As-You-Go Deployment of a Wireless Network with On-Line Measurements and Backtracking

TL;DR

This paper develops and analyzes algorithms for impromptu wireless sensor network deployment along a line, incorporating path outage considerations and backtracking to optimize relay placement using Markov decision processes.

Contribution

It introduces new deployment algorithms that include path outage and backtracking, extending previous work and providing optimal policies for different cost models.

Findings

Backtracking improves deployment efficiency and network reliability.

Optimal policies depend on the statistical distribution of source distance.

Numerical results demonstrate advantages over simple heuristics.

Abstract

We are motivated by the need, in some applications, for impromptu or as-you-go deployment of wireless sensor networks. A person walks along a line, making link quality measurements with the previous relay at equally spaced locations, and deploys relays at some of these locations, so as to connect a sensor placed on the line with a sink at the start of the line. In this paper, we extend our earlier work on the problem (see [1]) to incorporate two new aspects: (i) inclusion of path outage in the deployment objective, and (ii) permitting the deployment agent to make measurements over several consecutive steps before selecting a placement location among them (which we call backtracking). We consider a light traffic regime, and formulate the problem as a Markov decision process. Placement algorithms are obtained for two cases: (i) the distance to the source is geometrically distributed with known mean, and (ii) the average cost per step case. We motivate the per-step cost function in terms of several known forwarding protocols for sleep-wake cycling wireless sensor networks. We obtain the structures of the optimal policies for the various formulations, and provide some sensitivity results about the policies and the optimal values. We then provide a numerical study of the algorithms, thus providing insights into the advantage of backtracking, and a comparison with simple heuristic placement policies.