Wireless Personal Communications最新文献

For remote health monitoring, activity tracking, and other applications in healthcare such as sports, Wireless Body Area Networks (WBANs) have become a feasible technology. However, the limited resources and dynamic nature of WBANs pose significant challenges to designing efficient and reliable routing protocols. To address these challenges, the proposed work suggests a thermal-aware, energy-efficient, and congestion-aware routing protocol (TECRP) for WBAN. TECRP focuses on improving transmission in both inter-WBAN and intra-WBAN scenarios. It addresses three key Quality of Service (QoS) parameters: energy efficiency, node temperature, and congestion, aiming to enhance overall WBAN communication efficiency. To achieve all these parameters, the algorithm is considered a multi-objective problem. The analysis shows that the temperature rises and delay increases with the number of data transmission, but the multi-objective approach helps to mitigate such effects. The result analysis shows that the path loss values fluctuate with increasing data transmission and network traffic. But the temperature rise increases with more data transmission and larger packets. On the other hand, the Packet Delivery Ratio (PDR) decreases with an increase in data transmission and with larger packet sizes. This shows that with higher congestion ratios, a higher likelihood of packet loss is seen. But in overall performance, the proposed TECRP shows better efficiency and congestion management as compared to other existing state-of-art-models and achieves high PDR, and minimizes packet loss. The proposed approach shows 0.42% improvement in energy efficiency as compared to existing approaches.

A low-profile multi-port dual-band antenna with pattern and polarization diversity is presented for 2.45 and 5.8 GHz bands. The antenna consists of a monopolar patch for vertically polarized (VP) omnidirectional radiation and four combining printed-dipole and tapered-slot elements arranged in a circular shape for horizontally polarized (HP) multibeam radiation, and consequently, both pattern and polarization diversities are achieved in the full azimuth plane. The monopolar patch employs four vias and a square-ring slot to excite (TM_{01}) and (TM_{02}) modes at 2.45 and 5.8 GHz, respectively. Each HP element utilizes printed-dipole and tapered-slot modes for the lower and upper bands, respectively. The VP and HP elements are collocated with a common ground plane, which not only allows a low profile but also broadens the lower band of the monopolar mode. The final design with 0.03(lambda _{2.45-GHz}) height yields a measured 10-dB return loss bandwidth of 8.6% and 6.7% at 2.45 and 5.8 GHz, respectively, and an isolation (ge) 20 dB among all the ports. Its pattern and polarization diversities are validated by far-field measurements.

The increasing need for swift, low-latency, and reliable wireless transmission in the age of 5G has led to rapid advancements in 5G technology. MIMO systems, originally implemented in 4G applications, remain an integral part of 5G networks. The sub-6-GHz frequency band holds promise for 5G applications, such as improved link and reduced transmission losses. This comprehensive review explores advancements in patch antenna (PA) structure for sub-6 GHz 5G smartphone utilization, addressing the challenges posed by space constraints in mobile devices and the need for efficient integration of antennas. The paper discusses the significance of MIMO antennas in modern cellular communication, particularly in the context of 5G technology, which utilizes sub-6 GHz. Reconfigurable antennas (RAs) and Microstrip Patch Antennas (MPAs) are introduced as solutions, highlighting their compact, cost-effective, and flexible nature. The review delves into critical factors such as geometry, substrate selection, and feed design in optimizing patch antenna performance. It also covers the importance of addressing both sub-6 GHz and mm-wave bands within a single antenna system.

High-level security is crucial for protecting sensitive information transmitted across wireless sensor networks (WSNs). Secure routing plays a key role in preventing impersonation attacks within the network. Although numerous state-of-the-art routing models based on trusted metrics aim to enhance WSN security, challenges remain due to the network's dynamic nature. This paper introduces an enhanced multi-attribute-based trusted attack resistance (EMBTR) algorithm designed to securely manage routing using nodes' trust values. The proposed approach leverages Quality of Service (QoS) parameters such as stability rate (SR), reliability rate (RR), and elapsed time (ET) to improve network performance and combat trust-related attacks. By isolating misbehaving nodes based on their trust metrics, the algorithm establishes a reliable communication route. Comparisons with existing routing algorithms like TSRM (Trust-based Secure Routing Model) and TARF (Trust-aware Routing Framework for WSNs) demonstrate that the EMBTR algorithm effectively detects and eliminates malicious nodes, providing secure routing with higher detection rates and limited energy consumption compared to current solutions.

In the rapidly evolving domain of edge computing, efficient task scheduling emerges as a pivotal challenge due to the increasing complexity and volume of tasks. This study introduces a sophisticated dual-layer hybrid scheduling model that harnesses the strengths of Graph Neural Networks and Deep Reinforcement Learning to enhance the scheduling process. By simplifying task dependencies with Graph Neural Network at the upper layer and integrating Deep Reinforcement Learning with heuristic algorithms at the lower layer, this model optimally allocates tasks, significantly improving scheduling efficiency and reducing response times, particularly beneficial for logistics cloud robots operating in edge computing contexts. We validated the effectiveness of this innovative model through rigorous simulation experiments on the EdgeCloudSim platform, comparing its performance against traditional heuristic methods such as Shortest Job First, First Come First Serve and Heterogeneous Earliest Finish Time. The results confirm that our model consistently achieves superior task scheduling performance across various task volumes, effectively meeting the scheduling demands. This study demonstrates the effectiveness of integrating advanced machine learning techniques with heuristic algorithms to enhance task scheduling processes, making it particularly suitable for scenarios with high demands on response times. This approach not only facilitates more efficient task management but also aligns with the needs of modern edge computing applications, streamlining operations and boosting overall system performance.

This paper deals with the characterization of mobility models for unmanned aerial vehicles (UAVs) cellular network regarding handovers. The UAVs act as base stations to serve terrestrial users using service models, namely user dependent model and user independent model. The state-of-the-art mobility models, such as straight line, random waypoint (RWP) and modified random direction (M-RD) are employed under two different mobility scenarios for UBSs: same speed model and different speed model. The RWP mobility model achieves lower bound in handover probability followed by M-RD mobility model with an infinitesimal change. The M-RD mobility model exhibiting lowest handover rate has greatly increased its usability in UAVs cellular network.

Energy is still one of the most important problems in wireless sensor network (WSN). With the application of wireless charging vehicle (WCV), wireless charging technology can be used to solve the energy problem of WSN. In the wireless rechargeable sensor network (WRSN), how to reasonably deploy the WCV has become a problem in planning. The existing studies only consider the impact of independent coverage contribution of a single node on the charging path planning, but do not consider the redundant coverage area that may occur when a single node works independently, resulting in a small or even zero coverage contribution of some nodes. This paper propose Improved Soft-k-Means Clustering Charging Based on Node Collaborative Scheduling (ISKCC-NCS) to keep the coverage quality of WSN. At first, this algorithm uses Improved Soft-k-Means to cluster WRSN, and realizes confident information coverage through collaborative sensing between adjacent nodes. The second, we evaluate the coverage contribution of each node to be charged and calculate the priority of the node. The third, we form new charging path by inserting and deleting tasks. At last, a carrier charging vehicle (CCV) is used to carry several micro charging vehicle (MCV) to each cluster to charge the sensor node. Through a large number of simulation experiments, it is found that compared with other algorithms, ISKCC-NCS can significantly enhance the coverage rate and monitoring stability of the WSN.

Nowadays, MIMO technology is one of the hot issues in the development of communication technology. The power dividers have a large role in microwave circuits and it is very important in antenna array systems. This letter presents a broadband eight-way power divider based on the Wilkinson power divider for MIMO communication systems. The power divider is composed of a radial cavity equipped with coaxial probes serving as the power divider circuits, along with microstrip lines loaded with resistors that function as the isolation network. By combining these two parts, the power divider achieves more than 25% operating bandwidth with a theoretical 22.5 dB port isolation. Experimental verification for a C-band (5.8–7.3 GHz) broadband eight-way power divider is presented. Measured results show that the output return loss is better than 18 dB, the isolation is better than 20 dB, and the insertion loss is within 9.03±0.25 dB.

The article examined the physical layer security of cooperative non-orthogonal multiple access (NOMA) network over Rayleigh fading channels by considering one eavesdropper. Relaying techniques like decode-and-forward (DF) and amplify–and–forward (AF) protocols are analyzed. The secrecy rate and secrecy outage probability of the system considering NOMA-AF and NOMA-DF systems were derived. Two cases were considered for the analysis, one system model where a near user operates as relay, forwarding the signal to far user; another where a relay is used in forwarding the signal to both users. Simulation results were obtained for different values of power allocation coefficients. These results show that the system model proposed in case 2 provides a better secrecy performance result than NOMA based models proposed in case 1 under both relaying techniques.

The decision-making process in Industrial Wireless Sensor Networks heavily relies on the information provided by smart sensors. Ensuring the trustworthiness of these sensors is essential to prolong the lifetime of the network. Additionally, dependable data transmission by sensor nodes is crucial for effective decision-making. Trust management approaches play a vital role in safeguarding industrial sensor networks from internal threats, enhancing security, dependability, and network resilience. However, existing trust management schemes often focus solely on communication behaviour to calculate trust values, potentially leading to incorrect decisions amidst prevalent malicious attacks. Moreover, these schemes often fail to meet the resource and dependability requirements of IWSNs. To address these limitations, this paper proposes a novel hybrid Trust Management Scheme called the Multi-layered Assessment System for Trustworthiness Enhancement and Reliability (MASTER). The MASTER scheme employs a clustering approach within a hybrid architecture to reduce communication overhead, effectively detecting and mitigating various adversarial attacks such as Sybil, Blackhole, Ballot stuffing, and On–off attacks with minimal overheads. This multifactor trust scheme integrates both communication-based trust and data-based trust during trust estimation, aiming to improve the lifetime of industrial sensor networks. Furthermore, the proposed MASTER scheme utilizes a flexible weighting scheme that assigns more weight to recent interactions during both direct and recommendation (indirect) trust evaluation. This approach ensures robust and precise trust values tailored to the specific network scenario. To efficiently process and glean insights from dispersed data, machine learning algorithms are employed, offering a suitable solution. Experimental results demonstrate the superior performance of the MASTER scheme in several key metrics compared to recent trust models. For instance, when 30% of malicious Sensor Nodes (SNs) exist in a network comprising 500 sensor nodes, the MASTER scheme achieves a malicious behaviour detection rate of 97%, surpassing the rates of other models. Even after the occurrence of malicious SNs exceeding 30%, the False Negative Rate (FNR) in the MASTER scheme remains lower than other models due to adaptive trust functions employed at each level. With 50% malicious SNs in the network, the MASTER scheme achieves a malicious behaviour detection accuracy of 91%, outperforming alternative models. Moreover, the average energy consumption of SNs in the MASTER scheme is significantly lower compared to other schemes, owing to its elimination of unnecessary transactions through clustered topology utilization. Specifically, with 30% and 50% malicious SNs in the network, the MASTER scheme achieves throughput rates of 150 kbps and 108 kbps, respectively, demonstrating its efficiency in challenging network scenarios. Overall, the proposed MASTER scheme