Towards even coverage monitoring with opportunistic sensor networks

Opportunistic sensor networks typically rely on node mobility to monitor an area by collecting samples at different locations. In this paper we show how the mobility in combination with the periodic sampling of nodes causes large differences in the sensor coverage. We address this issue by leveraging simple heuristics based on local knowledge, employing an adaptive sampling scheme. The main insight is that areas where over-sampling is prevalent exhibit a high correlation with node contacts. Results obtained from both synthetic and real-world traces show that a dramatic decrease in oversampling of affected areas is achievable alongside a smaller increase of samples in more sparse areas.