Abstract
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.
Highlights
Transmission line monitoring is a critical issue in safety guarantee of smart grids [1], [2]
The transmission line monitoring network is a new type of self-organizing wireless sensor network (WSN), consisting of multiple sensor nodes deployed in monitoring areas [14]
This paper proposes a data acquisition mechanism based on spatiotemporal correlation perception in event-driven sensor networks
Summary
Transmission line monitoring is a critical issue in safety guarantee of smart grids [1], [2]. Data fusion algorithm based on grey support vector machine prediction model in wireless sensor networks proposed in literature [23] can reduce network energy consumption, but due to the complex prediction algorithm, it is not suitable for event-driven sensor networks. To solve these problems, this paper proposes a data acquisition mechanism based on spatiotemporal correlation perception in event-driven sensor networks. Data analysis shows that the data aggregation of sensor nodes can meet the real-time requirements of event-driven sensor network applications, and reduce the transmission of redundant data and transmission energy consumption within the considered network, which thereby extends the network life cycle.
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