We study a forwarding strategy in opportunistic networks which are one of the most challenging networks among mobile ad-hoc networks. In opportunistic networks, a node does not have knowledge about the entire network topology, which is essential in the mobile ad-hoc network’s forwarding strategy. Thus, node behavior is exploited to calculate future contact opportunities for forwarding a message. Utilizing social network analysis (e.g., similarity and centrality) has been proposed to improve the accuracy of the calculation task. This paper proposes a forwarding strategy based on an analysis of network topology. In the proposed strategy, each node takes a sequence of snapshots of its first-order neighbors during a warm-up period. Each node exchanges its own snapshots with each other, and then aggregates the snapshots in order to extract the network topology information. The extracted network topology is analyzed by social network analysis methods: compactness and algebraic connectivity. Forwarding decisions are made using the analysis of the features (compactness and algebraic connectivity). We present simulations using NS-2 and the home-cell community-based mobility model to show that the proposed forwarding strategy results in delay performances similar to the epidemic forwarding scheme, while maintaining reasonable network traffic. In addition, we demonstrate that the proposed strategy outperforms the SimBet and PRoPHET forwarding schemes with various communication ranges and memory space.
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Networks and Communications
- Electrical and Electronic Engineering