ALESSA: MDS - based localization algorithm for Wireless Sensor Networks

Self-localization in wireless sensor networks (WSN) should be precise and reliable. Alternative least-square scaling algorithm (ALESSA) is a recently proposed centralized multidimensional scaling (MDS)-based localization algorithm, which uses an iterative approach to solve for the coordinates of discrete points. While ALESSA converges most of the time, like most iterative algorithm, it can be trapped in local minima causing large errors in the location estimates. In this paper, we propose the reseeding of the initial random estimates to improve the convergence of the algorithm. Performance of the proposed algorithm is evaluated under different network topologies with limited connectivity. We also analyzed the effects of low signal to noise ratio (SNR) and the population of the nodes deployed in the network to the algorithm´s localization precision. Simulation results show that at 26 dB SNR reseeding always results in convergence with the estimation errors within 5% of the reference communication range. Analysis and test runs also verified that our algorithm provides accurate and consistent localization estimates under range-based localization with limited network connectivity, even with a low SNR. The algorithm also performs well with limited number of nodes.