| Summary: | Lifetime maximization is a key challenge in the design of sensor-network-based tracking applications. In this dissertation, formation of optimal coalitions of nodes is investigated for data acquisition in bearings-only target localization such that the average sleep times allocated to the nodes are maximized. Targets are assumed to be localized with a pre-defined accuracy where the determinant of the Bayesian Fisher information matrix (B-FIM) is used as the metric for estimation accuracy. Cooperative game theory is utilized as a tool to devise a distributed dynamic coalition formation algorithm in which nodes autonomously decide which coalition to join, while maximizing their feasible sleep times. Nodes in the sleep mode do not record any measurements; hence, save power in both sensing and transmitting the sensed data. The proposed scheme reduces the number of sensor measurements by capturing the spatio-temporal correlation of the information provided by the sensors from one side and bounding the localization accuracy to the pre-defined value from the other side. It is proved that if each node operates according to this algorithm, the average sleep time for the entire network converges to its maximum feasible value. In numerical examples, we illustrate the inherent trade-off between the localization accuracy and the average sleep time allocated to the nodes and demonstrate the superior performance of the proposed algorithm via Monte Carlo simulations. === Applied Science, Faculty of === Electrical and Computer Engineering, Department of === Graduate
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