Search

Multimodality sentiment analysis in the social Internet of Things is a developing field, which is basic to empathetic mechanisms, affective computing, and artificial intelligence. Current works in this domain do not explicitly consider the influence of contextual information fusion based on correlation coefficient and memory network with branch structure for sentiment analysis. Unlike present works, this article presents a hierarchical self-attention fusion (H-SATF) model for capturing contextual information better among utterances, a contextual self-attention temporal convolutional network (CSAT-TCN) for sentiment recognition in the social Internet of Things, and a multibranch memory (MBM) network that stores self-speaker and interspeaker sentimental states into global memories. For MOSI data sets, the hybrid H-SATF-CSAT-TCN-MBM model outperforms the state-of-the-art networks and shows 0.31%–9.93% improvement.

As the fifth-generation (5G) and beyond (5G+/6G) networks move forward, and a wide variety of new advanced Internet of Things (IoT) applications are offered, effective methodologies for discovering time-relevant information, services, and resources are being demanded. To this end, computing-, storage-, and battery-constrained IoT devices are progressively augmented via digital twins (DTs) hosted on edge servers. According to recent research results, a further feature these devices may acquire is social behavior; this latter offers enormous possibilities for fast and trustworthy service discovery, although it requires new orchestration policies of DTs at the network edge. This work addresses the dynamic placement of DTs with social capabilities [social digital twins (SDTs)] at the edge, by providing an optimal solution under IoT device mobility and by accounting for edge network deployment specifics, types of devices, and their social peculiarities. The optimization problem is formulated as a particular case of the quadratic assignment problem (QAP); also, an approximation algorithm is proposed and two relaxation techniques are applied to reduce computation complexity. Results show that the proposed placement policy ensures a latency among SDTs up to 1.4 times lower than the one obtainable with a traditional proximity-based only placement while still guaranteeing appropriate proximity between physical devices and their virtual counterparts. Moreover, the proposed heuristic closely approximates the optimal solution while guaranteeing the lowest computational time. publishedVersion

Social Internet of Things (SIoT) is an extension of the Internet of Things (IoT) that converges with social networking concepts to create social networks of interconnected smart objects. This convergence allows the enrichment of the two paradigms, resulting into new ecosystems. While IoT follows two interaction paradigms, human to human (H2H) and thing to thing (T2T), SIoT adds on human-to-thing (H2T) interactions. SIoT enables smart “ social objects ” that intelligently mimic the social behavior of human in the daily life. These social objects (SOs) are equipped with social functionalities capable of discovering other SOs in the surroundings and establishing social relationships. They crawl through the social network of objects for the sake of searching for services and information of interest. The notion of trust and trustworthiness in social communities formed in SIoT is still new and in an early stage of investigation. In this article, our contributions are threefold. First, we present the fundamentals of SIoT and trust concepts in SIoT, clarifying the similarities and differences between IoT and SIoT. Second, we categorize the trust management solutions proposed so far in the literature for SIoT over the last six years and provide a comprehensive review. We then perform a comparison of the state-of-the-art trust management schemes devised for SIoT by performing comparative analysis in terms of trust management process. Third, we identify and discuss the challenges and requirements in the emerging new wave of SIoT, and also highlight the challenges in developing trust and evaluating trustworthiness among the interacting SOs.

The things in the Internet of Things are becoming more and more socially aware. What social means for these things (more often termed as “social objects”) is predominately determined by how and when objects interact with each other. In this paper, an agent-based model for Social Internet of Things is proposed, which features the realization of various interaction modalities, along with possible network structures and mobility modes, thus providing a novel model to ask interesting “what-if” questions. The scenario used, which is the acquisition of shared resources in a common spatial and temporal world, demands agents to have ad-hoc communication and a willingness to cooperate with others. The model was simulated for all possible combinations of input parameters to study the implications of competitive vs. cooperative social behavior while agents try to acquire shared resources/services in a peer-to-peer fashion. However, the main focus of the paper was to analyze the impact of profile-based mobility, which has an underpinning on parameters of extent and scale of a mobility profile. The simulation results, in addition to others, reveal that there are substantial and systematic differences among different combinations of values for extent and scale.

Social Internet of Things (SIoT), integrating the social networks and Internet of Things (IoT), leads to heterogeneous interactions of thing to thing, human to human, and human to thing, which in turn generates exploded information. Hence, as the soul of SIoT, information with its interaction and diffusion, records the track of humans and things and contains the hidden value for social administration and people’s lives. Therefore, how to characterize the interplay between behavior spreading and information diffusion in SIoT is essential to predict and manage the information. Motivated by this, a more comprehensive understanding of the coupled modeling of social interaction and information diffusion processes in SIoT is conceived first. With the widespread adoption of cloud-edge computing, different nodes have different consciousness on information. Hence, a cloud-edge-aided information diffusion model is proposed for efficient interactions, which incorporates the role of edge in timely processing and feedback. On this basis, a blockchain-based cloud-edge SIoT architecture is proposed for traceability and security of information diffusion. Furthermore, the dynamical analysis of the coupled model in SIoT is provided, which illustrates the outbreak threshold, stability, and scale of information propagation. An interesting finding is that interactive behavior spreading only influences the final size of information propagation, not the spreading threshold. Extensive simulation results and detailed performance analysis verify the theoretical results, which are beneficial to provide traceable dissemination so as to find the most influential node and control the scale of information diffusion.

Social Internet of Things (SIoT) is a new standard resulting from the integration of the Internet of Things (IoT) and social networking. IoT is a visionary, one-paradigm, whereas social networks are platforms where voluminous collaborations between humans exist. The SIoT is defined as a social network of objects that are not only smarter but also socially conscious. Fundamental requirements of both IoT and SIoT networks include efficient service search and the discovery of object mechanisms. Therefore, our paper proposes a simple model for social networks to discover the object or services by using a small-world network. Our proposed model comprised a set of objects, and where each object is looking for a service. The service search is initiated in different hops by an object using a service query message to the nearest object. If the requested object is identified immediately or at the first hop, a permanent link is established between the service requester and the service provider otherwise the search process is repeated until the service is discovered. In this study, we integrate the SIoT with the small world concept for the building of our model. Our proposed model guarantees that the object containing the information is in a bounded path length and is treatable owing to the structure of small-world networks. The process of search is efficient because it is initiated only when an object asks for another object or service. Our intention here is to increase the navigability of the network. We carefully performed numerical analyses for our model and presented the simulation results based on efficiencies such as average path length, clustering coefficient, service execution time, and the giant component. After conducting various experiments, we conclude that our proposed model is efficient and reflects the real network structures of small-world networks, therefore, it can be suitable for social networks.

Recently, the consolidation of Social Network and the Internet of Things (IoT) acquiring appreciation and consideration among researchers due to its immense and adaptable behavior. Social Internet of Things (SIoT) comprehends the adequacy to support the inventive applications and network services with adept and persuasive demeanors. The author’s exertion for this research provides the subsistence to the SIoT amelioration Currently, the SIoT technique proven to be competent, but the exponential growth of heterogeneous smart devices may arise a challenging scenario. Searching the right object, service, or best path to the informative nodes in such highly convoluted networks is always a challenging task. This research strengthen the SIoT paradigm by consolidating the properties of the individual’s Small World. In addition, this research also advises a Smart Social Agent (SSA) to minimize the human intervention. The absolute objective of this paper is two-fold, (i) transformation of SIoT network into Individual’s Small World SIoT network, (ii) searching the right information efficiently by exploiting SSA. To accomplish the goal we have proposed an efficient Query based Searching algorithm which exploits Service Oriented properties of Individual’s Small World SIoT network and searches information. First result shows the significant alleviation in the network complexity, reduces the average path length, and network diameter. Eventually, it improves the network scalability and navigability. While second result shows that the proposed Query based searching algorithm proves to be efficient with its mean execution time not more than 2.66 ms for complete network on two different machines. Whereas, for Individual’s small world network the mean execution time is not more than 2.47 ms for both machine.

The existence of botnet puts people in an extremely insecure environment, which has seriously affected the development of Internet of Things (IoT). In order to prevent the formation of a botnet, it is necessary to understand the propagation behaviors and influence factors. As IoT devices become more intelligent, they can gradually generate social characteristics by mimicking human behaviors. However, in the process of coping with the botnet problem, little consideration has been given to the potential social characteristics of IoT. In this article, we build a dynamic botnet propagation model (i.e., IoT-BSI model) to study the influences of two social characteristics (i.e., device’s spread capability and device’s identification ability) on botnet formation. First, in terms of device’s spread capability, this article makes great improvements on the K- shell decomposition algorithm and calculates this characteristic more accurately. Second, this article divides the device’s identification ability into the rational identification ability and the irrational identification ability based on the sociological theory, and the calculation of the former is mainly realized by utilizing the PageRank idea. Third, this article applies the mean-field equation theory to analyze the dynamic characteristics of botnet propagation theoretically. Finally, the comprehensive results show that our model is not merely more consistent with the actual situation but also performs better than four compared models.

The emergence of the social Internet of things (SIoT) paradigm opens a new path for the development of the next generation of the Internet of things, but it also brings new security risks. The social attributes contained in SIoT exacerbate the harm of the virus to the network, so it is essential to analyze the characteristics of the virus and the core factors that influence the spread of the virus. This paper put forward a virus propagation model (susceptible-propagation ability-exposed-infected-recovered, SPEIR) which mainly considers the differences in intelligent devices’ propagation ability. Then, this paper proposes a novel method(i.e., DKP), which combines K-shell theory that we add the delete order and the idea of PageRank that determines the importance of web pages to calculate intelligent devices’ propagation ability. And the experiments have proved the accuracy of DKP is high.

– In the recent year, with the emergence of various smart devices, the data is explosively increasing in the social Internet of Things (IoT) such as human healthcare. These data mainly involve information about user behaviors collected from various heterogeneous wireless sensor and social networks. Therefore, it is vital to analyze the data to find hidden meaning and convert it into valuable information. Due to the lack of capability to handle a wide range of queries, a traditional relational database provides inefficient analysis for the data. A graph database can easily store and analyze the data from various heterogeneous wireless sensor and social network using team formation algorithm. In the healthcare field, it is important to form a team that manages patients' health efficiently. The final goal of team formation is to organize experts who can perform task of data analysis. However, the existing team formation algorithms rely on a centralized computing environment and require high communication cost among experts to form a team. In this paper, we propose a parallel team formation method on apache spark (PTFS) to analyze graph data considering the crowd intelligence capability that exists in the graph data and social network. The PTFS employs two computation stages - a find skill and a merger subgraph and provides the parallel execution of many map tasks of graph data analysis. The experimental evaluation of the proposed method on a graph dataset demonstrates that it minimizes the communicating cost of the team members to form an optimized expert team in which a desired skill set is assigned to accomplish the graph data analysis.

The Internet of Things (IoT) has evolved at a revolutionary pace in the last two decades of computer science. It is becoming increasingly fashionable for the IoT to be rebranded as the “Social Internet of Things” (SIoT), and this is drawing the attention of the scientific community. Smart items in the Internet of Things (IoT) ecosystem can locate relevant services based on the social ties between neighbors. As a result, SIoT displays the interplay between various items as a problem in the context of the social IoT ecosystem. Navigating a network can be difficult because of the number of friends and the complexity of social ties. By identifying difficulties with standard SIoT devices’ interaction with social objects, truthful friend computing (TFC) is a new paradigm for tracing such difficulties by utilising a relationship management component to improve network navigability. The concept of trust management can be useful as a strategy during collaborations among social IoT nodes. As a result, the trustor can use a variety of measures to evaluate a smart object’s trustworthiness. Hence, this article demonstrates the need for the trustor to evaluate the extent to which a given metric has contributed to the overall trust score and illustrates profitability when engaging in a transaction with other nodes. With the help of the SIoT, this paper used a unified fuzzy-based computational technique and a multiple-criteria decision-making approach to evaluate the trust weights. The statistical findings show that the computing of “truthful friends” is the biggest challenge for successful SIoT implementation at the initial level.

Over the last decade, the Internet of Things (IoT) domain has grown dramatically, from ultra-low-power hardware design to cloud-based solutions, and now, with the rise of 5G technology, a new horizon for edge computing on IoT devices will be introduced. A wide range of communication technologies has steadily evolved in recent years, representing a diverse range of domain areas and communication specifications. Because of the heterogeneity of technology and interconnectivity, the true realisation of the IoT ecosystem is currently hampered by multiple dynamic integration challenges. In this context, several emerging IoT domains necessitate a complete re-modeling, design, and standardisation from the ground up in order to achieve seamless IoT ecosystem integration. The Internet of Nano-Things (IoNT), Internet of Space-Things (IoST), Internet of Underwater-Things (IoUT) and Social Internet of Things (SIoT) are investigated in this paper with a broad future scope based on their integration and ability to source other IoT domains by highlighting their application domains, state-of-the-art research, and open challenges. To the best of our knowledge, there is little or no information on the current state of these ecosystems, which is the motivating factor behind this article. Finally, the paper summarises the integration of these ecosystems with current IoT domains and suggests future directions for overcoming the challenges.

The Social Internet of Things (SIoT) can be defined as an IoT where objects can establish social links with each other, in which the capability of humans and objects to select, determine, and use services in the IoT is improved. Hence, without a security mechanism to ensure trustworthy SIoT nodes' interactions, this paradigm cannot reach enough popularity to be a well-established technology. A Trust Management (TM) model becomes a major challenge in SIoT systems to ensure qualified and secure services. Nevertheless, the defined TM models don't show their performance towards trust related attacks performed at the aim of disrupting the management of the trust values. In this work, we propose a TM model dedicated not only to identify trustworthy nodes, but also to detect and prevent malicious attacks. Based on Machine Learning (ML) techniques, this model can identify these attacks by learning proposed trust features that are derived from the description of malicious node behaviors. Experimentation results generated with simulated data sets based on real data concur the effectiveness of our proposed model.

For the Social Internet of Things (SIoT), the interaction among ever increasing number of smart devices results in an exponential increase of services, which leads to an extreme difficulty for users to find suitable services. To address this issue, most existing recommendation algorithms are based on the data stored on the centralized server and distributed schemes are ignored. Meanwhile, distributed recommendation algorithms face the problems of privacy leakage and efficiency, which decrease the quality of experience (QoE). Therefore, we propose a novel SIoT service recommendation scheme called SIoT- SR, which adopts LSH Forest while combining with collaborative filtering algorithm to predict the Quality of Service (QoS) data of users. The LSH forest implements binary search by sorting and also has the ability to self-correct parameters. It can achieve a good tradeoff among memory, accuracy, efficiency, and privacy. Finally, we validate the effectiveness of the scheme based on the dataset WS-DREAM. The experimental results show that SIoT-SR has high prediction accuracy and efficiency while saving computing resources and are suitable for service recommendation of SIoT with resource-constrained devices.

Internet of Things is the future era of computing, where every conventional object are equipped with sensing and communicating capabilities to co-ordinate with each other autonomously to achieve a common goal. Social Internet of Things is the recent enhancement of Internet of Things where the communication between objects employs theories and observations from human social behavior with various types of relationships. The number of objects directly communicating with a device needs to be managed intelligently to cope with the memory and computational capacity of each object. Heuristic which are developed earlier uses graphical property of the device; this is the first work to the best of our knowledge to adopt the type of relationship in relationship management heuristic. An analysis is also presented after applying the heuristic in an Social Internet of Things environment.

Social Internet of Things (SIoT) integrates the social network with the Internet of Things (IoT) and has become a hot research issue for its potential to support novel IoT applications and networking services in more effective and efficient ways. The search engine, which is primarily designed for social network users to acquire interested information, also plays an important role in SIoT. There even exist search engines specially designed for SIoT. Using a search engine, people can easily find the smart devices in SIoT. Specifically, a search engine assists the information dissemination, i.e., enabling users (both humans and things) to access interested objects (both humans and things) with keywords-searching and transferring contents from the source directly to potential interested users. Accompanying such processes, the SIoT evolves as new links emerge between users and their interested objects. In this paper, we aim to quantitatively characterize how a search engine influences the social relationship in the IoT. Firstly, we point out that the search engine serves as the medium between the social network and the IoT, and then we propose a Search Social Internet of Things (SSIoT) model for SIoT evolution. Secondly, we adopt six performance metrics, namely, degree distribution, network diameter, average distance, network density, network stability, and user betweenness. Theoretically, we prove that the degree distribution follows an intensified power-law, the network diameter and the average distance shrink, network density, network stability, and user betweenness are greater in SSIoTs. Thirdly, we quantitatively show that a search engine accelerates malicious code propagation in SIoTs. Finally, based on four real-world data sets (i.e., CDBLP, Facebook, Weibo, and P2P), we verify our theoretical findings.

Online services typically collect data that are explicitly provided by users and metadata that are implicitly inferred from users’ activity patterns. The proclaimed goal of metadata collection is to support the quality of service, thus enhancing the Social Internet of Things (SIoT). To most users, it is not obvious that metadata express people’s lives to a large degree. Traditionally, security has focused on protecting communication content rather than the metadata associated with it. This provides a thin layer of privacy protection. Thus, a demand exists for privacy-preserving technologies that prevent metadata collection and aggregation. This paper focuses on a service’s ability to observe communication metadata that can be exploited to learn users’ identities and behavior patterns. Thus, a novel system, Misty Clouds, is proposed as a platform for creating anonymous Internet connections to address both security and performance issues. The performance evaluation shows that the desired level of anonymity can be achieved with tolerable performance overheads. Through a comparison analysis, it is shown that the new algorithm outperforms an existing algorithm, Tor. Additionally, the features that can facilitate the growth of Misty Clouds into a holistic privacy-preserving platform are discussed. Furthermore, a user survey was conducted to study users’ perceptions and attitudes.

Social-Internet of Things (S-IoT) fortifies the relationship between computing entities for efficient utilization of resources. S-IoT accentuates on building convivial circles between the devices, which sanction sharing of information without compromising own performance. Trust and privacy are the two factors responsible for the maintenance of these social circles. Trust establishes faith between the entities and privacy supports abstraction of device information. There exist a plethora of approaches, which fixate on trust and privacy in social networks. However, the subsisting solutions rely on a trust scoring system and utilize a single centralized server for these calculations. Such scoring systems are manipulable by eavesdroppers, which engender erroneous reputation leading to high trust values. Considering the desideratum of an efficient trust and privacy-preserving solution, this paper proposes a novel solution in the form of fission computing. The proposed approach relies on the edge-crowd integration for maintenance of trust and preservation of privacy rules in S-IoT. The proposed solution uses crowdsources as mini-edge servers and entropy modeling for defining trust between the entities. Fission managers are responsible for maintaining the privacy rules, which operate for every S-IoT application. The proposed approach is analyzed through numerical simulations by using a safe network as a performance benchmark. Further, the article presents a case study on the detection of fake news sources in S-IoT as an application of the proposed approach.

The interactions between enterprise systems (ESs) are unsatisfactory in numerous cases, which has a significant impact on the robustness of social internet of things (SIoT) for customization manufacturing (CM). In this paper, we present a novel methodology for managing the robustness of SIoT in the CM environment from a complex systems standpoint. First, this methodology decomposes SIoT into multiple ESs, determines the state variables of those ESs, and dissects the interactions between them. Second, it develops a nonlinear dynamic model along with algorithms revealing the bottom-up operation mechanisms of SIoT and identifying the threshold conditions in integration continuum for the SIoT to achieve a robust operation. Finally, extensive numerical experiments and an application involving manufacturing supply chains are given to illustrate this new methodology. The overall results suggest that interoperability and low incompatibility outperform high incompatibility and tightly-coupled integration in improving the robustness of SIoT under the CM paradigm.

The Internet of Things (IoT) is currently considered the new frontier of the Internet, and a lot of research results about this topic can be found in the literature. One of the most effective ways to investigate and implement IoT is based on the use of the social network paradigm: Social Internet of Things (SIoT) is an excellent attempt in this direction. In the last years, social network researchers have introduced new paradigms capable of capturing the growing complexity of this scenario. One of the most known of them is Social Internetworking System, which models a scenario comprising several related social networks. In this paper, we investigate the possibility of applying the ideas underlying Social Internetworking System to IoT, and we propose a new paradigm, called MIoT (Multiple Internets of Things), capable of modeling and handling the increasing complexity of this last context. Furthermore, in order to facilitate knowledge extraction and exploitation in presence of a huge number of things, we also propose a crawler specifically designed for an MIoT. Finally, through an experimental campaign, we show that classical crawlers are not adequate for MIoTs, whereas our own is well suited and outperforms all of them in this context.

The core concept of IoT is to equip real world objects with computing, processing and communicating capabilities to enable socializing between them. Internet of Vehicles (IoV) is an adherent of IoT that has realized significant advancements using communication technologies. Vehicles connected through Internet are capable of sharing information that can substantially enhance the quality of traffic on roads. Social Internet of Things (SIoT) is an instance of IoT that deals specifically in socialization of connected objects. SIoT enables the notion of Social Internet of Vehicles (SIoV) where vehicles are the key entities for sharing information between themselves and the infrastructure (commonly known as Road Side Units (RSUs)). Vehicles in SIoV socialize by exchanging data such as traffic congestions, weather conditions, infotainment, vacant parking slots, alternate routes and discount coupons for restaurants etc. In SIoV, vehicles can communicate with other vehicles and infrastructure through traditional communication technologies like Wi-Fi, Cellular networks or through Dedicated Short Range Communication (DSRC) etc. SIoV will be confronted with ethical dilemmas and expected to function in an ethically responsible manner. This paper highlights the ethical implications of SIoV systems. Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) involves autonomous decision making that requires setting ethical and moral rules before taking verdict. The article discusses the lack of ethical guidelines in designing and deploying of SIoV systems that are of utmost importance. Finally, an addition to SIoV architecture is proposed to incorporate the ethical and moral principles for scheming the SIoV systems.

Internet of things (IoT) has initiated a few interesting research directions such as Social Internet of Things(SIoT), Cloud of Things(CoT), and Edge of Things(EoT) etc. Nowadays, a large number of IoT nodes are available in our surroundings and there is a need to ensure automated access and communication among these IoT nodes. An emerging area of study is to make a social platform for IoT nodes so that devices can communicate with each other and create automated and on-demand services. As these IoT nodes and services are going to co-exist with us (human), we foresee establishing of cognitive contributory skills where IoT nodes, services and even human skills can collectively form a cognitive society to share resources, information and skills. However, building such intelligent societies automatically using SIoT is a big challenge, mainly due to the complexity of the systems and availability of a large number of nodes. In such scenarios, it is not trivial to find a suitable SIoT service node correctly to avail a service in real-time. We propose a common platform to virtualize the physical objects and make them available in the cyber-world and at the same time to ensure resource sharing. In this paper, we focus on virtual object management and selection process for SIoT platform. We propose a QoS aware object selection using Integer Programming solutions to find a right service at the right time.

Traffic congestion is expected to be an inescapable problem in the near future, as traffic is growing exponentially and becoming congested with thousands of millions of vehicles across the world. All existing approaches to examine this traffic domain are centralized and not feasible in terms of cost, time, and scalability issue. To investigate the probable reasons for traffic congestion with efficiency, this paper proposes a novel algorithm to reform any traffic domain into a complex network using the principles of decentralized Social Internet of Things (SIoT). By integrating social networking concepts into the Internet of Things (IoT), the concept of SIoT has been proposed. The idea presented below is that every vehicle acts as a smart Thing, communicate with nearby vehicles within a particular distance in a decentralized manner and together form a complex network. We have built a generalized mathematical model to measure the least amount of distance between two vehicles within the complex network. By extracting key network properties from the complex network of vehicles built from our proposed algorithm, probable reasons for traffic congestion can be investigated efficiently.

The Internet of Things (IoT) paradigm has integrated the sensor network silos to the Internet and enabled the provision of value-added services across these networks. These smart devices are now becoming socially conscious by following the social Internet of Things (SIoT) model that empowers them to create and maintain social relationships among them. The Social Internet of Vehicle (SIoV) is one application of SIoT in the vehicular domain that has evolved the existing intelligent transport system (ITS) and vehicular ad-hoc networks (VANETs) to the next phase of Intelligent by adding socializing aspect and constant connectivity. SIoV generates a massive amount of real-time data enriched with context and social relationship information about vehicles, drivers, passengers, and the surrounding environment. Therefore, the role of privacy management becomes essential in SIoV, as data is collected and stored at different layers of its architecture. The challenge of privacy is aggravated because the dynamic nature of SIoV poses a major threat in its adoption. Motivated by the need to address these aspects, this paper identifies the challenges involved in managing privacy in SIoV. Furthermore, the paper analyzes the privacy issues and factors that are essential to be considered for preserving privacy in SIoV environments from different perspectives including the privacy of a person, behavior and action, communication, data and image, thoughts and feelings, location and space, and association. In addition, the paper discusses the blockchain-based solutions to preserve privacy for SIoV.

As a cluster between Internet of Things (IoT) and mobile social networks (MSNs), social IoT allows close interaction between humans and things. Many applications of IoT and MSN are based on the community structure to realize efficient services, and thus the detection of the community structure has become a key problem and attracted much academic attention. In this paper, a compressive sensing (CS) over graphs-based intercommunity detection scheme is proposed for the social IoT. By exploiting the probability of two nodes encountering each other, the encounter probability and the edge-clustering coefficient are utilized to define a novel metric for each connection and construct the measurement matrix. Then a CS-based detection algorithm is proposed to detect the intercommunity links. Moreover, the edge-clustering coefficient is exploited as the prior information to further improve accuracy and reduce complexity. Simulations show that the proposed detection scheme outperforms the conventional random walk-based scheme in the social IoT.

As IoT systems would have an economic impact, they are gaining growing interest. Millions of IoT devices are expected to join the internet of things, which will carny both major benefits and significant security threats to consumers. For IoT systems that secure data and preserve privacy of users, trust management is an essential component. IoT objects carry on the ownership settings of their owners, allowing them to interact with each other. Social relationships are believed to be important in confidence building. In this paper, we explain how to compute trust in social IoT environments using a cloud-based approach.

While it is well understood that the emerging Social Internet of Things offers the capability of effectively integrating and managing massive heterogeneous IoT objects, it also presents new challenges for suggesting useful objects with certain service for users due to complex relationships in Social Internet of Things, such as user’s object usage pattern and various social relationships among Social Internet of Things objects. In this study, we focus on the problem of service recommendation in Social Internet of Things, which is very important for many applications such as urban computing, smart cities, and health care. We propose a graph-based service recommendation framework by jointly considering social relationships of heterogeneous objects in Social Internet of Things and user’s preferences. More exactly, we learn user’s preference from his or her object usage events with a latent variable model. Then, we model users, objects, and their relationships with a knowledge graph and regard Social Internet of Things service recommendation as a knowledge graph completion problem, where the “like” property that connects users to services needs to be predicted. To demonstrate the utility of the proposed model, we have built a Social Internet of Things testbed to validate our approach and the experimental results demonstrate its feasibility and effectiveness.

The emergence of Internet of Medical Things (IoMT) has opened up enormous opportunities in the path of advanced healthcare. The advancement in wearable sensors and integration of IoT technologies into medical applications is transforming the way we deliver healthcare. Also, the advent of Social Internet of Things (SIoT) paradigm has introduced a scalable approach to handle interactions among IoT devices. Recognizing the merits of a SIoT network over traditional IoT network, this paper envisages the incorporation of socially enabled IoT into smart healthcare termed as Social Internet of Medical Things (SIoMT) by proposing a cloud-based framework for SIoMT systems. The proposed framework is modeled semantically and instantiated with the help of semantic web languages. In addition, a relationship management approach has been defined to handle relationships among SIoMT devices.

The Social Internet of Things (SIoT) is the result of the development of Internet of Things from intelligence to socialization. In the social internet of things, different nodes can automatically establish social relationships through social networks to obtain the services they need. Trust management is very important to such an open environment. This paper proposes an improved trust management model for social internet of things, which is divided into two parts: the improved node-level trust model and server-level trust model. In this paper, we propose an innovative trust model at the SIoT server-level, by introducing the deep learning model to predict the trust value of the new nodes in the social internet of things, to solve the problem that the network delay may affect the trust value evaluation in the actual social internet of things network. The simulation results show that the model based on deep learning prediction can get more successful transaction experience, and it is still effective against the high proportion of malicious nodes. The system performance is significantly better than the model without deep learning.

Artificial intelligence (AI) has been in existence for over six decades and is used in various domains. The increase in supercomputing power and big data technologies seem to have empowered AI in recent years. The Social Internet of Things (SIoT) is used in diverse fields and networking services for the IoT. This chapter introduces an intelligent feature selection-based classification of big data in the SIoT through the map-reduced model using a supervised classification approach. Here, the extraction of features takes place using the Elephant Herd Optimization (EHO) algorithm. Then, the radial basis function (RBF) model is applied to classify the big data. Finally, a comprehensive simulation analysis is performed to ensure the goodness of the presented EHO-RBF model on a set of four benchmark datasets. The experimental outcome stated that the presented EHO-RBF model attained maximum accuracy values of 98.8, 98.6, 98.4, and 98.5 for the datasets I–IV, respectively.

Machine-to-Machine (M2M) is one of the emerging technologies that has caught the attention of many researchers. The heterogeneous connected objects, constrained by interoperability, security and confidentiality issues, is driving the emergence of the concept of the Social Internet of Things (SIoT). The latter consists of providing semi-autonomous communicating objects (CO) with interaction capacities inspired by human social relations. The SIoT concept contributes to the evolution of interaction between industrial objects, by improving deterministic mechanisms towards intelligent interactions. We propose a new concept of M2M interactions inspired by social paradigms. These paradigms are deduced from a synthesis of several sociological approaches.

The Internet of Things (IoT) is a paradigm that has made everyday objects intelligent by giving them the ability to connect to the Internet, communicate and interact. The integration of the social component in the IoT has given rise to the Social Internet of Things (SIoT), which has overcome various issues such as interoperability, navigability and resource/service discovery. In this type of environment, participants compete to offer a variety of attractive services. Some of them resort to malicious behavior to propagate poor quality services. They launch so-called Trust-Attacks (TA) and break the basic functionality of the system. Several works in the literature have addressed this problem and have proposed different trust-models. Most of them have attempted to adapt and reapply trust models designed for traditional social networks or peer-to-peer networks. Despite the similarities between these types of networks, SIoT ones have specific particularities. In SIoT, there are different types of entities that collaborate: humans, devices, and services. Devices can have very limited computing and storage capacities, and their number can be as high as a few million. The resulting network is complex and highly dynamic, and the impact of Trust-Attacks can be more compromising. In this work, we propose a Multidimensional, Dynamic, Resources-efficient and Scalable trust-model that is specifically designed for SIoT environments. We, first, propose features to describe the behavior of the three types of nodes involved in SIoT networks and to quantify the degree of trust according to the three resulting Trust-Dimensions. We propose, secondly, an aggregation method based on Supervised Machine-Learning and Deep Learning that allows, on the one hand, to aggregate the proposed features to obtain a trust score allowing to rank the nodes, but also to detect the different types of Trust-Attacks and to counter them. We then propose a hybrid propagation method that allows spreading trust values in the network, while overcoming the drawbacks of centralized and distributed methods. The proposed method ensures scalability and dynamism on the one hand, and minimizes resource consumption (computing and storage), on the other. Experiments applied to synthetic data have enabled us to validate the resilience and performance of the proposed model., L'internet des Objets (IoT) est un paradigme qui a rendu les objets du quotidien, intelligents en leur offrant la possibilité de se connecter à Internet, de communiquer et d'interagir. L'intégration de la composante sociale dans l'IoT a donné naissance à l'Internet des Objets Social (SIoT), qui a permis de surmonter diverse problématiques telles que l'interopérabilité et la découverte de ressources. Dans ce type d'environnement, les participants rivalisent afin d'offrir une variété de services attrayants. Certains d'entre eux ont recours à des comportements malveillants afin de propager des services de mauvaise qualité. Ils lancent des attaques, dites de confiance, et brisent les fonctionnalités de base du système. Plusieurs travaux de la littérature ont abordé ce problème et ont proposé différents modèles de confiance. La majorité d'entre eux ont tenté de réappliquer des modèles de confiance conçus pour les réseaux sociaux ou les réseaux pair-à-pair. Malgré les similitudes entre ces types de réseaux, les réseaux SIoT présentent des particularités spécifiques. Dans les SIoT, nous avons différents types d'entités qui collaborent, à savoir des humains, des dispositifs et des services. Les dispositifs peuvent présenter des capacités de calcul et de stockage très limitées et leur nombre peut atteindre des millions. Le réseau qui en résulte est complexe et très dynamique et les répercussions des attaques de confiance peuvent être plus importantes. Nous proposons un nouveau modèle de confiance, multidimensionnel, dynamique et scalable, spécifiquement conçu pour les environnements SIoT. Nous proposons, en premier lieu, des facteurs permettant de décrire le comportement des trois types de nœuds impliqués dans les réseaux SIoT et de quantifier le degré de confiance selon les trois dimensions de confiance résultantes. Nous proposons, ensuite, une méthode d'agrégation basée sur l'apprentissage automatique et l'apprentissage profond qui permet d'une part d'agréger les facteurs proposés pour obtenir un score de confiance permettant de classer les nœuds, mais aussi de détecter les types d'attaques de confiance et de les contrer. Nous proposons, ensuite, une méthode de propagation hybride qui permet de diffuser les valeurs de confiance dans le réseau, tout en remédiant aux inconvénients des méthodes centralisée et distribuée. Cette méthode permet d'une part d'assurer la scalabilité et le dynamisme et d'autre part, de minimiser la consommation des ressources. Les expérimentations appliquées sur des de données synthétiques nous ont permis de valider le modèle proposé.

Social Internet of Things (SIoT) is an associative paradigm of social networking concepts in Internet of Things (IoT). In SIoT, physical devices act smartly as well as communally with effective navigability. Device recommendation, network navigability, relationship management, service discovery and trustworthiness management are the essential units of SIoT. In this work, an effective Cognitive based Device Recommendation (CDR) model for SIoT environment is proposed based on the user predilections and societal operability. The proposed method is evaluated using MIT dataset containing 161 sensors. Investigational results reveal that the CDR approach beats the state-of-art approaches with the reduction in mean absolute and root mean square errors.

There is a rapid increase in online social networks (OSN) due to the development of information and communication technologies (ICT). People around the globe are adopting the new OSN to keep in touch with friends and family members, to remain updated with the latest trends and news, etc. In almost every OSN, few influential users are active and attract other members and make clusters. Many users connect with these influential users to remain in touch with the latest ongoing trends as the influential nodes come up with the latest news. In this work, the structural analysis of the Facebook network is undergone. The influential nodes have been identified based on betweenness centrality (BC) and degree centrality (DC) measures. Further, community detection has been made in the whole network especially around the influential nodes. Upon filtration of four top-ranked nodes selected based on the high value of BC and DC, only 22.75% nodes and 8.87% remain in the network. Hence, there is a big question about the sustainability of OSN.