Model-based object tracking in wireless sensor networks
Tracking moving objects is one of the most common requirements in wireless sensor network applications. Most tracking schemes predict a targets location based on a single object movement model and periodically activate nearby sensors to monitor the target. However, in most real-world situations, a target exhibits multiple movement patterns. Thus, multiple movement models are required to accurately describe the targets movement. This paper proposes a tracking framework, called model-based object tracking system (MOTS), that allows a sensor network to adaptively apply the most suitable tracking mechanism to monitor the target under various circumstances. To fairly and accurately evaluate all tracking modules, this study further develops a monitoring-cost evaluator to evaluate the monitoring cost of the inactive tracking modules, and then designs three tracking module selection strategies, the Greedy Strategy, Min-Max Strategy, and Weighted Moving Average Strategy, to select the most effective tracking module to monitor the target in each period. A set of experiments is conducted to evaluate MOTS and compare it against existing tracking systems. The obtained results reveal that the cost efficiency of MOTS is considerably better than that of existing tracking systems.
object tracking sensor network prediction movement model selection strategy algorithm design simulation