Wireless communication highly depends on the cellular ground base station (GBS). A failure of the cellular GBS, fully or partially, during natural or man-made disasters creates a communication gap in the disaster-affected areas. In such situations, public safety communication (PSC) can significantly save the national infrastructure, property, and lives. Throughout emergencies, the PSC can provide mission-critical communication and video transmission services in the affected area. Unmanned aerial vehicles (UAVs) as flying base stations (UAV-BSs) are particularly suitable for PSC services as they are flexible, mobile, and easily deployable. This manuscript considers a multi-UAV-assisted PSC network with an observational UAV receiving videos from the affected area’s ground users (AGUs) and transmitting them to the nearby GBS via a relay UAV. The objective of the proposed study is to maximize the average utility of the video streams generated by the AGUs upon reaching the GBS. This is achieved by optimizing the positions of the observational and relay UAVs, as well as the distribution of communication resources, such as bandwidth, and transmit power, while satisfying the system-designed constraints, such as transmission rate, rate outage probability, transmit power budget, and available bandwidth. To this end, a joint UAVs placement and resource allocation problem is mathematically formulated. The proposed problem poses a significant challenge for a solution. Considering the block coordinate descent and successive convex approximation techniques, an efficient iterative algorithm is proposed. Finally, simulation results are provided which show that our proposed approach outperforms the existing methods.

The Internet of Vehicles (IoV), which is an extension of traditional vehicular ad hoc networks (VANETs), ensures the provision of safe and efficient vehicular communications. However, the burgeoning IoV faces several challenges in terms of traffic information security and vehicle identity privacy during wireless transmission. Generally, vehicles move faster on the highway necessitating the use of reliable technology to communicate with infrastructure on the roadside efficiently and securely. Lately, researchers have proposed several different message dissemination methods for vehicle-to-infrastructure (V2I) communications. However, they suffer from different concerns (i.e., security weaknesses, computationally heavy, and high communication/storage overhead); therefore, they are unreliable for delay-sensitive communications. To cope with this, we come up with an enhanced certificateless and identity-based offline/online heterogeneous signcryption (CIOOHSC) protocol for V2I communications in edge computing-based IoV to simultaneously achieve confidentiality, integrity, authentication, non-repudiation, and identity anonymity in a logically single step. The CIOOHSC protocol allows a vehicle in certificateless cryptography to transmit a message to an edge node in identity-based cryptography. In the online phase, there are no point multiplication operations as they are already carried out in the offline phase. This reduces the computational load considerably in the online phase. Furthermore, our protocol enables the edge node to aggregate multiple messages and process them simultaneously to boost performance. The CIOOHSC protocol’s security is proved in the random oracle model under the hard problems’ assumptions. Our protocol outperforms recent similar schemes in terms of computational complexity, communication/storage overhead, and average transmission delay, according to the performance evaluation findings.

In the presence of non-Gaussian impulsive noise (IN) with a heavy tail, active noise control (ANC) algorithms often encounter stability problems. While adaptive filters based on the higher-order error power principle have shown improved filtering capability compared to the least mean square family algorithms for IN, however, the performance of the filtered-x least mean absolute third (FxLMAT) algorithm tends to degrade under high impulses. To address this issue, this paper proposes three modifications to enhance the performance of the FxLMAT algorithm for IN. To improve stability, the first alteration i.e. variable step size FxLMAT (VSSFxLMAT)algorithm is suggested that incorporates the energy of input and error signal but has slow convergence. To improve its convergence, the second modification i.e. filtered x robust normalized least mean absolute third (FxRNLMAT) algorithm is presented but still lacks robustness. Therefore, a third modification i.e. modified filtered-x RNLMAT (MFxRNLMAT) is devised, which is relatively stable when encountered with high impulsive noise. With comparable computational complexity, the proposed MFxRNLMAT algorithm gives better robustness and convergence speed than all variants of the filtered-x least cos hyperbolic algorithm, and filtered-x least mean square algorithm.