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The purpose of this work is to bridge FL and blockchain technology through designing a blockchain-based smart agent system architecture and applying in FL. and blockchain technology through designing a blockchain-based smart agent system architecture and applying in FL. FL is an emerging collaborative machine learning technique that trains a model across multiple devices or servers holding private data samples without exchanging their data. The locally trained results are aggregated by a centralized server in a privacy-preserving way. However, there is an assumption where the centralized server is trustworthy, which is impractical. Fortunately, blockchain technology has opened a new era of data exchange among trustless strangers because of its decentralized architecture and cryptography-supported techniques.

In this study, the author proposes a novel design of a smart agent inspired by the smart contract concept. Specifically, based on the proposed smart agent, a fully decentralized, privacy-preserving and fair deep learning blockchain-FL framework is designed, where the agent network is consistent with the blockchain network and each smart agent is a participant in the FL task. During the whole training process, both the data and the model are not at the risk of leakage.

A demonstration of the proposed architecture is designed to train a neural network. Finally, the implementation of the proposed architecture is conducted in the Ethereum development, showing the effectiveness and applicability of the design.

The author aims to investigate the feasibility and practicality of linking the three areas together, namely, multi-agent system, FL and blockchain. A blockchain-FL framework, which is based on a smart agent system, has been proposed. The author has made several contributions to the state-of-the-art. First of all, a concrete design of a smart agent model is proposed, inspired by the smart contract concept in blockchain. The smart agent is autonomous and is able to disseminate, verify the information and execute the supported protocols. Based on the proposed smart agent model, a new architecture composed by these agents is formed, which is a blockchain network. Then, a fully decentralized, privacy-preserving and smart agent blockchain-FL framework has been proposed, where a smart agent acts as both a peer in a blockchain network and a participant in a FL task at the same time. Finally, a demonstration to train an artificial neural network is implemented to prove the effectiveness of the proposed framework.

This paper is premised upon an analysis of 26 cities within the UK regarding their smart city projects. Each city was analyzed through news articles, reports and policy documents to ascertain the level of each city's development as a smart city. Each was coded by separating the projects into five types, which were ranked on a scale from 0 (no plans for use) to 5 (project type in use). The most common types are the provision of open data and the creation of business ecosystems as the primary driver of the smart city. However, many councils and enterprises proclaim smartness before the technology is actually in use, making it difficult to separate what is utilised and what is under development. Therefore, this paper further carried out an analysis of 20 cities and their intended plans to usher in the smart city, to observe the expected emergence of smart city technology. This was achieved by interrogating various roadmaps and policy documents produced by the respective cities. It was found that the most prevalent form of emergent smart city technology is the rollout of 5G and increased educational programmes alongside a proliferation of internet of things and electric vehicle usage.

This paper addresses a gap in research literature in the fields of blockchain technology (BC), supply chain network dynamics (SC) and network effect phenomena (NE). Extant BC and SC literature describes the potential benefits to be reaped through the adoption of BC technology. While BC technology does not yet meet the researched expectations of adoption, performance and efficacy, the authors analyze the three inter-related fields (BC, SC and NE) to bridge this gap in theory.

This paper begins with a research review correlating the technological fundamentals of BC technology into fundamental value propositions for SC logistics contexts. The authors review the gap between these theoretical technological functions and the current ecosystem of BC applications. With an overarching understanding of BC in SC contexts, this paper then explores the phenomena of NE and attempts to synthesize various interrelated aspects of the three fields (BC, SC and NE). Research frameworks from extant literature are used for cross-comparing legacy software/information system solutions with potential and existing BC-based solutions. Case studies are utilized to support this analysis.

Several key considerations and themes are identified to better inform practitioner and researcher decision-making. Novel insights pertain to BC platform architecture and application modularity, integrated governance and decision-making capabilities, and the automation capabilities that arise from a healthy application and smart contract ecosystem.

The core contribution is the synthesis of network effect theory with SC phenomena and BC theory and the exploration of how these three fields are inter-related in the maturation of BC technology. Specifically, the authors deepen insights from extant literature by contextualizing findings with relevant interdisciplinary theoretical frameworks.

This work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.

The proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.

The proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.

Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.

The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.

This paper aims to describe a method for Internet-of-Things-devices to achieve industrial grade reliability for information transfer from wireless sensor systems to production systems using blockchain technologies.

An increased security and reliability of submitted data within the sensor network could be achieved on an application level. Therefore, a lightweight, high-level communication protocol based on blockchain principles was designed.

Blockchain mechanisms can secure the wireless communication of Internet-of-Things-devices in a lightweight and scalable manner.

The innovation of this research is the successful application of general blockchain mechanisms to increase security of a wireless sensor system without binding to a dedicated blockchain technology.

This study aims to provide a systematic review about the Internet of Things (IoT) and its impacts on happiness. It intends to serve as a platform for further research as it is sparse in in-depth analysis.

This systematic review initially observed 2,501 literary articles through the ScienceDirect and WorldCat search engines before narrowing it down to 72 articles based on subject matter relevance in the abstract and keywords. Accounting for duplicates between search engines, the count was reduced to 66 articles. To finally narrow down all the literature used in this systematic review, 66 articles were given a critical readthrough. The count was finally reduced to 53 total articles used in this systematic review.

This paper necessitates the claim that IoT will likely impact many aspects of our everyday lives. Through the literature observed, it was found that IoT will have some significant and positive impacts on people's welfare and lives. The unprecedented nature of IoTs impacts on society should warrant further research moving forward.

While the literature presented in this systematic review shows that IoT can positively impact the perceived or explicit happiness of people, the amount of literature found to supplement this argument is still on the lower end. They also necessitate the need for both greater depth and variety in this field of research.

Since technology is already a pervasive element of most people’s contemporary lives, it stands to reason that the most important factors to consider will be in how we might benefit from IoT or, more notably, how IoT can enhance our levels of happiness. A significant implication is its ability to reduce the gap in happiness levels between urban and rural areas.

Currently, the literature directly tackling the quantification of IoTs perceived influence on happiness has yet to be truly discussed broadly. This systematic review serves as a starting point for further discussion in the subject matter. In addition, this paper may lead to a better understanding of the IoT technology and how we can best advance and adapt it to the benefits of the society.