Distributed localization in wireless sensor networks: a quantitative comparison
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Wireless sensor networks
Robust wireless localization to attacks on access points
SARNOFF'09 Proceedings of the 32nd international conference on Sarnoff symposium
An Adaptive Indoor Location Detection Method Using Hybrid of Radio Signal and Image Sensors
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
Toward attack-resistant localization under infrastructure attacks
Security and Communication Networks
Decentralized indoor wireless localization using compressed sensing of signal-strength fingerprints
Proceedings of the 7th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks
Guoguo: enabling fine-grained indoor localization via smartphone
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
Beacon selection for localisation in IEEE 802.11 wireless infrastructure
International Journal of Ad Hoc and Ubiquitous Computing
Beacon selection for localisation in IEEE 802.11 wireless infrastructure
International Journal of Ad Hoc and Ubiquitous Computing
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Location estimation is a critical step for many location-aware applications. To obtain location information, localization methods employing Received Signal Strength (RSS) are attestative since it can reuse the existing wireless infrastructure for localization. Among the large class of localization schemes, RSS-based lateration methods have the advantage of providing closed-form solutions for mathematical analysis as compared to heuristic-based localization approaches. However, the localization accuracy of RSS-based lateration methods are significantly affected by the unpredictable setup in indoor environments. To improve the applicability of RSS-based lateration methods in indoors, we propose two approaches, regression-based and correlation-based. The regression-based approach uses linear regression to discover a better fit of signal propagation model between RSS and the distance, while the correlation-based approach utilizes the correlation among RSS in local area to obtain more accurate signal propagation. Our results using both simulation as well as real experiments demonstrate that our improved methods outperform the original RSS-based lateration methods significantly.