A Novel Mathematical Framework for Similarity-based Opportunistic Social Networks

ElSherief, Mai; Alipour, Babak; Qathrady, Mimonah Al; ElBatt, Tamer; Zahran, Ahmed H.

doi:10.1016/j.pmcj.2017.08.004

Cited by 3 publications

(3 citation statements)

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“…In f o_gain(D_set, attr i ) Intr_value(attr i ) (17) where In f o_gr(D_set, attr i ) is the information gain ratio, In f o_gain(D_set, attr i ) represents the information gain, and Intr_value(attr i ) denotes the internal value.…”

Section: In F O_gr(d_set Attr I ) =mentioning

confidence: 99%

“…At present, various routing and forwarding algorithms have been proposed to deal with data dissemination problems in different scenarios. However, most of these algorithms only consider a single factor, either the mobile similarity [17] between nodes or the social similarity [18] of nodes. For algorithms that only consider the mobile similarity of nodes, they typically use the number of real-time common neighbor nodes of the nodes and destination nodes to evaluate the mobile similarity between them.…”

Section: Introductionmentioning

confidence: 99%

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MSSN: An Attribute-Aware Transmission Algorithm Exploiting Node Similarity for Opportunistic Social Networks

Guo

Xiao

2019

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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 algorithms find suitable next-hop nodes by comparing the similarity degree between nodes. However, when evaluating the similarity between two mobile nodes, these routing algorithms either consider the mobility similarity between nodes, or only consider the social similarity between nodes. To improve the data dissemination environment, this paper proposes an effective data transmission strategy (MSSN) utilizing mobile and social similarities in opportunistic social networks. In our proposed strategy, we first calculate the mobile similarity between neighbor nodes and destination, set a mobile similarity threshold, and compute the social similarity between the nodes whose mobile similarity is greater than the threshold. The nodes with high mobile similarity degree to the destination node are the reliable relay nodes. After simulation experiments and comparison with other existing opportunistic social networks algorithms, the results show that the delivery ratio in the proposed algorithm is 0.80 on average, the average end-to-end delay is 23.1% lower than the FCNS algorithm (A fuzzy routing-forwarding algorithm exploiting comprehensive node similarity in opportunistic social networks), and the overhead on average is 14.9% lower than the Effective Information Transmission Based on Socialization Nodes (EIMST) algorithm.

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Section: In F O_gr(d_set Attr I ) =mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MSSN: An Attribute-Aware Transmission Algorithm Exploiting Node Similarity for Opportunistic Social Networks

Guo

Xiao

2019

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“…Pairwise mobile encounter events provide opportunities for dissemination events such as content dissemination [9,22] and infection spreading through direct encounter [23].…”

Section: Mobility Encountersmentioning

confidence: 99%

Learning the Relation Between Mobile Encounters and Web Traffic Patterns

Alipour

Qathrady

2018

Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

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Mobility and network traffic have been traditionally studied separately. Their interaction is vital for generations of future mobile services and effective caching, but has not been studied in depth with real-world big data. In this paper, we characterize mobility encounters and study the correlation between encounters and web traffic profiles using large-scale datasets (30TB in size) of WiFi and NetFlow traces. The analysis quantifies these correlations for the first time, across spatio-temporal dimensions, for device types grouped into on-the-go Flutes and sit-to-use Cellos. The results consistently show a clear relation between mobility encounters and traffic across different buildings over multiple days, with encountered pairs showing higher traffic similarity than nonencountered pairs, and long encounters being associated with the highest similarity. We also investigate the feasibility of learning encounters through web traffic profiles, with implications for dissemination protocols, and contact tracing. This provides a compelling case to integrate both mobility and web traffic dimensions in future models, not only at an individual level, but also at pairwise and collective levels. We have released samples of code and data used in this study on GitHub, to support reproducibility and encourage further research (https://github.com/BabakAp/encounter-traffic).

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Mobility management—A personal perspective

Das

2018

Computer Communications

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A Novel Mathematical Framework for Similarity-based Opportunistic Social Networks

Cited by 3 publications

References 20 publications

MSSN: An Attribute-Aware Transmission Algorithm Exploiting Node Similarity for Opportunistic Social Networks

MSSN: An Attribute-Aware Transmission Algorithm Exploiting Node Similarity for Opportunistic Social Networks

Learning the Relation Between Mobile Encounters and Web Traffic Patterns

Mobility management—A personal perspective

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