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Neighbour-disjoint multipath for low-power and lossy networks

Neighbour-disjoint multipath for low-power and lossy networks

Hossain, A. K. M. Mahtab, Sreenan, Cormac J. and Alberola, Rodolfo De Paz (2016) Neighbour-disjoint multipath for low-power and lossy networks. ACM Transactions on Sensor Networks, 12 (3):23. pp. 1-25. ISSN 1550-4859 (Print), 1550-4867 (Online) (doi:

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In this article, we describe a neighbour disjoint multipath (NDM) scheme that is shown to be more resilient amidst node or link failures compared to the two well-known node disjoint and edge disjoint multipath techniques. A centralised NDM was first conceptualised in our initial published work utilising the spatial diversity among multiple paths to ensure robustness against localised poor channel quality or node failures. Here, we further introduce a distributed version of our NDM algorithm adapting to the low-power and lossy network (LLN) characteristics. We implement our distributed NDM algorithm in Contiki OS on top of LOADng—a lightweight On-demand Ad hoc Distance Vector Routing protocol. We compare this implementation's performance with a standard IPv6 Routing Protocol for Low power and Lossy Networks (RPL), and also with basic LOADng, running in the Cooja simulator. Standard performance metrics such as packet delivery ratio, end-to-end latency, overhead and average routing table size are identified for the comparison. The results and observations are provided considering a few different application traffic patterns, which serve to quantify the improvements in robustness arising from NDM. The results are confirmed by experiments using a public sensor network testbed with over 100 nodes.

Item Type: Article
Uncontrolled Keywords: LOADng; Neighbour disjoint multipath (NDM); RPL; Edge-disjoint multipath; Node-disjoint multipath; Wireless sensor networks
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > Internet of Things and Security (ISEC)
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
Last Modified: 26 Nov 2020 22:34
Selected for GREAT 2016: None
Selected for GREAT 2017: GREAT a
Selected for GREAT 2018: None
Selected for GREAT 2019: GREAT 2
Selected for REF2021: REF 2

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