Algorithm for Multicast Opportunistic Routing in Wireless Mesh Networks
Title | Algorithm for Multicast Opportunistic Routing in Wireless Mesh Networks |
Publication Type | Conference Paper |
Year of Publication | 2017 |
Authors | Das, Debasis, Kumar, Amritesh |
Conference Name | Proceedings of the 6th International Conference on Software and Computer Applications |
Publisher | ACM |
Conference Location | New York, NY, USA |
ISBN Number | 978-1-4503-4857-7 |
Keywords | betweenness centrality, community detection, composability, dynamic networks, incremental algorithm, Metrics, pubcrawl, resilience, Resiliency, streaming graphs, wireless mesh networks |
Abstract | Multi-hop Wireless Mesh Networks (WMNs) is a promising new technique for communication with routing protocol designs being critical to the effective and efficient of these WMNs. A common approach for routing traffic in these networks is to select a minimal distance from source to destination as in wire-line networks. Opportunistic Routing(OR) makes use of the broadcasting ability of wireless network and is especially very helpful for WMN because all nodes are static. Our proposed scheme of Multicast Opportunistic Routing(MOR) in WMNs is based on the broadcast transmissions and Learning Au-tomata (LA) to expand the potential candidate nodes that can aid in the process of retransmission of the data. The receivers are required to be in sync with one another in order to avoid duplicated broadcasting of data which is generally achieved by formulating the forwarding candidates according to some LA based metric. The most adorable aspect of this protocol is that it intelligently "learns" from the past experience and improves its performance. The results obtained via this approach of MOR, shows that the proposed scheme outperforms with some existing sachems and is an improved and more effective version of opportunistic routing in mesh network. |
URL | https://dl.acm.org/citation.cfm?doid=3056662.3056688 |
DOI | 10.1145/3056662.3056688 |
Citation Key | das_algorithm_2017 |