POOT: An efficient object tracking strategy based on short-term optimistic predictions for face-structured sensor networks

The advance of wireless sensor networks has enabled the development of a great number of applications in various areas, such as biology, military and environmental surveillance. Among these applications, object tracking systems have particularly useful functions, and have been studied by many researchers in recent years. In the design of a sensor network system, energy consumption is a critical consideration. In this paper, we propose a short-term Prediction-based Optimistic Object Tracking strategy (POOT) to reduce energy consumption and prolong the lifetime of sensor nodes while sacrificing only minimal tracking precision. Furthermore, we present two schemes, a Time-efficient Object Recovery Scheme (TORS) and a Communication-efficient Object Recovery Scheme   (CORS), to improve object recovery. We also derive cost models for POOT. Through a set of experiments, our proposed prediction-based optimistic object tracking scheme can save up to 23%23% energy consumption compared to the related scheme, DOT. Meanwhile, the accuracy of POOT is still higher than 97.5%97.5% which reveals the optimistic design does not affect the tracking accuracy. Hence, POOT is shown to effectively conserve energy and achieve the objective of tracking of moving objects.