Social phenomena and fog computing networks: A novel perspective for future networks

Abstract

Fog computing is an emerging technology that aims at reducing the load on cloud data centers by migrating some computation and storage toward end-users. It leverages the intermediate servers for local processing and storage while making it possible to offload part of the computation and storage to the cloud. Inspired by the benefits of fog computing, we present a novel paradigm that considers the context of social phenomena. Online and off-line human interactions and the mobile social network's relentless growth allowed real-world data and created users' traces. We categorize social phenomena into two main groups to integrate with fog computing from social interactions' continuous development. In this regard, the first contribution addresses the social relationship between the end-users and fog nodes based on personal benefits. The social relationship considers trust, reciprocity, incentives, and selfishness mechanisms. The second contribution describes the group-based social behavior, i.e., centrality, community, and colocation in fog computing networks (FCNs). We also discuss the impact of social phenomena on FCNs in network performance, resource allocations, security, and privacy. We present open challenges and highlight future directions on social perception to encourage follow-up work.

RAS ID

39654

Document Type

Journal Article

Date of Publication

2022

Funding Information

Beijing Natural Science Foundation Natural Science Foundation of China China Ministry of Education - China Mobile Scientific Research Foundation China Postdoctoral Science Foundation Beijing Municipal Commission of Education Foundation

School

School of Engineering

Copyright

subscription content

Publisher

IEEE

Identifier

Iftekhar Ahmad

ORCID : 0000-0003-4441-9631

Comments

Tu, S., Waqas, M., Rehman, S. U., Mir, T., Halim, Z., & Ahmad, I. (2022). Social phenomena and fog computing networks: A novel perspective for future networks. IEEE Transactions on Computational Social Systems, 9(1), 32-44. https://doi.org/10.1109/TCSS.2021.3082022

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Link to publisher version (DOI)

10.1109/TCSS.2021.3082022