Smart City Deployments Research Articles

Cost-Effective Power Management for Smart Homes: Innovative Scheduling Techniques and Integrating Battery Optimization in 6G Networks

This paper presents an Optimal Power Management System (OPMS) for smart homes in 6G environments, which are designed to enhance the sustainability of Green Internet of Everything (GIoT) applications. The system employs a brute-force search using an exact solution to identify the optimal decision for adapting power consumption to renewable power availability. Key techniques, including priority-based allocation, time-shifting, quality degradation, battery utilization and service rejection, will be adopted. Given the NP-hard nature of this problem, the brute-force approach is feasible for smaller scenarios but sets the stage for future heuristic methods in large-scale applications like smart cities. The OPMS, deployed on Multi-Access Edge Computing (MEC) nodes, integrates a novel demand response (DR) strategy to manage real-time power use effectively. Synthetic data tests achieved a 100% acceptance rate with zero reliance on non-renewable power, while real-world tests reduced non-renewable power consumption by over 90%, demonstrating the system’s flexibility. These results provide a foundation for further AI-based heuristics optimization techniques to improve scalability and power efficiency in broader smart city deployments.

A Study Article of DLMS to IOT Protocol

Abstract: The DLMS (Device Language Message Specification) protocol plays a crucial role in energy metering and management. It provides a standardized communication framework for exchanging data between utility meters and data collection devices. The protocol enables interoperability, remote meter reading, and supports efficient management of energy consumption, billing, and grid operations. The DLMS protocol is essential for Internet of Things (IoT) applications as it provides a standardized method for communication between Internet of Things devices and energy meters. This facilitates the seamless integration of energy data into IoT platforms, enabling efficient monitoring, analysis, and management of energy consumption in smart grid and smart city deployments. To this end, we have developed a prototype DLMS to IoT gateway that is based on either STM32 or any other IoT module. However, we encountered a challenge in testing the prototype since we couldn't get any digital meter to conduct hands-on testing..

What Makes a City “Smart” and Who Decides? From Vision to Reality in the USDOT Smart City Challenge

ABSTRACT The concept of a “smart city” has gained currency in urban policy as a recognition of the potentially transformative role advanced information technology will play in city operations as the twenty-first century progresses. Who decides what makes a city smart and who is that smart city for are crucial questions for the urbanist literature to systematically address. Despite the concept’s recent popularity, “smart city” is a contested term and remains a chaotic dimension of urban theory. Theorizing the smart city in a US context must address a particular mix of built forms, public policies, and social conditions—all of which shape how transportation and other systems help constitute the city. To deepen understanding of the smart city concept in both theory and practice, this research explores the framing, construction, and contestation of the smart city concept through the U.S. Department of Transportation’s (USDOT’s) Smart City Challenge (SCC), launched in 2015 as a competitive grant program with the intent to provide a spark for incentivizing the development of smart infrastructure in mid-sized US cities. We compare the action plans of the seven finalist cities selected to create plans to implement their visions, including Columbus, Ohio, the eventual winner. We employ interpretivist content and discourse analysis of the applications and award decision, as well as of early implementation in Columbus, to explore shared meanings of recurring themes and key concepts, building a grounded theory of the smart city in the US context. The findings of the comparative analysis reveal further insights into prevailing challenges in smart city deployment, particularly for areas lacking inherent technology platforms.

Are smart cities more sustainable? An exploratory study of 103 U.S. cities

Cities play a vital role in tackling sustainability challenges. Smart cities have emerged as solutions to urban sustainability. However, whether smart city practices lead to environmental, economic, and social sustainability outcomes is still not clear. This is due to a lack of thorough knowledge of how local governments have deployed the smart city notion, as well as a lack of holistic evaluation of sustainability outcomes achieved by smart cities. To fill in this gap, this research evaluates whether local implementation of smart cities is associated with sustainability outcomes at the city level. We studied 103 US cities by integrating a sustainability assessment conducted by the United Nations Sustainable Development Solutions Network and text mining of city official websites. Our analysis shows that more than 80% of the 103 cities have smart city statements on their websites. In addition, smart cities generally score higher on sustainability outcomes than non-smart cities. Furthermore, when controlling for population size and geographic region, smart city mentions are positively associated with economic sustainability outcomes. However, the relationship between smart city mentions and environmental and social sustainability is not statistically significant. This study contributes to the literature by providing empirical evidence on the prevalence of local governments’ deployment of smart cities, which is scarce at present, and gives novel insight into the relationship between the use of technologies and urban sustainability.

Pedestrian Detection with LiDAR Technology in Smart-City Deployments–Challenges and Recommendations

This paper describes a real case implementation of an automatic pedestrian-detection solution, implemented in the city of Aveiro, Portugal, using affordable LiDAR technology and open, publicly available, pedestrian-detection frameworks based on machine-learning algorithms. The presented solution makes it possible to anonymously identify pedestrians, and extract associated information such as position, walking velocity and direction in certain areas of interest such as pedestrian crossings or other points of interest in a smart-city context. All data computation (3D point-cloud processing) is performed at edge nodes, consisting of NVIDIA Jetson Nano and Xavier platforms, which ingest 3D point clouds from Velodyne VLP-16 LiDARs. High-performance real-time computation is possible at these edge nodes through CUDA-enabled GPU-accelerated computations. The MQTT protocol is used to interconnect publishers (edge nodes) with consumers (the smart-city platform). The results show that using currently affordable LiDAR sensors in a smart-city context, despite the advertising characteristics referring to having a range of up to 100 m, presents great challenges for the automatic detection of objects at these distances. The authors were able to efficiently detect pedestrians up to 15 m away, depending on the sensor height and tilt. Based on the implementation challenges, the authors present usage recommendations to get the most out of the used technologies.

Cellular Traffic Prediction: A Deep Learning Method Considering Dynamic Nonlocal Spatial Correlation, Self-Attention, and Correlation of Spatiotemporal Feature Fusion

Cellular traffic prediction will play a key role in the deployment of future smart cities. Although the current traffic prediction methods based on deep learning show better performance than traditional prediction methods, they still have the following problems: (1) In spatial domain, the correlations between cellular traffic features cannot be captured accurately in non-local (including “geographic adjacency” and long-distance) spatial areas. (2) In temporal domain, the correlation of different time-grained features is failed to consider. To address these problems, a deep learning method considering dynamic non-local spatial correlation, self-attention, and correlation of spatio-temporal feature fusion is proposed. In spatial domain, our method can accurately capture the spatial correlation and highlight the contribution of more relevant traffic in the non-local area by designing a NLG-NLAM model. In temporal domain, the correlations of time-periodic features with different granularities are considered to clarify the key roles of different periodic features and eliminate the influence of irrelevant cellular traffic features on the prediction by designing a calibration layer. Experimental results indicate that the proposed method shows better performance than other mainstream prediction methods on three real-world cellular traffic datasets.

Communication, sensing, computing and energy harvesting in smart cities

A smart city provides diverse services based on real-time data obtained from different devices deployed in urban areas. These devices are largely battery-powered and widely placed. Therefore, providing continuous energy to these devices and ensuring their efficient sensing and communications are critical for the wide deployment of smart cities. To achieve frequent and effective data exchange, advanced enabling information and communication technology (ICT) infrastructure is in urgent demand. An ideal network in future smart cities should be capable of sensing the physical environment and intelligently mapping the digital world. Therefore, in this paper, we propose design guidelines on how to integrate communications with sensing, computing and/or energy harvesting in the context of smart cities, aiming to offer research insights on developing integrated communications, sensing, computing and energy harvesting (ICSCE) for promoting the development ICT infrastructure in smart cities. To put these four pillars of smart cities together and to take advantage of ever-increasing artificial intelligence (AI) technologies, the authors propose a promising AI-enabled ICSCE architecture by leveraging the digital twin network. The proposed architecture models the physical deep neural network-aided ICSCE system in a virtual space, where offline training is performed by using the collected real-time data from the environment and physical devices.

Technologies Trend in the Integration of 5G Networks and IoT in Smart Cities: A Survey

This paper presents a survey of 5G and suitable concerns in the planning and development of communication model infrastructure for the deployment of Smart Cities, also called Intelligent Cities or Digital Cities. The paper first provides an overview of 5G technology, its evolution, and how it revolutionised worldwide communication. The drivers of 5G, such as enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC), will enable 5G to deliver higher speeds compared to the previous 4G and 3G generations and enable service providers to provide massive bandwidth catering to data-intensive applications such as Smart Cities. Besides human-computer interaction (HCI), smart cities include machine type communication (MTC), in which massive communication takes place between devices and the information infrastructure through gateways. A 5G network can successfully integrate the diversified service requirements and demands of smart city applications. Smart cities require massive IoT communication built into 5G. Multiple technologies are deployed in smart cities to enhance the standard of living for people. Citizens are provided with better health services, improved transportation, efficient energy, quality education, and safety and security to public. In this survey paper we present the contribution of 5G in the deployment of smart city applications and its pillars that play a very important role in the construction of smart cities. Better healthcare systems have been developed that integrate new communication technologies and conventional health care procedures. One of the vital segments of a smart city is its transportation system, which affects the environs and health of people. The energy efficiency of smart cities can be greatly improved by 5G by using smart appliances. Smart metres deliver real-time data about the energy consumed. Users can save energy and money. Similarly, it can assist the water board team by giving real-time statistics regarding water consumption to the consumers. Almost all the smart city infrastructure mentioned above has different requirements for delay, movement, dependability, and resilience of the network. Incorporating diverse applications into a communication network is a difficult chore. 5G has been designed for forward compatibility to support future amenities not known today. The paper outlines diverse requirements for smart cities based on application and technology requirements. Lastly, the supporting technologies are highlighted for the forthcoming integration of smart cities and communication infrastructure.

Comparative study of low power wide area network based on internet of things for smart city deployment in Bandung city

Smart city implementation, such as smart energy and utilities, smart mobility & transportation, smart environment, and smart living in urban areas is expanding rapidly worldwide. However, one of the biggest challenges that need to be solved is the selection of the appropriate internet of things (IoT) connectivity technologies. This research will seek for the best candidate low power wide area network (LPWAN) technologies such as long-range wide area network (LoRaWAN), narrow-band internet of things (NB-IoT), and random phase multiple access (RPMA) for IoT smart city deployment in Bandung city is based on IoT network connectivity between with six technical evaluation criteria: gateway requirements, traffic/data projection, the best signal level area distribution, and overlapping zones. Bass model is carried out to determine the capacity forecast. While in coverage prediction, LoRaWAN and NB-IoT use the Okumura-Hata propagation, and Erceg-Greenstein (SUI) model is used for RPMA. Based on the simulation and performance evaluation results, RPMA outperforms LoRaWAN and NB-IoT. It required the least gateway number to cover Bandung city with the best signal levels and overlapping zones.

AI based Adaptive Network for Smart Cities

Self-aware, Self-Defending Adaptive Network is a network that defends itself from security breaches in the deployment of smart cities. An adaptive system that knows and recognizes the level of threat faced by an intrusion across a network of smart cities. To detect and separate security threats, the AI program uses a new method of machine learning to track any aspect of a network. Threats include ransomware, high-jacking code, intrusion and illegal entry, theft, and unauthorized use. An autonomous system comprises of a collection of autonomous modules that are introduced and removed dynamically. To order to achieve machine objectives, nodes inside such an ensemble will cooperate. In response to changes in its operating environment, the self-adaptive network modifies its own behavior. We mean anything that the network can observe, such as user interaction, network devices and sensors, or instrumentation, by operating environment.

DXN: Dynamic AI-Based Analysis and Optimisation of IoT Networks’ Connectivity and Sensor Nodes’ Performance

Most IoT networks implement one-way messages from the sensor nodes to the “application host server” via a gateway. Messages from any sensor node in the network are sent when its sensor is triggered or at regular intervals as dictated by the application, such as a Smart-City deployment of LoRaWAN traps/sensors for rat detection. However, these traps can, due to the nature of this application, be moved out of signal range from their original location, or obstructed by objects, resulting in under 69% of the messages reaching the gateway. Therefore, applications of this type would benefit from control messages from the “application host server” back to the sensor nodes for enhancing their performance/connectivity. This paper has implemented a cloud-based AI engine, as part of the “application host server”, that dynamically analyses all received messages from the sensor nodes and exchanges data/enhancement back and forth with them, when necessary. Hundreds of sensor nodes in various blocked/obstructed IoT network connectivity scenarios are used to test our DXN solution. We achieved 100% reporting success if access to any blocked sensor node was possible via a neighbouring node. DXN is based on DNN and Time Series models.

Smart City Design Differences: Insights from Decision-Makers in Germany and the Middle East/North-Africa Region

Smart cities offer solutions to environmental, economic, and societal problems in urban agglomerations. We investigate the potential for mutual learning in smart city implementation by comparing German approaches (smaller, local projects) to projects implemented in the MENA region (bigger, national designs). We contrast the outside view on these projects with an inside perspective, surveying key decision-makers in five German and seven MENA smart cities. We assess motivation, technology options, and factors that drive or impede smart city implementation. We find strong similarities in the motives to engage in smart cities, offering common ground for mutual good practice exchange. Energy efficiency solutions and - to a lesser extent - renewable energies are of strong interest to policymakers in all countries. In contrast, the appraisal of mobility solutions strongly diverges, showing that technology deployment is far from being a simple plug and play solution. Considering these insights can facilitate the overall deployment of smart cities, not only in the surveyed countries but also in global manner.

Smart City Design Differences: Insights from Decision-Makers in Germany and the Middle East/North-Africa Region

Smart cities offer solutions to environmental, economic, and societal problems in urban agglomerations. We investigate the potential for mutual learning in smart city implementation by comparing German approaches (smaller, local projects) to projects implemented in the MENA region (bigger, national designs). We contrast the outside view on these projects with an inside perspective, surveying key decision-makers in five German and seven MENA smart cities. We assess motivation, technology options, and factors that drive or impede smart city implementation. We find strong similarities in the motives to engage in smart cities, offering common ground for mutual good practice exchange. Energy efficiency solutions and—to a lesser extent—renewable energies are of strong interest to policymakers in all countries. In contrast, the appraisal of mobility solutions strongly diverges, showing that technology deployment is far from being a simple “plug and play” solution. Considering these insights can facilitate the overall deployment of smart cities, not only in the surveyed countries but also in global manner.

Toward a Secure and Resilient All-Renewable Energy Grid for Smart Cities

The concept of smart cities is driven by the need to enhance citizens’ quality of life. It is estimated that 70% of the world population will live in urban areas by 2050. The electric grid is the energy backbone of smart city deployments. An electric energy system immune to adverse events, both cyber and physical risks, and able to support the integration of renewable sources will drive a transformational development approach for future smart cities. This article describes how the future electric energy system with 100% electricity supply from renewable energy sources requires the “birth of security and resiliency” incorporated with its ecosystem.

Use of Receiver Operating Characteristic Curve to Evaluate a Street Lighting Control System

Intelligent control of public lighting is nowadays one of the most challenging issues in smart city deployment. Lighting optimization entails a compromise between comfort, safety, and power consumption, affecting both vehicles and pedestrians. Smart solutions must estimate their characteristics to trade-off users’ needs and energy requirements. This paper proposes an intelligent street lighting control system and the Receiver Operating Characteristic (ROC) curve method to evaluate the best number of street lamps to achieve a balance between public road user comfort and system power consumption. The control system is based on the detection of users, mainly pedestrians, using presence sensors. From the detection of a pedestrian by two or more consecutive street lamps it is possible to determine their speed. Knowing the pedestrian speed, allows the system to anticipate and adjust the light intensity of the remaining street lamps, and provide a comfortable view of the street. Using the ROC curve, we evaluate the control algorithm in terms of the number of previous street lamps used. We have tested the system and the method in a model of pedestrians walking down a street. The obtained results show that ROC analysis used to control street lighting allows measuring the whole control system’s efficiency by providing a concrete number of previous street lamps.

Towards the sustainable development of smart cities through mass video surveillance: A response to the COVID-19 pandemic

Sustainable smart city initiatives around the world have recently had great impact on the lives of citizens and brought significant changes to society. More precisely, data-driven smart applications that efficiently manage sparse resources are offering a futuristic vision of smart, efficient, and secure city operations. However, the ongoing COVID-19 pandemic has revealed the limitations of existing smart city deployment; hence; the development of systems and architectures capable of providing fast and effective mechanisms to limit further spread of the virus has become paramount. An active surveillance system capable of monitoring and enforcing social distancing between people can effectively slow the spread of this deadly virus. In this paper, we propose a data-driven deep learning-based framework for the sustainable development of a smart city, offering a timely response to combat the COVID-19 pandemic through mass video surveillance. To implementing social distancing monitoring, we used three deep learning-based real-time object detection models for the detection of people in videos captured with a monocular camera. We validated the performance of our system using a real-world video surveillance dataset for effective deployment.

Blockchain-based batch authentication protocol for Internet of Vehicles

The vehicles in Internet of Vehicles (IoV) can be used to opportunistically gather and distribute the data in a smart city environment. However, at the same time, various security threats arise due to insecure communication happening among various entities in an IoV-based smart city deployment. To address this issue, we aim to design a novel blockchain-enabled batch authentication scheme in Artificial Intelligence (AI)-envisioned IoV-based smart city deployment. The latest trends and revolutions in technologies incorporate AI/Machine Learning (ML) in blockchaining to produce a secure, efficient and intelligent blockchain based system. The data stored in the blocks in the blockchain are authentic and genuine, which makes the AI/ML algorithms to work at their exceptions in order produce correct predictions on the blockchain data. Through the signing phase of the proposed scheme, each vehicle in a dynamically formed cluster broadcasts a message to its own member and respective road-side unit (RSU). In the proposed scheme, two types of authentication take place: vehicle to vehicle (V2V) authentication allows a vehicle to authenticate its neighbor vehicles in its cluster, while batch authentication permits a group of cluster vehicles to be authenticated by their R S U . At the end, a group key is established among the vehicles and R S U in their cluster. R S U then gathers securely data from its vehicles and form several transactions including the information of vehicles and its own given information to the cluster member vehicles. The transactions are formed later by the nearby fog server associated with R S U and then by the cloud server to form a complete block. The created blocks are mined by the cloud servers in a Peer-to-Peer (P2P) cloud server network through the voting-based Practical Byzantine Fault Tolerance (PBFT) consensus algorithm. The authentic and genuine data of the blockchain are utilized for Big data analytics through AI/ML algorithms. It is shown that the proposed scheme is highly robust against various attacks through formal and informal security analysis, and also through formal security verification tool. A detailed comparative analysis reveals that the proposed scheme achieves superior security and functionality features, and offers comparable storage, communication and computational costs as compared to other existing competing schemes. Finally, the blockchain implementation has been carried out on the proposed scheme to show its effectiveness.

On the design of biometric-based user authentication protocol in smart city environment

Among the security services, like authentication, access control, key management and intrusion detection, user authentication is very much needed for a smart city environment because an external authorized user may require the real time data to be accessed directly from the deployed Internet of Things (IoT) enabled smart devices. Using the established session key between the user and an access smart device though mutual authentication and key agreement process, the real time data can be securely accessed. To deal with this issue, we propose a new user authentication scheme in smart city environment using three factors of a legal registered user (mobile device, password and biometrics). The proposed scheme is shown to be robust against a number of potential attacks needed in an IoT-based smart city deployment. The simulation study for formal security verification using the widely-accepted “Automated Validation of Internet Security Protocols and Applications (AVISPA)” tool demonstrates that the proposed scheme is also secure. Furthermore, experiments on various cryptographic primitives have been carried out using “MIRACL Cryptographic SDK: Multiprecision Integer and Rational Arithmetic Cryptographic Library” under both server and Raspberry PI 3 settings. Finally, a comprehensive comparative analysis shows the effectiveness and better security of the proposed scheme as compared with other state of art user authentication schemes.

Fostering IoT Service Replicability in Interoperable Urban Ecosystems

Worldwide cities are involved in a digital transformation phase specially focused on sustainability and improving citizen’s quality of life. However, such objectives are hard to achieve if the migration of the urban processes are not performed following a common approach. Under the paradigm of smart city, different Information and Communication Technologies (ICT) have been deployed over urban environments to enable such digital transformation. However, actual implementations differ from one city to another, and even between services within the same city. As a consequence, the deployment of urban services is hindered, since they need to be tailored to each city. In addition, the isolation of urban services obstructs its optimization, since it cannot harness contextual information coming from other services. All in all, it is necessary to implement tools and mechanisms that allow us to ensure that city solutions and their vertical services are interoperable. In order to tackle this issue, different initiatives have proposed architectures that homogenize the interaction with smart cities from different angles. However, so far the compliance with such architectures has not been assessed. Having this in mind, in this work we present a validation framework, developed under the umbrella of the SynchroniCity project, which aims to verify that interfaces and data exposed by cities are aligned with the adopted standards and data models. In this regard, the validation framework presented here is the technical enabler for the creation of an interoperability certificate for smart cities. To assess the benefits of the validation framework, we have used it to check the interoperability of 21 smart city deployments worldwide that adhered the SynchroniCity guidelines. Afterwards, during an open call a total number of 37 services have been deployed over such SynchroniCity instances, thus confirming the goodness of uniform and validated smart cities to foster service replicability.

Design of a Trustless Smart City system: The #SmartME experiment

A Smart City is an example of a domain where citizens and institutions, at any level, should be empowered to cooperate for the greater good of the respective communities, and one of the most powerful ways to attain that is by actually decentralizing any process that can escape single-authority control and the grip of centrally-managed bureaucracies, whilst at the same time not relying on any single party trusting any other. In line with this key insight, in this paper we have designed and implemented a decentralized, trustless DLT-based system for (environmental) sensing, data acquisition, storage, and consumption, in the context of a real-world Smart City deployment, #SmartME, and with the participation of independent, institutional, stakeholders. This Open Data system has been devised to be user-friendly, with an end-user wizard for trustless (i.e., independent) data audit. Indeed, the data collection layer does not trust any IoT node by default, but requires nodes to authenticate themselves when sending readings. Last but not least, the storage layer is again trustless, as institutions can pool together their resources, without trusting one another, nor entrusting the administration of the system to any third party. In this work we extend our preliminary proof-of-concept design, and explore the expected outcomes of scaling the system to comprise an even greater number of active institutions participating in the validation process, by carrying out simulation experiments. Results from these experiments help us in validating our approach and assessing its scalability.