This thesis presents and discusses research associated with locating wireless devices. Several algorithms have been developed to determine the physical location of the wireless device and a subset of these algorithms only rely on received signal strength (RSS). Two of the most promising RSS-based algorithms are the LC and dwMDS algorithms; however each algorithm has only been tested via computer simulations with different environmental parameters. To determine which algorithm performs better (i.e., produces estimates that are closer to the true location of the wireless device), a fair comparison needs to be made using the same set of data. The goal of this research is to compare the performance of these two algorithms using not only the same set of data, but data that is collected from the field. An extensive measurement campaign at different environments provided a vast amount of data as input to these algorithms. Both of these algorithms are evaluated in a onedimensional (straight line) and two-dimensional (grid) setting. In total, six environments were used to test these algorithms; three environments for each setting. The results show that on average, the LC algorithm outperforms dwMDS in most of the environments. Since the same data was inputted for each algorithm, a fair comparison can be made and doesn’t give any unfair advantage to any particular algorithm. In addition, since the data was taken directly from the field as opposed to computer simulations, this provides a better degree of confidence for a successful realworld implementation.