Continuous objects detection and tracking in wireless sensor networks

Most research, in the area of target detection and tracking in wireless sensor networks (WSN), is focused on a single or multiple targets tracking. However, limited research is aimed at tracking and detection of continuous objects such as forest fires, biochemical materials and mudflows, etc. These continuous objects pose new challenges due to their nature and characteristics of changing in size and shape, shrinking and expanding, splitting into multiple objects, or merging of multiple objects into one object. Continuous objects tracking and detection require extensive communication, which consumes a considerable amount of network energy. To this end, this paper proposes a new algorithm named Continuous Object Detection and Tracking (CODAT). This paper also introduces a new data structure for reporting data. This new data structure reduces the communication cost of the overall algorithm without compromising the accuracy for reconstructing the boundary of a continuous object at the base station. A concept for differentiating between the holes in the phenomenon and overall phenomenon changes at the base station level is also introduced which provides additional information to the user as an added improvement while maintaining the high accuracy and efficiency. To demonstrate the feasibility and efficiency of this algorithm, it is implemented and compared its results with two known algorithms, including Continuous Boundary Monitoring (COBOM) and Detection and Monitoring for Continuous Objects (DEMOCO). The simulation results show that CODAT outperforms COBOM and DEMOCO with dense WSNs.