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Journal articles on the topic 'Opportunistic mobile social networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

Yan, Yeqing, Zhigang Chen, Jia Wu, and Leilei Wang. "An Effective Data Transmission Algorithm Based on Social Relationships in Opportunistic Mobile Social Networks." Algorithms 11, no. 8 (2018): 125. http://dx.doi.org/10.3390/a11080125.

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With the popularization of mobile communication equipment, human activities have an increasing impact on the structure of networks, and so the social characteristics of opportunistic networks become increasingly obvious. Opportunistic networks are increasingly used in social situations. However, existing routing algorithms are not suitable for opportunistic social networks, because traditional opportunistic network routing does not consider participation in human activities, which usually causes a high ratio of transmission delay and routing overhead. Therefore, this research proposes an effec

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2

Yang, Yibo, Honglin Zhao, Jinlong Ma, and Xiaowei Han. "Social-aware data dissemination in opportunistic mobile social networks." International Journal of Modern Physics C 28, no. 09 (2017): 1750115. http://dx.doi.org/10.1142/s0129183117501157.

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Opportunistic Mobile Social Networks (OMSNs), formed by mobile users with social relationships and characteristics, enhance spontaneous communication among users that opportunistically encounter each other. Such networks can be exploited to improve the performance of data forwarding. Discovering optimal relay nodes is one of the important issues for efficient data propagation in OMSNs. Although traditional centrality definitions to identify the nodes features in network, they cannot identify effectively the influential nodes for data dissemination in OMSNs. Existing protocols take advantage of

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3

Zhang, Junbao, Haojun Huang, Geyong Min, Wang Miao, and Dapeng Wu. "Social-Aware Routing in Mobile Opportunistic Networks." IEEE Wireless Communications 28, no. 2 (2021): 152–58. http://dx.doi.org/10.1109/mwc.001.2000189.

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4

Vidhya Lakshmi, Vimitha R., and Gireesh Kumar T. "Opportunistic mobile social networks: architecture, privacy, security issues and future directions." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (2019): 1145. http://dx.doi.org/10.11591/ijece.v9i2.pp1145-1152.

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Mobile Social Networks and its related applications have made a very great impact in the society. Many new technologies related to mobile social networking are booming rapidly now-a-days and yet to boom. One such upcoming technology is Opportunistic Mobile Social Networking. This technology allows mobile users to communicate and exchange data with each other without the use of Internet. This paper is about Opportunistic Mobile Social Networks, its architecture, issues and some future research directions. The architecture and issues of Opportunistic Mobile Social Networks are compared with that

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5

Wang, Ranyin, Xiaoming Wang, Fei Hao, et al. "Social identity-aware opportunistic routing in mobile social networks." Transactions on Emerging Telecommunications Technologies 29, no. 5 (2018): e3297. http://dx.doi.org/10.1002/ett.3297.

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6

Zhang, Sheng, Houzhong Liu, Caisen Chen, Zhaojun Shi, and William Wei Song. "Activity-based routing algorithm in opportunistic mobile social networks." International Journal of Distributed Sensor Networks 17, no. 9 (2021): 155014772110412. http://dx.doi.org/10.1177/15501477211041272.

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In opportunistic mobile social networks, nodes are clustered according to their interests or hobbies and take part in different activities regularly. We delve into the temporal and spatial mobility characteristics of network nodes and put forward an activity-based message opportunistic forwarding algorithm. The main idea of the algorithm is that we choose different message forwarding methods according to the situation of nodes participating in activities. If the source node and the destination node are both attend in the same activities, we select the best relay node which has the biggest deli

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7

Xiao, Mingjun, Jie Wu, and Liusheng Huang. "Community-Aware Opportunistic Routing in Mobile Social Networks." IEEE Transactions on Computers 63, no. 7 (2014): 1682–95. http://dx.doi.org/10.1109/tc.2013.55.

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8

GAO, Zhenxiang, Yan SHI, Shanzhi CHEN, and Qihan LI. "Exploiting Social Relationship for Opportunistic Routing in Mobile Social Networks." IEICE Transactions on Communications E98.B, no. 10 (2015): 2040–48. http://dx.doi.org/10.1587/transcom.e98.b.2040.

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9

Socievole, Annalisa, Antonio Caputo, Floriano De Rango, and Peppino Fazio. "Routing in Mobile Opportunistic Social Networks with Selfish Nodes." Wireless Communications and Mobile Computing 2019 (February 3, 2019): 1–15. http://dx.doi.org/10.1155/2019/6359806.

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When the connection to Internet is not available during networking activities, an opportunistic approach exploits the encounters between mobile human-carried devices for exchanging information. When users encounter each other, their handheld devices can communicate in a cooperative way, using the encounter opportunities for forwarding their messages, in a wireless manner. But, analyzing real behaviors, most of the nodes exhibit selfish behaviors, mostly to preserve the limited resources (data buffers and residual energy). That is the reason why node selfishness should be taken into account whe

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10

Wang, Eric Ke, Yueping Li, Yunming Ye, S. M. Yiu, and Lucas C. K. Hui. "A Dynamic Trust Framework for Opportunistic Mobile Social Networks." IEEE Transactions on Network and Service Management 15, no. 1 (2018): 319–29. http://dx.doi.org/10.1109/tnsm.2017.2776350.

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11

Wu, Yahui, Su Deng, and Hongbin Huang. "Information Propagation through Opportunistic Communication in Mobile Social Networks." Mobile Networks and Applications 17, no. 6 (2012): 773–81. http://dx.doi.org/10.1007/s11036-012-0401-3.

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12

Wu, Dapeng, Feng Zhang, Honggang Wang, and Ruyan Wang. "Security-oriented opportunistic data forwarding in Mobile Social Networks." Future Generation Computer Systems 87 (October 2018): 803–15. http://dx.doi.org/10.1016/j.future.2017.07.028.

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13

Lenando, Halikul, Mohamad Alrfaay, and Haithem Ben Chikha. "Multiple Social Metrics Based Routing Protocol in Opportunistic Mobile Social Networks." Advances in Science, Technology and Engineering Systems Journal 4, no. 2 (2019): 176–82. http://dx.doi.org/10.25046/aj040223.

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14

Zhao, Ruonan, Xiaoming Wang, Lichen Zhang, and Yaguang Lin. "A social-aware probabilistic routing approach for mobile opportunistic social networks." Transactions on Emerging Telecommunications Technologies 28, no. 12 (2017): e3230. http://dx.doi.org/10.1002/ett.3230.

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15

Liu, Kanghuai, Zhigang Chen, Jia Wu, and Leilei Wang. "FCNS: A Fuzzy Routing-Forwarding Algorithm Exploiting Comprehensive Node Similarity in Opportunistic Social Networks." Symmetry 10, no. 8 (2018): 338. http://dx.doi.org/10.3390/sym10080338.

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At the dawn of big data and 5G networks, end-to-end communication with large amounts of data between mobile devices is difficult to be implemented through the traditional face-to-face transmission mechanism in social networks. Consequently, opportunistic social networks proposed that message applications should choose proper relay nodes to perform effective data transmission processes. At present, several routing algorithms, based on node similarity, attempt to use the contextual information related to nodes and the special relationships between them to select a suitable relay node among neigh

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16

Guo, Mei, and Min Xiao. "MSSN: An Attribute-Aware Transmission Algorithm Exploiting Node Similarity for Opportunistic Social Networks." Information 10, no. 10 (2019): 299. http://dx.doi.org/10.3390/info10100299.

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Recently, with the development of big data and 5G networks, the number of intelligent mobile devices has increased dramatically, therefore the data that needs to be transmitted and processed in the networks has grown exponentially. It is difficult for the end-to-end communication mechanism proposed by traditional routing algorithms to implement the massive data transmission between mobile devices. Consequently, opportunistic social networks propose that the effective data transmission process could be implemented by selecting appropriate relay nodes. At present, most existing routing algorithm

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17

Xu, Qichao, Zhou Su, Kuan Zhang, Pinyi Ren, and Xuemin Sherman Shen. "Epidemic Information Dissemination in Mobile Social Networks With Opportunistic Links." IEEE Transactions on Emerging Topics in Computing 3, no. 3 (2015): 399–409. http://dx.doi.org/10.1109/tetc.2015.2414792.

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18

Cai, Ying, Haochen Zhang, Yanfang Fan, and Hongke Xia. "A survey on routing algorithms for opportunistic mobile social networks." China Communications 18, no. 2 (2021): 86–109. http://dx.doi.org/10.23919/jcc.2021.02.007.

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19

Xiao, Yutong, and Jia Wu. "Data Transmission and Management Based on Node Communication in Opportunistic Social Networks." Symmetry 12, no. 8 (2020): 1288. http://dx.doi.org/10.3390/sym12081288.

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Due to the rapid popularization of various short distance communication mobile devices, the use scenarios of opportunistic networks are increasing day by day. However, in opportunistic networks, because of the complexity of community structure, many methods lack of symmetry between application and theoretical research. Thus, the connection strength between nodes is different, and the degree of message diffusion is different. If the above factors cannot be accurately estimated and analyzed, and effective data forwarding and scheduling strategies cannot be formulated, the delivery ratio will be

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20

Lenando, Halikul, and Mohamad Alrfaay. "EpSoc: Social-Based Epidemic-Based Routing Protocol in Opportunistic Mobile Social Network." Mobile Information Systems 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/6462826.

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In opportunistic networks, the nature of intermittent and disruptive connections degrades the efficiency of routing. Epidemic routing protocol is used as a benchmark for most of routing protocols in opportunistic mobile social networks (OMSNs) due to its high message delivery and latency. However, Epidemic incurs high cost in terms of overhead and hop count. In this paper, we propose a hybrid routing protocol called EpSoc which utilizes the Epidemic routing forwarding strategy and exploits an important social feature, that is, degree centrality. Two techniques are used in EpSoc. Messages’ TTL

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21

Liu, Li. "Friend-Based Prediction Routing Protocol in Socially-Aware Opportunistic Networks." Applied Mechanics and Materials 519-520 (February 2014): 241–44. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.241.

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Mobile devices are popular used in peoples life. Generally, most of portable mobile devices are carried by people. Thus, the mobility of mobile devices is influenced heavily by peoples social relationship. Socially-aware Opportunistic Networks are used in intermittently connected networks by use of store-carry-and-forward fashion. It is mainly based on social relationship to design solutions for problem such as routing protocol or data dissemination. In this paper, we exploit social relationship about friendships information among people and use them to predict the contact opportunities. We pr

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22

Chen, Xiao, Charles Shang, Britney Wong, Wenzhong Li, and Suho Oh. "Efficient Multicast Algorithms in Opportunistic Mobile Social Networks using Community and Social Features." Computer Networks 111 (December 2016): 71–81. http://dx.doi.org/10.1016/j.comnet.2016.07.007.

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23

Anh Duong, Dat Van, Dae-Young Kim, and Seokhoon Yoon. "TSIRP: A Temporal Social Interactions-Based Routing Protocol in Opportunistic Mobile Social Networks." IEEE Access 9 (2021): 72712–29. http://dx.doi.org/10.1109/access.2021.3079443.

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24

Arnaboldi, Valerio, Marco Conti, and Franca Delmastro. "CAMEO: A novel context-aware middleware for opportunistic mobile social networks." Pervasive and Mobile Computing 11 (April 2014): 148–67. http://dx.doi.org/10.1016/j.pmcj.2013.09.010.

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25

Chilipirea, Cristian, Andreea Cristina Petre, and Ciprian Dobre. "Social-based routing algorithm for energy preservation in mobile opportunistic networks." International Journal of Embedded Systems 6, no. 1 (2014): 14. http://dx.doi.org/10.1504/ijes.2014.060922.

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26

M, Syed Rabiya, and Ramalakshmi Ramar. "Multiattribute-based routing for lifetime maximization in opportunistic mobile social networks." International Journal of Communication Systems 33, no. 10 (2020): e4312. http://dx.doi.org/10.1002/dac.4312.

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27

Vastardis, Nikolaos, Kun Yang, and Supeng Leng. "Socially-Aware Multi-phase Opportunistic Routing for Distributed Mobile Social Networks." Wireless Personal Communications 79, no. 2 (2014): 1343–68. http://dx.doi.org/10.1007/s11277-014-1933-6.

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28

Lu, Yu, Liu Chang, Jingwen Luo, and Jia Wu. "Routing Algorithm Based on User Adaptive Data Transmission Scheme in Opportunistic Social Networks." Electronics 10, no. 10 (2021): 1138. http://dx.doi.org/10.3390/electronics10101138.

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With the rapid popularization of 5G communication and internet of things technologies, the amount of information has increased significantly in opportunistic social networks, and the types of messages have become more and more complex. More and more mobile devices join the network as nodes, making the network scale increase sharply, and the tremendous amount of datatransmission brings a more significant burden to the network. Traditional opportunistic social network routing algorithms lack effective message copy management and relay node selection methods, which will cause problems such as hig

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29

Zhou, Huan, Linping Tong, Shouzhi Xu, Chungming Huang, and Jialu Fan. "Predicting temporal centrality in Opportunistic Mobile Social Networks based on social behavior of people." Personal and Ubiquitous Computing 20, no. 6 (2016): 885–97. http://dx.doi.org/10.1007/s00779-016-0958-0.

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30

Zeng, Feng, Nan Zhao, and Wenjia Li. "Effective Social Relationship Measurement and Cluster Based Routing in Mobile Opportunistic Networks." Sensors 17, no. 5 (2017): 1109. http://dx.doi.org/10.3390/s17051109.

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31

Mordacchini, Matteo, Andrea Passarella, and Marco Conti. "A social cognitive heuristic for adaptive data dissemination in mobile Opportunistic Networks." Pervasive and Mobile Computing 42 (December 2017): 371–92. http://dx.doi.org/10.1016/j.pmcj.2017.06.006.

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32

Zhou, Huan, Victor C. M. Leung, Chunsheng Zhu, Shouzhi Xu, and Jialu Fan. "Predicting Temporal Social Contact Patterns for Data Forwarding in Opportunistic Mobile Networks." IEEE Transactions on Vehicular Technology 66, no. 11 (2017): 10372–83. http://dx.doi.org/10.1109/tvt.2017.2740218.

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33

Moradi, Ashkan, and Vahid Shah‐Mansouri. "Opportunistic content dissemination in mobile social networks via adjustment of user selfishness." IET Networks 8, no. 2 (2019): 126–37. http://dx.doi.org/10.1049/iet-net.2018.5013.

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34

Deng, Xia, Le Chang, Jun Tao, and Jianping Pan. "Reducing the Overhead of Multicast Using Social Features in Mobile Opportunistic Networks." IEEE Access 7 (2019): 50095–108. http://dx.doi.org/10.1109/access.2019.2910238.

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35

Li, Fan, Lunan Zhao, Chao Zhang, Zhenmin Gao, and Yu Wang. "Routing with multi-level cross-community social groups in mobile opportunistic networks." Personal and Ubiquitous Computing 18, no. 2 (2013): 385–96. http://dx.doi.org/10.1007/s00779-013-0657-z.

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36

Zhu, Konglin, Wenzhong Li, Xiaoming Fu, and Lin Zhang. "Data routing strategies in opportunistic mobile social networks: Taxonomy and open challenges." Computer Networks 93 (December 2015): 183–98. http://dx.doi.org/10.1016/j.comnet.2015.10.018.

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37

Tang, Zhipeng, Anfeng Liu, and Changqin Huang. "Social-Aware Data Collection Scheme Through Opportunistic Communication in Vehicular Mobile Networks." IEEE Access 4 (2016): 6480–502. http://dx.doi.org/10.1109/access.2016.2611863.

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38

Wang, Tong, Yongzhe Zhou, Yunfeng Wang, and Mengbo Tang. "Novel Opportunistic Network Routing Based on Social Rank for Device-to-Device Communication." Journal of Computer Networks and Communications 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/2717403.

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In recent years, there has been dramatic proliferation of research concerned with fifth-generation (5G) mobile communication networks, among which device-to-device (D2D) communication is one of the key technologies. Due to the intermittent connection of nodes, the D2D network topology may be disconnected frequently, which will lead to failure in transmission of large data files. In opportunistic networks, in case of encountering nodes which never meet before a flood message blindly to cause tremendous network overhead, a novel opportunistic network routing protocol based on social rank and int

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39

Zhu, Yian, Lin Zhang, Haobin Shi, Kao-Shing Hwang, Xianchen Shi, and Shuyan Luo. "An Adaptive Routing-Forwarding Control Scheme Based on an Intelligent Fuzzy Decision-Making System for Opportunistic Social Networks." Symmetry 11, no. 9 (2019): 1095. http://dx.doi.org/10.3390/sym11091095.

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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 rou

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40

Ciobanu, Radu Ioan, Ciprian Dobre, Valentin Cristea, Florin Pop, and Fatos Xhafa. "SPRINT-SELF: Social-Based Routing and Selfish Node Detection in Opportunistic Networks." Mobile Information Systems 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/596204.

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Since mobile devices nowadays have become ubiquitous, several types of networks formed over such devices have been proposed. One such approach is represented by opportunistic networking, which is based on a store-carry-and-forward paradigm, where nodes store data and carry it until they reach a suitable node for forwarding. The problem in such networks is how to decide what the next hop will be, since nodes do not have a global view of the network. We propose using the social network information of a node when performing routing, since a node is more likely to encounter members of its own soci

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41

Dong, Yihan, Liu Chang, Jingwen Luo, and Jia Wu. "A Routing Query Algorithm Based on Time-Varying Relationship Group in Opportunistic Social Networks." Electronics 10, no. 13 (2021): 1595. http://dx.doi.org/10.3390/electronics10131595.

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With the fast development of IoT and 5G technologies, opportunity social networks composed of portable mobile devices have become a hot research topic in recent years. However, arbitrary node movement in opportunity networks and the absence of end-to-end pathways make node communication unstable. At the same time, the problem of ignoring human social preferences and relying on wrong message relay nodes lead to a low data transmission rate and high network overhead. Based on the above issues, we propose a time-varying relationship groups-based routing query algorithm for mobile opportunity netw

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42

Niu, Jianwei, Mingzhu Liu, and Han-Chieh Chao. "A P2P Query Algorithm for Opportunistic Networks Utilizing betweenness Centrality Forwarding." Mobile Information Systems 9, no. 4 (2013): 331–45. http://dx.doi.org/10.1155/2013/365497.

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With the proliferation of high-end mobile devices that feature wireless interfaces, many promising applications are enabled in opportunistic networks. In contrary to traditional networks, opportunistic networks utilize the mobility of nodes to relay messages in a store-carry-forward paradigm. Thus, the relay process in opportunistic networks faces several practical challenges in terms of delay and delivery rate. In this paper, we propose a novel P2P Query algorithm, namely Betweenness Centrality Forwarding (PQBCF), for opportunistic networking. PQBCF adopts a forwarding metric called Betweenne

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43

Chen, Shupei, Zhigang Chen, Jia Wu, and Kanghuai Liu. "An Adaptive Delay-Tolerant Routing Algorithm for Data Transmission in Opportunistic Social Networks." Electronics 9, no. 11 (2020): 1915. http://dx.doi.org/10.3390/electronics9111915.

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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 gene

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Jedari, Behrouz, Feng Xia, Honglong Chen, Sajal K. Das, Amr Tolba, and Zafer AL-Makhadmeh. "A social-based watchdog system to detect selfish nodes in opportunistic mobile networks." Future Generation Computer Systems 92 (March 2019): 777–88. http://dx.doi.org/10.1016/j.future.2017.10.049.

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45

Yu, Genghua, Zhigang Chen, Jia Wu, and Jian Wu. "A Transmission Prediction Neighbor Mechanism Based on a Mixed Probability Model in an Opportunistic Complex Social Network." Symmetry 10, no. 11 (2018): 600. http://dx.doi.org/10.3390/sym10110600.

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The amount of data has skyrocketed in Fifth-generation (5G) networks. How to select an appropriate node to transmit information is important when we analyze complex data in 5G communication. We could sophisticate decision-making methods for more convenient data transmission, and opportunistic complex social networks play an increasingly important role. Users can adopt it for information sharing and data transmission. However, the encountering of nodes in mobile opportunistic network is random. The latest probabilistic routing method may not consider the social and cooperative nature of nodes,

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46

Wang, Xiaofei, Zhengguo Sheng, Shusen Yang, and Victor C. M. Leung. "Tag-assisted social-aware opportunistic device-to-device sharing for traffic offloading in mobile social networks." IEEE Wireless Communications 23, no. 4 (2016): 60–67. http://dx.doi.org/10.1109/mwc.2016.7553027.

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47

Socievole, A., F. De Rango, and A. Caputo. "Opportunistic mobile social networks: From mobility and Facebook friendships to structural analysis of user social behavior." Computer Communications 87 (August 2016): 1–18. http://dx.doi.org/10.1016/j.comcom.2016.04.025.

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48

Wang, Zhu, Xingshe Zhou, Daqing Zhang, Zhiwen Yu, and Daqiang Zhang. "SOCKER: Enhancing Face-to-Face Social Interaction Based on Community Creation in Opportunistic Mobile Social Networks." Wireless Personal Communications 78, no. 1 (2014): 755–83. http://dx.doi.org/10.1007/s11277-014-1782-3.

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Luo, Jingwen, Jia Wu, and Yuzhou Wu. "Advanced Data Delivery Strategy Based on Multiperceived Community with IoT in Social Complex Networks." Complexity 2020 (February 11, 2020): 1–15. http://dx.doi.org/10.1155/2020/3576542.

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With the advent of 5G communication standards, the number of 5G base stations increases steadily, and the number of mobile terminals and IoT (Internet of Things) devices increases sharply, which sharps a large number of IoT devices and forms a complex network. These devices can take as nodes of a community in the opportunistic social network. However, in the environment of traditional opportunistic network algorithm and mass data transmission, information transmission is only carried out at several source nodes in the community, which usually leads to transmission delay, excessive energy consu

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50

Chen, Kang, and Haiying Shen. "FaceChange: Attaining Neighbor Node Anonymity in Mobile Opportunistic Social Networks With Fine-Grained Control." IEEE/ACM Transactions on Networking 25, no. 2 (2017): 1176–89. http://dx.doi.org/10.1109/tnet.2016.2623521.

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