IoT-Enabled Social Relationships Meet Artificial Social Intelligence

• URL: http://arxiv.org/abs/2103.01776v1
• Date: Sun, 21 Feb 2021 09:07:32 GMT
• Title: IoT-Enabled Social Relationships Meet Artificial Social Intelligence
• Authors: Sahraoui Dhelim, Huansheng Ning, Fadi Farha, Liming Chen, Luigi Atzori and Mahmoud Daneshmand
• Abstract summary: Artificial Social Intelligence (ASI) has the potential to tackle the social relationship explosion problem. This paper discusses the role of IoT in social relationships detection and management. It reviews the proposed solutions using ASI, including social-oriented machine-learning and deep-learning techniques.
• Score: 6.5576860060491065
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the recent advances of the Internet of Things, and the increasing accessibility of ubiquitous computing resources and mobile devices, the prevalence of rich media contents, and the ensuing social, economic, and cultural changes, computing technology and applications have evolved quickly over the past decade. They now go beyond personal computing, facilitating collaboration and social interactions in general, causing a quick proliferation of social relationships among IoT entities. The increasing number of these relationships and their heterogeneous social features have led to computing and communication bottlenecks that prevent the IoT network from taking advantage of these relationships to improve the offered services and customize the delivered content, known as relationship explosion. On the other hand, the quick advances in artificial intelligence applications in social computing have led to the emerging of a promising research field known as Artificial Social Intelligence (ASI) that has the potential to tackle the social relationship explosion problem. This paper discusses the role of IoT in social relationships detection and management, the problem of social relationships explosion in IoT and reviews the proposed solutions using ASI, including social-oriented machine-learning and deep-learning techniques.

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