Data aware, low cost error correction for wireless sensor networks

One or the main challenges in adoption and deployment of wireless networked sensing applications is ensuring reliable sensor data collection and aggregation, while satisfying the low-cost, low-energy operating constraints of such applications. A wireless sensor network is inherently vulnerable to different sources of unreliability resulting in transient failures. Existing reliability techniques that address transient failures in circuits and communication channels incur prohibitively high energy, bandwidth and cost overheads in the sensor nodes. In this paper we investigate application-level error correction techniques for sensor networks that exploit the properties of sensor data to eliminate any overhead on the sensor nodes, at the expense of nominal buffer requirements at the data aggregator nodes, which are much less cost/energy constrained. Our approach involves use of spatio-temporal correlations in sensor data, the goals of the application, and its vulnerability to various errors. We present our error-correction algorithm and evaluate it through simulations using real and synthetic sensor data. Experimental results validate the feasibility of our approach to provide high degree of reliability in sensor data aggregation, without imposing overheads on sensor nodes.