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The Symbiotic Relation of IoT and AI for Applications in Various Domains: Trends and Future Directions

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Data Analytics for Internet of Things Infrastructure

Part of the book series: Internet of Things ((ITTCC))

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Abstract

Organizations that want to increase their productivity, transparency, and profitability stand to gain the most from the recent rise of the Internet of Things (IoT) and artificial intelligence (AI). Both artificial intelligence and the Internet of Things are game-changing technologies. More companies than ever before are tapping into the IoT’s potential and recognizing its value. Machine learning, artificial intelligence, rapid feedback, and remote monitoring and operations are not far-off pipe dreams. They have already arrived, and they don’t plan on stopping any time soon. Businesses who get in on the IoT revolution early may take advantage of its growing popularity and benefit from its rapid expansion. As we look forward to 2023 and start to appreciate the effect IoT with AI will have on all sectors, the companies that successfully transform and empower themselves via the benefits of IoT may establish insurmountable competitive advantages. IoT technologies are expected to play ever-expanding roles in commercial and social contexts. Current trends in IoT and AI are extremely noticeable in most sectors, but sector-specific developments shouldn’t be overlooked. As both IoT and AI continue to expand, it’s important to keep an eye on emerging trends at the intersection of the two fields. The latest developments in IoT technology that make use of AI methods are the primary focus of this study. All the newest developments and potential future applications from combining IoT and AI are being reviewed. The objective of this study is to showcase the current trend of IoT with AI, along with future directions. Many domains have been analyzed and shown in tabular format, where the methodological advantages and future scope of AI-assisted IoT technologies are identified.

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References

  1. Rahman, A., et al. (2022). Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues. Cluster Computing, 1–41. https://doi.org/10.1007/s10586-022-03658-4

  2. Shumba, A. T., Montanaro, T., Sergi, I., Fachechi, L., De Vittorio, M., & Patrono, L. (2022). Leveraging IoT-aware technologies and AI techniques for real-time critical healthcare applications. Sensors, 22(19). https://doi.org/10.3390/S22197675

  3. Kamruzzaman, M. M., Alrashdi, I., & Alqazzaz, A. (2022). New opportunities, challenges, and applications of edge-AI for connected healthcare in internet of medical things for smart cities. Journal of Healthcare Engineering, 2022, 1. https://doi.org/10.1155/2022/2950699

    Article  Google Scholar 

  4. Alghamdi, A., et al. (2020). Detection of myocardial infarction based on novel deep transfer learning methods for urban healthcare in smart cities. Multimedia Tools and Applications, 1–22. https://doi.org/10.1007/S11042-020-08769-X/TABLES/10

  5. Lu, Z. X., et al. (2021). Application of AI and IoT in clinical medicine: Summary and challenges. Current Medical Science, 41(6), 1134–1150. https://doi.org/10.1007/S11596-021-2486-Z

    Article  Google Scholar 

  6. Junaid, S. B., et al. (2022). Recent advancements in emerging Technologies for healthcare management systems: A survey. Healthcare, 10(10), 1940. https://doi.org/10.3390/HEALTHCARE10101940

    Article  Google Scholar 

  7. Ramasamy, L. K., Khan, F., Shah, M., Prasad, B. V. V. S., Iwendi, C., & Biamba, C. (2022). Secure smart wearable computing through artificial intelligence-enabled internet of things and cyber-physical systems for health monitoring. Sensors, 22(3), 1076. https://doi.org/10.3390/s22031076

    Article  Google Scholar 

  8. Kollu, P. K., et al. (2022). Development of advanced artificial intelligence and IoT automation in the crisis of COVID-19 detection. Journal of Healthcare Engineering, 2022, 1. https://doi.org/10.1155/2022/1987917

    Article  Google Scholar 

  9. Sim, S., & Cho, M. (2021). Convergencemodel of AI and IoT for virus disease control system. Personal and Ubiquitous Computing,1–11. https://doi.org/10.1007/S00779-021-01577-6

  10. Huang, X. (2020). Applicationanalysis of AI reasoning engine in microblog culture industry.Personal and Ubiquitous Computing,24(3), 393–403. https://doi.org/10.1007/S00779-019-01338-6/FIGURES/13

    Article  Google Scholar 

  11. Priyadarshini, I., Kumar, R., Sharma, R.,Singh, P. K., & Satapathy, S. C. (2021). Identifying cyberinsecurities in trustworthy space and energy sector for smartgrids. Computers & ElectricalEngineering, 93, 107204.

    Google Scholar 

  12. Fraga-Lamas, P., Lopes, S. I., & Fernández-Caramés, T. M. (2021). Green IoT and edge AI as key technological enablers for a sustainable digital transition towards a smart circular economy: An industry 5.0 use case. Sensors, 21(17). https://doi.org/10.3390/S21175745

  13. Vo, M.T., Vo, A. H., Nguyen, T., Sharma, R., & Le, T. (2021).Dealing with the class imbalance problem in the detection offake job descriptions. Computers,Materials & Continua, 68(1),521–535.

    Article  Google Scholar 

  14. MIT-BIH Arrhythmia Database v1.0.0. https://physionet.org/content/mitdb/1.0.0/ (accessed Nov. 18, 2022).

  15. Sachan, S., Sharma, R., &Sehgal, A. (2021). Energy efficient scheme for betterconnectivity in sustainable mobile wireless sensor networks.Sustainable Computing: Informatics andSystems, 30, 100504.

    Google Scholar 

  16. Wagner, P., et al. (2020). PTB-XL, a large publicly available electrocardiography dataset. Sci. Data, 7(1). https://doi.org/10.1038/S41597-020-0495-6

  17. Ghanem, S., Kanungo, P., Panda, G., et al.(2021). Lane detection under artificial colored light intunnels and on highways: An IoT-based framework for smart cityinfrastructure. Complex &Intelligent Systems. https://doi.org/10.1007/s40747-021-00381-2

  18. Time Series Classification Website. http://timeseriesclassification.com/description.php?Dataset=ECG5000 (accessed Nov. 18, 2022).

  19. Sachan, S., Sharma, R., &Sehgal, A. (2021). SINR based energy optimization schemes for5G vehicular sensor networks. WirelessPersonal Communications, 127, 1023. https://doi.org/10.1007/s11277-021-08561-6

    Article  Google Scholar 

  20. State of the world's nursing 2020: investing in education, jobs and leadership. https://www.who.int/publications/i/item/9789240003279 (accessed Nov. 18, 2022).

  21. Priyadarshini, I., Mohanty, P.,Kumar, R., et al. (2021). A study on the sentiments andpsychology of twitter users during COVID-19 lockdown period.Multimedia Tools and Applications,81, 27009. https://doi.org/10.1007/s11042-021-11004-w

    Article  Google Scholar 

  22. World Population Prospects 2019: Highlights. Multimedia Library - United Nations Department of Economic and Social Affairs. https://www.un.org/development/desa/publications/world-population-prospects-2019-highlights.html. (accessed Nov. 18, 2022).

  23. Azad, C., Bhushan, B., Sharma,R., et al. (2021). Prediction model using SMOTE, geneticalgorithm and decision tree (PMSGD) for classification ofdiabetes mellitus. Multimedia Systems,28, 1289. https://doi.org/10.1007/s00530-021-00817-2

    Article  Google Scholar 

  24. Tang, X. (2019). The role of artificial intelligence in medical imaging research. BJR open, 2(1), 20190031. https://doi.org/10.1259/BJRO.20190031

    Article  Google Scholar 

  25. Priyadarshini, I., Kumar, R.,Tuan, L. M., et al. (2021). A new enhanced cyber securityframework for medical cyber physical systems. SICS Software-Intensive Cyber-Physical Systems,35, 159. https://doi.org/10.1007/s00450-021-00427-3

    Article  Google Scholar 

  26. Coronavirus disease (COVID-19). https://www.who.int/emergencies/diseases/novel-coronavirus-2019 (accessed Nov. 19, 2022).

  27. Singh, R.,Sharma, R., Akram, S. V., Gehlot, A., Buddhi, D., Malik, P. K.,& Arya, R. (2021). Highway 4.0: Digitalization of highwaysfor vulnerable road safety development with intelligent IoTsensors and machine learning. SafetyScience, 143, 105407. ISSN0925-7535.

    Google Scholar 

  28. Sun, H., & Bai, S. (2022). Enterprise information security management using internet of things combined with artificial intelligence technology. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/7138515

  29. Bi, S., et al. (2022). A survey on artificial intelligence aided internet-of-things technologies in emerging smart libraries. Sensors, 22(8). https://doi.org/10.3390/S22082991

  30. Sahu, L., Sharma, R., Sahu, I., Das, M., Sahu,B., & Kumar, R. (2021). Efficient detection ofParkinson’s disease using deep learning techniques overmedical data. Expert Systems,39, e12787. https://doi.org/10.1111/exsy.12787

    Article  Google Scholar 

  31. Bai, R., Zhao, J., Li, D., Lv,X., Wang, Q., & Zhu, B. (2020). RNN-based demand awarenessin smart library using CRFID. ChinaCommunications, 17(5), 284–294. https://doi.org/10.23919/JCC.2020.05.021

    Article  Google Scholar 

  32. Gültekin, Ö.,Cinar, E., Özkan, K., & Yazıcı, A.(2022). Multisensory data fusion-based deep learning approachfor fault diagnosis of an industrial autonomous transfervehicle. Expert Systems withApplications, 200, 117055. https://doi.org/10.1016/J.ESWA.2022.117055

    Article  Google Scholar 

  33. Gültekin, Ö., Cinar, E.,Özkan, K., & Yazıcı, A. (2022).Real-time fault detection and condition monitoring forindustrial autonomous transfer vehicles utilizing edgeartificial intelligence. Sensors,22(9), 3208. https://doi.org/10.3390/s22093208

    Article  Google Scholar 

  34. Malik, P., Lu, J., Madhav, B. T. P.,Kalkhambkar, G., & Amit, S. (Eds.). Smart antennas: Latest trends in design andapplication. Springer. ISBN 978-3-030-76636-8.https://doi.org/10.1007/978-3-030-76636-8

  35. Roges, R., & Malik, P. K.(2021). Planar and printed antennas for internet ofthings-enabled environment: Opportunities and challenges.International Journal of CommunicationSystems, 34(15), e4940. https://doi.org/10.1002/dac.4940. (IF: 2.047) ISSN: 1099-1131.

    Article  Google Scholar 

  36. Rahim, A., & Malik, P. K. (2021). Analysisand design of fractal antenna for efficient communicationnetwork in vehicular model. SustainableComputing: Informatics and Systems, Elsevier, 31,100586. https://doi.org/10.1016/j.suscom.2021.100586. ISSN2210-5379.

    Article  Google Scholar 

  37. Yang, C. T., Chen, H. W., Chang,E. J., Kristiani, E., Nguyen, K. L. P., & Chang, J. S.(2021). Current advances and future challenges of AIoTapplications in particulate matter (PM) monitoring and control.Journal of Hazardous Materials,419, 126442. https://doi.org/10.1016/j.jhazmat.2021.126442

    Article  Google Scholar 

  38. Börner, K., et al.(2020). Mapping the co-evolution of artificial intelligence,robotics, and the internet of things over 20 years (1998-2017).PLoS One, 15(12), e0242984.https://doi.org/10.1371/JOURNAL.PONE.0242984

    Article  Google Scholar 

  39. Shaik, N., & Malik, P. K.(2021). A comprehensive survey 5G wireless communicationsystems: Open issues, research challenges, channel estimation,multi carrier modulation and 5G applications. Multimedia Tools and Applications, 80,28789. https://doi.org/10.1007/s11042-021-11128-z

    Article  Google Scholar 

  40. Malik, P. K., Wadhwa, D. S.,& Khinda, J. S. (2020). A survey of device to device andcooperative communication for the future cellular networks.International Journal of WirelessInformation Networks, Springer, 27, 411–432.https://doi.org/10.1007/s10776-020-00482-8

    Article  Google Scholar 

  41. Tiwari, P., & Malik, P. K. (2021). Wideband micro-strip antenna design for higher “X”band. International Journal ofe-Collaboration (IJeC), 17(4), 60–74.https://doi.org/10.4018/IJeC.2021100105. (ISSN: 1548-3673).

    Article  Google Scholar 

  42. Kaur, A., & Malik, P. K. (2021). Multibandelliptical patch fractal and defected ground structuresmicrostrip patch antenna for wireless applications. Progress In Electromagnetics Research B,91, 157–173. https://doi.org/10.2528/PIERB20102704. (ISSN: 1937-6472).

    Article  Google Scholar 

  43. Shaik, N., & Malik, P. K. (2020). ARetrospection of Channel Estimation Techniques for 5G WirelessCommunications: Opportunities and Challenges. International Journal of Advanced Science andTechnology, 29(05), 8469–8479. ISSN:2005-4238.

    Google Scholar 

  44. Malik, P. K., & Singh, M. (2019). Multiplebandwidth design of micro strip antenna for future wirelesscommunication. International Journal ofRecent Technology and Engineering, 8(2),5135–5138. https://doi.org/10.35940/ijrte.B2871.078219. ISSN:2277-3878.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of CSE, Delhi Technological University, Delhi, India

    Aman Jolly

  2. School of Engineering, Babu Banarasi Das University, Lucknow, Uttar Pradesh, India

    Vikas Pandey

  3. School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, Punjab, India

    Praveen Kumar Malik

  4. Electrical engineering department, Najran University, Najran, Saudi Arabia

    Turki Alsuwian

Authors
  1. Aman Jolly
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  2. Vikas Pandey
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  3. Praveen Kumar Malik
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  4. Turki Alsuwian
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Correspondence to Aman Jolly .

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Editors and Affiliations

  1. SRM Institute of Science and Technology, Delhi NCR Campus, Ghaziabad, Uttar Pradesh, India

    Rohit Sharma

  2. Incheon National University, Incheon, Korea (Republic of)

    Gwanggil Jeon

  3. Xi’an Shiyou University, Xi'An, China

    Yan Zhang

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Jolly, A., Pandey, V., Malik, P.K., Alsuwian, T. (2023). The Symbiotic Relation of IoT and AI for Applications in Various Domains: Trends and Future Directions. In: Sharma, R., Jeon, G., Zhang, Y. (eds) Data Analytics for Internet of Things Infrastructure. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-031-33808-3_13

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  • DOI: https://doi.org/10.1007/978-3-031-33808-3_13

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