Addressing big data challenges in smart cities: a systematic literature review

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.

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.

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.

Smart cities are constantly faced with the generated data resources. To effectively manage and utilize the big city data, data vitalization technology is proposed. Considering the complex and diverse relationships among the big data, data correlation is very important for data vitalization. This paper presents a framework for data correlation and depicts the discovery, representation and growth of data correlation. In particular, this paper proposes an innovative representation of data correlation, namely the data correlation diagram. Based on the basic and the multi-stage data relations, we optimize the data correlation diagrams according to the transitive rules. We also design dynamic data diagrams to support data and relation changes, reducing the response time to data changes and enabling the autonomous growth of the vitalized data and the relations. Finally an instance of smart behaviors is introduced which verifies the feasibility and efficiency of the data relation diagram.

The notion of smart cities is inherently connected with the notion of Big Data. It is Big Data that allows more and more intelligence to be added to our existing urban systems. This intelligence th ...

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

Since there is no uniform specification for building address name in China, the same building address maybe has many different representations in Chinese natural language. The goal of the non-standard Chinese building address standardization task is to uniformly convert the non-standard building addresses from different social institutions to the standard building address defined by the public security organ, so that the spatial location information corresponding to the standard building address can be obtained. This plays an important role in the analysis and processing of big data in smart cities. Due to the large number of non-standard building addresses and the semantic ambiguity of addresses expressed in Chinese natural language, traditional methods based on string matching are difficult to meet the task requirements. To address these above problems, we propose an innovative joint learning approach based on hash map principle and word frequency theory for standardizing Chinese non-standard building addresses. Experimental results on the dataset constructed via crowdsourced technology show that approach has outstanding accuracy and adaptability to data from different sources.

Several countries are planning to adopt the idea of smart city in their towns and enforcing large-scale data projects that promotes smart city features to achieve the recommended level of sustainable development to enhance the quality of life. The smart city tends to mean an advanced technological area that would be capable of understanding the environment by examining the data so it can improve the livability. The smart cities allow different kinds of wireless sensors to gather massive amounts, full speed and a broad range of city data allow the use of big data technology to learn important observations to control resources, services, and infrastructure effectively. The smart cities use numerous innovations to increase the efficiency of fitness, travel, resources, education and public infrastructure, resulting in a higher comfort level for their residents. Big data analytics is one of the most recent technologies with great potential to improve smart city facilities. Big data is a huge amount of data collected from different sensors, smart objects or smart devices connected with the Internet of Things (IoT). Smart cities allow regional officials to communicate directly to both society and city services as well as to track what might be happening in the city or how the city is progressing. This chapter presents a comprehensive discussion on the fundamental of data analytics, challenges, and a case study of NEOM smart city in Saudi Arabia.

An urgent want remains for cities to get smarter, as to handle huge-scale urbanization and finding new methods to manipulate complexity, increase efficiency and improve excellence of lifestyles. With the urbanization progress, urban control is going through a chain of evocations within the new state of affairs. Smart city which is a modern form of municipal construction, flourished gradually in quick development scheme of a new intelligence technology. For the construction and betterment of smart cities, big data technology serves important support. Therefore, by reviewing forty papers, this research represents the characteristics of smart city as well as villages, analyzes the solicitation of big data technologies in smart city or smart village design and shows the findings which could be used by the researchers to do further research.KeywordsBig DataInternet of ThingsSmart citySmart villageSmart sensorsCloud computingNetworks

Scholars are becoming increasingly interested in whether and how government use of Big Data will affect public policy outcomes. Despite such growing scholarly interests, however, little evidence exists on the role Big Data can play in improving government service. We undertake one of the first quantitative studies revealing the potential utility and limitations of “Big Data-based policymaking” by exploring its recent use by the Seoul Metropolitan Government. In 2013, the government introduced the “Owl Bus”—a late-night bus system—the routes of which were selected based on government analyses of Big Data relating to citizens’ late-night taxi and mobile phone use. The findings suggest that the average number of passengers utilizing the Owl Bus routes was significantly greater than that on other daytime bus routes with comparable characteristics. That said, we also present the potential limitations of evidence-based policymaking in general, especially when politics and equity considerations are factored in.

Research on the application of block chain big data platform in the construction of new smart city for low carbon emission and green environment

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.

In this work, a solution for the distributed storage of data in Smart Cities is presented. An Edge-Fog-Cloud architecture that partitions the data through the Sharding technique proposes a hierarchical model that manipulates IoT data generated by Smart Cities. The problem is related to the approaches used to promote an integrated environment. Related works tend to use cloud-focused approaches that generate high latency rates, and those that use Fog Computing only use the layer as middleware, not exploring greater possibilities for use. In this context, this work presents the DASTData model that aims to enable lower latency rates, more data security, fault tolerance, high availability, and concurrent queries to promote a better experience in data management and availability in smart cities. In addition, our contribution to the literature is related to the proposition of an architecture focused on enabling the traceability of users who have mobile behavior in the city, providing the ability to analyze patterns and occurrences through the consolidation of data from one or more individuals. In the results obtained through the tests carried out in this work, we observed that in queries DASTData is up to 73% more efficient.

The smart cities concept is strongly related to energy efficiency and low carbon patterns, what that refers to meeting the sustainability criteria established by regulatory mechanisms. The Smart City Index (SCI) data can reach a higher quality of living by observing aspects as mobility, health, safety, transparency, smart management, and many others, like the waste management. Thus, this set of indicators was used to evaluate the e-waste management as an emergent challenge in urban cities, providing both, the generation of waste from the intense consumption of electronic devices, as well meeting the questions arising from urbanization from the integration of digital solutions. This study proposes a model of analysis using simple linear regression and k-mean clustering as correlation methods. The findings suggested correlation between the SCI ranking, the E-waste generated, and the economic situation of the entire set of smart cities. Nevertheless, it was found weak correlation for the best rated cities. The results provided the perception that it is possible to identify correlation from the Smart City Index and aspects from the e-waste management system, enabling the discussion over the importance of next smart city indexes and given a new perspective to the e-waste management.