Abstract
Routing selection in opportunistic social networks is a complex and challenging issue due to intermittent communication connections among mobile devices and dynamic network topologies. The structural characteristics of opportunistic social networks indicate that the social attributes of mobile nodes play a significant role on data dissemination. To this end, in this paper, we propose an adaptive routing-forwarding control scheme (FPRDM) based on an intelligent fuzzy decision-making system. On the foundation of the conception of fuzzy inference logic, two techniques are used in the proposed routing algorithm. Information fusion of social characteristics of message users and node identification are implemented based on the fuzzy recognition strategy, and the fuzzy decision-making mechanism is applied to control message replication and optimize data transmission. Simulation results demonstrate that, in the best case, the proposed scheme presents an average delivery ratio of 0.8, reduces the average end-to-end delay by nearly 45% as compared with the Epidemic routing protocol, and lowers the network overhead by about 75% as compared to the Spray and Wait routing algorithm.
Highlights
Opportunistic social networks (OSNs) [1,2], a type of complex intermittently connected network architecture with a foundation in opportunistic communication and node profiles, have emerged from both a type of delay tolerant network (DTN) [3] and social network service (SNS) [4], and it has been considered a promising network model to improve data transmission reliability.the structure in OSNs is characterized by several features such as node mobility, network topology, and social attributes [5]
(2) Because each attribute feature of mobile users plays a different role in the process of message routing and forwarding, how to dynamically allocate a reasonable weight value to them is another key issue to be tackled [28]. To address these open questions, we propose and develop an adaptive routing-forwarding control scheme based on fuzzy pattern recognition and decision-making system (FPRDM) for opportunistic social networks, which mainly consists of four phases: fuzzy recognition for node classification, weight adjustment for social features, fuzzy relationship inference, and fuzzy decision-making process
For investigating the problem of data transmission in opportunistic mobile social networks, we propose and develop an adaptive routing-forwarding control scheme based on fuzzy recognition and decision-making model (FPRDM), which contains three interlocking steps: fuzzy recognition for node classification, weight adjustment for social features, and the inference process for the fuzzy relationship between node profile and data transmission
Summary
Opportunistic social networks (OSNs) [1,2], a type of complex intermittently connected network architecture with a foundation in opportunistic communication and node profiles, have emerged from both a type of delay tolerant network (DTN) [3] and social network service (SNS) [4], and it has been considered a promising network model to improve data transmission reliability. How to utilize the effective social features of humans to implement an efficient data dissemination process between source nodes and destinations, is one of the most long-standing and elusive challenges in OSNs. With the aim of yielding low overhead, low network delay, and high delivery ratio, multitudinous routing approaches employ various strategies to create a better data dissemination environment in the OSNs. As one of the dominant algorithms, flooding-based routing protocols [18] transmit message replications to every node that the carriers encounter, which causes the overspread of data packets and a huge waste of cache spaces. As a supplement to routing algorithms in multi-layer social networks, this FPRDM mechanism constructs an effective communication link between nodes in different online communities or the same online community by transforming the social attribute characteristics of nodes into a strong basis for data dissemination
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