Effective Social Relationship Measurement and Cluster Based Routing in Mobile Opportunistic Networks

Abstract

In mobile opportunistic networks, the social relationship among nodes has an important impact on data transmission efficiency. Motivated by the strong share ability of “circles of friends” in communication networks such as Facebook, Twitter, Wechat and so on, we take a real-life example to show that social relationships among nodes consist of explicit and implicit parts. The explicit part comes from direct contact among nodes, and the implicit part can be measured through the “circles of friends”. We present the definitions of explicit and implicit social relationships between two nodes, adaptive weights of explicit and implicit parts are given according to the contact feature of nodes, and the distributed mechanism is designed to construct the “circles of friends” of nodes, which is used for the calculation of the implicit part of social relationship between nodes. Based on effective measurement of social relationships, we propose a social-based clustering and routing scheme, in which each node selects the nodes with close social relationships to form a local cluster, and the self-control method is used to keep all cluster members always having close relationships with each other. A cluster-based message forwarding mechanism is designed for opportunistic routing, in which each node only forwards the copy of the message to nodes with the destination node as a member of the local cluster. Simulation results show that the proposed social-based clustering and routing outperforms the other classic routing algorithms.

Adaptive State Observer Design for Dynamic Links in Complex Dynamical Networks

“…In fact, due to the limitation of measurement methods, the state values of links in CDNs are more difficult to be measured accurately in practical situation, compared to the states of nodes. Hence, only a few papers have studied and discussed the effective measurement problem of the links between individuals (nodes), and the measurement method mainly depends on the physical interaction between individuals [ 21 ] or the adaptive weights of links [ 22 ]. Similar to the state values of nodes, not all state values of links' weights can be measured and obtained.…”

An Incentive Mechanism Based on Bertrand Game for Opportunistic Edge Computing

“…In some scenarios, the Prophet Routing can have high delivery ratio and low overhead. There are other routing algorithms [14]- [16] which make the tradeoff between delivery success and network overhead, and have good performance in some specific scenarios. However, those traditional routing algorithms have a premise hypothesis which is all intermediate nodes in opportunistic networks should be willing to forward messages for other nodes.…”

FSF: Applying Machine Learning Techniques to Data Forwarding in Socially Selfish Opportunistic Networks

“…In OppNets, at every contact opportunity, the sender tries to send its messages to some relay nodes having a good probability of meeting their destinations at some other places [3]. However, since the path from a source to a destination is intermittently connected, the conventional routing algorithms are not applicable [4]. Consequently, new routing algorithms are required to overcome the long delays and frequent disconnection problems [5].…”