Opportunistic sensing in wireless sensor networks: Theory and application

In real world, wireless heterogeneous sensor network (HSN) design and information integration are necessary in different applications. Traditionally, wireless sensor networks information integration is set up to passively fuse all received data. Such an approach is computationally challenging and operationally ineffective because improvements in information accuracy are not guaranteed. Opportunistic Sensing (OS) refers to a paradigm for signal and information processing in which a network of sensing systems can automatically discover and select sensor platforms based on an operational scenario. In this paper, we propose theory and algorithms of OS to simplify the HSN design and promote more efficient information integration. We propose an information theoretical criterion for opportunistic sensing in HSN, and show that HSN with correlated modalities needs less number of codewords than that with independent modalities. Our OS algorithm advances autonomous sensing that not only ensures effective utilization of sensing assets but also provides robust optimal performance. We apply our OS algorithm to radar sensor networks for surveillance and monitoring, and show that our approach works very well and much better than other approaches.