Collective multi agent deployment for wireless sensor network maintenance

In this paper, we study the problem of wireless sensor network (WSN) maintenance using a team of physical autonomous mobile agents. The agents are deployed in the area of the WSN in such a way that would minimize the time it takes them to reach a failed sensor and repair it. The team must constantly optimize its collective deployment to account for occupied agents. The objective is to define the optimal deployment and task allocation strategy, that minimize the solution cost. The solution cost is a linear combination of the weighted sensors' downtime, the agents' traveling distance, and penalties incurred due to unrepaired sensors within a certain time limit. Our proposed solution algorithms are inspired by research in the field of computational geometry and the design of our algorithms is based on state of the art approximation algorithms for the classical problem of facility location. We empirically compare and analyze the performance of several proposed algorithms. The sensitivity of the algorithms' performance to the following parameters is analyzed: agents to sensors ratio, sensors' sparsity, frequency and distribution of failures, repair duration, repair capacity, and communication limitations. Our results demonstrate that: (i) cooperation enhances the team's performance by orders of magnitude, (ii) k-Median based deployment algorithm provides up to 30% improvement in downtime, (iii) k-Center based deployment incurs 10% fewest penalties, and (iv) k-Centroid based deployment is most efficient in terms of minimizing the overall costs, with up to 21% lower cost than the next best algorithm.

Automated summary

The paper explores the use of physical autonomous mobile agents to maintain wireless sensor networks efficiently. By optimizing deployment and task allocation strategies, the team aims to minimize solution cost. Results show that cooperation significantly improves performance, with specific algorithms providing improvements in downtime, penalties, and overall costs compared to others.