Wireless sensor network-assisted, autonomous mapping with information-theoretic utility

A mobile, autonomous platform is assisted by a wireless sensor network in its task of inferring a map of the spatial distribution of a physical quantity that is measured by the sensor nodes. Sensor nodes initiate a broadcast in the network, when the measured quantity assumes a value in the range of interest. Specifically, we consider randomly deployed networks of location-agnostic wireless sensor nodes, which broadcast messages by flooding. The node-to-node delays are assumed to be random. In networks of this type, the hop count of a broadcast message, given the distance from the source node, can be approximated by a simple parametric distribution. The mobile platform can interrogate a nearby sensor node to obtain, with a given success probability, the hop counts of the broadcast messages originating from different source nodes. By fusing successive hop count observations, the mobile platform infers the locations of the source nodes and thereby, the spatial distribution of the quantity of interest. The path taken by the mobile platform should minimize the resulting mapping error as quickly as possible. We propose an information-driven path planning approach, in which the mobile platform acts by maximizing a weighted sum of myopic, mutual information gains. We show by simulation, that suitable control of the weights is effective at reducing the error between the true and the inferred map, by preventing the information gain to be dominated by only a few source nodes.