Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Computer
Range-free localization schemes for large scale sensor networks
Proceedings of the 9th annual international conference on Mobile computing and networking
IEEE Communications Magazine
A multiple power-level approach for wireless sensor network positioning
Computer Networks: The International Journal of Computer and Telecommunications Networking
A Logical Group Formation and Management Mechanism Using RSSI for Wireless Sensor Networks
APNOMS '08 Proceedings of the 11th Asia-Pacific Symposium on Network Operations and Management: Challenges for Next Generation Network Operations and Service Management
On accuracy of region based localization algorithms for wireless sensor networks
Computer Communications
Morphogenesis in computer networks
Sarnoff'10 Proceedings of the 33rd IEEE conference on Sarnoff
Computing methodologies for localization techniques in wireless sensor networks
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
Massively parallel cooperative localisation in scalable sensor networks
International Journal of Communication Networks and Distributed Systems
Acoustic sensor network node self-localization based on adaptive particle swarm optimization
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
An Interactive and Energy-efficient Node Localization Scheme for Wireless Sensor Networks
Wireless Personal Communications: An International Journal
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One challenging issue in sensor networks is to determine where a given sensor node is physically located. This problem is especially crucial for very small sensor nodes. This paper presents a GPS-less, outdoor, self-positioning method for wireless sensor networks. In our method, a set of nodes, called reference points (RPs), are deployed in the sensor network with overlapping regions of coverage. The RP periodically broadcasts beacon frames which contain localization data. The sensor node collects the beacon frames from RPs and process the data in the frame; it can then easily localize itself. The analysis of positioning accuracy is given to show how well a sensor node can correctly localize itself. In the optimal transmitting power, the worst-case accuracy for all data points is within 28.87% of the separation-distance between two adjacent RPs and the average accuracy is within 15.51%. The simulation results also show the robustness of the proposed method. Finally, we have implemented our positioning method on a sensor network test bed and the actual measurement show that the method can achieve average accuracy within 17.9% of the separation-distance between two adjacent RPs in an outdoor environment.