A virtual sensor scheduling framework for heterogeneous wireless sensor networks

We investigate the problem of scheduling sensor node up-times to maximize the utility of the data they collect while operating within their resource constraints. We show that the optimal scheduling algorithm can improve data utility by more than 70% compared to naive schedules. We consider a suite of sensors with different capabilities and resource demands and represent their subsets as virtual sensors. For each virtual sensor, we calculate its optimal data fusion parameters and evaluate the sensors´ performance in a given environment. The selection of virtual sensors best suited to collect data in a given environment can be modeled as an Integer Linear programming problem, and we study three different algorithms to solve the problem efficiently. We evaluate the performance of virtual sensor scheduling algorithms by extensive simulation. We show that even though the naive greedy scheduling approaches work well in some scenarios, none of them are able to match our best scheduling algorithm consistently, under varying environmental conditions and sensor resources.