Relevance-based balanced sink mobility in wireless visual sensor networks

Wireless visual sensor networks can provide significant information for a large set of monitoring and surveillance applications. In these networks, mobile sinks are often used to reduce energy consumption over the network, where many algorithms have been proposed for higher energy efficiency. Frequently, visual sensors may have different relevancies for the monitoring functions of the applications, according to their potential to provide significant data. Additionally, the relevancies of visual sensors may be quickly adjusted according to the occurrence of some critical event. In such cases, higher relevant source nodes may be concurrently transmitting visual information with higher quality or frequency, potentially increasing energy consumption in intermediate nodes from those sources toward the sink. We propose an autonomous balanced positioning algorithm for mobile sinks in order to shorten the transmission paths from higher relevant sources, directly benefiting multi-hop sensor networks with multiple active visual source nodes.