The bits and flops of the n-hop multilateration primitive for node localization problems
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Localization from mere connectivity
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
Localization from Connectivity in Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
An Analysis of Error Inducing Parameters in Multihop Sensor Node Localization
IEEE Transactions on Mobile Computing
ITNG '06 Proceedings of the Third International Conference on Information Technology: New Generations
Robust distributed node localization with error management
Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing
Distributed weighted-multidimensional scaling for node localization in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Wireless sensor network localization techniques
Computer Networks: The International Journal of Computer and Telecommunications Networking
Error control in distributed node self-localization
EURASIP Journal on Advances in Signal Processing
On the error characteristics of multihop node localization in ad-hoc sensor networks
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Relative location estimation in wireless sensor networks
IEEE Transactions on Signal Processing
A multidimensional scaling framework for mobile location using time-of-arrival measurements
IEEE Transactions on Signal Processing
Robust tracking algorithm for wireless sensor networks based on improved particle filter
Wireless Communications & Mobile Computing
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In this paper we propose two novel and computationally efficient metaheuristic algorithms based on tabu search (TS) and particle swarm optimization (PSO) principles for locating the sensor nodes in a distributed wireless sensor network (WSN) environment. The WSN localization problem is formulated as a non-linear optimization problem with mean squared range error resulting from noisy distance measurement as the objective function. Unlike gradient descent methods, both TS and PSO methods ensure minimization of the objective function without the solution being trapped into local optima. We further implement a refinement phase with error propagation control for improvement of the results. The performance of the proposed algorithms are compared with each other and also against simulated annealing based WSN localization. The effects of range measurement error, anchor node density and uncertainty in the anchor node position on localization performance are also studied through various simulations. The simulation results establish better accuracy, computational efficiency and convergence characteristics for TS and PSO methods. Further, the efficacy of the proposed methods is verified with data collected from an experimental sensor network reported in the literature.