Abstract
In opportunistic networks, the requirement of QoS (quality of service) poses several major challenges to wireless mobile devices with limited cache and energy. This implies that energy and cache space are two significant cornerstones for the structure of a routing algorithm. However, most routing algorithms tackle the issue of limited network resources from the perspective of a deterministic approach, which lacks an adaptive data transmission mechanism. Meanwhile, these methods show a relatively low scalability because they are probably built up based on some special scenarios rather than general ones. To alleviate the problems, this paper proposes an adaptive delay-tolerant routing algorithm (DTCM) utilizing curve-trapezoid Mamdani fuzzy inference system (CMFI) for opportunistic social networks. DTCM evaluates both the remaining energy level and the remaining cache level of relay nodes (two-factor) in opportunistic networks and makes reasonable decisions on data transmission through CMFI. Different from the traditional fuzzy inference system, CMFI determines three levels of membership functions through the trichotomy law and evaluates the fuzzy mapping from two-factor fuzzy input to data transmission by curve-trapezoid membership functions. Our experimental results show that within the error interval of 0.05~0.1, DTCM improves delivery ratio by about 20% and decreases end-to-end delay by approximate 25% as compared with Epidemic, and the network overhead from DTCM is in the middle horizon.
Highlights
Opportunistic social networks [1] are a special combination of opportunistic networks and wireless multi-hop networks, in which clients carrying wireless mobile devices communicate with their peers in a communication area [1]
The network overhead from DTCM is in the middle horizon
We introduce the rough process of each component and the implement details of curve-trapezoid Mamdani fuzzy inference system (CMFI) will be described in Appendix A
Summary
Opportunistic social networks [1] are a special combination of opportunistic networks and wireless multi-hop networks, in which clients carrying wireless mobile devices communicate with their peers in a communication area [1]. As the energy of the mobile nodes is going to be exhausted, they must delay the data transmission [18] To this end, this implies that energy and cache are two significant cornerstones for the establishment of an opportunistic network routing algorithm [19]. The relationship between the two-factor (energy and cache) and data transmission is a special type of fuzzy uncertain expression, while should be defined as the degree of membership in fuzzy inference system from the theoretical perspective. The fuzzy inference system is able to define a complex relationship as the membership degree or fuzzy subset, and evaluates whether the two-factor is closely related to data transmission through the method of defuzzification To this end, this paper proposes an adaptive two-factor routing algorithm using curve-trapezoid.
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