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Internet of Things for Energy-Efficient Smart Cities: Technologies, Big Data and Security

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "G1: Smart Cities and Urban Management".

Deadline for manuscript submissions: closed (24 April 2026) | Viewed by 3818

Special Issue Editors


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Guest Editor
Department of Computer Science and Engineering, University of Bologna, 40126 Bologna, Italy
Interests: Internet of Things; interoperability; distributed systems; distributed ledger technology; blockchain

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Guest Editor
Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, 40136 Bologna, Italy
Interests: the design of intelligent sensor systems enabled by edge/extreme-edge computing functionalities and tiny machine learning capabilities for a wide range of applications (including monitoring infrastructures, industry and agriculture); focusing on the joint optimization of hardware-software components

Special Issue Information

Dear Colleagues,

The rapid urbanization of our world presents unprecedented challenges and opportunities, with smart cities emerging as a key strategy to enhance the quality of life for citizens and ensure sustainable development. Central to the evolution of smart cities is the Internet of Things (IoT), a transformative paradigm that connects a vast array of devices, sensors, and systems, enabling intelligent data collection, analysis, and control. As urban environments grow, so does their energy consumption, making energy efficiency a critical imperative. The integration of IoT technologies offers powerful solutions to optimize energy usage across various urban sectors, from smart grids and buildings to transportation and public services. However, the proliferation of IoT devices also generates massive volumes of data—Big Data—that require sophisticated analytical techniques to unlock valuable insights. Furthermore, the interconnected nature of these systems raises significant security and privacy concerns that must be addressed to ensure the trust and reliability of smart city infrastructures.

This Special Issue aims to present and disseminate the most recent advances related to the theory, design, modelling, application, and security of IoT systems for energy-efficient smart cities. We seek high-quality, original research that explores innovative technologies, addresses the challenges of managing and leveraging Big Data, and proposes robust security frameworks to safeguard these critical urban infrastructures.

Topics of interest for publication include, but are not limited to the following:

  • Novel IoT architectures and communication protocols for energy-efficient smart cities;
  • Low-power IoT devices and energy harvesting techniques for urban deployments;
  • Big Data analytics, machine learning, and AI for energy optimization in smart cities;
  • Cybersecurity, privacy-preserving mechanisms, and trust management for smart city IoT ecosystems;
  • IoT-enabled smart grids, demand-response systems, and renewable energy integration;
  • Intelligent transportation systems and their impact on urban energy consumption;
  • Energy-efficient smart buildings, homes, and infrastructure management using IoT;
  • IoT applications for sustainable urban resource management (e.g., water, waste);
  • Edge, fog, and cloud computing paradigms for smart city IoT data processing;
  • Blockchain technology for secure and transparent energy management in smart cities;
  • Interoperability and standardization challenges in IoT for smart cities;
  • Case studies, pilot projects, and real-world deployments of IoT in energy-efficient smart cities;
  • Ethical, social, and policy implications of IoT adoption in smart cities.

We look forward to receiving your contributions to this timely and important Special Issue.

Dr. Lorenzo Gigli
Dr. Federica Zonzini
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 250 words) can be sent to the Editorial Office for assessment.

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. Energies 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

  • Internet of Things (IoT)
  • smart cities
  • energy efficiency
  • big data
  • cyber security
  • smart grids
  • low-power IoT
  • machine learning
  • blockchain
  • IoT security

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Published Papers (2 papers)

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Research

22 pages, 6194 KB  
Article
Innovative Cyber-Physical/Electronic AI-Assisted Digital Twin Model of Small Energy Harvesting Cantilever Power Generators
by Alessandro Massaro, Giuseppe Fanizza and Giuseppe Starace
Energies 2026, 19(2), 390; https://doi.org/10.3390/en19020390 - 13 Jan 2026
Viewed by 464
Abstract
The paper deals with the design of a Digital Twin model of an energy harvesting cantilever beam for low frequency energy harvesting applications and specifically with a digital model matching simulations corresponding with Finite Element Method solutions in order to validate the model. [...] Read more.
The paper deals with the design of a Digital Twin model of an energy harvesting cantilever beam for low frequency energy harvesting applications and specifically with a digital model matching simulations corresponding with Finite Element Method solutions in order to validate the model. The physical behavior is based on the main parameters to be investigated. The finite elements analysis is geometrically and parametrically carried out for a small PZT5A device of the orders of millimeters and is optimized to take into consideration the relationships between tip displacement, generated voltages and vibration gravitational forces for standard industrial applications in the acceleration range between 0.5 and 2 g. Then a procedure to integrate the Digital Twin into a design framework has been developed, including an artificial intelligence algorithm that supports the modelling of the real behavior of the device. The paper is devoted to help researchers involved in a Digital Twin adoption in the field of electronic design and of the physical characterization of low frequency energy harvesting devices exclusively using open-source tools. Full article
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50 pages, 6411 KB  
Article
AI-Enhanced Eco-Efficient UAV Design for Sustainable Urban Logistics: Integration of Embedded Intelligence and Renewable Energy Systems
by Luigi Bibbò, Filippo Laganà, Giuliana Bilotta, Giuseppe Maria Meduri, Giovanni Angiulli and Francesco Cotroneo
Energies 2025, 18(19), 5242; https://doi.org/10.3390/en18195242 - 2 Oct 2025
Cited by 10 | Viewed by 2821
Abstract
The increasing use of UAVs has reshaped urban logistics, enabling sustainable alternatives to traditional deliveries. To address critical issues inherent in the system, the proposed study presents the design and evaluation of an innovative unmanned aerial vehicle (UAV) prototype that integrates advanced electronic [...] Read more.
The increasing use of UAVs has reshaped urban logistics, enabling sustainable alternatives to traditional deliveries. To address critical issues inherent in the system, the proposed study presents the design and evaluation of an innovative unmanned aerial vehicle (UAV) prototype that integrates advanced electronic components and artificial intelligence (AI), with the aim of reducing environmental impact and enabling autonomous navigation in complex urban environments. The UAV platform incorporates brushless DC motors, high-density LiPo batteries and perovskite solar cells to improve energy efficiency and increase flight range. The Deep Q-Network (DQN) allocates energy and selects reference points in the presence of wind and payload disturbances, while an integrated sensor system monitors motor vibration/temperature and charge status to prevent failures. In urban canyon and field scenarios (wind from 0 to 8 m/s; payload from 0.35 to 0.55 kg), the system reduces energy consumption by up to 18%, increases area coverage by 12% for the same charge, and maintains structural safety factors > 1.5 under gust loading. The approach combines sustainable materials, efficient propulsion, and real-time AI-based navigation for energy-conscious flight planning. A hybrid methodology, combining experimental design principles with finite-element-based structural modelling and AI-enhanced monitoring, has been applied to ensure structural health awareness. The study implements proven edge-AI sensor fusion architectures, balancing portability and telemonitoring with an integrated low-power design. The results confirm a reduction in energy consumption and CO2 emissions compared to traditional delivery vehicles, confirming that the proposed system represents a scalable and intelligent solution for last-mile delivery, contributing to climate resilience and urban sustainability. The findings position the proposed UAV as a scalable reference model for integrating AI-driven navigation and renewable energy systems in sustainable logistics. Full article
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