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One of the fundamental purpose of sensing information is to immediately respond to any anomalies. Wireless sensor network (WSN) is a network of inexpensive, low-power nodes with embedded processors, radios, sensors, and actuators, often integrated on a single chip, to communicate with the physical world in applications, such as security and surveillance, smart classroom, monitoring of natural habitats, and medical monitoring. WSNs differ considerably from current networked and embedded systems and due to its extreme energy constraints its design requires a proper understanding of the interplay between network protocols, energy-aware design, signal-processing algorithms, and distributed programming. Though the small form-factor of sensor nodes makes them attractable for use in monitoring applications, at the same time their small size affects resources such as the energy, computational power, and storage. Therefore, improvising on the energy constraints of wireless sensor networks is crucial. We propose two base-station relocation policies that aim to minimize the energy consumed for transmitting the data to base station. Both the policies involve a mobile base station, and focus on moving the base station closer to the active sensors that detect the target. Our first policy involves having a mobile base station and relocating it to the geometric centroid of all the sensors detecting the target. This approach significantly reduces the energy overhead required for transmitting data from the sensors to the base station. Our second policy for performing network lifetime optimization is to move the base station to geometric centroid of the base station locations obtained over several time periods. However, in each case, moving the base station at each time period involves a considerable overhead and therefore we observe the effects of moving the base station after a specific number of time periods as opposed to moving after every time period. We evaluate the network life- - time performance of these two proposed policies over different network scenarios.