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, starting from a sink node (e.g., a base-station), and proceeds towards a source node (e.g., a sensor) which is at an a priori unknown location. At equally spaced locations, he makes link quality measurements to the previous relay, and deploys relays at some of these locations, with the aim to connect the source to the sink by a multihop wireless path. In this paper, we consider two approaches for impromptu deployment: (i) the deployment agent can only move forward (which we call a pure as-you-go approach), and (ii) the deployment agent can make measurements over several consecutive steps before selecting a placement location among them (the explore-forward approach). We consider a very light traffic regime, and formulate the problem as a Markov decision process, where the trade-off is among the power used by the nodes, the outage probabilities in the links, and the number of relays placed per unit distance. We obtain the structures of the optimal policies for the pure as-you-go approach as well as for the explore-forward approach. We also consider natural heuristic algorithms, for comparison. Numerical examples show that the explore-forward approach significantly outperforms the pure as-you-go approach in terms of network cost. Next, we propose two learning algorithms for the explore-forward approach, based on Stochastic Approximation, which asymptotically converge to the set of optimal policies, without using any knowledge of the radio propagation model. We demonstrate numerically that the learning algorithms can converge (as deployment progresses) to the set of optimal policies reasonably fast and, hence, can be practical model-free algorithms for deployment over large regions. Finally, we demonstrate the end-to-end traffic carrying capability of such networks via field deployment.
Highlights
We are motivated by the need, in some applications, for impromptu or as-you-go deployment of wireless sensor networks
Abstracting the above-mentioned problems, we consider the problem of deployment of relay nodes along a line, between a sink node and a source node, where a single deployment agent starts from the sink node, places relay nodes along the line, and places the source node where required
We have developed several approaches for as-you-go deployment of wireless relay networks using on-line measurements, under a very light traffic assumption
Summary
We are motivated by the need, in some applications, for impromptu or as-you-go deployment of wireless sensor networks. As-you-go deployment is of interest when deploying multihop wireless networks for sensor-sink interconnection over large terrains, such as forest trails (see [2] for an application of multi-hop WSNs in wildlife monitoring, and [3, Section 5] for application of WSN in forest fire detection), where it may be difficult to make exhaustive measurements at all possible deployment locations before placing the relay nodes. Abstracting the above-mentioned problems, we consider the problem of deployment of relay nodes along a line, between a sink node (e.g., the WSN base-station) and a source node (e.g., a sensor) (see Fig. 1), where a single deployment agent (the person who is carrying out the deployment) starts from the sink node, places relay nodes along the line, and places the source node where required. The location at which sensor placement is required might only be discovered as the deployment agent walks (e.g., in an animal monitoring application, by finding a concentration of pugmarks, or a watering hole)
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