Battery regression for guaranteed k-coverage in distributed sensor networks

In energy-constrain wireless sensor networks, maintaining k — coverage degree requested by an application while maximizing the network lifetime is a major challenge. Existing literature on k — coverage does not consider residual energy levels and actual battery discharge rate of ACTIVE nodes. OBSP (Optimized Backoff Sleep Protocol) considers the residual energy level information and battery discharge rate but ignores the k-coverage degree. This paper proposes k-CGP (k-Coverage Guarantee Protocol) based on battery discharge curve using polynomial regression for different coverage degrees (k). k-CGP determines the optimal wakeup rate of sleeping nodes by computing Optimal Sleep Time derived from battery discharge curve using polynomial regression and Received Signal Strength Indicator for distance estimation. The coverage redundancy is computed by using equi-distance test. Due to this, a sufficient number of ACTIVE nodes could be maintained while achieving lesser energy consumption in the network. Simulation results show that k-CGP achieves higher energy savings and sensing area coverage as compared to PEAS.