IEEE Access Special Section Editorial: Social Computing Applications for Smart Cities

Half of the world’s population resides in cities and this percentage is going to rise in the coming years. This rapid transformation of the economy and society is making traditional city management redundant. Intensive urbanization has brought with it some challenges. Scanty resources, population growth, increased carbon footprint, reduced air quality, congestion on roads, depriving health status, and falling food quality, all are prominent parts of cities. Does this bring a question in mind is accelerated growth sustainable? The answer and solution to this is an adaption of the Smart City Concept. A smart city integrates all the available technology with the requirements of city development and builds infrastructure which can improve the quality of life of citizens. Smart cities are designed in a manner to use minimum resources with maximum efficiency in all areas of urban life. It provides smart solutions using smart technologies. Urban needs and technological advancements meet in smart cities. It manages cities more quickly and effectively by using AI, IT technologies, and robotic process automation. Intelligent transportation, intelligent energy, intelligent governance, intelligent security, intelligent healthcare are all central to the idea of smart cities. The emphasis of this essay is on smart transit as a component of smart cities. The movement of people and goods across several geographical boundaries has been significantly aided by the transportation sector. Globally, the majority of smart cities face logistical and transportation issues. Both the fast growing human population and the increase in the number of cars on the road are to blame for this. In order to design and run a sustainable transportation system, technology may be of great assistance. Robotic systems for real-time vehicle data collection, traffic management, and using mobility on demand in trip planning through a single user interface have emerged as cities struggle with traffic congestion. The transportation industry, which faces difficulties including traffic congestion, unanticipated delays, and routing issues that cause businesses to lose money, has successfully used robotic process automation. Robotic process automation provides reliable results and affordable solutions while reducing uncertainty in the decision-making process. It is very helpful in designing and running a successful transit system. Robotic Process Automation (RPA) systems for obtaining real-time data from vehicles, buses, and trucks for traffic management and using mobility on demand in trip planning through a single user interface have emerged as cities struggle with poor management and congestion. By examining different uses of robotic process automation, it is possible to evaluate precise prediction and detection models that are intended to forecast traffic volume, an appropriate route, traffic conditions, accurate information about timing, incidents, and which time and roadway for transport are suitable for running and mobility. The application of RPA in smart cities could be seen in Intelligent Transportation Systems’ Traffic Management, Public Transportation, Safety Management, Manufacturing, and Logistics. The research focuses on areas of concern for smart cities in the sphere of the transportation sector and related difficulties that may be solved via robotic process automation. The article emphasizes the challenges and solutions for making smart transportation more effective by using Intelligent Robotic Transportation Systems.

The rapid development of Internet of Things (IoT) technology has significantly transformed the conceptualization and implementation of smart city applications. This study aims to analyze the performance of Internet of Things (IoT) architecture in supporting smart city applications across various urban service domains, including transportation, environmental monitoring, public safety, and energy management. The research focuses on evaluating key performance indicators such as latency, throughput, scalability, reliability, and energy efficiency within a multilayer IoT architecture consisting of perception, network, middleware, and application layers.A quantitative experimental approach was employed by simulating smart city scenarios using heterogeneous sensors and communication protocols. Performance metrics were collected and analyzed under varying traffic loads and data transmission frequencies. The results indicate that network latency and bandwidth utilization significantly affect real-time data processing capabilities, particularly in time-sensitive services such as intelligent traffic systems and emergency response monitoring. Furthermore, the study found that edge computing integration enhances response time and reduces cloud dependency, thereby improving overall system efficiency and scalability.The findings suggest that optimizing network configuration, adopting lightweight communication protocols, and implementing distributed processing mechanisms are critical factors in enhancing IoT architectural performance for smart city applications. This research contributes to the development of a more reliable and efficient IoT infrastructure framework to support sustainable and data-driven urban governance. Future studies are recommended to explore security resilience and interoperability challenges in large-scale smart city deployments.

Background: The deployment of 5G infrastructure is one of the vectors for new application scenarios since it enables enhanced data bandwidth, low latency, and comprehensive signal coverage. This communication system supports various vertical applications such as smart health, smart cities, smart grids, and transportation systems. However, these applications bring new challenges to 5G networks due to specific requirements for such scenarios. Furthermore, as software-based technologies, including network slicing, software-defined networks, network function virtualization, and multi-access edge computing, are a fundamental part of the 5G architecture, the network can expose these applications to new security and privacy concerns. Results: This study summarizes existing literature on 5G vertical applications security. We highlight vulnerabilities, threats, attacks, and solutions for 5G vertical applications. We conducted a systematic literature mapping to discuss security and privacy challenges regarding the 5G vertical applications. We reviewed 389 papers from 2,349 produced by searching with a curated search query and discussed vulnerabilities, threats, attacks, and solutions for 5G vertical applications. Conclusions: Smart cities, Industry 4.0, smart transportation, public services, smart grids, and smart health are vertical applications with relevant security concerns. We observed the need for more research since the 5G and vertical applications continuously evolve.

A proactive role of IoT devices in building smart cities

Nowadays, Smart Cities are positioned as one of the most challenging and important research topics, highlighting major changes in people's lifestyle. Technologies such as smart energy, smart transportation or smart health are being designed to improve citizen's quality of life. Smart Cities leverage the deployment of a network of devices - sensors and mobile devices-, all connected through different means and/or technologies, according to their network availability and capacities, setting a novel framework feeding end-users with an innovative set of smart services. Aligned to this objective, a typical Smart City architecture is organized into layers, including a sensing layer (generates data), a network layer (moves the data), a middleware layer (manages all collected data and makes it ready for usage) and an application layer (provides the smart services benefiting from this data). In this paper a real Smart City is analyzed, corresponding to the city of Barcelona, with special emphasis on the layers responsible for collecting the data generated by the deployed sensors. The amount of daily sensors data transmitted through the network has been estimated and a rough projection has been made assuming an exhaustive deployment that fully covers all city. Finally, we discuss some solutions to both reduce the data transmission and improve the data management.

DevOps is a software development and distribution process. It combines an entire culture of collaboration and fusion between management approach, technologies of coding and technics of integration. DevOps is a new paradigm and novel concept that is introduced in different industries that concerns software and embedded development like robotics and smart agent.The ecosystem of the Internet of Things is a set of physical devices such as sensors and actuators. It includes a set of servers and gateways that provide connectivity. These devices can be installed at three levels: the edge level, the fog level and the cloud level. Applications that monitor things and collect data from sensors are deployed at the edge, fog and cloud levels. These applications can be containerized and deployed in different devices by Docker, which takes advantage of its advantage to create executable containers that are isolated from each other. This paper presents a generic metamodel of the Internet of Things ecosystem based on microservices and supported by Docker as a containerization tool and Ansible as a monitoring tool. This metamodel allows us to generate a system of connected objects that can be deployed on three levels: Fog, Edge, Cloud. This metamodel can be used in different types of domains such as Smart Home - Smart City - Smart Vehicle - Smart Health - Smart Farm - Smart Factory. The metamodel proposed in this article is a fusion between 5 metamodels: Internet of Things - Microservices - Ansible – Docker - Kubernetes.

With the use of technologies, ‘cities’ are able to control everything from traffic lights to the distribution of water in the city, traffic, environment, social actions, health, education, urban planning, security, and public administration, are some of the main areas of activity. The implementation of technology also brings other concerns such as data security and vulnerability and the privacy of individuals. The evolution of intelligent transport systems (ITS) has been accelerated, multifaceted, and often based on the technological advances considered revolutionary for the Urban Mobility sector. Recently, the widespread use of ITS in the operation and management of urban mobility is part of everyday life. Numerous tools are now available for a variety of contexts and scales, with applications that directly impact both locally and globally. This dissertation seeks to analyze the security risk within smart cities. It addresses the theme of what are intelligent transport systems and what are the main solutions to ensure the safety of these systems. To this end, is performed an analysis and classification of ITS threats. With the help of the NS-3 network simulator to model, the ITS communications architecture and the Simulation of Urban Mobility (SUMO) traffic simulator an urban road area scenario was generated. Finally, lessons learned and future research challenges to improve the security of ITS systems are presented.KeywordsSmart citySafetyPrivacyDataInfrastructuresMobilityInternet of Things (IoT)Traffic controlE-GovernanceCybersecurityHackers

QoS-aware big data analysis is critical in Information-Centric Internet of Things (IC-IoT) system to support various applications like smart city, smart grid, smart health, intelligent transportation systems, and so on. The employment of non-volatile memory (NVM) in cloud or edge system provides good opportunity to improve quality of data analysis tasks. However, we have to face the data recovery problem led by NVM failure due to the limited write endurance. In this paper, we investigate the data recovery problem for QoS guarantee and system robustness, followed by proposing a rarity-aware data recovery algorithm. The core idea is to establish the rarity indicator to evaluate the replica distribution and service requirement comprehensively. With this idea, we give the lost replicas with distinguishing priority and eliminate the unnecessary replicas. Then, the data replicas are recovered stage by stage to guarantee QoS and provide system robustness. From our extensive experiments and simulations, it is shown that the proposed algorithm has significant performance improvement on QoS and robustness than the traditional direct data recovery method. Besides, the algorithm gives an acceptable data recovery time.

Smart mobility and transportation, in general, are significant elements of smart cities, which account for more than 25% of the total energy consumption related to smart cities. Smart transportation has seven essential sections: leisure, private, public, business, freight, product distribution, and special transport. From the management point of view, transportation can be classified as passive or non-cooperating, semi-active or simple cooperating, active or cooperating, contract-based, and priority transportation. This approach can be applied to public transport and even to passengers of public transport. The transportation system can be widely observed, analyzed, and managed using an extensive distribution network of sensors and actuators integrated into an Internet of Things (IoT) system. The paper briefly discusses the benefits that the IoT can offer for smart city transportation management. It deals with the use of a hierarchical approach to total transportation management, namely, defines the concept, methodology, and required sub-model developments, which describes the total system optimization problems; gives the possible system and methodology of the total transportation management; and demonstrates the required sub-model developments by examples of car-following models, formation motion, obstacle avoidances, and the total management system implementation. It also introduces a preliminary evaluation of the proposed concept relative to the existing systems.

Smart transportation is playing a vital role as an important building block in smart city, providing solutions to many issues that relate to traffic on the road. This influences safety and quality of living (QoL) the main goals of smart city development. In such case, investigation of different aspects like different functionalities of smart transportation, purposes, research methods and technologies are obligatory. This study presents a literature review on existing researches on smart transportation in smart city to identify the area as well as the future research needs and gaps to be fulfilled. Here it summarizes different perspectives of smart city and smart transportation considered in studies, types of the researches performed, reported problems which are addressed in studies, purposes of deploying smart transportation, reported benefits and problems and technologies which are compatible in this research area.

Long-Range (LoRa) transmission technology is potentially a suitable solution in abundant applications such as smart cities, smart industries, smart health, and others, although it is challenging and complex to implement. LoRa is a non-cellular modulation technology for Long-Range Wide-Area Networks (LoRaWAN) and is suitable for Internet of Things (IoT) solutions through its long-range and low-power consumption characteristics. The present paper provides a comprehensive analysis of LoRa wireless transactions through several measurements, which differ in LoRa parameter configuration. The results showed dependency of the power consumed by the transaction on the selected Effective Isotropic Radiated Power (EIRP). The quantity of energy consumed by the transaction also significantly depends on the selected data rate (combination of the spread factor and bandwidth) and payload.

Nowadays, the growing adoption of the Internet of Things (IoT) is reshaping the telecommunication landscape and has penetrated every aspect of our lives with influential applications on smart health, home automation, smart logistics, smart industries and smart cities. These advanced technologies bring about numerous benefits and has begun to play a major role in daily lives, particularly data mining applied in precision agriculture to discover knowledge. Also in agro-industrial production chain, the combination of wireless and distributed specific sensor devices with the simulation of climatic conditions in order to track the evolution of grapes for wineries is outstanding. Mobile IoT such as narrowband-IoT (NB-IoT) and the long-term evolution (LTE) for machines (LTE-M) are significant innovations of this accelerating development of IoT technologies. In the era of ubiquitous communication where everything is connected to the internet, NB-IoT systems are expected to offer better quality-of-services (QoS) to end users than the traditional IoT paradigm. However, offering better QoS satisfaction to end users will become a great challenge due to the bottleneck caused by the dual problem of increasing IoT use cases and the shortage of wireless spectrum resources. Whilst discussing the recent innovative solutions of NB-IoT resource allocation, significant challenges and open issues related to the real-time implementation of NB-IoT are identified and discussed. Therefore, this paper gives a general overview of the resource allocation solutions in this NB-IoT innovation and further suggests and motivates for the intelligentization of future resource allocation solutions (i.e., the use of artificial intelligence (AI) strategies).

Federated learning for green and sustainable 6G IIoT applications

IoT solution for smart water distribution networks based on a low-power wireless network, combined at the device-level: A case study

Smart manufacturing in Industry 5.0 leads the fifth industrial revolution via smart products and operations in factories to optimise production. The impact of combining artificial Intelligence (AI) and Internet of Things (loT) in industry leads to AI-driven decision-making and thus improves the intelligence and efficiency of industrial systems. The IoT is a new paradigm transforming traditional living into a high-tech lifestyle. IoT transformation includes smart cities, smart homes, pollution control, energy saving, smart transportation, smart industries, etc. Urbanisation is growing rapidly, leading to a high demand for sustainable smart city solutions that maximise resource utilisation, improve citizens’ quality of life, and focus on reducing environmental impact. Utilising the IoT and AI creates new and innovative ways to tackle this problem with the ability to collect, analyse, and react to data as it is received in real time. The article presents a succinct review of the industrial revolution with AI models and techniques for sustainable and intelligent construction 4.0, construction 5.0, and society. It emphasises the applications for smart and sustainable construction 4.0, construction 5.0. and society 5.0 with challenges of integrating AI, IoT, and big data technologies for smart and sustainable industry.