The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
GPSR: greedy perimeter stateless routing for wireless networks
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 in sensor networks
Wireless sensor networks
Localization for mobile sensor networks
Proceedings of the 10th annual international conference on Mobile computing and networking
Data collection, storage, and retrieval with an underwater sensor network
Proceedings of the 3rd international conference on Embedded networked sensor systems
Deploying a Wireless Sensor Network on an Active Volcano
IEEE Internet Computing
Localization in wireless sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Wireless sensor network localization techniques
Computer Networks: The International Journal of Computer and Telecommunications Networking
Monte Carlo localization for mobile wireless sensor networks
Ad Hoc Networks
A review of smart homes-Present state and future challenges
Computer Methods and Programs in Biomedicine
Fast Geometric Routing with Concurrent Face Traversal
OPODIS '08 Proceedings of the 12th International Conference on Principles of Distributed Systems
A Context and Content-Based Routing Protocol for Mobile Sensor Networks
EWSN '09 Proceedings of the 6th European Conference on Wireless Sensor Networks
Intelligent Products: A survey
Computers in Industry
Context-aware cluster-based hierarchical protocol for Wireless Sensor Networks
International Journal of Ad Hoc and Ubiquitous Computing
Accurate and Energy-Efficient Range-Free Localization for Mobile Sensor Networks
IEEE Transactions on Mobile Computing
Mobile Networks and Applications
Personal and Ubiquitous Computing
Constraint-Based distance estimation in ad-hoc wireless sensor networks
EWSN'06 Proceedings of the Third European conference on Wireless Sensor Networks
Range-Based localization in mobile sensor networks
EWSN'06 Proceedings of the Third European conference on Wireless Sensor Networks
Human motion recognition using a wireless sensor-based wearable system
Personal and Ubiquitous Computing
Segmenting sensor data for activity monitoring in smart environments
Personal and Ubiquitous Computing
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Wireless sensor networks (WSN) have great potential in ubiquitous computing. However, the severe resource constraints of WSN rule out the use of many existing networking protocols and require careful design of systems that prioritizes energy conservation over performance optimization. A key infrastructural problem in WSN is localization--the problem of determining the geographical locations of nodes. WSN typically have some nodes called seeds that know their locations using global positioning systems or other means. Non-seed nodes compute their locations by exchanging messages with nodes within their radio range. Several algorithms have been proposed for localization in different scenarios. Algorithms have been designed for networks in which each node has ranging capabilities, i.e., can estimate distances to its neighbours. Other algorithms have been proposed for networks in which no node has such capabilities. Some algorithms only work when nodes are static. Some other algorithms are designed specifically for networks in which all nodes are mobile. We propose a very general, fully distributed localization algorithm called range-based Monte Carlo boxed (RMCB) for WSN. RMCB allows nodes to be static or mobile and that can work with nodes that can perform ranging as well as with nodes that lack ranging capabilities. RMCB uses a small fraction of seeds. It makes use of the received signal strength measurements that are available from the sensor hardware. We use RMCB to investigate the question: "When does range-based localization work better than range-free localization?" We demonstrate using empirical signal strength data from sensor hardware (Texas Instruments EZ430-RF2500) and simulations that RMCB outperforms a very good range-free algorithm called weighted Monte Carlo localization (WMCL) in terms of localization error in a number of scenarios and has a similar computational complexity to WMCL. We also implement WMCL and RMCB on sensor hardware and demonstrate that it outperforms WMCL. The performance of RMCB depends critically on the quality of range estimation. We describe the limitations of our range estimation approach and provide guidelines on when range-based localization is preferable.