Biblio

The Internet of Things (IoT) is an evolving network of billions of interconnected physical objects, such as, numerous sensors, smartphones, wearables, and embedded devices. These physical objects, generally referred to as the smart objects, when deployed in real-world aggregates useful information from their surrounding environment. As-of-late, this notion of IoT has been extended to incorporate the social networking facets which have led to the promising paradigm of the `Social Internet of Things' (SIoT). In SIoT, the devices operate as an autonomous agent and provide an exchange of information and services discovery in an intelligent manner by establishing social relationships among them with respect to their owners. Trust plays an important role in establishing trustworthy relationships among the physical objects and reduces probable risks in the decision making process. In this paper, a trust computational model is proposed to extract individual trust features in a SIoT environment. Furthermore, a machine learning-based heuristic is used to aggregate all the trust features in order to ascertain an aggregate trust score. Simulation results illustrate that the proposed trust-based model isolates the trustworthy and untrustworthy nodes within the network in an efficient manner.

Social Internet of Things (SIoT) is an extension of 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 are equipped with social functionalities capable of discovering other social objects 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 paper, 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 social objects.

The concept of social networking is integrated into Internet of things (IoT) to socialize smart objects by mimicking human behaviors, leading to a new paradigm of Social Internet of Things (SIoT). A crucial problem that needs to be solved is how to establish reliable relationships autonomously among objects, i.e., building trust. This paper focuses on exploring an efficient context-aware trustworthiness inference framework to address this issue. Based on the sociological and psychological principles of trust generation between human beings, the proposed framework divides trust into two types: familiarity trust and similarity trust. The familiarity trust can be calculated by direct trust and recommendation trust, while the similarity trust can be calculated based on external similarity trust and internal similarity trust. We subsequently present concrete methods for the calculation of different trust elements. In particular, we design a kernel-based nonlinear multivariate grey prediction model to predict the direct trust of a specific object, which acts as the core module of the entire framework. Besides, considering the fuzziness and uncertainty in the concept of trust, we introduce the fuzzy logic method to synthesize these trust elements. The experimental results verify the validity of the core module and the resistance to attacks of this framework.

Internet of Things (IoT) is an evolving research area for the last two decades. The integration of the IoT and social networking concept results in developing an interdisciplinary research area called the Social Internet of Things (SIoT). The SIoT is dominant over the traditional IoT because of its structure, implementation, and operational manageability. In the SIoT, devices interact with each other independently to establish a social relationship for collective goals. To establish trustworthy relationships among the devices significantly improves the interaction in the SIoT and mitigates the phenomenon of risk. The problem is to choose a trustworthy node who is most suitable according to the choice parameters of the node. The best-selected node by one node is not necessarily the most suitable node for other nodes, as the trustworthiness of the node is independent for everyone. We employ some theoretical characterization of the soft-set theory to deal with this kind of decision-making problem. In this paper, we developed a weighted based trustworthiness ranking model by using soft set theory to evaluate the trustworthiness in the SIoT. The purpose of the proposed research is to reduce the risk of fraudulent transactions by identifying the most trusted nodes.

In an Internet of Things (IOT) network, each node (device) provides and requires services and with the growth in IOT, the number of nodes providing the same service have also increased, thus creating a problem of selecting one reliable service from among many providers. In this paper, we propose a scalable graph-based collaborative filtering recommendation algorithm, improved using trust to solve service selection problem, which can scale to match the growth in IOT unlike a central recommender which fails. Using this recommender, a node can predict its ratings for the nodes that are providing the required service and then select the best rated service provider.

The integration of social networking concepts into the Internet of things has led to the Social Internet of Things (SIoT) paradigm, according to which objects are capable of establishing social relationships in an autonomous way with respect to their owners with the benefits of improving the network scalability in information/service discovery. Within this scenario, we focus on the problem of understanding how the information provided by members of the social IoT has to be processed so as to build a reliable system on the basis of the behavior of the objects. We define two models for trustworthiness management starting from the solutions proposed for P2P and social networks. In the subjective model each node computes the trustworthiness of its friends on the basis of its own experience and on the opinion of the friends in common with the potential service providers. In the objective model, the information about each node is distributed and stored making use of a distributed hash table structure so that any node can make use of the same information. Simulations show how the proposed models can effectively isolate almost any malicious nodes in the network at the expenses of an increase in the network traffic for feedback exchange.