Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Tolerating failures of continuous-valued sensors
ACM Transactions on Computer Systems (TOCS)
Self-stabilization
Stability of long-lived consensus
Journal of Computer and System Sciences
Distributed localization in wireless sensor networks: a quantitative comparison
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Wireless sensor networks
Convex Optimization
Distributed event-region detection in wireless sensor networks
EURASIP Journal on Advances in Signal Processing
Stability of Multivalued Continuous Consensus
SIAM Journal on Computing
OCD: obsessive consensus disorder (or repetitive consensus)
Proceedings of the twenty-seventh ACM symposium on Principles of distributed computing
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
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Many challenging tasks in sensor networks, including sensor calibration, ranking of nodes, monitoring, event region detection, collaborative filtering, collaborative signal processing, etc. , can be formulated as a problem of solving a linear system of equations. Several recent works propose different distributed algorithms for solving these problems, usually by using linear iterative numerical methods. In this work, we extend the settings of the above approaches, by adding another dimension to the problem. Specifically, we are interested in self-stabilizing algorithms, that continuously run and converge to a solution from any initial state. This aspect of the problem is highly important due to the dynamic nature of the network and the frequent changes in the measured environment. In this paper, we link together algorithms from two different domains. On the one hand, we use the rich linear algebra literature of linear iterative methods for solving systems of linear equations, which are naturally distributed with rapid convergence properties. On the other hand, we are interested in self-stabilizing algorithms, where the input to the computation is constantly changing, and we would like the algorithms to converge from any initial state. We propose a simple novel method called SS-Iterative as a self-stabilizing variant of the linear iterative methods. We prove that under mild conditions the self-stabilizing algorithm converges to a desired result. We further extend these results to handle the asynchronous case. As a case study, we discuss the sensor calibration problem and provide simulation results to support the applicability of our approach.