An Efficient Approach to Updating Closeness Centrality and Average Path Length in Dynamic Networks
Opportunistic Mobile Sensor Networks (OMSN) can be applied in many scenarios where the sensor data usually need to be transmitted from the source node to one of the multiple sink nodes. We propose the DGCCF (Data Gathering based on Closeness Centrality Forwarding) algorithm, which adopts "store-carry-forward" paradigm to transmit data from sensors to the sink nodes. In DGCCF, each sensor node keeps the Closeness Centrality (CC) to all sink nodes based on encounter history between the node and all the sink nodes. When two nodes encounter, the node with the lower CC value forwards its messages to the other nodes, until the messages are delivered to one of the sink nodes. Experimental results reveal that DGCCF can provide better performance on both the delivery ration and delivery latency than ZebraNet and the Random Forwarding algorithm.