Drift Detection of Intelligent Sensor Networks Deployment Based on Graph Stream

Abstract

The optimal deployment of sensor nodes can effectively improve the monitoring performance of the sensor network. The existing optimization deployment strategies focus on how to achieve the optimal coverage or the maximal monitoring efficiency of the sensor network deployment, and lack research on the issue of when a deployed sensor network need to update its deployment. Considering that the parameters of the monitored targets by sensor network will vary over time, making the deployment a certain degree of time drift characteristic. In this paper, we propose a deployment drift detection algorithm based on graph stream for intelligent sensor networks (ISNs) to investigate the optimization deployment update time of sensor nodes for more efficient targets monitoring. We utilize graph stream to represent the topology of sensor network at different timestamps, and propose a projection fusion clustering algorithm based on Structural Clustering Algorithm for Networks (SCAN) to construct the tensor graph summarization within a sliding time window. According to the contrast between the node-level and the graph-level queries of the summary graph in adjacent windows, we refer Hoeffding boundary and graph contrastive loss function as the criterion to judge the optimal update time of the sensor network deployment. Experiments on Intel_Lab_Data and CIMIS datasets demonstrate that our approach can effectively detect the update moment of the optimal deployment of sensor network.