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Sensors Solutions for the Development of Climate Neutral and Smart Cities

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 4743

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Special Issue Editors

Dr. Alin-Mihai Cailean

E-Mail Website
Guest Editor

MANSiD, University Stefan cel Mare of Suceava, Suceava, Romania
Interests: smart lighting; adaptive lighting; user centered lighting; visible light communications; vehicle safety; intelligent vehicles; inter-vehicle communication systems; wireless sensors; energy management sensors and systems
Special Issues, Collections and Topics in MDPI journals

Dr. Andrei Ceclan

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Guest Editor

Energy Transition Research Center, Technical University of Cluj-Napoca, Baritiu Street no. 26, Cluj-Napoca, Romania
Interests: urban energy management; energy transition towards carbon neutrality
Special Issues, Collections and Topics in MDPI journals

Dr. Radu Ioan Ciobanu

E-Mail Website
Guest Editor

Faculty of Automatic Control and Computer Science, University Politehnica Bucharest, Bucharest, Romania
Interests: opportunistic networks; mobile networks; cloud computing; social knowledge; Internet of Things; mobile AI

Special Issue Information

Dear Colleagues,

In the context of accelerating climate change and rapid urbanization, cities are at the forefront of the transition toward sustainability and carbon neutrality. The achievement of these goals requires the integration of advanced sensor technologies, intelligent energy and carbon measurement infrastructure, and data-driven decision-making. The European Union’s Mission on Climate-Neutral and Smart Cities calls for transformative action, supporting selected cities to reach climate neutrality by 2030 and inspiring all others to follow by 2050. In support of this vision, the Romanian initiative NetZeRoCities established and made operational a national competence center to guide and support local efforts.

This Special Issue of Sensors seeks to highlight innovative sensor-based solutions that contribute to climate-neutral and smart urban development, with a particular focus on outcomes and methodologies developed under the NetZeRoCities project.

The goal of this Issue is to disseminate scientific results and technological innovations that can guide cities in their sustainable transformation, and to foster the popularization and scaling of solutions developed through the NetZeRoCities initiative.

Additionally, the purpose of this Special Issue is to advance the state of the art in the development of climate-neutral and smart cities, equipment, technologies, solutions. We are thus inviting the consortium members and collaborators of the NetZeRoCities project, researchers, developers, and practitioners to submit original research articles, innovative developments, experimental studies, and new technological solutions that contribute to this transformative field. Survey papers providing comprehensive overviews of emerging trends, challenges, and opportunities are also welcome.

Expected topics include, but are not limited to, the following areas:

Sensor-driven approaches to climate-neutral urban governance;
Environmental and energy monitoring systems for sustainable cities;
Smart buildings with integrated sensing and automation technologies;
IoT-enabled mobility, transport, and infrastructure systems;
Digital twins and smart campus platforms for urban management;
Real-time data analytics for urban planning and decision-making;
Multi-sensor systems for air quality, noise, and occupancy monitoring;
Interoperability and integration of urban sensor networks;
Citizen engagement and participatory sensing for sustainability;
Tools and platforms for monitoring progress toward climate neutrality;
Sensor-supported decision-making and governance frameworks;
Data-driven climate policy and urban monitoring tools;
Participatory sensing for public engagement and climate accountability;
IoT-enabled energy monitoring and control;
Sensing technologies for environmental quality and resilience;
Urban-scale integration of renewable energy systems;
Sensor systems for building energy management and automation;
Indoor environmental quality monitoring;
Digital retrofitting and building diagnostics;
Intelligent transport systems and mobility-as-a-service (MaaS);
Infrastructure-to-Vehicle (I2V), Vehicle-to-Vehicle (V2V) and Vehicle-to-Everything (V2X) communications;
Hybrid vehicular communications;
Advanced driver assistance systems;
In-vehicle communications systems and applications;
Traffic flow sensing, EV infrastructure monitoring;
Real-time public transit optimization and emissions tracking;
Sensor integration for campus-scale sustainability monitoring;
Urban digital twin models using real-time sensor data;
Simulation and forecasting tools for smart city planning.

Dr. Alin-Mihai Cailean
Dr. Andrei Ceclan
Dr. Radu Ioan Ciobanu
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. 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

smart cities
urban sensors
environmental monitoring
Internet of Things (IoT)
smart infrastructure
smart buildings
urban data analytics
sensor networks
participatory sensing
net-zero emissions
NetZeroCities
infrastructure-to-vehicle communications
V2X
vehicular communications
visible light communications

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Published Papers (5 papers)

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Research

27 pages, 1367 KB

Open AccessArticle

EMO-PEGASIS: A Dual-Phase Machine Learning Protocol for Energy Delay Optimisation in WSNs

by Abdulla Juwaied

Sensors 2026, 26(2), 611; https://doi.org/10.3390/s26020611 - 16 Jan 2026

Viewed by 94

Abstract

Wireless sensor networks (WSNs) contend with the critical challenge of balancing energy conservation against data transmission delay, a trade-off that protocols such as PEGASIS—while being strong in energy efficiency—fail to manage optimally due to resulting high latency, unbalanced load distribution, and suboptimal cluster formation. To address these limitations, this paper introduces the Enhanced Multi-Objective PEGASIS (EMO-PEGASIS) protocol, which is designed and implemented using a dual-phase machine learning strategy. This multi-objective approach works in two stages. First, it utilises K-means clustering to achieve robust spatial partitioning of the network. Second, it employs K-Nearest Neighbours (K-NN) classification to enable adaptive and intelligent routing. The simulation was performed using MATLAB R2025a, and the results show that EMO-PEGASIS addresses this multi-objective optimisation problem. The proposed EMO-PEGASIS protocol achieves a 45% reduction in average energy consumption, a 38% decrease in end-to-end delay, and a 67% increase in network lifetime compared to the original PEGASIS protocol. Additionally, EMO-PEGASIS demonstrates enhanced stability and effective load balancing under heterogeneous network configurations, while maintaining an excellent packet delivery ratio of 96.8%. These findings underscore the effectiveness of integrating machine learning techniques, which ultimately yield enhanced performance and enable reliable multi-objective optimisation within energy- and delay-constrained WSN environments. Full article

(This article belongs to the Special Issue Sensors Solutions for the Development of Climate Neutral and Smart Cities)

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33 pages, 4059 KB

Open AccessArticle

AI-Enabled Dynamic Edge-Cloud Resource Allocation for Smart Cities and Smart Buildings

by Marian-Cosmin Dumitru, Simona-Iuliana Caramihai, Alexandru Dumitrascu, Radu-Nicolae Pietraru and Mihnea-Alexandru Moisescu

Sensors 2025, 25(24), 7438; https://doi.org/10.3390/s25247438 - 6 Dec 2025

Viewed by 699

Abstract

The rapid expansion of IoT devices represents significant progress in areas such as smart buildings and smart cities, but at the same time, the volume of data generated represents a challenge, which can lead to real bottlenecks in the data analysis process, thus resulting in increased waiting times for end users. The use of cloud-based solutions may prove inefficient in some cases, as the bandwidth required for transmitting data generated by IoT devices is limited. The integration with Edge computing mitigates this issue, bringing data processing closer to the resource that generates it. Edge computing plays a key role in improving cloud performance by offloading tasks closer to the data source, optimizing resource allocation. Achieving the desired performance requires a dynamic approach to resource management, where task execution can be prioritized based on current load conditions: either at the Edge node or the Cloud node. This paper proposes an approach based on the Seasonal Auto Regressive Integrated Moving Average (SARIMA) model for seamlessly switching between the Cloud and Edge nodes in the event of a loss of connection between the Cloud and Edge nodes. Thereby ensuring the command loop remains closed by transferring the task to the Edge node until the Cloud node becomes available. In this way, the prediction that could underlie a command is not jeopardized by the lack of connection to the cloud node. The method was evaluated using real-world resource utilization data and compared against a Simple Moving Average (SMA) baseline using standard metrics: RMSE, MAE, MAPE, and MSE. Experimental results demonstrate that SRIMA significantly improves prediction accuracy, achieving up to 64% improvement for CPU usage and 35% for RAM usage compared to SMA. These findings highlight the effectiveness of incorporating seasonality and autoregressive components in predictive models for edge computing, contributing to more efficient resource allocation and enhanced performance in smart city environments. Full article

(This article belongs to the Special Issue Sensors Solutions for the Development of Climate Neutral and Smart Cities)

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24 pages, 3977 KB

Open AccessArticle

Contributions to the Development of Fire Detection and Intervention Capabilities Using an Indoor Air Quality IoT Monitoring System

by Radu Nicolae Pietraru, Adriana Olteanu, Maximilian Nicolae and Robert-Alexandru Crăciun

Sensors 2025, 25(20), 6375; https://doi.org/10.3390/s25206375 - 15 Oct 2025

Cited by 1 | Viewed by 1312

Abstract

This paper presents a method for functionally extending an IoT indoor air quality monitoring network by adding a cloud-level fire detection logic component. The proposed method does not aim to replace traditional fire detection systems at this stage of research, but to propose a solution for the development of fire detection capabilities and to improve the support provided to firefighting teams by providing a geospatial representation of the building in which a fire occurs. The proposed solution is based on a series of laboratory tests that demonstrated that air quality sensors can successfully detect the effects caused by an ignition event of common materials and can differentiate fire events from other events that can generate false-positive alarms by classic detection systems. The research involved five laboratory combustion tests based on the measurement of temperature, humidity, PM2.5 particle concentration, volatile organic compound index, and nitrogen oxide index. Following the tests, a warning mechanism and geospatial representation were designed using a system with ten IoT sensors to monitor the indoor air quality in a building on our university’s campus. Full article

(This article belongs to the Special Issue Sensors Solutions for the Development of Climate Neutral and Smart Cities)

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32 pages, 10402 KB

Open AccessArticle

Merging Visible Light Communications and Smart Lighting: A Prototype with Integrated Dimming for Energy-Efficient Indoor Environments and Beyond

by Cătălin Beguni, Eduard Zadobrischi and Alin-Mihai Căilean

Sensors 2025, 25(19), 6046; https://doi.org/10.3390/s25196046 - 1 Oct 2025

Viewed by 867

Abstract

This article proposes an improved Visible Light Communication (VLC) solution that, besides the indoor lighting and data transfer, offers an energy-efficient alternative for modern workspaces. Unlike Light-Fidelity (LiFi), designed for high-speed data communication, VLC primarily targets applications where fast data rates are not essential. The developed prototype ensures reliable communication under variable lighting conditions, addressing low-speed requirements such as test bench monitoring, occupancy detection, remote commands, logging or access control. Although the tested data rate was limited to 100 kb/s with a Bit Error Rate (BER) below 10⁻⁷, the key innovation is the light dimming dynamic adaptation. Therefore, the system self-adjusts the LED duty cycle between 10% and 90%, based on natural or artificial ambient light, to maintain a minimum illuminance of 300 lx at the workspace level. Additionally, this work includes a scalability analysis through simulations conducted in an office scenario with up to six users. The results show that the system can adjust the lighting level and maintain the connectivity according to users’ presence, significantly reducing energy consumption without compromising visual comfort or communication performance. With this light intensity regulation algorithm, the proposed solution demonstrates real potential for implementation in smart indoor environments focused on sustainability and connectivity. Full article

(This article belongs to the Special Issue Sensors Solutions for the Development of Climate Neutral and Smart Cities)

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15 pages, 3462 KB

Open AccessArticle

Numerical Assessment of Electric Underfloor Heating Enhanced by Photovoltaic Integration

by Hana Charvátová, Aleš Procházka, Martin Zálešák and Vladimír Mařík

Sensors 2025, 25(18), 5916; https://doi.org/10.3390/s25185916 - 22 Sep 2025

Viewed by 1291

Abstract

The integration of electric underfloor heating systems with photovoltaic (PV) panels presents a promising approach to enhance thermal efficiency and energy sustainability in residential heating. This study investigates the performance of such hybrid systems under different energy supply scenarios. Numerical modeling and simulations were employed to evaluate underfloor heating performance using three electricity sources: standard electric supply, solar-generated energy, and a combined configuration. Solar irradiance sensors were utilized to collect input solar radiation data, which served as a critical parameter for numerical modeling and simulations. The set outdoor air temperature used in the analysis represents an average value calculated from data measured by environmental sensors at the location of the building during the monitored period. Key metrics included indoor air temperature, time to thermal stability, and heat loss relative to outdoor conditions. The combined electric and solar-powered system demonstrated thermal efficiency, improving indoor air temperature by up to 63.6% compared to an unheated room and achieving thermal stability within 22 h. Solar-only configuration showed moderate improvements. Heat loss analysis revealed a strong correlation with indoor–outdoor temperature differentials. Hybrid underfloor heating systems integrating PV panels significantly enhance indoor thermal comfort and energy efficiency. These findings support the adoption of renewable energy technologies in residential heating, contributing to sustainable energy transitions. Full article

(This article belongs to the Special Issue Sensors Solutions for the Development of Climate Neutral and Smart Cities)

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