An Adaptive RoutingForwarding Control Scheme Based on an Intelligent Fuzzy DecisionMaking System for Opportunistic Social Networks
Abstract
:1. Introduction
 A fuzzy inference model is proposed to implement the fusion of multiple social information of mobile users, thereby providing a reliable and stable strategy for opportunistic message routing and forwarding.
 To synthetically evaluate the impact of each social characteristic on the data transmission process in OSNs, we combine the fuzzy inference logic with the analytic hierarchy process, and more importantly, with exploring the data transmission relationships among mobile users.
 On the basis of a feedback mechanism, we are able to build a relatively stable and sustained data transmission connectivity between the source nodes and destinations in opportunistic mobile social network environments.
 Ultimately, simulation results demonstrate that this scheme reduces the network delay and the overhead ratio, and enhances the delivery ratio as compared to several other typical or latest routing protocols in the OSNs.
2. Related Works
2.1. The Proposed ProfileAware Routing Algorithms for Opportunistic Mobile Social Networks
2.2. The Proposed ProfileIgnorant Routing Algorithms for Opportunistic Mobile Social Networks
3. System Model Design
3.1. The Overall Structure of Intelligent Fuzzy DecisionMaking System for Opportunistic Mobile Social Networks
3.2. Fuzzy Pattern Recognition Process for Node Classification in Opportunistic Mobile Social Networks
3.2.1. Information Quantification and Determining Membership Degrees for Fuzzy Input
3.2.2. Fuzzy Pattern Recognition for Node Classification in Opportunistic Mobile Social Networks
3.3. Reasonable Weight Allocation and Inference of Fuzzy Relationships Via the Analytic Hierarchy Process
3.4. Information Fusion and Fuzzy DecisionMaking for Message RoutingForwarding in Opportunistic Mobile Social Networks
3.5. Algorithm Complexity Analysis
Algorithm 1 Fuzzy pattern cognition and decision model 
Input: social attributes ${X}_{1},{X}_{2}\cdots {X}_{n}$ of users $nod{e}_{i}$ Output: $T{V}_{nod{e}_{1}},T{V}_{nod{e}_{2}}\cdots T{V}_{nod{e}_{n}}$

4. Simulation And Analysis
4.1. Setting of Experimental Parameters
4.2. Experimental Measurement Metrics
4.3. Analysis of Experimental Results
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
OSNs  opportunistic social networks 
DTNs  delay tolerant networks 
SNS  social network service 
AHP  analytic hierarchy process 
EpSoc  a floodingbased socialbased routing protocol 
Tanh  tanhyperbolic function 
Markov  markov Andrey chain 
CoA  center of area 
MoM  mean of maximum 
ONE  opportunistic networking environment 
FPRDM  an adaptive control scheme based on intelligent fuzzy decisionmaking system 
FCNS  fuzzy routingforwarding algorithm exploiting comprehensive node similarity 
ICMT  information cache management and data transmission algorithm 
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W  $\left\mathit{ei}\right({\mathit{X}}_{1}\left)\right$  $\left\mathit{ei}\right({\mathit{X}}_{2}\left)\right$  ⋯  $\left\mathit{ei}\right({\mathit{X}}_{\mathit{n}}\left)\right$ 

$\leftei\right({X}_{1}\left)\right$  1  $R{a}_{(1,2)}$  ⋯  $R{a}_{(1,n)}$ 
$\leftei\right({X}_{2}\left)\right$  $1/R{a}_{(1,2)}$  1  ⋯  $R{a}_{(2,n)}$ 
⋮  ⋮  ⋮  ⋱  ⋮ 
$\leftei\right({X}_{n}\left)\right$  $1/R{a}_{(1,n)}$  $1/R{a}_{(2,n)}$  ⋯  1 
Dataset  Infocom5  Infocom6  Cambridge  Intel 

Device  iMote  iMote  iMote  iMote 
Duration (days)  3.5  4  11  3.5 
Number of experimental devices  41  98  50  8 
Number of internal contacts iMote  2245  1706  1087  1364 
Simulation Environment  Description 

Simulator  Opportunistic Network Environment (ONE) 
Communication area  3000 × 3000 
Total simulation time (h)  10–20 
Number of nodes N  100 (initial value), 200, 400, 600 
Cache space of a node C (Mb)  10 (initial value), 15, 20, 25, 30, 35, 40 
Speed of a node (m/s)  125 
Initial energy for a node (J)  200 
Number of social features  8 
Three location parameters ${a}_{1}$, ${a}_{2}$, ${a}_{3}$  ${a}_{1}=0.75$, ${a}_{2}=0.5$, ${a}_{3}=0.25$ 
Three scale parameters ${\sigma}_{1}^{2}$, ${\sigma}_{2}^{2}$, ${\sigma}_{3}^{2}$  ${\sigma}_{1}^{2}={\sigma}_{2}^{2}={\sigma}_{3}^{2}=0.96$ 
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Zhu, Y.; Zhang, L.; Shi, H.; Hwang, K.S.; Shi, X.; Luo, S. An Adaptive RoutingForwarding Control Scheme Based on an Intelligent Fuzzy DecisionMaking System for Opportunistic Social Networks. Symmetry 2019, 11, 1095. https://doi.org/10.3390/sym11091095
Zhu Y, Zhang L, Shi H, Hwang KS, Shi X, Luo S. An Adaptive RoutingForwarding Control Scheme Based on an Intelligent Fuzzy DecisionMaking System for Opportunistic Social Networks. Symmetry. 2019; 11(9):1095. https://doi.org/10.3390/sym11091095
Chicago/Turabian StyleZhu, Yian, Lin Zhang, Haobin Shi, KaoShing Hwang, Xianchen Shi, and Shuyan Luo. 2019. "An Adaptive RoutingForwarding Control Scheme Based on an Intelligent Fuzzy DecisionMaking System for Opportunistic Social Networks" Symmetry 11, no. 9: 1095. https://doi.org/10.3390/sym11091095