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In recent years, IoT has transformed personal environments by integrating diverse smart devices. This paper presents an advanced IoT architecture that optimizes network infrastructure, focusing on the adoption of MQTT protocol and introducing Cognitive Smart Objects for managing personal IoT applications. These objects use Neural Networks to predict optimal actions based on user behavior patterns. A Continuous Learning mechanism enables real-time adaptation of the network to evolving user interactions. The study highlights the role of Cognitive Transformation in Personal IoT, driving intelligent automation and enhancing user experience. [ABSTRACT FROM AUTHOR]

This study conducts an in-depth review of the Social Internet of Things (SIoT), a significant advancement from the conventional Internet of Things (IoT) via the integration of socialization principles akin to human interactions. We explore the architecture, trust management, relationship dynamics, and other crucial aspects of SIoT, with a particular focus on the relatively neglected areas of fault tolerance, cloud–fog computing, and clustering. Our systematic literature analysis, spanning research from 2011 to April 2023, uncovers critical gaps and establishes a detailed taxonomy of emerging SIoT themes. This paper not only sheds light on the current state of SIoT research but also charts a course for future exploration and development in this burgeoning field. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) enables the connection of millions of disparate devices to the World Wide Web. Various smart devices must cooperate in order to complete the task. According to security experts, there are lot of risks related to IoT devices. Access control systems and protocols have faced a number of difficulties as a result of the development of the Internet of Things (IoT). The gadgets recognize other devices as part of their network service. Keeping participating devices safe is a crucial component of the Internet of Things. When gadgets communicate with one another, they require a promise of confidence. In order to increase security and usability of such modern technologies, trust between internet-connected devices and access control techniques is essential. Based on the facts presented, this article will help researchers create better access control techniques for the Internet of Things using trust based approach. The paper examines several access control and trust methods that could be applied in an IoT environment. An access control component is necessary to provide either access services to these recently connected devices or to those that have been joined to the IoT network for a long period. Scalable and dynamic trust computation is needed to provide dynamic access control. This review includes a thorough examination of trust management in a variety of situations and suggested design of trust computation model to provide access permission to IoT devices. [ABSTRACT FROM AUTHOR]

The authors propose an informed search greedy approach that efficiently identifies the influencer nodes in the social Internet of Things with the ability to provide legitimate information. Primarily, the proposed approach minimizes the network size and eliminates undesirable connections. For that, the proposed approach ranks each of the nodes and prioritizes them to identify an authentic influencer. Therefore, the proposed approach discards the nodes having a rank (α) lesser than 0.5 to reduce the network complexity. α is the variable value represents the rank of each node that varies between 0 to 1. Node with the higher value of α gets the higher priority and vice versa. The threshold value α =0.5 defined by the authors with respect to their network pruning requirements that can be vary with respect to other research problems. Finally, the algorithm in the proposed approach traverses the trimmed network to identify the authentic node to obtain the desired information. The performance of the proposed method is evaluated in terms of time complexity and accuracy by executing the algorithm on both the original and pruned networks. Experimental results show that the approach identifies authentic influencers on a resultant network in significantly less time than in the original network. Moreover, the accuracy of the proposed approach in identifying the influencer node is significantly higher than that of the original network. Furthermore, the comparison of the proposed approach with the existing approaches demonstrates its efficiency in terms of time consumption and network traversal through the minimum number of hops [ABSTRACT FROM AUTHOR]

This study conducts an in-depth review of the Social Internet of Things (SIoT), a significant advancement from the conventional Internet of Things (IoT) via the integration of socialization principles akin to human interactions. We explore the architecture, trust management, relationship dynamics, and other crucial aspects of SIoT, with a particular focus on the relatively neglected areas of fault tolerance, cloud–fog computing, and clustering. Our systematic literature analysis, spanning research from 2011 to April 2023, uncovers critical gaps and establishes a detailed taxonomy of emerging SIoT themes. This paper not only sheds light on the current state of SIoT research but also charts a course for future exploration and development in this burgeoning field.

The Social Internet of Things (SIoT) ecosystem tends to process and analyze extensive data generated by users from both social networks and Internet of Things (IoT) systems and derives knowledge and diagnoses from all connected objects. To overcome many challenges in the SIoT system, such as big data management, analysis, and reporting, robust algorithms should be proposed and validated. Thus, in this work, we propose a framework to tackle the high dimensionality of transferred data over the SIoT system and improve the performance of several applications with different data types. The proposed framework comprises two parts: Transformer CNN (TransCNN), a deep learning model for feature extraction, and the Chaos Game Optimization (CGO) algorithm for feature selection. To validate the framework's effectiveness, several datasets with different data types were selected, and various experiments were conducted compared to other methods. The results showed that the efficiency of the developed method is better than other models according to the performance metrics in the SIoT environment. In addition, the average of the developed method based on the accuracy, sensitivity, specificity, number of selected features, and fitness value is 88.30%, 87.20%, 92.94%, 44.375, and 0.1082, respectively. The mean rank obtained using the Friedman test is the best value overall for the competitive algorithms. [ABSTRACT FROM AUTHOR]

Recommending Smart Social Network (SSN) items which are in line with user preferences is one of the significant applications in SSNs such as Event-Based Social Networks (EBSNs) and Smart object Based Social Network (SBSN). It is well acknowledged that geographical and social contextual influences play an important role in SSN item Recommender System (RS). Whereas incorporating such contextual influences into SSN item RSs is a challenging issue which needs to be addressed along with the conventional RS challenges (cold-start problem and data sparsity). To this end, a novel SSN item recommendation architecture (named as 3T-IEC*) based on Graph Convolutional Network (GCN) is proposed. Specifically, 3T-IEC* constructs an SSN item location graph based on physical distance between two SSN items and leverages a GCN-based representation learning method to capture geographical influence of SSN items; which not only incorporates the geographical preferences but also delivers a convincing idea to solve the data sparsity problem. Sequentially, 3T-IEC* deploys one more GCN to incorporate users' social associations. Furthermore, 3T-IEC* leverages self-attention technique with multiple heads to incorporate user's preferences over SSN items' multiple features. The experimental results on four real life datasets demonstrate superior performance of 3T-IEC* compared with state-of-the-art methods such as SkyLine (149%), 3T-IEC (54%), DeepWalk (38%), SORec (9%), and NGCF (7%). [ABSTRACT FROM AUTHOR]

Recent evolutions in the Internet of Things (IoT) and Social IoT (SIoT) are facilitating collaboration as well as social interactions between entities in various environments, especially Smart Learning Ecosystems (SLEs). However, in these contexts, trust issues become more intense, learners feel suspicious and avoid collaborating with their peers, leading to their demotivation and disengagement. Hence, a Trust Management System (TMS) has become a crucial challenge to promote qualified collaboration and stimulate learners' engagement. In the literature, several trust models were proposed in various domains, but rarely those that address trust issues in SLEs, especially in MOOCs. While these models exclusively rank the best nodes and fail to detect the untrustworthy ones. Therefore, in this paper, we propose Machine Learning-based trust evaluation model that considers social and dynamic trust parameters to quantify entities' behaviors. It can distinguish trustworthy and untrustworthy behaviors in MOOCs to recommend benign peers while blocking malicious ones to build a dynamic trust-based peer recommendation in the future phase. Our model prevents learners from wasting their time in unprofitable interactions, protects them from malicious actions, and boosts their engagement. A simulation experiment using real-world SIoT datasets and encouraging results show the performance of our trust model. [ABSTRACT FROM AUTHOR]

Social Internet of Things (SIoT) is extended to integrate social networks in the Internet of Things (IoT). SIoT enriches IoT, and thus resource (or service) discovery and consolidation in SIoT becomes an important and challenging problem. In this paper, we represent the SIoT as the heterogeneous information networks (HINs) with multi-typed entities and relations, and resolve this problem from the perspective of cohesive subgraph search. Specifically, given a query node $q$ , we discover a cohesive subgraph containing $q$ from a HIN, where all nodes are of the same type as $q$ and have dense relationships. Yet existing solutions cannot be applied to various HINs, and the result subgraph is not accurate and close enough due to ignoring the connectivity among nodes within the subgraph. To this end, 1) we extend the connectivity with novel meta-path-based edge-disjoint paths to HINs, and propose the $k$ -path connected component ( $k$ -PCC) to measure the cohesiveness of subgraph in HINs; 2) we model the densest connected subgraph containing $q$ as the $k$ -PCC with the maximum connectivity including $q$ , called the Steiner Maximum Path-Connected Subgraph (SMPCS); 3) we develop efficient algorithms based on an index tree for searching the SMPCS. Extensive experiments on four real HINs are conducted to demonstrate the effectiveness and efficiency of our proposed approaches.

With the popularization of Internet of Things (IoT) and Social Internet of Things (SIoT), innumerable smart objects are allowed to establish associations among each other. Among numerous existing smart object recommendation methods, none have explored the failures of smart objects in smart social networks. As smart objects are subject to failure, we strongly believe that incorporation of smart objects' failure in addition to other contextual factors (such as geographical, social, manufacturer, and economic influence) play a crucial role in smart object recommendation. However, heterogeneous nature of these contextual factors makes it challenging for estimating user and smart object representations in the Smart object-Based Social Network (SBSN). The key contribution of this work is to propose a novel smart object recommendation architecture called SORec for SBSN. The aforementioned contextual influences are incorporated into SORec by deriving transition probabilities among two nodes in SBSN which help in attaining more precise representation of users and objects. The latent representations of smart objects and users are estimated by employing Node2Vec graph embedding technique which help in improved training of the model and superior recommendation performance. Extensive experiments have been conducted on the real-world dataset to evaluate the performance of the SORec. Experimental results clearly indicate that recommendations generated by SORec are more precise, when compared to its variants and baseline methods such as PMF (41.03%), KGE (19.59%), DeepWalk (25.49%), and LINE (18.11%). [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is an emerging trend referring to an interconnected network of ubiquitous intelligence. It is a revolution for Internet, computing and communication. Further extensions of IoT, Social Internet of Things (SIoT) provides a platform for people posting messages and photos, sharing knowledge, and connecting with each other. It is effective and efficient for people to manage their interpersonal relations through SIoT, but produces stress and tension issue coincidentally. Therefore, this study explores how users share knowledge through assessment and response under stress cognition. Three demand appraisals and three coping strategies are proposed to discuss user's behaviour on knowledge sharing, and Smart-PLS is used to test the conceptual framework. Results show that self-protection, anxiety, and avoidance increase when members of the community are threatened or injured. Nevertheless, members with high self-efficacy could reduce anxiety production and improve self-protection. Consequently, the purpose of knowledge sharing is achieved. This study discusses users' psychological perspectives when participating in the SIoT. It provides a better understanding of the human activities on the Internet through the SIot. Meanwhile, further prediction of users' behavior of knowledge sharing provides benefits and opportunities for businesses to establish their marketing strategy.

The rapid advancements in the field of Internet and Social Networks have brought us to the edge of a real-time connected world. As a result, a new paradigm named Social Internet of Things (SIoT) is born. In SIoT, social network merges with the Internet of Things (IoT) to facilitate information and resource sharing among devices in an improved way. Devices seek and provide information among their friends or friends-of-friends. However, the users of this fascinating paradigm meet with issues such as trust and privacy. Hence, trust assessment becomes one of the primary challenges. Existing trust assessment schemes in SIoT are mostly based on the trust of a service provider. The application of such schemes is related to the process of service discovery. However, the ability of a service to act maliciously is overlooked. Literature from related fields shows that services are also capable of acting maliciously. Inspired by that, we aim to define a trustworthiness assessment scheme that is based on trust of service. The application of this scheme is related to the process of service selection. To the best of our knowledge, this is a novel proposition in SIoT. A parameter named Service Trust is proposed. This parameter is mathematically modeled by aggregation of multiple Quality of Service (QoS) parameters. A real-world bike-sharing company dataset is used for evaluation of the proposed scheme. The results attained after analysis are positive. Finally, the conclusion and future work are presented.

The Social Internet of Things (SIoT) ecosystem tends to process and analyze extensive data generated by users from both social networks and Internet of Things (IoT) systems and derives knowledge and diagnoses from all connected objects. To overcome many challenges in the SIoT system, such as big data management, analysis, and reporting, robust algorithms should be proposed and validated. Thus, in this work, we propose a framework to tackle the high dimensionality of transferred data over the SIoT system and improve the performance of several applications with different data types. The proposed framework comprises two parts: Transformer CNN (TransCNN), a deep learning model for feature extraction, and the Chaos Game Optimization (CGO) algorithm for feature selection. To validate the framework’s effectiveness, several datasets with different data types were selected, and various experiments were conducted compared to other methods. The results showed that the efficiency of the developed method is better than other models according to the performance metrics in the SIoT environment. In addition, the average of the developed method based on the accuracy, sensitivity, specificity, number of selected features, and fitness value is 88.30%, 87.20%, 92.94%, 44.375, and 0.1082, respectively. The mean rank obtained using the Friedman test is the best value overall for the competitive algorithms.

With the smart grid (SG) and the social Internet of Things (SIoT), an electric vehicle operator can use reliable, flexible, and efficient charging service with vehicle-to-grid (V2G). However, open channels can be vulnerable to various attacks by a malicious adversary. Therefore, secure mutual authentication for V2G has become essential, and numerous related protocols have been proposed. In 2018, Shen et al. proposed a privacy-preserving and lightweight key agreement protocol for V2G in SIoT to ensure security. However, we demonstrate that their protocol does not withstand impersonation, privileged-insider, and offline password guessing attacks, and it does not also guarantee secure mutual authentication, session key security, and perfect forward secrecy. Therefore, this paper proposes a dynamic privacy-preserving and lightweight key agreement protocol for V2G in SIoT to resolve the security weaknesses of Shen et al.'s protocol. The proposed protocol resists several attacks including impersonation, offline password guessing, man-in-the-middle, replay, and trace attacks, ensures anonymity, perfect forward secrecy, session key security, and secure mutual authentication. We evaluate the security of the proposed protocol using formal security analysis under the broadly-accepted real-or-random (ROR) model, secure mutual authentication proof using the widely-accepted Burrows-Abadi-Needham (BAN) logic, informal (non-mathematical) security analysis, and also the formal security verification using the broadly-accepted automated validation of Internet security protocols and applications (AVISPA) tool. We then compare computation costs, and security and functionality features of the proposed protocol with related protocols. Overall, the proposed protocol provides superior security, and it can be efficiently deployed to practical SIoT-based V2G environment.

Growing synergies between the Internet of Things (IoT) and Social technologies are contributing to the advances in Cyber Physical Social Systems. Integration of new technologies is facing key challenges related to information security and privacy. This paper explores the interplay and synergetic relationships among these technologies, identifying relevant interactions, key challenges primarily focusing on cybersecurity and privacy. Specific contributions include (1) concise description of digital transformation based on the literature and resulting potential business impact (2) identification of opportunities related to the integration of social and IoT technologies and (3) challenges in technology convergence with a focus on cybersecurity challenges. [ABSTRACT FROM AUTHOR]

Social Internet of Things (SIoT) is the integration of social network (SN) and the Internet of Things (IOT). Community search in SIOT is an important problem beneficial to the resource/service discovery. In this article, we address the problem from the perspective of a dense subgraph query. Specifically, we propose a core-based static dense subgraph query and a graph kernel based dynamic dense subgraph query. The two algorithms consider the large scale and the time-varying nature of the SIoT, respectively. Unlike the existing works, the static method is inspired by the first-connection-last-expansion idea. Top-k neighbors of each query node are first found by a random walk. Then, all the query nodes and their top-k neighbors are connected as the core using Steiner tree expansion. The rank constraint random sampling is utilized to extend the core to a dense subgraph. Further, by leveraging a graph kernel index and identifying the updates that may affect the results, we conduct the dynamic query in an incremental update way instead of executing it from scratch. The experiments on synthetic and real datasets show that the proposed algorithms are both effective and efficient. [ABSTRACT FROM AUTHOR]

Despite advancements in the Internet of Things (IoT) and social networks, developing an intelligent service discovery and composition framework in the Social IoT (SIoT) domain remains a challenge. In the IoT, a large number of things are connected together according to the different objectives of their owners. Due to this extensive connection of heterogeneous objects, generating a suitable recommendation for users becomes very difficult. The complexity of this problem exponentially increases when additional issues, such as user preferences, autonomous settings, and a chaotic IoT environment, must be considered. For the aforementioned reasons, this paper presents an SIoT architecture with a personalized recommendation framework to enhance service discovery and composition. The novel contribution of this study is the development of a unique personalized recommender engine that is based on the knowledge–desire–intention model and is suitable for service discovery in a smart community. Our algorithm provides service recommendations with high satisfaction by analyzing data concerning users’ beliefs and surroundings. Moreover, the algorithm eliminates the prevalent cold start problem in the early stage of recommendation generation. Several experiments and benchmarking on different datasets are conducted to investigate the performance of the proposed personalized recommender engine. The experimental precision and recall results indicate that the proposed approach can achieve up to an approximately 28% higher F-score than conventional approaches. In general, the proposed hybrid approach outperforms other methods.

The future of communication involves 'things' in addition to people thus bringing about the term Internet of Things. When social networking concepts are incorporated into Internet of Things, it is called Social Internet of Things. Searching for a desired services may lead to high computational and memory demands as the number of friends of any node starts to grow over time. Hence, it is required to judiciously prune the friends of a node by applying some strategy in order to reduce computational load and at the same time preserving network navigability. In this paper, four new heuristics for efficient selection of friends are proposed. An analysis is performed based on various metrics and the results are presented. [ABSTRACT FROM AUTHOR]

Today's electrical and electronic gadgets have become smarter and intelligent. These devices such as sensors, actuators, RFIDs are becoming part of our fabric. Internet of Things (IoT) and Social networking paradigms are not new. The increased pervasiveness has resulted not only in human to thing communication but also thing to thing communication. A new paradigm integrating IoT and Social Networks has emerged in recent years called the Social Internet of Things (SIoT) where objects are not only smarter but also socially conscious. Social Internet of Things is analogous to social network of intelligent objects. Trust is considered as a crucial factor in SIoT for objects to establish reliable autonomous communication. This paper proposes a Trust Management scheme for Social Internet of Things where trust between objects is computed based on Direct Observations, Indirect Recommendations, Centrality, Energy and Service Score of the object. The proposed trust model outperforms the existing trust models leading to a better application performance. The trust model is also tested in the presence of On Off selective forwarding attacks. Experimental results prove that the proposed model is reliable and defendant against On Off selective forwarding attacks. [ABSTRACT FROM AUTHOR]

The paradigm of the Social Internet of Things (SIoT) boosts a new trend wherein the connectivity and user friendliness benefits of Social Network Services (SNS) are exhibited within the network of connected objects, i.e. the Internet of Things (IoT). The SIoT exceeds the more traditional paradigm of IoT with an enhanced intelligence and context-awareness. In this paper, a novel service framework based on a cognitive reasoning approach for dynamic SIoT services discovery in smart spaces is proposed. That is, reasoning about users’ situational needs, preferences, and other social aspects along with users’ surrounding environment is proposed for generating a list of situation-aware services which matches users’ needs. This reasoning approach is then implemented as a proof-of-concept prototype, namely Airport Dynamic Social, within a smart airport. Finally, an empirical study to evaluate the reasoning approach's efficiency shows improved services adaptability to situational needs compared to common approaches proposed in literature. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) has been at the center of attention among researchers for the last two decades. Their aim was to convert each real-world object into a virtual object. Recently, a new idea of integrating the Social Networking concept into the Internet of Things has merged and is gaining popularity and attention in the research society due to its vast and flexible nature. It comprises of the potential to provide a platform for innovative applications and network services with efficient and effective manners. In this paper, we provide the sustenance for the Social Internet of Things (SIoT) paradigm to jump to the next level. Currently, the SIoT technique has been proven to be efficient, but heterogeneous smart devices are growing exponentially. This can develop a problematic scenario while searching for the right objects or services from billions of devices. Small world phenomena have revealed some interesting facts and motivated many researchers to find the hidden links between acquaintances in order to reach someone across the world. The contribution of this research is to integrate the SIoT paradigm with the small world concept. By integrating the small world properties in SIoT smart devices, we empower the Smart Social Agent (SSA). The Smart Social Agent ensures the finding of appropriate friends (i.e., the IoT devices used by our friend circle) and services that are required by the user, without human intervention. The Smart Social Agent can be any smart device in SIoTs, e.g., mobile phones.

Internet of things (IoT) is a current trend that reveals the next generation Internet-based information architecture, the convergence of social networks and IoT solutions is helpful to optimize relationships among objects. In order for IoT to take off in the IT sector, providers and other stakeholders must integrate knowledge successfully. In this study, we investigate the role of social media in supporting knowledge integration from a social capital perspective. Specifically, we propose that social media have the potential to facilitate the formation of employees' social capital indicated by social networking, trust and shared language. These mediating variables will in turn positively affect knowledge integration. This research frame is validated with survey data collected from 262 Chinese working professionals. The results provide general empirical support for our hypotheses. In analogy with social media for human beings, the future direction of socialization among objects can be inspired by this study. [ABSTRACT FROM AUTHOR]

Despite advancements in the Internet of Things (IoT) and social networks, developing an intelligent service discovery and composition framework in the Social IoT (SIoT) domain remains a challenge. In the IoT, a large number of things are connected together according to the different objectives of their owners. Due to this extensive connection of heterogeneous objects, generating a suitable recommendation for users becomes very difficult. The complexity of this problem exponentially increases when additional issues, such as user preferences, autonomous settings, and a chaotic IoT environment, must be considered. For the aforementioned reasons, this paper presents an SIoT architecture with a personalized recommendation framework to enhance service discovery and composition. The novel contribution of this study is the development of a unique personalized recommender engine that is based on the knowledge–desire–intention model and is suitable for service discovery in a smart community. Our algorithm provides service recommendations with high satisfaction by analyzing data concerning users' beliefs and surroundings. Moreover, the algorithm eliminates the prevalent cold start problem in the early stage of recommendation generation. Several experiments and benchmarking on different datasets are conducted to investigate the performance of the proposed personalized recommender engine. The experimental precision and recall results indicate that the proposed approach can achieve up to an approximately 28% higher F-score than conventional approaches. In general, the proposed hybrid approach outperforms other methods. [ABSTRACT FROM AUTHOR]