Ad Hoc Networks最新文献

Compared with upper layer authentication, physical layer authentication (PLA) is essential in unmanned Industrial Internet of Things (IIoT) scenarios, owing to its low complexity and lightweight. However, in dynamic environments, as the amount of users expands, the accuracy of single-attribute-based authentication decreases drastically, which becomes an urgent issue for IIoT. Accordingly, this paper proposes a novel multi-attribute-based convolutional attention neural network (CANN) for multiuser PLA. Using characteristics such as amplitude, phase, and delay, the multiple attributes from a real industrial scene are first constructed into three-dimensional matrices fed into CANN. Then, attention blocks are designed to learn the correlation between attributes and extract the attribute parts that are more instrumental in the CANN to improve authentication accuracy. In addition, to avoid confusing multiple users, a center confidence loss is introduced, which adaptively adjusts the weight of the center loss and works together with the softmax loss to train the CANN. The effectiveness of the proposed CANN-based multiuser PLA and center confidence loss is supported by experimental results. Compared with the recently proposed latent perturbed convolutional neural network (LPCNN), the CANN-based scheme improves the authentication accuracy by 8.11%, which is superior to the existing learning-based approaches. As the CANN is further trained with the loss function that combines center confidence loss, the authentication accuracy can be improved by at least 2.22%.

Routing Protocol for Low Power and Lossy Networks (RPL) is the de-facto routing standard in IoT networks. It enables nodes to collaborate and autonomously build ad-hoc networks modeled by tree-like destination-oriented direct acyclic graphs (DODAG). Despite its widespread usage in industry and healthcare domains, RPL is susceptible to insider attacks. Although the state-of-the-art RPL ensures that only authenticated nodes participate in DODAG, such hard security measures are still inadequate to prevent insider threats. This entails a need to integrate soft security mechanisms to support decision-making. This paper proposes iTRPL, an intelligent and behavior-based framework that incorporates trust to segregate honest and malicious nodes within a DODAG. It also leverages multi-agent reinforcement learning (MARL) to make autonomous decisions concerning the DODAG. The framework enables a parent node to compute the trust for its child and decide if the latter can join the DODAG. It tracks the behavior of the child node, updates the trust, computes the rewards (or penalties), and shares them with the root. The root aggregates the rewards/penalties of all nodes, computes the overall return, and decides via its $ϵ$-Greedy MARL module if the DODAG will be retained or modified for the future. A simulation-based performance evaluation demonstrates that iTRPL learns to make optimal decisions with time.

In recent years, Internet of Vehicles (IoV) is in a booming stage. But at the same time, the methods of attack against IoV such as Denial of Service (DoS) and deception are great threats to personal and social security. Traditional IoV intrusion detection usually adopts a centralized detection model, which has the disadvantages of untimely detection results and is difficult to protect vehicle privacy in practical applications. Meanwhile, centralized computation requires a large amount of vehicle data transmission, which overloads the wireless bandwidth. Combined the distributed computing resources of Federated Learning (FL) and the decentralized features of blockchain, an IoV intrusion detection framework named IoV-BCFL is proposed, which is capable of distributed intrusion detection and reliable logging with privacy protection. FL is used for distributing training on vehicle nodes and aggregating the training models at Road Side Unit (RSU) to reduce data transmission, protect the privacy of training data, and ensure the security of the model. A blockchain-based intrusion logging mechanism is presented, which enhances vehicle privacy protection through Rivest-Shamir-Adleman (RSA) algorithm encryption and uses Inter Planetary File System (IPFS) to store the intrusion logs. The intrusion behavior can be faithfully recorded by logging smart contract after detecting the intrusion, which can be used to track intruders, analyze security vulnerabilities, and collect evidence. Experiments based on different open source datasets show that FL achieves a high detection rates on intrusion data and effectively reduce the communication overhead, the smart contract performs well on evaluation indicators such as sending rate, latency, and throughput.

Identifying individuals with similar behaviors and mobility patterns has become essential to improving the functioning of urban services. However, since these patterns can vary over time, such identification needs to be done periodically. Furthermore, once mobility data expresses the routine of individuals, privacy must be guaranteed. In this work, we propose a framework for periodically detecting and grouping individuals with behavioral similarities into communities. To accomplish this, we built an autoencoder model to extract spatio-temporal mobility features from raw user data at periodic intervals. We used Federated Learning (FL) as a training approach to preserve privacy and alleviate time-consuming training and communication costs. To determine the number of communities without risking an arbitrary number, we compared the choices of two probabilistic methods, the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Since the communities are updated periodically, we also analyzed the impact of aged samples on the proposed framework. Finally, we compared the performance of our FL-based solution to a centralized training approach. We analyzed similarity and dissimilarity metrics on mobility samples and the contact time of individuals in three different scenarios. Our results indicate that AIC outperforms BIC when choosing the number of communities, although both satisfy the evaluation metrics. We also found that using older samples benefits more complex spatio-temporal scenarios. Finally, no significant losses were detected when compared to a centralized training approach, reinforcing the advantages of using the FL-based method.

Traditional ground-based illumination equipment is limited in mobility and light source height, making it difficult to adapt to diverse living scenarios such as camping that require quick and flexible illumination solutions. With the rapid development of Unmanned Aerial Vehicle (UAV) technology, particularly in illumination services, UAVs have demonstrated unique advantages. Addressing the inadequacies of conventional illumination, this study proposes a prototype of an autonomously deployed illumination system based on the RoboMaster Tello Talent (Tello) UAV, designed to provide quick and flexible on-site illumination solutions. The system’s design encompasses three complementary modules to enhance its overall functionality and efficiency. Firstly, the illumination module equips the Tello UAV with a specialized illumination extension, ensuring flight stability and effective illumination. Secondly, the addressing module employs iterative algorithms to identify optimal UAV deployment locations and precisely plan flight paths. Lastly, the flight control module, guided by the results from the addressing module, scripts flight commands, integrates with the Tello UAV’s Application Programming Interface (API), and executes flight plans optimized for path efficiency, ensuring the UAV quickly and accurately reaches designated locations, coordinating with the illumination module to deliver effective illumination. Experimental results demonstrate that the proposed illumination system can swiftly respond to various user demands, autonomously deploy UAVs to optimal illumination positions, and provide high-quality service.

Federated learning presents a compelling approach to training artificial intelligence systems in decentralized settings, prioritizing data safety over traditional centralized training methods. Understanding correlations among higher-level threats exhibiting abnormal behavior in the data stream becomes paramount to developing cyber–physical systems resilient to diverse attacks within a continuous data exchange framework. This work introduces a novel vertical federated multi-agent learning framework to address the challenges of modeling attacker and defender agents in stationary and non-stationary vertical federated learning environments. Our approach uniquely applies synchronous Deep Q-Network (DQN) based agents in stationary environments, facilitating convergence towards optimal strategies. Conversely, in non-stationary contexts, we employ synchronous Advantage Actor–Critic (A2C) based agents, adapting to the dynamic nature of multi-agent vertical federated reinforcement learning (VFRL) environments. This methodology enables us to simulate and analyze the adversarial interplay between attacker and defender agents, ensuring robust policy development. Our exhaustive analysis demonstrates the effectiveness of our approach, showcasing its capability to learn optimal policies in both static and dynamic setups, thus significantly advancing the field of cyber-security in federated learning contexts. To evaluate the effectiveness of our approach, we have done a comparative analysis with its baseline schemes. The findings of our study show significant enhancements compared to the standard methods, confirming the efficacy of our methodology. This progress dramatically enhances the area of cyber-security in the context of federated learning by facilitating the formulation of substantial policies. The proposed scheme attains 15.93%, 32.91%, 31.02%, and 47.26% higher results as compared to the A3C, DDQN, DQN, and Reinforce, respectively.

The efficient deployment of Roadside Units (RSUs) in an infrastructure based on IEEE 802.11p is essential for delivering Internet-based services to vehicles. In this paper, we introduce novel strategies that, in contrast to prior works, exclusively rely on the average vehicular density within specific urban areas, and these strategies depend on a performance model of IEEE 802.11p for guidance and decision-making regarding RSU placement. This minimal upfront information contributes to the practicality and ease of implementation of our strategies. We apply our strategies to three real-world urban scenarios, utilizing the ns-3 and sumo simulators for validation. This study contributes to three fundamental aspects. First, we establish that any efficient deployment of RSUs is closely linked to the unique characteristics of the city under consideration such as the street layout and spatial density of vehicles. In other words, the characteristics of an efficient RSU deployment are unique to each city. Second, we show that the optimal strategy is not to place the RSUs at the locations with the highest traffic density. Instead, with the help of an analytical performance model of IEEE 802.11, we propose a more efficient strategy wherein the location of each RSU is determined to maximize the number of vehicles receiving the target QoS. This can lead to a significant drop in the number of RSUs required to equip a city. Finally, we demonstrate that, by preventing the use of the lowest transmission rate of IEEE 802.11p at each RSU, a collective benefit can be achieved, even though each RSU experiences a shorter radio range.

With the rapid development of Internet of Things (IoT) technology in industrial, agricultural, medical and other fields, IoT terminal devices face security and privacy challenges when sharing data. Among them, ensuring data confidentiality, achieving dual-side privacy protection, and performing reliable data integrity verification are basic requirements. Especially in resource-constrained environments, limitations in the storage, computing, and communication capabilities of devices increase the difficulty of implementing these security safeguards. To address this problem, this paper proposes a resource-constrained anonymous data-sharing scheme (ADS-RC) for the IoT. In ADS-RC, we use elliptic curve operations to replace computation-intensive bilinear pairing operations, thereby reducing the computational and communication burden on end devices. We combine an anonymous verifiable algorithm and an attribute encryption algorithm to ensure double anonymity and data confidentiality during the data-sharing process. To deal with potential dishonest behavior, this solution supports the revocation of malicious user permissions. In addition, we designed a batch data integrity verification algorithm and stored verification evidence on the blockchain to ensure the security and traceability of data during transmission and storage. Through experimental verification, the ADS-RC scheme achieves reasonable efficiency in correctness, security and efficiency, providing a new solution for data sharing in resource-constrained IoT environments.

Wireless body area network (WBAN) has revolutionized the healthcare sector by enabling remote monitoring and control of wearable and implantable devices, providing freedom of mobility to patients. However, wireless channel modeling in BAN is a crucial aspect for designing an efficient off-body, on-body and in-body communication. Due to the unique characteristics of the human body, it aims to characterize the signal propagation through skin, tissues, internal organs and biological fluids of a patient’s body. Moreover, it is important to enhance the battery life of the low-powered devices for a sustainable BAN. In this work, we provide a hybrid communication channel model for wireless power transfer in a BAN including both off-body and in-body communication channels. An indoor room scenario is considered in which a movable patient having an implant inside its body is present along with an RF power source (for example, a Wi-Fi access point) situated in a ceiling corner. Implant is assumed to inhibit energy harvesting capability. For practicability, we have considered the effect of path loss, partition walls, floor attenuation factor along with other important body parameters. Specifically, we aim to statistically characterize this hybrid communication system, for which unique closed-form expressions of the probability distribution functions of the received power have been derived, thereby first calculating the instantaneous power at different layers of human body and then obtaining the closed-form expression for average received power. All the derived mathematical expressions have been verified via numerical simulations. Further, for elongating the lifespan of implants, we investigated the average power harvested by an implant and its power outage probability for analyzing the sustainability of implants. The results are numerically validated, considering different types of indoor room scenarios, in addition to providing key design insights.