Coordination of Mobile Mules via Facility Location Strategies

TL;DR

This paper addresses optimizing the deployment and task allocation of mobile mules in wireless sensor networks to minimize sensor downtime and travel distance, using facility location algorithms inspired by computational geometry.

Contribution

It introduces novel deployment and task allocation strategies for mobile mules based on approximation algorithms from facility location research.

Findings

Cooperation improves team performance.

k-Median deployment with closest-available task allocation minimizes sensor downtime.

k-Centroid deployment balances sensor downtime and travel distance.

Abstract

In this paper, we study the problem of wireless sensor network (WSN) maintenance using mobile entities called mules. The mules 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 fix it. The mules must constantly optimize their collective deployment to account for occupied mules. The objective is to define the optimal deployment and task allocation strategy for the mules, so that the sensors' downtime and the mules' traveling distance are minimized. Our solutions 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. Our empirical results demonstrate how cooperation enhances the team's performance, and indicate that a combination of k-Median based deployment with closest-available task allocation provides the best results in terms of minimizing the sensors' downtime but is inefficient in terms of the mules' travel distance. A k-Centroid based deployment produces good results in both criteria.