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Keywords = data-driven marketplace

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38 pages, 1507 KB  
Review
A Comprehensive Analysis of Privacy-Preserving Solutions Developed for IoT-Based Systems and Applications
by Abdul Majeed, Sakshi Patni and Seong Oun Hwang
Electronics 2025, 14(11), 2106; https://doi.org/10.3390/electronics14112106 - 22 May 2025
Viewed by 2824
Abstract
In recent years, a large number of Internet of Things (IoT)-based products, solutions, and services have emerged from the industry to enter the marketplace, improving the quality of service. With the wide adoption of IoT-based systems/applications in real scenarios, the privacy preservation (PP) [...] Read more.
In recent years, a large number of Internet of Things (IoT)-based products, solutions, and services have emerged from the industry to enter the marketplace, improving the quality of service. With the wide adoption of IoT-based systems/applications in real scenarios, the privacy preservation (PP) topic has garnered significant attention from both academia and industry; as a result, many PP solutions have been developed, tailored to IoT-based systems/applications. This paper provides an in-depth analysis of state-of-the-art (SOTA) PP solutions recently developed for IoT-based systems and applications. We delve into SOTA PP methods that preserve IoT data privacy and categorize them into two scenarios: on-device and cloud computing. We categorize the existing PP solutions into privacy-by-design (PbD), such as federated learning (FL) and split learning (SL), and privacy engineering solutions (PESs), such as differential privacy (DP) and anonymization, and we map them to IoT-driven applications/systems. We further summarize the latest SOTA methods that employ multiple PP techniques like ϵ-DP + anonymization or ϵ-DP + blockchain + FL (rather than employing just one) to preserve IoT data privacy in both PES and PbD categories. Lastly, we highlight quantum-based methods devised to enhance the security and/or privacy of IoT data in real-world scenarios. We discuss the status of current research in PP techniques for IoT data within the scope established for this paper, along with opportunities for further research and development. To the best of our knowledge, this is the first work that provides comprehensive knowledge about PP topics centered on the IoT, and which can provide a solid foundation for future research. Full article
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13 pages, 2474 KB  
Article
Business Case for a Regional AI-Based Marketplace for Renewable Energies
by Jonas Holzinger, Anna Nagl, Karlheinz Bozem, Carsten Lecon, Andreas Ensinger, Jannik Roessler and Christina Neufeld
Sustainability 2025, 17(4), 1739; https://doi.org/10.3390/su17041739 - 19 Feb 2025
Cited by 4 | Viewed by 1507
Abstract
The global energy sector is rapidly changing due to decentralization, renewable energy integration, and digitalization, challenging traditional energy business models. This paper explores a startup concept for an AI-assisted regional marketplace for renewable energy, specifically suited for small- and medium-sized enterprises (SMEs). Driven [...] Read more.
The global energy sector is rapidly changing due to decentralization, renewable energy integration, and digitalization, challenging traditional energy business models. This paper explores a startup concept for an AI-assisted regional marketplace for renewable energy, specifically suited for small- and medium-sized enterprises (SMEs). Driven by advancements in artificial intelligence (AI), big data, and Internet of Things (IoT) technology, this marketplace enables efficient energy trading through real-time supply–demand matching with dynamic pricing. Decentralized energy systems, such as solar and wind power, offer benefits like enhanced energy security but also present challenges in balancing supply and demand due to volatility. This research develops and validates an AI-based pricing model to optimize regional energy consumption and incentivize efficient usage to support grid stability. Through a SWOT analysis, this study highlights the strengths, weaknesses, opportunities, and threats of such a platform. Findings indicate that, with scalability, the AI-driven marketplace could significantly support the energy transition by increasing renewable energy use and therefore reducing carbon emissions. This paper presents a viable, scalable solution for SMEs aiming to participate in a resilient, sustainable, and localized energy market. Full article
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29 pages, 5539 KB  
Article
Is Artificial Intelligence a Game-Changer in Steering E-Business into the Future? Uncovering Latent Topics with Probabilistic Generative Models
by Simona-Vasilica Oprea and Adela Bâra
J. Theor. Appl. Electron. Commer. Res. 2025, 20(1), 16; https://doi.org/10.3390/jtaer20010016 - 22 Jan 2025
Cited by 9 | Viewed by 3639
Abstract
Academic publications from the Web of Science Core Collection on “e-business” and “artificial intelligence” (AI) are investigated to reveal the role of AI, extract latent themes and identify potential research topics. The proposed methodology includes relevant graphical representations (trends, co-occurrence networks, Sankey diagrams), [...] Read more.
Academic publications from the Web of Science Core Collection on “e-business” and “artificial intelligence” (AI) are investigated to reveal the role of AI, extract latent themes and identify potential research topics. The proposed methodology includes relevant graphical representations (trends, co-occurrence networks, Sankey diagrams), sentiment analyses and latent topics identification. A renewed interest in these publications is evident post-2018, with a sharp increase in publications around 2020 that can be attributed to the COVID-19 pandemic. Chinese institutions dominate the collaboration network in e-business and AI. Keywords such as “business transformation”, “business value” and “e-business strategy” are prominent, contributing significantly to areas like “Operations Research & Management Science”. Additionally, the keyword “e-agribusiness” recently appears connected to “Environmental Sciences & Ecology”, indicating the application of e-business principles in sustainable practices. Although three sentiment analysis methods broadly agree on key trends, such as the rise in positive sentiment over time and the dominance of neutral sentiment, they differ in detail and focus. Custom analysis reveals more pronounced fluctuations, whereas VADER and TextBlob present steadier and more subdued patterns. Four well-balanced topics are identified with a coherence score of 0.66 using Latent Dirichlet Allocation, which is a probabilistic generative model designed to uncover hidden topics in large text corpora: Topic 1 (29.8%) highlights data-driven decision-making in e-business, focusing on AI, information sharing and technology-enabled business processes. Topic 2 (28.1%) explores AI and Machine Learning (ML) in web-based business, emphasizing customer service, innovation and workflow optimization. Topic 3 (23.6%) focuses on analytical methods for decision-making, using data modeling to enhance strategies, processes and sustainability. Topic 4 (18.5%) examines the semantic web, leveraging ontologies and knowledge systems to improve intelligent systems and web platforms. New pathways such as voice assistance, augmented reality and dynamic marketplaces could further enhance e-business strategies. Full article
(This article belongs to the Topic Data Science and Intelligent Management)
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25 pages, 4151 KB  
Article
System Design of an Online Marketplace Towards the Standardisation of Sustainable Energy Efficiency Investments in Buildings
by Ioanna Andreoulaki, Aikaterini Papapostolou, Daniela Stoian, Konstantinos Kefalas and Vangelis Marinakis
Eng 2025, 6(1), 13; https://doi.org/10.3390/eng6010013 - 11 Jan 2025
Cited by 2 | Viewed by 1375
Abstract
Nowadays, the increase in sustainable investments, especially when it comes to energy efficiency in buildings, has been recognised as an important pillar towards reductions in energy consumption. In this context, there is a need for efficient and user-friendly digital tools that can support [...] Read more.
Nowadays, the increase in sustainable investments, especially when it comes to energy efficiency in buildings, has been recognised as an important pillar towards reductions in energy consumption. In this context, there is a need for efficient and user-friendly digital tools that can support decision-making procedures for all involved parties in the energy efficiency value chain. The scope of this paper is to present a high-level architecture and system design of the energy efficiency marketplace developed within the framework of the ENERGATE project, an EU-funded initiative aiming to assist Building Owners, Project Implementors, and Financial Institutions to collaborate and execute energy-efficient building renovations. To this end, structured data through predefined information entries will be collected. This will facilitate the interactions between heterogenous stakeholders and contribute to the standardisation of processes. The paper focuses on the functional description of the system design of the ENERGATE platform by defining the architecture, components, modules, interfaces, and structured data, as well as highlighting the requirements of potential platform users and design principles to meet the necessary requirements while ensuring security, reliability, and effectiveness. Full article
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39 pages, 892 KB  
Article
Evaluating Artificial Intelligence Models for Resource Allocation in Circular Economy Digital Marketplace
by Arifuzzaman (Arif) Sheikh, Steven J. Simske and Edwin K. P. Chong
Sustainability 2024, 16(23), 10601; https://doi.org/10.3390/su162310601 - 3 Dec 2024
Cited by 8 | Viewed by 3700
Abstract
This study assesses the application of artificial intelligence (AI) algorithms for optimizing resource allocation, demand-supply matching, and dynamic pricing within circular economy (CE) digital marketplaces. Five AI models—autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), random forest (RF), gradient boosting regressor (GBR), [...] Read more.
This study assesses the application of artificial intelligence (AI) algorithms for optimizing resource allocation, demand-supply matching, and dynamic pricing within circular economy (CE) digital marketplaces. Five AI models—autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), random forest (RF), gradient boosting regressor (GBR), and neural networks (NNs)—were evaluated based on their effectiveness in predicting waste generation, economic growth, and energy prices. The GBR model outperformed the others, achieving a mean absolute error (MAE) of 23.39 and an R2 of 0.7586 in demand forecasting, demonstrating strong potential for resource flow management. In contrast, the NNs encountered limitations in supply prediction, with an MAE of 121.86 and an R2 of 0.0151, indicating challenges in adapting to market volatility. Reinforcement learning methods, specifically Q-learning and deep Q-learning (DQL), were applied for price stabilization, resulting in reduced price fluctuations and improved market stability. These findings contribute a conceptual framework for AI-driven CE marketplaces, showcasing the role of AI in enhancing resource efficiency and supporting sustainable urban development. While synthetic data enabled controlled experimentation, this study acknowledges its limitations in capturing full real-world variability, marking a direction for future research to validate findings with real-world data. Moreover, ethical considerations, such as algorithmic fairness and transparency, are critical for responsible AI integration in circular economy contexts. Full article
(This article belongs to the Section Economic and Business Aspects of Sustainability)
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18 pages, 596 KB  
Article
Exploring Determinants of Second-Hand Apparel Purchase Intention and Word of Mouth: A Stimulus–Organism–Response Approach
by Olga Tymoshchuk, Xingqiu Lou and Ting Chi
Sustainability 2024, 16(11), 4445; https://doi.org/10.3390/su16114445 - 24 May 2024
Cited by 9 | Viewed by 7302
Abstract
The U.S. second-hand clothing industry is experiencing rapid growth, driven by increasing environmental awareness among consumers. However, there is a gap in understanding the driving forces behind this trend. This study aims to investigate the impact of external factors, including product quality, information [...] Read more.
The U.S. second-hand clothing industry is experiencing rapid growth, driven by increasing environmental awareness among consumers. However, there is a gap in understanding the driving forces behind this trend. This study aims to investigate the impact of external factors, including product quality, information quality, and service quality, on consumers’ internal emotions and examines how these emotional states, encompassing hedonic value, utilitarian value, environmental value, functional risk, aesthetic risk, and sanitary risk, influence their purchase intentions and word-of-mouth recommendations. Data were collected from 448 consumers who have shopped for second-hand clothing through an online survey conducted on Qualtrics. Multiple regression was applied to test the hypotheses. The findings indicate that product quality, information quality, and service quality enhance consumers’ perceived hedonic, utilitarian, and environmental values. Furthermore, service quality significantly reduces consumers’ perceived risks in terms of functionality, aesthetics, and sanitation. Additionally, consumers’ purchase intentions and word of mouth regarding second-hand clothing are positively influenced by their perceived hedonic, utilitarian, and environmental values. This research enriches the understanding of consumer behavior in the second-hand marketplace and offers insightful implications for retailers and marketers in the second-hand clothing industry. Full article
(This article belongs to the Special Issue Recycling Materials for the Circular Economy—2nd Edition)
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16 pages, 4444 KB  
Article
Using Privacy-Preserving Algorithms and Blockchain Tokens to Monetize Industrial Data in Digital Marketplaces
by Borja Bordel Sánchez, Ramón Alcarria, Latif Ladid and Aurel Machalek
Computers 2024, 13(4), 104; https://doi.org/10.3390/computers13040104 - 18 Apr 2024
Cited by 3 | Viewed by 3033
Abstract
The data economy has arisen in most developed countries. Instruments and tools to extract knowledge and value from large collections of data are now available and enable new industries, business models, and jobs. However, the current data market is asymmetric and prevents companies [...] Read more.
The data economy has arisen in most developed countries. Instruments and tools to extract knowledge and value from large collections of data are now available and enable new industries, business models, and jobs. However, the current data market is asymmetric and prevents companies from competing fairly. On the one hand, only very specialized digital organizations can manage complex data technologies such as Artificial Intelligence and obtain great benefits from third-party data at a very reduced cost. On the other hand, datasets are produced by regular companies as valueless sub-products that assume great costs. These companies have no mechanisms to negotiate a fair distribution of the benefits derived from their industrial data, which are often transferred for free. Therefore, new digital data-driven marketplaces must be enabled to facilitate fair data trading among all industrial agents. In this paper, we propose a blockchain-enabled solution to monetize industrial data. Industries can upload their data to an Inter-Planetary File System (IPFS) using a web interface, where the data are randomized through a privacy-preserving algorithm. In parallel, a blockchain network creates a Non-Fungible Token (NFT) to represent the dataset. So, only the NFT owner can obtain the required seed to derandomize and extract all data from the IPFS. Data trading is then represented by NFT trading and is based on fungible tokens, so it is easier to adapt prices to the real economy. Auctions and purchases are also managed through a common web interface. Experimental validation based on a pilot deployment is conducted. The results show a significant improvement in the data transactions and quality of experience of industrial agents. Full article
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34 pages, 4209 KB  
Article
A Machine Learning as a Service (MLaaS) Approach to Improve Marketing Success
by Ivo Pereira, Ana Madureira, Nuno Bettencourt, Duarte Coelho, Miguel Ângelo Rebelo, Carolina Araújo and Daniel Alves de Oliveira
Informatics 2024, 11(2), 19; https://doi.org/10.3390/informatics11020019 - 15 Apr 2024
Cited by 5 | Viewed by 3589
Abstract
The exponential growth of data in the digital age has led to a significant demand for innovative approaches to assess data in a manner that is both effective and efficient. Machine Learning as a Service (MLaaS) is a category of services that offers [...] Read more.
The exponential growth of data in the digital age has led to a significant demand for innovative approaches to assess data in a manner that is both effective and efficient. Machine Learning as a Service (MLaaS) is a category of services that offers considerable potential for organisations to extract valuable insights from their data while reducing the requirement for heavy technical expertise. This article explores the use of MLaaS within the realm of marketing applications. In this study, we provide a comprehensive analysis of MLaaS implementations and their benefits within the domain of marketing. Furthermore, we present a platform that possesses the capability to be customised and expanded to address marketing’s unique requirements. Three modules are introduced: Churn Prediction, One-2-One Product Recommendation, and Send Frequency Prediction. When applied to marketing, the proposed MLaaS system exhibits considerable promise for use in applications such as automated detection of client churn prior to its occurrence, individualised product recommendations, and send time optimisation. Our study revealed that AI-driven campaigns can improve both the Open Rate and Click Rate. This approach has the potential to enhance customer engagement and retention for businesses while enabling well-informed decisions by leveraging insights derived from consumer data. This work contributes to the existing body of research on MLaaS in marketing and offers practical insights for businesses seeking to utilise this approach to enhance their competitive edge in the contemporary data-oriented marketplace. Full article
(This article belongs to the Section Machine Learning)
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27 pages, 3834 KB  
Article
DataMesh+: A Blockchain-Powered Peer-to-Peer Data Exchange Model for Self-Sovereign Data Marketplaces
by Mpyana Mwamba Merlec and Hoh Peter In
Sensors 2024, 24(6), 1896; https://doi.org/10.3390/s24061896 - 15 Mar 2024
Cited by 6 | Viewed by 3713
Abstract
In contemporary data-driven economies, data has become a valuable digital asset that is eligible for trading and monetization. Peer-to-peer (P2P) marketplaces play a crucial role in establishing direct connections between data providers and consumers. However, traditional data marketplaces exhibit inadequacies. Functioning as centralized [...] Read more.
In contemporary data-driven economies, data has become a valuable digital asset that is eligible for trading and monetization. Peer-to-peer (P2P) marketplaces play a crucial role in establishing direct connections between data providers and consumers. However, traditional data marketplaces exhibit inadequacies. Functioning as centralized platforms, they suffer from issues such as insufficient trust, transparency, fairness, accountability, and security. Moreover, users lack consent and ownership control over their data. To address these issues, we propose DataMesh+, an innovative blockchain-powered, decentralized P2P data exchange model for self-sovereign data marketplaces. This user-centric decentralized approach leverages blockchain-based smart contracts to enable fair, transparent, reliable, and secure data trading marketplaces, empowering users to retain full sovereignty and control over their data. In this article, we describe the design and implementation of our approach, which was developed to demonstrate its feasibility. We evaluated the model’s acceptability and reliability through experimental testing and validation. Furthermore, we assessed the security and performance in terms of smart contract deployment and transaction execution costs, as well as the blockchain and storage network performance. Full article
(This article belongs to the Section Internet of Things)
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22 pages, 1611 KB  
Article
Dynamic Pricing for the Open Online Ticket System: A Surrogate Modeling Approach
by Elizaveta Stavinova, Ilyas Varshavskiy, Petr Chunaev, Ivan Derevitskii and Alexander Boukhanovsky
Smart Cities 2023, 6(3), 1303-1324; https://doi.org/10.3390/smartcities6030063 - 9 May 2023
Cited by 2 | Viewed by 4667
Abstract
Dynamic pricing is frequently used in online marketplaces, ticket sales, and booking systems. The commercial principles of dynamic pricing systems are often kept secret; however, their application causes complex changes in human behavior. Thus, a scientific tool is needed to evaluate and predict [...] Read more.
Dynamic pricing is frequently used in online marketplaces, ticket sales, and booking systems. The commercial principles of dynamic pricing systems are often kept secret; however, their application causes complex changes in human behavior. Thus, a scientific tool is needed to evaluate and predict the impact of dynamic pricing strategies. Publications in the field lack a common quality evaluation methodology, public data, and source code, making them difficult to reproduce. In this paper, a data-driven method, DPRank, for evaluating dynamic pricing systems is proposed. DPRank first builds a surrogate price elasticity of demand model using public data generated by a hidden dynamic pricing model, and then applies the surrogate model to build an exposed dynamic pricing model. The hidden and exposed dynamic pricing models were then systematically compared in terms of quality using a Monte Carlo simulation in terms of a company’s revenue. The effectiveness of the proposed method was tested on the dataset collected from the website of a Russian railway passenger carrier company. Depending on the train type, the quality difference between the hidden and exposed models can vary by several dozen percent on average, indicating the potential for improving the existing (hidden) company’s dynamic pricing model. Full article
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24 pages, 2074 KB  
Article
Demand-Response Control in Smart Grids
by Atef Gharbi, Mohamed Ayari and Abdulsamad Ebrahim Yahya
Appl. Sci. 2023, 13(4), 2355; https://doi.org/10.3390/app13042355 - 12 Feb 2023
Cited by 17 | Viewed by 5484
Abstract
In the smart grid, electricity price is a key element for all participants in the electric power industry. To meet the smart grid’s various goals, Demand-Response (DR) control aims to change the electricity consumption behavior of consumers based on dynamic pricing or financial [...] Read more.
In the smart grid, electricity price is a key element for all participants in the electric power industry. To meet the smart grid’s various goals, Demand-Response (DR) control aims to change the electricity consumption behavior of consumers based on dynamic pricing or financial benefits. DR methods are divided into centralized and distributed control based on the communication model. In centralized control, consumers communicate directly with the power company, without communicating among themselves. In distributed control, consumer interactions offer data to the power utility about overall consumption. Online auctions are distributed systems with several software agents working on behalf of human buyers and sellers. The coordination model chosen can have a substantial impact on the performance of these software agents. Based on the fair energy scheduling method, we examined Vickrey and Dutch auctions and coordination models in an electronic marketplace both analytically and empirically. The number of software agents and the number of messages exchanged between these agents were all essential indicators. For the simulation, GridSim was used, as it is an open-source software platform that includes capabilities for application composition, resource discovery information services, and interfaces for assigning applications to resources. We concluded that Dutch auctions are better than Vickrey auctions in a supply-driven world where there is an abundance of power. In terms of equity, Dutch auctions are more equitable than Vickrey auctions. This is because Dutch auctions allow all bidders to compete on an equal footing, with each bidder having the same opportunity to win the item at the lowest possible price. In contrast, Vickrey auctions can lead to outcomes that favor certain bidders over others, as bidders may submit bids that are higher than necessary to increase their chances of winning. Full article
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28 pages, 2300 KB  
Review
Big Data Maturity Assessment Models: A Systematic Literature Review
by Zaher Ali Al-Sai, Mohd Heikal Husin, Sharifah Mashita Syed-Mohamad, Rosni Abdullah, Raed Abu Zitar, Laith Abualigah and Amir H. Gandomi
Big Data Cogn. Comput. 2023, 7(1), 2; https://doi.org/10.3390/bdcc7010002 - 20 Dec 2022
Cited by 21 | Viewed by 9051
Abstract
Big Data and analytics have become essential factors in managing the COVID-19 pandemic. As no company can escape the effects of the pandemic, mature Big Data and analytics practices are essential for successful decision-making insights and keeping pace with a changing and unpredictable [...] Read more.
Big Data and analytics have become essential factors in managing the COVID-19 pandemic. As no company can escape the effects of the pandemic, mature Big Data and analytics practices are essential for successful decision-making insights and keeping pace with a changing and unpredictable marketplace. The ability to be successful in Big Data projects is related to the organization’s maturity level. The maturity model is a tool that could be applied to assess the maturity level across specific key dimensions, where the maturity levels indicate an organization’s current capabilities and the desirable state. Big Data maturity models (BDMMs) are a new trend with limited publications published as white papers and web materials by practitioners. While most of the related literature might not have covered all of the existing BDMMs, this systematic literature review (SLR) aims to contribute to the body of knowledge and address the limitations in the existing literature about the existing BDMMs, assessment dimensions, and tools. The SLR strategy in this paper was conducted based on guidelines to perform SLR in software engineering by answering three research questions: (1) What are the existing maturity assessment models for Big Data? (2) What are the assessment dimensions for Big Data maturity models? and (3) What are the assessment tools for Big Data maturity models? This SLR covers the available BDMMs written in English and developed by academics and practitioners (2007–2022). By applying a descriptive qualitative content analysis method for the reviewed publications, this SLR identified 15 BDMMs (10 BDMMs by practitioners and 5 BDMMs by academics). Additionally, this paper presents the limitations of existing BDMMs. The findings of this paper could be used as a grounded reference for assessing the maturity of Big Data. Moreover, this paper will provide managers with critical insights to select the BDMM that fits within their organization to support their data-driven decisions. Future work will investigate the Big Data maturity assessment dimensions towards developing a new Big Data maturity model. Full article
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5 pages, 477 KB  
Opinion
Data Marketplaces: A Solution for Personal Data Control and Ownership?
by Sachit Mahajan
Sustainability 2022, 14(24), 16884; https://doi.org/10.3390/su142416884 - 16 Dec 2022
Cited by 2 | Viewed by 3217
Abstract
Data sharing is critical for advancing research, commercializing technologies, and informing people. However, data owners have been reluctant to share data due to concerns about data control, access, and a lack of secure data storage solutions. Frequent data breaches have also led to [...] Read more.
Data sharing is critical for advancing research, commercializing technologies, and informing people. However, data owners have been reluctant to share data due to concerns about data control, access, and a lack of secure data storage solutions. Frequent data breaches have also led to a lack of user trust in businesses. Despite widespread recognition of user concerns, few steps have been taken to empower users. While there are models for decentralized data sharing, the question of how to incentivize these structures to enable data sharing at scale is largely unexplored. This work discusses the potential of blockchain-based data marketplaces to empower users by giving them more control over their data and bringing transparency to the data-driven ecosystem. Full article
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24 pages, 1830 KB  
Review
Optimized Data-Driven Models for Short-Term Electricity Price Forecasting Based on Signal Decomposition and Clustering Techniques
by Athanasios Ioannis Arvanitidis, Dimitrios Bargiotas, Dimitrios Kontogiannis, Athanasios Fevgas and Miltiadis Alamaniotis
Energies 2022, 15(21), 7929; https://doi.org/10.3390/en15217929 - 25 Oct 2022
Cited by 17 | Viewed by 3473
Abstract
In recent decades, the traditional monopolistic energy exchange market has been replaced by deregulated, competitive marketplaces in which electricity may be purchased and sold at market prices like any other commodity. As a result, the deregulation of the electricity industry has produced a [...] Read more.
In recent decades, the traditional monopolistic energy exchange market has been replaced by deregulated, competitive marketplaces in which electricity may be purchased and sold at market prices like any other commodity. As a result, the deregulation of the electricity industry has produced a demand for wholesale organized marketplaces. Price predictions, which are primarily meant to establish the market clearing price, have become a significant factor to an energy company’s decision making and strategic development. Recently, the fast development of deep learning algorithms, as well as the deployment of front-end metaheuristic optimization approaches, have resulted in the efficient development of enhanced prediction models that are used for electricity price forecasting. In this paper, the development of six highly accurate, robust and optimized data-driven forecasting models in conjunction with an optimized Variational Mode Decomposition method and the K-Means clustering algorithm for short-term electricity price forecasting is proposed. In this work, we also establish an Inverted and Discrete Particle Swarm Optimization approach that is implemented for the optimization of the Variational Mode Decomposition method. The prediction of the day-ahead electricity prices is based on historical weather and price data of the deregulated Greek electricity market. The resulting forecasting outcomes are thoroughly compared in order to address which of the two proposed divide-and-conquer preprocessing approaches results in more accuracy concerning the issue of short-term electricity price forecasting. Finally, the proposed technique that produces the smallest error in the electricity price forecasting is based on Variational Mode Decomposition, which is optimized through the proposed variation of Particle Swarm Optimization, with a mean absolute percentage error value of 6.15%. Full article
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31 pages, 9124 KB  
Article
Decentralized Personal Data Marketplaces: How Participation in a DAO Can Support the Production of Citizen-Generated Data
by Mirko Zichichi, Stefano Ferretti and Víctor Rodríguez-Doncel
Sensors 2022, 22(16), 6260; https://doi.org/10.3390/s22166260 - 20 Aug 2022
Cited by 19 | Viewed by 6125
Abstract
Big Tech companies operating in a data-driven economy offer services that rely on their users’ personal data and usually store this personal information in “data silos” that prevent transparency about their use and opportunities for data sharing for public interest. In this paper, [...] Read more.
Big Tech companies operating in a data-driven economy offer services that rely on their users’ personal data and usually store this personal information in “data silos” that prevent transparency about their use and opportunities for data sharing for public interest. In this paper, we present a solution that promotes the development of decentralized personal data marketplaces, exploiting the use of Distributed Ledger Technologies (DLTs), Decentralized File Storages (DFS) and smart contracts for storing personal data and managing access control in a decentralized way. Moreover, we focus on the issue of a lack of efficient decentralized mechanisms in DLTs and DFSs for querying a certain type of data. For this reason, we propose the use of a hypercube-structured Distributed Hash Table (DHT) on top of DLTs, organized for efficient processing of multiple keyword-based queries on the ledger data. We test our approach with the implementation of a use case regarding the creation of citizen-generated data based on direct participation and the involvement of a Decentralized Autonomous Organization (DAO). The performance evaluation demonstrates the viability of our approach for decentralized data searches, distributed authorization mechanisms and smart contract exploitation. Full article
(This article belongs to the Collection Sensors and Communications for the Social Good)
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