Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Dynamic fine-grained localization in Ad-Hoc networks of sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Localization from mere connectivity
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
Experimenting an Indoor Bluetooth-Based Positioning Service
ICDCSW '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Autonomous Localization Method in Wireless Sensor Networks
PERCOMW '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications Workshops
The effects of ranging noise on multihop localization: an empirical study
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Relative location estimation in wireless sensor networks
IEEE Transactions on Signal Processing
Real-time trajectory estimation in mobile ad hoc networks
Proceedings of the 12th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
Indoor tracking for mission critical scenarios: A survey
Pervasive and Mobile Computing
Encounter based sensor tracking
Proceedings of the thirteenth ACM international symposium on Mobile Ad Hoc Networking and Computing
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Accurate location of people in indoor environments is a key aspect of many applications such as resource management or security. In this paper, we explore the use of short-range radio technologies to track people indoors. The network consists of two kind of radio nodes: static nodes (anchors) and mobile nodes (people). From a set of sparse connectivity matrices (people vs. people and people vs. anchors) at each time instant and people's dynamics, we infer people's trajectories. To combine connectivity and dynamic information, we propose an extension of Multidimensional Scaling(MDS), Dynamic Weighted MDS (DWMDS), that finds an embedding of people's trajectories (x and y coordinates of people through time). DWMDS has proven to be more accurate and effective, especially for low connectivity degree networks (i.e. sparse networks), compared to existing location algorithms. Extensive simulations show the effectiveness and robustness of the proposed algorithm.