A location-free Prediction-based Sleep Scheduling protocol for object tracking in sensor networks

Abstract

Sleep scheduling protocols are widely used in wireless sensor networks for saving energy in sensor nodes. However, without considering the special requirements of object tracking, conventional sleep scheduling protocols may lead to intolerable degradation of tracking qualities when they are used in object tracking applications. To handle this problem, sleep scheduling protocols tailed for object tracking have been proposed recently. For saving energy while maintaining satisfactory tracking qualities, these protocols pro-actively awaken sensors according to the prediction of objects' movement. Such sleep scheduling protocols are called the prediction-based sleep scheduling protocols. Most existing prediction-based sleep scheduling protocols require sensor nodes to know the locations of themselves, which may not always be available. In this paper we propose a Location-free Prediction-based Sleep Scheduling protocol (LPSS) for object tracking in sensor networks. LPSS guarantees the coverage level, an important tracking quality in most applications, which is defined as the number of sensors simultaneously detecting the object. In LPSS, when a sensor detects the object, it will emit a signal, namely the sensing stimulus. Sensors decide to wake up or not based on only the received sensing stimulus, the prediction models and the required coverage level, without the requirement of location information. We implement LPSS with two most popular prediction models: the Circle-based and the Probability-based prediction models. Experiment results show that LPSS not only provides qualified coverage levels, but also saves about 40% to 70% energy compared with existing location-free protocols. Moreover, the energy cost of LPSS is close to the ideal approach using accurate location information in terms of the number of awakened nodes.