An ecologically inspired intelligent agent assisted wireless sensor network for data reconstruction

One of the most important problems studied in data harvesting wireless sensor networks (WSNs) is the optimization of the tradeoff between the accuracy of the reconstructed field data and the resource consumption. In order to optimize the resource consumption, whilst not compromising the accuracy of the reconstructed field data, an ecologically inspired marginal value theorem strategy (MVTS) is proposed for a mobile agent for choosing the next sensor node to be visited in the data acquisition process. The proposed MVTS can adaptively gain new knowledge during the process of collecting observations from a WSN comprising of static sensor nodes. Therefore, only the relatively important sensor observations will be collected by the agent according to the variety of the background environmental data. This is thought as an efficient way to reserve the resources, such as energy and bandwidth, because only the important observations are collected. Illustrated analytical and simulation results confirm the above achievements.