A Reliability-Oriented Local-Area Model for Large-Scale Wireless Sensor Networks

Large-scale wireless sensor networks (WSNs) have demonstrated some complex features which are similar to those of other types of complex networks, such as social networks. Based on these complex features, evolution process and characteristic of WSNs, we represent a WSN topologically by building a suitable model, which is named as the reliability-oriented local-area model (ROLM) and aimed at improving the performance of WSNs. For analyzing the performance of the ROLM, we define the reliability as the probability of that the relative error between the measurement and the true value is equal to or less than ε  (ε≥0) and proposed a parameter η to measure the reliability of the network. Based on them, we use η to analyze the influence of network structure on the reliability, and compared the reliabilities of the ROLM and the existing WSNs. Experiment results prove that the large-scale WSN follows a power-law distribution, and it has scale-free characteristic and small world characteristic. And it also shows that, comparing with existing model, ROLM not only balances energy consumption by limiting the connectivity of each node to prolong the lifetime of the network, but also improves the reliability substantially. And the ROLM can be used to express the topology of reliability-oriented WSNs and analyze the structure preferably

Federated Learning with Privacy-Preserving Incentives for Aerial Computing Networks

With the help of artificial intelligence (AI) model, aerial computing can help analyze and predict the network dynamics and support intelligent decision-making to improve the performance of 6G space-air-ground integrated networks. Federated learning has been proposed to tackle the challenges of limited energy and data shortage for the application of AI models in aerial computing networks. A critical problem of FL for aerial computing is the lack of incentives due to privacy concerns. On the one hand, the information needed to measure users’ learning quality may be eavesdropped. On the other hand, users’ real costs for determining payments may also undertake inference attacks. In this paper, we design a privacy-preserving and learning quality-aware incentive mechanism for federated learning in aerial computing networks. We propose differential privacy based scheme to protect the privacy of the real cost. In addition, utilize Combinatorial Multi-Armed Bandit (CMAB) algorithm to evaluate the user learning quality without any participant information. Simulation results demonstrate that our scheme can significantly motivate high-quality participants with guaranteed privacy preservation and achieve effective federated learning under the constraint of the limited budget

Safety message broadcast in vehicular networks

This book presents the current research on safety message dissemination in vehicular networks, covering medium access control and relay selection for multi-hop safety message broadcast. Along with an overall overview of the architecture, characteristics, and applications of vehicular networks, the authors discuss the challenging issues in the research on performance improvement for safety applications, and provide a comprehensive review of the research literature. A cross layer broadcast protocol is included to support efficient safety message broadcast by jointly considering geographical location, physical-layer channel condition, and moving velocity of vehicles in the highway scenario. To further support multi-hop safety message broadcast in a complex road layout, the authors propose an urban multi-hop broadcast protocol that utilizes a novel forwarding node selection scheme. Additionally, a busy tone based medium access control scheme is designed to provide strict priority to safety applications in vehicle-to-infrastructure communications. This book offers useful insights into protocol design and inspires a new line of thinking in performance improvements for safety applications in vehicular networks. It is a valuable resource for professionals, researchers, or advanced-level students working in vehicular networks or quality of service

Webit&NEU: An Embedded Device for the Internet of Things

The Internet of Things (IoT) is regarded as the future generation Internet, which ranges from radio frequency identification (RFID) to the ubiquitous computing systems such as wireless sensor networks and mobile ad hoc networks. With the rapid development of IoT, designing an effective low-cost embedded terminal device for the IoT become very necessary. A new embedded device, Webit&NEU, and its reduced embedded real-time operating system used for IoT are implemented by our China Liaoning Province Embedded Technique Key Laboratory in this paper. Besides, related modules in terms of RFID technique, wireless communication, and network protocol are also provided in this paper. Compared with several current solutions of connecting devices and Internet, it has the advantages of good real-time performance, light weight, and low cost. Besides, this paper also puts forward a localization algorithm for the Webit&NEU, and experimental test results in terms of real-time system ability, network communication performance, and localization algorithm show that Webit&NEU can work well and meet the actual requirements of the IoT