Optimization of Resource Allocation in Unmanned Aerial Vehicles Based on Swarm Intelligence Algorithms

Abstract

Due to their adaptability, Unmanned Aerial Vehicles (UAVs) play an essential role in the Internet of Things (IoT). Using wireless power transfer (WPT) techniques, an UAV can be supplied with energy while in flight, thereby extending the lifetime of this energy-constrained device. This paper investigates the optimization of resource allocation in light of the fact that power transfer and data transmission cannot be performed simultaneously. In this paper, we propose an optimization strategy for the resource allocation of UAVs in sensor communication networks. It is a practical solution to the problem of marine sensor networks that are located far from shore and have limited power. A corresponding system model is summarized based on the scenario and existing theoretical works. The minimum throughputmaximizing object is then formulated as an optimization problem. As swarm intelligence algorithms are utilized effectively in numerous fields, this paper chose to solve the formed optimization problem using the Harris Hawks Optimization and Whale Optimization Algorithms. This paper introduces a method for translating multi-decisions into a row vector in order to adapt swarm intelligence algorithms to the problem, as joint time and energy optimization have two sets of variables. The proposed method performs well in terms of stability and duration. Finally, performance is evaluated through numerical experiments. Simulation results demonstrate that the proposed method performs admirably in the given scenario.

Adaptive Bandwidth Allocation for Massive MIMO Systems Based on Multiple Services

“…However, the algorithm complexity is high, and the default number of users meets antenna constraints, which does not meet the access situation of users in practical applications. Reference [20] used swarm intelligence optimization algorithms to study resource allocation problems in communication systems, and the results showed that swarm intelligence optimization algorithms performed well in the field of resource allocation. Literature [21] proposed a bald eagle search algorithm (BES), which can effectively solve various complex numerical optimization problems.…”