Distributed Data Aggregation Scheduling in Multi-Channel and Multi-Power Wireless Sensor Networks

Abstract

Large amounts of sensor data are frequently generated and streamed from sensors deployed on various buildings, in forests or in other application areas. In many of these areas, one difficulty is managing the velocity and volume of the big sensor data while still providing low time latency support for data analysis. Data aggregation can reduce the volume of big sensor data. However, data aggregation is a fundamental yet time-consuming operation in wireless sensor networks (WSNs), particularly in high-density WSNs. Therefore, researchers have started focusing on minimizing the latency of data aggregation, which has been proven to be an NP-hard problem. This paper proposes a cluster-based distributed data aggregation scheduling algorithm, distributed multi-power and multi-channel (DMPMC), that can minimize the data aggregation latency in multi-channel and multi-power WSNs. To save energy, low transmission power is used for packet transmissions inside a cluster, and high power is used for packet transmissions among clusters. Simulations are conducted to compare DMPMC with the best centralized algorithm in a single channel, named E-PAS, the best distributed algorithm in a single channel, named CLU-DDAS, and the best algorithm in multi-channels, named multi-channel. The results show that the DMPMC algorithm proposed in this paper achieves the lowest average latency.