Continuous object tracking protocol with selective wakeup based on practical boundary prediction in wireless sensor networks

Abstract

In wireless sensor networks, the selective wakeup approach to activate minimum sensor nodes for object tracking is considered as an attractive way for efficient energy consumption. Different from individual tracking, continuous object tracking is necessary to maintain only sensor nodes on the boundary of continuous objects in the active mode. Recently, a prediction-based scheme, named PRECO, is proposed to energy-efficiently track a continuous object using a selective wakeup scheme. It continuously predicts the next boundary position of a continuous object through mathematical computing and activates sleeping sensor nodes on the next boundary to detect the object in advance. However, PRECO has critical problems for practical implementation in terms of three domains: space, quantity, and time domains. We propose a new continuous object tracking protocol that practically predicts and accurately detects a continuous object at the right time by solving the problems in the domains. To avoid high complex prediction in the space domain, a virtual cell-based prediction scheme is applied to estimate next diffusing areas of continuous object. To reduce the number of nodes for the prediction participation in the quantity domain, our protocol requests only cell heads for estimating the diffusing area of the object and announcing the decision. To remove synchronous prediction in the time domain, each cell head asynchronously predicts the next diffusing area of the object without collaboration with other cell heads. Simulation results conducted in various environments verifies that our protocol is superior to PRECO in terms of energy efficiency and prediction accuracy.