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The growing number of distributed energy resources (DER) has driven the uptake of variable energy from renewable energy sources at both utility and distributed scale. This is driving the development of new market models from the traditional centralized energy trading models to distributed energy trading (DET) models. Peer‐to‐peer (P2P) energy trading is an example of DET model that allows consumers and prosumers in the distribution network to trade energy between themselves, lowering cost while providing balancing and other ancillary services to the network. However, DET requires trust in both DER sharing and P2P energy trading transaction. Therefore, enabling technologies such as blockchains can help build trust and track DET transactions efficiently, securely, and at low cost. In this chapter, we examine the factors governing successful deployment of blockchain‐based DET applications and their technical challenges.

Many countries across the world have not been entirely successful in their attempts to contain the spread of the COVID-19 virus, which has caused, quite possibly, one of the greatest disasters of the 21st century. A rich variety of contact tracing applications, each offering its own unique set of features, are currently being used to track the spread of the virus from person to person, and isolate/quarantine suspected individuals. However, existing approaches have not been very successful since they lack incentives to motivate users to use them actively. Although each solution provides some form of privacy protection and data security, loopholes do exist, and there is scope for improvement. In this context, we explore the use of blockchain to help in advancing contact tracing applications. In this article, we describe how blockchain could offer enhanced data security and functionality without compromising users' privacy. The effectiveness of contact tracing applications improves with the user base. In this regard, we explore how blockchain-based smart contracts can be used to offer incentives to users at various stages of the contact tracing process. Next, we lay down a plan for future contact tracing frameworks that provide the advantages of blockchain and user incentives, and all the best features available in current solutions.

Authorization or access control limits the actions a user may perform on a computer system, based on predetermined access control policies, thus preventing access by illegitimate actors. Access control for the Internet of Things (IoT) should be tailored to take inherent IoT network scale and device resource constraints into consideration. However, common authorization systems in IoT employ conventional schemes, which suffer from overheads and centralization. Recent research trends suggest that blockchain has the potential to tackle the issues of access control in IoT. However, proposed solutions overlook the importance of building dynamic and flexible access control mechanisms. In this paper, we design a decentralized attribute-based access control mechanism with an auxiliary Trust and Reputation System (TRS) for IoT authorization. Our system progressively quantifies the trust and reputation scores of each node in the network and incorporates the scores into the access control mechanism to achieve dynamic and flexible access control. We design our system to run on a public blockchain, but we separate the storage of sensitive information, such as user's attributes, to private sidechains for privacy preservation. We implement our solution in a public Rinkeby Ethereum test-network interconnected with a lab-scale testbed. Our evaluations consider various performance metrics to highlight the applicability of our solution for IoT contexts., 12 pages, 10 figures, submitted to IEEE Transactions on Network and Service Management

Recommendation is a critical tool for developing and promoting the benefits of the Internet of Things (IoT). In recent years, recommender systems have attracted considerable attention in many IoT-related fields such as smart health, smart home, smart tourism and smart marketing. However, traditional recommender system approaches fail to exploit ever-growing, dynamic and heterogeneous IoT data in building recommender systems for the IoT (RSIoT). This article aims to provide a comprehensive review of state-of-the-art RSIoT, including the related techniques, applications and a discussion on the limitations of applying recommendation systems to IoT. Finally, we propose a reference framework for comparing existing studies to guide future research and practices.

Herein, a human identification system for smart spaces called Vein‐ID (referred to as VID) is presented, which leverage the uniqueness of vein patterns embedded in dorsum of an individual's hand. VID extracts vein patterns using the depth information and infrared (IR) images, both obtained from a commodity depth camera. Two deep learning models (CNN and Stacked‐Autoencoders) are presented for precisely identifying a target individual from a set of N enrolled users. VID also incorporates a strategy for identifying an intruder—that is a person whose vein patterns are not included in the set of enrolled individuals. The performance of VID by collecting a comprehensive data set of approximately 17,500 images from 35 subjects is evaluated. The tests reveal that VID can identify an individual with an average accuracy of over 99% from a group of up to 35 individuals. It is demonstrated that VID can detect intruders with an average accuracy of about 96%. The execution time for training and testing the two deep learning models on different hardware platforms is also investigated and the differences are reported.

Each and every organisation releases information in a variety of forms ranging from annual reports to legal proceedings. Such documents may contain sensitive information and releasing them openly may lead to the leakage of confidential information. Detection of sentences that contain sensitive information in documents can help organisations prevent the leakage of valuable confidential information. This is especially challenging when such sentences contain a substantial amount of information or are paraphrased versions of known sensitive content. Current approaches to sensitive information detection in such complex settings are based on keyword-based approaches or standard machine learning models. In this paper, we wish to explore whether pre-trained transformer models are well suited to detect complex sensitive information. Pre-trained transformers are typically trained on an enormous amount of text and therefore readily learn grammar, structure and other linguistic features, making them particularly attractive for this task. Through our experiments on the Monsanto trial data set, we observe that the fine-tuned Bidirectional Encoder Representations from Transformers (BERT) transformer model performs better than traditional models. We experimented with four different categories of documents in the Monsanto dataset and observed that BERT achieves better F2 scores by 24.13\% to 65.79\% for GHOST, 30.14\% to 54.88\% for TOXIC, 39.22\% for CHEMI, 53.57\% for REGUL compared to existing sensitive information detection models.