We refer to localization as the problem of estimating the spatial coordinates of wireless nodes in an ad-hoc network. Wireless sensor network is an example of such a network, where localization as a problem has been a challenging topic for several years. The position of sensor nodes can be either manually configured before deployment or a GPS receiver can be built into each of these nodes. The former approach is very tedious and error-prone while the latter is a costly proposition in terms of volume, money and power consumption. In this thesis, we consider the problem of determining the positions of wireless nodes using range measurements from multiple, sparsely located, beacon stations with known locations. Clusters of unknown nodes collaborate among themselves in estimating their positions with the help of beacon stations. The major problem is overcoming range measurement inaccuracies. We propose a simple position estimation algorithm that features robustness with respect to range measurement inaccuracies, has low complexity and distributed implementation using only local information. We analyze the performance of the algorithm based on rigorous simulation and theory. We then extend the simple algorithm to a probabilistic algorithm that overcomes some of the drawbacks present in the simple algorithm. The algorithm was designed and implemented in a wireless test-bed consisting of IEEE 802.11 based iPAQs to study its performance. Most of the current localization systems are based on multiple beacons assisting unknown nodes. In an attempt to eliminate some of the drawbacks present in such systems, we also propose and study a single mobile beacon based localization method where a mobile beacon assists unknown nodes in estimating their positions. An implementation of this method in a wireless testbed was used to evaluate the performance.