Unmanned-aerial-vehicles (UAVs) have been gaining much attention in the next-generation wireless networks due to their ability to enhance coverage and provide advanced services, particularly for first responders. UAVs equipped with mobile-edge computing (MEC) capabilities can migrate computational resources to airborne platforms. However, it is crucial to manage resources efficiently to optimize overall network performance. Moreover, in public safety scenarios, UAVs can help charge low-power Internet of Things (IoT) devices to sustain system operations. A holistic approach to managing communication, computation, caching, and energy resources is necessary to leverage UAV-assisted MEC networks fully. We formulated an optimization problem to minimize latency and reduce resource costs associated with communication, computation, caching, and energy harvesting while maximizing the number of IoT devices served by UAVs. Therefore, we integrated digital twin technology to analyze the latency. The optimization problem is challenging as it involves a mixed-integer nonlinear programming problem. To address this complexity, we propose a multistage offloading algorithm named the penalty function method heuristic algorithm that combines a learning algorithm with an interior-point method, ultimately delivering a practical solution. Our simulation results validate the performance of the proposed algorithm, which yields superior results compared to the simple relaxation heuristic algorithm.

IP networks for live TV production have unique requirements such as the ultra high bandwidth and the high sensitivity to packet loss that causes video impairments. Existing multicast protocols are not bandwidth-aware and could cause links to over-subscribe leading to packet loss and negative user quality of experience. Existing video quality error detection tools are reactive by design with no insights into the video domain. In this paper, we introduceMediaFlow, a system for bandwidth aware multicast routing, active in-network detection of video errors, and proactive recovery.MediaFlowutilizes a novel greedy online multicast routing algorithm for efficient routing and admission control. It also introduces novel per-flow video quality metric utilizing unique switch ASIC capabilities for scalable in-network video quality monitoring and rerouting. We implementMediaFlowusing data center switches and our testing results confirm thatMediaFlowalgorithm increases fabric capacity up to 60% compared to state of art multicast routing.MediaFlowcan detect errors in video flow integrity at a granularity of 100 mSec at line rate for thousands of flows. The system can proactively recover impacted flows within 1 sec.MediaFlowincreases video detection and recovery scale by a thousandfold compared to network edge solutions.

Recently, unmanned aerial vehicles (UAVs) have been used as flying base stations (BSs) to take advantage of line‐of‐sight (LOS) connectivity and efficiently enable fifth‐generation (5G) and cellular network coverage and data rates. On the other hand, nonorthogonal multiple access (NOMA) is a promising technique to help achieve unprecedented requirements by simultaneously allowing multiple users to send data over the same resource block. In this paper, we study a UAV‐enabled uplink NOMA network, where the UAV collects data from ground users while flying at a certain altitude. Unlike all existing work on this topic, this study consists of two stages. In the first stage, we use the well‐known Particle Swarm Optimization (PSO) algorithm, which is a metaheuristic algorithm, to deploy the UAV in 3D space, so that the users’ sum pathlosses are minimized. In the second stage, we investigate the user pairing problem and propose a dynamic power allocation technique for determining the user’s power allocation coefficients, as well as a closed‐form equation for the ergodic sum‐rate. Results show our PSO‐based algorithm prevailing over the Genetic Algorithm (GA) and random deployment methods. The proposed dynamic power allocation strategy maximizes the network’s ergodic sum‐rate and outperforms the fixed power allocation strategy. Additionally, the results reveal that the best pairing scheme is the one that keeps uniform channel gain difference in the same pair.

Unmanned aerial vehicle (UAV) communication can be used in overcrowded areas and either during or postdisaster situations as an evolving technology to provide ubiquitous connections for wireless devices due to its flexibility, mobility, and good condition of the line of sight channels. In this paper, a single UAV is used as an aerial relay node to provide connectivity to wireless devices because of the considerable distance between wireless devices and the ground base station. Specifically, two path loss models have been utilized; a cellular‐to‐UAV path loss for a backhaul connection and an air‐to‐ground path loss model for a downlink connection scenario. Then, the tradeoff introduced by these models is discussed. The problem of efficient placement of an aerial relay node is formulated as an optimization problem, where the objective is to maximize the total throughput of wireless devices. To find an appropriate location for a relay aerial node that maximizes the overall throughput, we first use the particle swarm optimization algorithm to find the drone location; then, we use three different approaches, namely, (1) the equal power allocation approach, (2) water filling approach, and (3) modified water filling approach to maximize the total users’ throughput. The results show that the modified water filling outperforms the other two approaches in terms of the average sum rate of all users and the total number of served users. More specifically, in the best‐case scenario, it was observed that the average sum rate of the modified water filling is better than the equal power allocation and ensuring 100% coverage. In contrast, the water filling provides a very close average sum rate to the modified water filling, but it only provides a 28% user coverage.