Abstract: In recent years, various received signal strength (RSS)-based localization estimation approaches for wireless sensor networks (WSNs) have been proposed. RSS-based localization is regarded as a low-cost solution for many location-aware applications in WSNs. In previous studies, the radiation patterns of all sensor nodes are assumed to be spherical, which is an oversimplification of the radio propagation model in practical applications. In this study, we present an RSS-based cooperative localization method that estimates unknown coordinates of sensor nodes in a network. Arrangement of two external low-cost omnidirectional dipole antennas is developed by using the distance-power gradient model. A modified robust regression is also proposed to determine the relative azimuth and distance between a sensor node and a fixed reference node. In addition, a cooperative localization scheme that incorporates estimations from multiple fixed reference nodes is presented to improve the accuracy of the localization. The proposed method is tested via computer-based analysis and field test. Experimental results demonstrate that the proposed low-cost method is a useful solution for localizing sensor nodes in unknown or changing environments.
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Jiang, J.-A.; Chuang, C.-L.; Lin, T.-S.; Chen, C.-P.; Hung, C.-H.; Wang, J.-Y.; Liu, C.-W.; Lai, T.-Y. Collaborative Localization in Wireless Sensor Networks via Pattern Recognition in Radio Irregularity Using Omnidirectional Antennas. Sensors 2010, 10, 400-427.
Jiang J-A, Chuang C-L, Lin T-S, Chen C-P, Hung C-H, Wang J-Y, Liu C-W, Lai T-Y. Collaborative Localization in Wireless Sensor Networks via Pattern Recognition in Radio Irregularity Using Omnidirectional Antennas. Sensors. 2010; 10(1):400-427.
Jiang, Joe-Air; Chuang, Cheng-Long; Lin, Tzu-Shiang; Chen, Chia-Pang; Hung, Chih-Hung; Wang, Jiing-Yi; Liu, Chang-Wang; Lai, Tzu-Yun. 2010. "Collaborative Localization in Wireless Sensor Networks via Pattern Recognition in Radio Irregularity Using Omnidirectional Antennas." Sensors 10, no. 1: 400-427.