Gradient Descent Localization in Wireless Sensor Networks

Meaningful information sharing between the sensors of a wireless sensor network (WSN) necessitates node localization, especially if the information to be shared is the location itself, such as in warehousing and information logistics. Trilateration and multilateration positioning methods can be employed in two-dimensional and three-dimensional space respectively. These methods use distance measurements and analytically estimate the target location; they suffer from decreased accuracy and computational complexity especially in the three-dimensional case. Iterative optimization methods, such as gradient descent (GD), offer an attractive alternative and enable moving target tracking as well. This chapter focuses on positioning in three dimensions using time-of-arrival (TOA) distance measurements between the target and a number of anchor nodes. For centralized localization, a GD-based algorithm is presented for localization of moving sensors in a WSN. Our proposed algorithm is based on systematically replacing anchor nodes to avoid local minima positions which result from the moving target deviating from the convex hull of the anchors. We also propose a GD-based distributed algorithm to localize a fixed target by allowing gossip between anchor nodes. Promising results are obtained in the presence of noise and link failures compared to centralized localization. Convergence factor issues are discussed, and future work is outlined

Energy-efficient distributed localization for wireless sensor networks

A wireless sensor network (WSN) is an ad hoc communication network that consists of a large number of small, low-cost, low-energy sensors with limited processing capability. WSN localization has emerged as a very important and a most intensively studied issue and sparked a considerable amount of innovative research. This chapter deals with compressive sensing (CS) principles in view of the localization/tracking application under consideration assuming centralized localization. It describes distributed gradient descent (GD) localization in WSNs employing distributed averaging gossip algorithms, and at the same time introduces CS to this scenario for achieving energy-efficient target tracking. WSN energy efficiency has been achieved in the context of centralized moving target tracking by using CS. The GD localization problem is very well tailored to distributed computing such as the distributed localization technique proposed in Nuha A. S. Alwan and A. S. Mahmood with the aim of achieving resilience against node and link failures