Wireless Networks最新文献

Federated Learning (FL) is a privacy-preserving paradigm which enables multiple clients to jointly learn a model and keeps their data local. However, the nature of FL leaves the vulnerability to Byzantine attacks, where the malicious clients upload poisoned local models to the FL server, further corrupting the learnt global model. Most existing defenses against Byzantine attack still have the limitations when the ratio of malicious clients is greater than (50%) and the data among clients is not independent and identically distributed (non-IID). To address these issues, we propose a novel FL framework with Byzantine detection, which is robust against Byzantine attacks when the adversary has control of the majority of the clients and the data among clients is highly non-IID. The main idea is that the FL server supervises the clients via injecting a shadow dataset into the processes of the local training. Moreover, we design a Local Model Filter with an adaptive filtering policy that evaluates the local models’ performance on the shadow dataset and further filters out these local models compromised by the adversary. Finally, we evaluate our work on three real-world datasets, and the results show that our work outperforms the four existing Byzantine-robust defenses in defending against two state-of-the-art threatening Byzantine attacks.

A wireless sensor network (WSN) is made up of many sensor nodes with insufficient energy, storage, and processing capabilities. Data gathering and transmission to the base station are two of the main responsibilities of the sensor nodes (BS). As a result, the network lifespan becomes the key factor in the successful design of data collection strategies in WSN. In this study, we provide the Enriched energy optimized LEACH (EE-OLEACH) protocol for data transfer. Through a combination of efficient optimum clustering and an optimal route selection mechanism, it provides a means for energy-efficient routing in WSN. The Homogeneous Hunter-Wolf optimization (HHWO) is used for clustering, and a cluster head is chosen for each cluster to reduce energy loss among sensor nodes and maximize efficiency in their use of available resources. Nodes with the highest residual energy may receive the most energy-efficient routing. In order to send the data to BS, the nodes with the greatest residual energy are chosen. The pheromone-profound Ant optimization (PPAO) method was then used to reduce energy consumption throughout the path-selection process. It contributes to a higher packet delivery ratio while reducing power consumption. According to the experimental findings, the proposed EE-OLEACH performs better than the current Protocol in terms of packet delivery ratio, end-to-end latency, and energy usage. In this paper, we compare the performance of the existing hierarchical routing protocols under varying conditions (packet size, starting energy level, etc.) and demonstrate how the optimal CH selection based on a suggested algorithm improves both network lifetime and energy consumption. The Simulation results shows that the EE-OLEACH enhances energy efficiency by 30%, delay by 35%, node survived by 45%, network lifetime by 56%, packet delivery ratio by 47% and throughput by 38% when compared with other existing protocols. The results clearly show that the suggested EE-OLEACH extends the lifespan of the network and reduce the energy consumption.

Secure user authentication has grown importance in today’s modern culture. It is significant to authenticate the user identity in numerous consumer applications particularly financial transactions. Traditional authentication methods rely on easy-to-guess passwords, PIN numbers, or tokens with several security flaws, such as those printed on the back of credit cards for PIN numbers. As an alternative to current systems, biometric authentication techniques based on physical and behavioral characteristics have been proposed. Multibiometric systems, which combine several biometrics, are developed as a result of the difficulties that single-biometric authentication systems encountered in real-world applications including lack of precision and noisy data. The proposed system provides better performance and greater accuracy compared with other authentication techniques. The majority of them is inconvenient and demand complicated user interactions. This paper proposes Enhanced Elman Spike Neural Network along Glowworm Swarm Optimization (EESNN-GSO-AMT) for Multiple Transaction Authentication. The images are collected via SDUMLA-HMTalong CASIA V5 dataset. The pictures are provided to pre-processing to enhance the images quality utilizing Learnable Edge Collaborative Filter (LECF). The preprocessed images are fed to feature extraction using Adaptive and concise empirical wavelet transform (ACEWT) and the features are extracted such as entropy, homogeneity, energy and contrast. The extracting features are provided to EESNN classifier to categorize authorized or unauthorized persons. In general, the EESNN classifier does not express adapting optimization methods to determine ideal parameters to ensure accurately. Therefore, it is proposed to utilize the Glowworm Swarm Optimization to enhanceEESNN, which accurately categorizes the authorized and unauthorized person. The efficiency of the proposed approach is assessed usingsome metrics. The proposed EESNN-GSO-AMT method attains higher accuracy 20.54%, 21.76% and 23.89%; greater sensitivity 20.12% 20.34% and 21.43%; higher precision 23.34%, 22.68% and 24.34% are analyzed to the existing methods, like Optimal feature level fusion for safe human authentication in multimodal biometric scheme (OptGWO-AMT-FV), Joint attention network for finger vein authentication (JAnet-AMT-FV), Finger Vein Recognition Utilizing Deep Learning Technique (DCNN-AMT-FV) respectively.

Vehicular fog computing has emerged as a promising paradigm that provisions computing at the network edge and alleviates the computation workload of static edge computing servers. In this regard, building computing facilities on top of jammed vehicles is particularly attractive and practically viable. However, the respective offloading mechanisms and resource sharing have been less explored. In this work, we propose a novel jammed vehicular cloudlet (JVC) assisted task offloading framework that aggregates and leverages underutilized communication and computation resources of congested vehicles and nearby road side unit to serve resource-intensive tasks of mobile users. To motivate resource provisioning by the JVCs in a non-competitive environment, we design an incentive mechanism that charges offloading user and rewards the serving JVC. With aim to maximize the total profit earned by JVCs, we formulate joint task assignment and resource allocation problem in presence of data segmentation, task deadline, and budget constraints. The formulated problem is mixed integer non-linear programming problem, and we directly obtain its solution using genetic algorithm (GA). We further devise a greedy fractional-knapsack based resource allocation scheme named profit-aware task scheduling (PATS). The extensive evaluation under realistic human mobility trajectories demonstrates that, GA outperforms other baseline schemes in maximizing the total profit of JVCs while PATS achieves comparable performance and serves more users with much lower computation complexity.

6G incorporates Intelligent Reflecting Surfaces (IRS) and Unmanned Aerial Vehicles (UAVs) as effective solutions to overcome the limitations of terrestrial networks in terms of coverage and resource constraints. Compared to communications with conventional UAV networks which face restricted battery longevity, fluctuating channel conditions, and paucity of resources, IRS-assisted UAV communications is seen as an attractive strategy. In this paper, we present an extensive survey on IRS-assisted UAV communications for 6G networks. We highlight various application scenarios and key technologies for integrating IRS and UAVs in 6G architecture. We discuss primary issues along with their solutions and put forward the open research challenges that could serve as a potential area for further investigation in the related discipline. Key findings encompass an in-depth exploration of diverse application scenarios and pivotal technologies crucial for seamless integration of IRS and UAVs within the 6G architecture, providing valuable insights into optimizing communication efficiency and addressing network challenges. This survey serves as a valuable resource for scholars, practitioners, and policymakers in the fields of integrated UAV and IRS communication. It provides insights for making well-informed decisions and driving advancements to meet the constantly evolving demands of our connected world.

Cognitive Radio Wireless Sensor Networks (CRWSNs) promise optimized spectrum utilization but face challenges in sustaining energy balance, particularly due to the emergence of “hot spots.” In CRWSNs, Cluster Heads (CHs) closer to the sink experience higher traffic as compared to those farther away, primarily due to their role in data collaboration and relaying to the sink. This leads to early depletion of their energy reserves and potentially causing the network to partition creating hot spots or energy holes. Effective clustering algorithms are needed to mitigate these hot spots. The main objective of the paper is to propose a novel clustering scheme titled “Unequal Clustering Energy Hole Avoidance (UCEHA) algorithm” to address hot spot issues in CRWSNs. UCEHA partitions the network into clusters based on sink proximity, selecting CHs considering node energy, communication channels, neighbors, and sink distance. An enhanced spectrum-aware AODV mechanism facilitates efficient data routing. To test and validate the proposed methodology, extensive experimentations were conducted and the results demonstrate UCEHA’s superiority over existing methods, exhibiting reduced energy consumption (average 19%), improved network load balance (average 26%), increased network lifetime (average 40%), and enhanced throughput (average 8%). These results highlight the effectiveness of UCEHA algorithm in addressing energy imbalance and hot spot issues in CRWSNs, ultimately leading to enhanced network performance and longevity.

The rising craze of sensor enabled mobile devices promotes its usage in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC) and other distributed computing environments derived from the cloud computing environment. As the computing paradigm shifts from centralized to distributed computing and mobile devices are getting smarter and resource rich, it facilitate the user to do computation to its proximity. Hence, it is quite useful to incorporate the Wireless Sensor Networks (WSN) with distributed computing environment to better cater to the user needs. The proposed work enhances the MCC and MEC by incorporating sensor enabled computing along with the application of energy optimization techniques such as coyote optimization, Fuzzy Logic (FL), data redundancy and data compression. A new framework called Sensor Enabled-Scalable Key Parameter Yield of Resources (SE-SKYR) framework is proposed in this research work by integrating SKYR framework with cluster-based sensing mechanism. The proposed work uses SKYR framework which is a cloudlet based MCC framework and works well for MEC as well. Cloudlet is used as the main computing component available at the local level which suits both MEC and MCC. The existing system uses the concept of relay node to transmit data packets in transmission path from sensor nodes to server via edge cloud and hence causes delay in transmission of data. In the proposed work, we have introduced a Scalable Energy Optimization of Resource (SEOR) algorithm to optimize the energy consumption by various resources. SE-SKYR framework along with SEOR algorithm addresses the problems faced by the existing system. The complexity of the proposed SEOR algorithm is less as compared to its existing counterparts and is also comprehended from the results.

In the rapidly advancing field of edge computing, improving the end-to-end transmission rate is crucial to accommodating the needs of latency-sensitive applications. To address this, this article introduces Reconfigurable Intelligent Surfaces (RIS) to examine the challenge of maximizing the minimum attainable rate among users in a cell-free massive MIMO system from an edge computing perspective. In this article, a framework is proposed to improve the end-to-end user transmission rate by alternately optimizing the precoding matrix of Access Points (APs) and the phase shift matrix of the RIS. For the optimization of the APs’ precoding matrix, this framework utilizes a Second Order Cone Programming (SOCP) method. In order to optimize the continuous phase shifts at the RIS, this framework uses a Semidefinite Relaxation (SDR) technique. For the optimization of the discrete phase shifts at the RIS, a projection-based method is proposed in this framework. By integrating these two forms of beamforming, the proposed framework significantly improves the end-to-end transmission rate, meeting the critical requirements of latency-sensitive applications in edge computing scenarios.

The fusion of satellite technologies with the Internet of Things (IoT) has propelled the evolution of mobile computing, ushering in novel communication paradigms and data management strategies. Within this landscape, the efficient management of computationally intensive tasks in satellite-enabled mist computing environments emerges as a critical challenge. These tasks, spanning from optimizing satellite communication to facilitating blockchain-based IoT processes, necessitate substantial computational resources and timely execution. To address this challenge, we introduce APOLLO, a novel low-layer orchestration algorithm explicitly tailored for satellite mist computing environments. APOLLO leverages proximity-driven decision-making and load balancing to optimize task deployment and performance. We assess APOLLO’s efficacy across various configurations of mist-layer devices while employing a round-robin principle for equitable tasks distribution among the close, low-layer satellites. Our findings underscore APOLLO’s promising outcomes in terms of reduced energy consumption, minimized end-to-end delay, and optimized network resource utilization, particularly in targeted scenarios. However, the evaluation also reveals avenues for refinement, notably in CPU utilization and slightly low task success rates. Our work contributes substantial insights into advancing task orchestration in satellite-enabled mist computing with more focus on energy and end-to-end sensitive applications, paving the way for more efficient, reliable, and sustainable satellite communication systems.

Wireless sensor network (WSN) is widely used in indoor positioning, but indoor positioning is susceptible to non-line-of-sight (NLOS) propagation environment. The inertial navigation system (INS) does not depend on external information, but it will produce a large cumulative error when working for a long time. The combination of Ultra-wide band (UWB) positioning and inertial navigation positioning can not only effectively reduce the impact of NLOS interference, but also alleviate the impact of INS cumulative error. This paper proposes an algorithm based on yaw angle and UWB joint positioning. In order to weaken the cumulative error of the INS itself, this paper uses the UWB positioning results to correct the INS positioning data and yaw angle data through the extended Kalman filter (EKF), and then performs subsequent positioning according to the modified yaw angle until the next data correction. In addition, this algorithm uses a hypothesis test method for INS and UWB data processing, which weakens the error impact of environmental factors. The proposed algorithm is compared with existing algorithms using mean square error (RMSE) as an indicator. The simulation and experimental results show that the algorithm has better performance in NLOS interference environment.