Localized In-Network Detection and Tracking of Phenomena Clouds using Wireless Sensor Networks
Bose Raja, Sumi Helal · Ambient intelligence and smart environments · 2009
Phenomena clouds are characterized by non-deterministic, dynamic variations over time, of their shape, size and direction of motion along multiple axes. In the past, the utility of phenomena detection and tracking has been limited to applications such as tracking oil spills and gas clouds. However, through our collective experience over the years in a completely different deployment domain (Smart Spaces), we have discovered great utility and value in applying this concept to accurately and efficiently observe other types of phenomena. In this paper, we propose distributed sensor network algorithms which utilize localized in-network processing to simultaneously detect and track multiple phenomena clouds in a sensor space. Our algorithms not only ensure low processing and networking overhead but also minimize the number of sensors which are actively involved in the detection and tracking processes at any given time. We validate our approach using both real-life smart home applications as well as simulation experiments. We also show that our algorithms result in significant reduction in resource usage and power consumption as compared to contemporary stream-based approaches.