Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
GPSR: greedy perimeter stateless routing for wireless networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Location errors in wireless embedded sensor networks: sources, models, and effects on applications
ACM SIGMOBILE Mobile Computing and Communications Review
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Error characteristics of ad hoc positioning systems (aps)
Proceedings of the 5th ACM international symposium on Mobile ad hoc networking and computing
A wireless sensor network For structural monitoring
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Robust distributed network localization with noisy range measurements
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Detecting Malicious Beacon Nodes for Secure Location Discovery in Wireless Sensor Networks
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
SeRLoc: Robust localization for wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
Countersniper system for urban warfare
ACM Transactions on Sensor Networks (TOSN)
Distributed weighted-multidimensional scaling for node localization in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Attack-resistant location estimation in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
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Wireless Sensor Networks (WSN) monitor the physical world using small wireless devices known as sensor nodes, with high precision and in real time, without the intervention of a human operator. Location information plays a critical role in many of the applications where WSN are used. Though a simple and effective solution could be to equip every node with self-locating hardware such as a GPS, the resulting cost renders such a solution unefficient. A widely used self-locating mechanism consists in equipping a small subset of the nodes with some GPS-like hardware, while the rest of the nodes employ reference estimations (received signal strength, time-of-arrival, etc.) in order to determine their locations. The task of determining the node locations using node-to-node distances combined with a set of known node locations is referred to as location discovery (LD). The main difficulty found in LD is the presence of distance estimation errors, which result in node positioning errors. We describe in this work an error model for the estimations, and propose a two-stage search procedure that combines minimization of an error norm function with maximization of a maximum likelihood function to solve the problem. We perform an empirical study of the performance of several variants of the guiding functions, and several metaheuristics used to solve real LD problem instances. Finally, we test our proposed technique against the single phase techniques in order to evaluate its performance.