DASTData: a Fog-Cloud model for distributed storage and traceability of IoT data in Smart Cities

PurposeBig Data has become crucial for fulfilling the vision of smart cities; however, there are several challenges associated with its use. This study aims to provide a holistic view of challenges of using Big Data in smart cities and the solutions to deal with them by systematically selecting, reviewing and synthesizing the literature in this area.Design/methodology/approachA systematic literature review was conducted by following a rigorous search protocol that identified a total of 310 peer-reviewed academic research papers. These papers were subsequently filtered and finally 38 relevant papers were selected.FindingsA number of major challenges (human dynamics, privacy, security, volume, velocity, variety, veracity and value) associated with Big Data in a smart city and the respective solutions to address them were identified. This study primarily contributes in providing the process of effectively managing Big Data in smart cities.Practical implicationsThe study provides valuable information to the Big Data practitioners by illustrating the process of effective management of Big Data in smart cities. This study further provides the directions to the future researchers.Originality/valueThe study is original and is based on the existing literature and its interpretation.

Recently, the notion of a smart city, which includes smart well‐being, smart transit, and smart society, has attracted much attention due to its impact on people's quality of living. Data in smart cities are characterized by variety, velocity, volume, value, and veracity that are the well‐known characteristics of big data. The fast pace expanding of IoT devices and sensors in smart cities generates a huge volume of data that can help decision‐makers and managers in city management. The aim of this article is to wholly and systematically review big data handling approaches in smart cities, in which we analyze research efforts published between 2013 and February 2021, where these techniques are categorized based on their algorithms and architectures. Further, the main ideas, evaluation techniques, tools, evaluation metrics, algorithm types, advantages, and disadvantages are explored. Additionally, essential evaluation factors are introduced in which scalability and availability by 16%, time by 15% and accuracy by 11% are more in focus, and finally, some of the challenges, open issues, and future trends that are valuable for further research are suggested in big data handling approaches in smart cities.

PurposeThe purpose of this paper is to propose a distributed deep learning architecture for smart cities in big data systems.Design/methodology/approachWe have proposed an architectural multilayer to describe the distributed deep learning for smart cities in big data systems. The components of our system are Smart city layer, big data layer, and deep learning layer. The Smart city layer responsible for the question of Smart city components, its Internet of things, sensors and effectors, and its integration in the system, big data layer concerns data characteristics 10, and its distribution over the system. The deep learning layer is the model of our system. It is responsible for data analysis.FindingsWe apply our proposed architecture in a Smart environment and Smart energy. 10; In a Smart environment, we study the Toluene forecasting in Madrid Smart city. For Smart energy, we study wind energy foresting in Australia. Our proposed architecture can reduce the time of execution and improve the deep learning model, such as Long Term Short Memory10;.Research limitations/implicationsThis research needs the application of other deep learning models, such as convolution neuronal network and autoencoder.Practical implicationsFindings of the research will be helpful in Smart city architecture. It can provide a clear view into a Smart city, data storage, and data analysis. The 10; Toluene forecasting in a Smart environment can help the decision-maker to ensure environmental safety. The Smart energy of our proposed model can give a clear prediction of power generation.Originality/valueThe findings of this study are expected to contribute valuable information to decision-makers for a better understanding of the key to Smart city architecture. Its relation with data storage, processing, and data analysis.

It was analyzed the information of databases, encryption algorithms, and information security that are the main components of a Smart City. The problem is the lack of security of public or secret data that is stored in a repository, where there is access to the citizen, local government, and central government. The objective is to define a security prototype and adopt a cryptographic algorithm to mitigate the risks of the database in the management of a Smart City. It was used the deductive method and exploratory research to analyze the information of the reference articles. It turned out a general prototype of security; an algorithm to implement a database for Smart City; an algorithm of data protection expressed in flowcharts. It was concluded that information protected with an encryption algorithm, is a support to be more efficient, reduce costs, reduce the environmental footprint and improve the management of a Smart City.

Smart cities collect lots of data from sources like traffic systems and sensors. This data is vital for improving services such as transport management, public safety, energy saving, and citizen involvement. However, keeping people’s privacy secure by making data anonymous can make the data less useful. Anonymization methods include mix-zones, pseudonymization, and data masking. Existing systems often evaluate the usefulness of data after it’s processed, but usually can not adjust to changes in real-time or work well across different fields. It also does not typically consider fairness for individuals or integrate well with privacy-preserving data methods on devices, which are crucial for city services needing quick responses. This paper presents a new system to assess anonymized data in smart cities in real-time. The system includes four main components: 1. A real-time tool that continuously monitors how anonymization affects data quality across various areas. 2. A utility model that adjusts usefulness scores based on the needs of different smart city services. 3. A module that detects and addresses unfair differences in data usefulness caused by anonymization. 4. A data combination layer that operates well on devices, allowing on-site decision-making while complying with privacy regulations like GDPR. The framework is tested with multiple real-world transportation datasets, anonymized in different ways, and applied to tasks like traffic prediction, anomaly detection, and location-based services. The results demonstrate that the framework provides accurate data usefulness estimation with minimal computing effort, and it offers fair, situation-specific trade-offs in usefulness in real-time. This work connects privacy and usefulness, offering a scalable solution for responsibly using anonymized data in smart city environments. The framework is validated on both transportation and smart energy datasets, demonstrating consistent improvements in privacy–utility–fairness balance and confirming its generality across smart-city domains.

The explosive data growth in smart city is making domain big data a hot topic for knowledge extraction. Non-taxonomic relations refer to any relations between concept pairs except the is-a relation, which is an important part of Knowledge Graph. In this paper, toward big data in smart city, we present a multi-phase correlation search framework to automatically extract non-taxonomic relations from domain documents. Different kinds of semantic information are used to improve the performance of the system. First, inspired by the works of network representation; we propose a Semantic Graph-Based method to combine structure information of semantic graph and context information of terms together for non-taxonomic relationships identification. Second, different semantic types of verb sets are extracted based on the dependency syntactic information, which are ranked to act as non-taxonomic relationship labels. Extensive experiments demonstrate the efficiency of the proposed framework. The F1 value reaches 81.4% for identification of non-taxonomic relationships. The total precision of the non-taxonomic relationship labels extraction is 73.4%, and 87.8% non-taxonomic relations can be provided with “good” labels. We hope this article can provide a useful way for domain big data knowledge extraction in smart city.

Internet of Things (IoT) and Big Data have played a significant role in the development and sustainability of Smart Cities. They are the backbones of Smart Cities' efforts to connect human resources, social capital, and ICT infrastructure to address public challenges, achieve sustainable development and improve people's standard of life. It helps Smart Cities improve public services, such as water management, healthcare, education, security issues, transportation, energy usage, and other community to achieve a better life. Big Data helps to analyze, store and back-ups a massive amount of data generated in smart cities in seconds day by day. For this matter, different analytical tools are employed, such as data analytics, machine learning, statistics, and data mining. Even though there are many benefits offered to smart cities; the Smart Cities encounters challenges that need to be addressed. Many of these challenges focus on security and privacy, infrastructures and operational and handling massive data. This paper describes the overview of IoT, Big Data, and Smart Cities. The Applications of IoT and Smart Cities were also discussed; the features of Smart Cities and Big Data; Different challenges of IoT, Big Data, and Smart Cities are described. Also, the proposed research work has presented the relationship between IoT and Big Data to Smart Cities; the role of IoT and Big Data in smart cities was identified; finally, the future directions for IoT and Big Data regarding Smart Cities are discussed.

A massive wave of urbanisation has grasped both developed and developing nations in the last decade. Various studies showcase the rising trend of rapid urbanisation growth due to demographic shifts, catalysed by numerous global and local parameters. India is professed to house 50 percent of its population in urban areas by 2030. The urban citizen’s rising expectations regarding infrastructure, amenities and safety will dovetail with this phenomenon. To cater to the demands of increased urbanisation, the concept of Smart cities is being advocated as an apt solution. Even so, there is widespread ambiguity about the challenges associated with adopting smart solutions into the existing urban social form. Though there have been many tentative explanations regarding the basic framework of a smart city, there are no known validated definitions. The main notable feature is the use of information and communication technologies (ICTs) within the cyclic dynamics of a city to facilitate the smooth and cost-effective working of urban areas. This ICT infrastructure enables real-time data collection, analysis, response, and storage. This has been deemed beneficial due to the increase in efficiency of operation and management services involved with the day-to-day functioning of any urban area. Smart cities advocate using information and communication technologies (ICTs) within the dynamics of a city to enable the real-time collection, analysis, and storage of big data. This is beneficial due to the increased efficiency of operation and management services involved with an urban area’s daily functioning. One such technological intervention is the ‘drone’ or the ‘unmanned aerial vehicles (UAVs)’. UAVs have a wide variety of uses in a smart urban fabric, from geospatial integration to traffic management, surveillance, disaster response, etc. In the current nascent stage of research and development of UAVs as one of the innovative solutions for smart cities in India, questions arise regarding privacy, cost of production, technical knowledge, safety and security with their large-scale use. This paper aims to assess the applicability of UAVs in overall smart city planning and management. The feasibility analysis method is adopted to analyse the felicitousness of UAVs in a smart city’s planning and design phases. The results of the study undertaken in this paper highlight the challenges and opportunities in the planning and management of smart cities by integrating UAVs. The paper enumerates the relevance and appropriate benefits of using UAVs to plan, design, and perpetuate Indian smart cities.

This paper proposes a method to manage and utilize image and video data effectively in a smart city. Applying digital watermarking techniques, a framework for visual data security and management for smart cities is presented. In the framework, a reversible fragile or semi-fragile watermark embedded into the visual data is used to ensure trusted acquisition. Moreover, reversible metadata watermarks carrying information such as identification and other properties data is used to assist visual data management. A solution for tracing users on a large scale is presented using reversible watermarking.

Industrial Internet of Water Things architecture for data standarization based on blockchain and digital twin technology☆

This chapter aims to explore the application of Internet of Things (IoT) and machine learning (ML) in assessing the planning and management of smart cities and new towns. Additionally, we have discussed the possible solutions that combine IoT and ML opportunities in this field. Similarly, our results support that ML and IoT optimize resources, address environmental challenges and enhance citizen well-being by using real-time data and predictive models. However, challenges such as interoperability, cybersecurity, data protection, algorithmic bias and high costs must be addressed. Finally, we support that by adopting transparent governance and innovation, smart cities and new towns can build resilient urban ecosystems that prioritize the needs of their residents and ensure equitable development.

PurposeThe Covid-19 pandemic has profoundly affected urban communities, generating the need for an immediate response from local governance. The availability of urban data platforms in some smart cities helped the relevant actors to develop various solutions in an innovative and highly contextual way. The purpose of this paper is to explore the role of data platforms in smart cities in the context of the Covid-19 crisis.Design/methodology/approachA total of 85 studies were identified using the Clarivate Analytics Web of Science electronic library. After applying exclusion and inclusion criteria, 61 publications were considered appropriate and reasonable for the research, being read in-depth. Finally, only 52 studies presented relevant information for the topic and were synthesized following the defined research questions. During the research, the authors included in the paper other interesting references found in selected articles and important information regarding the role of data in the fight against Covid-19 in smart cities available on the Internet and social media, with the intention to capture both academic and practical perspectives.FindingsThe authors' main conclusion suggests that based on their previous expertise in collecting, processing and analyzing data from multiple sources, some smart cities quickly adapted their data platforms for an efficient response against Covid-19. The results highlight the importance of open data, data sharing, innovative thinking, the collaboration between public and private stakeholders, and the participation of citizens, especially in these difficult times.Practical implicationsThe city managers and data operators can use the presented case studies and findings to identify relevant data-driven smart solutions in the fight against Covid-19 or another crisis.Social implicationsThe performance of smart cities is a social concern since the population of urban communities is continuously growing. By reviewing the adoption of information technologies-based solutions to improve the quality of citizens' life, the paper emphasizes their potential in societies in which information technology is embedded, especially during a major crisis.Originality/valueThis research re-emphasizes the importance of collecting data in smart cities, the role of the diversity of their sources and the necessity of citizens, companies and government synergetic involvement, especially in a pandemic context. The existence of smart solutions to process and extract information and knowledge from large data sets was essential for many actors involved in smart cities, helping them in the decision-making process. Based on previous expertise, some smart cities quickly adapted their data platforms for an efficient response against Covid-19. The paper analyzes also these success cases that can be considered models to be adopted by other municipalities in similar circumstances.

New sensors and devices are being incorporated in modern buildings and cities to facilitate the understanding of its dynamics and improve its efficiency. With this in mind, the combination of information technology and sensors capable of capturing and sharing energy data with other devices can help solve power consumption problems. However, large volumes of these data are collected and stored uninterrupted, becoming a challenge to analyze it to confirm trends, identify hidden patterns, and outliers to aid in decision-making. As an alternative to address the issue, this paper presents an interactive visual analytics system to understand energy data coming from a smart building or city. This system provides ways to analyze the data at different levels of time granularities for the identification of energy trends, patterns, and data outliers. Moreover, it combines different algorithms that allow fulfilling predictive analysis. An analysis with domain experts demonstrates the feasibility and advantages of using the system to monitor energy data.

24 - Current trends and future challenges in smart waste management in smart cities

Smart city data processing is an important taskfor the promotion and development of smart cities. The articledescribes and presents the types of smart city data, discusses theexisting modern methods and approaches to the processing ofsmart city data, such as pre-processing, assessment and analysis,and their tasks. This article contains architectural solutions andmethods used in the developed automated smart city dataevaluation system. There is also a detailed description of theintegration of the developed system with the DriveCloud cloudserver for receiving and storing smart city data.