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The Social Internet of Things (SIoT) is an innovative fusion of IoT and smart devices that enable them to establish dynamic relationships. Securing sensitive data in a smart environment requires a model to determine the relationships between devices through object profiling and ontological models. To address this need, we have proposed an ontology-based operational security model for the SIoT. In our approach, the interpolation method is used to establish relationships, while fiduciary relationships are employed to detect threats. Furthermore, the encryption of heterogeneous device data, coupled with the implementation of an operationbased intrusion detection system, proves highly effective in identifying potential threats.Invalid relationships are identified as intruders and validated using machine learning techniques. Encrypting heterogeneous device data, along with an operationbased intrusion detection system, efficiently identifies threats in the ever-evolving dynamic nature of the SIoT environment. the encryption of heterogeneous device data, coupled with an operation-based intrusion detection system, effectively identifies threats. Invalid relationships are promptly identified as attackers and machine learning techniques are used to validate relationships encryption and machine learning stand as indispensable tools in the endeavor to secure sensitive information within the realm of the SIoT. The suggested model outperformed the results of the current model, as evidenced by its average accuracy of 85.67%, precision of 90.37%, recall of 92.06%, and F1-score of 91.03% when compared to the existing model. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a sophisticated network of objects embedded with electronic systems that enable devices to collect and exchange data. IoT is a recent trending leading technology and changing the way we live. However, it has several challenges especially efficiency, architecture, complexity, and network topology. The traditional technologies are not enough to provide support. It is evident from the literature that complex networks are used to study the topology and the structure of a network and are applied to modern technologies. Thus, the capability of powerful computational tools and the existence of theoretical frameworks enable complex networks to derive new approaches in analyzing IoT-based technologies in terms of improving efficiency, architecture, complexity, and topology. In this direction, limited research has been carried out. The integration aspect remains a key challenge. Therefore, in order to fill this gap. Herein, we design a comprehensive literature review. In this research effort, we explore a newly leading emerging technology named the Social Internet of Things (SIoT). It is developed to overcome the challenges in IoT. We discuss the importance and the key applications of SIoT. We first presented a conceptual view along with a recent technological roadmap. The big data play an important role in the modern world. We discuss big data and the 5 Vs along with suitable applications and examples. Then, we highlighted the key concepts in complex networks, scale-free, random networks, and small-world networks. We explored and presented various graph models and metrics aligned with social networks and the most recent trends. The novelty of this research is to propose a synergy of complex networks to the IoT, SIoT, and big data together. We discuss the advantages of integration in detail. We present a detailed discussion on complex networks emerging technologies and cyber-physical systems (CPS). Briefly, our literature review covers the most recent advancements and developments in 10 years. In addition, our critical analysis is based on up-to-date surveys and case studies. Finally, we outline the impact of recent emerging technologies on challenges applications, and solutions for the future. This paper provides a good reference for researchers and readers in the IoT domain. [ABSTRACT FROM AUTHOR]

Featured Application: Potential applications of the work include novel ML-based systems for sustainable smart cities and smart territory control. The rapid development of modern information technology (IT), power supply, communication and traffic information systems and so on is resulting in progress in the area of distributed and energy-efficient (if possible, powered by renewable energy sources) smart grid components securely connected to entire smart city management systems. This enables a wide range of applications such as distributed energy management, system health forecasting and cybersecurity based on huge volumes of data that automate and improve the performance of the smart grid, but also require analysis, inference and prediction using artificial intelligence. Data management strategies, but also the sharing of data by consumers, institutions, organisations and industries, can be supported by edge clouds, thus protecting privacy and improving performance. This article presents and develops the authors' own concept in this area, which is planned for research in the coming years. The paper aims to develop and initially test a conceptual framework that takes into account the aspects discussed above, emphasising the practical aspects and use cases of the Social Internet of Things (SIoT) and artificial intelligence (AI) in the everyday lives of smart sustainable city (SSC) residents. We present an approach consisting of seven algorithms for the integration of large data sets for machine learning processing to be applied in optimisation in the context of smart cities. [ABSTRACT FROM AUTHOR]

The Social Internet of Things (SIoT) integrates the Internet of Things (IoT) and social networks, taking into account the social attributes of objects and diversifying the relationship between humans and objects, which overcomes the limitations of the IoT's focus on associations between objects. Artificial Intelligence (AI) technology is rapidly evolving. It is critical to build trustworthy and transparent systems, especially with system security issues coming to the surface. This paper emphasizes the social attributes of objects and uses hypergraphs to model the diverse entities and relationships in SIoT, aiming to build an SIoT hypergraph generation model to explore the complex interactions between entities in the context of intelligent SIoT. Current hypergraph generation models impose too many constraints and fail to capture more details of real hypernetworks. In contrast, this paper proposes a hypergraph generation model that evolves dynamically over time, where only the number of nodes is fixed. It combines node wandering with a forest fire model and uses two different methods to control the size of the hyperedges. As new nodes are added, the model can promptly reflect changes in entities and relationships within SIoT. Experimental results exhibit that our model can effectively replicate the topological structure of real-world hypernetworks. We also evaluate the vulnerability of the hypergraph under different attack strategies, which provides theoretical support for building a more robust intelligent SIoT hypergraph model and lays the foundation for building safer and more reliable systems in the future. [ABSTRACT FROM AUTHOR]

Emerging distributed intelligence paradigms for the Internet of Things (IoT) call for flexible and dynamic reconfiguration of elementary services, resources and devices. In order to achieve such capability, this paper faces complex interoperability and autonomous decision problems by proposing a thorough framework based on the integration of the Semantic Web of Things (SWoT) and Social Internet of Things (SIoT) paradigms. SWoT enables low-power knowledge representation and autonomous reasoning at the edge of the network through carefully optimized inference services and engines. This layer provides service/resource management and discovery primitives for a decentralized collaborative social protocol in the IoT, based on the Linked Data Notifications(LDN) over Linked Data Platform on Constrained Application Protocol (LDP-CoAP). The creation and evolution of friend and follower relationships between pairs of devices is regulated by means of novel dynamic models assessing trust as a usefulness reputation score. The close SWoT-SIoT integration overcomes the functional limitations of existing proposals, which focus on either social device or semantic resource management only. A smart mobility case study on Plug-in Electric Vehicles (PEVs) illustrates the benefits of the proposal in pervasive collaborative scenarios, while experiments show the computational sustainability of the dynamic relationship management approach. [ABSTRACT FROM AUTHOR]

As an ingenious convergence between the Internet of Things and social networks, the Social Internet of Things (SIoT) can provide effective and intelligent information services and has become one of the main platforms for people to spread and share information. Nevertheless, SIoT is characterized by high openness and autonomy, multiple kinds of information can spread rapidly, freely and cooperatively in SIoT, which makes it challenging to accurately reveal the characteristics of the information diffusion process and effectively control its diffusion. To this end, with the aim of exploring multi-information cooperative diffusion processes in SIoT, we first develop a dynamics model for multi-information cooperative diffusion based on the system dynamics theory in this paper. Subsequently, the characteristics and laws of the dynamical evolution process of multi-information cooperative diffusion are theoretically investigated, and the diffusion trend is predicted. On this basis, to further control the multi-information cooperative diffusion process efficiently, we propose two control strategies for information diffusion with control objectives, develop an optimal control system for the multi-information cooperative diffusion process, and propose the corresponding optimal control method. The optimal solution distribution of the control strategy satisfying the control system constraints and the control budget constraints is solved using the optimal control theory. Finally, extensive simulation experiments based on real dataset from Twitter validate the correctness and effectiveness of the proposed model, strategy and method.

The study aims to fill the gap in the bibliometric analysis of the Social Internet of Things (SIoT) discourse, focusing on recurring patterns, exploring uncharted study domains, and proposing future directions in the developing interdisciplinary realm. The analysis used quantitative data from the Scopus database from 2012 to 2023, covering architecture, trust management, service composition, network navigability, and integration with upcoming technologies. The study revealed a 29.14% yearly expansion rate in SIoT research, demonstrating a dynamic and cooperative research environment. Keyword clustersincluded social interactions, multidisciplinary perspectives, network architecture, cybersecurity, trust administration, and social networking integration. The bibliometric study provides valuable insights for researchers, practitioners, and policymakers, enabling them to navigate the ever-changing Smart Internet of Things landscape and promote interdisciplinary collaboration for further advancements. Future studies should include qualitative assessments, examine societal implications, and explore future technologies within the Internet of Things (IoT) framework. [ABSTRACT FROM AUTHOR]

The methodologies based on neural networks are substantial to accomplish sentiment analysis in the Social Internet of Things (SIoT). With social media sentiment analysis, significant insights can produce efficient and intelligent applications. Neural networks such as recurrent neural networks (RNNs) and convolution neural networks (CNNs) have been considered widely in many text classification tasks. However, RNNs are computationally expensive and require complex training to capture contextual information and long-term dependencies. Similarly, traditional CNNs must stack multiple convolutional layers, requiring massive computations and additional parameters. To address these problems, this work initialized the novel architecture, in which contextual representations (CRs) based on the textual framework are proposed at the initial step. In CRs, state-of-the-art word representation models, such as GloVe (global vectors) and FastText (subword information), collectively produce word representations upon the input sequence using a weight mechanism. Secondly, a unique way is introduced: a three-parallel layered dilated convolutional network with global mean pooling. The experimental results show that the proposed methods when compared with baseline methods, the dilation in CNNs following CRs significantly increases the accuracy from 72.45 to 98.98% and reduces computational resources. [ABSTRACT FROM AUTHOR]

With the development of technology, more and more devices are connected to the Internet. According to statistics, Internet of Things (IoT) devices have reached tens of billions of units, which forms a massive Internet of Things system. Social Internet of Things (SIoT) is an essential extension of the IoT system. Because of the heterogeneity present in the SIoT system and the limited resources available, it is facing increasing security issues, which hinders the interaction of SIoT information. Consortium chain combined with the trust problem in SIoT systems has gradually become an important goal to improve the security of SIoT data interaction. Detection of malicious nodes is one of the key points to solve the trust problem. In this article, we focus on the consortium chain network. According to the information characteristics of nodes on the consortium chain, it can be analyzed that the SIoT malicious node detection combined with the consortium chain network should have the privacy protection, subjectivity, uncertainty, lightweight, dynamic timeliness and so on. In response to the features above and the concerns of existing malicious node detection methods, we propose an algorithm based on inter-block delay. We employ unsupervised clustering algorithms, including K-means and DBSCAN, to analyze and compare the data set intercepted from the consortium chain. The results indicate that DBSCAN exhibits the best clustering performance. Finally, we transmit the acquired data onto the chain. We conclude that the proposed algorithm is highly effective in detecting malicious nodes on the combination of SIoT and consortium chain networks. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a sophisticated network of objects embedded with electronic systems that enable devices to collect and exchange data. IoT is a recent trending leading technology and changing the way we live. However, it has several challenges especially efficiency, architecture, complexity, and network topology. The traditional technologies are not enough to provide support. It is evident from the literature that complex networks are used to study the topology and the structure of a network and are applied to modern technologies. Thus, the capability of powerful computational tools and the existence of theoretical frameworks enable complex networks to derive new approaches in analyzing IoT-based technologies in terms of improving efficiency, architecture, complexity, and topology. In this direction, limited research has been carried out. The integration aspect remains a key challenge. Therefore, in order to fill this gap. Herein, we design a comprehensive literature review. In this research effort, we explore a newly leading emerging technology named the Social Internet of Things (SIoT). It is developed to overcome the challenges in IoT. We discuss the importance and the key applications of SIoT. We first presented a conceptual view along with a recent technological roadmap. The big data play an important role in the modern world. We discuss big data and the 5 Vs along with suitable applications and examples. Then, we highlighted the key concepts in complex networks, scale-free, random networks, and small-world networks. We explored and presented various graph models and metrics aligned with social networks and the most recent trends. The novelty of this research is to propose a synergy of complex networks to the IoT, SIoT, and big data together. We discuss the advantages of integration in detail. We present a detailed discussion on complex networks emerging technologies and cyber-physical systems (CPS). Briefly, our literature review covers the most recent advancements and developments in 10 years. In addition, our critical analysis is based on up-to-date surveys and case studies. Finally, we outline the impact of recent emerging technologies on challenges applications, and solutions for the future. This paper provides a good reference for researchers and readers in the IoT domain.

Summary: Source location privacy is a developing research topic in the social Internet of Things. Source location privacy holds paramount importance in security critical wireless sensor network applications like tracking and monitoring. Several methods have been proposed for source location privacy in the social Internet of Things, but the existing methods have some issues such as improper path selection, the transmission of duplicate messages, and low network lifetime. To overcome these issues, a source location privacy protocol based on energy‐efficient and link‐reliable multi‐scale bifurcated deep Capsnet routing in the social Internet of Things is proposed in this manuscript. At first, the optimal route for the source is selected with the help of energy‐efficient and link‐reliable routing, this method helps to avoid improper path selection. To estimate the quality of the selected optimal path, the multi‐scale bifurcated deep Capsnet is applied. The introduced method is executed in MATLAB. The introduced method's performance is estimated with the aid of several performances evaluating metrics like sensitivity, energy consumption, network lifetime, safety period, and delay. [ABSTRACT FROM AUTHOR]

The Social Internet of Things (SIoT) is a fast growing technology that autonomously connects things, allowing for exchange of information depending on relationships. However, the open nature of SIoT exposes it to various security challenges, such as default passwords, brute-forcing attacks, data privacy breaches, and an increasing number of IoT botnets. To address these vulnerabilities, a novel operational security framework for SIoT, leveraging the robust properties of blockchain technology. The primary objectives of the operational security model are to establish a secure and trusted environment in SIoT, protect sensitive data, ensure reliable device authentication, and enable secure and encrypted communication in SIoT operations. The proposed model combines key features of blockchain, including decentralization, immutability, transparency, and cryptographic mechanisms, with SIoT security operations. Techniques like encryption, anonymization and differential privacy are employed to preserve privacy in SIoT devices and safeguard sensitive data. An empirical assessment of this operational security framework yields promising outcomes in the pursuit of its defined objectives. It furnishes a decentralized and tamper-proof foundation for device authentication, thereby cementing the trustworthiness of SIoT devices. Moreover, the proposed Graph Neural Network (GNN) model eclipses the performance of traditional models, boasting a 95% accuracy, a 96% precision, a 95% recall, and a 95% F1-score. This achievement can be attributed to the incorporation of computations that encapsulate both transitivity and composability among devices. In tandem, the suggested framework emerges triumphant when compared to other methodologies, spanning diverse metrics such as throughput, latency, security score, privacy score, and trust score. Notably, this framework not only secures communication but also upholds data integrity and confidentiality, ensuring exclusive access to SIoT devices and data by authorized entities. The proposed SIoT framework, with 280 Tx/s throughput, 2.2 seconds latency, 9.5 security score, 9.0 privacy score, and 9.5 trust score, outperforms other authors' models across all the parameters. [ABSTRACT FROM AUTHOR]

Abstract The Trust-Aware Aggregation Authentication Protocol for SIoT Networks is a security process intended for SIoT platforms. It concentrates on ensuring the reliability of communication and data aggregation between interrelated IoT devices. This protocol deploys authentication systems for verifying the identities of devices and integrates trust-aware mechanisms to estimate the trustworthiness of data exchanged from the social environment of SIoT. By establishing a trustworthy and secure communication structure, this protocol improves the entire integrity and security of SIoT networks, addressing potential vulnerabilities connected with social communications between IoT devices. Therefore, this study develops an enhanced Trust Management using Locally Weighted Salp Swarm Algorithm with Deep learning (ETM-LWSSADL) technique for SIoT Networks. The ETM-LWSSADL technique computes direct and indirect trust values and is assessed depending upon different weighing factors for maximizing the application performance and creating a secure data transmission process. During authentication process, when the SIoT device with total trust value (TTV)is not greater than the threshold value (THV) or authentication token is invalid, the gateways then disregard the node. Besides, bidirectional gated recurrent unit (BiGRU) model is applied to generate a THV on collected traffic data. Moreover, the ETM-LWSSADL technique exploits the LWSSA technique for optimum hyper parameter selection of the BiGRU algorithm. To highlight the enhanced performance of the ETM-LWSSADL methodology, an extensive range of simulations can be involved.

In the distributed environment of the Social Internet of Things (SIoT), where nodes rely primarily on the “store-carry-forward” mechanism for message delivery, it has been determined that forming community structures is advantageous for message propagation. Nevertheless, due to the dynamic nature of nodal social relationships and inaccurate community division, routing algorithm performance is inferior. This paper proposes a novel SIoT routing algorithm, the Free Node-based Routing Algorithm (FNRA) to resolve these issues. The FNRA algorithm presents a comprehensive framework for message forwarding, differentiating between in-community and out-of-community scenarios. The algorithm compares the forwarding capabilities of relay and source nodes within the community. A graph-theoretic approach is used to classify source, relay, and destination nodes into four distinct structures outside the community. Considering node-node, node-community, community-node, and community-community interactions, the algorithm addresses situations where the source, destination, and relay nodes do not belong to the same community. FNRA dynamically selects appropriate forwarding strategies to expedite message delivery to the destination node by leveraging the community structure and the presence of free nodes. The FNRA algorithm exhibits superior performance through detailed experimental simulations by enhancing message delivery rate, decreasing network overhead, and reducing average latency compared to existing routing algorithms. This demonstrates the algorithm's effectiveness in optimizing overall network performance.

Social IoT (SIoT) refers to socializing in the Internet of Things (IoT), where things generate social relationships. Due to the development of objects and issues such as delayed response, slow search, and composite service process, distributed object service discovery, selection, and composition based on the social structure have become essential challenges in the SIoT. Therefore, it is necessary to provide an efficient method for evaluating the effectiveness of service discovery in identifying suitable devices to offer requested services and the best composition strategy for combining requested services. This paper presents a new community detection algorithm that detects IoT devices with social connections in the SIoT network to facilitate service discovery and composition by reducing search space. Additionally, it introduced a new service provisioning algorithm to optimize service discovery and composition, called An Improved Genetic Algorithm based on Community Detection (IGA-CD). Its effectiveness in detected communities is better than other methods in computation modularity, execution time, and cluster assignment quality methods by determining the network’s ideal devices. Experimental results demonstrate the efficacy of the proposed algorithms, which outperform other approaches in terms of scalability, efficiency, and flexibility. The IGA-CD average execution time is 0.129 seconds, which proves its efficiency and faster composition.

The Social Internet of Things (SIoT) is presented as a new paradigm of the Internet of Things that solves the problems of network navigability and provides enhanced service discovery and composition. It aims to socialize the IoT devices and allow them to interact just like humans by creating multiple social relationships. In SIoT scenarios, a device can offer multiple services, and different devices can offer the same services with different parameters and factors of interest, which leads to data sparsity and sheer volume of services. However, this sheer volume of available services makes it difficult for devices to navigate and select the ones that best fit their needs or preferences. On the other hand, the heterogeneous nature and dynamic connectivity of SIoT networks raise the cold start problem in service recommendations. Few works explored the integration of trust-aware approaches with latent feature mining in the SIoT recommendation systems. To address these challenges, we proposed a hybrid latent feature mining and trust-aware model to provide a tailored service recommendation in the SIoT environment. Experimental results conducted on a public dataset reveal the increase of service recommendation accuracy and highlight the proposed framework’s effectiveness in meeting recommendation needs within the scope of SIoT environment.

The swift progress of 6G cellular networks responds to the urgent demand for seamlessly integrating Internet of Things (IoT) devices on a large scale. Among various emerging technologies, grant-free non-orthogonal multiple access (GF-NOMA) emerges as a standout, offering distinct advantages over traditional networks in accommodating extensive connectivity needs. GF-NOMA optimizes resource usage by reallocating each time-frequency slot to multiple devices with different power levels. Furthermore, it reduces the coordination burden typically associated with uplink communication by broadcasting random access channels. Nonetheless, GF-NOMA faces a significant challenge: the risk of multiple devices inadvertently selecting the same resource and power, resulting in data loss, particularly problematic during emergencies marked by uncoordinated communications. Additionally, the expanding deployment of IoT devices demands a proportional increase in resources, despite advancements in network technology. To address these challenges, this paper introduces an innovative architecture aimed at significantly boosting spatial capacity through the establishment of autonomous social interactions among IoT devices. The proposed SAFE-GF-NOMA aggregation scheme facilitates resource sharing in an independent and ad-hoc trust management environment by ensuring trustworthy sharing. The proposed Social IoT (SIoT) framework reduces uplink access by grouping devices based on trust metrics, resulting in a notable 50% reduction in collision probability, and over a 50% increase in success probability, and a threefold capacity increase compared to conventional systems. Additionally, our system achieves a substantial reduction in energy consumption, cutting it from 17 J to just 5 J per device within the cluster.

The rapid development of modern information technology (IT), power supply, communication and traffic information systems and so on is resulting in progress in the area of distributed and energy-efficient (if possible, powered by renewable energy sources) smart grid components securely connected to entire smart city management systems. This enables a wide range of applications such as distributed energy management, system health forecasting and cybersecurity based on huge volumes of data that automate and improve the performance of the smart grid, but also require analysis, inference and prediction using artificial intelligence. Data management strategies, but also the sharing of data by consumers, institutions, organisations and industries, can be supported by edge clouds, thus protecting privacy and improving performance. This article presents and develops the authors’ own concept in this area, which is planned for research in the coming years. The paper aims to develop and initially test a conceptual framework that takes into account the aspects discussed above, emphasising the practical aspects and use cases of the Social Internet of Things (SIoT) and artificial intelligence (AI) in the everyday lives of smart sustainable city (SSC) residents. We present an approach consisting of seven algorithms for the integration of large data sets for machine learning processing to be applied in optimisation in the context of smart cities.

Emerging distributed intelligence paradigms for the Internet of Things (IoT) call for flexible and dynamic reconfiguration of elementary services, resources and devices. In order to achieve such capability, this paper faces complex interoperability and autonomous decision problems by proposing a thorough framework based on the integration of the Semantic Web of Things (SWoT) and Social Internet of Things (SIoT) paradigms. SWoT enables low-power knowledge representation and autonomous reasoning at the edge of the network through carefully optimized inference services and engines. This layer provides service/resource management and discovery primitives for a decentralized collaborative social protocol in the IoT, based on the Linked Data Notifications(LDN) over Linked Data Platform on Constrained Application Protocol (LDP-CoAP). The creation and evolution of friend and follower relationships between pairs of devices is regulated by means of novel dynamic models assessing trust as a usefulness reputation score. The close SWoT-SIoT integration overcomes the functional limitations of existing proposals, which focus on either social device or semantic resource management only. A smart mobility case study on Plug-in Electric Vehicles (PEVs) illustrates the benefits of the proposal in pervasive collaborative scenarios, while experiments show the computational sustainability of the dynamic relationship management approach.

The emerging yet promising paradigm of the Social Internet of Things (SIoT) integrates the notion of the Internet of Things with human social networks. In SIoT, objects, i.e., things, have the capability to socialize with the other objects in the SIoT network and can establish their social network autonomously by modeling human behaviour. The notion of trust is imperative in realizing these characteristics of socialization in order to assess the reliability of autonomous collaboration. The perception of trust is evolving in the era of SIoT as an extension to traditional security triads in an attempt to offer secure and reliable services, and is considered as an imperative aspect of any SIoT system for minimizing the probable risk of autonomous decision-making. This research investigates the idea of trust quantification by employing trust measurement in terms of direct trust, indirect trust as a recommendation, and the degree of the SIoT relationships in terms of social similarities (community-of-interest, friendship, and co-work relationships). A weighted sum approach is subsequently employed to synthesize all the trust features in order to ascertain a single trust score. The experimental evaluation demonstrates the effectiveness of the proposed model in segregating the trustworthy and the untrustworthy objects, and illustrates the superior performance of the proposed trust model over state-of-the-art trust models. [ABSTRACT FROM AUTHOR]

Recently, a novel concept called the Social Internet of Things (SIoT) has emerged, which combines the Internet of Things (IoT) and social networks. SIoT plays a significant role in various aspects of modern human life, including smart transportation, online healthcare systems, and viral marketing. One critical challenge in SIoT-based advertising is identifying the most effective objects for maximizing advertising impact. This research paper introduces a highly efficient heuristic algorithm named Influence Maximization-Cost Minimization for Advertising in the Social Internet of Things (IMCMoT), inspired by real-world advertising strategies. The IMCMoT algorithm comprises three essential steps: Initial preprocessing, candidate objects selection and final seed set identification. In the initial preprocessing phase, the objects that are not suitable for advertising purposes are eliminated. Reducing the problem space not only minimizes computational overhead but also reduces execution time. Inspired by real-world advertising, we then select influential candidate objects based on their effective sociality rate, which accounts for both the object's sociality rate and relevant selection cost factors. By integrating these factors simultaneously, our algorithm enables organizations to reach a broader audience at a lower cost. Finally, in identifying the final seed set, our algorithm considers the overlapping of neighbors between candidate objects and their neighbors. This approach helps minimize the costs associated with spreading duplicate advertisements. Through experimental evaluations conducted on both real-world and synthetic networks, our algorithm demonstrates superior performance compared to other state-of-the-art algorithms. Specifically, it outperforms existing methods concerning attention to influence spread, achieves a reduction in advertising cost by more than 2–3 times and reduces duplicate advertising. Additionally, the running time of the IMCMoT algorithm is deemed acceptable, further highlighting its practicality and efficiency. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has enabled billions of objects around the world to be in continuous interaction. Social interactions between smart objects are recognized by the Social Internet of Things (SIoT) vision. An efficient resource selection remains a fundamental challenge for large-scale SIoT. In this paper, resources are categorized into computational and consumed. A computational resource processes data and a consumed resource captures data. A novel approach, in Web 2.0 context, is proposed which combines social networks of resources with the principles of recommender systems, in order to define the interactions between resources. These interactions are exploited to develop two algorithms for dynamic computational and consumed resources selection in case of substitution. The search space of resources is considerably reduced by their categorization and the creating of social networks associated with each resource. Each resource is engaged in one and only one social network, which considerably reduces the task of replacing unavailable or failing resources. The results obtained are encouraging and show the indisputable contribution of resources categorization for the purpose of their substitution. [ABSTRACT FROM AUTHOR]

In the social internet of things, community structure exists objectively and affects the transmission of network messages. If the social context such as community is fully utilized, the efficiency of data forwarding will be effectively improved. A community-based routing algorithm (MSAR) is proposed by studying the multiple social relationships. First, we propose four measures of social relationships. They are social closeness degree, in-community activeness, cross-community activeness and community interaction. Then, the design of routing algorithm considers two stages. One is in-community forwarding and the other is cross-community forwarding. The measurement of node forwarding capability depends on closeness degree and in-community activeness in the in-community forwarding stage. In the cross-community stage, the measurement of node forwarding capability depends on closeness degree, cross-community activeness and community interaction. The relay node with higher cross-community forwarding utility will be selected. This prevents messages from being limited to the local community. Therefore, messages can always travel in the direction of the destination node's community. Finally, a lot of simulation experiments and analyses are carried out. The analysis results show that the proposed algorithm has good performance in the following two aspects, the average latency and the message delivery rate respectively. [ABSTRACT FROM AUTHOR]

The intelligent transportation system seeks to reduce traffic and improve the driving experience. They give us a lot of data that we can use to improve services for both the public and transportation officials by feeding it into machine learning systems. Most importantly, Traffic environment refers to everything that might have an impact on how much traffic is moving down the road, including traffic signals, accidents, protests, and even road repairs that might result in a backup. A motorist or rider can make an informed choice if they have previous knowledge that is very close to approximate all the above and many more real-world circumstances that can affect traffic. Additionally, it aids in the development of driverless vehicles. Traffic data have been growing dramatically in recent decades, and we are moving toward big data concepts for transportation. The current approaches for predicting traffic flow use some traffic prediction models, however they are still inadequate to handle practical situations. We thus aimed to focus on the traffic flow forecast problem using the traffic data and prediction models. The proposed model called DRGNN, a dilated recurrent graph neural network framework aims to effectively analyze and predict the traffic pattern by considering the spatial (space) and temporal (time) aspects of the real-time traffic data considering social relationships between internet of vehicles which indeed produced accurate and valuable insights that could help in deploying the model in any suitable real-time traffic monitoring and prediction system. [ABSTRACT FROM AUTHOR]

In recent years, the Internet of Things (IoT) has played a vital role in providing various services to users in a smart city. However, searching for services, objects, data, and frameworks remains a concern. The technological advancements in Cyber-Physical Systems (CPSs) and the Social Internet of Things (SIoT) open a new era of research. Thus, we propose a Cyber-Physical-Social Systems (CPSs) for service search. Herein, service search and object discovery operation carries with the suitable selection of friends in the network. Our proposed model constructs a graph and performs social network analysis (SNA). We suggest degree centrality, clustering, and scalefree emergence and show that a rational selection of friends per service exploration increases the overall network navigability. The efficiency of our proposed system is verified using real-world datasets based on service processing time, path length, giant component, and network diameter. The simulation results proved that our proposed system is efficient, robust, and scalable. [ABSTRACT FROM AUTHOR]

The social internet of things （SIoT） is a new paradigm that combines social networks and the internet of things to enhance network scalability and navigability.However, with increased openness and mobility of intelligent objects, there are rising security risks related to information security and reliable service delivery, which poses a significant challenge to the SIoT.Trust is a critical factor in providing secure and reliable services.Therefore, a new trust assessment model was proposed from the perspective of social relationships.A trust transfer algorithm was proposed based on the explicit social relationships and potential features between nodes.The algorithm can tap and establish the implicit social relationships among nodes to solve the SIoT sparsity and cold start problem.Additionally, the social relations were fine-grained and divided to build a single-relationship sub-network.Multiple single-relationship sub-networks were fused to build a multiple-relationship composite network.Thus, the impact of multiple dynamically changing social relationships among nodes on trust evaluation can be effectively fused.The comprehensive trust assessment of intelligent objects was conducted based on three indicators: direct trust, indirect recommendation trust, and service satisfaction.The experimental results on real smart city datasets demonstrate the model’s robustness in non-sparse and sparse network scenarios.It can effectively evaluate trusted and untrusted objects in the network and improve the accuracy and convergence of trust evaluation.

In recent years, the social Internet of Things (SIoT) and its applications have developed rapidly and introduced convenience to people's lives. In view of the massive amount of information generated by the SIoT, it is necessary to model and analyze the communication among smart devices and study the impact of social attributes of smart devices on information dissemination. Therefore, in this study, we propose a layered information dissemination model for SIoT that abstracts the communication process in SIoT as the information dissemination process among nodes, describes the process of wireless communication in SIoT using the microscopic Markov chain method, and derives the information dissemination threshold based on theoretical analyses. In addition, this study proposes a dynamic cache strategy considering the influence of social attributes, such as user interestingness, information popularity, and the interaction between messages on communication. Finally, the correctness of the model and propagation threshold and the effectiveness of the caching strategy are verified by simulation experiments in artificial networks and real networks. [ABSTRACT FROM AUTHOR]

The reliability interception of network attack is realized by data access control in network security storage area, and the data security in network is guaranteed. The traditional free link decentralized control model is used to control the data access in the secure storage area of the network. The data access control system of network security storage area includes communication library, protocol base, core control library and resource library. The association rule feature extraction method is used to design the data mining and attack detection algorithm of the network security storage area, which is the core of the data access control system software in the network security storage area. The kernel of the optimal interface in the network security storage area is set up by SDICmdCon register, and the software development of data access control in the network security storage area is realized by the adaptive partition weighted interface scheduling under the embedded Linux environment. This paper considers the novel perspective the data access control method of cloud network secure storage under Social Internet of Things environment. The simulation results show that the model is accurate for data mining and attack detection in the network security storage area, and the security of data access control is improved, which ensures the network security 21% higher than the traditional methods. [ABSTRACT FROM AUTHOR]

Summary: Due to the increasing growth of objects and problems such as increased traffic, overload, delay in response, and low search volume in the service discovery process in the complex Social Internet of Things (SIoT) environment, we provide an effective mechanism in the service discovery process by grouping objects based on common criteria that help us improve service search performance. In this article, we present a new method for clustering objects so that we can group objects that have common services and can work together. Hence, we create a set of different associations for the type of service and reciprocal cooperation of objects. With its help, instead of a global network search, we can perform service searches locally more efficiently and ensure the accuracy and correctness of searches and their answers. Then, we have provided a new mechanism for the service discovery process. In addition, we categorized communities based on their size to compare our proposed algorithm with other approaches using factors such as modularity in SIoT. Finally, we achieved sufficient efficiency in service discovery (86.81% and 88.28%) and demonstrated better performance of the proposed approach in identifying communities. [ABSTRACT FROM AUTHOR]

Nowadays, smart wearable devices are used widely in the Social Internet of Things (IoT), which record human physiological data in real time. To protect the data privacy of smart devices, researchers pay more attention to federated learning. Although the data leakage problem is somewhat solved, a new challenge has emerged. Asynchronous federated learning shortens the convergence time, while it has time delay and data heterogeneity problems. Both of the two problems harm the accuracy. To overcome these issues, we propose an asynchronous federated learning scheme based on double compensation to solve the problem of time delay and data heterogeneity problems. The scheme improves the Delay Compensated Asynchronous Stochastic Gradient Descent (DC-ASGD) algorithm based on the second-order Taylor expansion as the delay compensation. It adds the FedProx operator to the objective function as the heterogeneity compensation. Besides, the proposed scheme motivates the federated learning process by adjusting the importance of the participants and the central server. We conduct multiple sets of experiments in both conventional and heterogeneous scenarios. The experimental results show that our scheme improves the accuracy by about 5% while keeping the complexity constant. We can find that our scheme converges more smoothly during training and adapts better in heterogeneous environments through numerical experiments. The proposed double-compensation-based federated learning scheme is highly accurate, flexible in terms of participants and smooth the training process. Hence it is deemed suitable for data privacy protection of smart wearable devices. [ABSTRACT FROM AUTHOR]

The social internet of things (SIoT) facilitates numerous networking services and novel applications for the IoT, making it more productive and powerful. The state-of-the-art machine learning architectures often face different challenges when processing big data such as increased memory volume, increased training time, and higher computational costs. To tackle these challenges, this paper presents a novel framework for big data classification. Initially, an adaptive filter removes unwanted data and noises. The dimensionality of the filter data is then reduced via the Hadoop map reducer. After that, the battle royale optimization (BRO) algorithm is applied for feature selection. The optimal feature selection process performed by the BRO algorithm improves the accuracy and performance of the proposed fuzzy optimal deep convolutional neural network (FO-DCNN). The FO-DCNN model is formed by integrating the fuzzy-based remora optimization (F-RO) algorithm and deep CNN architecture for robust SIoT data classification. When evaluated using the Twitter, Rotten tomato, skin disease, diabetes, and hepatitis datasets, the proposed model offers improvements in terms of classification accuracy, memory consumption, and computational time. [ABSTRACT FROM AUTHOR]

The number of people owning vehicles has been steadily growing, resulting in increased numbers of vehicles on the roads, making roads more congested, and increasing the risk of accidents. In addition, heavy rain, snow, and fog have increased due to abnormal weather caused by global warming. These bad weather conditions can also affect the safety of vehicles and drivers. The need to disseminate safety messages on the social Internet of Vehicles due to these problems has been steadily increasing. In this paper, we propose an efficient safety message dissemination scheme that focuses on urban environments with high vehicle density and mobility to address these problems. The proposed scheme reduces packet loss by considering frequent cluster departures and subscriptions through an efficient cluster management technique. In a vehicle-to-vehicle environment, the dissemination of safety messages is divided into intracluster and intercluster emergencies, as well as a general safety message dissemination technique. In a vehicle-to-infrastructure environment, the proposed scheme reduces the number of processing requests and duplicate messages made to roadside units (RSUs) through a request operation process for each vehicle and an RSU scheduling technique. We conducted several performance evaluations of message packet loss and the number of RSU processing requests to demonstrate the superiority of the proposed scheme. [ABSTRACT FROM AUTHOR]

There are many problems in Social Internet of Things(IoTs), such as complex topology information, different degree of association between nodes and overlapping communities. The idea of set pair information grain computing and clustering is introduced to solve the above problems so as to accurately describe the similarity between nodes and fully explore the multi-community structure. A Set Pair Three-Way Overlapping Community Discovery Algorithm for Weighted Social Internet of Things (WSIoT-SPTOCD) is proposed. In the local network structure, which fully considers the topological information between nodes, the set pair connection degree is used to analyze the identity, difference and reverse of neighbor nodes. The similarity degree of different neighbor nodes is defined from network edge weight and node degree, and the similarity measurement method of set pair between nodes based on the local information structure is proposed. According to the number of nodes' neighbors and the connection degree of adjacent edges, the clustering intensity of nodes is defined, and an improved algorithm for initial value selection of k-means is proposed. The nodes are allocated according to the set pair similarity between nodes and different communities. Three-way community structures composed of a positive domain, boundary domain and negative domain are generated iteratively. Next, the overlapping node set is generated according to the calculation results of community node membership. Finally, experiments are carried out on artificial networks and real networks. The results show that WSIoT-SPTOCD performs well in terms of standardized mutual information, overlapping community modularity and F1.

With the extensive integration of the Internet, social networks and the internet of things, the social internet of things has increasingly become a significant research issue. In the social internet of things application scenario, one of the greatest challenges is how to accurately recommend or match smart objects for users with massive resources. Although a variety of recommendation algorithms have been employed in this field, they ignore the massive text resources in the social internet of things, which can effectively improve the effect of recommendation. In this paper, a smart object recommendation approach named object recommendation based on topic learning and joint features is proposed. The proposed approach extracts and calculates topics and service relevant features of texts related to smart objects and introduces the “thing-thing” relationship information in the internet of things to improve the effect of recommendation. Experiments show that the proposed approach enables higher accuracy compared to the existing recommendation methods.

The Internet of Things (IoT) benefits from social networking platforms in establishing and enhancing social-oriented services, information, and autonomous social relationships. Social IoT (SIoT) systems can boost the user experience in the real world in several applications, including healthcare, transportation, and entertainment. However, the collected data from various interconnected SIoT systems is massive, demanding robust and efficient processing algorithms, feature extraction, selection, and inference. This work presents an enhanced Artificial Hummingbird algorithm (AHA) for feature selection (FS). The enhanced version of AHA is performed using the advantages of Quantum-based optimization. The main aim of using Quantum is to improve the population’s exploration ability while discovering feasible regions. Extensive experiments utilizing eighteen UCI datasets were conducted to validate the developed FS method, QAHA. The QAHA is compared with other FS methods, and the experimental established its efficiency. Moreover, a set of four datasets from SIoT are used to evaluate the applicability of QAHA to the real-world setting. The results using these datasets indicate the high performance of QAHA to increase the accuracy by decreasing the number of features. In the case of UCI datasets, the average accuracy of the developed QAHA is 93% among the eighteen datasets. Whereas, In the case of the SIoT datasets, the developed QAHA has an accuracy of nearly 90.7%, 98.7%, 92.2%, and 84.6% for the Trajectory, GAS sensors, Hepatitis, and MovementAAL datasets, respectively.

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During application development, various unknown factors can affect the application and cause failures. According to the traditional software engineering techniques, software testing is usually performed later in software development, and it is difficult to find and correct these failures at this time. More and more distributed software systems are deployed on public cloud computing platforms and use the Internet to provide services outward. The complexity, dynamics, and openness of cloud computing environments make distributed software systems more prone to failure, which can result in service failures that can affect the normal use of large numbers of users. Therefore, in this paper, we study the fault detection algorithm of multi-channel parallel data flow software based on cloud computing. The data fusion and clustering models are integrated to detect the data features with the numerical verification, and the theoretical framework is also assigned to a better robustness for the theoretical framework implementation. The final results are also validated through the systematic overview. Future research directions are also included in the conclusion. [ABSTRACT FROM AUTHOR]

As nodes in Social Internet of Things (SIoT) become more intelligent, malicious information occurs more frequently and spreads more widely. This problem can severely affect the trustworthiness of services and applications in SIoT. Methods to effectively control malicious information spreading in SIoT are essential and necessary. Reputation mechanism provides a powerful tool to tackle this challenge. In this paper, we propose a reputation-based mechanism to activate the self-purification capacity of the SIoT network by balancing information conflicts triggered by reporters and supporters. In order to find the best rewarding and punishment strategy, a bilateral cumulative-prospect-based evolutionary game model of SIoT network information conflict is constructed. Using local stability analysis and numerical simulation, the evolutionary trends of the proposed game model under different theoretical application scenarios are analyzed. The findings indicate that the basic income and deposit of both sides, the popularity of information as well as the importance of the conformity effect all have a significant impact on the system's steady state and evolutionary path. The specific conditions that both participating sides of the game tend to treat conflicts relatively rationally are analyzed. Dynamic evolution analysis and sensitivity analysis of selected parameters show that basic income is positively related to smart object's feedback strategies, while deposit is negatively related to that. While weight of conformity effect or the information popularity goes up, the rising of feedback probability is observed. Based on the above results, suggestions on dynamic reward and punishment strategies are given. The proposed model is a helpful attempt to model the evolution of information spreading in SIoT networks, with the ability to simulate several well-known regularities of message dissemination. Proposed model and suggested quantitative strategies can be helpful to build feasible malicious information control facilities in SIoT networks.

This article starts from the service population of long‐term care beds and estimates the spatial distribution of long‐term care beds in the area. At the same time, combined with the spatial distribution of beds in the area, the supply of long‐term care beds is based on the division of service areas. Long‐term care institutions are facilities for the care and care of disabled elderly people. The beds are long‐term care beds, and the clients are mainly elderly people who cannot take care of themselves. The demand for long‐term care beds is mainly related to the number of elderly people. The elderly population in this article mainly refers to the resident population aged 60 and above. Based on the layout map of the resident population aged 60 and above, combined with the area of the administrative divisions of the village committees, the population density of each village committee is calculated to produce a schematic diagram. We use the data connection analysis model and the pattern finding algorithm to optimize the traditional methodologies. [ABSTRACT FROM AUTHOR]

Computer forensics is accompanied by the development of computer technology and the emergence of a new form of crime, network crime. The computer log provides a lot of evidence for the computer system to attack crime. Due to the particularity of information technology, it also brings huge information security risks in the whole information process. There are more and more new types of criminal activities which take computer network information system as criminal object and computer network information system as criminal tool. Therefore, it is of great practical significance to crack down on computer network crimes and ensure information security for national economic development and social stability. Different from the traditional social crime, cyber crime is a typical high‐tech crime. The evidence of network crime is transmitted and stored in the form of binary digital data through computers or related network devices in the network. The network forensics model based on data mining technology proposed in this paper applies data mining technology to the evidence analysis of network forensics, and makes full use of various mining modes of data mining technology. [ABSTRACT FROM AUTHOR]

By capitalizing on object relationships and local navigability, the social internet of things (SIoT) is one of the burgeoning paradigms that could solve the technical challenges of conventional IoT. Because of this paradigm's capacity to combine conventional IoT with social media, it is possible to create smart objects and services with greater utility than those created using conventional IoT infrastructures. In recent years, scholars have become interested in SIoT, leading to a plethora of works examining various mechanisms for providing services and technologies within this context. In this vein, we present a comprehensive review of recent research covering important aspects of SIoT. In this research, we give a detailed justification for the function of several machine learning paradigms and provide a practical application of it to unexamined concerns relating to erroneous data and other social IoT. First, we give a classification of false news detection approaches and an analysis of these techniques. Second, the potential uses for detecting fake news are examined at length, including how it might be applied to the areas of fake profile detection, traffic management, bullying detection, etc. We also suggested a detailed review of the possibilities of machine learning algorithms for detecting bogus news and intervening in social networks. In our paper, we introduce categories of fake news detection methods providing a comparison between these methods. After that, the promising applications for false news detection are extensively discussed in terms of fake account detection, bot detection, bullying detection, and the security and privacy of SIoT. After all, A thorough discussion of the potential of machine learning approaches for fake news detection and interventions in SIoT networks along with the state-of-the-art challenges, opportunities, and future search prospects. This article seeks for aiding the readers and researchers in explaining the motive and role of the different machine learning paradigms to offer them a comprehensive realization of so far unexplored issues related to false information and other scenarios of SIoT networks. [ABSTRACT FROM AUTHOR]