Integration of data science with the intelligent IoT (IIoT): Current challenges and future perspectives

Abstract

The Intelligent Internet of Things (IIoT) involves real-world things that communicate or interact with each other through networking technologies by collecting data from these “things” and using intelligent approaches, such as Artificial Intelligence (AI) and machine learning, to make accurate decisions. Data science is the science of dealing with data and its relationships through intelligent approaches. Most state-of-the-art research focuses independently on either data science or IIoT, rather than exploring their integration. Therefore, to address the gap, this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT (IIoT) system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics. The paper analyzes the data science or big data security and privacy features, including network architecture, data protection, and continuous monitoring of data, which face challenges in various IoT-based systems. Extensive insights into IoT data security, privacy, and challenges are visualized in the context of data science for IoT. In addition, this study reveals the current opportunities to enhance data science and IoT market development. The current gap and challenges faced in the integration of data science and IoT are comprehensively presented, followed by the future outlook and possible solutions.

Keywords

Data science / Internet of things (IoT) / Big data / Communication systems / Networks / Security / Data science analytics

CRediT authorship contribution statement

Inam Ullah: Writing - original draft, Validation, Software, Methodology, Data curation, Conceptualization. Deepak Adhikari: Writing - review & editing, Investigation, Data curation. Xin Su: Validation, Formal analysis. Francesco Palmieri: Formal analysis, Validation, Resources. Celimuge Wu: Visualization, Validation, Resources. Chang Choi: Visualization, Validation, Supervision, Project administration, Funding acquisition.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

This work was supported in part by the National Natural Science Foundation of China under Grant 62371181, and in part by the Changzhou Science and Technology International Cooperation Program under Grant CZ20230029.

This work was also supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2021R1A2B5B02087169). This work was also supported under the framework of international cooperation program managed by the National Research Foundation of Korea (2022K2A9A1A01098051).

References

[1] M.N. Bhuiyan, M.M. Rahman, M.M. Billah, D. Saha, Internet of Things (IoT): a review of its enabling technologies in healthcare applications, standards protocols, security, and market opportunities, IEEE Int. Things J. 8 (13) (2021) 10474-10498.

[2] K. Rose, S. Eldridge, L. Chapin, The internet of things: an overview, The Internet So-ciety (ISOC) 80 (15) (2015) 1-53, https://www.mckinsey.com/mgi/overview/in-the-news/by-2025-internet-of-things-applications-could-have-11-trillion-impact.

[3] L. Guo, M. Dong, K. Ota, Q. Li, T. Ye, J. Wu, J. Li, A secure mechanism for big data collection in large scale Internet of vehicle, IEEE Int. Things J. 4 (2) (2017) 601-610.

[4] M. Mohammadi, A. Al-Fuqaha, S. Sorour, M. Guizani, Deep learning for IoT big data and streaming analytics: a survey, IEEE Commun. Surv. Tutor. 20 (4) (2018) 2923-2960.

[5] L. Zhang, P. Wang, Y. Zhang, Z. Chi, N. Tong, L. Wang, F. Li, An adaptive and robust secret key extraction scheme from high noise wireless channel in IIoT, Digit. Commun. Netw. 9 (4) (2023) 809-816.

[6] D. Adhikari, W. Jiang, J. Zhan, Z. He, D.B. Rawat, U. Aickelin, H.A. Khorshidi, A comprehensive survey on imputation of missing data in Internet of Things, ACM Comput. Surv. 55 (7) (2022) 1-38.

[7] D. Adhikari, W. Jiang, J. Zhan, M. Assefa, H.A. Khorshidi, U. Aickelin, D.B. Rawat, A lightweight window portion-based multiple imputation for extreme missing gaps in IoT systems, IEEE Int. Things J. 11 (3) (2024) 3676-3689.

[8] I.H. Sarker, Data science and analytics: an overview from data-driven smart com-puting, decision-making and applications perspective, SN Comput. Sci. 2 (5) (2021) 377.

[9] S.B. Atitallah, M. Driss, W. Boulila, H.B. Ghézala, Leveraging deep learning and IoT big data analytics to support the smart cities development: review and future directions, Comput. Sci. Rev. 38 (2020) 100303.

[10] E. Siow, T. Tiropanis, W. Hall, Analytics for the Internet of Things: a survey, ACM Comput. Surv. 51 (4) (2018) 1-36.

[11] Y. Sasaki, A survey on IoT big data analytic systems: current and future, IEEE Int. Things J. 9 (2) (2021) 1024-1036.

[12] C.-W. Tsai, C.-F. Lai, M.-C. Chiang, L.T. Yang, Data mining for Internet of Things: a survey, IEEE Commun. Surv. Tutor. 16 (1) (2013) 77-97.

[13] S. Verma, Y. Kawamoto, Z.M. Fadlullah, H. Nishiyama, N. Kato, A survey on net-work methodologies for real-time analytics of massive IoT data and open research issues, IEEE Commun. Surv. Tutor. 19 (3) (2017) 1457-1477.

[14] A. Daissaoui, A. Boulmakoul, L. Karim, A. Lbath, IoT and big data analytics for smart buildings: a survey, Proc. Comput. Sci. 170 (2020) 161-168.

[15] M.S. Mahdavinejad, M. Rezvan, M. Barekatain, P. Adibi, P. Barnaghi, A.P. Sheth, Machine learning for Internet of Things data analysis: a survey, Digit. Commun. Netw. 4 (3) (2018) 161-175.

[16] C.-W. Tsai, P.-W. Tsai, M.-C. Chiang, C.-S. Yang,Data analytics for Internet of Things: a review, Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 8 (5) (2018) e1261.

[17] A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, M. Ayyash, Internet of Things: a survey on enabling technologies, protocols, and applications, IEEE Com-mun. Surv. Tutor. 17 (4) (2015) 2347-2376.

[18] Y. Hajjaji, W. Boulila, I.R. Farah, I. Romdhani, A. Hussain, Big data and IoT-based applications in smart environments: a systematic review, Comput. Sci. Rev. 39 (2021) 100318.

[19] W. Li, Y. Chai, F. Khan, S.R.U. Jan, S. Verma, V.G. Menon, X. Li, A comprehen-sive survey on machine learning-based big data analytics for IoT-enabled smart healthcare system, Mob. Netw. Appl. 26 (2021) 234-252.

[20] O. Elijah, T.A. Rahman, I. Orikumhi, C.Y. Leow, M.N. Hindia, An overview of In-ternet of Things (IoT) and data analytics in agriculture: benefits and challenges, IEEE Int. Things J. 5 (5) (2018) 3758-3773.

[21] M. Koot, M.R. Mes, M.E. Iacob, A systematic literature review of supply chain de-cision making supported by the Internet of Things and big data analytics, Comput. Ind. Eng. 154 (2021) 107076.

[22] L. Cao, Data science: a comprehensive overview, ACM Comput. Surv. 50 (3) (2017) 1-42.

[23] S. Shadroo, A.M. Rahmani, Systematic survey of big data and data mining in Inter-net of things, Comput. Netw. 139 (2018) 19-47.

[24] O. Hahm, E. Baccelli, H. Petersen, N. Tsiftes, Operating systems for low-end devices in the Internet of Things: a survey, IEEE Int. Things J. 3 (5) (2015) 720-734.

[25] H. Cai, B. Xu, L. Jiang, A.V. Vasilakos, IoT-based big data storage systems in cloud computing: perspectives and challenges, IEEE Int. Things J. 4 (1) (2016) 75-87.

[26] V. Pandey, A. Kipf, T. Neumann, A. Kemper,How good are modern spatial analytics systems?, Proc. VLDB Endow. 11 (11) (2018) 1661-1673.

[27] M. Marjani, F. Nasaruddin, A. Gani, A. Karim, I.A.T. Hashem, A. Siddiqa, I. Yaqoob, Big IoT data analytics: architecture, opportunities, and open research challenges, IEEE Access 5 (2017) 5247-5261.

[28] G. Atluri, A. Karpatne, V. Kumar, Spatio-temporal data mining: a survey of prob-lems and methods, ACM Comput. Surv. 51 (4) (2018) 1-41.

[29] I.H. Sarker, Smart city data science: towards data-driven smart cities with open research issues, Int. Things 19 (2022) 100528.

[30] J.A. Stankovic, Research directions for the Internet of Things, IEEE Int. Things J. 1 (1) (2014) 3-9.

[31] L. Atzori, A. Iera, G. Morabito, The Internet of Things: a survey, Comput. Netw. 54 (15) (2010) 2787-2805.

[32] J. Gubbi, R. Buyya, S. Marusic, M. Palaniswami, Internet of Things (IoT): a vision, architectural elements, and future directions, Future Gener. Comput. Syst. 29 (7) (2013) 1645-1660.

[33] E. Borgia, The Internet of Things vision: key features, applications and open issues, Comput. Commun. 54 (2014) 1-31.

[34] Y. Liu, Y. Kuang, Y. Xiao, G. Xu, Sdn-based data transfer security for Internet of Things, IEEE Int. Things J. 5 (1) (2017) 257-268.

[35] H. Jiang, F. Shen, S. Chen, K.-C. Li, Y.-S. Jeong, A secure and scalable storage system for aggregate data in IoT, Future Gener. Comput. Syst. 49 (2015) 133-141.

[36] N.C. Luong, D.T. Hoang, P. Wang, D. Niyato, D.I. Kim, Z. Han, Data collection and wireless communication in Internet of Things (IoT) using economic analysis and pricing models: a survey, IEEE Commun. Surv. Tutor. 18 (4) (2016) 2546-2590.

[37] A. Gluhak, S. Krco, M. Nati, D. Pfisterer, N. Mitton, T. Razafindralambo, A survey on facilities for experimental Internet of Things research, IEEE Commun. Mag. 49 (11) (2011) 58-67.

[38] M. Luckner, M. Grzenda, R. Kunicki, J. Legierski, IoT architecture for urban data-centric services and applications, ACM Trans. Internet Technol. 20 (3) (2020) 1-30.

[39] A. Shah, B. Ali, F. Wahab, I. Ullah, K.T. Amesho, M. Shafiq, Entropy-based grid approach for handling outliers: a case study to environmental monitoring data, Environ. Sci. Pollut. Res. 30 (60) (2023) 125138-125157.

[40] A.L.R. Madureira, F.R.C. Araújo, L.N. Sampaio, On supporting IoT data aggregation through programmable data planes, Comput. Netw. 177 (2020) 107330.

[41] J. Xu, Y. Andrepoulos, Y. Xiao, M. van Der Schaar, Non-stationary resource al-location policies for delay-constrained video streaming: application to video over Internet-of-Things-enabled networks, IEEE J. Sel. Areas Commun. 32 (4) (2014) 782-794.

[42] D. Ilic, P.G. Da Silva, S. Karnouskos, M. Griesemer, An energy market for trading electricity in smart grid neighbourhoods, in: 2012 6th IEEE International Confer-ence on Digital Ecosystems and Technologies (DEST), IEEE, 2012, pp. 1-6.

[43] R. Buyya, C.S. Yeo, S. Venugopal, Market-oriented cloud computing: vision, hype, and reality for delivering it services as computing utilities, in: 2008 10th IEEE International Conference on High Performance Computing and Communications, IEEE, 2008, pp. 5-13.

[44] Y. Feng, B. Li, B. Li, Price competition in an oligopoly market with multiple IaaS cloud providers, IEEE Trans. Comput. 63 (1) (2013) 59-73.

[45] P.J. Nesse, S. Svaet, D. Strasunskas, A.A. Gaivoronski, Assessment and optimisation of business opportunities for telecom operators in the cloud value network, Trans. Emerg. Telecommun. Technol. 24 (5) (2013) 503-516.

[46] Z. Chen, F. Xia, T. Huang, F. Bu, H. Wang, A localization method for the Internet of Things, J. Supercomput. 63 (2013) 657-674.

[47] L. Jiang, L. Da Xu, H. Cai, Z. Jiang, F. Bu, B. Xu, An IoT-oriented data storage framework in cloud computing platform, IEEE Trans. Ind. Inform. 10 (2) (2014) 1443-1451.

[48] R. Ranjan, O. Rana, S. Nepal, M. Yousif, P. James, Z. Wen, S. Barr, P. Watson, P.P. Jayaraman, D. Georgakopoulos, et al., The next grand challenges: integrating the Internet of Things and data science, IEEE Cloud Comput. 5 (3) (2018) 12-26.

[49] T. Baltrušaitis, C. Ahuja, L.-P. Morency, Multimodal machine learning: a survey and taxonomy, IEEE Trans. Pattern Anal. Mach. Intell. 41 (2) (2018) 423-443.

[50] S. Sawalha, G. Al-Naymat, Towards an efficient big data management schema for IoT, J. King Saud Univ, Comput. Inf. Sci. 34 (9) (2022) 7803-7818.

[51] L. Sanchez, L. Muñoz, J.A. Galache, P. Sotres, J.R. Santana, V. Gutierrez, R. Ramd-hany, A. Gluhak, S. Krco, E. Theodoridis, et al., Smartsantander: IoT experimenta-tion over a smart city testbed, Comput. Netw. 61 (2014) 217-238.

[52] F. Rezaeibagha, Y. Mu, K. Huang, L. Chen, Secure and efficient data aggregation for IoT monitoring systems, IEEE Int. Things J. 8 (10) (2020) 8056-8063.

[53] P. Naur, Concise Survey of Computer Methods, Petrocelli Books, 1974.

[54] P.Naur, ‘Datalogy’, the science of data and data processes,in:IFIP Congress (2), 1968, pp. 1383-1387.

[55] B. Cheng, A. Papageorgiou, F. Cirillo, E. Kovacs, Geelytics: geo-distributed edge analytics for large scale IoT systems based on dynamic topology, in: 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), IEEE, 2015, pp. 565-570.

[56] W. Ding, X. Jing, Z. Yan, L.T. Yang, A survey on data fusion in Internet of Things: towards secure and privacy-preserving fusion, Inf. Fusion 51 ( 2019) 129-144.

[57] H. Fang, Managing data lakes in big data era: what’s a data lake and why has it became popular in data management ecosystem, in: 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), IEEE, 2015, pp. 820-824.

[58] T. Wang, Y. Liang, Y. Zhang, X. Zheng, M. Arif, J. Wang, Q. Jin, An intelligent dynamic offloading from cloud to edge for smart IoT systems with big data, IEEE Trans. Netw. Sci. Eng. 7 (4) (2020) 2598-2607.

[59] F. Terroso-Saenz, A. González-Vidal, A.P. Ramallo-González, A.F. Skarmeta, An open IoT platform for the management and analysis of energy data, Future Gener. Comput. Syst. 92 (2019) 1066-1079.

[60] L. Xu, H. Yin, H. Jia, W. Lin, X. Zhou, Y. Fu, X. Yu, Data secure transmission in-telligent prediction algorithm for mobile industrial IoT networks, Digit. Commun. Netw. 9 (2) (2023) 400-410.

[61] M. Devi, R. Dhaya, R. Kanthavel, F. Algarni, P. Dixikha, Data science for Internet of Things (IoT),in: Second International Conference on Computer Networks and Communication Technologies: ICCNCT 2019, Springer, 2020, pp. 60-70.

[62] T. Wang, L. Qiu, A.K. Sangaiah, A. Liu, M.Z.A. Bhuiyan, Y. Ma, Edge-computing-based trustworthy data collection model in the Internet of Things, IEEE Int. Things J. 7 (5) (2020) 4218-4227.

[63] I. Ullah, S. Qian, Z. Deng, J.-H. Lee, Extended Kalman filter-based localization algorithm by edge computing in wireless sensor networks, Digit. Commun. Netw. 7 (2) (2021) 187-195.

[64] D. Sun, J. Wu, J. Yang, H. Wu, Intelligent data collaboration in heterogeneous-device IoT platforms, ACM Trans. Sens. Netw. 17 (3) (2021) 1-17.

[65] F. Jameel, W.U. Khan, N. Kumar, R. Jäntti, Efficient power-splitting and resource allocation for cellular V2X communications, IEEE Trans. Intell. Transp. Syst. 22 (6) (2020) 3547-3556.

[66] M.R. Anawar, S. Wang, M. Azam Zia, A.K. Jadoon, U. Akram, S. Raza, et al., Fog computing: an overview of big IoT data analytics, Wirel. Commun. Mob. Comput. 2018 (1) (2018) 1-22.

[67] Y. Ai, M. Peng, K. Zhang, Edge computing technologies for Internet of Things: a primer, Digit. Commun. Netw. 4 (2) (2018) 77-86.

[68] K. Wang, Y. Shao, L. Xie, J. Wu, S. Guo, Adaptive and fault-tolerant data processing in healthcare IoT based on fog computing, IEEE Trans. Netw. Sci. Eng. 7 (1) (2018) 263-273.

[69] R. Goyat, G. Kumar, M. Alazab, M. Conti, M.K. Rai, R. Thomas, R. Saha, T.-H. Kim, Blockchain-based data storage with privacy and authentication in Internet of Things, IEEE Int. Things J. 9 (16) (2020) 14203-14215.

[70] F. Rezaeibagha, Y. Mu, K. Huang, L. Chen, L. Zhang, Toward secure data compu-tation and outsource for multi-user cloud-based IoT, IEEE Trans. Cloud Comput. 11 (1) (2023) 217-228.

[71] A.-T. Fadi, B.D. Deebak, Seamless authentication: for IoT-big data technologies in smart industrial application systems, IEEE Trans. Ind. Inform. 17 (4) (2020) 2919-2927.

[72] Y. Fathy, P. Barnaghi, Quality-based and energy-efficient data communication for the Internet of Things networks, IEEE Int. Things J. 6 (6) (2019) 10318-10331.

[73] T. Luo, J. Huang, S.S. Kanhere, J. Zhang, S.K. Das, Improving IoT data quality in mobile crowd sensing: a cross validation approach, IEEE Int. Things J. 6 (3) (2019) 5651-5664.

[74] L.B. Furstenau, P. Leivas, M.K. Sott, M.S. Dohan, J.R. López-Robles, M.J. Cobo, N.L. Bragazzi, K.-K.R. Choo, Big data in healthcare: conceptual network structure, key challenges and opportunities, Digit. Commun. Netw. 9 (4) (2023) 856-868.

[75] Y. Xu, Z. Wang, H. Gao, Z. Jiang, Y. Yin, R. Li, Towards machine-learning-driven effective mashup recommendations from big data in mobile networks and the Internet-of-Things, Digit. Commun. Netw. 9 (1) (2023) 138-145.

[76] F. Provost, T. Fawcett, Data Science for Business:What You Need to Know About Data Mining and Data-Analytic Thinking, O’Reilly Media, Inc., 2013.

[77] A. Marchand, P. Marx, Automated product recommendations with preference-based explanations, J. Retail. 96 (3) (2020) 328-343.

[78] J. Boudet, B. Gregg, K. Rathje, E. Stein, K. Vollhardt, The future of personal-ization—and how to get ready for it, Recuperado el. 12 (2019) 1-7, https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-future-of-personalization-and-how-to-get-ready-for-it.

[79] J. Grabis, K. Jegorova, K. Pinka,IoT data analytics in retail: framework and imple-mentation, in: IN4PL, 2020, pp. 93-100.

[80] United Nations, World Urbanization Prospects: the 2014 Revision, United Nations, New York, 2018.

[81] B. Resch, M. Szell, Human-centric data science for urban studies, ISPRS Int. J. Geo-Inf. 8 (12) (2019) 1-9, https://doi.org/10.3390/ijgi8120584.

[82] H. Yoo, R.C. Park, K. Chung, IoT-based health big-data process technologies: a survey, KSII Trans. Int. Inf. Syst. (TIIS) 15 (3) (2021) 974-992, https://doi.org/10.3837/tiis.2021.03.001.

[83] M. Nilashi, O. bin Ibrahim, H. Ahmadi, L. Shahmoradi, An analytical method for diseases prediction using machine learning techniques, Comput. Chem. Eng. 106 (2017) 212-223.

[84] G. Ayoade, A. El-Ghamry, V. Karande, L. Khan, M. Alrahmawy, M.Z. Rashad, Secure data processing for IoT middleware systems, J. Supercomput. 75 (2019) 4684-4709.

[85] A. Al-Abassi, H. Karimipour, H. HaddadPajouh, A. Dehghantanha, R.M. Parizi, In-dustrial big data analytics: challenges and opportunities,in: Handbook of Big Data Privacy, 2020, pp. 37-61.

[86] X. Yuan, X. Yuan, B. Li, C. Wang, Toward secure and scalable computation in Internet of Things data applications, IEEE Int. Things J. 6 (2) (2019) 3753-3763.

[87] M. Mahesh, S.R. Kawale, M.D. PraveenKumar, S.J. Veeresh, D.N. Sahu, M. Barote, A. Rangampet, Applications of Internet of Things in food and beverage industries, Ann. For. Res. 65 (1) (2022) 3957-3970.

[88] N. Lu, N. Cheng, N. Zhang, X. Shen, J.W. Mark, Connected vehicles: solutions and challenges, IEEE Int. Things J. 1 (4) (2014) 289-299.

[89] B. Ji, M. Zhang, L. Xing, X. Li, C. Li, C. Han, H. Wen, Research on optimal intel-ligent routing algorithm for IoV with machine learning and smart contract, Digit. Commun. Netw. 9 (1) (2023) 47-55.

[90] W.U. Khan, M.A. Javed, T.N. Nguyen, S. Khan, B.M. Elhalawany, Energy-efficient resource allocation for 6G backscatter-enabled NOMA IoV networks, IEEE Trans. Intell. Transp. Syst. 23 (7) (2021) 9775-9785.

[91] M. Dibaei, X. Zheng, K. Jiang, R. Abbas, S. Liu, Y. Zhang, Y. Xiang, S. Yu, Attacks and defences on intelligent connected vehicles: a survey, Digit. Commun. Netw. 6 (4) (2020) 399-421.

[92] J.M.K. Sri, V. Narendra, V. Pai, Implementing and testing of IoT technology in agriculture, Int. J. Innov. Technol. Explor. Eng. 8 (2S) (2018) 190-194.

[93] R. Akhter, S.A. Sofi, Precision agriculture using IoT data analytics and machine learning, J. King Saud Univ, Comput. Inf. Sci. 34 (8) (2022) 5602-5618.

[94] I.H. Sarker, A. Kayes, S. Badsha, H. Alqahtani, P. Watters, A. Ng, Cybersecurity data science: an overview from machine learning perspective, J. Big Data 7 (2020) 1-29.

[95] I.H. Sarker, Cyberlearning: effectiveness analysis of machine learning security modeling to detect cyber-anomalies and multi-attacks, Int. Things 14 (2021) 100393.

[96] Y. Xin, L. Kong, Z. Liu, Y. Chen, Y. Li, H. Zhu, M. Gao, H. Hou, C. Wang, Ma-chine learning and deep learning methods for cybersecurity, IEEE Access 6 (2018) 35365-35381.

[97] S.A. Shah, D.Z. Seker, S. Hameed, D. Draheim, The rising role of big data analytics and IoT in disaster management: recent advances, taxonomy and prospects, IEEE Access 7 (2019) 54595-54614.

[98] J. Lloret, J. Tomas, A. Canovas, L. Parra, An integrated IoT architecture for smart metering, IEEE Commun. Mag. 54 (12) (2016) 50-57.

[99] B. Xu, L. Da Xu, H. Cai, C. Xie, J. Hu, F. Bu, Ubiquitous data accessing method in IoT-based information system for emergency medical services, IEEE Trans. Ind. Inform. 10 (2) (2014) 1578-1586.

[100] P. Basanta-Val, N.C. Audsley, A.J. Wellings, I. Gray, N. Fernández-García, Archi-tecting time-critical big-data systems, IEEE Trans. Big Data 2 (4) (2016) 310-324.

[101] I. Ullah, H.Y. Youn, Intelligent data fusion for smart IoT environment: a survey, Wirel. Pers. Commun. 114 (2020) 409-430.

[102] W. He, G. Yan, L. Da Xu, Developing vehicular data cloud services in the IoT environment, IEEE Trans. Ind. Inform. 10 (2) (2014) 1587-1595.

[103] J. Li, H. Liu, Challenges of feature selection for big data analytics, IEEE Intell. Syst. 32 (2) (2017) 9-15.

[104] W.U. Khan, F. Jameel, A. Ihsan, O. Waqar, M. Ahmed, Joint optimization for se-cure ambient backscatter communication in NOMA-enabled IoT networks, Digit. Commun. Netw. 9 (1) (2023) 264-269.

[105] T.S. López, D.C. Ranasinghe, B. Patkai, D. McFarlane, Taxonomy, technology and applications of smart objects, Inf. Syst. Front. 13 (2011) 281-300.

[106] C. Xu, W. Zhao, J. Zhao, Z. Guan, X. Song, J. Li, Uncertainty-aware multiview deep learning for Internet of Things applications, IEEE Trans. Ind. Inform. 19 (2) (2022) 1456-1466.

[107] C. Bayılmı¸s, M.A. Ebleme, Ü. Çavu¸so˘ glu, K. Küçük, A. Sevin, A survey on com-munication protocols and performance evaluations for Internet of Things, Digit. Commun. Netw. 8 (6) (2022) 1094-1104.

[108] J.R. Bhat, S.A. AlQahtani, M. Nekovee, Fintech enablers, use cases, and role of fu-ture Internet of Things, J. King Saud Univ, Comput. Inf. Sci. 35 (1) (2023) 87-101.

[109] A. de Saint-Exupery, Internet of things, strategic research roadmap, in: Internet of Things - Global Technological and Societal Trends, 2009, pp. 9-52, https://www.semanticscholar.org/paper/Internet-of-Things-Strategic-Research-Roadmap-De-Saint-Exup%C3%A9ry/b5336e565b38f99fd3527fe68ad002d9e309ecfc, 2009.

[110] A. Asin, D. Gascon, 50 Sensor Applications for a Smarter World, Tech. Rep., Li-belium Comunicaciones Distribuidas, 2012, pp. 589-594.

[111] R. Baeza-Yates, B. Ribeiro-Neto, et al., Modern Information Retrieval, vol. 463, ACM Press, New York, 1999.

[112] O.J. Reichman, M.B. Jones, M.P. Schildhauer, Challenges and opportunities of open data in ecology, Science 331 (6018) (2011) 703-705.

[113] J. Wang, R. Zhu, S. Liu, A differentially private unscented Kalman filter for stream-ing data in IoT, IEEE Access 6 (2018) 6487-6495.

[114] E. Nazerfard, P. Rashidi, D.J. Cook,Using association rule mining to discover tem-poral relations of daily activities, in:Toward Useful Services for Elderly and People with Disabilities: 9th International Conference on Smart Homes and Health Telem-atics, ICOST 2011, Montreal, Canada, June 20-22, 2011. Proceedings 9, Springer, 2011, pp. 49-56.

[115] E. Nazerfard, P. Rashidi, D.J. Cook, Discovering temporal features and relations of activity patterns, in: 2010 IEEE International Conference on Data Mining Work-shops, IEEE, 2010, pp. 1069-1075.

[116] K.R. Sollins, IoT big data security and privacy versus innovation, IEEE Int. Things J. 6 (2) (2019) 1628-1635.

[117] X. Yao, F. Farha, R. Li, I. Psychoula, L. Chen, H. Ning, Security and privacy issues of physical objects in the IoT: challenges and opportunities, Digit. Commun. Netw. 7 (3) (2021) 373-384.

[118] T. Mazhar, D.B. Talpur, T.A. Shloul, Y.Y. Ghadi, I. Haq, I. Ullah, K. Ouahada, H. Hamam, Analysis of IoT security challenges and its solutions using artificial intel-ligence, Brain Sci. 13 (4) (2023) 683.

[119] J. Jiang, F. Liu, Y. Liu, Q. Tang, B. Wang, G. Zhong, W. Wang, A dynamic en-semble algorithm for anomaly detection in IoT imbalanced data streams, Comput. Commun. 194 (2022) 250-257.

[120] H. Bi, J. Liu, N. Kato, Deep learning-based privacy preservation and data analytics for IoT enabled healthcare, IEEE Trans. Ind. Inform. 18 (7) (2021) 4798-4807.

[121] R. Hai, S. Geisler, C. Quix, Constance: an intelligent data lake system,in: Pro-ceedings of the 2016 International Conference on Management of Data, 2016, pp. 2097-2100.

[122] E. Ahmed, I. Yaqoob, I.A.T. Hashem, I. Khan, A.I.A. Ahmed, M. Imran, A.V. Vasi-lakos, The role of big data analytics in Internet of Things, Comput. Netw. 129 (2017) 459-471.

[123] ProjectPro, Internet of Things (IoT)’s Impact on Big Data and Data Science, https://www.projectpro.io/article/internet-of-things-iot-s-impact-on-big-data-and-data-science/249, 2022. (Accessed 11 April 2023).

[124] B. Glavic, Big data provenance: challenges and implications for benchmarking,in: Specifying Big Data Benchmarks: First Workshop, WBDB 2012, San Jose, CA, USA, May 8-9, 2012, and Second Workshop, WBDB 2012, Pune, India, December 17-18, 2012, in: Revised Selected Papers, Springer, 2014, pp. 72-80.

[125] C. Perera, A. Zaslavsky, P. Christen, D. Georgakopoulos, Context aware computing for the Internet of Things: a survey, IEEE Commun. Surv. Tutor. 16 (1) (2013) 414-454.

[126] J. Dean, S. Ghemawat, Mapreduce: simplified data processing on large clusters, Commun. ACM 51 (1) (2008) 107-113.

[127] C.C. Aggarwal, Data streams: models and algorithms,in: Advances in Database Systems, vol. 31, Springer Science & Business Media, 2007, pp. 18-354.

[128] G. Krempl, I. Žliobaite, D. Brzezi´ nski, E. Hüllermeier, M. Last, V. Lemaire, T. Noack, A. Shaker, S. Sievi, M. Spiliopoulou, et al., Open challenges for data stream mining research, ACM SIGKDD Explor. Newsl. 16 (1) (2014) 1-10.

[129] O. Diallo, J.J. Rodrigues, M. Sene, J. Lloret, Distributed database management techniques for wireless sensor networks, IEEE Trans. Parallel Distrib. Syst. 26 (2) (2013) 604-620.

[130] M.A. Alsheikh, D. Niyato, S. Lin, H.-P. Tan, Z. Han, Mobile big data analytics using deep learning and apache spark, IEEE Netw. 30 (3) (2016) 22-29.

[131] G. Xie, H. Peng, Z. Li, J. Song, Y. Xie, R. Li, K. Li, Reliability enhancement to-ward functional safety goal assurance in energy-aware automotive cyber-physical systems, IEEE Trans. Ind. Inform. 14 (12) (2018) 5447-5462.

[132] M. Borkowski, S. Schulte, C. Hochreiner,Predicting cloud resource utilization, in:Proceedings of the 9th International Conference on Utility and Cloud Computing, 2016, pp. 37-42.

[133] A. Gharaibeh, A. Khreishah, M. Mohammadi, A. Al-Fuqaha, I. Khalil, A. Rayes, Online auction of cloud resources in support of the Internet of Things, IEEE Int. Things J. 4 (5) (2017) 1583-1596.

[134] H.U. Khan, M. Sohail, F. Ali, S. Nazir, Y.Y. Ghadi, I. Ullah, Prioritizing the multi-criterial features based on comparative approaches for enhancing security of IoT devices, Phys. Commun. 59 (2023) 102084.

[135] Y. Shi, J. Han, X. Wang, J. Gao, H. Fan, An obfuscatable aggregatable signcryption scheme for unattended devices in IoT systems, IEEE Int. Things J. 4 (4) (2017) 1067-1081.

[136] R. Lu, H. Zhu, X. Liu, J.K. Liu, J. Shao, Toward efficient and privacy-preserving computing in big data era, IEEE Netw. 28 (4) (2014) 46-50.

[137] M.H. Au, K. Liang, J.K. Liu, R. Lu, J. Ning, Privacy-preserving personal data op-eration on mobile cloud—chances and challenges over advanced persistent threat, Future Gener. Comput. Syst. 79 (2018) 337-349.

[138] J. Zhang, G. Cormode, C.M. Procopiuc, D. Srivastava, X. Xiao, Privbayes: private data release via Bayesian networks, ACM Trans. Database Syst. 42 (4) (2017) 1-41.

[139] R. Mendes, J.P. Vilela, Privacy-preserving data mining: methods, metrics, and ap-plications, IEEE Access 5 (2017) 10562-10582.

[140] B. Wang, B. Li, H. Li, Panda: public auditing for shared data with efficient user revocation in the cloud, IEEE Trans. Serv. Comput. 8 (1) (2013) 92-106.

[141] J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang, W. Zhao, A survey on Internet of Things: architecture, enabling technologies, security and privacy, and applications, IEEE Int. Things J. 4 (5) (2017) 1125-1142.

[142] J. Liu, M. Ahmed, M.A. Mirza, W.U. Khan, D. Xu, J. Li, A. Aziz, Z. Han, RL/DRL meets vehicular task offloading using edge and vehicular cloudlet: a survey, IEEE Int. Things J. 9 (11) (2022) 8315-8338.

[143] S. Chen, B. Tang, K. Wang, Twin delayed deep deterministic policy gradient-based intelligent computation offloading for IoT, Digit. Commun. Netw. 9 (4) (2023) 836-845.

[144] P. Mach, Z. Becvar, Mobile edge computing: a survey on architecture and compu-tation offloading, IEEE Commun. Surv. Tutor. 19 (3) (2017) 1628-1656.