Mohamed Essaaidi

Smart Mobility plays a key role in Smart Cities, given its ability to support the rollout of intelligent transport systems, allowing for more sustainable urban transportation and greater interoperability across diverse mobility modes. Furthermore, Smart Mobility is essential to maximize the quality of life for the community while advancing principles of sustainability, economic development, technological innovation, and collaborative governance. Real-Time Traffic Management (RTTM) emerges as a vital technology for optimizing traffic management in Smart Mobility. Using the PRISMA framework, the proposed systematic literature review examines 165 peer-reviewed publications related to RTTM research work published between 2019 and 2025. This review identified eleven application domains, with Urban Traffic Management Systems (36.97%) and Artificial Intelligence (AI) and Predictive Analytics (12.73%) representing the most prominent areas. A retrospective analysis of the literature on control architecture used in closed-loop feedback systems indicates that most studies (89%) have adopted a more dynamic control model, while 7.8% adopted a Digital Twin (DT)-based approach. However, several implementation barriers persist, including limited integration of online optimization and learning loops into RTTM systems, gaps in performance comparisons between simulation and reality, scalability issues due to heterogeneous environments, inconsistent data quality caused by various sensor types, and difficulties integrating sensors into a control system. In addition, this paper proposes a taxonomy of RTTM applications and control architectures, while outlining key practical barriers to implementation and charting future research directions for advancing Smart Mobility through robust RTTM.

In an era increasingly defined by uncertainty, interconnectedness and systemic transformation, complexity science has emerged not merely as a theoretical framework, but as an essential lens for making sense of the world. From network dynamics and artificial intelligence to climate tipping points and cultural epistemologies, the field of complex systems continues to expand its relevance and application. This special issue brings together six diverse yet interwoven contributions that collectively explore how intelligent infrastructures and emergent systems are reshaping our understanding of resilience, learning and adaptation in the twenty-first century.

The discourse on sustainable urban development has evolved to emphasize the critical synergy between Smart Mobility, Smart Governance, and Smart Environment, particularly in response to rapid urbanization and environmental challenges. While extant research provides valuable insights into these domains individually, a significant gap persists in understanding their dynamic interplay and co-evolution within the smart city ecosystem. This paper addresses this knowledge gap by conducting a comprehensive literature review, examining the intricate interdependencies among these domains, with a focus on their mutual impacts and synergies. Our analysis focuses on their mutual effects and synergies, aiming to provide novel insights for developing robust policy frameworks to guide smart city initiatives. By unraveling the interconnected pathways, we contribute to the current understanding of smart urban development and its co-evolutionary influence on sustainable urban growth. This holistic approach offers a nuanced perspective on how technological advancements in one domain can catalyze progress across the entire smart city landscape, ultimately shaping the trajectory of urban development

Smart cities are complex ecosystems that leverage digital transformation to generate and utilize substantial volumes of data from diverse sources, including social media, citizen interactions, sensors, cameras, and Internet of Things (IoT) devices. This data can be harnessed to improve the quality of life for urban residents by informing more effective decision-making processes regarding urban services and resource management. However, this necessitates the efficient visualization and analysis of data through user-friendly interfaces, such as dashboards. This paper aims to elucidate the concept of smart cities by providing a comprehensive definition and proposing a prototype for a smart city dashboard that displays data collected from sensors, IoT devices, and cameras. Our dashboard addresses key challenges in urban service management by effectively monitoring four critical aspects: weather conditions, air quality, and noise levels. This comprehensive monitoring approach provides stakeholders with valuable insights into the city’s dynamics. Our prototype contributes to the creation of more livable, sustainable, and efficient urban environments by harnessing the power of data-driven insights. This research advances the field of smart city management by demonstrating the potential of integrated data visualization in addressing complex urban challenges.

Living and working in comfort while a building’s energy consumption is kept under control requires monitoring a system’s consumption to optimize the energy performance. The way energy is generally used is often far from optimal, which requires the use of smart meters that can record the energy consumption and communicate the information to an energy manager who can analyze the consumption behavior, monitor, and optimize energy performance. Given that the heating, ventilation, and air conditioning (HVAC) systems are the largest electricity consumers in buildings, this paper discusses the importance of incorporating occupancy data in the energy efficiency analysis and unveils energy inefficiencies in the way the system operates. This paper uses 1-year data of a highly efficient certified office building located in the Houston area and shows the power of self-organizing maps and data analysis in identifying up to 4.6% possible savings in energy. The use of time series analysis and machine-learning techniques is conducive to helping energy managers discover more energy savings.

A smart city is one that uses digital technologies and other means to improve the quality of life of its citizens and reduce the cost of municipal services. Smart cities primarily use IoT to collect and analyze data to interact directly with the city’s infrastructure and monitor city assets and community developments in real time to improve operational efficiency and proactively respond to potential problems and challenges. Today, cybersecurity is considered one of the main challenges facing smart cities. Over the past few years, the cybersecurity research community has devoted a great deal of attention to this challenge. Among the different technologies proposed to address this challenge, Blockchain appears to offer the security and data privacy needed to enhance smart cities security. In this paper, we propose a comprehensive framework and architecture based on Blockchain, big data and artificial intelligence to improve smart cities cybersecurity. For the sake of illustration of the proposed framework, simulation results are presented for a smart grid dataset from the UCI Machine Learning Repository, demonstrating its potential and efficiency to deal with cybersecurity challenges in smart cities.

The authors wish to make the following correction to their paper [...]

During the past ten years, worldwide efforts have been pursuing an ambitious policy of sustainable development, particularly in the energy sector. This ambition was revealed by noticeable progress in the deployment and development of infrastructures for the production of renewable electrical energy. These infrastructures combined with the deployment of wired and wireless communications could support research actions in the field of connected electro-mobility. Also, this progress was manifested by the development of electric vehicles (EV), penetrating our transportation roads more and more. They are considered among the potential solutions, which are envisaged to further reduce road transport’s greenhouse gas emissions, relying on low-carbon energy production. However, the uncertainty caused by both external road disturbances and drivers’ behavior could influence the prediction of upcoming power demands. These latter are mainly affected by the unpredictability of the electric vehicles’ speed on transportation roads. In this work, we introduce an energy management platform, which interfaces with in-vehicle components, using a developed embedded system, and external services, using IoT and big data technologies, for efficient battery power use. The platform was deployed in real-setting scenarios and tested for EV speed prediction. In fact, we have used driving data, which have been collected on Rabat-Salé-Kénitra (RSK) urban roads by our Twizy EV. A multivariate Long Short Term Memory (LSTM) algorithm was developed and deployed for speed forecasting. The effectiveness of LSTM was evaluated against well-known algorithms: Auto Regressive Integrated Moving Average (ARIMA), Convolutional Neural Network (CNN) and Convolutional LSTM (ConvLSTM). Experiments have been conducted using two approaches; the whole trajectory dataset and segmented trajectory datasets to train the models. The experimentation results show that LSTM outperforms the other used algorithms in terms of forecasting the speed, especially when using the trajectory segmentation approach.

Building equipment accounts for almost 40% of total global energy consumption. More than half of which is used by active systems, such as heating, ventilation and air conditioning (HVAC) systems. These latter are responsible for the occupants’ well-being and considered among the main consumers of electricity in buildings. In order to improve both occupants’ comfort and energy efficiency in buildings, optimal control oriented models, such as Model Predictive Control (MPC), have proven to be promising techniques for developing intelligent control strategies for building energy management systems. This paper presents a real-time predictive control approach of an air conditioning (AC) system for thermal regulation in a single-zone building using MPC control framework. The proposed approach takes into account the physical parameters of the building, weather predictions (i.e. ambient temperature and solar radiation) and time-varying thermal comfort constraints to maintain optimal energy consumption of the AC while enhancing occupants’ comfort. For this purpose, a control-oriented thermal model for a room integrated with AC system is first developed using physics-based (white box) technique and then used to design and develop the MPC controller model. A numerical case study has been investigated and simulation results show the effectiveness of the proposed approach in reducing the energy consumption by about 68% while providing a significant indoor thermal improvement. A conventional On–Off controller was used as a baseline reference to evaluate the system performance against the proposed approach.

This paper presents the design of a 1×16-elements RDRA array for anti-collision radar SRR application at 24 GHz. A single RDRA with high dielectric constant of 41, fed by a simple microstrip line feeding technique, is initially designed to operate around 24 GHz. The RDRA element is further used within an array network structure made up of 16 linear antenna elements to cover the same frequency band. The simulated 1×16 RDRA array can reach a high gain, up to18.6 dB, very high radiation efficiency (97%), and ensure enough directional radiation pattern properties for radar applications with a 3-dB angular beam width of 6°. To validate our design, RDRA array’ radiation pattern computed results are compared to an equivalent fabricated patch antenna array reported in the literature.

In this work, we present a UHF-RFID-based noninvasive sensor to measure the concentration of ethanol in water using the volume fraction of liquids in mixture solutions. The sensing system operates at the UHF band (860–928 MHz). The concentration of ethanol in water affects the dielectric properties of the solution and therefore the antenna sensitivity of the RFID tag. This sensor operates by measuring the change in permittivity of a solution because of the change in concentration of ethanol in water. We propose a flexible RFID-Tag sensor a low-cost alternative to identify the possible sensitivity of tag changes and is able to detect a variation of 25% in ethanol in 9 ml of deionized water (DI-Water). The solution is useful in avoiding counterfeit ethanol solutions that may be toxic. The experimental setup is inexpensive, portable, quick, and contactless. We present results for ethanol solutions ranging from 25% to 100% in a small tube container.

A novel compact Ultra-Wide-Band Planar Inverted- L antenna is presented and investigated in this paper. The proposed antenna consists of a square planar radiating element with a U-shaped slot. The radiating element is supported by a shorting wall, and fed by a single 50 Ohms characteristic impedance microstripe line, printed on the top of the FR-4 substrate. The ground plane of the antenna is printed on the other side of the substrate. The entire antenna occupies only a small volume of 20mm × 35mm × 4mm, and is capable of operating from 4.2GHz to 8.6GHz (68.75%) and offers a maximum gain of 5.24dB. Therefore, it is suitable for UWB systems and other wireless and mobile technologies and, thus, can be integrated into smartwatch, mobile phones, tablets and laptops. The design of this antenna was carried out using 3D software such as CST studio and Ansoft HFSS to compare and validate the results.

Nowadays, electric vehicles (EV) are increasingly penetrating the transportation roads in most countries worldwide. Many efforts are oriented toward the deployment of the EVs infrastructures, including those dedicated to intelligent transportation and electro-mobility as well. For instance, many Moroccan organizations are collaborating to deploy charging stations in mostly all Moroccan cities. Furthermore, in Morocco, EVs are tax-free, and their users can charge for free their vehicles in any station. However, customers are still worried by the driving range of EVs. For instance, a new driving style is needed to increase the driving range of their EV, which is not easy in most cases. Therefore, the need for a companion system that helps in adopting a suitable driving style arise. The driving range depends mainly on the battery’s capacity. Hence, knowing in advance the battery’s state-of-charge (SoC) could help in computing the remaining driving range. In this paper, a battery SoC forecasting method is introduced and tested in a real case scenario on Rabat-Salé-Kénitra urban roads using a Twizy EV. Results show that this method is able to forecast the SoC up to 180 s ahead with minimal errors and low computational overhead, making it more suitable for deployment in in-vehicle embedded systems.

Intelligent reflecting surface (IRS) is deemed a promising technique for future wireless and mobile technologies due to its ability to reconfigure the radio propagation environment. In this paper, we analyze the physical layer security (PLS) of a downlink non-orthogonal multiple access (NOMA) network through an IRS intended for assisting the wireless transmission, taking into account the external and internal eavesdropping scenarios. Considering the hardware limitations, a 1-bit coding scheme is utilized to investigate the secrecy performance of the IRS-aided NOMA (IRS-NOMA) network. Exact and asymptotic expressions in terms of the secrecy outage probability (SOP) and the effective secrecy throughput (EST) are presented, which are in consideration of the imperfect successive interference cancellation (ipSIC) and perfect SIC (pSIC). Based on the analytical results, the secrecy diversity orders are related to the number of reflecting elements and EST ceilings are observed in the high SNR regime. Monte Carlo simulations are presented to corroborate the correctness of the analyses and illustrate that the IRS-NOMA scheme is superior in the secrecy outage performance to the IRS-aided orthogonal multiple access scheme. In addition, the SOP performance enhancement through the increased number of reflecting elements and the improvement of EST through proper target secrecy rate are also evidenced by the simulations.

<strong>Disclaimer</strong><br> This Impact report was produced by the StandICT.eu 2023, a Coordination and Support Action (CSA) project co-funded by the European Commission within the Research and Innovation Framework Programme, Framework Programme Horizon 2020 (H2020), under grant agreement no. 951972. The information and views set out in this report are those of the authors and do not necessarily reflect the official opinion of the European Commission and may not be held responsible for the use which may be made of the information contained therein. Reproduction is authorised provided the source is acknowledged. <br> <strong>About StandICT.eu 2023</strong><br> The StandICT.eu 2023 Coordination and Support Action project has received funding from the European Union’s Horizon 2020 - Research and Innovation programme - under grant agreement no. 951972. The project is coordinated by Trust-IT Srl (IT), supported by its partners from the Dublin City University (IE) and AUSTRALO (ES). The content of the present report does not represent the opinion of the European Union, and the European Union is not responsible for any use that might be made of such content. <strong>Acknowledgements</strong> Our consortium, formed by Trust-IT as the coordinator, Dublin City University and AUSTRALO Marketing Lab, is grateful to all experts of our StandICT.eu 2023 community for their competent work. This booklet is a tangible reflection of your continuous dedication to ICT Standardisation - Thank you! StandICT.eu 2023 would also like to thank Thomas Riebe, StandICT.eu Project Officer and Senior Expert at DG Connect European Commission, and Emilio Davila-Gonzales, Deputy Director at DG Connect leading Unit F3-Blockchain and Innovation for their leadership and guidance. The External Advisory Group (EAG) has given us valuable support and guidance throughout the course of the project, so far. Our appreciation for their effort and commitment goes to Ray Walshe (EAG Chair), Stefan Hallensbellen, Brian McAuliffe, Lindsay Frost, Jens Gayko, Karl Grun, Enrico Scarrone, Marc TavernerThe landscape, Nuria de Lama, Tom de Block, Martin Chapman, Annika Linck, Fergal Finn, Ana Garcia Robles, Stefan Weisgerber, Jochen Friedrich, Antonio Conte, and Stefano Nativi. Finally, we would like to thank all EUOS Technical Working Groups (European Observatory for ICT Standardisation) chairs and members for the investment in gathering expertise and producing outstanding landscape reports of the standardisation status across different ICT sectors. We warmly thank the TWG chairs guiding this work: Lindsay Frost, Ismael Arribas, Matthias Pocs, Dimosthenis Kyriazis, Jeroen Broekhuijsen, Joel Meyers and Fiona Delaney.<br>

The climate of Houston, classified as a humid subtropical climate with tropical influences, makes the heating, ventilation, and air conditioning (HVAC) systems the largest electricity consumers in buildings. HVAC systems in commercial buildings are usually operated by a centralized control system and/or an energy management system based on a fixed schedule and scheduled control of a zone setpoint, which is not appropriate for many buildings with changing occupancy rates. Lately, as part of energy efficiency analysis, attention has focused on collecting and analyzing smart meters and building-related data, as well as applying supervised learning techniques, to propose new strategies to operate HVAC systems and reduce energy consumption. On the other hand, unsupervised learning techniques have been used to study the consumption information and profile characterization of different buildings after cluster analysis is performed. This paper adopts a different approach by revealing the power of unsupervised learning to cluster data and unveiling hidden patterns. In this study, we also identify energy inefficiencies after exploring the cluster results of a single building’s HVAC consumption data and building usage data as part of the energy efficiency analysis. Time series analysis and the K-means clustering algorithm are successfully applied to identify new energy-saving opportunities in a highly efficient office building located in the Houston area (TX, USA). The paper uses 1-year data from a highly efficient Leadership in Energy and Environment Design (LEED)-, Energy Star-, and Net Zero-certified building, showing a potential energy savings of 6% using the K-means algorithm. The results show that clustering is instrumental in helping building managers identify potential additional energy savings.

<strong>Disclaimer</strong><br> This Impact report was produced by the StandICT.eu 2023, a Coordination and Support Action (CSA) project co-funded by the European Commission within the Research and Innovation Framework Programme, Framework Programme Horizon 2020 (H2020), under grant agreement no. 951972. The information and views set out in this report are those of the authors and do not necessarily reflect the official opinion of the European Commission and may not be held responsible for the use which may be made of the information contained therein. Reproduction is authorised provided the source is acknowledged. <br> <strong>About StandICT.eu 2023</strong><br> The StandICT.eu 2023 Coordination and Support Action project has received funding from the European Union’s Horizon 2020 - Research and Innovation programme - under grant agreement no. 951972. The project is coordinated by Trust-IT Srl (IT), supported by its partners from the Dublin City University (IE) and AUSTRALO (ES). The content of the present report does not represent the opinion of the European Union, and the European Union is not responsible for any use that might be made of such content. <strong>Acknowledgements</strong> Our consortium, formed by Trust-IT as the coordinator, Dublin City University and AUSTRALO Marketing Lab, is grateful to all experts of our StandICT.eu 2023 community for their competent work. This booklet is a tangible reflection of your continuous dedication to ICT Standardisation - Thank you! StandICT.eu 2023 would also like to thank Thomas Riebe, StandICT.eu Project Officer and Senior Expert at DG Connect European Commission, and Emilio Davila-Gonzales, Deputy Director at DG Connect leading Unit F3-Blockchain and Innovation for their leadership and guidance. The External Advisory Group (EAG) has given us valuable support and guidance throughout the course of the project, so far. Our appreciation for their effort and commitment goes to Ray Walshe (EAG Chair), Stefan Hallensbellen, Brian McAuliffe, Lindsay Frost, Jens Gayko, Karl Grun, Enrico Scarrone, Marc TavernerThe landscape, Nuria de Lama, Tom de Block, Martin Chapman, Annika Linck, Fergal Finn, Ana Garcia Robles, Stefan Weisgerber, Jochen Friedrich, Antonio Conte, and Stefano Nativi. Finally, we would like to thank all EUOS Technical Working Groups (European Observatory for ICT Standardisation) chairs and members for the investment in gathering expertise and producing outstanding landscape reports of the standardisation status across different ICT sectors. We warmly thank the TWG chairs guiding this work: Lindsay Frost, Ismael Arribas, Matthias Pocs, Dimosthenis Kyriazis, Jeroen Broekhuijsen, Joel Meyers and Fiona Delaney.<br>