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Component-based Self-Healing Algorithm with Dynamic Range Allocation for Fault-Tolerance in WSN

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Published:24 November 2017Publication History

ABSTRACT

In this paper we present a new fault-tolerant approach that establishes alternate path to the root node in a hierarchical aggregation tree in the event of intermediary node/link failures in WSNs. The Component-based Self-healing approach introduced in the paper exploits the inherent redundancy of WSN, to reconnect the nodes affected by a faulty parent back to the root component without using any redundant or backup parent nodes. The component approach is borrowed from graph theory to establish a connected path to the root. Based on this approach, two algorithms are developed (i) Self-healing Component-based Reconfiguration (SCR) and SCR-Dynamic Transmission Range Adjustment (SCR-DTRA). The SCR algorithm performs admirably well compared to two existing approaches in literature. However, depending on the location of nodes deployed, there could still be few estranged nodes that fail to get connected to root component. The SCR-DTRA algorithm counters this by dynamically adjusting the transmission range of estranged node so as to ensure its connectivity to the root component, without compromising the precedence relations. The results of simulations show that SCR-DTRA succeeds in connecting all affected nodes to root component, unlike most other approaches. The paper presents the algorithms, results of simulation and validation studies carried out on both algorithms.

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          • Published in

            cover image ACM Other conferences
            ICCCT-2017: Proceedings of the 7th International Conference on Computer and Communication Technology
            November 2017
            157 pages
            ISBN:9781450353243
            DOI:10.1145/3154979

            Copyright © 2017 ACM

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            Publication History

            • Published: 24 November 2017

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            Acceptance Rates

            ICCCT-2017 Paper Acceptance Rate33of124submissions,27%Overall Acceptance Rate33of124submissions,27%

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