A stochastic model for environment sensing correction

Wireless Sensor Networks (WSN) are growing in popularity and penetrating newer fields of applications more than ever. Gradually, we are relying on WSNs to perform more complex tasks with increasing cognitive abilities. At the core of the WSN research transpires, the need is to achieve accurate sensing at real-time. Unfortunately, confidence in sensors′ readings decreases in harsh environments and as a result of normal reading errors, message loss, or even low battery operations. The complex problem of dealing with corrections and in some cases shredding the outcome of entire deployments leads to loss of effort, time, and money. Classic approaches for correcting laboratory experiments like curve fitting and least square are well known and have been established for decades. But little research attempts have been made to correct and recalibrate sensors observations in real-time. Furthermore, classic approaches for correcting sensor observations require higher interaction between sensors to a level we cannot afford in deployments where battery, network, and memory represent scarce resource. In addition, classical corrections lack the ability to contemplate the physical properties of the underlying sensor environment. In this article, we present a sensor correction model that relies on clustered WSN. Our approach employs autonomous selection mechanism to elect cluster heads by applying a stochastic competition between cluster members while maintaining the underlying physical properties as the bases to locate competition winner. Then, we perform the stochastic competitive correction at real-time by referencing the underlying physical properties of the environment represented by selected relation as suggested by the physics of the environment. Finally, sensors adapt the minor changes and maintain a relation to their surroundings by continuously monitoring and assimilating information received from surrounding sensors. We show that this approach has smaller footprint in terms of processing, communicating, and storage. We present our approach and apply it on an environment of known physical property.