The Internet of Things has facilitated access to a large volume of sensitive information on each participating object in an ecosystem. This imposes many threats ranging from the risks of data management to the potential discrimination enabled by data analytics over delicate information such as locations, interests, and activities. To address these issues, the concept of trust is introduced as an important role in supporting both humans and services to overcome the perception of uncertainty and risks before making any decisions. However, establishing trust in a cyber world is a challenging task due to the volume of diversified influential factors from cyber-physical-systems. Hence, it is essential to have an intelligent trust computation model that is capable of generating accurate and intuitive trust values for prospective actors. Therefore, in this paper, a quantifiable trust assessment model is proposed. Built on this model, individual trust attributes are calculated numerically. Moreover, a novel algorithm based on machine learning principles is devised to classify the extracted trust features and combine them to produce a final trust value to be used for decision making. Finally, our model's effectiveness is verified through a simulation. The results show that our method has advantages over other aggregation methods.

Recent advancements in the Internet of Things (IoT) has enabled the collection, processing, and analysis of various forms of data including the personal data from billions of objects to generate valuable knowledge, making more innovative services for its stakeholders. Yet, this paradigm continuously suffers from numerous security and privacy concerns mainly due to its massive scale, distributed nature, and scarcity of resources towards the edge of IoT networks. Interestingly, blockchain based techniques offer strong countermeasures to protect data from tampering while supporting the distributed nature of the IoT. However, the enormous amount of energy consumption required to verify each block of data make it difficult to use with resource-constrained IoT devices and with real-time IoT applications. Nevertheless, it can expose the privacy of the stakeholders due to its public ledger system even though it secures data from alterations. Edge computing approaches suggest a potential alternative to centralized processing in order to populate real-time applications at the edge and to reduce privacy concerns associated with cloud computing. Hence, this paper suggests the novel privacy preserving blockchain called TrustChain which combines the power of blockchains with trust concepts to eliminate issues associated with traditional blockchain architectures. This work investigates how TrustChain can be deployed in the edge computing environment with different levels of absorptions to eliminate delays and privacy concerns associated with centralized processing and to preserve the resources in IoT networks.

Social Internet of Things (SIoT) is an evolutionary idea which combines traditional IoT models with social network paradigms. "Objects" in SIoT formulate social relationships with other "trusted objects" according to the relationships of their owners which deliver trustworthy services on request. From our trust platform concept to identify vital trust metrics, attributes, we propose a Recommendations plus Reputations based Trust Computational Model (RpR) that enables objects in SIoT to build associations in a trustworthy manner. A numerical model is developed to estimate trust of each object based on Recommendation, Reputation parameters. Next, both estimates are merged together, a robust algorithm is proposed. Finally, we demonstrate, validate the usefulness of RpR over prior approaches through simulations, analysis. The aim of our approach is to facilitate accurate modeling of trustworthiness in distributed SIoT environments.

Trust in Social Internet of Things has allowed to open new horizons in collaborative networking, particularly by allowing objects to communicate with their service providers, based on their relationships analogy to human world. However, strengthening trust is a challenging task as it involves identifying several influential factors in each domain of social-cyber-physical systems in order to build a reliable system. In this paper, we address the issue of understanding and evaluating honesty that is an important trust metric in trustworthiness evaluation process in social networks. First, we identify and define several trust attributes, which affect directly to the honesty. Then, a subjective computational model is derived based on experiences of objects and opinions from friendly objects with respect to identified attributes. Based on the outputs of this model a final honest level is predicted using regression analysis. Finally, the effectiveness of our model is tested using simulations.

The Internet of Things (IoT) is the novel paradigm of connectivity and the driving force behind state-of-the-art applications and services. However, the exponential growth of the number of IoT devices and services, their distributed nature, and scarcity of resources has increased the number of security and privacy concerns ranging from the risks of unauthorized data alterations to the potential discrimination enabled by data analytics over sensitive information. Thus, a blockchain based IoT-platform is introduced to address these issues. Built upon the tamper-proof architecture, the proposed access management mechanisms ensure the authenticity and integrity of data. Moreover, a novel approach called Block Analytics Tool (BAT), integrated with the platform is proposed to analyze and make predictions on data stored on the blockchain. BAT enables the data-analysis applications to be developed using the data stored in the platform in an optimized manner acting as an interface to off-chain processing. A pharmaceutical supply chain is used as the use case scenario to show the functionality of the proposed platform. Furthermore, a model to forecast the demand of the pharmaceutical drugs is investigated using a real-world data set to demonstrate the functionality of BAT. Finally, the performance of BAT integrated with the platform is evaluated.