Smart cities have been a very active research area in the past 20 years, while continuously adapting to new technological advancements and keeping up with the times regarding sustainability and climate change. In this context, there have been numerous proposals to expand the scope of smart cities, focusing on resilience and sustainability, among other aspects, resulting in terms like smart sustainable cities. At the same time, there is an ongoing discussion regarding the degree in which smart cities put people at their centre. In this work, we argue toward expanding the current smart city definition by integrating the circular economy as one of its central pillars and adopting the term smart (and) circular city. We discuss the ways a smart and circular city encompasses both sustainability and smartness in an integral manner, while also being well-positioned to foster novel business activity and models and helping to place citizens at the heart of the smart city. In this sense, we also argue that previous research in smart cities and technologies, such as those related to Industry 4.0, can serve as a cornerstone to implement circular economy activities within cities, at a scale that
Sorting a huge stream of waste accurately within a short period can be done with the support of digitalization, particularly Artificial Intelligence, instead of traditional methods. The overlap of Artificial Intelligence and Circular Economy can flourish many services in the environmental technology domain, in particular smart ewaste recycling, resulting in enabling circular smart cities. We analyse the growing need for automated ewaste recycling as an essential requirement to cope with the fast growing ewaste stream and we shed the light on the impact of Artificial Intelligence in supporting the recycling process through smart classification of devices, where the smartphone is our case study. Our study applies transfer learning as a special technique of Artificial Intelligence by finetuning the output layers of AlexNet as a pretrained model and perform the implementation on a small size dataset that contains 12 classes from 6 smartphone brands. We evaluate the performance of our model by tuning the learning rate, choosing the best optimizer, and augmenting the original dataset to avoid overfitting. We found that the optimizer of Stochastic Gradient Descent with Momentum and 3e-4 a
Smart tourism (ST) stems from the concepts of e-tourism - focused on the digitalization of processes within the tourism industry, and digital tourism - also considering the digitalization within the tourist experience. The earlier ST references found regard ST Destinations and emerge from the development of Smart Cities. Our initial literature review on the ST concept and Smart Tourism Tools (STT) revealed significant research uncertainties: ST is poorly defined and frequently linked to the concept of Smart Cities; different authors have different, sometimes contradictory, views on the goals of ST; STT claims are often only based on technological aspects, and their "smartness" is difficult to evaluate; often the term "Smart" describes developments fueled by cutting-edge technologies, which lose that status after a few years. This chapter is part of the ongoing initiative to build an online observatory that provides a comprehensive view of STTs' offerings in Europe, known as the European STT Observatory. To achieve this, the observatory requires methodologies and tools to evaluate "smartness" based on a sound definition of ST and STT, while also being able to adapt to technological
Successful smart services require seamless integration into existing corporate systems and an interdisciplinary approach that aligns the development of both business models and technical architectures. Multi-disciplinarity and cocreating with customers add a layer of complexity but are essential collaboration schemes for validating the value proposition of smart services and building longterm customer loyalty. This paper explores these challenges and distills the design principles for the architectures of technical smart service systems, based on empirical data from architecture projects in two manufacturing companies. These principles contribute to the sparse academic literature on this topic and help practitioners navigate several design trade-offs commonly arising in smart service projects.
This chapter explores the six core dimensions of smart cities (i.e. smart economy, mobility, environment, people, living, and governance) emphasizing their interdependence and the need for holistic orchestration. Building on Giffinger et al. (2007) and subsequent literature, it argues that integrating these dimensions is crucial for sustainable urban development. ICT plays a key enabling role but must be complemented by human and social capital. Through institutional examples, such as the creation of dedicated municipal offices for digital innovation, the chapter illustrates how governance and internal capacity shape smart transitions. A human-centric approach is also essential, ensuring inclusivity, creativity, and active civic participation. Ultimately, smart cities must be viewed as cohesive urban ecosystems where technology, people, and governance interact dynamically.
This document reports the State of the Art of science and practice on three topics related to smart and healthy ageing at home: furniture and habitats, Information and Communication Technologies (ICT), and healthcare. The reports were prepared by the working groups of COST Action CA16226, Sheld-on. Sheld-on is a network of researchers, user representatives, industry members, and other stakeholders. The three domains covered in this report were the areas of interest for three working groups from the COST Action. The aim of each working group was to assess the State of the Art for disciplinary understanding, identification of advances in smart furniture and habitat, products, industries and success stories. The findings on these topics of all working groups are compiled here. Due to the different backgrounds of the members of each of the working groups, the document is divided in three separate parts that can be considered as separate State of the Art reports. The goal of this document is to be used as input in the fourth working group of Sheld-on COST Action: Solutions for Ageing Well at Home, in the Community, and at Work, where experts from the three different domains converge to
Robotic systems have become integral to smart environments, enabling applications ranging from urban surveillance and automated agriculture to industrial automation. However, their effective operation in dynamic settings - such as smart cities and precision farming - is challenged by continuously evolving topographies and environmental conditions. Traditional control systems often struggle to adapt quickly, leading to inefficiencies or operational failures. To address this limitation, we propose a novel framework for autonomous and dynamic reconfiguration of robotic controllers using Digital Twin technology. Our approach leverages a virtual replica of the robot's operational environment to simulate and optimize movement trajectories in response to real-world changes. By recalculating paths and control parameters in the Digital Twin and deploying the updated code to the physical robot, our method ensures rapid and reliable adaptation without manual intervention. This work advances the integration of Digital Twins in robotics, offering a scalable solution for enhancing autonomy in smart, dynamic environments.
The rapid evolution of smart grids requires effective communication protocols to transfer data reliably and securely. Controller Area Network (CAN) is one of the most recognized protocols that offer reliable data transmission in smart grids due to its robustness, real-time capabilities, and relatively low initial cost of its required hardware. However, as a smart city becomes more interconnected, it also becomes more vulnerable to cyber-attacks. As there are many mechanisms to secure the CAN nodes from attacks, most of those mechanisms have computational overhead, resulting in more delay in the network. We implemented a solution that requires almost no overhead to any CAN node connected to the network. It depends on a single node responsible for securing the CAN network. This approach seeks to augment network security while reducing security mechanisms overhead to all CAN network nodes. The methodology and comprehensive test results will be presented in detail during a subsequent discussion. The used software for development is Code Composer Studio, and the used microcontroller evaluation boards (EVB) are TM4C 1294.
With the proliferation of smart grids, smart cities face growing challenges due to cyber-attacks and sophisticated electricity theft behaviors, particularly in residential photovoltaic (PV) generation systems. Traditional Electricity Theft Detection (ETD) methods often struggle to capture complex temporal dependencies and integrating multi-source data, limiting their effectiveness. In this work, we propose an efficient ETD method that accurately identifies fraudulent behaviors in residential PV generation, thus ensuring the supply-demand balance in smart cities. Our hybrid deep learning model, combining multi-scale Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Transformer, excels in capturing both short-term and long-term temporal dependencies. Additionally, we introduce a data embedding technique that seamlessly integrates time-series data with discrete temperature variables, enhancing detection robustness. Extensive simulation experiments using real-world data validate the effectiveness of our approach, demonstrating significant improvements in the accuracy of detecting sophisticated energy theft activities, thereby contributing to the stability and fairn
The Internet of Things (IoT) plays a major role today in smart building infrastructures, from simple smart-home applications, to more sophisticated industrial type installations. The vast amounts of data generated from relevant systems can be processed in different ways revealing important information. This is especially true in the era of edge computing, when advanced data analysis and decision-making is gradually moving to the edge of the network where devices are generally characterised by low computing resources. In this context, one of the emerging main challenges is related to maintaining data analysis accuracy even with less data that can be efficiently handled by low resource devices. The present work focuses on correlation analysis of data retrieved from a pilot IoT network installation monitoring a small smart office by means of environmental and energy consumption sensors. The research motivation was to find statistical correlation between the monitoring variables that will allow the use of machine learning (ML) prediction algorithms for energy consumption reducing input parameters. For this to happen, a series of hypothesis tests for the correlation of three different e
Topology reasoning is crucial for autonomous driving as it enables comprehensive understanding of connectivity and relationships between lanes and traffic elements. While recent approaches have shown success in perceiving driving topology using vehicle-mounted sensors, their scalability is hindered by the reliance on training data captured by consistent sensor configurations. We identify that the key factor in scalable lane perception and topology reasoning is the elimination of this sensor-dependent feature. To address this, we propose SMART, a scalable solution that leverages easily available standard-definition (SD) and satellite maps to learn a map prior model, supervised by large-scale geo-referenced high-definition (HD) maps independent of sensor settings. Attributed to scaled training, SMART alone achieves superior offline lane topology understanding using only SD and satellite inputs. Extensive experiments further demonstrate that SMART can be seamlessly integrated into any online topology reasoning methods, yielding significant improvements of up to 28% on the OpenLane-V2 benchmark.
The global agricultural sector is undergoing a transformative shift, driven by increasing food demands, climate variability and the need for sustainable practices. SUSTAINABLE is a smart farming platform designed to integrate IoT, AI, satellite imaging, and role-based task orchestration to enable efficient, traceable, and sustainable agriculture with a pilot usecase in viticulture. This paper explores current smart agriculture solutions, presents a comparative evaluation, and introduces SUSTAINABLE's key features, including satellite index integration, real-time environmental data, and role-aware task management tailored to Mediterranean vineyards.
The battery performance and lifespan of electric vehicles (EVs) degrade significantly in cold climates, requiring a considerable amount of energy to heat up the EV batteries. This paper proposes a novel technology, namely temperature-controlled smart charging, to coordinate the heating/charging power and reduce the total energy use of a solar-powered EV charging station. Instead of fixing the battery temperature setpoints, we analyze the thermal dynamics and inertia of EV batteries, and decide the optimal timing and proper amount of energy allocated for heating. In addition, a temperature-sensitive charging model is formulated with consideration of dynamic charging rates as well as battery health. We further tailor acceleration algorithms for large-scale EV charging, including the reduced-order dual decomposition and vehicle rescheduling. Simulation results demonstrate that the proposed temperature-controlled smart charging is superior in capturing the flexibility value of EV batteries and making full use of the rooftop solar energy. The proposed model typically achieves a 12.5--18.4% reduction in the charging cost and a 0.4--6.8% drop in the overhead energy use for heating.
A smart home is realized by setting up various services. Several methods have been proposed to create smart home services, which can be divided into knowledge-based and data-driven approaches. However, knowledge-based approaches usually require manual input from the inhabitant, which can be complicated if the physical phenomena of the concerned environment states are complex, and the inhabitant does not know how to adjust related actuators to achieve the target values of the states monitored by services. Moreover, machine learning-based data-driven approaches that we are interested in are like black boxes and cannot show the inhabitant in which situations certain services proposed certain actuators' states. To solve these problems, we propose a hybrid system called HKD-SHO (Hybrid Knowledge-based and Data-driven services based Smart HOme system), where knowledge-based and machine learning-based data-driven services are profitably integrated. The principal advantage is that it inherits the explicability of knowledge-based services and the dynamism of data-driven services. We compare HKD-SHO with several systems for creating dynamic smart home services, and the results show the bette
With the convergence of information and telecommunication technologies, the vision of the Smart City is fast becoming a reality. City governments in a growing number of countries are capitalizing on these advances to enhance the lives of their citizens and to increase efficiency and sustainability. In this paper, we elaborate on smartCityRA, a reference architecture for Smart City projects, which serves as the design language for creating smart cities blueprints. Such a blueprint caters for diverse stakeholders, devices, platforms, and technologies. We report on our experience in carrying out a proof-of-concept use case with a major telecommunication provider in the UAE. In doing so, we refined our multiple-view model of the initial smartCityRA reference architecture. We show that Data in smart city applications drive the entire development lifecycle and should be considered early in the development cycle. In addition, Data affects all the other views in the smartCityRA and hence the Data View needs to be at the heart of the entire smartCityRA. Realizing the Data view using a component like a Data Hub helped in creating a central integration location for disparate data from differe
The increasing digitization of smart grids has made addressing cybersecurity issues crucial in order to secure the power supply. Anomaly detection has emerged as a key technology for cybersecurity in smart grids, enabling the detection of unknown threats. Many research efforts have proposed various machine-learning-based approaches for anomaly detection in grid operations. However, there is a need for a reproducible and comprehensive evaluation environment to investigate and compare different approaches to anomaly detection. The assessment process is highly dependent on the specific application and requires an evaluation that considers representative datasets from the use case as well as the specific characteristics of the use case. In this work, we present an evaluation environment for anomaly detection methods in smart grids that facilitates reproducible and comprehensive evaluation of different anomaly detection methods.
Smart cities are an actual trend being pursued by research that, fundamentally, tries to improve city's management on behalf of a better human quality of live. This paper proposes a new autonomic complementary approach for smart cities management. It is argued that smart city management systems with autonomic characteristics will improve and facilitate management functionalities in general. A framework is also presented as use case considering specific application scenarios like smart-health, smart-grid, smart-environment and smart-streets.
Smart contract is a special program that manages digital assets on blockchain. It is difficult to recover the loss if users make transactions through buggy smart contracts, which cannot be directly fixed. Hence, it is important to ensure the correctness of smart contracts before deploying them. This paper proposes a systematic framework to mutation testing for smart contracts on Ethereum, which is currently the most popular open blockchain for deploying and running smart contracts. Fifteen novel mutation operators have been designed for Ethereum Smart Contracts (ESC), in terms of keyword, global variable/function, variable unit, and error handling. An empirical study on 26 smart contracts in four Ethereum DApps has been conducted to evaluate the effectiveness of mutation testing. The experimental results show that our approach can outperform the coverage-based approach on defect detection rate (96.01% vs. 55.68%). The ESC mutation operators are effective to reveal real defects and we found 117 out of 729 real bug reports are related to our operators. These show the great potential of using mutation testing for quality assurance of ESC.
An appealing feature of blockchain technology is smart contracts. A smart contract is executable code that runs on top of the blockchain to facilitate, execute and enforce an agreement between untrusted parties without the involvement of a trusted third party. In this paper, we conduct a systematic mapping study to collect all research that is relevant to smart contracts from a technical perspective. The aim of doing so is to identify current research topics and open challenges for future studies in smart contract research. We extract 24 papers from different scientific databases. The results show that about two thirds of the papers focus on identifying and tackling smart contract issues. Four key issues are identified, namely, codifying, security, privacy and performance issues. The rest of the papers focuses on smart contract applications or other smart contract related topics. Research gaps that need to be addressed in future studies are provided.
Although we have reached new levels in smart city installations and systems, efforts so far have focused on providing diverse sources of data to smart city services consumers while neglecting to provide ways to simplify making good use of them. In this context, one first step that will bring added value to smart cities is knowledge creation in smart cities through anomaly detection and data annotation, supported in both an automated and a crowdsourced manner. We present here LearningCity, our solution that has been validated over an existing smart city deployment in Santander, and the OrganiCity experimentation-as-a-service ecosystem. We discuss key challenges along with characteristic use cases, and report on our design and implementation, together with some preliminary results derived from combining large smart city datasets with machine learning.