2013 IEEE 7th International Conference on Self-Adaptive and Self-Organizing Systems (2013)
Philadelphia, PA, USA USA
Sept. 9, 2013 to Sept. 13, 2013
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SASO.2013.18
Most applications involving large-scale wireless networks need to know the connectivity of the network topology. Conventional approaches largely ignore the temporal aspects of node-to-node connectivity, and perform an offline analysis. In this paper, we characterize the temporal connectivity in a mobile wireless network, in a decentralized manner. We present Path Detect, a distributed algorithm that combines local broadcast with distributed consensus to achieve a spatial-temporal view of network connectivity. Additionally, the information gathered bypath Detect allows for the distributed computation of temporal efficiency, a metric that has until now only been computed centrally. Path Detect is adaptive, and can therefore track connectivity changes in real-time. We evaluate Path Detect under diverse test-cases featuring node and wireless link failures, and mobility patterns. Through these evaluations, we show that the comparison of Path Detect against the ground truth observation shows less than 10% relative error in estimation of temporal efficiency for most cases. Additionally, we also present our results of evaluating Path Detect on a real-wold network, showing that it is an attractive choice for real-world implementations.
Distributed consensus, Temporal connectivity, Distributed estimation
Venkatraman Iyer, Qingzhi Liu, Stefan Dulman, Koen Langendoen, "Adaptive Online Estimation of Temporal Connectivity in Dynamic Wireless Networks", 2013 IEEE 7th International Conference on Self-Adaptive and Self-Organizing Systems, vol. 00, no. , pp. 237-246, 2013, doi:10.1109/SASO.2013.18