CWBound: boundary node detection algorithm for complex non-convex mobile ad hoc networks

Abstract

Efficient message forwarding in mobile ad hoc network in disaster scenarios is challenging because location information on the boundary and interior nodes is often unavailable. Information related to boundary nodes can be used to design efficient routing protocols as well as to prolong the battery power of devices along the boundary of an ad hoc network. In this article, we developed an algorithm, CWBound, which discovers boundary nodes in a complex non-convex mobile ad hoc (CNCAH) networks. Experiments show that the CWBound algorithm is at least three times faster than other state-of-the-art algorithms, and up to 400 times faster than classical force-directed algorithms. The experiments also confirmed that the CWBound algorithm achieved the highest accuracy (above 97% for 3 out of the 4 types of CNCAH networks) and sensitivity (90%) among the algorithms evaluated.

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    http://www.cis.umac.mo/~fstasp/profile_eric.html.

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Acknowledgements

This research was funded by the Research Committee of University of Macau, Grants MYRG2016-00148-FST and MYRG2017-00029-FST.

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Correspondence to Yain-Whar Si.

Appendix: Chinese Whispers algorithm

Appendix: Chinese Whispers algorithm

We used the Chinese Whispers algorithm for clustering which is proposed by [37]. The algorithm partitions the nodes of a graph into clusters. The Chinese Whispers algorithm is able to good clustering results on large networks within an acceptable number of iterations [38]. The time complexity of the Chinese Whispers algorithm is \(O(n \times E)\), where n is the number of nodes and E is the number of edges in the network. This algorithm uses the following steps to partition the nodes into clusters. On initialisation, every node is labelled with a random, unique, class (cluster). For example, node-1 is labelled as class \(\#1\), node-2 is labelled as class \(\#2\) and so on. In each iteration, a randomly selected node is assigned to a class in its local neighbourhood. The class selected to be assigned is the one with the highest total edge weight of all classes in the neighbourhood of the node. Step 2 is repeated until the class assigned to the nodes remains the same for a specified number (f) of iterations (i.e. a local minimum is reached). However, the algorithm may not always be able to converge to a local minimum. In that case, a stopping criterion is used to terminate the iterations. The pseudocode for the Chinese Whispers algorithm is given in Algorithm 4.

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Cheong, SH., Si, YW. CWBound: boundary node detection algorithm for complex non-convex mobile ad hoc networks. J Supercomput 74, 5558–5577 (2018). https://doi.org/10.1007/s11227-018-2494-3

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Keywords

  • Mobile ad hoc network
  • Force-directed algorithm
  • Boundary node detection
  • Complex non-convex ad hoc network