Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Localization from mere connectivity
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
Range-free localization schemes for large scale sensor networks
Proceedings of the 9th annual international conference on Mobile computing and networking
Analyzing Connectivity-Based Multi-Hop Ad-hoc Positioning
PERCOM '04 Proceedings of the Second IEEE International Conference on Pervasive Computing and Communications (PerCom'04)
A simple 3-sweep LBFS algorithm for the recognition of unit interval graphs
Discrete Applied Mathematics
Health monitoring of civil infrastructures using wireless sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Perpendicular Intersection: Locating Wireless Sensors with Mobile Beacon
RTSS '08 Proceedings of the 2008 Real-Time Systems Symposium
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
Achieving range-free localization beyond connectivity
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
Radio Tomographic Imaging with Wireless Networks
IEEE Transactions on Mobile Computing
Proceedings of the ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Spatial Ordering Derivation for One-Dimensional Wireless Sensor Networks
ISPA '11 Proceedings of the 2011 IEEE Ninth International Symposium on Parallel and Distributed Processing with Applications
Component-based localization in sparse wireless networks
IEEE/ACM Transactions on Networking (TON)
Refining hop-count for localisation in wireless sensor networks
International Journal of Sensor Networks
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Sensors can be deployed along a road or a bridge to form a wireless sensor network with a linear topology where nodes have a spatial ordering. This spatial characteristic indicates relative locations for nodes and facilitates such functions as object tracking and monitoring in the network. We attempt to derive this ordering with RSSI (Received Signal Strength Indicator) as the only input, which does not require attaching extra hardware to the sensor nodes. This requires a method for translating RSSI into spatial constraints. There are two candidate methods in the literature. The first method uses connectivity information among the nodes to calculate their relative locations. But analysis of real-world trace data indicates that it does not work well in reality. The second method assumes that closer nodes receive higher RSSI. However, we have proved that the relative localization problem is actually NP-hard under such an assumption. Fortunately, the problem turns out to be efficiently solvable if we adopt a new observation slightly different from the above-mentioned closer-higher RSSI assumption. This observation that the closest node always receives the highest RSSI is verified by the analytical results of the same real-world trace data. We then propose a spatial ordering method based on the observation, and evaluate it through various field experiments. Results show that the proposed method achieves an accuracy of over 99%.