Autonomous Multi-Source Multi-Sink Routing in Wireless Sensor Networks

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This paper proposes a distributed algorithm to calculate a subnetwork of a given wireless sensor network (WSN) connecting a set of sources and a set of sinks, in such a way that: 1) the length of one of the shortest paths connecting from a source to a sink in the subgraph does not exceed the distance from the source to the farthest sink in the original graph, and 2) the number of links contained in the subgraph is smallest. The proposed algorithm tries to improve an initial solution generated by a heuristic scheme by repeatedly applying a local search. The result of simulations indicates that: 1) using a heuristic to generate an initial solution, the size of the initial solution is reduced by 10% compared with a simple shortest path tree; and 2) the local search reduces the size of the resultant subgraph by 20% and the cost required for such an improvement by the local search can be recovered by utilizing the resultant subgraph for a sufficiently long time such as a few days. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.20(2012) No.1 (online) DOI　http://dx.doi.org/10.2197/ipsjjip.20.319 ------------------------------

This paper proposes a distributed algorithm to calculate a subnetwork of a given wireless sensor network (WSN) connecting a set of sources and a set of sinks, in such a way that: 1) the length of one of the shortest paths connecting from a source to a sink in the subgraph does not exceed the distance from the source to the farthest sink in the original graph, and 2) the number of links contained in the subgraph is smallest. The proposed algorithm tries to improve an initial solution generated by a heuristic scheme by repeatedly applying a local search. The result of simulations indicates that: 1) using a heuristic to generate an initial solution, the size of the initial solution is reduced by 10% compared with a simple shortest path tree; and 2) the local search reduces the size of the resultant subgraph by 20% and the cost required for such an improvement by the local search can be recovered by utilizing the resultant subgraph for a sufficiently long time such as a few days. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.20(2012) No.1 (online) DOI　http://dx.doi.org/10.2197/ipsjjip.20.319 ------------------------------

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