A Statistical Modeling Approach to Location Estimation
IEEE Transactions on Mobile Computing
A high-accuracy, low-cost localization system for wireless sensor networks
Proceedings of the 3rd international conference on Embedded networked sensor systems
Bayesian localization in wireless networks using angle of arrival
Proceedings of the 3rd international conference on Embedded networked sensor systems
Cryptographic key exchange based on locationing information
Pervasive and Mobile Computing
Wired and wireless sensor networks for industrial applications
Microelectronics Journal
An ellipse-centroid localization algorithm in wireless sensor networks
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
sTrack: tracking in indoor symbolic space with RFID sensors
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Robot Navigation in a Decentralized Landmark-Free Sensor Network
Journal of Intelligent and Robotic Systems
Context awareness in network selection for dynamic environments
PWC'06 Proceedings of the 11th IFIP TC6 international conference on Personal Wireless Communications
International Journal of Grid and High Performance Computing
Obstacle detection and estimation in wireless sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Taxonomy of Fundamental Concepts of Localization in Cyber-Physical and Sensor Networks
Wireless Personal Communications: An International Journal
| Hi-index | 0.00 |
We characterize the fundamental limits of localization using signal strength in indoor environments. Signal strength approaches are attractive because they are widely applicable to wireless sensor networks and do not require additional localization hardware. We show that although a broad spectrum of algorithms can trade accuracy for precision, none has a significant advantage in localization performance. We found that using commodity 802.11 technology over a range of algorithms, approaches and environments, one can expect a median localization error of 10ft and 97th percentile of 30ft. We present strong evidence that these limitations are fundamental and that they are unlikely to be transcended without fundamentally more complex environmental models or additional localization infrastructure.