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Flying Ad‐Hoc Network (FANET) is a promising ad hoc networking paradigm that can offer new added value services in military and civilian applications. Typically, it incorporates a group of Unmanned Aerial Vehicles (UAVs), known as drones that collaborate and cooperate to accomplish several missions without human intervention. However, UAV communications are prone to various attacks and detecting malicious nodes is essential for efficient FANET operation. Trust management is an effective method that plays a significant role in the prediction and recognition of intrusions in FANETs. Specifically, evaluating node behaviour remains an important issue in this domain. For this purpose, the authors suggest using fuzzy logic, one of the most commonly used methods for trust computation, which classifies nodes based on multiple criteria to handle complex environments. In addition, the Received Signal Strength Indication (RSSI) is an important parameter that can be used in fuzzy logic to evaluate a drone's behaviour. However, in outdoor flying networks, the RSSI can be seriously influenced by the humidity of the air, which can dramatically impact the accuracy of the trust results. FUBA, a fuzzy‐based UAV behaviour analytics is presented for trust management in FANETs. By considering humidity as a new parameter, FUBA can identify insider threats and increase the overall network's trustworthiness under bad weather conditions. It is capable of performing well in outdoor flying networks. The simulation results indicate that the proposed model significantly outperforms FNDN and UNION in terms of the average end‐to‐end delay and the false positive ratio. [ABSTRACT FROM AUTHOR]

In this paper, we develop a novel demultiplexer design for Coarse Wavelength Division Multiplexer (CWDM). The device consists of multi-layer inhomogeneous semi-conductor material, where the refractive index of each layer is graded according to a predefined profile. The proposed design exploits the ray's spatial shift that results from the material dispersion as different wavelengths propagate through the different layers of the device. Our design forces the multiplexed light to refract after propagation for short distance within the device leading to smaller device size while providing the needed spatial shift between the ray's of the adjacent multiplexed wavelengths. The proposed structure can be easily implemented using the well-established technology utilized in fabricating existing graded-index fibers. The impacts of the various design parameters (such as the incident angle, number of layers, the layer thickness, the spacing between adjacent wavelengths, the refractive index difference) on the amount of achieved spatial shift between the adjacent wavelengths and the size of the device are investigated. Compared to previous proposed techniques, our device can be easily fabricated to provide higher spatial shift while reducing the device size with by controlling the different design parameters. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) technology is proposed to provide huge spectrum opportunities to enable a large-scale deployment of Internet-of-Things (IoT) sensing-based applications. An important challenge in this domain is how to design efficient channel assignment algorithms for delay-sensitive CRIoT-based sensor networks while being resilient to jamming attacks (the most common threat to IoT network reliability). Such channel assignment algorithms must enable parallel multi-channel transmissions in order to satisfy the imposed quality-of-service and delay requirements of the CRIoT devices while being spectrum efficient. This article introduces a multi-channel batch-based security-aware medium access control (MAC) design proposed for time-critical CRIoT deployments, referred to as BMRJA-MAC. The proposed design aims at improving the overall network performance by serving the largest possible number of CRIoT nodes by utilizing their multi-channel transmission capabilities while being reactive jamming-aware. The performance of the proposed MAC design is validated via simulations and experimentally using the Future Internet-of-Things (FIT) IoT-LAB testbed. Compared with reference protocols, the results show that BMRJA-MAC significantly improves network performance and spectrum efficiency. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) technology enables dynamic spectrum access for 5G and Beyond 5G (B5G) networks. Specifically, CR provides massive spectrum opportunities for enabling large-scale deployment of 5G/B5G networks. Multiple-input, multiple-output (MIMO) technology is another promising technique that can significantly improve the performance of 5G/B5G networks. Integrating the MIMO technology with the CR technology can further improve the wireless network's performance. However, due to the dynamic nature of the CR operating environment, such integration imposes several design challenges regarding the appropriate MIMO-mode selection, spectrum assignment, and user selection. Unlike the majority of previously developed MIMO-based spectrum access protocols that execute the channel assignment sequentially, we investigate the problem of maximizing the network throughput by simultaneously providing distributed CR-aware MIMO-mode channel-assignment decisions for multiple CR transmissions (batching approach) subject to a set of constraints. The joint batch-based MIMO-mode and channel-assignment optimization is, in general, an NP-hard combinatorial integer non-linear problem. To solve such a joint problem in polynomial-time, we propose a two-stage optimization algorithm. In the first stage, the proposed algorithm finds the highest number of packets that each contending CR user can send over each idle channel. Then, in the second stage, the best channel–user assignment that can result in the highest number of transmitted packets is computed. To realize the proposed batch-based algorithm in a distributed manner, we adopt an admission control process that allows CR users to announce the required control information. Compared with the existing MIMO-based CRN protocols, the results show that by jointly performing batch-based channel assignment and per-user MIMO-mode optimization, the proposed protocol significantly enhances the overall network throughput by up to 60%. [ABSTRACT FROM AUTHOR]