The tremendous increase in the number of Internet-of-Things (IoT) devices is leading to severe scarcity in the available frequency spectrum. Therefore, smart techniques are needed to efficiently utilize the available frequency bands. One main technique for achieving spectral efficiency is Cognitive radio (CR). By allowing unlicensed users to use the unused portions of the licensed frequency spectrum, CR significantly improves network throughput. Another technique is full-duplex (FD) communication, which achieves spectral efficiency by allowing concurrent transmission and reception over the same frequency channel. Interestingly, most of the existing routing schemes for multihop CR-IoT networks were designed assuming homogeneous setups, where all nodes are endowed with either half-duplex (HD), or FD radio capabilities. However, in reality, multihop CR-IoT networks are more likely to be heterogeneous; i.e., comprised of both HD and FD devices. Routing schemes designed for FD-homogeneous networks will not attain their intended optimal performance when deployed over heterogeneous networks, due to the existence of HD nodes. Similarly, Routing schemes designed for HD-homogeneous networks will not take advantage of the FD capabilities available in heterogeneous networks, resulting in suboptimal performance. In this paper, we propose the heterogeneity-aware path segmentation and optimization (PSO) routing scheme for heterogeneous multihop CR-IoT networks. For a given source-destination pair, PSO aims at selecting the path, and determining the channel assignment that results in maximizing the overall end-to-end throughput, while taking into account the HD/FD heterogeneity. Simulation experiments reveal that the proposed PSO algorithm leads to improved system performance compared to existing algorithms that overlook heterogeneity.

The increasing incorporation of Industrial Internet of Things (IIoT) devices into critical industrial operations and critical infrastructures necessitates robust security measures to safeguard confidential information and ensure dependable connectivity. Particularly in Cyber Physical Systems (CPSs), IIoT system security becomes critical as systems become more interconnected and digital. This paper introduces a novel Lightweight Industrial IoT Authentication (LI2A) method as a solution to address security concerns in the industrial sector and smart city infrastructure. Mutual authentication, authenticated message integrity, key agreement, soundness, forward secrecy, resistance to a variety of assaults, and minimal resource consumption are all features offered by LI2A. Critical to CPS operations, the approach prevents impersonation, man-in-the-middle, replay, eavesdropping, and modification assaults, according to a security study. The method proposed herein ensures the integrity of CPS networks by verifying communication reliability, identifying unauthorized message modifications, establishing a shared session key between users and IIoT devices, and periodically updating keys to ensure sustained security. A comprehensive assessment of performance takes into account each aspect of storage, communication, and computation. The communication and computing capabilities of LI2A, which are critical for the operation of CPS infrastructure, are demonstrated through comparisons with state-of-the-art systems from the literature. LI2A can be implemented in resource-constrained IIoT devices found in CPS and industrial environments, according to the results. By integrating IIoT devices into critical processes in CPS, it is possible to enhance security while also promoting urban digitalization and sustainability.

The advent of a wide variety of affordable communication devices and cameras has enabled IoT systems to provide effective solutions for a wide range of civil and military applications. One of the potential applications is a surveillance system in which several cameras collaborate to monitor a specific area. However, existing surveillance systems are often based on traditional camera distribution and come with additional communication costs and redundancy in the detection range. Thus, we propose a smart and efficient camera distribution system based on machine learning using two Reinforcement Learning (RL) methods: Q-Learning and neural networks. Our proposed approach initially uses a geometric distributed network clustering algorithm that optimizes camera placement based on the camera Field of View (FoV). Then, to improve the camera distribution system, we integrate it with an RL technique, the role of which is to dynamically adjust the previous/existing setup to maximize target coverage while minimizing the number of cameras. The reinforcement agent modifies system parameters—such as the overlap distance between adjacent cameras, the camera FoV, and the number of deployed cameras—based on changing traffic distribution and conditions in the surveilled area. Simulation results confirm that the proposed camera distribution algorithm outperforms the existing methods when comparing the required number of cameras, network coverage percentage, and traffic coverage.