Search Results (135)

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

While paratuberculosis control has been studied for over a century, knowledge gaps still exist regarding the uptake and efficacy of control programmes. This narrative review aims to summarise studies on control programmes presented at the IDF ParaTB Fora in 2021 and 2022 and the International Colloquium on Paratuberculosis in 2022. Studies were grouped by topic as follows: successful control, field studies, education and extension, voluntary and compulsory control programmes, and surveillance. Various Map control programmes resulted in a decreasing animal and herd level Map prevalence. Long-term stakeholder commitment, stable funding, involvement of herd veterinarians and incentives for farmers to participate were shown to be pivotal for long-term success. Control measures focused on vertical and calf-to-calf transmission may improve Map control in infected herds. Easy-to-capture visualisation of surveillance test results to inform participants on the progress of Map control in their herds was developed. The probability of freedom from disease and estimated within-herd prevalence were identified as good candidates for categorisation of herds to support low-risk trade of cattle. Results of the surveillance schemes may inform genetic selection for resistance to Map infection. In conclusion, successful paratuberculosis control is feasible at both the herd and country level provided that crucial prerequisites are met. Full article

Vehicle -to-vehicle (V2V) communications are crucial for enhancing road network safety and efficiency. With the increasing demand for bandwidth in V2V services, exploring innovative solutions has become imperative. This study explores a comparative analysis of mmWave and WiFi transmission technologies, with a specific focus on line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios in both 2D and 3D modeling environments. The use of stochastic geometry tools allows a realistic modeling of the random positioning of vehicles within the V2V system framework, resulting in accurate expressions for the successful transmission probability (STP) and average throughput (AT) for both communication systems. To validate our analytical findings, Monte Carlo simulations have been employed, offering a comprehensive evaluation of mmWave and WiFi performance. Simulation results highlight that mmWave systems outperform in scenarios with short transmission distances and low vehicle density while WiFi systems demonstrate greater efficiency for longer transmission distances. Full article

This article addresses challenges in the design of underground high-voltage transmission lines, focusing on thermal management and cable ampacity determination. It introduces an innovative proposal that adjusts the dimensions of the backfill to enhance ampacity, contrasting with the conventional approach of increasing the core cable’s cross-sectional area. The methodology employs a particle swarm optimization (PSO) technique with adaptive penalization and restart strategies, implemented in MATLAB for parameter autoadaptation. The article emphasizes more efficient solutions than traditional PSO, showcasing improved convergence and precise results (success probability of 66.1%). While traditional PSO is 81% faster, the proposed PSO stands out for its accuracy. The inclusion of thermal backfill results in an 18.45% increase in cable ampacity, considering variations in soil thermal resistivity, backfill properties, and ambient temperature. Additionally, a sensitivity analysis was conducted, revealing conservative values that support the proposal’s robustness. This approach emerges as a crucial tool for underground installation, contributing to continuous ampacity improvement and highlighting its impact on decision making in energy systems. Full article