Breaking Down Silos in Urban Services

A special issue of Smart Cities (ISSN 2624-6511).

Deadline for manuscript submissions: 31 March 2025 | Viewed by 945

Special Issue Editors


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Guest Editor
CeDInt-UPM, Universidad Politécnica de Madrid, Campus de Montegancedo, 28223 Pozuelo de Alarcón, Spain
Interests: Internet of Things; smart cities; urban smart spaces; wireless communications; energy efficiency; sustainability

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Guest Editor
CeDInt-UPM, Universidad Politécnica de Madrid, Campus de Montegancedo, 28223 Pozuelo de Alarcón, Spain
Interests: IoT; energy efficiency; RV/RA; urban mobility

Special Issue Information

Dear Colleagues,

Urban services often operate in isolation, each functioning independently with minimal coordination or integration. These silos have developed due to historical practices, bureaucratic structures, and specialized expertise, resulting in the fragmented delivery of services such as transportation, waste management, and street lighting. Each service typically operates within its own department or agency, focusing solely on its specific mandate and objectives, without considering the broader urban context or potential synergies with other services. Overcoming these silos is essential for creating more efficient, resilient, and sustainable urban environments. This Special Issue seeks to gather researchers, industry experts, urban planners, and policymakers to discuss the latest advancements, challenges, and opportunities in integrating urban services.

We invite submissions of original research papers, review articles, and case studies addressing the following topics, but not limited to them:

  • Standards, protocols, and data models for integrated urban services;
  • IoT platforms facilitating interservice coordination;
  • Communication technologies enabling seamless service integration;
  • Cross-service applications and interoperability frameworks;
  • Case studies on real-world implementations of integrated urban solutions;
  • Enabling technologies driving the convergence of urban services;
  • Data sharing and privacy concerns in integrated urban environments;
  • Policy and governance models promoting inter-service collaboration;
  • Innovative business models for integrated service delivery;
  • Role of artificial intelligence and machine learning in urban service integration;
  • Impact of integrated services on urban sustainability and resilience;
  • Citizen engagement and participatory approaches in urban service integration.

Dr. Guillermo del Campo Jiménez
Prof. Dr. Asun Santamaria
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Smart Cities is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • urban services integration
  • smart cities
  • Internet of Things
  • IoT platforms
  • Iot communication technologies
  • cross-service interoperability
  • urban data spaces

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Published Papers (1 paper)

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Research

31 pages, 4629 KiB  
Article
An Adaptive Energy Orchestrator for Cyberphysical Systems Using Multiagent Reinforcement Learning
by Alberto Robles-Enciso, Ricardo Robles-Enciso and Antonio F. Skarmeta Gómez
Smart Cities 2024, 7(6), 3210-3240; https://doi.org/10.3390/smartcities7060125 - 29 Oct 2024
Viewed by 695
Abstract
Reducing carbon emissions is a critical issue for the near future as climate change is an imminent reality. To reduce our carbon footprint, society must change its habits and behaviours to optimise energy consumption, and the current progress in embedded systems and artificial [...] Read more.
Reducing carbon emissions is a critical issue for the near future as climate change is an imminent reality. To reduce our carbon footprint, society must change its habits and behaviours to optimise energy consumption, and the current progress in embedded systems and artificial intelligence has the potential to make this easier. The smart building concept and intelligent energy management are key points to increase the use of renewable sources of energy as opposed to fossil fuels. In addition, cyber-physical systems (CPSs) provide an abstraction of the management of services that allows the integration of both virtual and physical systems in a seamless control architecture. In this paper, we propose to use multiagent reinforcement learning (MARL) to model the CPS services control plane in a smart house, with the purpose of minimising, by shifting or shutdown services, the use of non-renewable energy (fuel generator) by exploiting solar production and batteries. Furthermore, our proposal dynamically adapts its behaviour in real time according to current and historic energy production, thus being able to handle occasional changes in energy production due to meteorological phenomena or unexpected energy consumption. In order to evaluate our proposal, we have developed an open-source smart building energy simulator and deployed our use case. Finally, several simulations with different configurations are evaluated to verify the performance. The simulation results show that the reinforcement learning solution outperformed the priority-based and the heuristic-based solutions in both power consumption and adaptability in all configurations. Full article
(This article belongs to the Special Issue Breaking Down Silos in Urban Services)
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