Abstract
Wireless sensor networks have often been used to monitor and report the locations of moving objects. In the first part of the dissertation, we investigate how to efficiently find and report the locations of moving objects within wireless sensor networks. Since sensors can be used for storage, a wireless sensor network can be considered a distributed database, enabling us to update and query the location information of moving objects. Many researchers have studied the problem of how to construct message-pruning trees that can update a database and query objects with minimum cost (the Minimum Cost Message-Pruning Tree problem). The trees are constructed in such a way that the total cost of updating the database and querying objects is kept as minimum as possible, while the hardness of the Minimum Cost Message-Pruning Tree problem remains unknown. Here, we show that the Minimum Cost Message-Pruning Tree problem is NP-complete. Besides, we propose a new data aggregation structure, a message-pruning tree with shortcuts, instead of the message-pruning tree. In the second part of the dissertation, we investigate how to efficiently monitor the objects in a tracking system. In a wireless sensor network, sensors are usually in the sleep state to prolong the network life. The tracking system in a wireless sensor network usually uses a prediction model to predict the next location of the object and activates the appropriate sensors to keep monitoring the object. Once the prediction fails to track the object, additional sensors are activated in order to recapture the lost object. Therefore, a better prediction model can significantly reduce power consumption because fewer redundant sensors will be activated. The Gauss-Markov mobility model is one of the best mobility models to describe object trajectory because it can capture the correlation of object velocity in time. Traditionally, Gauss-Markov parameters are estimated using autocorrelation technique or recursive least square estimation technique; either of these techniques, however, requires a large amount of historical movement information of the mobile object, which is not suitable for tracking objects in a wireless sensor network because they demand a considerable amount of message communication overhead between wireless sensors which are usually battery-powered. Here, we develop a Gauss-Markov parameter estimator for wireless sensor networks (GMPE_MLH) using a maximum likelihood technique. The GMPE_MLH estimates Gauss-Markov parameters with few requirements in terms of message communication overhead.