Opportunistic networks exploit human mobility and consequent device-to-device contacts to opportunistically create data paths over time. Identifying influential nodes as relay is a crucial problem for efficient routing in opportunistic networks. The degree centrality method is very simple but of little relevance. Although closeness centrality and betweenness centrality can effectively identify influential nodes, they are incapable to be applied in large-scale networks due to the high computational complexity. In this paper, we focus on designing an effective centrality ranking metric with low computational complexity in opportunistic networks. We propose the semi locally evaluated centrality metric to identify influential nodes for message forwarding in opportunistic networks. We also present a simple message forwarding algorithm, and employ real world mobility traces and synthetic mobility traces to evaluate the benefits of the proposed semi locally evaluated centrality metric. Results demonstrate the efficiency of the proposed metric in opportunistic networks.
The peripheral malfunction will cause the nodes fail to work when there is no central control node in the large scale sensor network. The network cant guarantee the continuity of the communication and service and the communication efficiency will be low in this situation. In order to fix the issue, the paper proposes a correcting fusion algorithm for the sensor network data communication. When the collision of the communication data happens in the sensor network due to the failure of the nodes, the correcting fusion algorithm can ensure the effective data fusion between the fault nodes and other nodes to guarantee the communication to the maximum extent. The experiment indicates the algorithm can increase the efficiency of the sensor network to achieve the satisfactory results.
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