Search Results (138)

The proliferation of smart devices such as sensors and communication devices has necessitated the development of networks that can adopt device-to-device communication for delay-tolerant data transfer and energy efficiency. Therefore, there is a need to develop opportunistic networks to enhance energy efficiency through improved data routing. A sensor device equipped with computing, communication, and mobility capabilities can opportunistically transfer data to another device, either as a direct recipient or as an intermediary forwarding data to a third device. Routing algorithms designed for such opportunistic networks aim to increase the probability of successful message transmission by leveraging area information derived from historical data to forecast potential encounters. However, accurately determining the precise locations of mobile devices remains highly challenging and necessitates a robust prediction mechanism to provide reliable insights into mobility encounters. In this study, we propose incorporating a random forest regressor (RFR) to predict the future location of mobile users, thereby enhancing message routing efficiency. The RFR utilizes mobility traces from diverse users and is equipped with sensors for computing and communication purposes. These predictions improve message routing performance and reduce energy and bandwidth resource utilization during routine data transmissions. To evaluate the proposed approach, we compared the predictive performance of the RFR against existing benchmark schemes, including the Gaussian process, using real-world mobility data traces. The mobility traces from the University of Southern California (USC) were employed to underpin the simulations. Our findings demonstrate that the RFR significantly outperformed both the Gaussian process and existing methods in predicting mobility encounters. Furthermore, the integration of mobility predictions into device-to-device (D2D) communication and traditional internet networks showed potential energy consumption reductions of up to one-third, highlighting the practical benefits of the proposed approach. The contribution of this research is that it highlights the limitations of existing mobility prediction models and develops new resource optimization and energy-efficient opportunistic networks that overcome these limitations. Full article

Malaria, a climate-sensitive mosquito-borne disease, is widespread in tropical and subtropical regions, and its elimination is a global health priority. Malaria is endemic to Vanuatu, where elimination campaigns have been implemented with varied success. In this study, climate variables were assessed for their correlation with national malaria cases from 2014 to 2023 and used to develop a proof-of-concept model for estimating malaria incidence in Vanuatu. Maximum, minimum, and median temperatures; diurnal temperature variation; median temperature during the 18:00–21:00 mosquito biting period (VUT); median humidity; and precipitation (total and anomaly) were evaluated as predictors at different time lags. It was found that maximum temperature had the strongest correlation with malaria cases and produced the best-performing linear regression model, where malaria cases increased by approximately 43 cases for every degree (°C) increase in monthly maximum temperature. This aligns with similar findings from climate–malaria studies in the Southwest Pacific, where temperature tends to stimulate the development of both Anopheles farauti and Plasmodium vivax, increasing transmission probability. A Bayesian model using maximum temperature and total precipitation at a two-month time lag was more effective in predicting malaria incidence than using maximum temperature or precipitation alone. A Bayesian approach was preferred due to its flexibility with varied data types and prior information about malaria dynamics. This model for predicting malaria incidence in Vanuatu can be adapted to smaller regions or other malaria-affected areas, supporting malaria early warning and preparedness for climate-related health challenges. Full article

A series of poly(lactic acid) (PLA)/biochar (BC) bio-composites filled with low amounts (1–5 wt%) of BC were prepared and characterized. The synthesis involved the in situ ring-opening polymerization (ROP) of lactide in the presence of two different types of BC named SWP550 and SWP700, having been produced by pyrolysis of softwood pellets at two different temperatures, 550 and 700 °C, respectively. The bio-composites were characterized by complementary techniques. The successful synthesis of PLA and PLA/BC bio-composites was directly demonstrated by the formation of new bonds, most probably between PLA and BC. Indirect evidence for that was obtained by the systematic molar mass reduction in the presence of BC. BC was found by transmission electron microscopy (TEM) micrographs to be well dispersed at the nanosize level, indicating that in situ polymerization is a technique quite efficient for producing bio-composites with finely dispersed BC additive. The molecular dynamics mapping is performed here via dielectric spectroscopy, moreover, for the first time in these PLA/BC systems. The strong PLA/BC interactions (due to the grafting) led to a systematic deceleration of segmental mobility (elevation of the T_g) in the bio-composites despite the opposite effect expected by the decrease in molar mass with the BC content increasing. In addition, the same interactions and chain-length reduction are responsible for the slight suppression of the PLA’s crystallizability. The effects are slightly stronger for SWP700 as compared to SWP550. The crystal structure is rather similar between the unfilled matrix and the bio-composites, whereas, based on the overall data, the semicrystalline morphology is expected to be tighter in the bio-composites. The thermal stability and decomposition kinetics were also thoroughly studied. All materials exhibit good resistance to thermal degradation. Additionally, the mechanical properties of bio-composites were evaluated by tensile testing and found slightly enhanced at low biochar contents and decreasing thereafter due to the low molecular weight of bio-composites with the larger BC contents. Full article

As drone technology develops rapidly and many users emerge in airspace networks, various forms of interference have caused the wireless spectrum to exhibit a dense, diverse, and dynamic trend. This increases the probability of spectrum conflicts among users and seriously impacts the quality and transmission rate of communication. How to effectively improve the detection accuracy of each frequency component in the electromagnetic space mixed signals and avoid spectrum conflicts will become one of the crucial issues currently faced by unmanned aerial vehicle (UAV) communication technologies. However, the existing methods overlook the mutual interference among the component signals as well as the noise during the frequency detection process, which affects their detection performance. In this paper, we propose a mixed-signal frequency detection method based on the reconstruction of sparse feature signals. Without information such as frequency range, bandwidth, and the number of components, it can utilize the autoencoder network to learn the sparse features of each component signal in the high-dimensional frequency domain space and construct a nonlinear reconstruction function to reconstruct each component signal in the mixed signal, thereby realizing the separation of signals. On this basis, complex dilated convolution and deconvolution are used successively to perform feature extraction on the separated signals, which enhances the receptive field and frequency resolution ability of the network for signals, reduces the interference between noise and different component signals, and realizes the accurate estimation of the number of components and carrier frequencies. The simulation results show that when $SNR\ge$ 6 dB, the detection accuracy of the number of component signals is greater than 96.3%. The detection error and detection accuracy of component frequencies are less than 3.19% and greater than 90.7%, respectively. Full article

This paper examines two-way relaying cognitive radio networks utilizing fountain coding (FC), reconfigurable intelligent surfaces (RIS), and radio frequency energy harvesting (EH). In the proposed schemes, two secondary sources attempt to exchange data with each other through the assistance of an RIS deployed in the network. Using FC, one source sends its encoded packets to the other source, which must collect enough packets for a successful data recovery. The transmit power of the two sources is adjusted according to an interference constraint given by a primary user and the energy harvested from a power station. In the conventional scheme, one source continuously transmits FC packets to the other, using the maximum number of transmissions allowed. In the modified scheme, as soon as one source collects a sufficient number of FC packets, it notifies the other source to stop transmission. We derive closed-form expressions of outage probability (OP) at each source, system outage probability (SOP), and average number of FC-packet transmissions for the successful data exchange of the considered schemes over Rayleigh fading channels. Simulation results are provided to validate our analysis, to compare the performance of the considered schemes, and to examine the impact of key parameters on performance. Full article

In this article, we consider an UAV (unmanned aerial vehicle)-assisted free space optical (FSO) secure communication network. Since FSO signal is impossible to detect by eavesdroppers without proper beam alignment and security authentication, a BS employs FSO technique to transfer information to multiple authenticated sensors, to improve the transmission security and reliability with the help of an UAV relay with decode and forward (DF) mode. All the sensors need to first send information to the UAV to obtain security authentication, and then the UAV forwards corresponding information to them. Successive interference cancellation (SIC) is used to decode the information received at the UAV and all authenticated sensors. With consideration of fairness, we introduce a statistical metric for evaluating the network performance, i.e., the maximum decoding outage probability for all authenticated sensors. In particular, applying an intelligent approach, we obtain a near-optimal scheme for secure transmit power allocation. With a well-trained allocation scheme, approximate closed-form expressions for optimal transmit power levels can be obtained. Through some numerical examples, we illustrate the various design trade-offs for such a system. Additionally, the validity of our approach was verified by comparing with the result from exhaustive search. In particular, the result with DRL was only $0.3\%$ higher than that with exhaustive search. These results can provide some important guidelines for the fairness-aware design of UAV-assisted secure FSO communication networks. Full article

With the development of Internet of Vehicles (IoV) technology, the need for real-time data processing and communication in vehicles is increasing. Traditional request-based methods face challenges in terms of latency and bandwidth limitations. Mode 4 in cellular vehicle-to-everything (C-V2X), also known as autonomous resource selection, aims to address latency and overhead issues by dynamically selecting communication resources based on real-time conditions. However, semi-persistent scheduling (SPS), which relies on distributed sensing, may lead to a high number of collisions due to the lack of centralized coordination in resource allocation. On the other hand, non-orthogonal multiple access (NOMA) can alleviate the problem of reduced packet reception probability due to collisions. Age of Information (AoI) includes the time a message spends in both local waiting and transmission processes and thus is a comprehensive metric for reliability and latency performance. To address these issues, in C-V2X, the waiting process can be extended to the queuing process, influenced by packet generation rate and resource reservation interval (RRI), while the transmission process is mainly affected by transmission delay and success rate. In fact, a smaller selection window (SW) limits the number of available resources for vehicles, resulting in higher collisions when the number of vehicles is increasing rapidly. SW is generally equal to RRI, which not only affects the AoI part in the queuing process but also the AoI part in the transmission process. Therefore, this paper proposes an AoI estimation method based on multi-priority data type queues and considers the influence of NOMA on the AoI generated in both processes in C-V2X system under different RRI conditions. Our experiments show that using multiple priority queues can reduce the AoI of urgent messages in the queue, thereby providing better service about the urgent message in the whole vehicular network. Additionally, applying NOMA can further reduce the AoI of the messages received by the vehicle. Full article

Practical Byzantine Fault Tolerance (PBFT) is one of the most popular consensus mechanisms for the consortium and private blockchain technology. It has been recognized as a candidate consensus mechanism for the Internet of Things networks as it offers lower resource requirements and high performance when compared with other consensus mechanisms such as proof of work. In this paper, by considering the blockchain nodes are wirelessly connected, we model the network nodes distribution and transaction arrival rate as Poisson point process and we develop a framework for evaluating the performance of the wireless PBFT network. The framework utilizes slotted ALOHA as its multiple access technique. We derive the end-to-end success probability of the wireless PBFT network which serves as the basis for obtaining other key performance indicators namely, the optimal transmission interval, the transaction throughput and delay, and the viable area. The viable area represents the minimum PBFT coverage area that guarantees the liveness, safety, and resilience of the PBFT protocol while satisfying a predefined end-to-end success probability. Results show that the transmission interval required to make the wireless PBFT network viable can be reduced if either the end-to-end success probability requirement or the number of faulty nodes is lowered. Full article

In order to improve the concealment and security of a point-to-point transparent forwarding satellite communication system, a signal structure based on aperiodic long code spread spectrum is designed in this paper. This structure can achieve reliable signal acquisition without special physical layer synchronization overhead, which can effectively shorten signal transmission time and improve the concealment of communication. In addition, the performance of burst spread spectrum signal acquisition is analyzed in detail by establishing a mathematical model, and the influencing factors and design criteria of the matching filter length for aperiodic long code acquisition are determined. On this basis, a matched filter acquisition method based on high-power clock multiplexing and an adaptive decision threshold design method based on an auxiliary channel are proposed. The above methods effectively reduce hardware complexity and resource consumption caused by long code acquisition, and realize reliable acquisition under the condition of low SNR. The simulation results show that under the condition of Eb/N0 = 3 dB, the transmission efficiency for a 128-symbol burst frame can be increased by 50%, thereby significantly reducing the burst communication time. Furthermore, the acquisition success probability can reach 99.99%. Full article

The Onion Router (Tor), as the most widely used anonymous network, is vulnerable to traffic correlation attacks by powerful passive adversaries, such as Autonomous Systems (AS). AS-level adversaries increase their chances of executing correlation attacks by manipulating the underlying routing, thereby compromising anonymity. Furthermore, these underlying routing detours in the Tor client’s routing inference introduce extra latency. To address this challenge, we propose Toward Resisting AS-level Adversary Correlation Attacks Optimal Anonymous Routing (TOAR). TOAR is a two-stage routing mechanism based on Bayesian optimization within Software Defined Networks (SDN), comprising route search and route forwarding. Specifically, it searches for routes that conform to established policies, avoiding AS that could connect traffic between clients and destinations while maintaining anonymity in the selection of routes that minimize communication costs. To evaluate the anonymity of TOAR, as well as the effectiveness of route searching and the performance of route forwarding, we conduct a detailed analysis and extensive experiments. The analysis and experimental results show that the probability of routing being compromised by correlation attacks is significantly reduced. Compared to classical enumeration-based methods, the success rate of route searching increased by close to 2.5 times, and the forwarding throughput reached 70% of that of the packet transmission. The results show that TOAR effectively improves anonymity while maintaining communication quality, minimizing anonymity loss from AS-level adversaries and reducing high latency from routing detours. Full article

For the future of sixth-generation (6G) wireless communication, simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) technology is emerging as a promising solution to achieve lower power transmission and flawless coverage. To facilitate the performance analysis of RIS-assisted networks, the statistics of the sum of double random variables, i.e., the sum of the products of two random variables of the same distribution type, become vitally necessary. This paper applies the statistics of the sum of double random variables in the performance analysis of an integrated power beacon (PB) energy-harvesting (EH)-based NOMA-assisted STAR-RIS network to improve its outage probability (OP), ergodic rate, and average symbol error rate. Furthermore, the impact of imperfect successive interference cancellation (ipSIC) on system performance is also analyzed. The analysis provides the closed-form expressions of the OP and ergodic rate derived for both imperfect and perfect SIC (pSIC) cases. All analyses are supported by extensive simulation results, which help recommend optimized system parameters, including the time-switching factor, the number of reflecting elements, and the power allocation coefficients, to minimize the OP. Finally, the results demonstrate the superiority of the proposed framework compared to conventional NOMA and OMA systems. Full article

In 2022, global poliovirus modeling suggested that coordinated cessation of bivalent oral poliovirus vaccine (bOPV, containing Sabin-strain types 1 and 3) in 2027 would likely increase the risks of outbreaks and expected paralytic cases caused by circulating vaccine-derived polioviruses (cVDPVs), particularly type 1. The analysis did not include the implementation of planned, preventive supplemental immunization activities (pSIAs) with bOPV to achieve and maintain higher population immunity for types 1 and 3 prior to bOPV cessation. We reviewed prior published OPV cessation modeling studies to support bOPV cessation planning. We applied an integrated global poliovirus transmission and OPV evolution model after updating assumptions to reflect the epidemiology, immunization, and polio eradication plans through the end of 2023. We explored the effects of bOPV cessation in 2027 with and without additional bOPV pSIAs prior to 2027. Increasing population immunity for types 1 and 3 with bOPV pSIAs (i.e., intensification) could substantially reduce the expected global risks of experiencing cVDPV outbreaks and the number of expected polio cases both before and after bOPV cessation. We identified the need for substantial increases in overall bOPV coverage prior to bOPV cessation to achieve a high probability of successful bOPV cessation. Full article

Upconversion nanoparticles (UCNPs) are well-reported for bioimaging. However, their applications are limited by low luminescence intensity. To enhance the intensity, often the UCNPs are coated with macromolecules or excited with high laser power, which is detrimental to their long-term biological applications. Herein, we report a novel approach to prepare co-doped CaF₂:Yb³⁺ (20%), Er³⁺ with varying concentrations of Er (2%, 2.5%, 3%, and 5%) at ambient temperature with minimal surfactant and high-pressure homogenization. Strong luminescence and effective red emission of the UCNPs were seen even at low power and without functionalization. X-ray diffraction (XRD) of UCNPs revealed the formation of highly crystalline, single-phase cubic fluorite-type nanostructures, and transmission electron microscopy (TEM) showed co-doped UCNPs are of $~$12 nm. The successful doping of Yb and Er was evident from TEM–energy dispersive X-ray analysis (TEM-EDAX) and X-ray photoelectron spectroscopy (XPS) studies. Photoluminescence studies of UCNPs revealed the effect of phonon coupling between host lattice (CaF₂), sensitizer (Yb³⁺), and activator (Er³⁺). They exhibited tunable upconversion luminescence (UCL) under irradiation of near-infrared (NIR) light (980 nm) at low laser powers (0.28–0.7 W). The UCL properties increased until 3% doping of Er³⁺ ions, after which quenching of UCL was observed with higher Er³⁺ ion concentration, probably due to non-radiative energy transfer and cross-relaxation between Yb³⁺-Er³⁺ and Er³⁺-Er³⁺ ions. The decay studies aligned with the above observation and showed the dependence of UCL on Er³⁺ concentration. Further, the UCNPs exhibited strong red emission under irradiation of 980 nm light and retained their red luminescence upon internalization into cancer cell lines, as evident from confocal microscopic imaging. The present study demonstrated an effective approach to designing UCNPs with tunable luminescence properties and their capability for cellular imaging under low laser power. Full article

Wireless sensor networks (WSNs) and sensing devices are considered to be core components of the Internet of Things (IoT). The performance modeling of IoT–WSN is of key importance to better understand, deploy, and manage this technology. As sensor nodes are battery-constrained, a fundamental issue in WSN is energy consumption. Additional issues also arise in heterogeneous scenarios due to the coexistence of sensor nodes with different features. In these scenarios, the modeling process becomes more challenging as an efficient orchestration of the sensor nodes must be achieved to guarantee a successful operation in terms of medium access, synchronization, and energy conservation. We propose a novel methodology to determine the energy consumed by sensor nodes deploying a recently proposed synchronous duty-cycled MAC protocol named Priority Sink Access MAC (PSA-MAC). We model the operation of a WSN with two classes of sensor devices by a pair of two-dimensional Discrete-Time Markov Chains (2D-DTMC), determine their stationary probability distribution, and propose new expressions to compute the energy consumption based solely on the obtained stationary probability distribution. This new approach is more systematic and accurate than previously proposed ones. The new methodology to determine energy consumption takes into account different specific features of the PSA-MAC protocol as: (i) the synchronization among sensor nodes; (ii) the normal and awake operation cycles to ensure synchronization among sensor nodes and energy conservation; (iii) the two periods that compose a full operation cycle: the data and sleep periods; (iv) two transmission schemes, SPT (single packet transmission) and APT (aggregated packet transmission) (v) the support of multiple sensor node classes; and (vi) the support of different priority assignments per class of sensor nodes. The accuracy of the proposed methodology has been validated by an independent discrete-event-based simulation model, showing that very precise results are obtained. Full article

With the increasing demand for high data rates, low delay, and extended battery life, managing massive machine-type communication (mMTC) in the beyond 5G (B5G) context is challenging. MMTC devices, which play a role in developing the Internet of Things (IoT) and smart cities, need to transmit short amounts of data periodically within a specific time frame. Although blockchain technology is utilized for secure data storage and transfer while digital twin technology provides real-time monitoring and management of the devices, issues such as constrained time delays and network congestion persist. Without a proper data transmission strategy, most devices would fail to transmit in time, thus defying their relevance and purpose. This work investigates the problem of massive random access channel (RACH) attempts while emphasizing the energy efficiency and access latency for mMTC devices with critical missions in B5G networks. Using machine learning techniques, we propose an attention-based reinforcement learning model that orchestrates the device grouping strategy to optimize device placement. Thus, the model guarantees a higher probability of success for the devices during data transmission access, eventually leading to more efficient energy consumption. Through thorough quantitative simulations, we demonstrate that the proposed learning-based approach significantly outperforms the other baseline grouping methods. Full article