Linear Complexity for k-Coverage Sensor Redundancy Determination in IoT

Sensors play a crucial role in the IoT frameworks by enabling devices to collect and transmit data in k-coverage situations. However, maintaining k-coverage in a region of Interest (RoI) requires the concurrent activation of many sensors where at least $k$ nodes (k > 1), should cover each location in the sensing region. Due to overlaps in their sensing disks, sensors may be redundant and consume energy unnecessarily, in most k-coverage scenarios. In this paper, we propose SRA-Rot-k lmax ; a new redundancy algorithm that can affordably determine redundant sensors with a linear running time complexity even in k-coverage situations. A sensor is redundant in SRA- Rot-k lmax , if its neighbors belong to particular sub-regions within its sensing disk denoted Flower areas (FA). A logical rotation with a certain angle value $a$ is preformed to determine all possible Flower areas of a sensing disk and their local coverage degree k lmax . Finally, according to klmax of each sensor, the coverage degree of the entire RoI is obtained. Simulations show that the proposed algorithm outperforms well-known k-coverage protocols in terms of energy conservation, network lifetime, and coverage performance.