Smart City Big Data Research Articles

Addressing barriers to big data implementation in sustainable smart cities: Improved zero-sum grey game and grey best-worst method

The optimization of sustainable smart cities is an essential endeavor in modern urban development, aiming to enhance the quality of life for citizens while minimizing environmental impacts. Big data plays a critical role in achieving these goals by enabling the collection, analysis, and utilization of vast amounts of information to make informed decisions. However, implementing big data in smart cities faces significant barriers, including data-sharing challenges, technical limitations, and organizational non-cooperation. Addressing these barriers is crucial for the successful deployment of smart city initiatives. We propose a novel approach to tackle these challenges using the Improved Zero-Sum Grey Game (IZSGG) theory and the Grey Best-Worst Method (G-BWM). This method comprehensively analyzes the risks and uncertainties associated with big data implementation in smart cities. By modeling the interactions between different stakeholders and their competing interests, IZSGG theory provides a framework to identify optimal strategies for data management. The G-BWM further refines these strategies by evaluating and prioritizing the various factors influencing big data utilization. Our findings reveal that the worst-case scenario for a smart city involves the simultaneous occurrence of several risks, all of which have positive values, indicating their potential to significantly disrupt smart city operations. The specific risks identified include: the sharing of data and information, the collection and recording of data, technical limitations and challenges associated with technology, the non-cooperation of organizations, and issues related to the interpretation of complex information. The technical barrier is the most significant with a weight of w(T)=0.6152, indicating its critical role compared to other barriers. Within this category, the sub-barrier of technical and technological constraints is particularly critical, with a weight of 0.39267375.

Edge Perception Temporal Data Anomaly Detection Method Based on BiLSTM-Attention in Smart City Big Data Environment

The improvement of edge perception layer anomaly detection performance has an immeasurable driving effect on the development of smart cities. However, many existing anomaly detection methods often suffer from problems such as ignoring the correlation between multiple source temporal sequences and losing key features of a single temporal sequence. Therefore, a new anomaly detection method using BiLSTM and attention mechanism is proposed. First, a fusion algorithm TCDCD was formed by combining Data Correlation Detection (DCD) and Temporal Continuity Detection (TCD) to preprocess Edge Perception Data (EPD). Then, BiLSTM is employed to gather deep-level features of EPD, and the attention mechanism is utilized to enhance important features that contribute to anomaly detection. Ultimately, the SoftMax classifier is employed to categorize abnormal data. The experimental findings from the SWaT and WADI datasets demonstrate that the suggested method achieves better performance than other newer anomaly detection methods. Among them, the accuracy, precision, recall and F1 of the proposed method on the SWaT dataset were 96.62%, 94.32%, 96.02% and 94.30%, respectively. In terms of performance, it is superior to traditional EPD anomaly detection models, and has good representational and generalization capabilities.

The scheduling techniques in the Hadoop and Spark of smart cities environment: a systematic review

Processing extensive and diverse data in real-time is a significant challenge in the context of smart cities. Timely access to information and efficient analytics is essential for smart city services to make data-driven decisions and enhance urban living. Scheduling algorithms play a crucial role in ensuring the prompt delivery of services and efficient task completion. This paper explores various scheduling techniques, including static, dynamic, and hybrid schedulers, and compares their objectives and performance. Additionally, the study examines two prominent data processing frameworks, Hadoop and Spark, and compares their capabilities in handling big data in smart cities. With its ability to process large amounts of data quickly and efficiently, Spark has shown superiority over Hadoop in real-time data processing and performance optimization. The paper concludes by highlighting the strengths and limitations of each framework. It discusses the need for further research in optimizing scheduling techniques and exploring hybrid artificial intelligence scheduling for Spark. Overall, the findings contribute to a better understanding of data processing in real-time and provide insights for researchers and practitioners in smart cities.

Research trends in the application of big data in smart cities—A literature review

AbstractIn this study, we review articles published in academic journals. in the last 5 years at the intersection of big data and the smart city context. Based on a dataset of 192 articles, we use content analysis to identify recent trends and future research directions. The most influential research areas identified include using big data analytics, electric vehicle integration, and citizen‐centric big data, while the most recent research area that emerged is green space. Our results highlight how big data can influence eight different dimensions of smart cities, with smart mobility attracting the most attention from researchers in the application of big data in smart cities. In contrast, smart people and smart economy are the least explored dimensions from this perspective.

Big Data, Artificial Intelligence and Smart Cities

Cities are gradually relying on significant segments to handle problems with civilization, the environment, topography, and many other topics. This possibility is greatly aided by the burgeoning idea of “Green Infrastructure,” which fosters the integration of monitors and Big Data through the Internet of Things (IoT). In addition to improving employment future, this data explosion also opens up new opportunities for city planning and administration. Machine life big data processing may significantly improve the urban fabric, but ecology and livability factors shouldn’t be ignored in favour of technical ones. To comply with the Sustainability Goa and the New Urban Objectives, this paper analyses the urban AI’s potential and suggests a new feel great Artificial intelligence and metropolises while helping to ensure the incorporation of key facets of Culture, Insulin sensitivity, and Leadership. These aspects are known to be critical for the successful integration of Smart Cities. In order to improve the life quality of the urban fabric and foster job creation and opportunity, this document is directed at government officials, computer scientists, and engineering who are interested in strengthening the convergence of artificial intelligence and big data in smart cities.

The Concept of a Smart City in the Context of Modern Research

It was defined that a smart city is a concept of city development that involves the integration of the newest information and communication technologies in all spheres of its functioning. It was found that the components of a smart city are: smart infrastructure, smart transport, smart energy, smart health care, smart governance, smart economy, smart citizens, smart technologies. The attributes of a smart city are: sustainability, quality of life, urbanization, smartness. It was determined that the main directions of development of a smart city are society, economy, environment, governance. It was found that the main challenges of implementing the concept of a smart city are: the cost of smart city projects, the integration of ICT and physical infrastructure, the amount of data that creates a smart city, operational efficiency, the level of hydrocarbon emissions, achieving sustainability, information security, protection from natural disasters cataclysms. It was found that smart city sustainability is linked to urban infrastructure, governance, energy, climate change, pollution, waste, as well as social, economic and health issues. It was determined that the Internet of Things and big data are the most important elements of implementing the concept of a smart city. Using the Internet of Things makes smart cities possible. The main components of the Internet of Things are: an item, local network, Internet, cloud. Big data is a collection of complex data sets that are difficult to process using conventional database management tools or traditional data processing applications. It was found that the Internet of Things, big data and smart cities are closely related. The data generated by a smart city and associated with spatial and temporal tags form the basis of big data. Big data in smart cities is accumulated as a result of the work of Internet of Things sensors, websites, mobile applications, and social networks.

Blockchain Security Using Merkle Hash Zero Correlation Distinguisher for the IoT in Smart Cities

Internet of Things (IoT) data is one of the most important assets in business models for offering various ubiquitous and brilliant services. The IoT is provided with the advantage of susceptibility that cybercriminals and other malicious users. Even though smart cities are intended to extend productivity and efficiency, residents and authorities face risks when they avoid cybersecurity. The conventional blockchain methods were introduced to ensure the secure management and examination of the smart city big data. But, the blockchains are found to have computationally high costs, and failed to improve the security, not adequate resource-constrained IoT devices have been designated for smart cities. In order to address these issues, the proposed novel blockchain model called blockchain secured Merkle hash zero correlation distinguisher (BSMH-ZCD) is suitable for IoT devices within the cloud infrastructure. The objective of the BSMH-ZCD method is to enhance security and reduce the run time and computational overhead. Initially, the Merkle hash tree is used to create the hash value with every transaction. Next, the zero correlation distinguisher is applied to perform the data encryption and decryption operation for the ARX block for obtaining proficient secure data access in the IoT devices. Experimental assessment of the proposed BSMH-ZCD method and existing methods are carried out by using the taxi driver data set and Novel Corona Virus 2019 data set with different factors, such as running time, computational complexity, and security with respect to a number of blocks and executions. By using the taxi driver data set, the experimental results reveal that the BSMH-ZCD method performs better with a 19% improvement in security, 20% reduction of computational complexity, and 29% faster running time for IoT compared to existing works.

Dynamic Analysis of Multicenter Spatial Structure with Big Data in Smart City

Based on dynamic big data, the multicenter spatial structure of cities is studied, which provides help for scientific planning of urban space and rational use of urban land. Firstly, the research background and significance of this topic is expounded in the Introduction, and then, the related concepts and theories, which lay a theoretical foundation for this research, are summarized. After that, the paper focuses on the design of the scheme of urban multicenter spatial structure and puts forward the method of multicenter identification, method of aggregation feature analysis, and method of spatial structure feature. Finally, the proposed scheme is verified by a case.

A Sustainable Development Neural Network Model for Big Data in Smart Cities

The study aims to prepare a neural network model considering predictor variables such as big data in urban planning, effective governance, resource management, and knowledge based economy for sustainable development in smart cities. A sample of 212 survey responses was collected randomly from Oman and analysed in a neural network model. The predictors’ contributing to sustainable development in smart cities are big data in knowledge based economy (.299), big data in governance (.251), big data in urban planning (.225), and big data in resource management (.224). Overall the neural network model consisting of predictors’ was able to predict 60.0% correctly during training and 72.6% during testing for sustainable development in smart cities.

High Performance Priority Packets Scheduling Mechanism for Big Data in Smart Cities

Today, Internet of Things (IoT) is a technology paradigm which convinces many researchers for the purpose of achieving high performance of packets delivery in IoT applications such as smart cities. Interconnecting various physical devices such as sensors or actuators with the Internet may causes different constraints on the network resources such as packets delivery ratio, energy efficiency, end-to-end delays etc. However, traditional scheduling methodologies in large-scale environments such as big data smart cities cannot meet the requirements for high performance network metrics. In big data smart cities applications which need fast packets transmission ratio such as sending priority packets to hospitals for an emergency case, an efficient scheduling mechanism is mandatory which is the main concern of this paper. In this paper, we overcome the shortcoming issues of the traditional scheduling algorithms that are utilized in big data smart cities emergency applications. Transmission information about the priority packets between the source nodes (i.e., people with emergency cases) and the destination nodes (i.e., hospitals) is performed before sending the packets in order to reserve transmission channels and prepare the sequence of transmission of theses priority packets between the two parties. In our proposed mechanism, Software Defined Networking (SDN) with centralized communication controller will be responsible for determining the scheduling and processing sequences for priority packets in big data smart cities environments. In this paper, we compare between our proposed Priority Packets Deadline First scheduling scheme (PPDF) with existing and traditional scheduling algorithms that can be used in urgent smart cities applications in order to illustrate the outstanding network performance parameters of our scheme such as the average waiting time, packets loss rates, priority packets end-to-end delay, and efficient energy consumption.

What makes a city ‘smart’ in the Anthropocene? A critical review of smart cities under climate change

In recent years, smart cities have grown in popularity, both in research and in practice. The focus of smart city studies and policy initiatives has historically been on technology, particularly how information and communication technology can be leveraged to improve city functions. This focus has begun to shift towards sustainability, with many researchers calling for the development of smart, sustainable cities, which can aid efforts of climate change mitigation and adaptation. The connection between smart cities and climate change, however, is not clear. In this review article, we aim to synthesize the recent literature surrounding smart cities and climate change, and to discuss the benefits (or costs) of smart cities with regard to climate change mitigation and adaptation efforts. In particular, we focus on five key aspects of urban resilience to climate change: infrastructure, public health and well-being, accessibility and equity, sustainable systems, and governance. The literature reveals a higher level of emphasis on infrastructure resilience to climate change than the other categories. Moreover, the research areas differ in the level of connection between smart city initiatives and climate change adaptation and mitigation efforts. For example, within the critical infrastructure research area, studies on smart energy systems focus on climate change mitigation, particularly reducing emissions, while studies on smart water systems emphasize adaptation to future floods and droughts. Going beyond the aforementioned research areas, we discuss the role of big data in smart cities, including the benefits and challenges associated with collecting large amounts of data from smart technology, as well as the techniques needed to analyze such data. Finally, we highlight future directions that we believe the research on smart cities needs to focus on, based on the results from our literature review. These include infrastructure and disaster resilience, public health and social equity, and sustainability.

Security threshold setting algorithm of distributed optical fiber monitoring and sensing system based on big data in smart city

Security threshold setting algorithm of distributed optical fiber monitoring and sensing system based on big data in smart city

Analytics of location-based big data for smart cities: Opportunities, challenges, and future directions

The growing ubiquity of location/activity sensing technologies and location-based services (LBS) has led to a large volume and variety of location-based big data (LocBigData), such as location tracking or sensing data, social media data, and crowdsourced geographic information. The increasing availability of such LocBigData has created unprecedented opportunities for research on urban systems and human environments in general. In this article, we first review the common types of LocBigData: mobile phone network data, GPS data, Location-based social media data, LBS usage/log data, smart card travel data, beacon log data (WiFi or Bluetooth), and camera imagery data. Secondly, we describe the opportunities fueled by LocBigData for the realization of smart cities, mainly via answering questions ranging from “what happened” and “why did it happen” to “what's likely to happen in the future” and “what to do next”. Thirdly, pitfalls of dealing with LocBigData are summarized, such as high volume/velocity/variety; non-random sampling; messy and not clean data; and correlations rather than causal relationships. Finally, we review the state-of-the-art research trends in this field, and conclude the article with a list of open research challenges and a research agenda for LocBigData research to help achieve the vision of smart and sustainable cities.

Leveraging big data in smart cities: A systematic review

AbstractRecently, 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.

Big Data in Smart City: Management Challenges

Smart cities use digital technologies such as cloud computing, Internet of Things, or open data in order to overcome limitations of traditional representation and exchange of geospatial data. This concept ensures a significant increase in the use of data to establish new services that contribute to better sustainable development and monitoring of all phenomena that occur in urban areas. The use of the modern geoinformation technologies, such as sensors for collecting different geospatial and related data, requires adequate storage options for further data analysis. In this paper, we suggest the biG dAta sMart cIty maNagEment SyStem (GAMINESS) that is based on the Apache Spark big data framework. The model of the GAMINESS management system is based on the principles of the big data modeling, which differs greatly from standard databases. This approach provides the ability to store and manage huge amounts of structured, semi-structured, and unstructured data in real time. System performance is increasing to a higher level by using the process parallelization explained through the five V principles of the big data paradigm. The existing solutions based on the five V principles are focused only on the data visualization, not the data themselves. Such solutions are often limited by different storage mechanisms and by the ability to perform complex analyses on large amounts of data with expected performance. The GAMINESS management system overcomes these disadvantages by conversion of smart city data to a big data structure without limitations related to data formats or use standards. The suggested model contains two components: a geospatial component and a sensor component that are based on the CityGML and the SensorThings standards. The developed model has the ability to exchange data regardless of the used standard or the data format into proposed Apache Spark data framework schema. The verification of the proposed model is done within the case study for the part of the city of Novi Sad.

Research on the Construction of Smart Cities by the Big Data Platform of the Blockchain

In the process of rapid development of information technology, to promote the further construction of new smart cities, comprehensive utilization of cloud computing technology, big data technology and Internet of Things technology will help improve the convenience of residents’ lives. The development of blockchain technology provides more possibilities for the construction of new smart cities. The use of blockchain and other new technologies to build a smart city big data platform can realize a point-to-point communication mechanism. At the same time, this can also establish a more reliable trust agreement. It is conducive to the realization of applications such as smart IoT, government affairs, and big data. Using the blockchain big data platform to build a new type of smart city can break information islands and achieve the purpose of data sharing and information security. This is a key issue that must be paid attention to in the current construction of new smart cities. In this link, it is necessary to strengthen the research and analysis of blockchain technology to understand the problems that exist in the process of building a smart city on a big data platform. This article discusses the application advantages and related strategies of a smart city big data platform based on blockchain technology.

IoT and Big Data Applications in Smart Cities: Recent Advances, Challenges, and Critical Issues

The notion of smart cities has remained under evolution as its global implementations are challenged by numerous technological, economic, and governmental obstacles. Moreover, the synergy of the Internet of Things (IoT) and big data technologies could result in promising horizons in terms of smart city development which has not been explored yet. Thus, the current research aims to address the essence of smart cities. To this end, first, the concept of smart cities is briefly overviewed; then, their properties and specifications as well as generic architecture, compositions, and real-world implementations are addressed. Furthermore, possible challenges and opportunities in the field of smart cities are described. Numerous issues and challenges such as analytics and using big data in smart cities introduced in this study offers an enhancement in developing applications of the above-mentioned technologies. Hence, this study paves the way for future research on the issues and challenges of big data applications in smart cities.

Deep learning-based urban big data fusion in smart cities: Towards traffic monitoring and flow-preserving fusion

ObjectiveIn the last few years, several techniques and models are used for retrieving significant information from urban big data of smart cities. This research work aims at developing a data fusion-based traffic congestion control system in smart cities using a deep learning model. MethodologyA hybrid model based on the convolution neural network (CNN) and long short term memory (LSTM) architectures are used for region-based traffic flow predictions in smart cities. CNN is used for the classification of spatial data while LSTM for temporal data. ConclusionThe experiments used the CityPulse Traffic and CityPulse Pollution datasets, and measured root mean square error (RMSE), time consumption and accuracy. A small RMSE value of 49 and highest accuracy of 92.3% compared to other baseline models depicts the applicability of the proposed model in the region-based traffic flow prediction problems in the smart cities.

Using a distributed deep learning algorithm for analyzing big data 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.

AKILLI KENTLERDE VERİNİN GİZLİLİĞİ VE GÜVENLİĞİ: İLKELER VE YAKLAŞIMLAR

Innovations emerging in the dynamic environment of technological developments lead to new situations in social and economic fields. Cities are one of the important areas where these developments are reflected. Thus, smart cities and big data concepts come up and these developments cause changes in services provided in cities. However, these developments bring along some negativities, as well. The main purpose of this study is to examine the approaches and principles that have come to fore in order to ensure the privacy and security of big data in smart cities. Basically, the answer to this question is sought: what principles, approaches and practices come to the forefront in order to ensure the confidentiality and security of big data that is an important part of smart cities? The answer is sought through the relevant literature, legal regulations and international decisions. According to the general results of the study, it is understood that there are not any consensual standards on data security at global level. However, some regulations draw attention. At this point, the “General Data Protection Regulation (GDPR)” which came into force in 2018 is important. However, some principles and approaches are gaining importance. At the end of the study, some suggestions about policies are made according to the results obtained