Self-regulating supply-demand systems
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TLDR
In this paper, a generic decentralized self-regulatory framework is proposed for matching supply and demand in smart-grid systems, which is shaped around standardized control system concepts and Internet of Things technologies.About:
This article is published in Future Generation Computer Systems.The article was published on 2017-11-01 and is currently open access. It has received 28 citations till now. The article focuses on the topics: Smart city & Smart grid.read more
Citations
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Decentralized Collective Learning for Self-managed Sharing Economies
TL;DR: This article envisions an alternative unsupervised and decentralized collective learning approach that preserves privacy, autonomy, and participation of multi-agent systems self-organized into a hierarchical tree structure as well as findings on techno-socio-economic tradeoffs and global optimality.
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Decentralized Edge-to-Cloud Load Balancing: Service Placement for the Internet of Things
TL;DR: The proposed solution, EPOS Fog, introduces a decentralized multi-agent system for collective learning that utilizes edge-to-cloud nodes to jointly balance the input workload across the network and minimize the costs involved in service execution.
Journal ArticleDOI
So) Big Data and the transformation of the city
Gennady Andrienko,Gennady Andrienko,Natalia Andrienko,Natalia Andrienko,Chiara Boldrini,Guido Caldarelli,Paolo Cintia,Stefano Cresci,Angelo Facchini,Fosca Giannotti,Aristides Gionis,Aristides Gionis,Riccardo Guidotti,Michael Mathioudakis,Cristina Ioana Muntean,Luca Pappalardo,Dino Pedreschi,Evangelos Pournaras,Francesca Pratesi,Maurizio Tesconi,Roberto Trasarti +20 more
TL;DR: The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media, and concludes the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.
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Socio-technical smart grid optimization via decentralized charge control of electric vehicles
TL;DR: In this article, a fully decentralized and participatory learning mechanism for privacy-preserving coordinated charging control of electric vehicles that regulates three Smart Grid socio-technical aspects (i) reliability, (ii) discomfort and (iii) fairness) is proposed.
Proceedings ArticleDOI
TRAPPed in traffic?: a self-adaptive framework for decentralized traffic optimization
TL;DR: A self-adaptive framework to support this research is introduced: TRAPP – Traffic Reconfigurations via Adaptive Participatory Planning, which relies on a microscopic traffic simulator for simulating urban mobility scenarios, and on a decentralized multi-agent planning system, EPOS, for decentralized combinatorial optimization, applied here in traffic flows.
References
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Internet of Things (IoT): A vision, architectural elements, and future directions
TL;DR: In this article, the authors present a cloud centric vision for worldwide implementation of Internet of Things (IoT) and present a Cloud implementation using Aneka, which is based on interaction of private and public Clouds, and conclude their IoT vision by expanding on the need for convergence of WSN, the Internet and distributed computing directed at technological research community.
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College Admissions and the Stability of Marriage
David Gale,Lloyd S. Shapley +1 more
TL;DR: In this article, the authors studied the relationship between college admission and the stability of marriage in the United States, and found that college admission is correlated with the number of stable marriages.
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Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments
TL;DR: Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting peak-to-average ratio in load demand for various load scenarios.
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Demand side management: Benefits and challenges ☆
TL;DR: In this paper, the major benefits and challenges of electricity demand side management (DSM) are discussed in the context of the UK electricity system, particularly in the residential, commercial and small business sectors.
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On the rationale of maximum-entropy methods
TL;DR: The relations between maximum-entropy (MAXENT) and other methods of spectral analysis such as the Schuster, Blackman-Tukey, maximum-likelihood, Bayesian, and Autoregressive models are discussed, emphasizing that they are not in conflict, but rather are appropriate in different problems.