Biblio

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.

Centrality measures have perpetually been helpful to find the foremost central or most powerful node within the network. There are numerous strategies to compute centrality of a node however in social networks betweenness centrality is the most widely used approach to bifurcate communities within the network, to find out the susceptibility within the complex networks and to generate the scale free networks whose degree distribution follows the power law. In this paper, we've computed betweenness centrality by identifying communities lying within the network. Our algorithm efficiently updates the centrality of the nodes whenever any edge or vertex addition or deletion takes place within the dynamic network by modifying solely a subset of vertices. For the vertex addition, Incremental Algorithm has been used in which Streaming graphs has also been considered. Brandes approach is the most widely used approach for finding out the betweenness centrality however it's still expensive for growing networks since it takes O(mn+n2logn) amount of time and O(n+m) space however our approach efficiently updates the centrality of the nodes by taking O(textbarStextbarn+textbarStextbarnlogn) amount of time where textbarStextbar is the subset of the vertices,m is the number of edges, n is the number of vertices and textbarStextbar≤n holds true.