A Distributed RSS-Based Localization Using a Dynamic Circle Expanding Mechanism
| Autor: | Joe-Air Jiang, Cheng-Long Chuang, Yu-Fan Chen, Chien-Hao Wang, Po-Tang Chen, Xiang-Yao Zheng, Chia-Pang Chen |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Engineering
Smart objects business.industry RSS Node (networking) Distributed computing Intelligent decision support system computer.file_format Key distribution in wireless sensor networks Smart environment Electrical and Electronic Engineering business Instrumentation Wireless sensor network computer Trilateration |
| Zdroj: | IEEE Sensors Journal. 13:3754-3766 |
| ISSN: | 1558-1748 1530-437X |
| DOI: | 10.1109/jsen.2013.2258905 |
| Popis: | This paper focuses on localization that serves as a smart service. Among the primary services provided by Internet of Things (IoT), localization offers automatically discoverable services. Knowledge relating to an object's position, especially when combined with other information collected from sensors and shared with other smart objects, allows us to develop intelligent systems to fast respond to changes in an environment. Today, wireless sensor networks (WSNs) have become a critical technology for various kinds of smart environments through which different kinds of devices can connect with each other coinciding with the principles of IoT. Among various WSN techniques designed for positioning an unknown node, the trilateration approach based on the received signal strength is the most suitable for localization due to its implementation simplicity and low hardware requirement. However, its performance is susceptible to external factors, such as the number of people present in a room, the shape and dimension of an environment, and the positions of objects and devices. To improve the localization accuracy of trilateration, we develop a novel distributed localization algorithm with a dynamic-circle-expanding mechanism capable of more accurately establishing the geometric relationship between an unknown node and reference nodes. The results of real world experiments and computer simulation show that the average error of position estimation is 0.67 and 0.225 m in the best cases, respectively. This suggests that the proposed localization algorithm outperforms other existing methods. |
| Databáze: | OpenAIRE |
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