Integration of Blockchain and Auction Models: A Survey, Some Applications, and Challenges

In recent years, blockchain has gained widespread attention as an emerging technology for decentralization, transparency, and immutability in advancing online activities over public networks. As an essential market process, auctions have been well studied and applied in many business fields due to their efficiency and contributions to fair trade. Complementary features between blockchain and auction models trigger a great potential for research and innovation. On the one hand, the decentralized nature of blockchain can provide a trustworthy, secure, and cost-effective mechanism to manage the auction process; on the other hand, auction models can be utilized to design incentive and consensus protocols in blockchain architectures. These opportunities have attracted enormous research and innovation activities in both academia and industry; however, there is a lack of an in-depth review of existing solutions and achievements. In this paper, we conduct a comprehensive state-of-the-art survey of these two research topics. We review the existing solutions for integrating blockchain and auction models, with some application-oriented taxonomies generated. Additionally, we highlight some open research challenges and future directions towards integrated blockchain-auction models

When energy trading meets blockchain in electrical power system: The state of the art

With the rapid growth of renewable energy resources, energy trading has been shifting from the centralized manner to distributed manner. Blockchain, as a distributed public ledger technology, has been widely adopted in the design of new energy trading schemes. However, there are many challenging issues in blockchain-based energy trading, e.g., low efficiency, high transaction cost, and security and privacy issues. To tackle these challenges, many solutions have been proposed. In this survey, the blockchain-based energy trading in the electrical power system is thoroughly investigated. Firstly, the challenges in blockchain-based energy trading are identified and summarized. Then, the existing energy trading schemes are studied and classified into three categories based on their main focuses: energy transaction, consensus mechanism, and system optimization. Blockchain-based energy trading has been a popular research topic, new blockchain architectures, models and products are continually emerging to overcome the limitations of existing solutions, forming a virtuous circle. The internal combination of different blockchain types and the combination of blockchain with other technologies improve the blockchain-based energy trading system to better satisfy the practical requirements of modern power systems. However, there are still some problems to be solved, for example, the lack of regulatory system, environmental challenges and so on. In the future, we will strive for a better optimized structure and establish a comprehensive security assessment model for blockchain-based energy trading system.This research was funded by Beijing Natural Science Foundation (grant number 4182060).Scopu

Understanding human-machine networks: A cross-disciplinary survey

© 2017 ACM. In the current hyperconnected era, modern Information and Communication Technology (ICT) systems form sophisticated networks where not only do people interact with other people, but also machines take an increasingly visible and participatory role. Such Human-Machine Networks (HMNs) are embedded in the daily lives of people, both for personal and professional use. They can have a significant impact by producing synergy and innovations. The challenge in designing successful HMNs is that they cannot be developed and implemented in the same manner as networks of machines nodes alone, or following a wholly human-centric view of the network. The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. Extending the previous theoretical concepts of sociotechnical systems, actor-network theory, cyber-physical-social systems, and social machines, we concentrate on the interactions among humans and between humans and machines. We identify eight types of HMNs: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. We systematically select literature on each of these types and review it with a focus on implications for designing HMNs. Moreover, we discuss risks associated with HMNs and identify emerging design and development trends

Self-Tuning Service Provisioning for Decentralised Cloud Applications

Cloud computing has revolutionized service delivery by providing on-demand invocation and elasticity. To reap these benefits, computation has been displaced from client devices and into data centers. This partial centralization is undesirable for applications that have stringent locality requirements, e.g., low latency. This problem could be addressed with large numbers of smaller cloud resources closer to users. However, as cloud computing diffuses from within data centers and into the network, there will be a need for cloud resource allocation algorithms that operate on resource-constrained computational units that serve localized subsets of customers. In this paper, we present a mechanism for service provisioning in distributed clouds where applications compete for resources. The mechanism operates by enabling execution zones to assign resources based on Vickrey auctions and provides high-quality probabilistic models that applications can use to predict the outcomes of such auctions. This allows applications to use knowledge of the locality distribution of their clients to accurately select the number of bids to be sent to each execution zone and their value. The proposed mechanism is highly scalable, efficient, and validated by extensive simulations

μGIM - Microgrid intelligent management system based on a multi-agent approach and the active participation of end-users

[ES] Los sistemas de potencia y energía están cambiando su paradigma tradicional, de sistemas centralizados a sistemas descentralizados. La aparición de redes inteligentes permite la integración de recursos energéticos descentralizados y promueve la gestión inclusiva que involucra a los usuarios finales, impulsada por la gestión del lado de la demanda, la energía transactiva y la respuesta a la demanda. Garantizar la escalabilidad y la estabilidad del servicio proporcionado por la red, en este nuevo paradigma de redes inteligentes, es más difícil porque no hay una única sala de operaciones centralizada donde se tomen todas las decisiones. Para implementar con éxito redes inteligentes, es necesario combinar esfuerzos entre la ingeniería eléctrica y la ingeniería informática. La ingeniería eléctrica debe garantizar el correcto funcionamiento físico de las redes inteligentes y de sus componentes, estableciendo las bases para un adecuado monitoreo, control, gestión, y métodos de operación. La ingeniería informática desempeña un papel importante al proporcionar los modelos y herramientas computacionales adecuados para administrar y operar la red inteligente y sus partes constituyentes, representando adecuadamente a todos los diferentes actores involucrados. Estos modelos deben considerar los objetivos individuales y comunes de los actores que proporcionan las bases para garantizar interacciones competitivas y cooperativas capaces de satisfacer a los actores individuales, así como cumplir con los requisitos comunes con respecto a la sostenibilidad técnica, ambiental y económica del Sistema. La naturaleza distribuida de las redes inteligentes permite, incentiva y beneficia enormemente la participación activa de los usuarios finales, desde actores grandes hasta actores más pequeños, como los consumidores residenciales. Uno de los principales problemas en la planificación y operación de redes eléctricas es la variación de la demanda de energía, que a menudo se duplica más que durante las horas pico en comparación con la demanda fuera de pico. Tradicionalmente, esta variación dio como resultado la construcción de plantas de generación de energía y grandes inversiones en líneas de red y subestaciones. El uso masivo de fuentes de energía renovables implica mayor volatilidad en lo relativo a la generación, lo que hace que sea más difícil equilibrar el consumo y la generación. La participación de los actores de la red inteligente, habilitada por la energía transactiva y la respuesta a la demanda, puede proporcionar flexibilidad en desde el punto de vista de la demanda, facilitando la operación del sistema y haciendo frente a la creciente participación de las energías renovables. En el ámbito de las redes inteligentes, es posible construir y operar redes más pequeñas, llamadas microrredes. Esas son redes geográficamente limitadas con gestión y operación local. Pueden verse como áreas geográficas restringidas para las cuales la red eléctrica generalmente opera físicamente conectada a la red principal, pero también puede operar en modo isla, lo que proporciona independencia de la red principal. Esta investigación de doctorado, realizada bajo el Programa de Doctorado en Ingeniería Informática de la Universidad de Salamanca, aborda el estudio y el análisis de la gestión de microrredes, considerando la participación activa de los usuarios finales y la gestión energética de lascarga eléctrica y los recursos energéticos de los usuarios finales. En este trabajo de investigación se ha analizado el uso de conceptos de ingeniería informática, particularmente del campo de la inteligencia artificial, para apoyar la gestión de las microrredes, proponiendo un sistema de gestión inteligente de microrredes (μGIM) basado en un enfoque de múltiples agentes y en la participación activa de usuarios. Esta solución se compone de tres sistemas que combinan hardware y software: el emulador de virtual a realidad (V2R), el enchufe inteligente de conciencia ambiental de Internet de las cosas (EnAPlug), y la computadora de placa única para energía basada en el agente (S4E) para permitir la gestión del lado de la demanda y la energía transactiva. Estos sistemas fueron concebidos, desarrollados y probados para permitir la validación de metodologías de gestión de microrredes, es decir, para la participación de los usuarios finales y para la optimización inteligente de los recursos. Este documento presenta todos los principales modelos y resultados obtenidos durante esta investigación de doctorado, con respecto a análisis de vanguardia, concepción de sistemas, desarrollo de sistemas, resultados de experimentación y descubrimientos principales. Los sistemas se han evaluado en escenarios reales, desde laboratorios hasta sitios piloto. En total, se han publicado veinte artículos científicos, de los cuales nueve se han hecho en revistas especializadas. Esta investigación de doctorado realizó contribuciones a dos proyectos H2020 (DOMINOES y DREAM-GO), dos proyectos ITEA (M2MGrids y SPEAR), tres proyectos portugueses (SIMOCE, NetEffiCity y AVIGAE) y un proyecto con financiación en cascada H2020 (Eco-Rural -IoT)

Revealing the Landscape of Privacy-Enhancing Technologies in the Context of Data Markets for the IoT: A Systematic Literature Review

IoT data markets in public and private institutions have become increasingly relevant in recent years because of their potential to improve data availability and unlock new business models. However, exchanging data in markets bears considerable challenges related to disclosing sensitive information. Despite considerable research focused on different aspects of privacy-enhancing data markets for the IoT, none of the solutions proposed so far seems to find a practical adoption. Thus, this study aims to organize the state-of-the-art solutions, analyze and scope the technologies that have been suggested in this context, and structure the remaining challenges to determine areas where future research is required. To accomplish this goal, we conducted a systematic literature review on privacy enhancement in data markets for the IoT, covering 50 publications dated up to July 2020, and provided updates with 24 publications dated up to May 2022. Our results indicate that most research in this area has emerged only recently, and no IoT data market architecture has established itself as canonical. Existing solutions frequently lack the required combination of anonymization and secure computation technologies. Furthermore, there is no consensus on the appropriate use of blockchain technology for IoT data markets and a low degree of leveraging existing libraries or reusing generic data market architectures. We also identified significant challenges remaining, such as the copy problem and the recursive enforcement problem that-while solutions have been suggested to some extent-are often not sufficiently addressed in proposed designs. We conclude that privacy-enhancing technologies need further improvements to positively impact data markets so that, ultimately, the value of data is preserved through data scarcity and users' privacy and businesses-critical information are protected.Comment: 49 pages, 17 figures, 11 table