In this chapter, the sensing coverage area of surveillance wireless sensor networks is considered. The sensing coverage is determined by applying Neyman-Pearson detection and defining the breach probability on a grid-modeled field. Using a graph model for the perimeter, Dijkstra's shortest path algorithm is used to find the weakest breach path. The breach probability is linked to parameters such as the false alarm rate, size of the data record and the signal-to-noise ratio. Consequently, the required number of sensor nodes and the surveillance performance of the network axe determined. For target tracking applications, small wireless sensors provide accurate information since they can be deployed and operated near the phenomenon. These sensing devices have the opportunity of collaboration amongst themselves to improve the target localization and tracking accuracies. Distributed data fusion architecture provides a collaborative tracking framework. Due to the present energy constraints of these small sensing and wireless communicating devices, a common trend is to put some of them into a dormant state. We adopt a mutual information based metric to select the most informative subset of the sensors to achieve reduction in the energy consumption, while preserving the desired accuracies of the target position estimation.