A Dynamic Energy-Aware Algorithm for Self-Optimizing Wireless Sensor Networks

Wireless sensor networks are distributed ad-hoc networks that consist of small sensor devices collecting and transmitting data. In such devices, optimizing power consumption due to sensing and routing data is an open area for researchers. In earlier works, approaches based on the Gur game, ant algorithms and evolutionary algorithms were introduced. These approaches have been employed to control the number of active sensors in wireless networks required to guarantee high performance parameters while taking energy conservation into consideration. These studies though ignored the coverage redundancy, which is a key issue in sensor networks. In this paper, we use the mathematical paradigm of the Gur Game in order to achieve the optimal assignment of active sensors while maximizing the number of regions covered by sensor nodes. We use a dynamic clustering algorithm that employs the concept of connected dominating sets. The proposed algorithm addresses this problem by playing Gur Game among the cluster nodes. We also further develop the earlier developed ants algorithm and genetic algorithm to take into consideration node addition and deletion. A simulation study was used to test the proposed algorithms under different network scenarios.