A Wireless Sensor Network (WSN) is formed of low-cost, low-power, multifunctional, tiny sensor nodes that scattered in one area and can collect physical information, process and forward them to sink nodes which act as data collectors. Sensors exhibit self-configuring and self-organizing capabilities and can be deployed densely and randomly without predetermining their positions. Localization has been a major challenge in wireless sensor networks because without finding the position of the sensor that is reporting sensed data, the data reported will not be useful. However, most of the previous localization approaches demand that sensors have plenty of resources like power, memory, or extra hardware used for ranging. In this thesis we propose a new Hybrid Distance Vector based localization algorithm, referred to as HDV-Hop, that accurately localizes sensors while minimizing their power consumption and memory requirements, and without depending on any extra hardware. HDV-Hop splits the task of localization between sensor nodes and the base station (i.e., sink). Moreover, the proposed algorithm does not necessitate the presence of many nodes with preconfigured positions, or anchor nodes. In HDV-Hop, anchors are placed in a circle or a semi-circle around the perimeter of the WSN. Not only this placement strategy leads to more accurate localization, but also it is very convenient for WSNs used for environment monitoring or military surveillance or tracking applications. Using NS-2 and MATLAB simulations, we have shown that the proposed HDV-Hop outperforms other peer approaches in terms of localization accuracy and power efficiency.