DRAGON: Detection and Tracking of Dynamic Amorphous Events in Wireless Sensor Networks

Wireless sensor networks may be deployed in many applications to detect and track events of interest. Events can be either point events with an exact location and constant shape, or region events which cover a large area and have dynamic shapes. While both types of events have received attention, no event detection and tracking protocol in existing wireless sensor network research is able to identify and track region events with dynamic identities, which arise when events are created or destroyed through splitting and merging. In this paper, we propose DRAGON, an event detection and tracking protocol which is able to handle all types of events including region events with dynamic identities. DRAGON employs two physics metaphors: event center of mass, to give an approximate location to the event; and node momentum, to guide the detection of event merges and splits. Both detailed theoretical analysis and extensive performance studies of DRAGON's properties demonstrate that DRAGON's execution is distributed among the sensor nodes, has low latency, is energy efficient, is able to run on a wide array of physical deployments, and has performance which scales well with event size, speed, and count.