Energy efficiency and security issues are the main concerns in wireless sensor networks (WSNs) because of limited energy resources and the broadcast nature of wireless communication. Therefore, how to improve the energy efficiency of WSNs while enhancing security performance has attracted widespread attention. In order to solve this problem, this paper proposes a new deep reinforcement learning (DRL)-based strategy, i.e., DeepNR strategy, to enhance the energy efficiency and security performance of WSN. Specifically, the proposed DeepNR strategy approximates the Q-value by designing a deep neural network (DNN) to adaptively learn the state information. It also designs DRL-based multi-level decision-making to learn and optimize the data transmission paths in real time, which eventually achieves accurate prediction and decision-making of the network. To further enhance security performance, the DeepNR strategy includes a defense mechanism that responds to detected attacks in real time to ensure the normal operation of the network. In addition, DeepNR adaptively adjusts its strategy to cope with changing network environments and attack patterns through deep learning models. Experimental results show that the proposed DeepNR outperforms the conventional methods, demonstrating a remarkable 30% improvement in network lifespan, a 25% increase in network data throughput, and a 20% enhancement in security measures.

Upon the spreading of intelligent transportation technology, reconfigurable intelligent surface (RIS) can be applied in traditional vehicular networks to assist autonomous driving, so as to deal with blind spots and high energy consumption of long-distance communication in vehicles communication system. However, achieving bidirectional reliable communication for vehicular networks is challenging because of the high mobility of vehicles. The existing solution relies on constantly reconstructing end-to-end communication links for bidirectional communication in vehicular networks. Moreover, waiting timers on the data transmission path can double the transmission delay for data return, not to mention the potential communication delays and overheads triggered by the rapid movement of the vehicle. In order to avoid those tricky situations, this article proposes a novel two-way reliable forwarding strategy (TRFS) for vehicular named data networks and adopts the RIS symbiotic communication scheme to further reduce the transmission overhead, which has the ability to establish and maintain a reliable end-to-end communication link. On the one hand, the crucial insight of the proposed TRFS is to achieve low delay by eliminating wait timers on the data return path. Specifically, a topology-free policy is used to reduce transmission overhead and to accommodate sudden changes in network topology. On the other hand, the proposed RIS-assisted symbiotic communication scheme expands the coverage of vehicle communication and acts as a relay to compensate for the energy consumption from the long-distance transmission. Moreover, we also investigate the reliability of the proposed TRFS in vehicle communication under various vehicle densities, vehicle communication distances, and speed variations. The experiments show that the proposed TRFS scheme outperforms the classic listen first broadcast last (LFBL) fast-forwarding strategy and the novel reinforcement-based lightweight forwarding (R-LF) strategy in terms of transmission overhead, satisfaction rate, and average delay. The results reiterates the proposed RIS-assisted scheme’s ability to further reduce the transmission overhead of vehicles communication.

WiFi-based sensing technology has become a popular research direction in the Internet of Things (IoT) field. However, the accuracy of action sensing across different environmental domains is severely degraded when the model is deployed at the real IoT edge. Existing cross-domain sensing methods are unsuitable for real-life applications due to the large amount of expensive channel state information (CSI) data required for training. Meanwhile, pre-trained predictive models in the cloud may not perform well in edge-side deployment environments. To address these issues, we propose a few-shot cross-domain WiFi sensing (FewCS) system with online learning. The model aggregates unlabelled samples from the same target domain and separates samples from different domains while minimizing sample cost and training an accurate WiFi-sensing system. The core idea of FewCS is to capture the sensing features of data through intra-domain prototype clustering and perform cross-domain prototype extraction in a shared embedding space of multiple data. Moreover, we extend the model to the IoT edge segment for online learning and fine-tune the model parameters to fit the new domain on the cloud by using data collected in the field. Extensive experimental results on real datasets show that the proposed scheme significantly outperforms the current state-of-the-art WiFi sensing methods in terms of sensing accuracy and achieves satisfactory online learning performance with fewer training samples and times.

In the rapidly evolving landscape of Internet of Things (IoT), Zigbee networks have emerged as a critical component for enabling wireless communication in a variety of applications. Despite their widespread adoption, Zigbee networks face significant security challenges, particularly in key management and network resilience against cyber attacks like distributed denial of service (DDoS). Traditional key rotation strategies often fall short in dynamically adapting to the ever-changing network conditions, leading to vulnerabilities in network security and efficiency. To address these challenges, this paper proposes a novel approach by implementing a reinforcement learning (RL) model for adaptive key rotation in Zigbee networks. We developed and tested this model against traditional periodic, anomaly detection-based, heuristic-based, and static key rotation methods in a simulated Zigbee network environment. Our comprehensive evaluation over a 30-day period focused on key performance metrics such as network efficiency, response to DDoS attacks, network resilience under various simulated attacks, latency, and packet loss in fluctuating traffic conditions. The results indicate that the RL model significantly outperforms traditional methods, demonstrating improved network efficiency, higher intrusion detection rates, faster response times, and superior resource management. The study underscores the potential of using artificial intelligence (AI)-driven, adaptive strategies for enhancing network security in IoT environments, paving the way for more robust and intelligent Zigbee network security solutions.

Security and privacy are vital challenges in Industrial Internet of Things (IIoT) systems due to the inherent broadcast property of industrial wireless communication. Therefore, how to improve the security of smart devices in IIoT systems has attracted widespread attention. In this article, a novel security optimization scheme for space shift keying (SSK)-based IIoT systems is proposed by applying secrecy enhancing techniques in the physical layer. Specifically, two methods are adopted in the proposed optimization scheme. First, the sender transmits artificial noise (AN) to interfere with the eavesdropper, while the legitimate receiver can eliminate AN. Second, the sender can select optimal antennas based on the proposed minmax criteria to transmit AN and information, which further promotes the security of industrial wireless communication. The optimal antenna selection pattern increases the difference of the average mutual information between the legitimate receiver and the eavesdropper. In addition, the proposed minmax antenna selection approach only requires the channel state information of the legitimate receiver, which satisfies the application scenario of unknown eavesdropper attacks in IIoT. Finally, we adopt a deep learning-based maximum likelihood (ML) detection algorithm to detect the optimal antenna index and analyze the bit error rate (BER) and secrecy rate (SR) of the proposed optimization scheme. Simulation results show that the proposed scheme can achieve similar SR improvements and superior BER performance compared with the state-of-the-art scheme, namely the signal-leakage-to-noise ratio (SLNR) scheme. The BER performance of the proposed scheme achieves a maximum of 100 times superior to that of the SLNR scheme when the signal-to-noise ratio (SNR) is 27 dB. Moreover, the proposed scheme outperforms the traditional antenna selection scheme (random selection) in terms of BER and SR.