Smart City Systems Research Articles

The design of intelligent highway transportation system in smart city based on the internet of things.

The design of intelligent expressway transportation system based on the Internet of Things is studied to improve the safety, travel experience, and operation management of expressway. The characteristics of the Internet of Things and cloud computing technology and its application on the expressway are analyzed, and the system design requirements of expressway intelligent transportation are understood. Besides, the overall architecture of the system is studied and designed. The IaaS layer, PaaS layer, and SaaS layer of the cloud platform are designed and deployed. The intelligent information system can make expressway highly informative. The simulation experiments reveal that the system only needs 120 milliseconds in accident processing time, which is far lower than the intelligent transportation system that only uses edge computing technology (201 milliseconds) and the intelligent transportation system that only uses cloud computing technology (443 milliseconds). Meanwhile, the accident response time is only 12s, which is also superior to other models. In terms of cost-effectiveness, the monthly cost of the system is 7004 yuan, with a CPU utilization rate of 53%, demonstrating good cost-effectiveness and resource utilization efficiency. In addition, compared with the existing system, the average traffic congestion time has been reduced by 25%, the traffic accident rate has been reduced by 18%, and the accident rate has been reduced by 27%. The intelligent traffic system design of expressway, expressway safety, travel service, and operation management is effectively improved by researching the intelligent traffic system design of expressway.

Retraction Note: The power of AI, IoT, and advanced quantum based optical systems in smart cities

Retraction Note: The power of AI, IoT, and advanced quantum based optical systems in smart cities

RTPL: A Real-Time Communication Protocol for LoRa Network

The industrial Internet of Things (IIoT) is prominently emerging in applications of large-scale and wide-area applications, such as oilfield management, smart grid management, real-time equipment monitoring, and integration of traffic management systems for smart cities. Relying on short-range wireless technologies (e.g., WirelessHART and ISA100.11a), traditional wireless solutions for industrial automation find it challenging to support the expansive scale of today’s IIoT. To address this limitation, we propose to adopt LoRaWAN, a prominent low-power wide-area network technology, for industrial automation. LoRaWAN for industrial automation poses some unique challenges. The fundamental building blocks of any industrial automation system are feedback control loops that largely rely on real-time communication. LoRaWAN traditionally adopts a simple protocol based on ALOHA with no collision avoidance or Listen Before Talk with Clear Channel Assessment and Random Backoff mechanisms to minimize energy consumption, which are less suitable for real-time communication. Existing real-time protocols for short-range technologies cannot be applied to a LoRaWAN network due to its unique characteristics such as asymmetry between downlink and the uplink spectrum, predefined modes (or classes) of operation, and concurrent reception through orthogonal spreading factors. In this paper, we address these challenges and propose RTPL - a Real-Time communication Protocol for LoRaWAN networks. RTPL is a low-overhead and conflict-free communication protocol allowing autonomous real-time communication of low-energy devices and exploits LoRa’s capability of parallel communication. We implement our approach on LoRa devices and evaluate through both physical experiments and extensive simulations. All results show that RTPL achieves on average 75% improvement in real-time performance without sacrificing throughput or energy compared to traditional LoRaWAN.

AI-Driven Optimization of Renewable Energy Storage Systems in Smart Cities Using Improved Binary Genetic Algorithm

AI-Driven Optimization of Renewable Energy Storage Systems in Smart Cities Using Improved Binary Genetic Algorithm

Towards sustainable IoT-based smart mobility systems in smart cities

Towards sustainable IoT-based smart mobility systems in smart cities

Low Network Power Challenges in IoT-Based Applications in Smart Cities

The emergence of Internet of Things (IoT) technologies has led to the development of smart cities, potentially improving urban life through efficient and effective use of resources. The success of IoT-based applications in smart cities is contingent on several factors, including network power challenges. Deploying IoT-based applications in smart cities requires a reliable and efficient network infrastructure to ensure smooth and uninterrupted data communication. One of the significant challenges faced in this regard is low network power, which can significantly impact the performance of IoT devices and, consequently, the entire smart city system. This article addresses the issue of low network power in IoT-based applications in smart cities. It investigates the causes of this problem and the potential solutions that can be adopted to mitigate its impact. The article also highlights the importance of network power management in the context of IoT-based applications in smart cities. By implementing effective network power management techniques, it is possible to optimize the performance of IoT devices and extend their battery life, thus ensuring the overall efficiency and sustainability of the smart city system. The article concludes by emphasizing the need for continuous research and development to overcome the challenges of low network power and further enhance IoT technology's capabilities in smart cities.

A Recent trends in waste Management Systems for Smart Cities in India – A Review

A Recent trends in waste Management Systems for Smart Cities in India – A Review

Machine Learning for Blockchain and IoT Systems in Smart Cities: A Survey

The integration of machine learning (ML), blockchain, and the Internet of Things (IoT) in smart cities represents a pivotal advancement in urban innovation. This convergence addresses the complexities of modern urban environments by leveraging ML’s data analytics and predictive capabilities to enhance the intelligence of IoT systems, while blockchain provides a secure, decentralized framework that ensures data integrity and trust. The synergy of these technologies not only optimizes urban management but also fortifies security and privacy in increasingly connected cities. This survey explores the transformative potential of ML-driven blockchain-IoT ecosystems in enabling autonomous, resilient, and sustainable smart city infrastructure. It also discusses the challenges such as scalability, privacy, and ethical considerations, and outlines possible applications and future research directions that are critical for advancing smart city initiatives. Understanding these dynamics is essential for realizing the full potential of smart cities, where technology enhances not only efficiency but also urban sustainability and resilience.

A Review On Revolutionizing Waste Water Collection and Recycling Processes with IoT

This paper provides an extensive review of IoT-driven solutions addressing critical challenges in sectors like wastewater treatment, agriculture, waste management, e-waste recycling, and water quality monitoring. Innovative models leverage IoT technology for real-time monitoring, control, and optimization. Examples include an industrial IoT cloud-based model for wastewater treatment, sensor integration in aquaculture systems for improved resource management, and intelligent waste collection systems in smart cities. The review identifies gaps and opportunities for advancements, highlighting the importance of real-time data collection, cloud-based analytics, and machine learning for informed decision- making in environmental and resource management

ResiSC: A system for building resilient smart city communication networks

AbstractSmart city networks are critical for delivering essential services such as healthcare, education, and business operations. However, these networks are highly susceptible to a range of threats, including natural disasters and intentional cyberattacks, which can severely disrupt their functionality. To address these vulnerabilities, we present the resilient smart city (ResiSC) system, designed to enhance the resilience of smart city communication networks through a topological design approach. Our system employs a graph‐theoretic algorithm to determine the optimal network topology for a given set of nodes, aiming to maximize connectivity while minimizing link provisioning costs. We introduce two novel connectivity measurements, All Nodes Reachability (ANR) and Sum of All Nodes Reachability (SANR), to evaluate network resilience. We applied our approach to data from two public universities of different sizes, simulating various attack scenarios to assess the robustness of the resulting network topologies. Evaluation results indicate that our solution improves network resilience against targeted attacks by 38% compared to baseline methods such as k‐nearest neighbours (k‐NN) graphs, while also reducing the number of additional links and their associated costs. Results also indicate that our proposed solution outperforms baseline methods like k‐NN in terms of network resilience against targeted attacks by 41%. This work provides a practical framework for developing robust smart city networks capable of withstanding diverse threats.

Blockchain and Smart Cities for Inclusive and Sustainable Communities: A Bibliometric and Systematic Literature Review

Smart cities are urban areas that leverage technological solutions to enhance traditional network management and efficiency to benefit residents and businesses. Based on the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol, this study presents a systematic literature review aimed at analyzing the existing literature on smart cities research. The literature review specifically focuses on the impact of blockchain technology on the urban environment and its potential to contribute to the development of inclusive and sustainable communities, including financial systems and infrastructures with similar characteristics to serve these societies. The findings reveal a lack of studies on the practical applications of distributed ledger technologies (DLTs), particularly blockchain, that specifically focus on the urban context capable of developing the (financial) ecosystem of smart cities. To address this gap, a future research agenda is proposed, highlighting several research questions that could guide academics and practitioners interested in exploring the development of smart city systems, with particular attention on the financial framework.

Clustered remote sensing target distribution detection aided by density-based spatial analysis

Small target detection in remote sensing is integral to a range of applications, including smart city systems and emergency rescue operations. However, the challenges posed by weak features and complex backgrounds in remote sensing images have hindered the efficacy of detection. Current models tend to focus on identifying individual targets, resulting in algorithms with larger parameters, slower detection efficiency, and difficulty in striking a balance between false positives and negatives. Given that many tasks do not require precise target location, a more efficient approach involves swiftly predicting target areas with models involving fewer parameters. This paper introduces the concept of group target distribution detection, gathering targets with similar distances and semantic similarities for clustered detection. A Gaussian probability map, formed from target density, is used to train a probability prediction model. We propose a new metric for evaluating this innovative group target distribution detection paradigm and provide a comparative assessment with traditional single-object detection models. In experimental evaluation, our proposed DenseUGE network — employing ResNet34 and ResNet50 as its backbone — surpasses the best baseline method by 3.37% on the AI-TOD dataset using our metrics. Additionally, visualizations demonstrate the ability of our proposed methodology to effectively identify the concentrated distribution of small target groups.

Personalization of the Car-Sharing Fleet Selected for Commuting to Work or for Educational Purposes—An Opportunity to Increase the Attractiveness of Systems in Smart Cities

Car-sharing services, which provide short-term vehicle rentals in urban centers, are rapidly expanding globally but also face numerous challenges. A significant challenge is the effective management of fleet selection to meet user expectations. Addressing this challenge, as well as methodological and literature gaps, the objective of this article is to present an original methodology that supports the evaluation of the suitability of vehicle fleets used in car-sharing systems and to identify the vehicle features preferred by users necessary for specific types of travel. The proposed methodology, which incorporates elements of transportation system modeling and concurrent analysis, was tested using a real-world case study involving a car-sharing service operator. The research focused on the commuting needs of car-sharing users for work or educational purposes. The study was conducted for a German car-sharing operator in Berlin. The research was carried out from 1 January to 30 June 2022. The findings indicate that the best vehicles for the respondents are large cars representing classes D or E, equipped with a combustion engine with a power of 63 to 149 kW, at least parking sensors, navigation, hands-free, lane assistant, heated seats, and high safety standards as indicated by Euro NCAP ratings, offered at the lowest possible rental price. The results align with market trends in Germany, which focus on the sale of at least medium-sized vehicles. This suggests a limitation of small cars in car-sharing systems, which were ideologically supposed to be a key fleet in those kinds of services. The developed methodology supports both system operators in verifying whether their fleet meets user needs and urban policymakers in effectively managing policies towards car-sharing services, including fleet composition, pricing regulations, and vehicle equipment standards. This work represents a significant step towards enhancing the efficiency of car-sharing services in the context of smart cities, where personalization and optimizing transport are crucial for sustainable development.

Exploring the Application of Digital Twin Technology in the Energy Sector using MEREC and MAIRCA Methods

Smart city sustainability initiatives prioritize creating environmentally, economically, and socially sustainable urban environments. Digital Twin (DT) technology creates precise digital replicas of physical assets, systems, or processes. These digital twins play a crucial role in advancing the goals of smart city sustainability. This paper explores the development and application of DT technology for integrated regional energy systems in smart cities, emphasizing its potential to optimize energy consumption, reduce costs, and enhance overall system performance. The CloudIEPS platform, an energy internet planning platform based on digital twin technology, is a great example of how digital twin technology can be applied in practice, helping optimize energy efficiency and reduce costs. Integrating digital twin technology with the Multi-Criteria Decision-Making (MCDM) methods offers a novel approach to managing and optimizing energy systems in smart cities. The paper aims to create a consistent and robust approach to determining the best digital twin solution for energy systems in smart cities. The paper identifies critical factors for decision-making and establishes a method for assessing the significance of criteria using Triangular Neutrosophic Sets (TNS) through the MEthod based on Removal Effects of Criteria (MEREC) and the Multi-Attributive Ideal Real Comparative Analysis (MAIRCA) approach. These methods are used to evaluate and prioritize multiple criteria in decision-making processes. Furthermore, the methods are combined with Triangular Neutrosophic Sets (TNS) to support decision-making for smart cities' energy systems, better accounting for the complex and uncertain nature of energy systems. A case study is conducted to apply and validate the developed methodology and perform a sensitivity analysis of the experimental results. The research outcomes indicated that the proposed methodology is robust and effective in handling the uncertainty and complexity inherent in smart cities' energy systems. The sensitivity analysis further confirms the stability and adaptability of the proposed methodology across different scenarios, making it a valuable tool for policymakers and stakeholders in the energy sector.

Human‐centered Smart Cities: An evaluation of a small community using the Smart Cities Initiative framework

AbstractIn September 2023, the INCOSE Smart Cities Initiative released a framework to evaluate and define smart city systems. The framework includes a human‐centered definition of a smart city and offers metrics of a smart city. The definition, metrics, and framework are based on a systematic process that allows consistent evaluation of city that focuses on providing for fundamental human needs.Many smart city applications still focus on technology, regardless of whether that technology provides a clear benefit to the stakeholders of the city. Systems engineering tools and practices offer potential for improving smart city implementations by improving the alignment of needs and solutions. Unfortunately, in new domains, such as the plethora domains involved with smart cities, systems engineering can be viewed as unnecessary overhead. Therefore, the usefulness of the systems engineering concepts that underlie the INCOSE Smart Cities framework could be overlooked unless it can provide immediate value. As a demonstration of how the new framework could be used by someone interested in engaging INCOSE Smart Cities concepts, a high‐level application of the definitions and framework was performed.The analysis demonstrated that a simple review of the city system, using the perspectives of the INCOSE Smart Cities initiative, could reveal strengths and weaknesses of a smart city and identify potential next steps for improvement. This paper is a case study of how to use qualitative analysis to apply the human‐centered definition and framework to evaluate a Smart City. The case study provides an opportunity to evaluate the strengths and weaknesses of the new INCOSE framework.

Decentralized AIoT based intelligence for sustainable energy prosumption in local energy communities: A citizen-centric prosumer approach

The role of prosumers who are consumers who produce, store, and consume energy is vital to the uptake of renewable energies in Local Energy Communities (LEC). However, the integration of prosumers in the smart grid to facilitate bidirectional flows of energy and information depends on intelligent operations of energy systems and flexible structures of the existing energy markets. But existing energy trading mechanisms are faced with issues of trust, privacy, security, and energy pricing determination. Also, there are fewer studies based on a citizen-centric prosumer approach. Thus, there is need to provide reliable solutions that addresses the aforementioned challenges faced by prosumers in LEC. Advancements in disruptive technologies, such as Distributed Ledger Technologies (DLT), Artificial Intelligence (AI), and the Internet of Things (IoT) have transformed a broad spectrum of intelligent systems in smart cities. Therefore, this study examines the integration of AI and IoT as AIoT and DLT towards a citizen-centric prosumer approach for decentralized energy markets trading. Additionally, this article develops an architectural model for energy prosumption in LEC using design science approach based on a user-centred design method that shows a possible implementation concept to support energy sharing and trading in LEC. The architectural model supports trust, data privacy, security, and energy pricing determination using AI and smart contracts to provides real-time energy trading monitoring, easy access, control, and immutable logs to unearth underlying energy demand and supply patterns thereby supporting citizen-centric prosumer approach. Finally, a use case scenario of DLT and AIoT for prosumption operations is presented.

Rule based complex event processing for an air quality monitoring system in smart city

Smart city development is accelerating, aiming for systematic urban living, but challenges like air quality and traffic management need addressing for sustainability in cities. The World Health Organization (WHO) links air pollution to severe respiratory diseases, emphasizing the need for real-time air quality monitoring and timely decisions based on varying pollutant concentrations (NH3, O3, SO2, NO2) across locations. This research work proposes a framework combining Siddhi Complex Event Processing (CEP) and SPARQL queries for air quality monitoring. Dataset collected from Central Pollution Control Board (CPCB) India, is preprocessed, passed through Apache Kafka, and transformed into a knowledge graph. The RDF data is integrated with a knowledge graph via Apache Jena to enhance CEP performance. Decision trees generate rules and enrich CEP engines with CPCB parameters to identify complex patterns. SPARQL queries on real-time RDF data categorize air quality conditions for all stakeholders. The model's effectiveness is validated by deploying RDF chunks to the CEP engine and evaluating performance through various query execution times. Our model efficiently processed numerous air pollutant events (200, 400, 600 etc.), filtering out about 80% and demonstrating robust event processing capabilities, which enhances air quality management and supports sustainable urban ecosystems and societies.

A Process Algebraic Approach to Predict and Control Uncertainty in Smart IoT Systems for Smart Cities Based on Permissible Probabilistic Equivalence.

Process algebra is one of the most suitable formal methods to model smart IoT systems for smart cities. Each IoT in the systems can be modeled as a process in algebra. In addition, the nondeterministic behavior of the systems can be predicted by defining probabilities on the choice operations in some algebra, such as PALOMA and PACSR. However, there are no practical mechanisms in algebra either to measure or control uncertainty caused by the nondeterministic behavior in terms of satisfiability of the system requirements. In our previous research, to overcome the limitation, a new process algebra called dTP-Calculus was presented to verify probabilistically the safety and security requirements of smart IoT systems: the nondeterministic behavior of the systems was defined and controlled by the static and dynamic probabilities. However, the approach required a strong assumption to handle the unsatisfied probabilistic requirements: enforcing an optimally arbitrary level of high-performance probability from the continuous range of the probability domain. In the paper, the assumption from the previous research is eliminated by defining the levels of probability from the discrete domain based on the notion of Permissible Process and System Equivalences so that satisfiability is incrementally enforced by both Permissible Process Enhancement in the process level and Permissible System Enhancement in the system level. In this way, the unsatisfied probabilistic requirements can be incrementally enforced with better-performing probabilities in the discrete steps until the final decision for satisfiability can be made. The SAVE tool suite has been developed on the ADOxx meta-modeling platform to demonstrate the effectiveness of the approach with a smart EMS (emergency medical service) system example, which is one of the most practical examples for smart cities. SAVE showed that the approach is very applicable to specify, analyze, verify, and especially, predict and control uncertainty or risks caused by the nondeterministic behavior of smart IoT systems. The approach based on dTP-Calculus and SAVE may be considered one of the most suitable formal methods and tools to model smart IoT systems for smart cities.

Health of Things Melanoma Detection System—detection and segmentation of melanoma in dermoscopic images applied to edge computing using deep learning and fine-tuning models

According to the World Health Organization (WHO), melanoma is a type of cancer that affects people globally in different parts of the human body, leading to deaths of thousands of people every year worldwide. Intelligent diagnostic tools through automatic detection in medical images are extremely effective in aiding medical diagnosis. Computer-aided diagnosis (CAD) systems are of utmost importance for image-based pre-diagnosis, and the use of artificial intelligence–based tools for monitoring, detection, and segmentation of the pathological region are increasingly used in integrated smart solutions within smart city systems through cloud data processing with the use of edge computing. This study proposes a new approach capable of integrating into computational monitoring and medical diagnostic assistance systems called Health of Things Melanoma Detection System (HTMDS). The method presents a deep learning–based approach using the YOLOv8 network for melanoma detection in dermatoscopic images. The study proposes a workflow through communication between the mobile device, which extracts captured images from the dermatoscopic device and uploads them to the cloud API, and a new approach using deep learning and different fine-tuning models for melanoma detection and segmentation of the region of interest, along with the cloud communication structure and comparison with methods found in the state of the art, addressing local processing. The new approach achieved satisfactory results with over 98% accuracy for detection and over 99% accuracy for skin cancer segmentation, surpassing various state-of-the-art works in different methods, such as manual, semi-automatic, and automatic approaches. The new approach demonstrates effective results in the performance of different intelligent automatic models with real-time processing, which can be used in affiliated institutions or offices in smart cities for population use and medical diagnosis purposes.

Deep learning–based urban energy forecasting model for residential building energy efficiency

Sustainable and inventive city design is becoming more and more dependent on the use of cutting-edge technology as smart cities develop further. Energy efficiency optimization in residential structures is an essential part of the puzzle as it helps conserve resources and keeps the planet habitable. An enhanced Deep Neural Network (DNN) model for household energy efficiency predictions is presented in this research. Our model uses a large dataset of building features, weather, occupancy patterns and energy usage histories. Data is preprocessed, features are engineered and hyperparameters are tweaked to improve DNN prediction. Scalable, easy-to-understand models are essential, as are shifting urban areas and energy landscapes. In this work, the authors have evaluated the proposed model with basic model with different optimizers. Initially, the Stochastic Gradient Descent optimizer applied that gained 91.02% Recall, 93.47% Precision, 93.28% F1-Score, 0.0153 MSE, 0.0166 RMSE and 0.0165 MAE. The proposed model gained 99.52% Recall, 98.91% Precision, 99.09% F1-Score, 0.0140 MSE, 0.0137 RMSE and 0.0139 MAE. By monitoring, analyzing and making decisions in real time, smart city systems can help planners understand energy usage trends. The optimized DNN model advances smart city development by promoting sustainability and resource optimization. Predicting residential buildings’ energy efficiency provides proactive energy savings, cost reduction and environmental impact mitigation. The suggested DNN model shows how smart cities use cutting-edge urban planning to become more sustainable, efficient and resilient.