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Urban Intelligence at the Edge
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Urban Intelligence at the Edge

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 15752

Special Issue Editor

Dr. Giandomenico Spezzano

E-Mail Website
Guest Editor
National Research Council of Italy (CNR), Institute for High Performance Computing and Networking (ICAR), Via Pietro Bucci, 8-9C, 87036 Rende (CS), Italy
Interests: cognitive IoT; urban computing; swarm intelligence; edge computing; reinforcement learning; cognitive buildings
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Building a smart city aims to optimize transport, energy distribution, and services provided to residents by installing sensors in parking lots, public transport stations, garbage trucks, urban lighting systems, etc. to collect the data needed to help cities in decision-making processes.

The huge volume of data generated in a city provides enormous amounts of information about the behaviors, habits, and needs of its inhabitants. At the heart of all smart cities are digital technologies that offer significant potential for transformation. Urban intelligence and its constituent elements resulting from the digital transformation process of the city will lead to a paradigm shift in which the city will become a platform where digital services for urban planning and management will be supported by urban analysis and real-time data.

As cities become “smarter”, urban digital services will continue to evolve. Edge computing will play a key role in making digital services possible in smart cities. Edge computing allows large amounts of complex data to be processed and analyzed instantly on the devices themselves, rather than in large data centers.

The goal of this Special Issue is to call for a coordinated effort to understand the opportunities and challenges to developing intelligent solutions for urban issues.

Prof. Giandomenico Spezzano
Guest Editor

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. Sensors 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 2600 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

  • Intelligent urban ecosystems
  • AI and machine learning for an urban dataset
  • Deep reinforcement learning
  • Urban computing
  • Big data analytics for smart communities
  • Edge computing, cloud computing, and mobile computing for urban intelligence
  • Robotics for community services
  • Food and consumer goods distribution and management
  • Pollution monitoring and management

Published Papers (3 papers)

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Research

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14 pages, 2297 KiB  
Article
Smart Scheduling of Electric Vehicles Based on Reinforcement Learning
by Andrei Viziteu, Daniel Furtună, Andrei Robu, Stelian Senocico, Petru Cioată, Marian Remus Baltariu, Constantin Filote and Maria Simona Răboacă
Sensors 2022, 22(10), 3718; https://0-doi-org.brum.beds.ac.uk/10.3390/s22103718 - 13 May 2022
Cited by 7 | Viewed by 3317
Abstract
As the policies and regulations currently in place concentrate on environmental protection and greenhouse gas reduction, we are steadily witnessing a shift in the transportation industry towards electromobility. There are, though, several issues that need to be addressed to encourage the adoption of [...] Read more.
As the policies and regulations currently in place concentrate on environmental protection and greenhouse gas reduction, we are steadily witnessing a shift in the transportation industry towards electromobility. There are, though, several issues that need to be addressed to encourage the adoption of EVs on a larger scale, starting from enhancing the network interoperability and accessibility and removing the uncertainty associated with the availability of charging stations. Another issue is of particular interest for EV drivers travelling longer distances and is related to scheduling a recharging operation at the estimated time of arrival, without long queuing times. To this end, we propose a solution capable of addressing multiple EV charging scheduling issues, such as congestion management, scheduling a charging station in advance, and allowing EV drivers to plan optimized long trips using their EVs. The smart charging scheduling system we propose considers a variety of factors such as battery charge level, trip distance, nearby charging stations, other appointments, and average speed. Given the scarcity of data sets required to train the Reinforcement Learning algorithms, the novelty of the recommended solution lies in the scenario simulator, which generates the labelled datasets needed to train the algorithm. Based on the generated scenarios, we created and trained a neural network that uses a history of previous situations to identify the optimal charging station and time interval for recharging. The results are promising and for future work we are planning to train the DQN model using real-world data. Full article
(This article belongs to the Special Issue Urban Intelligence at the Edge)
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16 pages, 3336 KiB  
Article
Electric Vehicle Smart Charging Reservation Algorithm
by Radu Flocea, Andrei Hîncu, Andrei Robu, Stelian Senocico, Andrei Traciu, Baltariu Marian Remus, Maria Simona Răboacă and Constantin Filote
Sensors 2022, 22(8), 2834; https://0-doi-org.brum.beds.ac.uk/10.3390/s22082834 - 07 Apr 2022
Cited by 8 | Viewed by 4429
Abstract
The widespread adoption of electromobility constitutes one of the measures designed to reduce air pollution caused by traditional fossil fuels. However, several factors are currently impeding this process, ranging from insufficient charging infrastructure, battery capacity, and long queueing and charging times, to psychological [...] Read more.
The widespread adoption of electromobility constitutes one of the measures designed to reduce air pollution caused by traditional fossil fuels. However, several factors are currently impeding this process, ranging from insufficient charging infrastructure, battery capacity, and long queueing and charging times, to psychological factors. On top of range anxiety, the frustration of the EV drivers is further fuelled by the uncertainty of finding an available charging point on their route. To address this issue, we propose a solution that bypasses the limitations of the “reserve now” function of the OCPP standard, enabling drivers to make charging reservations for the upcoming days, especially when planning a longer trip. We created an algorithm that generates reservation intervals based on the charging station’s reservation and transaction history. Subsequently, we ran a series of test cases that yielded promising results, with no overlapping reservations and the occupation of several stations without queues, assuring, thus, a proper distribution of the available energy resources, while increasing end-user satisfaction. Our solution is independent from the OCPP reservation method; therefore, the authentication and reservation processes performed by the proposed algorithm run only through the central system, authorizing only the creator of the reservation to start the charging transaction. Full article
(This article belongs to the Special Issue Urban Intelligence at the Edge)
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Review

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23 pages, 1394 KiB  
Review
Smart Technologies for Water Resource Management: An Overview
by Stefania Anna Palermo, Mario Maiolo, Anna Chiara Brusco, Michele Turco, Behrouz Pirouz, Emilio Greco, Giandomenico Spezzano and Patrizia Piro
Sensors 2022, 22(16), 6225; https://0-doi-org.brum.beds.ac.uk/10.3390/s22166225 - 19 Aug 2022
Cited by 10 | Viewed by 7022
Abstract
The latest progress in information and communication technology (ICT) and the Internet of Things (IoT) have opened up new opportunities for real-time monitoring and controlling of cities’ structures, infrastructures, and services. In this context, smart water management technology provides the data and tools [...] Read more.
The latest progress in information and communication technology (ICT) and the Internet of Things (IoT) have opened up new opportunities for real-time monitoring and controlling of cities’ structures, infrastructures, and services. In this context, smart water management technology provides the data and tools to help users more effectively manage water usage. Data collected with smart water devices are being integrated with building management systems to show how much water is used by occupants as well as to identify the consumption areas to use water more efficiently. By this approach, smart buildings represent an innovative solution that enhances a city’s sustainability and contributes to overcoming environmental challenges due to increasing population and climate change. One of the main challenges is resource-saving and recovery. Water is an all-important need of all living beings, and the concerns of its scarcity impose a transition to innovative and sustainable management starting from the building scale. Thus, this manuscript aims to provide an updated and valuable overview for researchers, consumers, and stakeholders regarding implementing smart and sustainable technologies for water resource management, primarily for building-scale uses. Full article
(This article belongs to the Special Issue Urban Intelligence at the Edge)
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