Secure and Energy-efficient Unmanned Aerial Vehicle-enabled Visible Light Communication via A Multi-objective Optimization Approach

TL;DR

Simulation outcomes show that the proposed approach to provide communication service for terrestrial receivers via using unmanned aerial vehicle-enabled visible light communication is superior to other approaches, and is efficient at improving the security and energy efficiency of visible light communication system.

Abstract

In this research, a unique approach to provide communication service for terrestrial receivers via using unmanned aerial vehicle-enabled visible light communication is investigated. Specifically, we take into account a unmanned aerial vehicle-enabled visible light communication scenario with multiplex transmitters, multiplex receivers, and a single eavesdropper, each of which is equipped with a single photodetector. Then, a unmanned aerial vehicle deployment multi-objective optimization problem is formulated to simultaneously make the optical power received by receiving surface more uniform, minimize the amount of information collected by a eavesdropper, and minimize the energy consumption of unmanned aerial vehicles, while the locations and transmission power of unmanned aerial vehicles are simultaneously optimized under certain constraints. Since the formulated unmanned aerial vehicle deployment multi-objective optimization problem is complex and nonlinear, it is challenging to be tackled by using conventional methods. For the purpose of solving the problem, a multi-objective evolutionary algorithm based on decomposition with chaos initiation and crossover mutation is proposed. Simulation outcomes show that the proposed approach is superior to other approaches, and is efficient at improving the security and energy efficiency of visible light communication system.

Figures (9)

• Figure 1: The UAV-enabled VLC network consisting of UAVs and receiving surface.
• Figure 2: Solution distributions achieved by different algorithms in Case 1 (8 UAVs) at different iterations. (a) 50th iteration. (b) 100th iteration. (c) 150th iteration. (d) 200th iteration.
• Figure 3: The deployments of UAVs obtained by different methods for Case 1 (8 UAVs). (a) Initial deployments of UAVs. (b) Random deployments. (c) Uniform deployment. (d) MOEA/D-CICM.
• Figure 4: The maps of the received optical power distribution obtained by different methods for Case 1 (8 UAVs). (a) 3D power distributions at initial deployment of UAVs. (b) 2D power distributions at initial deployment of UAVs. (c) 3D power distributions of random deployment. (d) 2D power distributions of random deployment. (e) 3D power distributions of uniform deployment. (f) 2D power distributions of uniform deployment. (g) 3D power distributions of MOEA/D-CICM. (h) 2D power distributions of MOEA/D-CICM.
• Figure 5: Transmission power of 8 UAVs obtained by different algorithms.