Path Planning of Data Mules in Sensor Networks

Abstract

We study the problem of planning the motion of “data mules” for collecting the data from stationary sensor nodes in wireless sensor networks. Use of data mules significantly reduces energy consumption at sensor nodes compared to commonly used multihop forwarding approaches, but has a drawback in that it increases the latency of data delivery. Optimizing the motion of data mules, including path and speed, is critical for improving the data delivery latency and making the data mule approach more useful in practice. In this article, we focus on the path selection problem: finding the optimal path of data mules so that the data delivery latency can be minimized. We formulate the path selection problem as a graph problem that is capable of expressing the benefit from larger communication range. The problem is NP-hard and we present approximation algorithms for both single-data mule case and multiple-data mules case. We further consider the case in which we have only partial knowledge of communication range, where we design semionline algorithms that improve the offline plan using online knowledge at runtime. Simulation experiments on Matlab and ns2 demonstrate that our offline and semionline algorithms produce significantly shorter path lengths and data delivery latency compared to previously proposed methods, suggesting that controlled mobility can be exploited much more effectively.