Collaborative scheduling in highly dynamic environments using error inference

Energy constraint is a critical hurdle hindering the practical deployment of long-term wireless sensor network applications. Turning off (i.e., duty cycling) sensors could reduce energy consumption, however at the cost of low sensing fidelity due to sensing gaps introduced. Existing techniques have studied how to collaboratively reduce the sensing gap in space and time, however none of them provides a rigorous approach to confine sensing error within desirable bounds. In this work, we propose a collaborative scheme called CIES, based on the novel concept of error inference between collaborative sensor pairs. Within a node, we use a sensing probability bound to control tolerable sensing error. Within a neighborhood, nodes can trigger additional sensing activities of other nodes when inferred sensing error has aggregately exceed the tolerance. We conducted simulations to investigate system performance using historical soil temperature data in Wisconsin-Minnesota area. The simulation results demonstrate that the system error is confined within the specified error tolerance bounds and that a maximum of 60 percent of the energy savings can be achieved, when the CIES is compared to several fixed probability sensing schemes such as eSense. We further validated the simulation and algorithms by constructing a lab test-bench to emulate actual environment monitoring applications. The results show that our approach is effective and efficient in tracking the dramatic temperature shift in highly dynamic environments.