Fault Tolerant Data Collection in Heterogeneous Intelligent Monitoring Networks

In this work, we focus on the problem of fault tolerant data collection in heterogeneous Intelligent Monitoring Networks(IMNs). IMNs are expected to have a wide range of applications in many fields such as forest monitoring, structural monitoring, and industrial plant monitoring. We present our fault tolerant data collection scheme in the hierarchical structure of IMNs. We use an interesting technique borrowed from the popular BitTorrent software to maintain a highly efficient and robust data collection in IMNs with heterogeneous and faulty devices. In our proposed scheme, monitoring sensors are instructed to randomly select some overheard transmissions and process them in data fusion. Our preliminary study confirmed the benefits of the fault tolerant data collection strategy.