Self-Organizing Localization with Adaptive Weights for Wireless Sensor Networks

Self-organizing localization is a crucial feature of wireless sensor networks that cannot employ positioning techniques such as the global positioning system. If the number of anchor nodes is insufficient, it is difficult to estimate each node's location using the anchor nodes' information by employing the existing algorithms, the localization problem becoming complicated. In this paper, we propose a recursive self-organizing localization scheme, solely based on the neighbors' connectivity information. This scheme utilizes a mass-spring-relaxation algorithm in which each node finds its location by iteratively balancing the geometric relationships with neighboring nodes until the system reaches an equilibrium state. We propose a simple distance correction factor to consider the accuracy of distance measurements, and adopt the adaptive step size control based on the gradient method to improve the system stability. Our simulations show that the proposed scheme improves the system performance in terms of convergence speed, system stability, and estimation accuracy.