Anycast Routing in Mobile Opportunistic Networks

A mobile opportunistic network consists of sparsely scattered mobile nodes communicating via short range radios. It is characterized by frequent and unpredictable network partitions and intermittent connectivity. Anycast in opportunistic networks is anticipated in many application scenarios, and deserves great attention. In this paper, we propose an anycast routing algorithm in which each node is associated with a forwarding metric indicating its delivery probability to the destination anycast group and the node with lower value hands over the message to the encountered node with higher metric. The forwarding metric is determined according to historical node encounter information. We use three different forwarding metrics (variables) to guide the transmission of messages. The Group Forwarding Metric (GFM) treats the entire group as a whole, and it is defined as the probability of meeting any member in the anycast group to deliver a message. Similar to GFM, Probability Forwarding Metric (PFM) is defined as the probability of encountering at least one anycast group member, but it relies on the probability of meeting individual group members. The Distance Forwarding Metric (DFM) takes a function of the delivery probability to an anycast group member as the distance to the member. The DFM is the combination of these distances to forward messages towards the higher member density. Different metrics can be adopted for different mobile opportunistic networks based on the connectivity characteristics of the networks. We analyze the control overhead of the anycast algorithm and the message delivery delay of the routing protocol. Extensive simulations are carried out to evaluate the performance of the proposed solution under synthetic and realistic traces. The results show that our algorithm will significantly improve the anycast delivery performance when compared with simple routing algorithms in term of average message delivery delay and transmission overhead.1This work is supported by the State Key Program of NSFC (Grant No. 60933011),Important National Science & Technology Specific Projects of China (Grant No. 2009ZX03006-001-01) and National 863 High-tech R&D Program of China (Grant No. 2009AA11Z209) This work is supported by the State Key Program of NSFC (Grant No. 60933011),Important National Science & Technology Specific Projects of China (Grant No. 2009ZX03006-001-01) and National 863 High-tech R&D Program of China (Grant No. 2009AA11Z209)