Impromptu Deployment of Wireless Relay Networks: Experiences Along a Forest Trail

TL;DR

This paper explores impromptu wireless relay deployment along a forest trail, comparing algorithms based on Markov decision processes and measurement-driven strategies through real-world experiments, providing insights into effective deployment methods.

Contribution

It presents experimental evaluation of various deployment algorithms, including MDP-based and measurement-based strategies, for impromptu wireless relay network deployment in forest environments.

Findings

MDP-based algorithms require a radio propagation model.

Measurement-based strategies can be effective without a model.

Proper theoretical formulation improves deployment success.

Abstract

We are motivated by the problem of impromptu or as- you-go deployment of wireless sensor networks. As an application example, a person, starting from a sink node, walks along a forest trail, makes link quality measurements (with the previously placed nodes) at equally spaced locations, and deploys relays at some of these locations, so as to connect a sensor placed at some a priori unknown point on the trail with the sink node. In this paper, we report our experimental experiences with some as-you-go deployment algorithms. Two algorithms are based on Markov decision process (MDP) formulations; these require a radio propagation model. We also study purely measurement based strategies: one heuristic that is motivated by our MDP formulations, one asymptotically optimal learning algorithm, and one inspired by a popular heuristic. We extract a statistical model of the propagation along a forest trail from raw measurement data, implement the algorithms experimentally in the forest, and compare them. The results provide useful insights regarding the choice of the deployment algorithm and its parameters, and also demonstrate the necessity of a proper theoretical formulation.