Towards sustainable industry 4.0: A survey on greening IoE in 6G networks

Authors

  • Saeed Hamood Alsamhi Insight Centre for Data Analytics, University of Galaway, Ireland Faculty of Engineering, IBB University, 70270, Ibb, Yemen Department of Computer Science and Engineering, College of Informatics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea Corresponding author at: Insight Centre for Data Analytics, University of Galaway, Ireland. Author https://orcid.org/0000-0003-2857-6979
  • Ammar Hawbani School of Computer Science, Shenyang Aerospace University, Shenyang 110136, China Author
  • Radhya Sahal Computer Science, Hudida University, Hudida Ibb, Yemen Author
  • Sumit Srivastava Department of Electronics and Communication Engineering, FET, MJP Rohilkhand University, Bareilly, Uttar Pradesh 243006, India Author
  • Santosh Kumar Department of CSE, IIIT Naya Raipur, Chhattisgarh, India Author
  • Liang Zhao School of Computer Science, Shenyang Aerospace University, Shenyang 110136, China Author
  • Mohammed A A Al-Qaness Emirates International University image/svg+xml Author
  • Jahan Hassan School of Engineering and Technology, College of ICT, Central Queensland University, Australia Author https://orcid.org/0000-0002-0939-2106
  • Mohsen Guizani Machine Learning Department, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates Author
  • Edward Curry Insight Centre for Data Analytics, University of Galaway, Ireland Author https://orcid.org/0000-0001-8236-6433

DOI:

https://doi.org/10.1016/j.adhoc.2024.103610

Keywords:

6G , Connectivity , Energy efficiency , Green IoE , Industry 4.0 , Sustainability

Abstract

The dramatic recent increase of the smart Internet of Everything (IoE) in Industry 4.0 has significantly increased energy consumption, carbon emissions, and global warming. IoE applications in Industry 4.0 face many challenges, including energy efficiency, heterogeneity, security, interoperability, and centralization. Therefore, Industry 4.0 in Beyond the Sixth-Generation (6G) networks demands moving to sustainable, green IoE and identifying efficient and emerging technologies to overcome sustainability challenges. Many advanced technologies and strategies efficiently solve issues by enhancing connectivity, interoperability, security, decentralization, and reliability. Greening IoE is a promising approach that focuses on improving energy efficiency, providing a high Quality of Service (QoS), and reducing carbon emissions to enhance the quality of life at a low cost. This survey provides a comprehensive overview of how advanced technologies can contribute to green IoE in the 6G network of Industry 4.0 applications. This survey provides a comprehensive overview of advanced technologies, including Blockchain, Digital Twins (DTs), Unmanned Aerial Vehicles (UAVs, a.k.a. drones), and Machine Learning (ML), to improve connectivity, QoS, and energy efficiency for green IoE in 6G networks. We evaluate the capability of each technology in greening IoE in Industry 4.0 applications and analyse the challenges and opportunities to make IoE greener using the discussed technologies.

Author Biographies

  • Saeed Hamood Alsamhi, Insight Centre for Data Analytics, University of Galaway, Ireland Faculty of Engineering, IBB University, 70270, Ibb, Yemen Department of Computer Science and Engineering, College of Informatics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea Corresponding author at: Insight Centre for Data Analytics, University of Galaway, Ireland.
    Saeed Hamood Alsamhi received an M.Tech. degree in communication systems and a Ph.D. degree from the Department of Electronics Engineering, Indian Institute of Technology (Banaras Hindu University), IIT (BHU), Varanasi, India, in 2012 and 2015. In 2009, he worked as a Lecturer Assistant in the Engineering faculty at IBB University. Afterward, he held a postdoctoral position with the School of Aerospace Engineering, Tsinghua University, Beijing, China. Since 2019, he has been an Assistant Professor at Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen. In 2020, he worked as MSCA SMART 4.0 FELLOW at Athlone Institute of Technology, Athlone, Ireland. Currently, he is a Senior Research Fellow, at Insight Centre for Data Analytics, University of Galway, Ireland; Adjunct Professor at the Department of Computer Science and Engineering, College of Informatics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea; and Assistant Professor at Faculty of Engineering, IBB University, Ibb, Yemen. His areas of interest include green and semantic communication, green Internet of Things, QoE, QoS, Cybersecurity, multi-robot collaboration, blockchain technology, federated learning, and space technologies (high altitude platforms, drones, and tethered balloon technologies).
  • Ammar Hawbani, School of Computer Science, Shenyang Aerospace University, Shenyang 110136, China
    Ammar Hawbani received the B.S., M.S. and Ph.D. degrees in computer software and theory from the University of Science and Technology of China (USTC), Hefei, China, in 2009, 2012, and 2016, respectively. He served as a Post-Doctoral Researcher and an Associate Researcher at the School of Computer Science and Technology, USTC, from 2016 to 2023. Currently, He is a Full Professor with the School of Computer Science, Shenyang Aerospace University. His research interests include the IoT, WSNs, WBANs, WMNs, VANETs and SDN.
  • Radhya Sahal, Computer Science, Hudida University, Hudida Ibb, Yemen
    Radhya Sahal is a SMART 4.0 Research Fellow in Confirm Centre for Smart Manufacturing, University College Cork. She has received her M.Sc. and Ph.D. in computer science in 2013 and 2018 from Faculty of Computers and Information, Cairo University, Egypt respectively. Radhya has publications in the area of Big Data, Cloud Database, IoT, Industry 4.0 and Healthcare. Her research interests include Big Data, Blockchain, Stream Processing, Query Optimization over large-scale distributed data systems, Internet of Things (IoT), Industry 4.0, Digital Twins, Smart Manufacturing and Smart Cities.
  • Sumit Srivastava, Department of Electronics and Communication Engineering, FET, MJP Rohilkhand University, Bareilly, Uttar Pradesh 243006, India
    Sumit Srivastava Sumit Srivastava received the Ph.D. from Indian Institute of Technology (B.H.U.), Varanasi. He has more than 18 years of teaching experience. He is working as Assistant Professor (Senior Scale) in the Department of Electronics and Communication Engineering, FET, MJP Rohilkhand University Bareilly, INDIA. He has Published more than 30 research Papers in different reputed Journals and Conferences. His research interests include, IoT, Artificial Intelligence, Machine Learning, Smart Sensors, Microstrip Patch Antennas and Federated Learning.
  • Santosh Kumar, Department of CSE, IIIT Naya Raipur, Chhattisgarh, India
    Santosh Kumar is an Assistant Professor in Computer Science and Engineering discipline. Prior to joining IIIT-NR, Dr. Santosh Kumar was a Ph.D. scholar in the Department of Computer Science and Engineering, IIT (B.H.U.), Varanasi, Uttar Pradesh, India. He is an active member of the Computer Society and the Association for Computing Machinery. He has published over 14 journal (SCI-index journal) and 5 conference papers (including 2 tier-1 international conferences (ACM multimedia-2016 and Mobisys-2016); filed 1patent, and 14 book chapters in edited books (Springer publication and IGI publication).
  • Liang Zhao, School of Computer Science, Shenyang Aerospace University, Shenyang 110136, China
    Liang Zhao (Member, IEEE) is a Professor at Shenyang Aerospace University, China. He received his Ph.D. degree from the School of Computing at Edinburgh Napier University in 2011. Before joining Shenyang Aerospace University, he worked as associate senior researcher in Hitachi (China) Research and Development Corporation from 2012 to 2014. He is also a JSPS invitational Fellow (2023) and a visiting professor at the University of Electro- Communications, Japan. He was listed as Top 2% of scientists in the world by Standford University (2022 and 2023). His research interests include ITS, VANET, WMN and SDN.
  • Mohammed A A Al-Qaness , Emirates International University
    Mohammed A. A. Al-qaness received the B.S., M.S., and Ph.D. degrees from Wuhan University of Technology, in 2010, 2014, and 2017, respectively, all in information and communication engineering. He was an Assistant Professor with the School of Computer Science, Wuhan University. He was also a Postdoc Researcher at the State Key Laboratory for Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University. Now, he is a Professor at the College of Physics and Electronic Information Engineering, Zhejiang Normal University. His current research interests include Wi-Fi sensing, wireless sensing, mobile computing, machine learning, signal and image processing, and natural language processing.
  • Jahan Hassan, School of Engineering and Technology, College of ICT, Central Queensland University, Australia
    Jahan Hassan a Senior Lecturer at the Central Queensland University, holds a PhD from the University of New South Wales and a Bachelor’s degree from Monash University, Australia, both in Computer Science. She is an Area Editor for Elsevier’s Ad Hoc Networks and has guest-edited for IEEE Communications Magazine, Elsevier Ad Hoc Networks, and IEEE Network. Dr. Hassan has published in top journals like IEEE Journal on Selected Areas in Communications and IEEE Transactions on Mobile Computing and has edited a book on machine learning in drone networks. Her research focuses on civilian applications of drones, IoT, and AI, including leading a grant-funded project on AI-assisted weed management. A Senior Member of the IEEE and a Certified Professional Member of ACS, she has served on Technical Program Committees for over 30 international conferences. Dr. Hassan champions women in technology, co-chairing the N2Women initiative at IEEE WoWMoM 2024 and chairing the Women in Technology workshop at IEEE ITNAC 2020. Additionally, she is a co-chair of the 2024 DroneSense-AI workshop.
  • Mohsen Guizani, Machine Learning Department, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates
    Mohsen Guizani is currently a Professor of Machine Learning at Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE. His research interests include applied machine learning, artificial intelligence, Internet of Things, smart city, and cybersecurity. He was elevated to the IEEE Fellow in 2009 and was listed as a Clarivate Analytics Highly Cited Researcher in Computer Science in 2019, 2020 and 2021.
  • Edward Curry, Insight Centre for Data Analytics, University of Galaway, Ireland
    Edward Curry is the Established Professor of Data Science and Director of the Insight SFI Research Centre for Data Analytics at the University of Galway. Edward has made substantial contributions to semantic technologies, incremental data management, event processing middleware, software engineering, as well as distributed systems and information systems. He combines strong theoretical results with high-impact practical applications. The excellence and impact of his research have been acknowledged by numerous awards, including best paper awards and the University of Galway President’s Award for Societal Impact in 2017. His team’s technology enables intelligent systems for smart environments in collaboration with several industrial partners. He is organizer and programme co-chair of major international conferences, including CIKM 2020, ECML 2018, IEEE Big Data Congress, and European Big Data Value Forum. Edward is co-founder and elected Vice President of the Big Data Value Association, an industry-led European big data community, has built consensus on a joint European big data research and innovation agenda, and influenced European data innovation policy to deliver on the agenda.

References

Adebisi, J. A., Prabhat, T., &固定 Ghanshyam, S. (2022). Potential, concepts, and key advances for a ubiquitous adaptive indigenous microengineering and nanoengineering in 6G network. International Journal of Communication Systems, 35, Article e5410. https://doi.org/10.1002/dac.5410

Almalki, F., Alsamhi, S. H., Sahal, R., Hassan, J., Hawbani, A., Rajput, N. S., ... & Breslin, J. (2021). Green IoT for eco-friendly and sustainable smart cities: Future directions and opportunities. Mobile Networks and Applications, 26(5), 1–25. https://doi.org/10.1007/s11036-021-01746-y

Aloqaily, M., Bouachir, O., Boukerche, A., & Ridhawi, I. A. (2021). Design guidelines for blockchain-assisted 5G-UAV networks. IEEE Network, 35(1), 64–71. https://doi.org/10.1109/MNET.011.2000171

Aloqaily, M., Jararweh, Y., & Bouachir, O. (2021). Trustworthy cooperative UAV-based data management in densely crowded environments. IEEE Communications Standards Magazine, 5(4), 18–24. https://doi.org/10.1109/MCOMSTD.001.2100031

Alsamhi, S. H. (2015). Quality of Service (QoS) Enhancement Techniques in High Altitude Platform (HAP) Based Communication Networks (Ph.D. thesis). Banaras Hindu University, Varanasi, India.

Alsamhi, S. H., Ansari, M. S., & Rajput, N. S. (2018). Disaster coverage prediction for the emerging tethered balloon technology: Capability for preparedness, detection, mitigation, and response. Disaster Medicine and Public Health Preparedness, 12(2), 222–231. https://doi.org/10.1017/dmp.2017.55

Alsamhi, S. H., Ma, O., Ansari, M. S., & Gupta, S. K. (2019). Collaboration of drone and internet of public safety things in smart cities: An overview of QoS and network performance optimization. Drones, 3(1), Article 13. https://doi.org/10.3390/drones30103

Alsamhi, S. H., Ma, O., Ansari, M. S., & Meng, Q. (2019). Greening internet of things for greener and smarter cities: A survey and future prospects. Telecommunication Systems, 72(4), 609–632. https://doi.org/10.1007/s11235-019-00587-4

Alsamhi, S. H., Almalki, F. A., Al-Dois, H., Shvetsov, A. V., Ansari, M. S., Hawbani, A., ... & Lee, B. (2021). Multi-drone edge intelligence and SAR smart wearable devices for emergency communication. Wireless Communications and Mobile Computing, 2021, Article 6710074. https://doi.org/10.1155/2021/6710074

Alsamhi, S. H., Almalki, F. A., Ma, O., Ansari, M. S., & Lee, B. (2021). Predictive estimation of optimal signal strength from drones over IoT frameworks in smart cities. IEEE Transactions on Mobile Computing, 22(1), 402–416. https://doi.org/10.1109/TMC.2021.3061115

Alsamhi, S. H., Almalki, F. A., Afghah, F., Hawbani, A., Shvetsov, A. V., Lee, B., & Song, H. (2021). Drones' edge intelligence over smart environments in B5G: Blockchain and federated learning synergy. IEEE Transactions on Green Communications and Networking, 6(1), 295–312. https://doi.org/10.1109/TGCN.2021.3132152

Alsamhi, S. H., Lee, B., Guizani, M., Kumar, N., Qiao, Y., & Liu, X. (2021). Blockchain for decentralized multi-drone to combat COVID-19 and future pandemics: Framework and proposed solutions. Transactions on Emerging Telecommunications Technologies, 32(9), Article e4255. https://doi.org/10.1002/ett.4255

Alsamhi, S. H., Shvetsov, A. V., Kumar, S., Hassan, J., Alhartomi, M. A., Shvetsova, S. V., ... & Hawbani, A. (2022). Computing in the sky: A survey on intelligent ubiquitous computing for UAV-assisted 6G networks and Industry 4.0/5.0. Drones, 6(7), Article 177. https://doi.org/10.3390/drones6070177

Alsamhi, S. H., Shvetsov, A. V., Kumar, S., Shvetsova, S. V., Alhartomi, M. A., Hawbani, A., ... & Nyangaresi, V. O. (2022). UAV computing-assisted search and rescue mission framework for disaster and harsh environment mitigation. Drones, 6(7), Article 154. https://doi.org/10.3390/drones6070154

Alsamhi, S. H., Shvetsov, A. V., Hawbani, A., Shvetsova, S. V., Kumar, S., & Zhao, L. (2023). Survey on federated learning enabling indoor navigation for Industry 4.0 in B5G. Future Generation Computer Systems, 148, 250–265. https://doi.org/10.1016/j.future.2023.05.025

Alsamhi, S. H., Hawbani, A., Kumar, S., Gravina, R., Fortino, G., & Curry, E. (2023). Metaverse-driven drone edge intelligence in B5G: A conceptual framework for empowering CPSS. In 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 1289–1294). IEEE. https://doi.org/10.1109/SMC53903.2023.1012894

Alsamhi, S. H., & Rajput, N. S. (2014). HAP antenna radiation pattern for providing coverage and service characteristics. In 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 1434–1439). IEEE. https://doi.org/10.1109/ICACCI.2014.6968214

Alsamhi, S. H., & Rajput, N. S. (2014). Performance and analysis of propagation models for efficient handoff in high altitude platform system to sustain QoS. In 2014 IEEE Students' Conference on Electrical, Electronics and Computer Science (pp. 1–6). IEEE. https://doi.org/10.1109/SCEECS.2014.6804444

Alsamhi, S. H., & Rajput, N. S. (2015). An intelligent hand-off algorithm to enhance quality of service in high altitude platforms using neural network. Wireless Personal Communications, 82(4), 2059–2073. https://doi.org/10.1007/s11277-015-2332-2

Alsamhi, S. H., & Rajput, N. S. (2015). An intelligent HAP for broadband wireless communications: Developments, QoS and applications. International Journal of Electronics and Electrical Engineering, 3(2), 134–143. https://doi.org/10.12720/ijeee.3.2.134-143

Alsamhi, S. H., & Rajput, N. S. (2016). An efficient channel reservation technique for improved QoS for mobile communication deployment using high altitude platform. Wireless Personal Communications, 91(3), 1095–1108. https://doi.org/10.1007/s11277-016-3516-9

Alsamhi, S. H., & Rajput, N. S. (2016). Implementation of call admission control technique in HAP for enhanced QoS in wireless network deployment. Telecommunication Systems, 63(2), 141–151. https://doi.org/10.1007/s11235-015-0110-4

Alsamhi, S. H., Shvetsov, A. V., Shvetsova, S. V., Hawbani, A., Guizani, M., Alhartomi, M. A., & Ma, O. (2022). Blockchain-empowered security and energy efficiency of drone swarm consensus for environment exploration. IEEE Transactions on Green Communications and Networking, 7(1), 328–338. https://doi.org/10.1109/TGCN.2022.3175124

Alsamhi, S. H., Curry, E., Hawbani, A., Kumar, S.,固定 Hassan, U. U., & Rajput, N. S. (2023). DataSpace in the sky: A novel decentralized framework to secure drones data sharing in B5G for Industry 4.0 toward Industry 5.0. arXiv preprint arXiv:2305.10549.

Alsamhi, S. H., Myrzashova, R., Hawbani, A., Kumar, S., Srivastava, S., Zhao, L., ... & Guizani, M. (2024). Federated learning meets blockchain in decentralized data-sharing: Healthcare use case. IEEE Internet of Things Journal, 11(4), 6012–6025. https://doi.org/10.1109/JIOT.2024.3321524

Alsamhi, S. H., Shvetsov, A. V., Shvetsova, S. V.,固定 Hawbani, A., Guizani, M., Alhartomi, M. A., & Ma, O. (2022). Blockchain-empowered security and energy efficiency of drone swarm consensus for environment exploration. IEEE Transactions on Green Communications and Networking, 7(1), 328–338. https://doi.org/10.1109/TGCN.2022.3175124

Alsamhi, S. H., Almalki, F. A., Al-Dois, H., Ben Othman, S., Hassan, J.,固定 Hawbani, A., ... & Saleh, H. (2021). Machine learning for smart environments in B5G networks: Connectivity and QoS. Computational Intelligence and Neuroscience, 2021, Article 6805151. https://doi.org/10.1155/2021/6805151

Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15), 2787–2805. https://doi.org/10.1016/j.comnet.2010.05.010

Bortolini, M., Faccio, M., Gamberi, M., & Pilati, F. (2020). Motion analysis system (MAS) for production and ergonomics assessment in the manufacturing processes. Computers & Industrial Engineering, 139密, Article 105485. https://doi.org/10.1016/j.cie.2019.105485

Botta, A., De Donato, W., Persico, V., & Pescapé, A. (2016). Integration of cloud computing and internet of things: A survey. Future Generation Computer Systems, 56, 684–700. https://doi.org/10.1016/j.future.2015.09.021

Curry, E., Derguech, W., Hasan, S., Kouroupetroglou, C., & ul Hassan, U. (2019). A real-time linked dataspace for the Internet of things: Enabling 'pay-as-you-go' data management in smart environments. Future Generation Computer Systems, 90, 405–422. https://doi.org/10.1016/j.future.2018.04.025

Curry, E. (2020). Real-Time Linked Dataspaces: Enabling Data Ecosystems for Intelligent Systems. Springer Nature.

Curry, E., Fabritius, W., Hasan, S., Kouroupetroglou, C., & Derguech, W. (2020). A model for Internet of things enhanced user experience in smart environments. In Real-Time Linked Dataspace (pp. 271–294). Springer, Cham.

Curry, E., Derguech, W., Hasan, S., Kouroupetroglou, C., & Fabritius, W. (2020). Building Internet of things-enabled digital twins and intelligent applications using a real-time linked dataspace. In Real-Time Linked Dataspace (pp. 255–270). Springer, Cham.

Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660. https://doi.org/10.1016/j.future.2013.01.010

Gu, M., Li, X., & Cao, Y. (2014). Optical storage arrays: A perspective for future big data storage. Light: Science & Applications, 3(5), Article e177. https://doi.org/10.1038/lsa.2014.58

Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., &固定 Khan, S. U. (2015). The rise of 'big data' on cloud computing: Review and open research issues. Information Systems, 47, 98–115. https://doi.org/10.1016/j.is.2014.07.006

IEEE Internet Initiative. (2015). Towards a definition of the internet of things (IoT) (Revision 1). IEEE Standards White Paper.

Linde, L., Sjödin, D., Parida, V., & Wincent, J. (2021). Dynamic capabilities for ecosystem orchestration: A capability-based framework for smart city innovation initiatives. Technological Forecasting and Social Change, 166, Article 120614. https://doi.org/10.1016/j.techfore.2021.120614

Meng, Z., Wu, Z., Muvianto, C., & Gray, J. (2016). A data-oriented M2M messaging mechanism for industrial IoT applications. IEEE Internet of Things Journal, 4(1), 236–246. https://doi.org/10.1109/JIOT.2016.2646438

Myrzashova, R., Alsamhi, S. H., Shvetsov, A. V., Hawbani, A., & Wei, X. (2023). Blockchain meets federated learning in healthcare: A systematic review with challenges and opportunities. IEEE Internet of Things Journal, 11(3), 3593–3601.

Myrzashova, R., Alsamhi, S. H., Hawbani, A., Curry, E., Guizani, M., & Wei, X. (2024). Safeguarding patient data-sharing: Blockchain-enabled federated learning in medical diagnostics. IEEE Transactions on Sustainable Computing, 9(2), 1416–1427.

Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2014). Context aware computing for the internet of things: A survey. IEEE Communications Surveys & Tutorials, 16(1), 414–454. https://doi.org/10.1109/SURV.2013.042313.00125

Raja, S. A., & Muthuswamy, P. (2023). Industry 5.0 or industry 4.0 s? Introduction to industry 4.0 and a peek into the prospective industry 5.0 technologies. International Journal on Interactive Design and Manufacturing (IJIDeM), 17(2), 947–979. https://doi.org/10.1007/s12008-022-00920-x

Saif, A., Alsamhi, S. H., & Curry, E. (2023). Climate-resilient UAVs: Enhancing energy-efficient B5G communication in harsh environments. arXiv preprint arXiv:2309.09387.

Saif, A., Dimyati, K., Noordin, K. A., Mosali, N. A.,固定 Deepak, G. C., & Alsamhi, S. H. (2023). Skyward bound: Empowering disaster resilience with multi-UAV-assisted B5G networks for enhanced connectivity and energy efficiency. Internet of Things, 23, Article 100885. https://doi.org/10.1016/j.iot.2023.100885

Saif, A., Dimyati, K., Noordin, K. A., Shah, N. S. M., Alsamhi, S. H., & Abdullah, Q. (2021). Energy-efficient tethered UAV deployment in B5G for smart environments and disaster recovery. In 2021 1st International Conference on Emerging Smart Technologies and Applications (eSmarTA) (pp. 1–5). IEEE. https://doi.org/10.1109/eSmarTA52301.2021.107340

Shah, J., &固定 Mishra, B. (2016). IoT enabled environmental monitoring system for smart cities. In International Conference on Internet of Things and Applications (IOTA) (pp. 383–388). IEEE.

Shuja, J., Ahmad, R. W., Gani, A., Ahmed, A. I. A., Siddiqa, A., Nisar, K., ... & Zomaya, A. Y. (2017). Greening emerging IT technologies: Techniques and practices. Journal of Internet Services and Applications, 8(1), Article 9. https://doi.org/10.1186/s13174-017-0060-1

Singh, S. P., Kumar, N., Singh, A., Singh, K. K., Askar, S. S., & Abouhawwash, M. (2024). Energy efficient hybrid evolutionary algorithm for internet of everything (IoE)-enabled 6G. IEEE Access, 12, 1658–1670.

Tallat, R., Hawbani, A., Wang, X., Al-Dubai, A., Zhao, L., Liu, Z., ... & Alsamhi, S. H. (2023). Navigating Industry 5.0: A survey of key enabling technologies, trends, challenges, and opportunities. IEEE Communications Surveys & Tutorials, 25(4), 1767–1816.

Tellez, M., El-Tawab, S., & Heydari, H. M. (2016). Improving the security of wireless wireless sensor networks in an IoT environmental monitoring system. In Systems and Information Engineering Design Symposium (SIEDS) (pp. 72–77). IEEE.

Verma, A., Bhattacharya, P., Madhani, N., Trivedi, C., Bhushan, B., Tanwar, S., ... & Sharma, R. (2022). Blockchain for Industry 5.0: Vision, opportunities, key enablers, and future directions. IEEE Access, 10, 69160–69199. https://doi.org/10.1109/ACCESS.2022.3221999

13

Downloads

Published

2024-12-01

Issue

2024

Section

Articles

Categories

License

Copyright (c) 2024 CC BY 4.0

How to Cite

Alsamhi, S. H., Hawbani, A., Sahal, R., Srivastava, S., Kumar, S., Zhao, L., Al-qaness, M. A., Hassan, J., Guizani, M., & Curry, E. (2024). Towards sustainable industry 4.0: A survey on greening IoE in 6G networks. Emirates International University Digital Repository, 1(1). https://doi.org/10.1016/j.adhoc.2024.103610

Similar Articles

11-20 of 46

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)