Intelligent distributed routing scheme based on social similarity for mobile social networks

Xu, Fang; Xu, Qiong; Xiong, Zenggang; Xiao, Nan; Xie, Yong; Deng, Min; Hao, Huibing

doi:10.1016/j.future.2019.02.033

Cited by 13 publications

(7 citation statements)

References 15 publications

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“…So the parameters of their relative random variables would be different. In this case, the indices of formulas (7) and (10) just need to change and come up as P n i¼1…”

Section: The Mcsf Criterion Definitionmentioning

confidence: 99%

“…The MSN exploits human social behavior and characteristics, such as similarities, daily routes, mobility patterns, and interests in carrying out the message routing and data dissemination. [8][9][10] Hence, some efforts such as Xu et al 10 try to predict the regularity of mobile nodes movements in terms of time and space dimensions in order to design routing algorithms. But the assumption of regularity applies only in certain circumstances such as those employed special nodes called ferry which move in a scheduled trace.…”

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confidence: 99%

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MSN‐CDF: New community detection framework to improve routing in mobile social networks

Moradi

Mohasefi

Mahdipour

2021

Int J Communication

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In mobile social networks (MSN), portable devices of mobile users communicate with each other. The contact and intercontact times are the two most important parameters that specify the behavior of MSN. Many social features such as similarity, centrality, closeness, and distance are derived from contact and intercontact time patterns. Those parameters and features can be used in the community detection process in community-based routing as a vector weight of network graph edges. But there is no solution for proper exploitation of them as a multicriteria optimization of community detection algorithms so that the communities become more adaptive with the MSN quiddity and the computational complexity of the algorithms to be stay constant. In this paper, we propose the MSN-adaptive community detection framework, MSN-CDF, which meets these demands. We improve the quality function of community detection methods, for example, the Intensity in clique percolation methods and the Modularity in modularity-based methods, through our new modelbased joint optimization of their quality functions. To this end, to express the MSN contact and social features, we develop a criterion called MCSF. It holds the contact time and intercontact time distributions parameters and implies their social features for MSN purposes. This criterion has as little traffic as possible and enjoys from the probabilistic joint distribution model. The simulations show that MSN-CDF reduces the network overhead significantly compared with the DiBUBB, Epidemic, and Dynamic Routing. Moreover, it outperforms DiBUBB and Dynamic Routing in terms of delivery rate and delivery latency.K E Y W O R D S community-based routing, community detection, delay-tolerant networks, mobile social networks 1 | INTRODUCTION Delay tolerant networks (DTN) communicate in disconnected environments where traditional protocols, such as TCP, would fail to work. It has been used in many fields including pocket switched networks (PSN), mobile social networks (MSN), delay-tolerant sensor networks (DTSN), mobile delay-tolerant ad hoc networks (MDTANET), interplanetary networks (IPN), underwater networks (UWN), and vehicular delay-tolerant ad hoc networks (VDTANET). Nowadays,

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“…So the parameters of their relative random variables would be different. In this case, the indices of formulas (7) and (10) just need to change and come up as P n i¼1…”

Section: The Mcsf Criterion Definitionmentioning

confidence: 99%

mentioning

confidence: 99%

MSN‐CDF: New community detection framework to improve routing in mobile social networks

Moradi

Mohasefi

Mahdipour

2021

Int J Communication

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“…And they proved that the proposed algorithm can achieve the highest data transmission rate with the highest routing efficiency in the social environment. Xu et al [43] proposed an intelligent distributed routing algorithm based on social similarity. is algorithm can use the social environment information in the network to predict the mobile attributes of network nodes through the BP neural network.…”

Section: Related Workmentioning

confidence: 99%

Effective Data Transmission and Control Based on Social Communication in Social Opportunistic Complex Networks

Yang

Luo

2020

Complexity

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In opportunistic complex networks, information transmission between nodes is inevitable through broadcast. The purpose of broadcasting is to distribute data from source nodes to all nodes in the network. In opportunistic complex networks, it is mainly used for routing discovery and releasing important notifications. However, when a large number of nodes in the opportunistic complex networks are transmitting information at the same time, signal interference will inevitably occur. Therefore, we propose a low-latency broadcast algorithm for opportunistic complex networks based on successive interference cancellation techniques to improve propagation delay. With this kind of algorithm, when the social network is broadcasting, this algorithm analyzes whether the conditions for successive interference cancellation are satisfied between the broadcast links on the assigned transmission time slice. If the conditions are met, they are scheduled at the same time slice, and interference avoidance scheduling is performed when conditions are not met. Through comparison experiments with other classic algorithms of opportunistic complex networks, this method has outstanding performance in reducing energy consumption and improving information transmission efficiency.

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“…The forwarding metric is combined with two utility functions to derive the social strength among users and their importance, and it is used to determine the best relay node. Moreover, [22] used BP neural network to predict the encounter regularity of mobile nodes in terms of time and space dimensions. Simulation analysis and experimental results show that the proposed routing algorithm can effectively improve the message delivery ratio and reduce the network overhead.…”

Section: Related Workmentioning

confidence: 99%

Routing Scheme Based on Community Correlation in Socially Aware Networking

Deng

et al. 2020

IEEE Access

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Intelligent distributed routing scheme based on social similarity for mobile social networks

Cited by 13 publications

References 15 publications

MSN‐CDF: New community detection framework to improve routing in mobile social networks

MSN‐CDF: New community detection framework to improve routing in mobile social networks

Effective Data Transmission and Control Based on Social Communication in Social Opportunistic Complex Networks

Routing Scheme Based on Community Correlation in Socially Aware Networking

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