IoT-Enabled Social Relationships Meet Artificial Social Intelligence

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.Comment: Submitted to IEEE internet of things journa

A Reputation and Knowledge Based Trust Service Platform for Trustworthy Social Internet of Things

The Internet of Things has attracted a plenty of research in this decade and imposed fascinating services where large numbers of heterogeneous-features entities socially collaborate together to solve complex scenarios. However, these entities need to trust each other prior to exchanging data or offering services. In this paper, we briefly present our ongoing project called Trust Service Platform, which offers trust assessment of any two entities in the Social Internet of Things to applications and services. We propose a trust model that incorporates both reputation properties as Recommendation and Reputation trust metrics; and knowledge-based property as Knowledge trust metric. For the trust service platform deployment, we propose a reputation system and a functional architecture with Trust Agent, Trust Broker and Trust Analysis and Management modules along with mechanisms and algorithms to deal with the three trust metrics. We also present a utility theory-based mechanism for trust calculation. To clarify our trust service platform, we describe the trust models and mechanisms in accordance with a trust car-sharing service. We believe this study offers the better understanding of the trust as a service in the platform and will impose many trust-related research challenges as the future work

Exploiting social internet of things features in cognitive radio

Cognitive radio (CR) represents the proper technological solution in case of radio resources scarcity and availability of shared channels. For the deployment of CR solutions, it is important to implement proper sensing procedures, which are aimed at continuously surveying the status of the channels. However, accurate views of the resources status can be achieved only through the cooperation of many sensing devices. For these reasons, in this paper, we propose the utilization of the Social Internet of Things (SIoT) paradigm, according to which objects are capable of establishing social relationships in an autonomous way, with respect to the rules set by their owners. The resulting social network enables faster and trustworthy information/service discovery exploiting the social network of friend'' objects.We first describe the general approach according to which members of the SIoT collaborate to exchange channel status information. Then, we discuss the main features, i.e., the possibility to implement a distributed approach for a low-complexity cooperation and the scalability feature in heterogeneous networks. Simulations have also been run to show the advantages in terms of increased capacity and decreased interference probability

The 10 Research Topics in the Internet of Things

Since the term first coined in 1999 by Kevin Ashton, the Internet of Things (IoT) has gained significant momentum as a technology to connect physical objects to the Internet and to facilitate machine-to-human and machine-to-machine communications. Over the past two decades, IoT has been an active area of research and development endeavours by many technical and commercial communities. Yet, IoT technology is still not mature and many issues need to be addressed. In this paper, we identify 10 key research topics and discuss the research problems and opportunities within these topics.Comment: 10 pages. IEEE CIC 2020 vision pape

IoT Design Challenges and the Social IoT Solution

The IoT (Internet of Things) promises to be the major phenomenon in information technology in the near term. By some forecasts more than half of all new IT system deployments by 2020 will incorporate some form of IoT technology. Currently, however, there is no dominant IoT platform and no universal IoT design standards currently in use. This contributes to Architectural Heterogeneity which in turn contributes to high integration costs and inhibits IoT benefits realisation. The use of universal design standards presents one solution to this problem. Social Internet of Things (SIoT) methods use the way that people manage social relationships as a reference architecture for the way to manage the interaction between the various Things in an IoT network. This paper discusses some of the current IoT design challenges and presents solutions couched in SIoT that can be used as standards for future IoT designs to reduce Architectural Heterogeneity

An effective communication and computation model based on a hybridgraph-deeplearning approach for SIoT.

Social Edge Service (SES) is an emerging mechanism in the Social Internet of Things (SIoT) orchestration for effective user-centric reliable communication and computation. The services are affected by active and/or passive attacks such as replay attacks, message tampering because of sharing the same spectrum, as well as inadequate trust measurement methods among intelligent devices (roadside units, mobile edge devices, servers) during computing and content-sharing. These issues lead to computation and communication overhead of servers and computation nodes. To address this issue, we propose the HybridgrAph-Deep-learning (HAD) approach in two stages for secure communication and computation. First, the Adaptive Trust Weight (ATW) model with relation-based feedback fusion analysis to estimate the fitness-priority of every node based on directed graph theory to detect malicious nodes and reduce computation and communication overhead. Second, a Quotient User-centric Coeval-Learning (QUCL) mechanism to formulate secure channel selection, and Nash equilibrium method for optimizing the communication to share data over edge devices. The simulation results confirm that our proposed approach has achieved effective communication and computation performance, and enhanced Social Edge Services (SES) reliability than state-of-the-art approaches

Leveraging Machine Learning for Network Intrusion Detection in Social Internet Of Things (SIoT) Systems

This research investigates the application of machine learning models for network intrusion detection in the context of Social Internet of Things (SIoT) systems. We evaluate Convolutional Neural Network with Generative Adversarial Network (CNN+GAN), Generative Adversarial Network (GAN), and Logistic Regression models using the CIC IoT Dataset 2023. CNN+GAN emerges as a promising approach, exhibiting superior performance in accurately identifying diverse intrusion types. Our study emphasizes the significance of advanced machine learning techniques in enhancing SIoT security by effectively detecting anomalous behaviours within socially interconnected environments. The findings provide practical insights for selecting suitable intrusion detection methods and highlight the need for ongoing research to address evolving intrusion scenarios and vulnerabilities in SIoT ecosystems