An adaptive routing algorithm considering position and social similarities in an opportunistic network

An opportunistic network (OPPNET) is a wireless networks without an infrastructure. In OPPNET, communication intermittently occurs when one node meets with another node. Thus, a connected path between the source and destination nodes rarely exists. For this reason, nodes need not only to forward messages but are also to store and carry messages as relay nodes. In OPPNET, several routing algorithms that rely on relay nodes with appropriate behavior have been proposed. Some of these are referred to as context-ignorant routing algorithms, which manipulate flooding, and others are referred to as context-aware routing algorithms, which utilize the contextual information. We propose a routing algorithm that employs a novel similarity based on both position and social information. We combine the position similarity with the social similarity using the fuzzy inference method to obtain the enhanced performance. Through this method, the proposed algorithm utilizes more proper relay nodes in forwarding adaptively and achieves significant improvement on the performance especially under memory constrained environment. We analyze the proposed algorithm on the NS-2 network simulator with the home-cell community-based mobility model. Experimental results show that the proposed algorithm outperforms typical routing algorithms in terms of the network traffic and delivery delay.