Environmental field estimation of mobile sensor networks using support vector regression

This paper presents a distributed algorithm for mobile sensor networks to monitor the environment. With this algorithm, multiple mobile sensor nodes can collectively sample the environmental field and recover the environmental field function via machine learning approaches. The mobile sensor nodes are able to self-organise so that the distribution of mobile sensor nodes matches to the estimated environmental field function. In this way, it is possible to make the next-step sampling more accurate and efficient. The machine learning approach used for function regression is support vector regression (SV R) algorithm. A distributed SV R learning algorithm is used for on-line learning. The self-organised algorithm used for deployment is based on locational optimisation techniques. In particular, Lloyd's algorithm for optimising centroidal Voronoi tessellations (CVT) is used to spread mobile sensor nodes over the monitored environment. The environmental field function is simulated in static and dynamic settings and the demonstration on the simulated environments shows the proposed algorithm is effective.