Target tracking using Wireless Sensor Networks (WSNs) has drawn lots of attentions after the recent advances of wireless technologies. Target tracking aims at locating one or several mobile objects and depicting their trajectories over time. The applications of Object Tracking Sensor Networks (OSTNs) include but not limited to environmental and wildlife monitoring, industrial sensing, intrusion detection, access control, traffic monitoring, patient monitoring in the health-related studies and location awareness in the battle eld. One of the most rewarding applications of target tracking is wildlife monitoring. Wildlife monitoring is used to protect the animals which are endangered to extinction. Road safety applications are another popular usage of wildlife monitoring using WSNs. In this thesis, the issues and challenges of energy-efficient wildlife monitoring and target tracking using WSNs are discussed. This study provides a survey of the proposed tracking algorithms and analyzes the advantages and disadvantages of these algorithms. Some of the tracking algorithms are proposed to increase the energy e ciency of the tracking algorithm and to prolong the network lifetime; while, other algorithms aim at improving the localization accuracy or decreasing the missing rate. Since improving the energy efficiency of the system provides more alive sensors over time to locate the target; it helps to decrease the missing rate as the network ages. Thus, this study proposes to adjust the sensing radius of the sensor nodes in real-time to decrease the sensing energy consumption and prolong the network lifetime. The proposed VAriable Radius Sensor Activation (VARSA) mechanism for target tracking using wireless sensor networks tackles the energy consumption issues due to resource constraints of the WSNs. VARSA reduces the radio covered area of each sensor node to only cover the Area of Interest (AoI) which is the location of the target in tracking applications. Thus, VARSA aims at decreasing the sensing energy consumption which leads to encreasing the network life time. In addition, VARSA decreases the missing rate over time as it provides more alive sensors to detect the target compared to previous activation algorithms as the network ages. VARSA is compared to PRediction-based Activation (PRA) and Periodic PRediction-based Activation (PPRA) algorithms which are two of the most promising algorithms proposed for sensor activation. The simulation results show that VARSA outperforms PRA and PPRA. VARSA prolongs the lifetime of the network and decreases the missing rate of the target over time.