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Future Internet
Volume 14
Issue 7
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Future Internet, Volume 14, Issue 7 (July 2022) – 27 articles

Cover Story (view full-size image): Unmanned aerial vehicles (UAVs), commonly known as drones, are an emerging facilitator of several smart city services, such as observing weather phenomena, aerial photography, product delivery and surveillance. However, drones can be compromised by an adversary, causing altered flight routes and possible sabotage. In this article, a systematic framework for identifying malicious drone behavior through the use of a deep analysis of routine (normal) drone operations is presented. Normal drone flight behavior is reverse-engineered and malicious drone behavior is synthesized for hijacking, GPS signal jamming and DoS attacks. Subsequently, machine learning techniques are adopted to identify malicious drone activity for the study. View this paper
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48 pages, 1458 KiB  
Review
Internet of Things and Blockchain Integration: Security, Privacy, Technical, and Design Challenges
by Yehia Ibrahim Alzoubi, Ahmad Al-Ahmad, Hasan Kahtan and Ashraf Jaradat
Future Internet 2022, 14(7), 216; https://doi.org/10.3390/fi14070216 - 21 Jul 2022
Cited by 31 | Viewed by 5676
Abstract
The Internet of things model enables a world in which all of our everyday devices can be integrated and communicate with each other and their surroundings to gather and share data and simplify task implementation. Such an Internet of things environment would require [...] Read more.
The Internet of things model enables a world in which all of our everyday devices can be integrated and communicate with each other and their surroundings to gather and share data and simplify task implementation. Such an Internet of things environment would require seamless authentication, data protection, stability, attack resistance, ease of deployment, and self-maintenance, among other things. Blockchain, a technology that was born with the cryptocurrency Bitcoin, may fulfill Internet of things requirements. However, due to the characteristics of both Internet of things devices and Blockchain technology, integrating Blockchain and the Internet of things can cause several challenges. Despite a large number of papers that have been published in the field of Blockchain and the Internet of things, the problems of this combination remain unclear and scattered. Accordingly, this paper aims to provide a comprehensive survey of the challenges related to Blockchain–Internet of things integration by evaluating the related peer-reviewed literature. The paper also discusses some of the recommendations for reducing the effects of these challenges. Moreover, the paper discusses some of the unsolved concerns that must be addressed before the next generation of integrated Blockchain–Internet of things applications can be deployed. Lastly, future trends in the context of Blockchain–Internet of things integration are discussed. Full article
(This article belongs to the Section Internet of Things)
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<p>BC layered architecture.</p> Full article ">Figure 2
<p>BIoT integration current challenges.</p> Full article ">
15 pages, 936 KiB  
Article
Multifractal Cross-Correlations of Bitcoin and Ether Trading Characteristics in the Post-COVID-19 Time
by Marcin Wątorek, Jarosław Kwapień and Stanisław Drożdż
Future Internet 2022, 14(7), 215; https://doi.org/10.3390/fi14070215 - 21 Jul 2022
Cited by 15 | Viewed by 3281
Abstract
Unlike price fluctuations, the temporal structure of cryptocurrency trading has seldom been a subject of systematic study. In order to fill this gap, we analyse detrended correlations of the price returns, the average number of trades in time unit, and the traded volume [...] Read more.
Unlike price fluctuations, the temporal structure of cryptocurrency trading has seldom been a subject of systematic study. In order to fill this gap, we analyse detrended correlations of the price returns, the average number of trades in time unit, and the traded volume based on high-frequency data representing two major cryptocurrencies: bitcoin and ether. We apply the multifractal detrended cross-correlation analysis, which is considered the most reliable method for identifying nonlinear correlations in time series. We find that all the quantities considered in our study show an unambiguous multifractal structure from both the univariate (auto-correlation) and bivariate (cross-correlation) perspectives. We looked at the bitcoin–ether cross-correlations in simultaneously recorded signals, as well as in time-lagged signals, in which a time series for one of the cryptocurrencies is shifted with respect to the other. Such a shift suppresses the cross-correlations partially for short time scales, but does not remove them completely. We did not observe any qualitative asymmetry in the results for the two choices of a leading asset. The cross-correlations for the simultaneous and lagged time series became the same in magnitude for the sufficiently long scales. Full article
(This article belongs to the Special Issue Blockchain Security and Privacy)
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Figure 1
<p>Evolution of the quantities of interest over the time period considered in this study for two principal cryptocurrencies: BTC (red circles) and ETH (blue squares). (<b>a</b>) Price <math display="inline"><semantics> <mrow> <mi>P</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> of the cryptocurrencies expressed in US dollars; (<b>b</b>) logarithmic returns <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>t</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math> s; (<b>c</b>) mean volume traded <math display="inline"><semantics> <mrow> <mo>〈</mo> <msub> <mi>V</mi> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>〉</mo> </mrow> </semantics></math> in 10 s intervals; (<b>d</b>) mean number of transactions <math display="inline"><semantics> <mrow> <mo>〈</mo> <msub> <mi>N</mi> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>〉</mo> </mrow> </semantics></math> in <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>t</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math> s. The averaging was carried out over a rolling window of 1 month with a step of 6 days.</p> Full article ">Figure 2
<p>Pearson’s cross-correlation coefficients calculated for all possible pairs of time series considered in this study. All values are statistically significant.</p> Full article ">Figure 3
<p>Univariate fluctuation functions <math display="inline"><semantics> <mrow> <msubsup> <mi>F</mi> <mi>q</mi> <mi>XX</mi> </msubsup> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> calculated for time series of price returns <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (<b>left column</b>), volume traded <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (<b>middle column</b>), and the number of transactions <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (<b>right column</b>) for two cryptocurrencies expressed in USDT: BTC (<b>top</b>) and ETH (<b>bottom</b>). In each panel, vertical dashed lines denote a range of time scales <span class="html-italic">s</span> for which a power-law model can be fitted to the fluctuation functions. A range of values of <span class="html-italic">q</span> is also shown.</p> Full article ">Figure 4
<p>(<b>Main panels</b>) Fluctuation functions <math display="inline"><semantics> <mrow> <msubsup> <mi>F</mi> <mi>q</mi> <mi>XY</mi> </msubsup> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> calculated for the time series of logarithmic price returns <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> for X = BTC and Y = ETH. In each panel, vertical dashed lines denote a range of time scales <span class="html-italic">s</span> for which a power-law model can be fitted to <math display="inline"><semantics> <mrow> <msubsup> <mi>F</mi> <mrow> <mi>XY</mi> </mrow> <mi>q</mi> </msubsup> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. Extreme values of <span class="html-italic">q</span> are also shown. Three cases are considered: both time series are simultaneous (<b>top</b>), time series representing BTC is advanced by <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> min (<b>middle</b>), and time series representing ETH is advanced by <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> min (<b>bottom</b>). (<b>Insets</b>) The bivariate scaling exponent <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </semantics></math> vs. the mean univariate scaling exponent <math display="inline"><semantics> <mrow> <msub> <mi>h</mi> <mi>XY</mi> </msub> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> calculated for the same time series. Error bars denote the standard errors.</p> Full article ">Figure 5
<p>(<b>a</b>) The <span class="html-italic">q</span>-dependent detrended cross-correlation coefficient <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mi>q</mi> </msub> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> (<b>left</b>) and <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> (<b>right</b>) calculated for time series of price returns <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (<b>top</b>), volume traded <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (<b>middle</b>), and the number of transactions <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> in <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </semantics></math> (<b>bottom</b>) for X = BTC and Y = ETH. Three cases are considered: both time series are simultaneous (solid blue), time series representing BTC is advanced by <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> min (short-dashed green), and time series representing ETH is advanced by <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> min (long-dashed red). (<b>b</b>) Difference <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi>XY</mi> </msub> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> between the bivariate scaling exponent <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </semantics></math> and the mean univariate exponent <math display="inline"><semantics> <mrow> <msub> <mi>h</mi> <mi>XY</mi> </msub> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p> Full article ">Figure 6
<p>(<b>Main panels</b>) Fluctuation functions <math display="inline"><semantics> <mrow> <msubsup> <mi>F</mi> <mi>q</mi> <mi>XY</mi> </msubsup> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> calculated for the time series of volume traded <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> for X = BTC and Y = ETH. In each panel, vertical dashed lines denote a range of time scales <span class="html-italic">s</span> for which a power-law model can be fitted to <math display="inline"><semantics> <mrow> <msubsup> <mi>F</mi> <mrow> <mi>XY</mi> </mrow> <mi>q</mi> </msubsup> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. Extreme values of <span class="html-italic">q</span> are also shown. Three cases are considered: both time series are simultaneous (<b>top</b>), time series representing BTC is advanced by <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> min (<b>middle</b>), and time series representing ETH is advanced by <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> min (<b>bottom</b>). (<b>Insets</b>) The bivariate scaling exponent <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </semantics></math> vs. the mean univariate scaling exponent <math display="inline"><semantics> <mrow> <msub> <mi>h</mi> <mi>XY</mi> </msub> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> calculated for the same time series. Error bars denote the standard errors.</p> Full article ">Figure 7
<p>(<b>Main panels</b>) Fluctuation functions <math display="inline"><semantics> <mrow> <msubsup> <mi>F</mi> <mi>q</mi> <mi>XY</mi> </msubsup> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> calculated for time series of the number of transactions <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> in <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </semantics></math> for X = BTC and Y = ETH. In each panel, vertical dashed lines denote a range of time scales <span class="html-italic">s</span> for which a power-law model can be fitted to <math display="inline"><semantics> <mrow> <msubsup> <mi>F</mi> <mi>q</mi> <mi>XY</mi> </msubsup> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. Extreme values of <span class="html-italic">q</span> are also shown. Three cases are considered: both time series are simultaneous (<b>top</b>), time series representing BTC is advanced by <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> min (<b>middle</b>), and time series representing ETH is advanced by <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> min (<b>bottom</b>). (<b>Insets</b>) The bivariate scaling exponent <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </semantics></math> vs. the mean univariate scaling exponent <math display="inline"><semantics> <mrow> <msub> <mi>h</mi> <mi>XY</mi> </msub> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> calculated for the same time series. Error bars denote the standard errors.</p> Full article ">
24 pages, 623 KiB  
Article
Enhanced Geographic Routing with One- and Two-Hop Movement Information in Opportunistic Ad Hoc Networks
by Mohd-Yaseen Mir, Hengbing Zhu and Chih-Lin Hu
Future Internet 2022, 14(7), 214; https://doi.org/10.3390/fi14070214 - 20 Jul 2022
Cited by 2 | Viewed by 2439
Abstract
Opportunistic ad hoc networks are characterized by intermittent and infrastructure-less connectivity among mobile nodes. Because of the lack of up-to-date network topology information and frequent link failures, geographic routing utilizes location information and adopts the store–carry–forward data delivery model to relay messages in [...] Read more.
Opportunistic ad hoc networks are characterized by intermittent and infrastructure-less connectivity among mobile nodes. Because of the lack of up-to-date network topology information and frequent link failures, geographic routing utilizes location information and adopts the store–carry–forward data delivery model to relay messages in a delay-tolerant manner. This paper proposes a message-forwarding policy based on movement patterns (MPMF). First, one- and two-hop location information in a geographic neighborhood is exploited to select relay nodes moving closer to a destination node. Message-forwarding decisions are made by referring to selected relay nodes’ weight values obtained by calculating the contact frequency of each node with the destination node. Second, when relays in the vicinity of a message-carrying node are not qualified due to the sparse node density and nodal motion status, the destination’s movement and the location information of a one-hop relay are jointly utilized to improve the message-forwarding decision. If the one-hop relay is not closer to the destination node or moving away from it, its centrality value in the network is used instead. Based on both synthetic and real mobility scenarios, the simulation results show that the proposed policy performs incomparable efforts to some typical routing policies, such as Epidemic, PRoPHETv2, temporal closeness and centrality-based (TCCB), transient community-based (TC), and geographic-based spray-and-relay (GSaR) routing policies. Full article
(This article belongs to the Topic Wireless Communications and Edge Computing in 6G)
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Figure 1

Figure 1
<p>(<b>a</b>) Front-side scanning with respect to a destination node, and (<b>b</b>) geometric angle formation at one- and two-hop distance.</p> Full article ">Figure 2
<p>Two-hop based routing between <span class="html-italic">s</span>, <math display="inline"><semantics> <msub> <mi>n</mi> <mi>i</mi> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mi>n</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> </semantics></math>.</p> Full article ">Figure 3
<p>Results by MPMF with different values of <math display="inline"><semantics> <mi>ϵ</mi> </semantics></math> and <math display="inline"><semantics> <msub> <mi>W</mi> <mn>0</mn> </msub> </semantics></math> under TVCM trace (TTL = 5 h and buffer size = 10 MB).</p> Full article ">Figure 4
<p>Results by MPMF with different values of <math display="inline"><semantics> <mi>ϵ</mi> </semantics></math> and <math display="inline"><semantics> <msub> <mi>W</mi> <mn>0</mn> </msub> </semantics></math> under NCCU trace (TTL = 5 h and buffer size = 10 MB).</p> Full article ">Figure 5
<p>Results by MPMF with different values of <math display="inline"><semantics> <mi>α</mi> </semantics></math> and <math display="inline"><semantics> <mi>ϵ</mi> </semantics></math> under TVCM trace (TTL = 5 h and buffer size = 10 MB).</p> Full article ">Figure 6
<p>Results by MPMF with different values of <math display="inline"><semantics> <mi>α</mi> </semantics></math> and <math display="inline"><semantics> <mi>ϵ</mi> </semantics></math> under NCCU trace (TTL = 5 h and buffer size = 10 MB).</p> Full article ">Figure 7
<p>Results by MPMF with different values of <span class="html-italic">L</span> and TTL under TVCM trace (buffer size = 10 MB).</p> Full article ">Figure 8
<p>Results by MPMF with different values of <span class="html-italic">L</span> and TTL under NCCU trace (buffer size = 10 MB).</p> Full article ">Figure 9
<p>Performance comparison of MPMF in TVCM trace (<math display="inline"><semantics> <msub> <mi>W</mi> <mn>0</mn> </msub> </semantics></math> = 0.6, <span class="html-italic">L</span> = 10, <math display="inline"><semantics> <mi>ϵ</mi> </semantics></math> = 50, <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 0.5, and buffer size = 10 MB).</p> Full article ">Figure 9 Cont.
<p>Performance comparison of MPMF in TVCM trace (<math display="inline"><semantics> <msub> <mi>W</mi> <mn>0</mn> </msub> </semantics></math> = 0.6, <span class="html-italic">L</span> = 10, <math display="inline"><semantics> <mi>ϵ</mi> </semantics></math> = 50, <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 0.5, and buffer size = 10 MB).</p> Full article ">Figure 10
<p>Performance comparison of MPMF in NCCU trace (<math display="inline"><semantics> <msub> <mi>W</mi> <mn>0</mn> </msub> </semantics></math> = 0.5, <span class="html-italic">L</span> = 10, <math display="inline"><semantics> <mi>ϵ</mi> </semantics></math> = 60, <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 0.6, and buffer size = 10 MB).</p> Full article ">Figure 11
<p>Performance comparison of MPMF in TVCM trace (<math display="inline"><semantics> <msub> <mi>W</mi> <mn>0</mn> </msub> </semantics></math> = 0.5, <span class="html-italic">L</span> = 20, <math display="inline"><semantics> <mi>ϵ</mi> </semantics></math> = 60, <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 0.6, and buffer size = 10 MB).</p> Full article ">
26 pages, 2456 KiB  
Article
A Survey on Monitoring Quality Assessment for Wireless Visual Sensor Networks
by Thiago C. Jesus, Daniel G. Costa, Paulo Portugal and Francisco Vasques
Future Internet 2022, 14(7), 213; https://doi.org/10.3390/fi14070213 - 19 Jul 2022
Cited by 11 | Viewed by 2330
Abstract
Wireless visual sensor networks have been adopted in different contexts to provide visual information in a more flexible and distributed way, supporting the development of different innovative applications. Although visual data may be central for a considerable set of applications in areas such [...] Read more.
Wireless visual sensor networks have been adopted in different contexts to provide visual information in a more flexible and distributed way, supporting the development of different innovative applications. Although visual data may be central for a considerable set of applications in areas such as Smart Cities, Industry 4.0, and Vehicular Networks, the actual visual data quality may be not easily determined since it may be associated with many factors that depend on the characteristics of the considered application scenario. This entails several aspects from the quality of captured images (sharpness, definition, resolution) to the characteristics of the networks such as employed hardware, power consumption, and networking efficiency. In order to better support quality analysis and performance comparisons among different wireless visual sensor networks, which could be valuable in many monitoring scenarios, this article surveys this area with special concern on assessment mechanisms and quality metrics. In this context, a novel classification approach is proposed to better categorize the diverse applicable metrics for quality assessment of visual monitoring procedures. Hence, this article yields a practical guide for analyzing different visual sensor network implementations, allowing fairer evaluations and comparisons among a variety of research works. Critical analysis are also performed regarding the relevance and usage of the proposed categories and identified quality metrics. Finally, promising open issues and research directions are discussed in order to guide new developments in this research field. Full article
(This article belongs to the Section Internet of Things)
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<p>Proposed classification and commonly associated coverage aspects.</p> Full article ">Figure 2
<p>WVSN for target coverage.</p> Full article ">Figure 3
<p>WVSN for area coverage.</p> Full article ">Figure 4
<p>WVSN for barrier coverage.</p> Full article ">Figure 5
<p>Content quality assuming regions of sharpness.</p> Full article ">Figure 6
<p>Content quality assuming sharpness continuous variation.</p> Full article ">Figure 7
<p>Monitoring model considering the angle of view.</p> Full article ">Figure 8
<p>Published works per year.</p> Full article ">Figure 9
<p>Percentage distribution of articles according to the application monitoring purpose.</p> Full article ">Figure 10
<p>Classification groups.</p> Full article ">Figure 11
<p>Distribution of the groups and metrics.</p> Full article ">
21 pages, 283655 KiB  
Article
Intuitively Searching for the Rare Colors from Digital Artwork Collections by Text Description: A Case Demonstration of Japanese Ukiyo-e Print Retrieval
by Kangying Li, Jiayun Wang, Biligsaikhan Batjargal and Akira Maeda
Future Internet 2022, 14(7), 212; https://doi.org/10.3390/fi14070212 - 18 Jul 2022
Cited by 2 | Viewed by 2889
Abstract
In recent years, artworks have been increasingly digitized and built into databases, and such databases have become convenient tools for researchers. Researchers who retrieve artwork are not only researchers of humanities, but also researchers of materials science, physics, art, and so on. It [...] Read more.
In recent years, artworks have been increasingly digitized and built into databases, and such databases have become convenient tools for researchers. Researchers who retrieve artwork are not only researchers of humanities, but also researchers of materials science, physics, art, and so on. It may be difficult for researchers of various fields whose studies focus on the colors of artwork to find the required records in existing databases, that are color-based and only queried by the metadata. Besides, although some image retrieval engines can be used to retrieve artwork by text description, the existing image retrieval systems mainly retrieve the main colors of the images, and rare cases of color use are difficult to find. This makes it difficult for many researchers who focus on toning, colors, or pigments to use search engines for their own needs. To solve the two problems, we propose a cross-modal multi-task fine-tuning method based on CLIP (Contrastive Language-Image Pre-Training), which uses the human sensory characteristics of colors contained in the language space and the geometric characteristics of the sketches of a given artwork in order to gain better representations of that artwork piece. The experimental results show that the proposed retrieval framework is efficient for intuitively searching for rare colors, and that a small amount of data can improve the correspondence between text descriptions and color information. Full article
(This article belongs to the Special Issue Big Data Analytics, Privacy and Visualization)
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<p>The example of rare and attractive colors in ukiyo-e prints are shown in red circles.</p> Full article ">Figure 2
<p>The (<span class="html-italic">H</span>, <span class="html-italic">L</span>) = (0, 60) color group. the colors in the red boxes may tend to be described more as gray based on human senses.</p> Full article ">Figure 3
<p>A variety of different beer colors and tea colors.</p> Full article ">Figure 4
<p>Results for ‘yellowish blue’ using text-image search and image-image search.</p> Full article ">Figure 5
<p>Architecture of our cross-modal multi-task fine-tuning representation learning framework.</p> Full article ">Figure 6
<p>Structure of the space sampler module. (<b>1</b>) refers to process image sketch extraction, (<b>2</b>) refers to process HOG feature extraction and (<b>3</b>) refers to process triplet data sampling.</p> Full article ">Figure 7
<p>Example sketch image output from AODA Net. Original input image is extracted from the ukiyo-e prints series Ogura Imitations of One Hundred Poems by One Hundred Poets (小倉擬百人一首).</p> Full article ">Figure 8
<p>An example of a HOG feature map.</p> Full article ">Figure 9
<p>Example of color information extraction.</p> Full article ">Figure 10
<p>Extracting the color-proportion index and a color description document from the image data. In the color description document, the name of each color is treated as a word unit.</p> Full article ">Figure 11
<p>Colors with top 6 highest frequencies.</p> Full article ">Figure 12
<p>Example of color name <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>Score</mi> </mrow> <mrow> <mrow> <mi>color</mi> <mo> </mo> <mi>name</mi> </mrow> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <msup> <mi mathvariant="normal">x</mi> <mi mathvariant="normal">i</mi> </msup> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p> Full article ">Figure 13
<p>Example of training data of the cross-modal fine-tuning task.</p> Full article ">Figure 14
<p>Cosine, Euclidean, Manhattan, and Dot Pearson correlation values calculated at 60 epochs.</p> Full article ">Figure 15
<p>Example of correlation scores: −0.3, 0, and +0.3.</p> Full article ">Figure 16
<p>Linguistic space visualization.</p> Full article ">Figure 17
<p>The same ukiyo-e print that was digitized at the different institutions.</p> Full article ">Figure 18
<p>Example of extracting rare color information based on our proposed method.</p> Full article ">Figure 19
<p>Demo application implementation.</p> Full article ">Figure 20
<p>Retrieval results of inputting the pigment name ‘Cinnabar dark’.</p> Full article ">
11 pages, 1342 KiB  
Article
Zero-Inflated Patent Data Analysis Using Generating Synthetic Samples
by Daiho Uhm and Sunghae Jun
Future Internet 2022, 14(7), 211; https://doi.org/10.3390/fi14070211 - 16 Jul 2022
Cited by 5 | Viewed by 2061
Abstract
Due to the expansion of the internet, we encounter various types of big data such as web documents or sensing data. Compared to traditional small data such as experimental samples, big data provide more chances to find hidden and novel patterns with big [...] Read more.
Due to the expansion of the internet, we encounter various types of big data such as web documents or sensing data. Compared to traditional small data such as experimental samples, big data provide more chances to find hidden and novel patterns with big data analysis using statistics and machine learning algorithms. However, as the use of big data increases, problems also occur. One of them is a zero-inflated problem in structured data preprocessed from big data. Most count values are zeros because a specific word is found in only some documents. In particular, since most of the patent data are in the form of a text document, they are more affected by the zero-inflated problem. To solve this problem, we propose a generation of synthetic samples using statistical inference and tree structure. Using patent document and simulation data, we verify the performance and validity of our proposed method. In this paper, we focus on patent keyword analysis as text big data analysis, and we encounter the zero-inflated problem just like other text data. Full article
(This article belongs to the Special Issue Trends of Data Science and Knowledge Discovery)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Part of structured patent data.</p> Full article ">Figure 2
<p>Patent data preprocessing and analysis.</p> Full article ">Figure 3
<p>Proposed method for zero-inflated data analysis.</p> Full article ">Figure 4
<p>Sequential modeling of CART method to generate synthetic patent data.</p> Full article ">Figure 5
<p>Generating synthetic samples from patent-keyword matrix.</p> Full article ">
24 pages, 4895 KiB  
Article
Blockchain for Doping Control Applications in Sports: A Conceptual Approach
by Flavio Pinto, Yogachandran Rahulamathavan and James Skinner
Future Internet 2022, 14(7), 210; https://doi.org/10.3390/fi14070210 - 14 Jul 2022
Cited by 4 | Viewed by 3276
Abstract
Doping is a well-known problem in competitive sports. Along the years, several cases have come to public, evidencing corrupt practices from within the sports environment. To guarantee fair play and prevent public health issues, anti-doping organizations and sports authorities are expected to cooperate [...] Read more.
Doping is a well-known problem in competitive sports. Along the years, several cases have come to public, evidencing corrupt practices from within the sports environment. To guarantee fair play and prevent public health issues, anti-doping organizations and sports authorities are expected to cooperate in the fight against doping. To achieve this mission, doping-related data must be produced, stored, accessed, and shared in a secure, tamperproof, and privacy-preserving manner. This paper investigates the processes and tools established by the World Anti-Doping Agency for the global harmonization of doping control activities. From this investigation, it is possible to conclude that there is an inherent trust problem, in part due to a centralized data management paradigm and to the lack of fully digitalized processes. Therefore, this paper presents two main contributions: the concept of a multiorganizational decentralized data governance model and a blockchain-based design for one of the most sensitive data-sharing processes within the anti-doping ecosystem. Throughout this article, it is shown that the adoption of a permissioned blockchain can benefit the whole anti-doping community, creating more reliable processes for handling data, where privacy and security are enhanced. Full article
(This article belongs to the Collection Innovative People-Centered Solutions Applied to Industries, Cities and Societies)
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<p>Examples of the current doping control and TUE request processes and their vulnerabilities. References: [<a href="#B7-futureinternet-14-00210" class="html-bibr">7</a>,<a href="#B31-futureinternet-14-00210" class="html-bibr">31</a>,<a href="#B55-futureinternet-14-00210" class="html-bibr">55</a>,<a href="#B56-futureinternet-14-00210" class="html-bibr">56</a>,<a href="#B57-futureinternet-14-00210" class="html-bibr">57</a>,<a href="#B58-futureinternet-14-00210" class="html-bibr">58</a>].</p> Full article ">Figure 2
<p>Research strategy workflow.</p> Full article ">Figure 3
<p>(<b>a</b>) A decentralized multiorganizational model for the AD ecosystem. (<b>b</b>) The governance hierarchy proposed for a decentralized AD ecosystem. Reference: [<a href="#B78-futureinternet-14-00210" class="html-bibr">78</a>].</p> Full article ">Figure 4
<p>General network architecture for consortium application in HLF, with an example with different channels for different applications. References: [<a href="#B14-futureinternet-14-00210" class="html-bibr">14</a>,<a href="#B74-futureinternet-14-00210" class="html-bibr">74</a>].</p> Full article ">Figure 5
<p>TUE request–asset lifecycle. References: [<a href="#B83-futureinternet-14-00210" class="html-bibr">83</a>,<a href="#B84-futureinternet-14-00210" class="html-bibr">84</a>].</p> Full article ">Figure 6
<p>Mapping of ABE functionalities/AD organizations. Reference: [<a href="#B85-futureinternet-14-00210" class="html-bibr">85</a>].</p> Full article ">Figure 7
<p>Data format (TUE request)/transaction structure/examples of ABE policies.</p> Full article ">
15 pages, 1704 KiB  
Article
N-Trans: Parallel Detection Algorithm for DGA Domain Names
by Cheng Yang, Tianliang Lu, Shangyi Yan, Jianling Zhang and Xingzhan Yu
Future Internet 2022, 14(7), 209; https://doi.org/10.3390/fi14070209 - 13 Jul 2022
Cited by 8 | Viewed by 2531
Abstract
Domain name generation algorithms are widely used in malware, such as botnet binaries, to generate large sequences of domain names of which some are registered by cybercriminals. Accurate detection of malicious domains can effectively defend against cyber attacks. The detection of such malicious [...] Read more.
Domain name generation algorithms are widely used in malware, such as botnet binaries, to generate large sequences of domain names of which some are registered by cybercriminals. Accurate detection of malicious domains can effectively defend against cyber attacks. The detection of such malicious domain names by the use of traditional machine learning algorithms has been explored by many researchers, but still is not perfect. To further improve on this, we propose a novel parallel detection model named N-Trans that is based on the N-gram algorithm with the Transformer model. First, we add flag bits to the first and last positions of the domain name for the parallel combination of the N-gram algorithm and Transformer framework to detect a domain name. The model can effectively extract the letter combination features and capture the position features of letters in the domain name. It can capture features such as the first and last letters in the domain name and the position relationship between letters. In addition, it can accurately distinguish between legitimate and malicious domain names. In the experiment, the dataset is the legal domain name of Alexa and the malicious domain name collected by the 360 Security Lab. The experimental results show that the parallel detection model based on N-gram and Transformer achieves 96.97% accuracy for DGA malicious domain name detection. It can effectively and accurately identify malicious domain names and outperforms the mainstream malicious domain name detection algorithms. Full article
(This article belongs to the Special Issue Trends of Data Science and Knowledge Discovery)
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<p>Parallel detection model. <span class="html-italic">x</span> is the number of features obtained. It describes the process by which the model detects malicious domains. The model is divided into three stages: data pre-processing, parallel training, and domain name category prediction.</p> Full article ">Figure 2
<p>Word-hashing to process “<span class="html-italic"><a href="http://www.baidu.com" target="_blank">www.baidu.com</a></span>”.</p> Full article ">Figure 3
<p>Parallel model structure.</p> Full article ">Figure 4
<p>N-gram (<span class="html-italic">N</span> = 2) algorithm to process “<span class="html-italic"><a href="http://linkedin.com" target="_blank">linkedin.com</a></span>”.</p> Full article ">Figure 5
<p>Distribution of legitimate and malicious domain phrase element frequencies. After using N-gram processing, the phrase elements were sorted from highest to lowest frequency. The frequency of the top 20 ranked phrase elements in legitimate domain names and those corresponding to malicious domain names were selected for comparison. (<b>a</b>) The processing result when <span class="html-italic">N</span> = 2. (<b>b</b>) The processing result when <span class="html-italic">N</span> = 3.</p> Full article ">Figure 6
<p>Observe the trends in <span class="html-italic">Acc</span> and <span class="html-italic">recall</span> by changing the number of heads in Transformer.</p> Full article ">
23 pages, 6966 KiB  
Article
Analytical Modeling and Empirical Analysis of Binary Options Strategies
by Gurdal Ertek, Aysha Al-Kaabi and Aktham Issa Maghyereh
Future Internet 2022, 14(7), 208; https://doi.org/10.3390/fi14070208 - 6 Jul 2022
Cited by 2 | Viewed by 5316
Abstract
This study analyzes binary option investment strategies by developing mathematical formalism and formulating analytical models. The binary outcome of binary options represents either an increase or a decrease in a parameter, typically an asset or derivative. The investor receives only partial returns if [...] Read more.
This study analyzes binary option investment strategies by developing mathematical formalism and formulating analytical models. The binary outcome of binary options represents either an increase or a decrease in a parameter, typically an asset or derivative. The investor receives only partial returns if the prediction is correct but loses all the investment otherwise. Mainstream research on binary options aims to develop the best dynamic trading strategies. This study focuses on static tactical easy-to-implement strategies and investigates the performance of such strategies in relation to prediction accuracy, payout percentage, and investment strategy decisions. Full article
(This article belongs to the Special Issue Big Data Analytics, Privacy and Visualization)
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<p>Typical user interface for binary options trading.</p> Full article ">Figure 2
<p>Components of typical user interface for binary options trading.</p> Full article ">Figure 3
<p>Order of events in each period assumed in the research study.</p> Full article ">Figure 4
<p>Relation between binary option parameters and return on investment (<math display="inline"><semantics> <mrow> <mi>r</mi> <mi>o</mi> <mi>i</mi> </mrow> </semantics></math> ) across all four strategies.</p> Full article ">Figure 5
<p>Change in (average of median) <math display="inline"><semantics> <mrow> <mi>r</mi> <mi>o</mi> <mi>i</mi> </mrow> </semantics></math> of the strategies in relation to prediction accuracy <math display="inline"><semantics> <mi>p</mi> </semantics></math> on the <span class="html-italic">x</span>-axis.</p> Full article ">Figure 6
<p>Change in (median) <math display="inline"><semantics> <mrow> <mrow> <mi>r</mi> <mi>o</mi> <mi>i</mi> <mtext> </mtext> <mi>S</mi> <mi>t</mi> <mi>d</mi> <mi>D</mi> <mi>e</mi> <mi>v</mi> </mrow> </mrow> </semantics></math> of the strategies in relation to prediction accuracy <math display="inline"><semantics> <mi>p</mi> </semantics></math> on the <span class="html-italic">x</span>-axis.</p> Full article ">Figure 7
<p>Change in (average of median) <math display="inline"><semantics> <mrow> <mi>r</mi> <mi>o</mi> <mi>i</mi> </mrow> </semantics></math> of the strategies in relation to payout percentage <math display="inline"><semantics> <mi>r</mi> </semantics></math> on the <span class="html-italic">x</span>-axis.</p> Full article ">Figure 8
<p>Change in (median) <math display="inline"><semantics> <mrow> <mi>r</mi> <mi>o</mi> <mi>i</mi> <mo> </mo> <mi>S</mi> <mi>t</mi> <mi>d</mi> <mi>D</mi> <mi>e</mi> <mi>v</mi> </mrow> </semantics></math> of the strategies in relation to payout percentage <math display="inline"><semantics> <mi>r</mi> </semantics></math> on the <span class="html-italic">x</span>-axis.</p> Full article ">Figure 9
<p>Impact of prediction accuracy <math display="inline"><semantics> <mi>p</mi> </semantics></math>, payout percentage <math display="inline"><semantics> <mi>r</mi> </semantics></math>, and investment proportion <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>’</mo> </mrow> </semantics></math> on (average of median) <math display="inline"><semantics> <mrow> <mi>r</mi> <mi>o</mi> <mi>i</mi> <mi>B</mi> </mrow> </semantics></math> for Strategy B.</p> Full article ">Figure 10
<p>Relation between binary options parameters <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mrow> <mi>p</mi> <mo>,</mo> <mi>r</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> and near-optimal investment proportion <math display="inline"><semantics> <mrow> <mi>z</mi> <msup> <mo>’</mo> <mo>*</mo> </msup> </mrow> </semantics></math> values of strategy B.</p> Full article ">Figure 11
<p>Relation between decision variables <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mrow> <mi>z</mi> <mo>,</mo> <mi>w</mi> <mo>,</mo> <mi>v</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> of Strategy C and return on investment (<math display="inline"><semantics> <mrow> <mi>r</mi> <mi>o</mi> <mi>i</mi> <mi>C</mi> </mrow> </semantics></math> ) under constant (<math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0.65</mn> <mo>,</mo> <mtext> </mtext> <mi>r</mi> <mo>=</mo> <mn>0.65</mn> </mrow> </semantics></math> ).</p> Full article ">
18 pages, 1941 KiB  
Article
Research on Routing Equalization Algorithm of Inter-Satellite Partition for Low-Orbit Micro-Satellites
by Hengfei Cheng, Zhaobin Xu, Xiaoxu Guo, Jia Yang, Kedi Xu, Shuqin Liu, Zhonghe Jin and Xiaojun Jin
Future Internet 2022, 14(7), 207; https://doi.org/10.3390/fi14070207 - 4 Jul 2022
Viewed by 2211
Abstract
Low-orbit micro-satellite technology has developed rapidly in recent years due to its advantages of low time delay, low cost and short research period. However, among the existing inter-satellite routing algorithms, the classical flooding and greedy algorithms and their derivatives also have some limitations. [...] Read more.
Low-orbit micro-satellite technology has developed rapidly in recent years due to its advantages of low time delay, low cost and short research period. However, among the existing inter-satellite routing algorithms, the classical flooding and greedy algorithms and their derivatives also have some limitations. The path delay calculated by the flooding algorithm is small but the calculation is large, while the greedy algorithm is the opposite. In this paper, a balanced inter-satellite routing algorithm based on partition routing is proposed. This paper presents the simulation experiments for the following indexes of the classic inter-satellite routing algorithms and the balanced partition routing algorithm: computation complexity, single-node computation pressure, routing path delay, path delay variance (data in Topo table satisfy μ =5, σ2=10). The results reveal that the balanced partition routing algorithm achieves better performance. In this paper, two optimization directions of the balanced partition routing algorithm are simulated under conditions that the data in the Topo table satisfy μ =5, σ2= 6, σ2=10 and σ2=15, respectively, when comparing their performance indicators. The experiments show that these two optimization methods can be adapted to various application scenarios and can further reduce the hardware cost of satellite nodes. Full article
(This article belongs to the Special Issue Security in Mobile Communications and Computing)
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<p>Simulation of computational complexity of three algorithms.</p> Full article ">Figure 2
<p>Simulation of calculated pressure for a single node.</p> Full article ">Figure 3
<p>Simulation of average path length for four algorithms.</p> Full article ">Figure 4
<p>Simulation of the variance of average path length for four algorithms.</p> Full article ">Figure 5
<p>Two kinds of optimized partitioning (method 2 is left, method 3 is right).</p> Full article ">Figure 6
<p>Simulation of average path length (<math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">μ</mi> <mo>=</mo> <mn>5</mn> <mo>,</mo> <msup> <mi mathvariant="sans-serif">σ</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>).</p> Full article ">Figure 7
<p>Simulation of average path length (<math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">μ</mi> <mo>=</mo> <mn>5</mn> <mo>,</mo> <msup> <mi mathvariant="sans-serif">σ</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>).</p> Full article ">Figure 8
<p>Simulation of average path length (<math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">μ</mi> <mo>=</mo> <mn>5</mn> <mo>,</mo> <msup> <mi mathvariant="sans-serif">σ</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>).</p> Full article ">Figure 9
<p>Simulation of the variance of average path length (<math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">μ</mi> <mo>=</mo> <mn>5</mn> <mo>,</mo> <msup> <mi mathvariant="sans-serif">σ</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>).</p> Full article ">Figure 10
<p>Simulation of the variance of average path length (<math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">μ</mi> <mo>=</mo> <mn>5</mn> <mo>,</mo> <msup> <mi mathvariant="sans-serif">σ</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>).</p> Full article ">Figure 11
<p>Simulation of the variance of average path length (<math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">μ</mi> <mo>=</mo> <mn>5</mn> <mo>,</mo> <msup> <mi mathvariant="sans-serif">σ</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>).</p> Full article ">
15 pages, 1784 KiB  
Review
Mapping Art to a Knowledge Graph: Using Data for Exploring the Relations among Visual Objects in Renaissance Art
by Alexandros Kouretsis, Iraklis Varlamis, Laida Limniati, Minas Pergantis and Andreas Giannakoulopoulos
Future Internet 2022, 14(7), 206; https://doi.org/10.3390/fi14070206 - 3 Jul 2022
Cited by 2 | Viewed by 3190
Abstract
Graph-like structures, which are increasingly popular in data representation, stand out since they enable the integration of information from multiple sources. At the same time, clustering algorithms applied on graphs allow for group entities based on similar characteristics, and discover statistically important information. [...] Read more.
Graph-like structures, which are increasingly popular in data representation, stand out since they enable the integration of information from multiple sources. At the same time, clustering algorithms applied on graphs allow for group entities based on similar characteristics, and discover statistically important information. This paper aims to explore the associations between the visual objects of the Renaissance in the Europeana database, based on the results of topic modeling and analysis. For this purpose, we employ Europeana’s Search and Report API to investigate the relations between the visual objects from this era, spanning from the 14th to the 17th century, and to create clusters of similar art objects. This approach will lead in transforming a cultural heritage database with semantic technologies into a dynamic digital knowledge representation graph that will relate art objects and their attributes. Based on associations between metadata, we will conduct a statistic analysis utilizing the knowledge graph of Europeana and topic modeling analysis. Full article
(This article belongs to the Special Issue Knowledge Graph Mining and Its Applications)
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<p>Intertopic Distance Map (via multidimensional scaling).</p> Full article ">Figure 2
<p>Top-30 most relevant terms for Topic 1.</p> Full article ">Figure 3
<p>Top-30 most relevant terms for Topic 2.</p> Full article ">Figure 4
<p>Top-30 most relevant terms for Topic 3.</p> Full article ">Figure 5
<p>Top-30 most relevant terms for Topic 4.</p> Full article ">Figure 6
<p>Top-30 most relevant terms for Topic 5.</p> Full article ">
19 pages, 1202 KiB  
Article
Securing the Smart City Airspace: Drone Cyber Attack Detection through Machine Learning
by Zubair Baig, Naeem Syed and Nazeeruddin Mohammad
Future Internet 2022, 14(7), 205; https://doi.org/10.3390/fi14070205 - 30 Jun 2022
Cited by 22 | Viewed by 4575
Abstract
Drones are increasingly adopted to serve a smart city through their ability to render quick and adaptive services. They are also known as unmanned aerial vehicles (UAVs) and are deployed to conduct area surveillance, monitor road networks for traffic, deliver goods and observe [...] Read more.
Drones are increasingly adopted to serve a smart city through their ability to render quick and adaptive services. They are also known as unmanned aerial vehicles (UAVs) and are deployed to conduct area surveillance, monitor road networks for traffic, deliver goods and observe environmental phenomena. Cyber threats posed through compromised drones contribute to sabotage in a smart city’s airspace, can prove to be catastrophic to its operations, and can also cause fatalities. In this contribution, we propose a machine learning-based approach for detecting hijacking, GPS signal jamming and denial of service (DoS) attacks that can be carried out against a drone. A detailed machine learning-based classification of drone datasets for the DJI Phantom 4 model, compromising both normal and malicious signatures, is conducted, and results obtained yield advisory to foster futuristic opportunities to safeguard a drone system against such cyber threats. Full article
(This article belongs to the Special Issue Machine Learning Integration with Cyber Security)
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<p>Smart city with drones—illustration of a potential threat.</p> Full article ">Figure 2
<p>Pitch vs. roll in flight trajectory clusters obtained from GMM.</p> Full article ">Figure 3
<p>Identifying the flight number with abnormal pitch values.</p> Full article ">Figure 4
<p>Abnormal operations in flight-log-19.</p> Full article ">Figure 5
<p>Unreliable GPS signal observed in the drone.</p> Full article ">Figure 6
<p>Sudden drop in motor speeds observed in the drone.</p> Full article ">Figure 7
<p>Continuous frame loss due to DoS attacks against the drone.</p> Full article ">Figure 8
<p>Drone and radio control disconnected.</p> Full article ">Figure 9
<p>Framework to detect malicious attacks in UAVs.</p> Full article ">Figure 10
<p>Impact on random forest accuracy for increasing numbers of estimators.</p> Full article ">Figure 11
<p>ROC curve comparing the performances with varying values of max_depth.</p> Full article ">Figure 12
<p>ROC curve comparing the performances with varying values of numbers of estimators.</p> Full article ">Figure 13
<p>Comparison of model accuracy for datasets obtained from (blue) single and (orange) combined (two drones).</p> Full article ">
21 pages, 5986 KiB  
Article
Aesthetic Trends and Semantic Web Adoption of Media Outlets Identified through Automated Archival Data Extraction
by Aristeidis Lamprogeorgos, Minas Pergantis, Michail Panagopoulos and Andreas Giannakoulopoulos
Future Internet 2022, 14(7), 204; https://doi.org/10.3390/fi14070204 - 30 Jun 2022
Cited by 1 | Viewed by 2405
Abstract
The last decade has been a time of great progress in the World Wide Web and this progress has manifested in multiple ways, including both the diffusion and expansion of Semantic Web technologies and the advancement of the aesthetics and usability of Web [...] Read more.
The last decade has been a time of great progress in the World Wide Web and this progress has manifested in multiple ways, including both the diffusion and expansion of Semantic Web technologies and the advancement of the aesthetics and usability of Web user interfaces. Online media outlets have often been popular Web destinations and so they are expected to be at the forefront of innovation, both in terms of the integration of new technologies and in terms of the evolution of their interfaces. In this study, various Web data extraction techniques were employed to collect current and archival data from news websites that are popular in Greece, in order to monitor and record their progress through time. This collected information, which took the form of a website’s source code and an impression of their homepage in different time instances of the last decade, has been used to identify trends concerning Semantic Web integration, DOM structure complexity, number of graphics, color usage, and more. The identified trends were analyzed and discussed with the purpose of gaining a better understanding of the ever-changing presence of the media industry on the Web. The study concluded that the introduction of Semantic Web technologies in online media outlets was rapid and extensive and that website structural and visual complexity presented a steady and significant positive trend, accompanied by increased adherence to color harmony. Full article
(This article belongs to the Special Issue Theory and Applications of Web 3.0 in the Media Sector)
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<p>Visual representation of the process of website information collection.</p> Full article ">Figure 2
<p>Visual representation of the process of instance information gathering.</p> Full article ">Figure 3
<p>Visual representation of the HTML code collecting process.</p> Full article ">Figure 4
<p>Visual representation of the screenshot collecting process.</p> Full article ">Figure 5
<p>Visual representation of the complete instance data collecting process.</p> Full article ">Figure 6
<p>Example of the homepage of <a href="http://euronews.com" target="_blank">euronews.com</a> displaying increased structural and visual complexity.</p> Full article ">Figure 7
<p>Example of a homepage screenshot from <a href="http://nytimes.com" target="_blank">nytimes.com</a> with the empty space turned orange.</p> Full article ">Figure 8
<p>The evolution of the homepage of <a href="http://hellomagazine.com" target="_blank">hellomagazine.com</a> throughout the last two decades.</p> Full article ">Figure 9
<p>Major schemes of color combination based on the RYB color wheel.</p> Full article ">Figure 10
<p>Example of measuring distances on the RYB color wheel.</p> Full article ">Figure 11
<p>Percentage of websites using various Semantic Web technologies by year.</p> Full article ">Figure 12
<p>Website complexity as inferred through the average number of hyperlinks and div elements.</p> Full article ">Figure 13
<p>Graph presenting the number of graphical elements (normalized).</p> Full article ">Figure 14
<p>Graph presenting the usage of fluid or responsive design techniques.</p> Full article ">Figure 15
<p>Number of basic RYB colors used besides black and white by year.</p> Full article ">Figure 16
<p>Basic RYB color usage by year.</p> Full article ">Figure 17
<p>Basic RYB color usage by year (normalized) for (<b>a</b>) black and white, (<b>b</b>) warm colors, and (<b>c</b>) cool colors.</p> Full article ">Figure 18
<p>Usage of the colors white and black as empty space colors.</p> Full article ">Figure 19
<p>Usage of basic colors besides white and black as empty space colors.</p> Full article ">Figure 20
<p>Usage of color schemes by year.</p> Full article ">
13 pages, 3898 KiB  
Article
Correlation between Human Emotion and Temporal·Spatial Contexts by Analyzing Environmental Factors
by Minwoo Park and Euichul Lee
Future Internet 2022, 14(7), 203; https://doi.org/10.3390/fi14070203 - 30 Jun 2022
Viewed by 1968
Abstract
In this paper, we propose a method for extracting emotional factors through audiovisual quantitative feature analysis from images of the surrounding environment. Nine features were extracted such as time complexity, spatial complexity (horizontal and vertical), color components (hue and saturation), intensity, contrast, sound [...] Read more.
In this paper, we propose a method for extracting emotional factors through audiovisual quantitative feature analysis from images of the surrounding environment. Nine features were extracted such as time complexity, spatial complexity (horizontal and vertical), color components (hue and saturation), intensity, contrast, sound amplitude, and sound frequency. These nine features were used to infer “pleasant-unpleasant” and “arousal-relaxation” scores through two support vector regressions. First, the inference accuracy for each of the nine features was calculated as a hit ratio to check the distinguishing power of the features. Next, the difference between the position in the two-dimensional emotional plane inferred through SVR and the ground truth determined subjectively by the subject was examined. As a result of the experiment, it was confirmed that the time-complexity feature had the best classification performance, and it was confirmed that the emotion inferred through SVR can be valid when the two-dimensional emotional plane is divided into 3 × 3. Full article
(This article belongs to the Special Issue Trends of Data Science and Knowledge Discovery)
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<p>Russell’s human emotion model [<a href="#B20-futureinternet-14-00203" class="html-bibr">20</a>].</p> Full article ">Figure 2
<p>Geneva emotion wheel model [<a href="#B24-futureinternet-14-00203" class="html-bibr">24</a>].</p> Full article ">Figure 3
<p>The overall process of the proposed method.</p> Full article ">Figure 4
<p>Detection of temporal complexity. (<b>a</b>) Example of low temporal complexity case; (<b>b</b>) Example of high temporal complexity case.</p> Full article ">Figure 5
<p>Detection of spatial complexity (*: convolution). (<b>a</b>) Example of high spatial complexity in the horizontal edge case; (<b>b</b>) Example of high spatial complexity in the vertical edge case.</p> Full article ">Figure 6
<p>Mapping the 6 colors into the X-axis of “Unpleasant-Pleasant” [<a href="#B27-futureinternet-14-00203" class="html-bibr">27</a>].</p> Full article ">Figure 7
<p>Developed Android application for capturing the surrounding environment and performing the subjective emotional evaluation. (<b>a</b>) The initial screen; (<b>b</b>) Screen after capturing video and subjective evaluation.</p> Full article ">Figure 8
<p>Fully connected dual SVR networks with nine inputs and two outputs.</p> Full article ">Figure 9
<p>Data acquisition process for experiment.</p> Full article ">Figure 10
<p>The distance of results between estimated emotion and subjective evaluation on the 2D emotional plane divided into a 3 × 3 grid.</p> Full article ">
21 pages, 2684 KiB  
Article
Towards Strengthening the Resilience of IoV Networks—A Trust Management Perspective
by Yingxun Wang, Hushairi Zen, Mohamad Faizrizwan Mohd Sabri, Xiang Wang and Lee Chin Kho
Future Internet 2022, 14(7), 202; https://doi.org/10.3390/fi14070202 - 30 Jun 2022
Cited by 7 | Viewed by 3192
Abstract
Over the past decade or so, considerable and rapid advancements in the state of the art within the promising paradigms of the Internet of Things (IoT) and Artificial Intelligence (AI) have accelerated the development of conventional Vehicular Ad Hoc Networks (VANETS) into the [...] Read more.
Over the past decade or so, considerable and rapid advancements in the state of the art within the promising paradigms of the Internet of Things (IoT) and Artificial Intelligence (AI) have accelerated the development of conventional Vehicular Ad Hoc Networks (VANETS) into the Internet of Vehicles (IoV), thereby bringing both connected and autonomous driving much closer to realization. IoV is a new concept in the Intelligent Traffic System (ITS) and an extended application of IoV in intelligent transportation. It enhances the existing capabilities of mobile ad hoc networks by integrating them with IoT so as to build an integrated and unified vehicle-to-vehicle network. It is worth mentioning that academic and industrial researchers are paying increasing attention to the concept of trust. Reliable trust models and accurate trust assessments are anticipated to improve the security of the IoV. This paper, therefore, focuses on the existing trustworthiness management models along with their corresponding trust parameters, as well as the corresponding trust evaluation parameters and simulation, which provide the basis for intelligent and efficient model suggestions and optimal parameter integration. In addition, this paper also puts forward some open research directions that need to be seriously solved before trust can play its due role in enhancing IoV network elasticity. Full article
(This article belongs to the Special Issue Software-Defined Vehicular Networking)
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<p>The relationship between the VANET and the Internet.</p> Full article ">Figure 2
<p>C-V2X schematic.</p> Full article ">Figure 3
<p>Evolution history of the VANET.</p> Full article ">Figure 4
<p>IoV system’s “End Management Cloud” three-layer system.</p> Full article ">Figure 5
<p>Classification of trust.</p> Full article ">Figure 6
<p>Classification of trust models.</p> Full article ">
17 pages, 2618 KiB  
Article
Misuse Patterns from the Threat of Modification of Non-Control Data in Network Function Virtualization
by Abdulrahman K. Alnaim
Future Internet 2022, 14(7), 201; https://doi.org/10.3390/fi14070201 - 30 Jun 2022
Cited by 2 | Viewed by 2053
Abstract
Network Function Virtualization (NFV) is a virtual network model, the goal of which is a cost-efficient transition of the hardware infrastructure into a flexible and reliable software platform. However, this transition comes at the cost of more security threats. A key part of [...] Read more.
Network Function Virtualization (NFV) is a virtual network model, the goal of which is a cost-efficient transition of the hardware infrastructure into a flexible and reliable software platform. However, this transition comes at the cost of more security threats. A key part of this virtualization environment is the hypervisor, which emulates the hardware resources to provide a runtime environment for virtual machines (VMs). The hypervisor is considered a major attack vector and must be secured to ensure network service continuity. The virtualization environment contains critical non-control data where compromise could lead to several misuses, including information leakage and privilege and resource modification. In this paper, we present a misuse pattern for an attack that exploits the security vulnerabilities of the hypervisor to compromise the integrity of non-control data in the NFV environment. Misuse patterns are used to describe how attacks are carried out from the attackers’ perspective. The threat of modification of non-control data can lead to several misuses, and in this paper, we discuss three of them. The defenses to this attack can be incorporated into the Security Reference Architecture (SRA) of the NFV system to prevent these misuses. Full article
(This article belongs to the Section Network Virtualization and Edge/Fog Computing)
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<p>NFV Reference Architecture Framework [<a href="#B20-futureinternet-14-00201" class="html-bibr">20</a>].</p> Full article ">Figure 2
<p>Class diagram for modifying a non-control attack in NFV.</p> Full article ">Figure 3
<p>Sequence diagram for use case “Gain unauthorized access to hypervisor files by executing a malicious hypercall.”.</p> Full article ">Figure 4
<p>Sequence diagram for use case “Degrade the performance of the victim’s network service”.</p> Full article ">Figure 5
<p>Sequence diagram for use case “Upgrade the performance of the attacker’s network service”.</p> Full article ">Figure 6
<p>A pattern diagram for the NFV SRA [<a href="#B18-futureinternet-14-00201" class="html-bibr">18</a>].</p> Full article ">Figure 7
<p>An extended version of the NFV SRA.</p> Full article ">
12 pages, 2031 KiB  
Communication
A Public Infrastructure for a Trusted Wireless World
by Renee Carnley and Sikha Bagui
Future Internet 2022, 14(7), 200; https://doi.org/10.3390/fi14070200 - 30 Jun 2022
Cited by 2 | Viewed by 2474
Abstract
The novelty of this work lies in examining how 5G, blockchain-based public key infrastructure (PKI), near field communication (NFC), and zero trust architecture securely provide not only a trusted digital identity for telework but also a trusted digital identity for secure online voting. [...] Read more.
The novelty of this work lies in examining how 5G, blockchain-based public key infrastructure (PKI), near field communication (NFC), and zero trust architecture securely provide not only a trusted digital identity for telework but also a trusted digital identity for secure online voting. The paper goes on to discuss how blockchain-based PKI, NFC, and the cloud provide a roadmap for how industry and governments can update existing frameworks to obtain a trusted digital identity in cyberspace that would provide secure telework and online voting capabilities. Full article
(This article belongs to the Special Issue AI, Machine Learning and Data Analytics for Wireless Communications)
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<p>BPES integrated with the ICAM system.</p> Full article ">Figure 2
<p>BPES city, county, and state integration.</p> Full article ">Figure 3
<p>An example of the BPES blockchain.</p> Full article ">Figure 4
<p>Distributed online e-voting within a county as well as in-person voting working simultaneously.</p> Full article ">
16 pages, 13747 KiB  
Article
Real-Time Detection of Vine Trunk for Robot Localization Using Deep Learning Models Developed for Edge TPU Devices
by Khadijeh Alibabaei, Eduardo Assunção, Pedro D. Gaspar, Vasco N. G. J. Soares and João M. L. P. Caldeira
Future Internet 2022, 14(7), 199; https://doi.org/10.3390/fi14070199 - 29 Jun 2022
Cited by 10 | Viewed by 3533
Abstract
The concept of the Internet of Things (IoT) in agriculture is associated with the use of high-tech devices such as robots and sensors that are interconnected to assess or monitor conditions on a particular plot of land and then deploy the various factors [...] Read more.
The concept of the Internet of Things (IoT) in agriculture is associated with the use of high-tech devices such as robots and sensors that are interconnected to assess or monitor conditions on a particular plot of land and then deploy the various factors of production such as seeds, fertilizer, water, etc., accordingly. Vine trunk detection can help create an accurate map of the vineyard that the agricultural robot can rely on to safely navigate and perform a variety of agricultural tasks such as harvesting, pruning, etc. In this work, the state-of-the-art single-shot multibox detector (SSD) with MobileDet Edge TPU and MobileNet Edge TPU models as the backbone was used to detect the tree trunks in the vineyard. Compared to the SSD with MobileNet-V1, MobileNet-V2, and MobileDet as backbone, the SSD with MobileNet Edge TPU was more accurate in inference on the Raspberrypi, with almost the same inference time on the TPU. The SSD with MobileDet Edge TPU achieved the second-best accurate model. Additionally, this work examines the effects of some features, including the size of the input model, the quantity of training data, and the diversity of the training dataset. Increasing the size of the input model and the training dataset increased the performance of the model. Full article
(This article belongs to the Special Issue Advances in Agriculture 4.0)
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<p>An example of the variety of images in the database.</p> Full article ">Figure 2
<p>SSD architecture.</p> Full article ">Figure 3
<p><math display="inline"><semantics> <mrow> <mn>8</mn> <mo>×</mo> <mn>8</mn> </mrow> </semantics></math> Feature Map with 4 default boxes at position <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>3</mn> <mo>,</mo> <mn>3</mn> <mo>)</mo> </mrow> </semantics></math>. For each of the boxes, four offsets and class values are predicted for <span class="html-italic">p</span> classes.</p> Full article ">Figure 4
<p>(<b>A</b>) shows a normal convolution with <math display="inline"><semantics> <mrow> <mn>8</mn> <mo>×</mo> <mn>8</mn> <mo>×</mo> <mn>1</mn> </mrow> </semantics></math> output and (<b>B</b>) shows a depthwise convolution with three kernels to get an image with <math display="inline"><semantics> <mrow> <mn>8</mn> <mo>×</mo> <mn>8</mn> <mo>×</mo> <mn>3</mn> </mrow> </semantics></math> image.</p> Full article ">Figure 5
<p>Blocks used in MobileNet-V2.</p> Full article ">Figure 6
<p>Blocks used in MobileDet.</p> Full article ">Figure 7
<p>From left to right: Raspberrypi (<a href="https://www.raspberrypi.com/" target="_blank">https://www.raspberrypi.com/</a>, accessed on 15 November 2021), and Google coral USB Accelerator (<a href="https://coral.ai/" target="_blank">https://coral.ai/</a>, accessed on 8 November 2021).</p> Full article ">Figure 8
<p>Loss of models during training. Top left: MobileNet-V1, top right: MobileNet-V2, bottom left: MobileNet Edge TPU and bottom right: MobileDet Edge TPU.</p> Full article ">Figure 9
<p>mAP of models during training (threshold = 50). Top left: MobileNet-V1, top right: MobileNet-V2, bottom left: MobileNet Edge TPU and bottom right: MobileDet Edge TPU.</p> Full article ">Figure 10
<p>Detection results using SSD MobileDet Edge TPU.</p> Full article ">Figure 11
<p>Comparison of mean average precision vs. latency for mobile models.</p> Full article ">Figure 12
<p>mAP of the SSD MobileDet model, trained on the images without leaves and tested on the images with leaves.</p> Full article ">Figure 13
<p>Detection results of the trained models with and without enhanced images in the training dataset. Top image: ground truth, bottom left: Model trained with enhanced images, bottom right: Model trained without enhanced images.</p> Full article ">Figure 14
<p>The left images are the detection results of the model, and the left side is the ground truth. The model has found the trunks in the image that are not marked as tree trunks.</p> Full article ">Figure 15
<p>Two trunks are close together and the model cannot detect both.</p> Full article ">Figure 16
<p>The result of the model in the complex environment.</p> Full article ">
14 pages, 2136 KiB  
Article
Designing an Interactive Communication Assistance System for Hearing-Impaired College Students Based on Gesture Recognition and Representation
by Yancong Zhu, Juan Zhang, Zhaoxi Zhang, Gina Clepper, Jingpeng Jia and Wei Liu
Future Internet 2022, 14(7), 198; https://doi.org/10.3390/fi14070198 - 29 Jun 2022
Cited by 8 | Viewed by 2703
Abstract
Developing a smart classroom can make the modern classroom more efficient and intelligent. Much research has been conducted pertaining to smart classrooms for hearing-impaired college students. However, there have been few significant breakthroughs in mobilizing students’ learning efficiency as measured by information transmission, [...] Read more.
Developing a smart classroom can make the modern classroom more efficient and intelligent. Much research has been conducted pertaining to smart classrooms for hearing-impaired college students. However, there have been few significant breakthroughs in mobilizing students’ learning efficiency as measured by information transmission, communication, and interaction in class. This research collects data through nonparticipatory observation and in-depth interviews and analyzes available data on classroom interaction needs of these students. We found that diversified explanations, recordable interactive contents, and interaction between teachers and students could improve the learning effects in the classroom. We also propose a tracking-processing method based on gesture recognition and representation and present a design for a processing system based on AT89C52 microcontroller and Kinect. In this way, sign language can be translated into text and all students can receive the information and participate in the interaction, which greatly improves students’ autonomy and enthusiasm of learning. This design enables deaf students to fully use classroom learning resources, reduces learning time costs, and improves learning efficiency. It can assist teachers in teaching and tutoring students to enhance their experience. Full article
(This article belongs to the Special Issue Advances and Perspectives in Human-Computer Interaction)
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<p>Hierarchical content of each node in the coding system.</p> Full article ">Figure 2
<p>The system.</p> Full article ">Figure 3
<p>The prototype.</p> Full article ">Figure 4
<p>The flow of the sign-language recognition system.</p> Full article ">
24 pages, 5308 KiB  
Article
Energy-Optimized Content Refreshing of Age-of-Information-Aware Edge Caches in IoT Systems
by Martina Pappalardo, Antonio Virdis and Enzo Mingozzi
Future Internet 2022, 14(7), 197; https://doi.org/10.3390/fi14070197 - 28 Jun 2022
Cited by 1 | Viewed by 2229
Abstract
The Internet of Things (IoT) brings internet connectivity to everyday devices. These devices generate a large volume of information that needs to be transmitted to the nodes running the IoT applications, where they are processed and used to make some output decisions. On [...] Read more.
The Internet of Things (IoT) brings internet connectivity to everyday devices. These devices generate a large volume of information that needs to be transmitted to the nodes running the IoT applications, where they are processed and used to make some output decisions. On the one hand, the quality of these decisions is typically affected by the freshness of the received information, thus requesting frequent updates from the IoT devices. On the other hand, the severe energy, memory, processing, and communication constraints of IoT devices and networks pose limitations in the frequency of sensing and reporting. So, it is crucial to minimize the energy consumed by the device for sensing the environment and for transmitting the update messages, while taking into account the requirements for information freshness. Edge-caching can be effective in reducing the sensing and the transmission frequency; however, it requires a proper refreshing scheme to avoid staleness of information, as IoT applications need timeliness of status updates. Recently, the Age of Information (AoI) metric has been introduced: it is the time elapsed since the generation of the last received update, hence it can describe the timeliness of the IoT application’s knowledge of the process sampled by the IoT device. In this work, we propose a model-driven and AoI-aware optimization scheme for information caching at the network edge. To configure the cache parameters, we formulate an optimization problem that minimizes the energy consumption, considering both the sampling frequency and the average frequency of the requests sent to the device for refreshing the cache, while satisfying an AoI requirement expressed by the IoT application. We apply our caching scheme in an emulated IoT network, and we show that it minimizes the energy cost while satisfying the AoI requirement. We also compare the case in which the proposed caching scheme is implemented at the network edge against the case in which there is not a cache at the network edge. We show that the optimized cache can significantly lower the energy cost of devices that have a high transmission cost because it can reduce the number of transmissions. Moreover, the cache makes the system less sensitive to higher application-request rates, as the number of messages forwarded to the devices depends on the cache parameters. Full article
(This article belongs to the Special Issue Big Data Analytics for the Industrial Internet of Things)
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<p>IoT system overview and model: (<b>a</b>) a typical IoT system, (<b>b</b>) the considered scenario, (<b>c</b>) the interplay over time between the arrival process of IoT-application requests, the sampling process, and the cache operations.</p> Full article ">Figure 2
<p>Discrete Time Markov Chain.</p> Full article ">Figure 3
<p>Constraints for <math display="inline"><semantics> <mrow> <mi>A</mi> <mi>o</mi> <msub> <mi>I</mi> <mi>α</mi> </msub> <mo>=</mo> <mn>420</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> <mo>∕</mo> <mn>1800</mn> <msup> <mrow> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>=</mo> <mn>60</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math> (log scale on the <span class="html-italic">y</span>-axis).</p> Full article ">Figure 4
<p>Values of <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mover accent="true"> <mi>β</mi> <mo>¯</mo> </mover> </msub> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>A</mi> <mi>o</mi> <msub> <mi>I</mi> <mi>α</mi> </msub> <mo>=</mo> <mn>420</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> <mo>∕</mo> <mn>1800</mn> <msup> <mrow> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>=</mo> <mn>60</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>, varying <math display="inline"><semantics> <mover accent="true"> <mi>β</mi> <mo>¯</mo> </mover> </semantics></math>: (<b>a</b>) <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>β</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>β</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>0.994</mn> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>β</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>0.997</mn> </mrow> </semantics></math>, (<b>d</b>) <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>β</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> (log scale on the <span class="html-italic">y</span>-axis).</p> Full article ">Figure 5
<p>Values of <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mover accent="true"> <mi>β</mi> <mo>¯</mo> </mover> </msub> <mo> </mo> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>β</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, varying <math display="inline"><semantics> <mi>λ</mi> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>A</mi> <mi>o</mi> <msub> <mi>I</mi> <mi>α</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 6
<p>Optimum values of <math display="inline"><semantics> <mi>w</mi> </semantics></math> and <math display="inline"><semantics> <mi>s</mi> </semantics></math> for (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> <mo>∕</mo> <mn>90</mn> <msup> <mrow> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> <mo>∕</mo> <mn>180</mn> <msup> <mrow> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, and (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> <mo>∕</mo> <mn>360</mn> <msup> <mrow> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>A</mi> <mi>o</mi> <msub> <mi>I</mi> <mi>α</mi> </msub> <mo>=</mo> <mn>420</mn> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>, varying <math display="inline"><semantics> <mover accent="true"> <mi>β</mi> <mo>¯</mo> </mover> </semantics></math>.</p> Full article ">Figure 7
<p><math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mover accent="true"> <mi>β</mi> <mo>¯</mo> </mover> </msub> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>A</mi> <mi>o</mi> <msub> <mi>I</mi> <mi>α</mi> </msub> <mo>=</mo> <mn>420</mn> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> <mo>∕</mo> <mn>10</mn> <msup> <mrow> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> <mo>∕</mo> <mn>180</mn> <msup> <mrow> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> <mo>∕</mo> <mn>500</mn> <msup> <mrow> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> <mo>∕</mo> <mn>1000</mn> <msup> <mrow> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> <mo>∕</mo> <mn>1800</mn> <msup> <mrow> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>.</p> Full article ">Figure 8
<p>Percentage variation of the normalized energy cost for <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> <mo>∕</mo> <mn>1800</mn> <mo> </mo> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>β</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>0.95</mn> </mrow> </semantics></math> (circles), <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>β</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>0.995</mn> </mrow> </semantics></math> (diamonds), <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>β</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> (crosses).</p> Full article ">Figure 9
<p>Percentage variation of <math display="inline"><semantics> <mrow> <mi>A</mi> <mi>o</mi> <msub> <mi>I</mi> <mi>α</mi> </msub> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> <mo>∕</mo> <mn>1800</mn> <mo> </mo> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>β</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>0.95</mn> </mrow> </semantics></math> (circles), <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>β</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>0.995</mn> </mrow> </semantics></math> (diamonds), <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>β</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> (crosses).</p> Full article ">Figure 10
<p>Percentage variation of the cost: <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> <mo>∕</mo> <mn>1800</mn> <msup> <mrow> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> (circles), <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> <mo>∕</mo> <mn>3600</mn> <msup> <mrow> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> (diamonds), <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> <mo>∕</mo> <mn>7200</mn> <msup> <mrow> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> (crosses), <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>β</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>0.995</mn> </mrow> </semantics></math>.</p> Full article ">Figure 11
<p>Theoretical AoI CDF and empirical AoI CDF for <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> <mo>∕</mo> <mn>180</mn> <msup> <mrow> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>A</mi> <mi>o</mi> <msub> <mi>I</mi> <mi>α</mi> </msub> <mo>=</mo> <mn>420</mn> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>.</p> Full article ">Figure 12
<p>AoI CDF with 10 periodic servers: (<b>a</b>) first scenario, (<b>b</b>) second scenario.</p> Full article ">Figure 13
<p><math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mover accent="true"> <mi>β</mi> <mo>¯</mo> </mover> </msub> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>A</mi> <mi>o</mi> <msub> <mi>I</mi> <mi>α</mi> </msub> <mo>=</mo> <mn>420</mn> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> <mo>∕</mo> <mn>360</mn> <msup> <mrow> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> <mo>∕</mo> <mn>180</mn> <msup> <mrow> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> <mo>∕</mo> <mn>90</mn> <msup> <mrow> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>.</p> Full article ">Figure 14
<p>Service delay for <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> <mo>∕</mo> <mn>180</mn> <msup> <mrow> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>.</p> Full article ">
18 pages, 6015 KiB  
Article
Insights from the Experimentation of Named Data Networks in Mobile Wireless Environments
by Luís Gameiro, Carlos Senna and Miguel Luís
Future Internet 2022, 14(7), 196; https://doi.org/10.3390/fi14070196 - 27 Jun 2022
Cited by 2 | Viewed by 2435
Abstract
The Information-Centric Network (ICN) paradigm has been touted as one of the candidates for the Internet of the future, where the Named Data Network (NDN) architecture is the one leading the way. Despite the large amount of works published in the literature targeting [...] Read more.
The Information-Centric Network (ICN) paradigm has been touted as one of the candidates for the Internet of the future, where the Named Data Network (NDN) architecture is the one leading the way. Despite the large amount of works published in the literature targeting new implementations of such architecture, covering different network topologies and use cases, there are few NDN implementations in real networks. Moreover, most of these real-world NDN implementations, especially those addressing wireless and wired communication channels, are at a small scale, in laboratory environments. In this work, we evaluate the performance of an NDN-based implementation in a mobile wireless network, as part of a smart city infrastructure, making use of multiple wireless interfaces. We start by showing how we have implemented the NDN stack in current network nodes of the smart city infrastructure, following a hybrid solution where both TCP/IP and NDN paradigms can coexist. The implementation is evaluated in three scenarios, targeting different situations: mobility, the simultaneous use of different wireless interfaces and the network characteristics. The results show that our implementation works properly and insights about the correct NDN parameterization are derived. Full article
(This article belongs to the Special Issue Recent Advances in Information-Centric Networks (ICNs))
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<p>TCP/IP vs. NDN paradigm.</p> Full article ">Figure 2
<p>NDN basic principle. (<b>a</b>) Consumer and Producer interaction. (<b>b</b>) Node’s control structures.</p> Full article ">Figure 3
<p>Aveiro Tech City Living Lab: communication architecture (on the <b>left</b>) and infrastructure’s map (on the <b>right</b>).</p> Full article ">Figure 4
<p>Deployment of NDN over ATCLL mobile communication units.</p> Full article ">Figure 5
<p>Forwarding process.</p> Full article ">Figure 6
<p>Messages exchanged following the NDN architecture and heterogeneous radio access technologies.</p> Full article ">Figure 7
<p>Route performed by the mobile node in the first experiment. (<b>a</b>) Between <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> s and <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>574</mn> </mrow> </semantics></math> s the vehicle connects with four RSUs. (<b>b</b>) Between <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>574</mn> </mrow> </semantics></math> s and <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math> s both vehicle and pedestrian are in communication range. (<b>c</b>) Between <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math> s and <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>1534</mn> </mrow> </semantics></math> s the vehicle travels around the city, connecting with three RSUs. (<b>d</b>) Finally, the vehicle ends its journey next to the pedestrian.</p> Full article ">Figure 8
<p>Number of unique data (chunks) received during the experiment. (<b>a</b>) Data downloaded by the vehicle. (<b>b</b>) Data downloaded by the pedestrian.</p> Full article ">Figure 9
<p>Network overhead. (<b>a</b>) Total amount of packets transmitted/received by the vehicle. (<b>b</b>) Total contact time between each mobile node and the infrastructure.</p> Full article ">Figure 10
<p>NDN in multiple wireless interfaces—requesting a 6 MB file using both ITS-G5 and Wi-Fi.</p> Full article ">Figure 11
<p>Download durations and number of timeout events for different MTUs and Interest timeouts. (<b>a</b>) Time to download file. (<b>b</b>) Timeouts.</p> Full article ">
16 pages, 8595 KiB  
Article
Cooperative D-GNSS Aided with Multi Attribute Decision Making Module: A Rigorous Comparative Analysis
by Thanassis Mpimis, Theodore T. Kapsis, Athanasios D. Panagopoulos and Vassilis Gikas
Future Internet 2022, 14(7), 195; https://doi.org/10.3390/fi14070195 - 27 Jun 2022
Cited by 4 | Viewed by 2192
Abstract
Satellite positioning lies within the very core of numerous Intelligent Transportation Systems (ITS) and Future Internet applications. With the emergence of connected vehicles, the performance requirements of Global Navigation Satellite Systems (GNSS) are constantly pushed to their limits. To this end, Cooperative Positioning [...] Read more.
Satellite positioning lies within the very core of numerous Intelligent Transportation Systems (ITS) and Future Internet applications. With the emergence of connected vehicles, the performance requirements of Global Navigation Satellite Systems (GNSS) are constantly pushed to their limits. To this end, Cooperative Positioning (CP) solutions have attracted attention in order to enhance the accuracy and reliability of low-cost GNSS receivers, especially in complex propagation environments. In this paper, the problem of efficient and robust CP employing low-cost GNSS receivers is investigated over critical ITS scenarios. By adopting a Cooperative-Differential GNSS (C-DGNSS) framework, the target’s vehicle receiver can obtain Position–Velocity–Time (PVT) corrections from a neighboring vehicle and update its own position in real-time. A ranking module based on multi-attribute decision-making (MADM) algorithms is proposed for the neighboring vehicle rating and optimal selection. The considered MADM techniques are simulated with various weightings, normalization techniques, and criteria associated with positioning accuracy and reliability. The obtained criteria values are experimental GNSS measurements from several low-cost receivers. A comparative and sensitivity analysis are provided by evaluating the MADM algorithms in terms of ranking performance and robustness. The positioning data time series and the numerical results are then presented, and comments are made. Scoring-based and distance-based MADM methods perform better, while L1 RMS, HDOP, and Hz std are the most critical criteria. The multi-purpose applicability of the proposed scheme, not only for land vehicles, is also discussed. Full article
(This article belongs to the Collection Innovative People-Centered Solutions Applied to Industries, Cities and Societies)
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<p>ITS scenarios and connected vehicle services.</p> Full article ">Figure 2
<p>Diagram of the Cooperative-Differential GNSS (C-DGNSS) positioning configuration.</p> Full article ">Figure 3
<p>Mean ranking of I/II/III positions of alternative vehicles for the entire trajectory using all normalizations and 13 MADM methods. (<b>Top</b>) Equal weights; (<b>Bottom</b>) Unequal weights.</p> Full article ">
18 pages, 538 KiB  
Article
A Novel Text Classification Technique Using Improved Particle Swarm Optimization: A Case Study of Arabic Language
by Yousif A. Alhaj, Abdelghani Dahou, Mohammed A. A. Al-qaness, Laith Abualigah, Aaqif Afzaal Abbasi, Nasser Ahmed Obad Almaweri, Mohamed Abd Elaziz and Robertas  Damaševičius
Future Internet 2022, 14(7), 194; https://doi.org/10.3390/fi14070194 - 27 Jun 2022
Cited by 22 | Viewed by 3673
Abstract
We propose a novel text classification model, which aims to improve the performance of Arabic text classification using machine learning techniques. One of the effective solutions in Arabic text classification is to find the suitable feature selection method with an optimal number of [...] Read more.
We propose a novel text classification model, which aims to improve the performance of Arabic text classification using machine learning techniques. One of the effective solutions in Arabic text classification is to find the suitable feature selection method with an optimal number of features alongside the classifier. Although several text classification methods have been proposed for the Arabic language using different techniques, such as feature selection methods, an ensemble of classifiers, and discriminative features, choosing the optimal method becomes an NP-hard problem considering the huge search space. Therefore, we propose a method, called Optimal Configuration Determination for Arabic text Classification (OCATC), which utilized the Particle Swarm Optimization (PSO) algorithm to find the optimal solution (configuration) from this space. The proposed OCATC method extracts and converts the features from the textual documents into a numerical vector using the Term Frequency-Inverse Document Frequency (TF–IDF) approach. Finally, the PSO selects the best architecture from a set of classifiers to feature selection methods with an optimal number of features. Extensive experiments were carried out to evaluate the performance of the OCATC method using six datasets, including five publicly available datasets and our proposed dataset. The results obtained demonstrate the superiority of OCATC over individual classifiers and other state-of-the-art methods. Full article
(This article belongs to the Special Issue Deep Learning and Natural Language Processing)
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<p>General Framework of OCATC.</p> Full article ">Figure 2
<p>Document distribution for: (<b>a</b>) DatasetB; (<b>b</b>) DatasetC; (<b>c</b>) DatasetE.</p> Full article ">Figure 3
<p>Precision, recall, and f1-score for: (<b>a</b>) DatasetA; (<b>b</b>) DatasetB; (<b>c</b>) DatasetC; (<b>d</b>) DatasetD; (<b>e</b>) DatasetE; and (<b>f</b>) DatasetF.</p> Full article ">
18 pages, 4245 KiB  
Article
Characterization of Dynamic Blockage Probability in Industrial Millimeter Wave 5G Deployments
by Anastasia Kondratyeva, Daria Ivanova, Vyacheslav Begishev, Ekaterina Markova, Evgeni Mokrov, Yuliya Gaidamaka and Konstantin Samouylov
Future Internet 2022, 14(7), 193; https://doi.org/10.3390/fi14070193 - 27 Jun 2022
Cited by 7 | Viewed by 2398
Abstract
5G New Radio (NR) systems promise to expand offered services to enable industrial automation scenarios. To enable ultra-low latency at the air interface and to exploit spatial redundancy for applications such as synchronization and motion control, user equipment (UE) will naturally require device-to-device [...] Read more.
5G New Radio (NR) systems promise to expand offered services to enable industrial automation scenarios. To enable ultra-low latency at the air interface and to exploit spatial redundancy for applications such as synchronization and motion control, user equipment (UE) will naturally require device-to-device (D2D) and base station (BS) to UE communications and directional transmissions provided by millimeter wave (mmWave) frequencies. However, the performance of such systems is affected by the blockage phenomenon. In this paper, we propose a simple line-of-sight (LoS) blockage model for Industrial mmWave-based industrial Internet of Things (IIoT) deployments. The model is based on two sub-models, where each part can be changed/replaced to fit the scenario of interest. The first part is based on photogrammetry and provides the transparency probability for a single element on the factory floor. The second one utilizes these models of industrial elements to form the deployment and then applies stochastic geometry to derive the blockage probability. The proposed model can be utilized for any type of industrial machine, accounts for their inherent regular deployments on the factory floor, and provides the final results in an easy-to-compute form. Our results indicate that direct UE-UE communications are feasible in sparse deployments (less than 0.1 machine/m2) or at small communications distances (less than 5–10 m) or in deployments with highly transparent machines (machine transparency less than 0.5). Otherwise, BS-UE communications need to be utilized. In this case, BS height becomes a critical parameter affecting the LoS probability. Specifically, using a BS height of 10 m allows blockage probability to be improved by 20–30% as compared to a BS of height 4 m. Finally, as UE height produces more impact on the blockage probability as compared to a machine height, in real deployments, one needs to ensure that the height of communications modules at UEs are maximized. Full article
(This article belongs to the Special Issue Internet of Things and Cyber-Physical Systems)
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<p>The considered deployment scenario and two-dimensional view of the abstracted model. (<b>a</b>) The considered deployment scenario. (<b>b</b>) Abstracted model.</p> Full article ">Figure 2
<p>Methodological steps for LoS blockage probability assessment. (<b>a</b>) Setting up the scene. (<b>b</b>) Tracking objects in motion. (<b>c</b>) Visibility paths.</p> Full article ">Figure 2 Cont.
<p>Methodological steps for LoS blockage probability assessment. (<b>a</b>) Setting up the scene. (<b>b</b>) Tracking objects in motion. (<b>c</b>) Visibility paths.</p> Full article ">Figure 3
<p>LoS blockage probability for selected objects.</p> Full article ">Figure 4
<p>The side view of the BS-UE communications scenario.</p> Full article ">Figure 5
<p>Accuracy assessment of the proposed model.</p> Full article ">Figure 6
<p>UE to UE communications scenario. (<b>a</b>) The effect of machines dimensions. (<b>b</b>) The effect of machine’s transparency.</p> Full article ">Figure 6 Cont.
<p>UE to UE communications scenario. (<b>a</b>) The effect of machines dimensions. (<b>b</b>) The effect of machine’s transparency.</p> Full article ">Figure 7
<p>BS to UE communications with random machine heights. (<b>a</b>) The effect of machine height. (<b>b</b>) The effect of UE height.</p> Full article ">Figure 8
<p>Comparison of random and constant machine heights.</p> Full article ">Figure 9
<p>Average blockage probability.</p> Full article ">Figure 10
<p>Blockage probability in deployments with considered machines.</p> Full article ">
31 pages, 1560 KiB  
Review
Convergence of Information-Centric Networks and Edge Intelligence for IoV: Challenges and Future Directions
by Salahadin Seid Musa, Marco Zennaro, Mulugeta Libsie and Ermanno Pietrosemoli
Future Internet 2022, 14(7), 192; https://doi.org/10.3390/fi14070192 - 25 Jun 2022
Cited by 13 | Viewed by 5723
Abstract
Recently the Internet of Vehicles (IoV) has become a promising research area in the field of the Internet of Things (IoT), which enables vehicles to communicate and exchange real-time information with each other, as well as with infrastructure, people, and other sensors and [...] Read more.
Recently the Internet of Vehicles (IoV) has become a promising research area in the field of the Internet of Things (IoT), which enables vehicles to communicate and exchange real-time information with each other, as well as with infrastructure, people, and other sensors and actuators through various communication interfaces. The realization of IoV networks faces various communication and networking challenges to meet stringent requirements of low latency, dynamic topology, high data-rate connectivity, resource allocation, multiple access, and QoS. Advances in information-centric networks (ICN), edge computing (EC), and artificial intelligence (AI) will transform and help to realize the Intelligent Internet of Vehicles (IIoV). Information-centric networks have emerged as a paradigm promising to cope with the limitations of the current host-based network architecture (TCP/IP-based networks) by providing mobility support, efficient content distribution, scalability and security based on content names, regardless of their location. Edge computing (EC), on the other hand, is a key paradigm to provide computation, storage and other cloud services in close proximity to where they are requested, thus enabling the support of real-time services. It is promising for computation-intensive applications, such as autonomous and cooperative driving, and to alleviate storage burdens (by caching). AI has recently emerged as a powerful tool to break through obstacles in various research areas including that of intelligent transport systems (ITS). ITS are smart enough to make decisions based on the status of a great variety of inputs. The convergence of ICN and EC with AI empowerment will bring new opportunities while also raising not-yet-explored obstacles to realize Intelligent IoV. In this paper, we discuss the applicability of AI techniques in solving challenging vehicular problems and enhancing the learning capacity of edge devices and ICN networks. A comprehensive review is provided of utilizing intelligence in EC and ICN to address current challenges in their application to IIoV. In particular, we focus on intelligent edge computing and networking, offloading, intelligent mobility-aware caching and forwarding and overall network performance. Furthermore, we discuss potential solutions to the presented issues. Finally, we highlight potential research directions which may illuminate efforts to develop new intelligent IoV applications. Full article
(This article belongs to the Special Issue Recent Advances in Information-Centric Networks (ICNs))
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<p>Converged Architecture for IIoV.</p> Full article ">Figure 2
<p>The organization of the remaining parts of the paper.</p> Full article ">Figure 3
<p>ITS Spectrum allocation in US and Europe.</p> Full article ">Figure 4
<p>Comparison of host-centric and information-centric stack [<a href="#B46-futureinternet-14-00192" class="html-bibr">46</a>].</p> Full article ">Figure 5
<p>Typical interest and data communication in NDN communication.</p> Full article ">Figure 6
<p>CNN and example of object-detection application of autonomous vehicle in IoV (<b>a</b>) Typical CNN learning process. (<b>b</b>) Object detection in IoV context.</p> Full article ">Figure 7
<p>RNN and example of position prediction application of autonomous vehicle in IoV [<a href="#B68-futureinternet-14-00192" class="html-bibr">68</a>]. (<b>a</b>) Typical RNN. (<b>b</b>) Position prediction.</p> Full article ">Figure 8
<p>Typical reinforcement learning process.</p> Full article ">Figure 9
<p>IoV FL process [<a href="#B69-futureinternet-14-00192" class="html-bibr">69</a>]. (<b>a</b>) Federated learning performed on RSU (V2I). (<b>b</b>) Federated learning among vehicles (V2V).</p> Full article ">Figure 10
<p>Typical IoV TinyML.</p> Full article ">Figure 11
<p>The convergence of ICN-Edge Computing-AI for IIoV.</p> Full article ">Figure 12
<p>Protocol stack of converged architecture for ICN IIoV.</p> Full article ">Figure 13
<p>Collaborative driving—see through.</p> Full article ">
10 pages, 335 KiB  
Article
Polarity and Subjectivity Detection with Multitask Learning and BERT Embedding
by Ranjan Satapathy, Shweta Rajesh Pardeshi and Erik Cambria
Future Internet 2022, 14(7), 191; https://doi.org/10.3390/fi14070191 - 22 Jun 2022
Cited by 13 | Viewed by 3052
Abstract
In recent years, deep learning-based sentiment analysis has received attention mainly because of the rise of social media and e-commerce. In this paper, we showcase the fact that the polarity detection and subjectivity detection subtasks of sentiment analysis are inter-related. To this end, [...] Read more.
In recent years, deep learning-based sentiment analysis has received attention mainly because of the rise of social media and e-commerce. In this paper, we showcase the fact that the polarity detection and subjectivity detection subtasks of sentiment analysis are inter-related. To this end, we propose a knowledge-sharing-based multitask learning framework. To ensure high-quality knowledge sharing between the tasks, we use the Neural Tensor Network, which consists of a bilinear tensor layer that links the two entity vectors. We show that BERT-based embedding with our MTL framework outperforms the baselines and achieves a new state-of-the-art status in multitask learning. Our framework shows that the information across datasets for related tasks can be helpful for understanding task-specific features. Full article
(This article belongs to the Special Issue Machine Learning Perspective in the Convolutional Neural Network Era)
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<p>Framework of our proposed model.</p> Full article ">Figure 2
<p>MTL based on BERT embedding. (<b>a</b>) Accuracy graph. (<b>b</b>) Loss graph.</p> Full article ">
13 pages, 1804 KiB  
Article
First Steps of Asthma Management with a Personalized Ontology Model
by Hicham Ajami, Hamid Mcheick and Catherine Laprise
Future Internet 2022, 14(7), 190; https://doi.org/10.3390/fi14070190 - 22 Jun 2022
Cited by 4 | Viewed by 3152
Abstract
Asthma is a chronic respiratory disease characterized by severe inflammation of the bronchial mucosa. Allergic asthma is the most common form of this health issue. Asthma is classified into allergic and non-allergic asthma, and it can be triggered by several factors such as [...] Read more.
Asthma is a chronic respiratory disease characterized by severe inflammation of the bronchial mucosa. Allergic asthma is the most common form of this health issue. Asthma is classified into allergic and non-allergic asthma, and it can be triggered by several factors such as indoor and outdoor allergens, air pollution, weather conditions, tobacco smoke, and food allergens, as well as other factors. Asthma symptoms differ in their frequency and severity since each patient reacts differently to these triggers. Formal knowledge is selected as one of the most promising solutions to deal with these challenges. This paper presents a new personalized approach to manage asthma. An ontology-driven model supported by Semantic Web Rule Language (SWRL) medical rules is proposed to provide personalized care for an asthma patient by identifying the risk factors and the development of possible exacerbations. Full article
(This article belongs to the Special Issue Smart Objects and Technologies for Social Good)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Proposed system architecture.</p> Full article ">Figure 2
<p>Part of our ontology model.</p> Full article ">Figure 3
<p>Design assessments.</p> Full article ">Figure 4
<p>An example of risk recognition reasoning based on rules.</p> Full article ">Figure 5
<p>Part of proposed ontology.</p> Full article ">Figure 6
<p>SWRL rules in SWRLTab.</p> Full article ">
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