A Novel On-Demand Framework for Collaborative Object Detection in Sensor Networks

Abstract

Quality of object detection and network lifetime hold critical importance to many sensor network applications such as military surveillance. Unfortunately, improving one of these aspects comes at the expense of the other. In this paper, based on the probabilistic sensing model, we propose a novel framework for object detection in sensor networks, called DeCODe (on-demand framework for collaborative object detection), which provides a desired object detection performance (characterized in terms of detection probability and false detection probability), while attempting to prolong the network lifetime. The design of DeCODe is motivated by a counterintuitive observation that simple collaboration among active sensors indeed degrades the object detection performance. By contrast, each active sensor in DeCODe can trigger its neighboring inactive sensors to participate in the detection process in an on-demand fashion, so as to achieve the same low false detection probability while increasing the probability of detection. The effectiveness of the proposed DeCODe framework is supported by theoretical analysis and simulation-based validation.