A distributed deployment algorithm for communication coverage in wireless robotic networks

Wireless Robotic Networks (WRNs), composed of numerous mobile robotic agents with the ability of moving, computing, sensing, and communicating, are able to provide wireless communication services and thus implement complicated tasks for user equipments. In order to guarantee the performance of coverage rate and ensure providing the services timely and effectively, mobile robotics in WRNs are usually deployed flexibly and quickly. However, coverage overlaps and energy redundancy may be generated by excessive deployed agents. In order to provide maximum coverage area with a minimum number of agents, we study the 3-dimensional coverage deployment problem in WRNs and propose a distributed deployment algorithm. Firstly, we give the scenario model, communication model, and coverage model to define the 3-dimensional coverage problem. Secondly, we propose a distributed coverage deployment algorithm that can set redundant agents to idle mode iteratively. Herein, we decouple the coverage deployment problem in the altitude and horizontal dimensions without any loss of optimality. On the one hand, this algorithm can find the optimal altitude for agents mathematically. On the other hand, this algorithm contains a local deployment algorithm based on Particle Swarm Optimization (PSO) which is used for a particular active agent to find a better position with a larger local coverage area. In particular, the local coverage area is obtained depending on the Voronoi Diagram (VD). Our proposed algorithm is distributed which only requires local information. Finally, performance evaluation are given in three aspects, which demonstrate the effectiveness of the proposed distributed deployment algorithm.

Automated summary

The study investigates the 3-dimensional coverage deployment problem in Wireless Robotic Networks (WRNs) and proposes a distributed deployment algorithm that maximizes coverage area by decoupling the problem in altitude and horizontal dimensions. The algorithm utilizes Particle Swarm Optimization for local deployment and evaluates local coverage area using Voronoi Diagram. Performance evaluations demonstrate the effectiveness of the proposed algorithm.