Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
Range-free localization schemes for large scale sensor networks
Proceedings of the 9th annual international conference on Mobile computing and networking
Distributed localization in wireless sensor networks: a quantitative comparison
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Wireless sensor networks
Computer Networking: A Top-Down Approach Featuring the Internet
Computer Networking: A Top-Down Approach Featuring the Internet
Localization for mobile sensor networks
Proceedings of the 10th annual international conference on Mobile computing and networking
Analysis of hop-distance relationship in spatially random sensor networks
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing
Distributed weighted-multidimensional scaling for node localization in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Rendered path: range-free localization in anisotropic sensor networks with holes
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Bounds on hop distance in greedy routing approach in wireless ad hoc networks
International Journal of Wireless and Mobile Computing
On the hop count statistics for randomly deployed wireless sensor networks
International Journal of Sensor Networks
Sensor network localization, euclidean distance matrix completions, and graph realization
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
Geographic Random Forwarding (GeRaF) for Ad Hoc and Sensor Networks: Multihop Performance
IEEE Transactions on Mobile Computing
Proceedings of the 3rd International Conference on Performance Evaluation Methodologies and Tools
Distributed sensor localization in random environments using minimal number of anchor nodes
IEEE Transactions on Signal Processing
Organizing a global coordinate system from local information on an ad hoc sensor network
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Loopy belief propagation as a basis for communication in sensor networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
A Survey on TOA Based Wireless Localization and NLOS Mitigation Techniques
IEEE Communications Surveys & Tutorials
The generalized distributive law
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Hierarchical Cooperation Achieves Optimal Capacity Scaling in Ad Hoc Networks
IEEE Transactions on Information Theory
Nonparametric belief propagation for self-localization of sensor networks
IEEE Journal on Selected Areas in Communications
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Wireless sensor networks can often be viewed in terms of a uniform deployment of a large number of nodes in a region of Euclidean space. Following deployment, the nodes self-organize into a mesh topology with a key aspect being self-localization. Having obtained a mesh topology in a dense, homogeneous deployment, a frequently used approximation is to take the hop distance between nodes to be proportional to the Euclidean distance between them. In this work, we analyze this approximation through two complementary analyses. We assume that the mesh topology is a random geometric graph on the nodes; and that some nodes are designated as anchors with known locations. First, we obtain high probability bounds on the Euclidean distances of all nodes that are h hops away from a fixed anchor node. In the second analysis, we provide a heuristic argument that leads to a direct approximation for the density function of the Euclidean distance between two nodes that are separated by a hop distance h. This approximation is shown, through simulation, to very closely match the true density function. Localization algorithms that draw upon the preceding analyses are then proposed and shown to perform better than some of the well-known algorithms present in the literature. Belief-propagation-based message-passing is then used to further enhance the performance of the proposed localization algorithms. To our knowledge, this is the first usage of message-passing for hop-count-based self-localization.