Enhancing Transparency of MQTT Brokers For IoT Applications Through Provenance Streams

ACKNOWLEDGMENTS The work described here was funded by the award made by the UK Engineering & Physical Sciences research council to the University of Aberdeen for the Trusted Things & Communities project (EP/N028074/1).Postprin

Decentralized brokered enabled ecosystem for data marketplace in smart cities towards a data sharing economy

Presently data are indispensably important as cities consider data as a commodity which can be traded to earn revenues. In urban environment, data generated from internet of things devices, smart meters, smart sensors, etc. can provide a new source of income for citizens and enterprises who are data owners. These data can be traded as digital assets. To support such trading digital data marketplaces have emerged. Data marketplaces promote a data sharing economy which is crucial for provision of available data useful for cities which aims to develop data driven services. But currently existing data marketplaces are mostly inadequate due to several issues such as security, efficiency, and adherence to privacy regulations. Likewise, there is no consolidated understanding of how to achieve trust and fairness among data owners and data sellers when trading data. Therefore, this study presents the design of an ecosystem which comprises of a distributed ledger technology data marketplace enabled by message queueing telemetry transport (MQTT) to facilitate trust and fairness among data owners and data sellers. The designed ecosystem for data marketplaces is powered by IOTA technology and MQTT broker to support the trading of sdata sources by automating trade agreements, negotiations and payment settlement between data producers/sellers and data consumers/buyers. Overall, findings from this article discuss the issues associated in developing a decentralized data marketplace for smart cities suggesting recommendations to enhance the deployment of decentralized and distributed data marketplaces.publishedVersio

Decentralised, trustless marketplace for brokered IoT data trading

PhD ThesisTrading data as valuable assets has become a trend. The use of real-time data generated from IoT devices provides a new insight into how to conduct a profitable business. As data marketplaces are becoming ubiquitous, it is also becoming clear that IoT data hold value for potential third-party consumers. This work introduces a marketplace for IoT data streams that can unlock such potential value in a scalable way, by enabling any pairs of data providers and consumers to engage in data exchange transactions without any prior assumption of mutual trust. It investigates the use of the power of blockchain technology in automating data trade agreements in a decentralised architecture. We present a marketplace protocol to support trading of streaming data, from the advertising of data assets and the stipulation of legally binding trading agreements, to their fulfilment and payment settlement, and managing trade participants’ reputations. This work has two outcomes: a marketplace model and a reputation model. We present a decentralised, trustless marketplace for brokered IoT data trading, using Blockchain in Ethereum network that enables producers and consumers to start trading in the absence of trust; however, it is managed by a reputation model. Our marketplace is powered by a reputation system that is designed to address participants’ trust and the reputation management of these traders in this marketplace. We mathematically define the reputation model by applying a reputation function to the marketplace participants – either producers or consumers – to quantify their trustworthiness in trading, based on various criteria. We evaluate the marketplace functionalities and its reputation model by designing a marketplace simulator. It is designed to simulate participant trading in the marketplace and how reputations are quantified based on rules and criteria defined in the system protocol. It is configured to replicate the behaviour of multiple pairs of producers and consumers in different trading scenarios and show how reputations are measured in these different scenarios. We experimentally show the trade-off between a trade overhead cost and the level of participant trust. On Blockhain Ethereum Mainnet, our system evaluates the latency of transactions an Ethereum takes to process and confirm our marketplace transactions

Emerging approaches for data-driven innovation in Europe: Sandbox experiments on the governance of data and technology

Europe’s digital transformation of the economy and society is one of the priorities of the current Commission and is framed by the European strategy for data. This strategy aims at creating a single market for data through the establishment of a common European data space, based in turn on domain-specific data spaces in strategic sectors such as environment, agriculture, industry, health and transportation. Acknowledging the key role that emerging technologies and innovative approaches for data sharing and use can play to make European data spaces a reality, this document presents a set of experiments that explore emerging technologies and tools for data-driven innovation, and also deepen in the socio-technical factors and forces that occur in data-driven innovation. Experimental results shed some light in terms of lessons learned and practical recommendations towards the establishment of European data spaces

Emerging approaches for data-driven innovation in Europe

Europe's digital transformation of the economy and society is one of the priorities of the current Commission and is framed by the European strategy for data. This strategy aims at creating a single market for data through the establishment of a common European data space, based in turn on domain-specific data spaces in strategic sectors such as environment, agriculture, industry, health and transportation. Acknowledging the key role that emerging technologies and innovative approaches for data sharing and use can play to make European data spaces a reality, this document presents a set of experiments that explore emerging technologies and tools for data-driven innovation, and also deepen in the socio-technical factors and forces that occur in data-driven innovation. Experimental results shed some light in terms of lessons learned and practical recommendations towards the establishment of European data spaces

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical

New Waves of IoT Technologies Research – Transcending Intelligence and Senses at the Edge to Create Multi Experience Environments

The next wave of Internet of Things (IoT) and Industrial Internet of Things (IIoT) brings new technological developments that incorporate radical advances in Artificial Intelligence (AI), edge computing processing, new sensing capabilities, more security protection and autonomous functions accelerating progress towards the ability for IoT systems to self-develop, self-maintain and self-optimise. The emergence of hyper autonomous IoT applications with enhanced sensing, distributed intelligence, edge processing and connectivity, combined with human augmentation, has the potential to power the transformation and optimisation of industrial sectors and to change the innovation landscape. This chapter is reviewing the most recent advances in the next wave of the IoT by looking not only at the technology enabling the IoT but also at the platforms and smart data aspects that will bring intelligence, sustainability, dependability, autonomy, and will support human-centric solutions.acceptedVersio