An Efficient Data Collection Protocol for Maximum Sensor Network Data Persistence

Sensor network has lot applications in the early warning and assistant of disaster environment such as debris flows, floods and forest fires. However, such disaster environment pose an interesting challenge for data collection since sensor nodes may be destroyed unpredictably and centrally, resulting in the decrease of data persistence in the network. Growth Codes Protocol (GCP) first focuses on increase sensor network data persistent in the disaster. However, the completely random data transmission way in GCP may cause a large number of invalid data transmissions and therefore, the efficiency of data collection of the protocol is not ideal in the late stage of data collection. In this paper, we propose an efficient data collection protocol (DGCP) to maximize sensor network data persistence by changing the completely random data transmission way. Packet classification mechanism and a novel dynamic probability model of data transmission in DGCP are proposed to control the effective direction of data flow. Furthermore, we found that the parameter optimization problem of the probabilistic model is a problem of searching the optimal solution in a mathematical view. Based on this property, we propose a genetic algorithm to optimize the dynamic probability model. The performance of the proposed DGCP is shown by a comparative experimental study. When compared with GCP, our DGCP has better performance in a variety of environments