Leveraging big data in smart cities: A systematic review

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.

[1]  Tao Wang,et al.  Analysis of urban environmental problems based on big data from the urban municipal supervision and management information system , 2016, Ecological Indicators.

[2]  Yu Zheng,et al.  U-Air: when urban air quality inference meets big data , 2013, KDD.

[3]  M. H. Kashani,et al.  PARALLEL MIN-MAX ANT COLONY SYSTEM ( MMAS ) FOR DYNAMIC PROCESS SCHEDULING IN DISTRIBUTED OPERATING SYSTEMS CONSIDERING LOAD BALANCING , 2007 .

[4]  R. Kalpana,et al.  Optimized traffic control and data processing using IoT , 2018, Cluster Computing.

[5]  Jordan Frith,et al.  Big Data, Technical Communication, and the Smart City , 2017 .

[6]  Zhiyuan Tan,et al.  Urban data management system: Towards Big Data analytics for Internet of Things based smart urban environment using customized Hadoop , 2019, Future Gener. Comput. Syst..

[7]  Jian Zhang,et al.  Research on the application of block chain big data platform in the construction of new smart city for low carbon emission and green environment , 2020, Comput. Commun..

[8]  Towards effective offloading mechanisms in fog computing , 2021, Multim. Tools Appl..

[9]  Baoming Han,et al.  A Review of Big Data Applications in Urban Transit Systems , 2021, IEEE Transactions on Intelligent Transportation Systems.

[10]  Pietro Manzoni,et al.  Evolutionary gaming approach for decision making of Tier‐3 Internet service provider networks migration to SoDIP6 networks , 2020, Int. J. Commun. Syst..

[11]  Noise and the city: Leveraging crowdsourced big data to examine the spatio-temporal relationship between urban development and noise annoyance , 2019, Environment and Planning B: Urban Analytics and City Science.

[12]  Fu Gu,et al.  Internet of things and Big Data as potential solutions to the problems in waste electrical and electronic equipment management: An exploratory study. , 2017, Waste management.

[13]  Houman Zarrabi,et al.  A new metaheuristic approach to task assignment problem in distributed systems , 2017, 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI).

[14]  S. Giest.  Big data analytics for mitigating carbon emissions in smart cities: opportunities and challenges , 2017 .

[15]  Carl Marcus Wallenburg,et al.  Dealing with supply chain risks , 2012 .

[16]  Nitin Auluck,et al.  Load balancing aware scheduling algorithms for fog networks , 2020, Softw. Pract. Exp..

[17]  S. Chauhan,et al.  Addressing big data challenges in smart cities: a systematic literature review , 2016 .

[18]  Nima Jafari Navimipour,et al.  Quality of service‐aware approaches in fog computing , 2020, Int. J. Commun. Syst..

[19]  Awais Ahmad,et al.  Urban planning and building smart cities based on the Internet of Things using Big Data analytics , 2016, Comput. Networks.

[20]  Nathalie Japkowicz,et al.  Big Data Analysis: New Algorithms for a New Society , 2015 .

[21]  Enrico Capobianco,et al.  Smart Cities, Big Data, and Communities: Reasoning From the Viewpoint of Attractors , 2016, IEEE Access.

[22]  Michael Batty,et al.  Planning support systems and the new logic of computation , 1995 .

[23]  Awais Ahmad,et al.  Real-time secure communication for Smart City in high-speed Big Data environment , 2017, Future Gener. Comput. Syst..

[24]  J. Manyika.  Big data: The next frontier for innovation, competition, and productivity , 2011 .

[25]  Miguel Macias Macias,et al.  Analysis of the factors generating vehicular traffic in the city of Quito and its relation to the application of sensorial and social data with big data as a basis for decision making , 2016, 2016 Third International Conference on eDemocracy & eGovernment (ICEDEG).

[26]  P. Maglio,et al.  Smart cities with big data: Reference models, challenges, and considerations , 2018, Cities.

[27]  Nima Jafari Navimipour,et al.  MapReduce and Its Applications, Challenges, and Architecture: a Comprehensive Review and Directions for Future Research , 2017, Journal of Grid Computing.

[28]  Alex Pentland,et al.  Big Data and Management , 2014 .

[29]  Yuan Yao,et al.  Big data in smart cities , 2015, Science China Information Sciences.

[30]  Avita Katal,et al.  Big data: Issues, challenges, tools and Good practices , 2013, 2013 Sixth International Conference on Contemporary Computing (IC3).

[31]  Terry L. Cooper,et al.  Citizen-Centered Collaborative Public Management , 2006 .

[32]  Antonio F. Gómez-Skarmeta,et al.  Smart Lighting Solutions for Smart Cities , 2013, 2013 27th International Conference on Advanced Information Networking and Applications Workshops.

[33]  Ebrahim Mahdipour,et al.  Twitter Spam Detection: A Systematic Review , 2020, ArXiv.

[34]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[35]  Luning Liu,et al.  Citizen-centered big data analysis-driven governance intelligence framework for smart cities , 2018, Telecommunications Policy.

[36]  Nazareno Andrade,et al.  An Integrated Big and Fast Data Analytics Platform for Smart Urban Transportation Management , 2019, IEEE Access.

[37]  Yan Song,et al.  The impact of urban growth patterns on urban vitality in newly built-up areas based on an association rules analysis using geographical ‘big data’ , 2018, Land Use Policy.

[38]  Dirk Draheim,et al.  Towards Disaster Resilient Smart Cities: Can Internet of Things and Big Data Analytics Be the Game Changers? , 2019, IEEE Access.

[39]  Dedao Gu,et al.  Urban Big Data and the Development of City Intelligence , 2016 .

[40]  Peter Nijkamp,et al.  Big data dashboards as smart decision support tools for i-cities – An experiment on stockholm , 2018 .

[41]  Athanasios V. Vasilakos,et al.  Exploring Data Validity in Transportation Systems for Smart Cities , 2017, IEEE Communications Magazine.

[42]  Pearl Brereton,et al.  Systematic literature reviews in software engineering - A systematic literature review , 2009, Inf. Softw. Technol..

[43]  Ernesto Damiani,et al.  Privacy-aware Big Data Analytics as a service for public health policies in smart cities , 2018 .

[44]  Giancarlo Fortino,et al.  A Mobility-Aware Optimal Resource Allocation Architecture for Big Data Task Execution on Mobile Cloud in Smart Cities , 2018, IEEE Communications Magazine.

[45]  Marimuthu Palaniswami,et al.  Fast and Scalable Big Data Trajectory Clustering for Understanding Urban Mobility , 2018, IEEE Transactions on Intelligent Transportation Systems.

[46]  Zhaoquan Gu,et al.  Automatic Non-Taxonomic Relation Extraction from Big Data in Smart City , 2018, IEEE Access.

[47]  Carl Marcus Wallenburg,et al.  Dealing with supply chain risks Linking risk management practices and strategies to performance , 2017 .

[48]  Euripidis Loukis,et al.  An Ontology for G2G Collaboration in Public Policy Making, Implementation and Evaluation , 2007, Artificial Intelligence and Law.

[49]  Weijun Yang,et al.  A Distributed Collaborative Urban Traffic Big Data System Based on Cloud Computing , 2019, IEEE Intelligent Transportation Systems Magazine.

[50]  Vahideh Hayyolalam,et al.  A systematic literature review on QoS-aware service composition and selection in cloud environment , 2018, J. Netw. Comput. Appl..

[51]  Ebrahim Mahdipour,et al.  Correction to: Big Data Analytics in Weather Forecasting: A Systematic Review , 2021, Archives of Computational Methods in Engineering.

[52]  Joseph Amankwah-Amoah,et al.  Safety or no safety in numbers? Governments, big data and public policy formulation , 2015, Ind. Manag. Data Syst..

[53]  Ahmed Ghenabzia,et al.  An intelligent system for energy management in smart cities based on big data and ontology , 2020 .

[54]  D. Boyd,et al.  CRITICAL QUESTIONS FOR BIG DATA , 2012 .

[55]  Edwin H.W. Chan,et al.  A framework to simplify pre-processing location-based social media big data for sustainable urban planning and management , 2021 .

[56]  Yang Xiao,et al.  Real-time identification of urban rainstorm waterlogging disasters based on Weibo big data , 2018, Natural Hazards.

[57]  Luis Muñoz,et al.  Business model analysis of public services operating in the smart city ecosystem: The case of SmartSantander , 2017, Future Gener. Comput. Syst..

[58]  Wei Ni,et al.  Big Data Analytics and Network Calculus Enabling Intelligent Management of Autonomous Vehicles in a Smart City , 2019, IEEE Internet of Things Journal.

[59]  Shunjiang Ni,et al.  Crime risk analysis through big data algorithm with urban metrics , 2020 .

[60]  Majid Hussain,et al.  A methodology for real-time data sustainability in smart city: Towards inferencing and analytics for big-data , 2017 .

[61]  Neeraj Kumar,et al.  A unified framework for big data acquisition, storage, and analytics for demand response management in smart cities , 2020, Future Gener. Comput. Syst..

[62]  Jia Liu,et al.  Urban big data fusion based on deep learning: An overview , 2020, Inf. Fusion.

[63]  Nader Mohamed,et al.  Collaborative data analytics for smart buildings: opportunities and models , 2019, Cluster Computing.

[64]  Wadii Boulila,et al.  Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions , 2020, Comput. Sci. Rev..

[65]  Fu-sheng Zhang,et al.  The Smart City Ecological Construction Based on Big Data in FIOT Operation Model , 2019, DEStech Transactions on Social Science, Education and Human Science.

[66]  K. Pfeffer,et al.  Big Data for Better Urban Life? – An Exploratory Study of Critical Urban Issues in Two Caribbean Cities: Paramaribo (Suriname) and Port of Spain (Trinidad and Tobago) , 2015, The European Journal of Development Research.

[67]  Houbing Song,et al.  Internet of Things and Big Data Analytics for Smart and Connected Communities , 2016, IEEE Access.

[68]  Xin-She Yang,et al.  Bat algorithm: literature review and applications , 2013, Int. J. Bio Inspired Comput..

[69]  Wei Li,et al.  Prediction of Passenger Flow in Urban Rail Transit Based on Big Data Analysis and Deep Learning , 2019, IEEE Access.

[70]  M. H. Kashani,et al.  A GENETIC ALGORITHM FOR PROCESS SCHEDULING IN DISTRIBUTED OPERATING SYSTEMS CONSIDERING LOAD BALANCING , 2007 .

[71]  Divyakant Agrawal,et al.  Big data and cloud computing: current state and future opportunities , 2011, EDBT/ICDT '11.

[72]  Surya Nepal,et al.  Scheduling Real-Time Security Aware Tasks in Fog Networks , 2019, IEEE Transactions on Services Computing.

[73]  Raheleh Sarvizadeh,et al.  A swarm intelligence based memetic algorithm for task allocation in distributed systems , 2012, International Conference on Machine Vision.

[74]  Tomás Soukup,et al.  Digital world meets urban planet – new prospects for evidence-based urban studies arising from joint exploitation of big earth data, information technology and shared knowledge , 2018, Int. J. Digit. Earth.

[75]  Mostafa Haghi Kashani,et al.  A Novel Bee Colony Approach to Distributed Systems Scheduling , 2012 .

[76]  Ross Maciejewski,et al.  Urban form and composition of street canyons: A human-centric big data and deep learning approach , 2019, Landscape and Urban Planning.

[77]  Weiming Tian,et al.  Impacts of ionospheric scintillation on geosynchronous SAR focusing: preliminary experiments and analysis , 2015, Science China Information Sciences.

[78]  Victor C. M. Leung,et al.  Software-Defined Networks with Mobile Edge Computing and Caching for Smart Cities: A Big Data Deep Reinforcement Learning Approach , 2017, IEEE Communications Magazine.

[79]  Amir Masoud Rahmani,et al.  Systematic survey of big data and data mining in internet of things , 2018, Comput. Networks.

[80]  Mattias Höjer,et al.  Smart Sustainable Cities: Definition and Challenges , 2015, ICT Innovations for Sustainability.

[81]  Nima Jafari Navimipour,et al.  Big data handling mechanisms in the healthcare applications: A comprehensive and systematic literature review , 2018, J. Biomed. Informatics.

[82]  Wolfgang Kastner,et al.  Communication systems for building automation and control , 2005, Proceedings of the IEEE.

[83]  Zhihan Lv,et al.  Virtual Reality Smart City Based on WebVRGIS , 2016, IEEE Internet of Things Journal.

[84]  Hadi Nasrabadi,et al.  Well placement optimization using imperialist competitive algorithm , 2016 .

[85]  Mohak Shah.  Big Data and the Internet of Things , 2015, ArXiv.

[86]  Michael Luca,et al.  Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life , 2015 .

[87]  Ebrahim Mahdipour,et al.  A systematic review of IoT in healthcare: Applications, techniques, and trends , 2021, J. Netw. Comput. Appl..

[88]  A. Rezaee Jordehi.  A chaotic artificial immune system optimisation algorithm for solving global continuous optimisation problems , 2014, Neural Computing and Applications.

[89]  Xiangjian He,et al.  A Survey on Big Multimedia Data Processing and Management in Smart Cities , 2019, ACM Comput. Surv..

[90]  Hafiz Mahfooz Ul Haque,et al.  A context‐aware framework for modelling and verification of smart parking systems in urban cities , 2019, Concurr. Comput. Pract. Exp..

[91]  Zhanyu Liu.  Research on the Internet of Things and the development of smart city industry based on big data , 2017, Cluster Computing.

[92]  Francesco Archetti,et al.  Smart water in urban distribution networks: limited financial capacity and Big Data analytics , 2014 .

[93]  Simon Elias Bibri,et al.  The IoT for smart sustainable cities of the future: An analytical framework for sensor-based big data applications for environmental sustainability , 2018 .

[94]  Christopher B. Kuhn,et al.  Introspective Failure Prediction for Autonomous Driving Using Late Fusion of State and Camera Information , 2020, IEEE Transactions on Intelligent Transportation Systems.

[95]  Ebrahim Mahdipour,et al.  Load balancing mechanisms in fog computing: A systematic review , 2020, ArXiv.

[96]  M. Akbari,et al.  Utilizing Bee Colony to Solve Task Scheduling Problem in Distributed Systems , 2011, 2011 Third International Conference on Computational Intelligence, Communication Systems and Networks.

[97]  Xu Li,et al.  Performance Evaluation of Vehicle-Based Mobile Sensor Networks for Traffic Monitoring , 2009, IEEE Transactions on Vehicular Technology.

[98]  Mehdi Hosseinzadeh,et al.  Load Balancing Mechanisms in the Software Defined Networks: A Systematic and Comprehensive Review of the Literature , 2018, IEEE Access.

[99]  Andrea Zanella,et al.  Internet of Things for Smart Cities , 2014, IEEE Internet of Things Journal.

[100]  Bo Tang,et al.  Incorporating Intelligence in Fog Computing for Big Data Analysis in Smart Cities , 2017, IEEE Transactions on Industrial Informatics.

[101]  Nima Jafari Navimipour,et al.  Toward Efficient Service Composition Techniques in the Internet of Things , 2018, IEEE Internet of Things Journal.

[102]  Ken Cai,et al.  Big Data Analysis Technology for Electric Vehicle Networks in Smart Cities , 2020, IEEE Transactions on Intelligent Transportation Systems.

[103]  Murad Khan,et al.  Integration of Big Data analytics embedded smart city architecture with RESTful web of things for efficient service provision and energy management , 2017, Future Gener. Comput. Syst..

[104]  M. Angelidou.  Smart cities: A conjuncture of four forces , 2015 .

[105]  Gang Xiong,et al.  A Multi-Stream Feature Fusion Approach for Traffic Prediction , 2022, IEEE Transactions on Intelligent Transportation Systems.

[106]  Mahdi Jameii,et al.  A new distributed systems scheduling algorithm: a swarm intelligence approach , 2011, Other Conferences.

[107]  Wenbin Hu,et al.  IQGA: A route selection method based on quantum genetic algorithm- toward urban traffic management under big data environment , 2018, World Wide Web.

[108]  Zhifeng Liu,et al.  A systematic review of big data-based urban sustainability research: State-of-the-science and future directions , 2020 .

[109]  Mimi Sheller,et al.  Mobile Publics: Beyond the Network Perspective , 2004 .

[110]  Mostafa Haghi Kashani,et al.  Fog Computing Approaches in Smart Cities: A State-of-the-Art Review , 2020, ArXiv.

[111]  Ala I. Al-Fuqaha,et al.  Enabling Cognitive Smart Cities Using Big Data and Machine Learning: Approaches and Challenges , 2018, IEEE Communications Magazine.

[112]  Franco Cicirelli,et al.  An edge-based platform for dynamic Smart City applications , 2017, Future Gener. Comput. Syst..

[113]  Wenjie Wu,et al.  The Geography of Cultural Ties and Human Mobility: Big Data in Urban Contexts , 2016 .

[114]  Jerome P. Lynch,et al.  A summary review of wireless sensors and sensor networks for structural health monitoring , 2006 .

[115]  Ate Poorthuis,et al.  How to Draw a Neighborhood? The Potential of Big Data, Regionalization, and Community Detection for Understanding the Heterogeneous Nature of Urban Neighborhoods , 2018 .

[116]  Nima Jafari Navimipour,et al.  A taxonomy of software-based and hardware-based approaches for energy efficiency management in the Hadoop , 2019, J. Netw. Comput. Appl..

[117]  M. H. Kashani,et al.  A New Method Based on Memetic Algorithm for Task Scheduling in Distributed Systems , 2010 .

[118]  Kartik Shankar,et al.  An effect of big data technology with ant colony optimization based routing in vehicular ad hoc networks: Towards smart cities , 2019, Journal of Cleaner Production.

[119]  Rafal Dlugosz,et al.  New technologies for smart cities – high‐resolution air pollution maps based on intelligent sensors , 2020, Concurr. Comput. Pract. Exp..

[120]  Xiangfeng Luo,et al.  Crowdsourcing Based Description of Urban Emergency Events Using Social Media Big Data , 2020, IEEE Transactions on Cloud Computing.

[121]  M. Shamim Hossain,et al.  Urban Healthcare Big Data System Based on Crowdsourced and Cloud-Based Air Quality Indicators , 2018, IEEE Communications Magazine.

[122]  Simon Elias Bibri,et al.  Smart sustainable cities of the future: An extensive interdisciplinary literature review , 2017 .

[123]  Nima Jafari Navimipour,et al.  Comprehensive and systematic review of the service composition mechanisms in the cloud environments , 2017, J. Netw. Comput. Appl..

[124]  Michael Batty,et al.  Big data, smart cities and city planning , 2013, Dialogues in human geography.

[125]  Murad Khan,et al.  Big Data Processing using Internet of Software Defined Things in Smart Cities , 2018, International Journal of Parallel Programming.

[126]  Nanning Zheng,et al.  Guest Editorial Special Issue on IoT on the Move: Enabling Technologies and Driving Applications for Internet of Intelligent Vehicles (IoIV) , 2019, IEEE Internet Things J..

[127]  A-Xing Zhu,et al.  Estimation of Travel Time Based on Ensemble Method With Multi-Modality Perspective Urban Big Data , 2020, IEEE Access.

[128]  Ebrahim Mahdipour,et al.  Big data analytics meets social media: A systematic review of techniques, open issues, and future directions , 2020, Telematics and Informatics.

[129]  Mohsen Jahanshahi,et al.  Using Simulated Annealing for Task Scheduling in Distributed Systems , 2009, 2009 International Conference on Computational Intelligence, Modelling and Simulation.

[130]  Xavier Vilajosana,et al.  Bootstrapping smart cities through a self-sustainable model based on big data flows , 2013, IEEE Communications Magazine.

[131]  M. H. Kashani,et al.  A novel method for task scheduling in distributed systems using Max-Min Ant Colony Optimization , 2011, 2011 3rd International Conference on Advanced Computer Control.

[132]  Marta C. González,et al.  Big Data Fusion to Estimate Urban Fuel Consumption: A Case Study of Riyadh , 2017, ArXiv.

[133]  Qi Shi,et al.  Big Data applications in real-time traffic operation and safety monitoring and improvement on urban expressways , 2015 .

[134]  Wei Dong,et al.  Mosaic: A low-cost mobile sensing system for urban air quality monitoring , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[135]  Devin K. Harris,et al.  A big data analytics strategy for scalable urban infrastructure condition assessment using semi-supervised multi-transform self-training , 2020 .

[136]  Biagio F. Giannetti,et al.  Emergy-based comparative analysis of urban metabolic efficiency and sustainability in the case of big and data scarce medium-sized cities: A case study for Jing-Jin-Ji region (China) , 2018, Journal of Cleaner Production.

[137]  Amir Masoud Rahmani,et al.  Service composition approaches in IoT: A systematic review , 2018, J. Netw. Comput. Appl..

[138]  Arif Ur Rahman,et al.  SMART TSS: Defining transportation system behavior using big data analytics in smart cities , 2018, Sustainable Cities and Society.

[139]  Dominique Genoud,et al.  Internet of Things for Cultural Heritage of Smart Cities and Smart Regions , 2015, 2015 IEEE 29th International Conference on Advanced Information Networking and Applications Workshops.

[140]  Zhihan Lv,et al.  Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics , 2017, IEEE Transactions on Industrial Informatics.

[141]  Harri Niska,et al.  Improved route planning and scheduling of waste collection and transport , 2006, Expert Syst. Appl..

[142]  Harry Haoxiang Wang,et al.  Visualization model of big data based on self-organizing feature map neural network and graphic theory for smart cities , 2018, Cluster Computing.

[143]  R. Kitchin,et al.  The real-time city? Big data and smart urbanism , 2013, GeoJournal.

[144]  Ebrahim Mahdipour,et al.  A hybrid classification method for Twitter spam detection based on differential evolution and random forest , 2021, Concurr. Comput. Pract. Exp..

[145]  Urvinder Singh,et al.  Modified Grey Wolf Optimizer for Global Engineering Optimization , 2016, Appl. Comput. Intell. Soft Comput..

[146]  Anna Corinna Cagliano,et al.  Current trends in Smart City initiatives: some stylised facts , 2014 .

[147]  Akramul Azim,et al.  Improving the Schedulability of Real-Time Tasks Using Fog Computing , 2019, IEEE Transactions on Services Computing.

[148]  Simon Elias Bibri,et al.  The core enabling technologies of big data analytics and context-aware computing for smart sustainable cities: a review and synthesis , 2017, Journal of Big Data.

[149]  David Edgar,et al.  Big data and smart cities: a public sector organizational learning perspective , 2018, Inf. Syst. E Bus. Manag..

[150]  Mostafa Haghi Kashani,et al.  Fog-based smart homes: A systematic review , 2020, J. Netw. Comput. Appl..