Probabilistic Data Aggregation Protocol Based on ACO-GA Hybrid Approach in Wireless Sensor Networks

Abstract

In Wireless Sensor Networks (WSNs), data aggregation techniques have the ability of reducing the data redundancy and the communication load. The probabilistic aggregation protocols make the dynamic routing decision, the nodes do not have explicit knowledge of downstream and upstream neighbors, and then it is difficult to obtain high aggregation efficiency. In order to address this problem, this paper proposes a new probabilistic aggregation protocol based on Ant Colony Optimization (ACO)-Genetic Algorithm (GA) hybrid approach. The Multi-Objective Steiner Tree (MOST) is defined as the optimal structure for data aggregation, which can be explored and frequently exploited during the routing process. Moreover, by using the prediction model based on the sliding window for the future arriving packets, the adaptive timing policy can adjust the timer interval to reduce the transmission delay and to enhance the aggregation probability. Finally, simulation results validate the feasibility and the high efficiency of the novel protocol when compared against other existing approaches.