Similarity based image selection with frame rate adaptation and local event detection in wireless video sensor networks

Wireless Video Sensor Networks (WVSNs7unding environmental information. Those sensor nodes can locally process the information and then wirelessly transmit it to the coordinator and to the sink to be further processed. As a consequence, more abundant video and image data are collected. In such densely deployed networks, the problem of data redundancy arises when information are gathered from neighboring nodes. To overcome this problem, one important enabling technology for WVSN is data aggregation, which is essential to be cost-efficient. In this paper, we propose a new approach for data aggregation in WVSN based on images and shot similarity functions. It is deployed on two levels: the video-sensor node level and the coordinator level. At the sensor node level the proposed algorithms aim at reducing the number of frames sensed by the sensor nodes and sent to the coordinator. At the coordinator level, after receiving shots from different neighbouring sensor nodes, the similarity between these shots is computed to eliminate redundancies and to only send the frames which meet a certain condition to the sink. The similarity between shots is evaluated based on their color, edge and motion information. We evaluate our approach on a live scenario and compare the results with another approach from the literature in terms of data reduction and energy consumption. The results show that the two approaches have a significant data reduction to reduce the energy consumption, thus our approach tends to overcome the other one in terms of reducing the energy consumption related to the sensing process, and to the transmitting process while guaranteeing the detection of all the critical events at the node and the coordinator levels.