A Lightweight Algorithm To Optimize Power Consumption in Sensor Node Through Parametric Tuning of Communication Interface

Abstract

Wireless sensor network is widely used to monitor sparsely generated events through a centralized system. The coverage provided by the network to monitor region of interest vary and rely on the system components of sensor node. While monitoring events from cluster head or sink through sensor nodes located at a distant, it experiences increased communication cost due to prone errors which are proportional to losses due to distance and interferences. Biotelemetry application is reviewed for the derivation of the research problem which is worked upon in the present paper. The animals move around in their habitat performing various activities which are to be monitored. The sensing tags are mounted on the animals to study the behaviour of the animals. These animals roam around in their habitat generating variation in the interferences caused for communication. Due to wide variations, the inefficiency of the sensor node in communication results in draining battery rapidly and thereby life of sensor node. So in order to improve lifetime the optimization at the sensor node is very essential to keep network alive. The technique that performs optimization needs to be lightweight in terms of processing required as well as tuning of parameter which are required for model based optimization methods. In the paper, we proposed a technique which is lightweight and can dynamically optimize operating state. It is done by adaptively configuring communication parameters to patch losses and conserve energy to enhance Sensor node lifetime. The optimization technique proposed, does parameter tuning to minimize the communication cost in sensor node through efficient search method. The results are compared with traditionally employed technique with static setting and Online Greedy Optimization Algorithm. NI LabVIEW is used to do simulation of the model and for estimating the effect of parameter reconfiguration upon application of optimization techniques.