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29 pages, 4830 KiB  
Article
Enabling Seamless Connectivity: Networking Innovations in Wireless Sensor Networks for Industrial Application
by Shathya Duobiene, Rimantas Simniškis and Gediminas Račiukaitis
Sensors 2024, 24(15), 4881; https://doi.org/10.3390/s24154881 - 27 Jul 2024
Viewed by 1160
Abstract
The wide-ranging applications of the Internet of Things (IoT) show that it has the potential to revolutionise industry, improve daily life, and overcome global challenges. This study aims to evaluate the performance scalability of mature industrial wireless sensor networks (IWSNs). A new classification [...] Read more.
The wide-ranging applications of the Internet of Things (IoT) show that it has the potential to revolutionise industry, improve daily life, and overcome global challenges. This study aims to evaluate the performance scalability of mature industrial wireless sensor networks (IWSNs). A new classification approach for IoT in the industrial sector is proposed based on multiple factors and we introduce the integration of 6LoWPAN (IPv6 over low-power wireless personal area networks), message queuing telemetry transport for sensor networks (MQTT-SN), and ContikiMAC protocols for sensor nodes in an industrial IoT system to improve energy-efficient connectivity. The Contiki COOJA WSN simulator was applied to model and simulate the performance of the protocols in two static and moving scenarios and evaluate the proposed novelty detection system (NDS) for network intrusions in order to identify certain events in real time for realistic dataset analysis. The simulation results show that our method is an essential measure in determining the number of transmissions required to achieve a certain reliability target in an IWSNs. Despite the growing demand for low-power operation, deterministic communication, and end-to-end reliability, our methodology of an innovative sensor design using selective surface activation induced by laser (SSAIL) technology was developed and deployed in the FTMC premises to demonstrate its long-term functionality and reliability. The proposed framework was experimentally validated and tested through simulations to demonstrate the applicability and suitability of the proposed approach. The energy efficiency in the optimised WSN was increased by 50%, battery life was extended by 350%, duplicated packets were reduced by 80%, data collisions were reduced by 80%, and it was shown that the proposed methodology and tools could be used effectively in the development of telemetry node networks in new industrial projects in order to detect events and breaches in IoT networks accurately. The energy consumption of the developed sensor nodes was measured. Overall, this study performed a comprehensive assessment of the challenges of industrial processes, such as the reliability and stability of telemetry channels, the energy efficiency of autonomous nodes, and the minimisation of duplicate information transmission in IWSNs. Full article
(This article belongs to the Special Issue IoT Sensors Development and Application for Environment & Safety)
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<p>LoWPAN system architecture.</p>
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<p>MQTT-SN system architecture.</p>
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<p>Proposed IIoT architecture based on PSO-DC algorithm.</p>
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<p>Flowchart of the proposed PSO-DC algorithm.</p>
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<p>Simulation models of network topologies: star, mesh, and tree.</p>
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<p>Schematic diagram for initial planning and visualisation.</p>
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<p>Topology of the WSN (<b>a</b>) deployed in the FTMC laboratory (<b>b</b>).</p>
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<p>Data delivery latency for fixed and movable nodes on four square areas of 25 × 25 m<sup>2</sup>, 50 × 50 m<sup>2</sup>, 75 × 75 m<sup>2</sup>, and 100 × 100 m<sup>2</sup>.</p>
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<p>Average hops count in the networks after WSN optimisation with different strategies: optimal mesh, IMR, and RL network in the areas of 25 × 25 m<sup>2</sup>, 50 × 50 m<sup>2</sup>, 75 × 75 m<sup>2</sup>, and 100 × 100 m<sup>2</sup>.</p>
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<p>Number of received packets per node versus nodes.</p>
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<p>Average radio duty cycle.</p>
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<p>Average power consumption of the nodes in different operation modes and components: LPM, the consumption of the CPU, radio listen, and transmission modes.</p>
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<p>Temperature readings collected by the WSN in the FTMC laboratory.</p>
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<p>Humidity readings collected by the WSN in the FTMC laboratory.</p>
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11 pages, 262 KiB  
Article
Analysis of Oral Health Literacy in Caregivers of Special Needs Individuals in Special Schools and Social Institutions in Jakarta
by Esther Rotiur Hutagalung, Anandina Irmagita Soegyanto, Mas Suryalis Ahmad and Masita Mandasari
Dent. J. 2023, 11(9), 221; https://doi.org/10.3390/dj11090221 - 19 Sep 2023
Viewed by 1591
Abstract
Background: Individuals with special needs (IWSN) are susceptible to oral conditions such as caries and periodontal disease. In order to improve oral health of IWSN, it is important to improve the oral health literacy (OHL) of caregivers, as they play an important role [...] Read more.
Background: Individuals with special needs (IWSN) are susceptible to oral conditions such as caries and periodontal disease. In order to improve oral health of IWSN, it is important to improve the oral health literacy (OHL) of caregivers, as they play an important role in the daily hygiene and personal care of these people. Objective: This study aimed to analyze the OHL in caregivers of IWSN in special schools (informal caregivers) and social institutions for people with disabilities (professional caregivers) in Jakarta, Indonesia. Methods: The study was conducted with a cross-sectional and descriptive analytic design with a cluster sampling method of 400 informal and professional caregivers. The study utilized the validated Health Literacy Dentistry-Indonesian Version (HeLD-ID) questionnaire to measure OHL. Quantitative data was analyzed using non-parametric Kruskal Wallis and Mann Whitney tests (significant level p < 0.05). Results: The median total OHL score of respondents was 3.14 (0.24–4) for informal caregivers and 3.21 (0–4) for professional caregivers. The OHL score of the two populations showed significant differences in the domains of receptivity (p = 0.036), understanding (p = 0.030), and economic barriers (p = 0.022). Significant differences in OHL scores were also noted among caregivers according to their sociodemographic characteristics, such as level of education, and number of IWSN handled. Conclusion: Informal and professional caregivers in this study showed good level of OHL. To elucidate the relationship between caregiver’s level of OHL with IWSN, further study is necessary. Full article
14 pages, 345 KiB  
Article
Fast and Low-Overhead Time Synchronization for Industrial Wireless Sensor Networks with Mesh-Star Architecture
by Zhaowei Wang, Tailiang Yong and Xiangjin Song
Sensors 2023, 23(8), 3792; https://doi.org/10.3390/s23083792 - 7 Apr 2023
Cited by 1 | Viewed by 1734
Abstract
Low-overhead, robust, and fast-convergent time synchronization is important for resource-constrained large-scale industrial wireless sensor networks (IWSNs). The consensus-based time synchronization method with strong robustness has been paid more attention in wireless sensor networks. However, high communication overhead and slow convergence speed are inherent [...] Read more.
Low-overhead, robust, and fast-convergent time synchronization is important for resource-constrained large-scale industrial wireless sensor networks (IWSNs). The consensus-based time synchronization method with strong robustness has been paid more attention in wireless sensor networks. However, high communication overhead and slow convergence speed are inherent drawbacks for consensus time synchronization due to inefficient frequent iterations. In this paper, a novel time synchronization algorithm for IWSNs with a mesh–star architecture is proposed, namely, fast and low-overhead time synchronization (FLTS). The proposed FLTS divides the synchronization phase into two layers: mesh layer and star layer. A few resourceful routing nodes in the upper mesh layer undertake the low-efficiency average iteration, and the massive low-power sensing nodes in the star layer synchronize with the mesh layer in a passive monitoring manner. Therefore, a faster convergence and lower communication overhead time synchronization is achieved. The theoretical analysis and simulation results demonstrate the efficiency of the proposed algorithm in comparison with the state-of-the-art algorithms, i.e., ATS, GTSP, and CCTS. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in Industrial Applications)
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<p>Schematic illustration of two-layered IWSNs. The number in figure denotes the node ID.</p>
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<p>The convergence of the logical clock in the mesh layer. (<b>a</b>) Logical clock skew; (<b>b</b>) Logical clock offset.</p>
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<p>Convergence comparison of the logical clock in whole network. (<b>a</b>) Logical clock skew; (<b>b</b>) Logical clock offset.</p>
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<p>Comparison of communication overhead.</p>
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<p>Convergence comparison of the logical clock in intra-cluster synchronization. (<b>a</b>) Logical clock skew; (<b>b</b>) Logical clock offset.</p>
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<p>Convergence comparison of the logical clock in inter-cluster synchronization. (<b>a</b>) Logical clock skew; (<b>b</b>) Logical clock offset.</p>
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<p>Convergence comparison of the logical clock against node mobility. (<b>a</b>) Logical clock skew; (<b>b</b>) Logical clock offset.</p>
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<p>Convergence comparison of the logical clock against communication delay. (<b>a</b>) Logical clock skew; (<b>b</b>) Logical clock offset.</p>
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14 pages, 2877 KiB  
Article
AoI-Bounded Scheduling for Industrial Wireless Sensor Networks
by Chenggen Pu, Han Yang, Ping Wang and Changjie Dong
Electronics 2023, 12(6), 1499; https://doi.org/10.3390/electronics12061499 - 22 Mar 2023
Cited by 2 | Viewed by 1988
Abstract
Age of information (AoI) is an emerging network metric that measures information freshness from an application layer perspective. It can evaluate the timeliness of information in industrial wireless sensor networks (IWSNs). Previous research has primarily focused on minimizing the long-term average AoI of [...] Read more.
Age of information (AoI) is an emerging network metric that measures information freshness from an application layer perspective. It can evaluate the timeliness of information in industrial wireless sensor networks (IWSNs). Previous research has primarily focused on minimizing the long-term average AoI of the entire system. However, in practical industrial applications, optimizing the average AoI does not guarantee that the peak AoI of each data packet is within a bounded interval. If the AoI of certain packets exceeds the predetermined threshold, it can have a significant impact on the stability of the industrial control system. Therefore, this paper studies the scheduling problem subject to a hard AoI performance requirement in IWSNs. First, we propose a low-complexity AoI-bounded scheduling algorithm for IWSNs that guarantees that the AoI of each packet is within a bounded interval. Then, we analyze the schedulability conditions of the algorithm and propose a method to decrease the peak AoI of nodes with higher AoI requirements. Finally, we present a numerical example that illustrates the proposed algorithm step by step. The results demonstrate the effectiveness of our algorithm, which can guarantee bounded AoI intervals (BAIs) for all nodes. Full article
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<p>Evolution of node’s AoI with respect to different transmission intervals. (<bold>a</bold>) The change of node’s AoI when <inline-formula><mml:math id="mm184"><mml:semantics><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>&gt;</mml:mo><mml:msub><mml:mi>T</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula>; (<bold>b</bold>) the change of node’s AoI when <inline-formula><mml:math id="mm185"><mml:semantics><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>T</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula> under ideal conditions; (<bold>c</bold>) the change of node’s AoI when <inline-formula><mml:math id="mm186"><mml:semantics><mml:mrow><mml:mfrac><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow><mml:mn>2</mml:mn></mml:mfrac><mml:mo>&lt;</mml:mo><mml:msub><mml:mi>I</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>&lt;</mml:mo><mml:msub><mml:mi>T</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula>; (<bold>d</bold>) the change of node’s AoI when <inline-formula><mml:math id="mm187"><mml:semantics><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>&lt;</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow><mml:mn>2</mml:mn></mml:mfrac></mml:mrow></mml:semantics></mml:math></inline-formula>.</p>
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<p>The superframe is divided into multiple minimum transmission units, where the length of each transmission unit is equal to the minimum sampling period in all nodes, and <inline-formula><mml:math id="mm188"><mml:semantics><mml:mi>σ</mml:mi></mml:semantics></mml:math></inline-formula> time slots are reserved for aperiodic data at the end of each transmission unit.</p>
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<p>Illustration of the time slot allocation.</p>
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<p>The real-time AoI, with the corresponding peak AoI and benchmark of three sample nodes: (<bold>a</bold>) node #2, (<bold>b</bold>) node #6, (<bold>c</bold>) node #9.</p>
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<p>Boxplot of AoI for all ten sensor nodes.</p>
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<p>The AoI of node #9 with different <inline-formula><mml:math id="mm189"><mml:semantics><mml:mrow><mml:msub><mml:mi>α</mml:mi><mml:mn>9</mml:mn></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula>.</p>
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<p>Boxplot of AoI for node #9 with different TIC <inline-formula><mml:math id="mm190"><mml:semantics><mml:mrow><mml:msub><mml:mi>α</mml:mi><mml:mn>9</mml:mn></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula>.</p>
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<p>Schedulability analysis with respect to varying numbers of nodes and sampling periods.</p>
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17 pages, 767 KiB  
Article
RESEMBLE: A Real-Time Stack for Synchronized Mesh Mobile Bluetooth Low Energy Networks
by Luca Leonardi, Lucia Lo Bello and Gaetano Patti
Appl. Syst. Innov. 2023, 6(1), 19; https://doi.org/10.3390/asi6010019 - 26 Jan 2023
Cited by 3 | Viewed by 2029
Abstract
Bluetooth Low Energy (BLE) is a wireless technology for low-power, low-cost and lowcomplexity short-range communications. On top of the BLE stack, the Bluetooth Mesh profile can be adopted to handle large networks with mesh topologies. BLE is a promising candidate for the implemention [...] Read more.
Bluetooth Low Energy (BLE) is a wireless technology for low-power, low-cost and lowcomplexity short-range communications. On top of the BLE stack, the Bluetooth Mesh profile can be adopted to handle large networks with mesh topologies. BLE is a promising candidate for the implemention of Industrial Wireless Sensor Networks (IWSNs), thanks to its wide diffusion (e.g., on smartphones and tablets) and the lower cost of the devices compared to other wireless industrial communication technologies. However, neither the BLE nor the Bluetooth Mesh specifications can provide real-time messages with bounded delays. To overcome this limitation, this work proposes RESEMBLE, a real-time stack developed on top of BLE that is able to realize low-cost IWSNs over mesh topologies. RESEMBLE offers support to both real-time and non-real-time communications on the same network. Moreover, RESEMBLE provides clock synchronization, thus allowing for Time Division Multiple Access (TDMA) transmissions. The clock synchronization provided by RESEMBLE can be also exploited by the upper layers’ industrial applications to implement timecoordinated actions. Full article
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<p>Bluetooth Mesh Profile stack.</p>
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<p>Advertisement event.</p>
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<p>Advertisement collision example.</p>
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<p>RESEMBLE stack.</p>
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<p>Example of RESEMBLE superframe.</p>
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<p>RESEMBLE software components.</p>
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<p>Scenario for clock synchronization accuracy assessment.</p>
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<p>Clock skew distribution (from N1 to N0).</p>
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<p>Clock skew distribution (from N2 to N0).</p>
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<p>Clock skew distribution (from N3 to N0).</p>
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<p>Maximum absolute clock skew.</p>
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<p>Scenario for end-to-end delay assessment.</p>
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<p>End-to-end delay distributions for the flows generated by the Nodes 1 and 2.</p>
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<p>End-to-end delay distributions for the flows generated by the Nodes 0 and 3.</p>
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34 pages, 10096 KiB  
Article
Maintenance 5.0: Towards a Worker-in-the-Loop Framework for Resilient Smart Manufacturing
by Alejandro Cortés-Leal, César Cárdenas and Carolina Del-Valle-Soto
Appl. Sci. 2022, 12(22), 11330; https://doi.org/10.3390/app122211330 - 8 Nov 2022
Cited by 21 | Viewed by 4533
Abstract
Due to the global uncertainty caused by social problems such as COVID-19 and the war in Ukraine, companies have opted for the use of emerging technologies, to produce more with fewer resources and thus maintain their productivity; that is why the market for [...] Read more.
Due to the global uncertainty caused by social problems such as COVID-19 and the war in Ukraine, companies have opted for the use of emerging technologies, to produce more with fewer resources and thus maintain their productivity; that is why the market for wearable artificial intelligence (AI) and wireless sensor networks (WSNs) has grown exponentially. In the last decade, maintenance 4.0 has achieved best practices due to the appearance of emerging technologies that improve productivity. However, some social trends seek to explore the interaction of AI with human beings to solve these problems, such as Society 5.0 and Industry 5.0. The research question is: could a human-in-the-loop-based maintenance framework improve the resilience of physical assets? This work helps to answer this question through the following contributions: first, a search for research gaps in maintenance; second, a scoping literature review of the research question; third, the definition, characteristics, and the control cycle of Maintenance 5.0 framework; fourth, the maintenance worker 5.0 definition and characteristics; fifth, two proposals for the calculation of resilient maintenance; and finally, Maintenance 5.0 is validated through a simulation in which the use of the worker in the loop improves the resilience of an Industrial Wireless Sensor Network (IWSN). Full article
(This article belongs to the Special Issue Smart Industrial System)
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<p>Maintenance Space State: (1) Asset life cycle without maintenance events, (2) Failure event with maintenance events, and (3) No failure events. Own elaboration inspired from [<a href="#B25-applsci-12-11330" class="html-bibr">25</a>].</p>
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<p>Wearable ecosystem.</p>
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<p>Research framework.</p>
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<p>Flow diagram of search and selection process.</p>
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<p>Selected papers by year of publication.</p>
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<p>General human-in-the-loop control framework for maintenance 5.0.</p>
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<p>Worker 5.0 pyramid.</p>
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<p>Worker 5.0 ecosystem.</p>
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<p>A general setup for RL with a human in the loop. Own elaboration, adapted from [<a href="#B119-applsci-12-11330" class="html-bibr">119</a>].</p>
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<p>Connected worker 5.0 inside ISA 95.</p>
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<p>Resilience for real-time data. A is the <span class="html-italic">No_Failure Level</span> plus B, where B is the cosine amplitude.</p>
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<p>MTBF, MTTF and MTTR maintenance metrics.</p>
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<p>Resilience configuration.</p>
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<p>Worker 5.0 collaboration in Jamming Detection and Mitigation.</p>
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<p>Distribution of the IWSN nodes of the experiment. Image obtained from [<a href="#B107-applsci-12-11330" class="html-bibr">107</a>].</p>
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<p>Jamming Detection Algorithm. Image obtained from [<a href="#B107-applsci-12-11330" class="html-bibr">107</a>].</p>
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<p>The worker-in-the-loop jamming attack control mechanism.</p>
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<p>Node-red: Communication to Telegram Bot and Historic data through MQTT protocol.</p>
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<p>Resilience analysis for IWSN Jamming attack.</p>
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<p>MTBF, MTTF y MTTR calculation.</p>
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<p>Worker 5.0—IWSN interaction in Reinforcement Learning.</p>
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<p>Network simulated.</p>
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<p>Simulation results: Iterations vs. Score. (<b>a</b>) No human, (<b>b</b>) Human.</p>
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<p>Resilience with simulated data.</p>
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10 pages, 383 KiB  
Article
rSEM: System-Entropy-Measure-Guided Routing Algorithm for Industrial Wireless Sensor Networks
by Xiaoxiong Xiong, Chao Dong and Kai Niu
Sensors 2022, 22(21), 8291; https://doi.org/10.3390/s22218291 - 29 Oct 2022
Cited by 1 | Viewed by 1457
Abstract
In this paper, a new system entropy measure is used to optimize the routing algorithm in power consumption. We introduce the system entropy measure into the problem of industrial wireless sensor networks (iWSNs) routing and propose a high-performance routing algorithm guided by the [...] Read more.
In this paper, a new system entropy measure is used to optimize the routing algorithm in power consumption. We introduce the system entropy measure into the problem of industrial wireless sensor networks (iWSNs) routing and propose a high-performance routing algorithm guided by the system entropy measure (rSEM). Based on the cluster iWSNs architecture, the rSEM selects the cluster heads and cluster member nodes successively, according to the system entropy measure, and constructs the iWSNs with the minimum system entropy. The method of the cluster head selection is traversal, while the method of the cluster member selection is a greedy algorithm to reduce the complexity. The experiments show that the power consumption of the iWSNs generated by the rSEM is in the same order of magnitude as that of Dijkstra in both 2D and 3D scenarios. In addition, the delay of the rSEM is slightly higher than that of LEACH. Therefore, the rSEM is suitable for networks that are sensitive to both the delay and power consumption. The rSEM puts forward a new idea for the design of routing for the next-generation iWSNs, which improves the overall network performance according to the network topology, instead of relying on the power consumption or delay performance only. Full article
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<p>The simulation scenario discussed in this paper.</p>
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<p>Schematic diagram of cluster structure iWSNs in 2D factory.</p>
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<p>Schematic diagram of cluster structure iWSNs in 3D factory.</p>
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<p>Simulation results in 2D factory scenario.</p>
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<p>Simulation results in 3D factory scenario.</p>
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16 pages, 5393 KiB  
Article
Semantic Interconnection Scheme for Industrial Wireless Sensor Networks and Industrial Internet with OPC UA Pub/Sub
by Chenggen Pu, Xiwu Ding, Ping Wang, Shunji Xie and Junhua Chen
Sensors 2022, 22(20), 7762; https://doi.org/10.3390/s22207762 - 13 Oct 2022
Cited by 7 | Viewed by 2042
Abstract
In the Industry 4.0 era, with the continuous integration of industrial field systems and upper-layer facilities, interconnection between industrial wireless sensor networks (IWSNs) and industrial Internet networks is becoming increasingly pivotal. However, when deployed in real industrial scenarios, IWSNs are often connected to [...] Read more.
In the Industry 4.0 era, with the continuous integration of industrial field systems and upper-layer facilities, interconnection between industrial wireless sensor networks (IWSNs) and industrial Internet networks is becoming increasingly pivotal. However, when deployed in real industrial scenarios, IWSNs are often connected to legacy control systems, through some wired industrial network protocols via gateways. Complex protocol translation is required in these gateways, and semantic interoperability is lacking between IWSNs and the industrial Internet. To fill this gap, our study focuses on realizing the interconnection and interoperability between an IWSN and the industrial Internet. The Open Platform Communications Unified Architecture (OPC UA) and joint publish/subscribe (pub/sub) communication between the two networks are used to achieve efficient transmission. Taking the Wireless Networks for Industrial Automation Process Automation (WIA-PA), a typical technology in IWSNs, as an example, we develop a communication architecture that adopts OPC UA as a communication bridge to integrate the WIA-PA network into the industrial Internet. A WIA-PA virtualization method for OPC UA pub/sub data sources is designed to solve the data mapping problem between WIA-PA and OPC UA. Then, the WIA-PA/OPC UA joint pub/sub transmission mechanism and the corresponding configuration mechanism are designed. Finally, a laboratory-level verification system is implemented to validate the proposed architecture, and the experimental results demonstrate its promising feasibility and capability. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in Industrial Applications)
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<p>WIA-PA network topology.</p>
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<p>Broker-based OPC UA pub/sub communication architecture.</p>
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<p>Feasibility analysis of OPC UA P/S applied to WIA-PA.</p>
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<p>System architecture of WIA-PA/OPC UA joint pub/sub scheme.</p>
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<p>WIA-PA/OPC UA pub/sub software architecture.</p>
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<p>OPC UA pub/sub communication architecture for WIA-PA.</p>
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<p>OPC UA pub/sub virtual device.</p>
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<p>Resource discovery process.</p>
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<p>WIA-PA/OPC UA joint communication mechanism.</p>
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<p>Time sequence flowchart.</p>
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<p>WIA-PA/OPC UA joint pub/sub interaction process.</p>
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<p>WIA-PA/OPC UA joint pub/sub experimental verification system.</p>
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<p>Memory occupancy of publisher and subscriber.</p>
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<p>Communication success rate of WIA-PA/OPC UA joint pub/sub.</p>
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<p>Joint publishing delay of WIA-PA field devices.</p>
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21 pages, 3908 KiB  
Article
Enhancing Graph Routing Algorithm of Industrial Wireless Sensor Networks Using the Covariance-Matrix Adaptation Evolution Strategy
by Nouf Alharbi, Lewis Mackenzie and Dimitrios Pezaros
Sensors 2022, 22(19), 7462; https://doi.org/10.3390/s22197462 - 1 Oct 2022
Cited by 4 | Viewed by 2364
Abstract
The emergence of the Industrial Internet of Things (IIoT) has accelerated the adoption of Industrial Wireless Sensor Networks (IWSNs) for numerous applications. Effective communication in such applications requires reduced end-to-end transmission time, balanced energy consumption and increased communication reliability. Graph routing, the main [...] Read more.
The emergence of the Industrial Internet of Things (IIoT) has accelerated the adoption of Industrial Wireless Sensor Networks (IWSNs) for numerous applications. Effective communication in such applications requires reduced end-to-end transmission time, balanced energy consumption and increased communication reliability. Graph routing, the main routing method in IWSNs, has a significant impact on achieving effective communication in terms of satisfying these requirements. Graph routing algorithms involve applying the first-path available approach and using path redundancy to transmit data packets from a source sensor node to the gateway. However, this approach can affect end-to-end transmission time by creating conflicts among transmissions involving a common sensor node and promoting imbalanced energy consumption due to centralised management. The characteristics and requirements of these networks encounter further complications due to the need to find the best path on the basis of the requirements of IWSNs to overcome these challenges rather than using the available first-path. Such a requirement affects the network performance and prolongs the network lifetime. To address this problem, we adopt a Covariance-Matrix Adaptation Evolution Strategy (CMA-ES) to create and select the graph paths. Firstly, this article proposes three best single-objective graph routing paths according to the IWSN requirements that this research focused on. The sensor nodes select best paths based on three objective functions of CMA-ES: the best Path based on Distance (PODis), the best Path based on residual Energy (POEng) and the best Path based on End-to-End transmission time (POE2E). Secondly, to enhance energy consumption balance and achieve a balance among IWSN requirements, we adapt the CMA-ES to select the best path with multiple-objectives, otherwise known as the Best Path of Graph Routing with a CMA-ES (BPGR-ES). A simulation using MATALB with different configurations and parameters is applied to evaluate the enhanced graph routing algorithms. Furthermore, the performance of PODis, POEng, POE2E and BPGR-ES is compared with existing state-of-the-art graph routing algorithms. The simulation results reveal that the BPGR-ES algorithm achieved 87.53% more balanced energy consumption among sensor nodes in the network compared to other algorithms, and the delivery of data packets of BPGR-ES reached 99.86%, indicating more reliable communication. Full article
(This article belongs to the Collection Wireless Sensor Networks towards the Internet of Things)
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<p>Schematic view of proposed Graph Routing model.</p>
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<p>Two-way time message exchange between node <span class="html-italic">i</span> and node <span class="html-italic">j</span>.</p>
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<p>A network topology example with 50 sensor nodes.</p>
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<p>PDR and PMR boxplots for different topologies: (<b>a</b>) PDR and PMR results of the 100 × 100 m<sup>2</sup> network area of 50 and 100 nodes; (<b>b</b>) PDR and PMR results of the 200 × 200 m<sup>2</sup> network area of 50 and 100 nodes.</p>
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<p>Total Consumed Energy for different topologies: (<b>a</b>) energy consumption results of the 100 × 100 m<sup>2</sup> network area of 50 and 100 sensor nodes; (<b>b</b>) energy consumption results of the 200 × 200 m<sup>2</sup> network area of 50 and 100 sensor nodes.</p>
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<p>Average of energy imbalance factor for different topologies: (<b>a</b>) average EIF results of the 100 × 100 m<sup>2</sup> network area of 50 and 100 sensor nodes; (<b>b</b>) average EIF results of the 200 × 200 m<sup>2</sup> network area of 50 and 100 sensor nodes.</p>
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<p>End-to-End transmission time for different topologies: (<b>a</b>) E2ET results of 100 × 100 m<sup>2</sup> network area of 50 and 100 sensor nodes; (<b>b</b>) E2ET results of 200 × 200 m<sup>2</sup> network area of 50 and 100 sensor nodes.</p>
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14 pages, 2163 KiB  
Article
Cascading Failure Analysis of Hierarchical Industrial Wireless Sensor Networks under the Impact of Data Overload
by Hongchi Lv, Zhengtian Wu, Xin Zhang, Baoping Jiang and Qing Gao
Machines 2022, 10(5), 380; https://doi.org/10.3390/machines10050380 - 16 May 2022
Cited by 4 | Viewed by 1995
Abstract
As industrialization accelerates, the industrial sensor network environment becomes more complex. Hierarchical multi-cluster wireless sensing network topology is generally used due to large-scale industrial environments, harsh environments, and data overload impact. In industrial wireless sensor networks, the overload of some nodes may lead [...] Read more.
As industrialization accelerates, the industrial sensor network environment becomes more complex. Hierarchical multi-cluster wireless sensing network topology is generally used due to large-scale industrial environments, harsh environments, and data overload impact. In industrial wireless sensor networks, the overload of some nodes may lead to the failure of the whole network, which is called cascading failure. This phenomenon has incalculable impact on industrial production. However, cascading failure models have mainly been studied for planar structures, and there is no cascading failure model for hierarchical topologies in industrial environments. Therefore, this paper built a cascading failure model for hierarchical industrial wireless sensor networks (IWSNs) for realistic industrial network topologies. By establishing an evaluation mechanism considering the efficiency of the network and the viability of nodes, the network communication efficiency that is not considered in the traditional evaluation mechanism is solved. In addition, aiming at the problem of network topology changes caused by node failure, dynamic load distribution methods (ADD, SLD) are used to improve network invulnerability. Theoretical analysis and experimental results show that the traditional allocation method (SMLD) does not apply in hierarchical topologies; when the general cluster head node capacity is moderate, increasing the capacity of single-hop cluster head nodes can prevent cascading failures more effectively. Full article
(This article belongs to the Section Industrial Systems)
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<p>Hierarchical topology of industrial wireless sensor networks.</p>
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<p>Cascading-failure process between cluster head nodes.</p>
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<p>The topology of the cluster head nodes: (<b>a</b>) the complete cluster head nodes topology and (<b>b</b>) the partially enlarged view of the attacked node.</p>
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<p>Effect of capacity regulation parameters: (<b>a</b>) the effect of exponential adjustment parameter on invulnerability performance and (<b>b</b>) the effect of overload tolerance on invulnerability performance.</p>
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<p>The influence of single-hop cluster head node capacity on G.</p>
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<p>The influence of single-hop cluster head node capacity on M.</p>
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<p>The influence of different distributions working on M.</p>
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14 pages, 924 KiB  
Article
K-Means Clustering-Based Safety System in Large-Scale Industrial Site Using Industrial Wireless Sensor Networks
by Dongyeong Seo, Sangdae Kim, Seungmin Oh and Sang-Ha Kim
Sensors 2022, 22(8), 2897; https://doi.org/10.3390/s22082897 - 9 Apr 2022
Cited by 3 | Viewed by 1962
Abstract
A large number of workers and heavy equipment are used in most industrial sizes, and the prevention of safety accidents is one of the most important issues. Therefore, although a number of systems have been proposed to prevent accidents, existing studies assume that [...] Read more.
A large number of workers and heavy equipment are used in most industrial sizes, and the prevention of safety accidents is one of the most important issues. Therefore, although a number of systems have been proposed to prevent accidents, existing studies assume that workers are gathered in some areas. These assumptions are not suitable for large-scale industrial sites in which workers form as a group and work in a large area. In other words, in a large-scale industrial site, existing schemes are unsuitable for the timely notifying of warnings of threats, and excessive energy is consumed. Therefore, we propose a k-means clustering-based safety system for a large-scale industrial site. In the proposed scheme, workers deployed over a large area are divided into an appropriate number of groups, and threat notification is delivered by a multicasting tree toward each cluster. The notification to workers is delivered through local flooding in each cluster. The simulation results show that the system is able to deliver the notification within a valid time, and it is energy efficient compared to the existing scheme. Full article
(This article belongs to the Special Issue Recent Trends in Wireless Sensor and Actuator Networks)
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<p>An overview of proposed system.</p>
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<p>A flowchart of operation process of proposed system.</p>
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<p>An example of k-means clustering.</p>
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<p>An example of elbow method.</p>
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<p>Calculation of longest distance in the cluster.</p>
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<p>An example of multicasting tree construction.</p>
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<p>Point selection for main and branch forwarding.</p>
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<p>An example of industrial site topology.</p>
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<p>Comparison of in-time packet transmission success ratio according to the number of workers.</p>
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<p>Comparison of in-time packet transmission success ratio according to the radio range.</p>
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<p>Comparison of energy consumption according to the number of workers.</p>
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<p>Comparison of energy consumption according to the radio range.</p>
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22 pages, 7214 KiB  
Article
Predictive Energy-Aware Routing Solution for Industrial IoT Evaluated on a WSN Hardware Platform
by Eusebiu Jecan, Catalin Pop, Ovidiu Ratiu and Emanuel Puschita
Sensors 2022, 22(6), 2107; https://doi.org/10.3390/s22062107 - 9 Mar 2022
Cited by 7 | Viewed by 2037
Abstract
In industrial wireless sensors networks (IWSNs), the sensor lifetime predictability is critical for ensuring continuous system availability, cost efficiency and suitability for safety applications. When deployed in a real-world dynamic and centralised network, the sensor lifetime is highly dependent on the network topology, [...] Read more.
In industrial wireless sensors networks (IWSNs), the sensor lifetime predictability is critical for ensuring continuous system availability, cost efficiency and suitability for safety applications. When deployed in a real-world dynamic and centralised network, the sensor lifetime is highly dependent on the network topology, deployment configuration and application requirements. (In the absence of an energy-aware mechanism, there is no guarantee for the sensor lifetime). This research defines a conceptual model for enhancing the energy predictability and efficiency of IWSNs. A particularization of this model is the predictive energy-aware routing (PEAR) solution that assures network lifetime predictability through energy-aware routing, energy balancing and profiling. The PEAR solution considers the requirements and constraints of the industrial ISA100.11a communication standard and the VR950 IIoT Gateway hardware platform. The results demonstrate the PEAR ability to ensure predictable energy consumption for one or multiple network clusters. The PEAR solution is capable of intracluster energy balancing, reducing the overconsumption 10.4 times after 210 routing changes as well as intercluster energy balancing, increasing the cluster lifetime 2.3 times on average and up to 3.2 times, while reducing the average consumption by 23.6%. The PEAR solution validates the feasibility and effectiveness of the energy-aware conceptual indicating its suitability within IWSNs having real world applications and requirements. Full article
(This article belongs to the Special Issue Energy-Aware Networks for Industrial Internet of Things (IIoT))
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<p>The top-down approach conceptual model of an IWSN energy-aware solution.</p>
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<p>The PEAR implementation of the energy aware conceptual model respecting the ISA100.11a IWSN standard.</p>
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<p>The PEAR post-network-formation pseudocode algorithm.</p>
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<p>The PEAR impact on network topology formation: phase 1. The device highlighted with the green square belongs to a low energy profile whereas the device belonging to a high energy profile is highlighted with a blue square.</p>
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<p>The PEAR impact on network topology evolution: phase 2. The device highlighted with the green square belongs to a low energy profile whereas the device belonging to a high energy profile is highlighted with a blue square.</p>
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<p>The PEAR impact on network topology evolution: phase 3. The device highlighted with the green square belongs to a low energy profile whereas the device belonging to a high energy profile is highlighted with a blue square.</p>
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<p>The ISA100.11a certified hardware devices employed in the experimental setup at CDS premises (Cluj-Napoca, Romania)—two out of three test batches.</p>
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<p>Average field device energy consumption per day exceeding the energy profile.</p>
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<p>Average field device overconsumption for a 24-month profile (i.e., 200 DPDU/min) with respect to the number of topology changes triggered by PEAR.</p>
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<p>Average field device energy consumption for all devices in the network differentiated by the energy profile when PEAR is enabled.</p>
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<p>Average field device energy consumption for all devices in the network differentiated by the energy profile when PEAR is disabled.</p>
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<p>Comparison between PEAR-Enabled and PEAR-Disabled low cluster lifetime for different cluster configurations.</p>
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23 pages, 786 KiB  
Article
Reliable Fault Tolerant-Based Multipath Routing Model for Industrial Wireless Control Systems
by Hakim Abdulrab, Fawnizu Azmadi Hussin, Azrina Abd Aziz, Azlan Awang, Idris Ismail and P. Arun Mozhi Devan
Appl. Sci. 2022, 12(2), 544; https://doi.org/10.3390/app12020544 - 6 Jan 2022
Cited by 23 | Viewed by 2849
Abstract
Communication in industrial wireless networks necessitates reliability and precision. Besides, the existence of interference or traffic in the network must not affect the estimated network properties. Therefore, data packets have to be sent within a certain time frame and over a reliable connection. [...] Read more.
Communication in industrial wireless networks necessitates reliability and precision. Besides, the existence of interference or traffic in the network must not affect the estimated network properties. Therefore, data packets have to be sent within a certain time frame and over a reliable connection. However, the working scenarios and the characteristics of the network itself make it vulnerable to node or link faults, which impact the transmission reliability and overall performance. This article aims to introduce a developed multipath routing model, which leads to cost-effective planning, low latency and high reliability of industrial wireless mesh networks, such as the WirelessHART networks. The multipath routing model has three primary paths, and each path has a backup node. The backup node stores the data transmitted by the parent node to grant communication continuity when primary nodes fail. The multipath routing model is developed based on optimal network planning and deployment algorithm. Simulations were conducted on a WirelessHART simulator using Network Simulator (NS2). The performance of the developed model is compared with the state-of-the-art. The obtained results reveal a significant reduction in the average network latency, low power consumption, better improvement in expected network lifetime, and enhanced packet delivery ratio which improve network reliability. Full article
(This article belongs to the Special Issue Wireless Sensor Networks: Technologies, Applications, Prospects)
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<p>WirelessHART mesh network structure.</p>
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<p>An example of the broadcast graph (<math display="inline"><semantics> <msub> <mi>G</mi> <mi>B</mi> </msub> </semantics></math>) (<b>a</b>), uplink graph (<math display="inline"><semantics> <msub> <mi>G</mi> <mi>U</mi> </msub> </semantics></math>) (<b>b</b>), and downlink graph (<math display="inline"><semantics> <msub> <mi>G</mi> <mi>d</mi> </msub> </semantics></math>) (<b>c</b>).</p>
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<p>(<b>a</b>) Original layout of factory, (<b>b</b>) Phase 1: forming multipath triangular cells grid.</p>
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<p>(<b>a</b>) Phase 2: place access point, (<b>b</b>) Phase 3: plan routes and place the routers.</p>
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<p>Proposed network structure of the deployment.</p>
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<p>Average Network Latency (ANL) performance comparison.</p>
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<p>ANL with and without packet drop.</p>
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<p>Expected network lifetime (ENL) performance comparison.</p>
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<p>ENL with and without packet drop.</p>
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<p>Energy consumption performance comparison.</p>
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<p>Packet delivery ratio (PDR) performance comparison.</p>
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<p>Packet delivery ratio (PDR) with and without packet drop.</p>
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19 pages, 7417 KiB  
Article
TSCH Multiflow Scheduling with QoS Guarantees: A Comparison of SDN with Common Schedulers
by Federico Orozco-Santos, Víctor Sempere-Payá, Javier Silvestre-Blanes and Teresa Albero-Albero
Appl. Sci. 2022, 12(1), 119; https://doi.org/10.3390/app12010119 - 23 Dec 2021
Cited by 5 | Viewed by 2664
Abstract
Industrial Wireless Sensor Networks (IWSN) are becoming increasingly popular in production environments due to their ease of deployment, low cost and energy efficiency. However, the complexity and accuracy demanded by these environments requires that IWSN implement quality of service mechanisms that allow them [...] Read more.
Industrial Wireless Sensor Networks (IWSN) are becoming increasingly popular in production environments due to their ease of deployment, low cost and energy efficiency. However, the complexity and accuracy demanded by these environments requires that IWSN implement quality of service mechanisms that allow them to operate with high determinism. For this reason, the IEEE 802.15.4e standard incorporates the Time Slotted Channel Hopping (TSCH) protocol which reduces interference and increases the reliability of transmissions. This standard does not specify how time resources are allocated in TSCH scheduling, leading to multiple scheduling solutions. Schedulers can be classified as autonomous, distributed and centralised. The first two have prevailed over the centralised ones because they do not require high signalling, along with the advantages of ease of deployment and high performance. However, the increased QoS requirements and the diversity of traffic flows that circulate through the network in today’s Industry 4.0 environment require strict, dynamic control to guarantee parameters such as delay, packet loss and deadline, independently for each flow. That cannot always be achieved with distributed or autonomous schedulers. For this reason, it is necessary to use centralised protocols with a disruptive approach, such as Software Defined Networks (SDN). In these, not only is the control of the MAC layer centralised, but all the decisions of the nodes that make up the network are configured by the controller based on a global vision of the topology and resources, which allows optimal decisions to be made. In this work, a comparative analysis is made through simulation and a testbed of the different schedulers to demonstrate the benefits of a fully centralized approach such as SDN. The results obtained show that with SDN it is possible to simplify the management of multiple flows, without the problems of centralised schedulers. SDN maintains the Packet Delivery Ratio (PDR) levels of other distributed solutions, but in addition, it achieves greater determinism with bounded end-to-end delays and Deadline Satisfaction Ratio (DSR) at the cost of increased power consumption. Full article
(This article belongs to the Topic Wireless Sensor Networks)
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<p>(<b>a</b>) 6TiSCH Protocol Stack (<b>b</b>) Time Slotted Channel Hopping Slotframe.</p>
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<p>Control plane distribution between the main approaches of TSCH schedulers. (<b>a</b>) Autonomous, (<b>b</b>) Distributed, (<b>c</b>) Centralized (SDN), (<b>d</b>) Centralized.</p>
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<p>Topology with 10 fixed nodes.</p>
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<p>End-to-End Delay for different transmission periods.</p>
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<p>Radio duty cycle for each scheduler with different packet period.</p>
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<p>Packet Inter-Arrival Time for each Scheduling protocol with different Packet Period.</p>
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<p>Deadline Satisfaction ratio for each protocol.</p>
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<p>Packet Inter-arrival Time for each Scheduling protocol with multiple data Flows.</p>
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<p>Testbed using OpenMote B.</p>
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<p>End-to-End Delay and Packet Inter-arrival time in testbed for SDN WISE-TSCH and Orchestra.</p>
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<p>Deadline Satisfaction Ratio obtained in testbed for each Tx node.</p>
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12 pages, 2017 KiB  
Article
Efficient Link Scheduling Based on Estimated Number of Packets in Queue on Industrial Wireless Sensor Networks
by Myung-Kyun Kim
Energies 2021, 14(19), 6370; https://doi.org/10.3390/en14196370 - 5 Oct 2021
Cited by 3 | Viewed by 1613
Abstract
The links of low power wireless sensor networks are error prone and the transmission on a wireless link is determined probabilistically by the packet reception rate (PRR) of the link. On the other hand, there is a very strict requirement in the end-to-end [...] Read more.
The links of low power wireless sensor networks are error prone and the transmission on a wireless link is determined probabilistically by the packet reception rate (PRR) of the link. On the other hand, there is a very strict requirement in the end-to-end reliability and delay of sensor data in industrial wireless sensor networks (IWSNs). The existing approaches to provide the end-to-end reliability in IWSNs is retransmitting the packet when failure occurs. These approaches transmit a packet multiple times in successive time slots to provide the required reliability. These approaches, however, can increase the average delay of packets and the number of packets buffered in a queue. This paper proposes a new scheme to estimate the probabilistic amount of packets, called queue level (QL), in the buffer of each node based on the PRRs of the wireless links. This paper also proposes a QL-based centralized scheduling algorithm to assign time slots efficiently in TDMA-based IWSNs. The proposed scheduling algorithm gives higher priority to the nodes with higher QL. By assigning time slots first to the node with the highest QL, we can reduce the average end-to-end delay of packets and reduce the amount of buffered packets in the queue while satisfying the required end-to-end reliability. The performance of the proposed scheduling algorithm have been evaluated through a simulation using the Cooja simulator and compared with the existing approach. In the simulation on an sample network with the target end-to-end reliability of 99%, all of the flows were shown to guarantee the target reliability in both algorithms: on average, 99.76% in the proposed algorithm and 99.85% in the existing approach. On the other hand, the proposed algorithm showed much better performance than the existing approach in terms of the average end-to-end delay of packets (about 47% less) and the number of maximally buffered packets in the queue of each node (maximally, more than 90% less). Full article
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<p>An example network graph.</p>
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<p>PA and TS values for each node in the example network of <a href="#energies-14-06370-f001" class="html-fig">Figure 1</a> (<b>a</b>), and QL change after transmission on link &lt;3,2&gt; (<b>b</b>).</p>
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<p>Scheduling steps (<b>a</b>) and the assignment result (<b>b</b>) for the sample graph in <a href="#energies-14-06370-f001" class="html-fig">Figure 1</a> with 2-channels.</p>
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<p>Scheduling results of the proposed algorithm and flow-based algorithm on the example network with 3-channels.</p>
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<p>End-to-end reliability of the proposed algorithm and the flow-based approach.</p>
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<p>Average end-to-end delay of packets in the proposed algorithm and the flow-based approach.</p>
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<p>Average number of maximally buffered packets of each node in the proposed algorithm and the flow-based approach.</p>
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