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

Energy-aware Demand Selection and Allocation for Real-time IoT Data Trading

Personal IoT data is a new economic asset that individuals can trade to generate revenue on the emerging data marketplaces. Typically, marketplaces are centralized systems that raise concerns of privacy, single point of failure, little transparency and involve trusted intermediaries to be fair. Furthermore, the battery-operated IoT devices limit the amount of IoT data to be traded in real-time that affects buyer/seller satisfaction and hence, impacting the sustainability and usability of such a marketplace. This work proposes to utilize blockchain technology to realize a trusted and transparent decentralized marketplace for contract compliance for trading IoT data streams generated by battery-operated IoT devices in real-time. The contribution of this paper is two-fold: (1) we propose an autonomous blockchain-based marketplace equipped with essential functionalities such as agreement framework, pricing model and rating mechanism to create an effective marketplace framework without involving a mediator, (2) we propose a mechanism for selection and allocation of buyers' demands on seller's devices under quality and battery constraints. We present a proof-of-concept implementation in Ethereum to demonstrate the feasibility of the framework. We investigated the impact of buyer's demand on the battery drainage of the IoT devices under different scenarios through extensive simulations. Our results show that this approach is viable and benefits the seller and buyer for creating a sustainable marketplace model for trading IoT data in real-time from battery-powered IoT devices.Comment: Accepted in SmartComp 202

Cost-effective blockchain-based IoT data marketplaces with a credit invariant

Billions of Internet of Things (IoT) devices deployed today collect massive amounts of potentially valuable data. To efficiently utilize this data, markets must be developed where data can be traded in real time. Blockchain technology offers a potential platform for these types of markets. However, previous proposals using blockchain technology either require trusted third parties such as data brokers, or necessitate a large number of on-chain transactions to operate, incurring excessive overhead costs. This paper proposes a trustless data trading system that minimizes both the risk of fraud and the number of transactions performed on chain. In this system, data producers and consumers come to binding agreements while trading data off chain and they only settle on chain when a deposit or withdrawal of funds is required. A credit mechanism is also developed to further reduce the incurred fees. Additionally, the proposed marketplace is benchmarked on a private Ethereum network running on a lab-scale testbed and the proposed credit system is simulated so to analyze its risks and benefits

Modeling and Analysis of Data Trading on Blockchain-based Market in IoT Networks

Mobile devices with embedded sensors for data collection and environmental sensing create a basis for a cost-effective approach for data trading. For example, these data can be related to pollution and gas emissions, which can be used to check the compliance with national and international regulations. The current approach for IoT data trading relies on a centralized third-party entity to negotiate between data consumers and data providers, which is inefficient and insecure on a large scale. In comparison, a decentralized approach based on distributed ledger technologies (DLT) enables data trading while ensuring trust, security, and privacy. However, due to the lack of understanding of the communication efficiency between sellers and buyers, there is still a significant gap in benchmarking the data trading protocols in IoT environments. Motivated by this knowledge gap, we introduce a model for DLT-based IoT data trading over the Narrowband Internet of Things (NB-IoT) system, intended to support massive environmental sensing. We characterize the communication efficiency of three basic DLT-based IoT data trading protocols via NB-IoT connectivity in terms of latency and energy consumption. The model and analyses of these protocols provide a benchmark for IoT data trading applications.Comment: 10 pages, 8 figures, Accepted at IEEE Internet of Things Journa

FAST DATA: A Fair, Secure and Trusted Decentralized IIoT Data Marketplace enabled by Blockchain

As the world calls it, data is the new oil. With vast installments of Industrial Internet-of-Things (IIoT) infrastructure, data is produced at a rate like never before. Similarly, artificial intelligence (AI) and machine learning (ML) solutions are getting integrated to numerous services, making them "smarter". However, the data remains fragmented in individual organizational silos inhibiting data value extraction to it’s full potential. Digital marketplaces are emerging to allow data owners to monetize this data. Yet concerns like privacy, security and unfair payment settlement deter adoption of such platforms. In addition, the state-of-the-art platforms are under the control of large multinational corporations with no transparency between buyer and seller in terms of payment details, listing, data discovery and storage. In this work, a novel decentralized platform of digital data marketplace for IoT data has been proposed. The platform leverages a decentralized data streaming network to host IoT data in a reliable and fault tolerant manner. The platform ensures fair trading, data storage and delivery in a privacy preserving manner and trust metric calculation for actors in the network. In order to study the feasibility of the proposed platform, an open source library is developed using Hyperledger Fabric and data network layer built on VerneMQ, the library is deployed on a real-time Google cloud platform. The library is tested and results are analysed for throughput, overheads and scalability

Autonomy, Efficiency, Privacy and Traceability in Blockchain-enabled IoT Data Marketplace

Personal data generated from IoT devices is a new economic asset that individuals can trade to generate revenue on the emerging data marketplaces. Blockchain technology can disrupt the data marketplace and make trading more democratic, trustworthy, transparent and secure. Nevertheless, the adoption of blockchain to create an IoT data marketplace requires consideration of autonomy and efficiency, privacy, and traceability. Conventional centralized approaches are built around a trusted third party that conducts and controls all management operations such as managing contracts, pricing, billing, reputation mechanisms etc, raising concern that providers lose control over their data. To tackle this issue, an efficient, autonomous and fully-functional marketplace system is needed, with no trusted third party involved in operational tasks. Moreover, an inefficient allocation of buyers’ demands on battery-operated IoT devices poses a challenge for providers to serve multiple buyers’ demands simultaneously in real-time without disrupting their SLAs (service level agreements). Furthermore, a poor privacy decision to make personal data accessible to unknown or arbitrary buyers may have adverse consequences and privacy violations for providers. Lastly, a buyer could buy data from one marketplace and without the knowledge of the provider, resell bought data to users registered in other marketplaces. This may either lead to monetary loss or privacy violation for the provider. To address such issues, a data ownership traceability mechanism is essential that can track the change in ownership of data due to its trading within and across marketplace systems. However, data ownership traceability is hard because of ownership ambiguity, undisclosed reselling, and dispersal of ownership across multiple marketplaces. This thesis makes the following novel contributions. First, we propose an autonomous and efficient IoT data marketplace, MartChain, offering key mechanisms for a marketplace leveraging smart contracts to record agreement details, participant ratings, and data prices in blockchain without involving any mediator. Second, MartChain is underpinned by an Energy-aware Demand Selection and Allocation (EDSA) mechanism for optimally selecting and allocating buyers' demands on provider’s IoT devices while satisfying the battery, quality and allocation constraints. EDSA maximizes the revenue of the provider while meeting the buyers’ requirements and ensuring the completion of the selected demands without any interruptions. The proof-of-concept implementation on the Ethereum blockchain shows that our approach is viable and benefits the provider and buyer by creating an autonomous and efficient real-time data trading model. Next, we propose KYBChain, a Know-Your-Buyer in the privacy-aware decentralized IoT data marketplace that performs a multi-faceted assessment of various characteristics of buyers and evaluates their privacy rating. Privacy rating empowers providers to make privacy-aware informed decisions about data sharing. Quantitative analysis to evaluate the utility of privacy rating demonstrates that the use of privacy rating by the providers results in a decrease of data leakage risk and generated revenue, correlating with the classical risk-utility trade-off. Evaluation results of KYBChain on Ethereum reveal that the overheads in terms of gas consumption, throughput and latency introduced by our privacy rating mechanism compared to a marketplace that does not incorporate a privacy rating system are insignificant relative to its privacy gains. Finally, we propose TrailChain which generates a trusted trade trail for tracking the data ownership spanning multiple decentralized marketplaces. Our solution includes mechanisms for detecting any unauthorized data reselling to prevent privacy violations and a fair resell payment sharing scheme to distribute payment among data owners for authorized reselling. We performed qualitative and quantitative evaluations to demonstrate the effectiveness of TrailChain in tracking data ownership using four private Ethereum networks. Qualitative security analysis demonstrates that TrailChain is resilient against several malicious activities and security attacks. Simulations show that our method detects undisclosed reselling within the same marketplace and across different marketplaces. Besides, it also identifies whether the provider has authorized the reselling and fairly distributes the revenue among the data owners at marginal overhead

Secure Decentralized IoT Service Platform using Consortium Blockchain

Blockchain technology has gained increasing popularity in the research of Internet of Things (IoT) systems in the past decade. As a distributed and immutable ledger secured by strong cryptography algorithms, the blockchain brings a new perspective to secure IoT systems. Many studies have been devoted to integrating blockchain into IoT device management, access control, data integrity, security, and privacy. In comparison, the blockchain-facilitated IoT communication is much less studied. Nonetheless, we see the potential of blockchain in decentralizing and securing IoT communications. This paper proposes an innovative IoT service platform powered by consortium blockchain technology. The presented solution abstracts machine-to-machine (M2M) and human-to-machine (H2M) communications into services provided by IoT devices. Then, it materializes data exchange of the IoT network through smart contracts and blockchain transactions. Additionally, we introduce the auxiliary storage layer to the proposed platform to address various data storage requirements. Our proof-of-concept implementation is tested against various workloads and connection sizes under different block configurations to evaluate the platform's transaction throughput, latency, and hardware utilization. The experiment results demonstrate that our solution can maintain high performance under most testing scenarios and provide valuable insights on optimizing the blockchain configuration to achieve the best performance

The Rise and Fall of Cryptocurrencies: Defining the Economic and Social Values of Blockchain Technologies, assessing the Opportunities, and defining the Financial and Cybersecurity Risks of the Metaverse

This paper contextualises the common queries of "why is crypto crashing?" and "why is crypto down?", the research transcends beyond the frequent market fluctuations to unravel how cryptocurrencies fundamentally work and the step-by-step process on how to create a cryptocurrency. The study examines blockchain technologies and their pivotal role in the evolving Metaverse, shedding light on topics such as how to invest in cryptocurrency, the mechanics behind crypto mining, and strategies to effectively buy and trade cryptocurrencies. Through an interdisciplinary approach, the research transitions from the fundamental principles of fintech investment strategies to the overarching implications of blockchain within the Metaverse. Alongside exploring machine learning potentials in financial sectors and risk assessment methodologies, the study critically assesses whether developed or developing nations are poised to reap greater benefits from these technologies. Moreover, it probes into both enduring and dubious crypto projects, drawing a distinct line between genuine blockchain applications and Ponzi-like schemes. The conclusion resolutely affirms the continuing dominance of blockchain technologies, underlined by a profound exploration of their intrinsic value and a reflective commentary by the author on the potential risks confronting individual investors