Data Collection Study Based on Spatio-Temporal Correlation in Event-Driven Sensor Networks

In this paper, a spatial-temporal correlation aware data collection mechanism is proposed for a event-driven sensor network in terms of the realistic requirements such as real-time data sensing and dynamic network topology. Firstly, in order to reduce the path congestion and the data transmission delay, the perceived data states are classified based on binary representation. Secondly, a low cost manner is studied to aggregate the perceived data at the representative nodes and aggregation nodes respectively based on the spatial-temporal correlation. Furthermore, the best data collection path is obtained by carrying out a particle swarm optimization (PSO). Simulation results validate that the proposed algorithm can effectively reduce the amount of data transmissions in the network event area. Besides, the proposed mechanism also has advantages in reducing the delay and energy consumption.