Coverage Aware Battery Regression Curve Node Scheduling in Wireless Sensor Networks

In energy-limited wireless sensor networks (WSN’s), optimized node scheduling is an important technique for maximizing coverage and network lifetime. Existing coverage protocols present periodical, random and conditional node scheduling based on some coverage metrics. However, these scheduling techniques causes the frequent and un-necessary wake-ups of sleeping nodes which would increase the energy consumption and reduced network lifetime. In this paper, we propose Coverage Aware Battery Regression Node Scheduling (CABR) algorithm using battery discharge curve. In CABR, the coverage computation test determines that there are adequate numbers of sensing nodes in the network field while battery curve regression decides an optimal wakeup rate of sleeping nodes. The coverage computation test ensures minimum coverage redundancy within the network and optimal backoff sleep time derived from regression fit to the battery curve avoids unnecessary, random and frequent wake-ups of sleeping nodes. Due to this, sleeping nodes wake-up only close to the death of sensing node. In addition, coverage computation test maintains adequate/sufficient number of sensing nodes which would reduce energy consumption and maximize the coverage and network lifetime in the WSN. Simulation results show that, CABR achieves lesser energy consumption and longer network coverage lifetime as compared to RBSP and PEAS.