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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 threedimensional 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.

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Energy efficiency is an important requirement in wireless sensor networks in order to achieve cost-effectiveness and practical implementation. The present work deals with the problem of minimising node power consumption in the context of moving-node localisation and tracking. Time-of-arrival measurements are sent from anchor nodes to a powerful, usually sophisticated, central node, called the fusion centre, where all computations are performed. Low data rates are desirable to economise on node energy but result in sub-optimal localisation accuracy. It makes sense, therefore, to sample measurements at a low data rate while interpolating the data stream at the fusion centre to improve localisation. The localisation error is remarkably reduced and energy efficiency increased by using this conventional sample rate conversion technique. A further improvement in terms of localisation error is achieved using compressive sensing (via random sampling and interpolation), whereby the localisation error function is shown to decrease with higher-average random sampling periods.