MOTION: Multi-models correlation framework for energy-saving in wireless video sensor networks

Abstract

Wireless Video Sensor Networks (WVSNs) face critical challenges in energy consumption and data bandwidth utilization due to multiple sensor nodes transmitting redundant data of the observed phenomenon. Thus, identifying and reducing such redundancies is becoming essential for extending the network lifetime and emphasizing the quality of the collected data. In this paper, we propose Multi-mOdels correlaTION framework (MOTION) that efficiently removes data duplication and conserve sensor energies in WVSNs. MOTION proposes new data reduction methods based on the temporal–spatial correlations that could be applied at different node levels, e.g. sensors and cluster-heads (CHs). At the sensor level, MOTION introduces two temporal-based correlation mechanisms to search the similarity among frames collected during each period; the first mechanism aims to detect short-term duplication, e.g. among consecutive frames, while the second one allows to detect scene changes then trigger transmissions when the variation is significant. At the second level, CH geographically groups video sensors into clusters to find the spatial correlation between them, then a scheduling strategy is applied to switch spatially-correlated ones into sleep/active modes. Extensive simulations using real-world video data sets as a benchmark are conducted to show the efficiency of the proposed framework. The results demonstrated that MOTION can reduce up to 92.4% of collected video data, leading to tremendous energy savings and network lifetime up to 74.3% compared to existing approaches.