Distributed Sensor Localization in Random Environments Using Minimal Number of Anchor Nodes

By Usman A. Khan, Soummya Kar, José M. F. Moura. IEEE Transactions on Signal Processing, 2008

352 citations recorded by the source corpus.

The paper introduces DILOC, a distributed, iterative algorithm to locate M sensors (with unknown locations) in Rm, m ges 1, with respect to a minimal number of m + 1 anchors with known locations. The sensors and anchors, nodes in the network, exchange data with their neighbors only; no centralized data processing or communication occurs, nor is there a centralized fusion center to compute the sensors' locations. DILOC uses the barycentric coordinates of a node with respect to its neighbors; these coordinates are computed using the Cayley-Menger determinants, i.e., the determinants of matrices of internode distances. We show convergence of DILOC by associating with it an absorbing Markov chain whose absorbing states are the states of the anchors. We introduce a stochastic approximation version extending DILOC to random environments, i.e., when the communications among nodes is noisy, the communication links among neighbors may fail at random times, and the internodes distances are subject to errors. We show a.s. convergence of the modified DILOC and characterize the error between the true values of the sensors' locations and their final estimates given by DILOC. Numerical studies illustrate DILOC under a variety of deterministic and random operating conditions.

Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/1e3b1548bae743de43d35375898a8812eda2135c

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Citations in the corpus

Gossip Algorithms for Distributed Signal Processing (2010, methodology): “This can also be accomplished using a gossip-style algorithm, as described in [113].”
A Theoretical Foundation of Network Localization and Navigation (2018, neutral): “With the aid of a few nodes that have perfectly known positions (called anchors), internode measurements to these anchors can be used to estimate the agent position through multilateration or triangulation [73]–[75].”