Robust Positioning Algorithms for Distributed Ad-Hoc Wireless Sensor Networks
ATEC '02 Proceedings of the General Track of the annual conference on USENIX Annual Technical Conference
Simulated Annealing based Localization in Wireless Sensor Network
LCN '05 Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary
Semidefinite programming based algorithms for sensor network localization
ACM Transactions on Sensor Networks (TOSN)
A Theory of Network Localization
IEEE Transactions on Mobile Computing
Two-Phase Stochastic Optimization to Sensor Network Localization
SENSORCOMM '07 Proceedings of the 2007 International Conference on Sensor Technologies and Applications
Bacterial foraging oriented by particle swarm optimization strategy for PID tuning
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Localization systems for wireless sensor networks
IEEE Wireless Communications
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems
IEEE Transactions on Evolutionary Computation
IEEE Communications Magazine
A swarm intelligence based distributed localization technique for wireless sensor network
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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Many applications of wireless sensor networks (WSNs) require location information of the randomly deployed nodes. A common solution to the localization problem is to deploy a few special beacon nodes having location awareness, which help the ordinary nodes to localize. In this approach, non-beacon nodes estimate their locations using noisy distance measurements from three or more non-collinear beacons they can receive signals from. In this paper, the ranging-based localization task is formulated as a multidimensional optimization problem, and addressed using bio-inspired algorithms, exploiting their quick convergence to quality solutions. An investigation on distributed iterative localization is presented in this paper. Here, the nodes that get localized in an iteration act as references for remaining nodes to localize. The problem has been addressed using particle swarm optimization (PSO) and bacterial foraging algorithm (BFA). A comparison of the performances of PSO and BFA in terms of the number of nodes localized, localization accuracy and computation time is presented.