Skip to main content

Advertisement

SpringerLink
Log in

Green IoT: A Short Survey on Technical Evolution & Techniques

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The Internet of Things (IoT) embodies the confluence of the virtual & physical world. IoT will play an important role in managing the managing depleting resource such as water, fuel, food, etc. However, to realize these applications enormous IoT devices will communicate with each other. This massive connectivity will directly or indirectly aid in Green House Gas emissions. Hence, to admissibly reduce this environmental impact of IoT, it must be greened in terms of energy consumption. Green IoT will reduce environmental exploitation by slashing carbon emission effectively and thus will help in achieving sustainability of the planet. This paper describes the journey of IoT to Green IoT. Along with this, the survey on recent Green-IoT techniques that will effectively help in reducing required energy consumption is presented. Along with this ability of unmanned aerial vehicle (UAV) technology to provide Green IoT and survey on recent energy-efficient UAV assisted communication is presented. In addition to this, a dual battery enabled Unmanned Aerial vehicle base station, an energy-efficient clustering algorithm, has also been proposed to prolong the battery life.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data Availability

Author can provide the data on demand.

Code Availability

Manuscript based on survey.

References

  1. Al-Fuqaha, A., et al. (2015). Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys and Tutorials, 17(4), 2347–2376.

    Article  Google Scholar 

  2. Li, S., Xu, L. D., et al. (2015). The internet of things: A survey. Information Systems Frontiers, 17(2), 243–259.

    Article  MathSciNet  Google Scholar 

  3. Berthelsen, E., et al. (2015) “The global IoT market opportunity will reach usd4.3 trillion by 2024.” Internet: https://machinaresearch.com/news/the-global-iot-market-opportunity-will-reach-usd43-trillion-by-2024/, [Dec. 20, 2017].

  4. Liu, X., & Ansari, N. (2019). Toward green IoT: Energy solutions and key challenges. IEEE Communications Magazine, 57(3), 104–110.

    Article  Google Scholar 

  5. Huang, J., et al. (2014). A novel deployment scheme for green internet of things. IEEE Internet of Things Journal, 1(2), 196–205.

    Article  Google Scholar 

  6. Huang, H., et al. (2019). Green data-collection from geo-distributed IoT networks through low-earth-orbit satellites. IEEE Transactions on Green Communications and Networking, 3(3), 806–816.

    Article  Google Scholar 

  7. Li, J., et al. (2017). Towards green IoT networking: Performance optimization of network coding based communication and reliable storage. IEEE Access, 5, 8780–8791.

    Article  Google Scholar 

  8. Rico-Alvarino, A., et al. (2016). An overview of 3GPP enhancements on machine to machine communications. IEEE Communications Magazine, 54(6), 14–21.

    Article  Google Scholar 

  9. Elsaadany, M., et al. (2017). Cellular LTE-A technologies for the future internet-of-things: Physical layer features and challenges. IEEE Communications Surveys and Tutorials, 19(4), 2544–2572.

    Article  Google Scholar 

  10. Sakshi, Jha, R. K., & Jain, S. (2021). A Comprehensive Survey on Green ICT with 5G-NB-IoT: Towards Sustainable Planet. Computer Networks, 108433.

  11. Popli, S., Jha, R. K., & Jain, S. (2021). Adaptive Small Cell position algorithm (ASPA) for green farming using NB-IoT. Journal of Network and Computer Applications, 173, 102841.

    Article  Google Scholar 

  12. Datta, S. K., Dugelay, J. L., & Bonnet, C. (2018). “IoT based UAV platform for emergency services.” In 2018 international conference on information and COMMUNICATION technology convergence (ICTC). IEEE, pp. 144–147.

  13. Nath, B., Reynolds, F., & Want, R. (2006). RFID technology and applications. IEEE Pervasive Computing, 5(1), 22–24.

    Article  Google Scholar 

  14. Opasjumruskit, K., et al. (2006). Self-powered wireless temperature sensors exploit RFID technology. IEEE Pervasive computing, 5(1), 54–61.

    Article  Google Scholar 

  15. Bhuptani, M., & Moradpour, M. (2005). RFID field guide: Deploying radio frequency identification systems. Prentice Hall PTR.

  16. Hossain, M. M., & Prybutok, V. R. (2008). Consumer acceptance of RFID technology: An exploratory study. IEEE Transactions on Engineering Management, 55(2), 316–328.

    Article  Google Scholar 

  17. Jia, X., Feng, Q., Fan, T., & Lei, Q. (2012). RFID technology and its applications in Internet of Things (IoT) (pp. 1282–1285). In Consumer Electronics.

  18. Sheng, Q. Z., Li, X., & Zeadally, S. (2008). Enabling next-generation RFID applications: Solutions and challenges. Computer, 41(9), 21–28.

    Article  Google Scholar 

  19. Goudos, S. K., et al. (2014). Novel spiral antenna design using artificial bee colony optimization for UHF RFID applications. IEEE Antennas and Wireless Propagation Letters, 13, 528–531.

    Article  Google Scholar 

  20. Arnitz, D., & Reynolds, M. S. (2013). Multi transmitter wireless power transfer optimization for backscatter RFID transponders. IEEE Antennas and Wireless Propagation Letters, 12, 849–852.

    Article  Google Scholar 

  21. Sohraby, K., Minoli, D., & Znati, T. (2007). Wireless sensor networks: Technology, protocols, and applications. John Wiley & Sons.

  22. Kumar, V., & Kumar, S. (2016). Energy balanced position-based routing for lifetime maximization of wireless sensor networks. Ad Hoc Networks, 52, 117–129.

    Article  Google Scholar 

  23. Thirukrishna, J. T., Karthik, S., & Arunachalam, V. P. (2018). Revamp energy efficiency in homogeneous wireless sensor networks using optimized radio energy algorithm (OREA) and power-aware distance source routing protocol. Future Generation Computer Systems, 81, 331–339.

    Article  Google Scholar 

  24. Ari, A. A. A., Yenke, B. O., Labraoui, N., Damakoa, I., et al. (2016). A power efficient cluster-based routing algorithm for wireless sensor networks: Honeybees swarm intelligence based approach. Journal of Network and Computer Applications, 69, 77–97.

    Article  Google Scholar 

  25. Kurt, S., Yildiz, H. U., Yigit, M., Tavli, B., & Gungor, V. C. (2017). Packet size optimization in wireless sensor networks for smart grid applications. IEEE Transactions on Industrial Electronics, 64(3), 2392–2401.

    Article  Google Scholar 

  26. Rahman, M. N., & Matin, M. A. (2011). Efficient algorithm for prolonging network lifetime of wireless sensor networks. Tsinghua Science and Technology, 16(6), 561–568.

    Article  Google Scholar 

  27. Wang, Y., Chen, R., & Wang, D. C. (2013). A survey of mobile cloud computing applications: Perspectives and challenges. Wireless Personal Communications, 80(4), 1607–1623.

    Article  Google Scholar 

  28. Atta ur Rehman, K., et al. (2014). A survey of mobile cloud computing application models. IEEE Communications Surveys and Tutorials, 16(1), 393–413.

    Article  Google Scholar 

  29. De, D. (2016). Mobile cloud computing: Architectures, algorithms and applications. CRC Press.

  30. Fernando, N., Loke, S. W., & Rahayu, W. (2013). Mobile cloud computing: A survey. Future generation computer systems, 29(1), 84–106.

    Article  Google Scholar 

  31. Abolfazli, S., & Sanaei, Z. (2014). Cloud-based augmentation for mobile devices: Motivation, taxonomies, and open challenges. IEEE Communications Surveys and Tutorials, 16(1), 337–368.

    Article  Google Scholar 

  32. Akherfi, K., Gerndt, M., & Harroud, H. (2018). Mobile cloud computing for computation offloading: Issues and challenges. Applied Computing and Informatics, 14(1), 1–16.

    Article  Google Scholar 

  33. Aminzadeh, N., Sanaei, Z., & Ab Hamid, S. H. (2015). Mobile storage augmentation in mobile cloud computing: Taxonomy, approaches, and open issues. Simulation Modelling Practice and Theory, 50, 96–108.

    Article  Google Scholar 

  34. Liu, K., Peng, J., Li, H., Zhang, X., & Liu, W. (2016). Multi-device task offloading with time-constraints for energy efficiency in mobile cloud computing. Future Generation Computer Systems, 64, 1–14.

    Article  Google Scholar 

  35. Li, Y., Chen, M., Dai, W., & Qiu, M. (2017). Energy optimization with dynamic task scheduling mobile cloud computing. IEEE Systems Journal, 11(1), 96–105.

    Article  Google Scholar 

  36. Shah-Mansouri, H., Wong, V. W., & Schober, R. (2017). Joint optimal pricing and task scheduling in mobile cloud computing systems. IEEE Transactions on Wireless Communications, 16(8), 5218–5232.

    Article  Google Scholar 

  37. Zhang, J., Xia, W., Yan, F., & Shen, L. (2018). Joint computation offloading and resource allocation optimization in heterogeneous networks with mobile edge computing. IEEE Access, 6, 19324–19337.

    Article  Google Scholar 

  38. Nawrocki, P., & Reszelewski, W. (2017). Resource usage optimization in mobile cloud computing. Computer Communications, 99, 1–12.

    Article  Google Scholar 

  39. Tiwary, M., Puthal, D., Sahoo, K. S., Sahoo, B., & Yang, L. T. (2018). Response time optimization for cloudlets in mobile edge computing. Journal of Parallel and Distributed Computing, 119, 81–91.

    Article  Google Scholar 

  40. Geng, H. (2017). Internet of things and data analytics handbook. John Wiley & Sons.

  41. Zhu, C., Leung, V. C., Shu, L., & Ngai, E. C. H. (2015). Green internet of things for smart world. IEEE Access, 3, 2151–2162.

    Article  Google Scholar 

  42. Elhattab, M. K., Elmesalawy, M. M., & Ibrahim, I. I. (2017). Opportunistic device association for heterogeneous cellular networks with H2H/IoT co-existence under QoS guarantee. IEEE Internet of Things Journal, 4(5), 1360–1369.

    Article  Google Scholar 

  43. Yang, Q., Wang, H. M., Zheng, T. X., Han, Z., & Lee, M. H. (2018). Wireless powered asynchronous backscatter networks with sporadic short packets: Performance analysis and optimization. IEEE Internet of Things Journal, 5(2), 984–997.

    Article  Google Scholar 

  44. Malmodin, J., and Lundén, D. (2018). “The energy and carbon footprint of the global ICT and E&M sectors 2010–2015.” 5th International Conference on Information and Communication Technology for Sustainability, EPiC Series in Computing, 52, 187:208.

  45. Belkhir, L., & Elmeligi, A. (2018). Assessing ICT global emissions footprint: Trends to 2040 & recommendations. Journal of Cleaner Production, 177, 448–463.

    Article  Google Scholar 

  46. Albreem, M. A. M., El-Saleh, A. A., Isa, M., Salah, W., Jusoh, M., Azizan, M. M., and Ali, A. (2017). “Green internet of things (IoT): An overview.” In 2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA), IEEE. pp. 1–6.

  47. Jeong, H., Lee, J., Yoo, H., & Park, I. (2016). A low-power high-performance SoC platform for IoT applications. IDEC Journal of Integrated Circuits and Systems, 2.

  48. Arshad, R., Zahoor, S., Shah, M. A., Wahid, A., & Yu, H. (2017). Green IoT: An investigation on energy saving practices for 2020 and beyond. IEEE Access, 5, 15667–15681.

    Article  Google Scholar 

  49. Raza, U., Kulkarni, P., & Sooriyabandara, M. (2017). Low power wide area networks: An overview. IEEE Communications Surveys and Tutorials, 19(2), 855–873.

    Article  Google Scholar 

  50. Popli, S., Jha, R. K., & Jain, S. (2018). A survey on energy efficient narrowband internet of things (NBIoT): Architecture, application and challenges. IEEE Access, 7, 16739–16776.

    Article  Google Scholar 

  51. Gandotra, P., Jha, R. K., & Jain, S. (2018). Prolonging user battery lifetime using green communication in spectrum sharing networks. IEEE Communications Letters, 22(7), 1490–1493.

    Article  Google Scholar 

  52. Chen, Y., et al. (2014). Time-reversal wireless paradigm for green internet of things: An overview. IEEE Internet of Things Journal, 1(1), 81–98.

    Article  Google Scholar 

  53. Danilak, R. (2017). Why energy is a big And rapidly growing problem for data centers, 12–17.

  54. Dayarathna, M., et al. (2016). Data center energy consumption modeling: A survey. IEEE Communications Surveys and Tutorials, 18(1), 732–794.

    Article  Google Scholar 

  55. Varasteh, A., & Goudarzi, M. (2015). Server consolidation techniques in virtualized data centers: A survey. IEEE Systems Journal, 11(2), 772–783.

    Article  Google Scholar 

  56. Bari, M. F., Boutaba, R., Esteves, R., Granville, L. Z., Podlesny, M., Rabbani, M. G., Zhang, Q., & Zhani, M. F. (2012). Data center network virtualization: A survey. IEEE Communications Surveys and Tutorials, 15(2), 909–928.

    Article  Google Scholar 

  57. Lyu, X., et al. (2018). Selective offloading in mobile edge computing for the green internet of things. IEEE Network, 32(1), 54–60.

    Article  Google Scholar 

  58. Din, S., Ahmad, A., Paul, A., & Rho, S. (2018). MGR: Multi-parameter green reliable communication for internet of things in 5G network. Journal of Parallel and Distributed Computing, 118, 34–45.

    Article  Google Scholar 

  59. Said, O., Al-Makhadmeh, Z., & Tolba, A. M. R. (2020). EMS: An energy management scheme for green IoT environments. IEEE Access, 8, 44983–44998.

    Article  Google Scholar 

  60. Deng, D., Xia, J., Fan, L., & Li, X. (2020). Link selection in buffer-aided cooperative networks for green IoT. IEEE Access, 8, 30763–30771.

    Article  Google Scholar 

  61. Na, Z., Wang, X., Shi, J., Liu, C., Liu, Y., & Gao, Z. (2020). Joint resource allocation for cognitive OFDM-NOMA systems with energy harvesting in green IoT. Ad Hoc Networks, 107, 102221.

    Article  Google Scholar 

  62. Liu, Q., Sun, S., Wang, H., & Zhang, S. (2021). 6G green IoT network: Joint design of intelligent reflective surface and ambient backscatter communication. Wireless Communications and Mobile Computing, 2021, 1–10.

    Google Scholar 

  63. Amjad, M., Chughtai, O., Naeem, M., & Ejaz, W. (2021). SWIPT-assisted energy efficiency optimization in 5G/B5G cooperative IoT network. Energies, 14(9), 2515.

    Article  Google Scholar 

  64. Verma, S., Kaur, S., Khan, M. A., & Sehdev, P. S. (2020). Toward green communication in 6G-enabled massive internet of things. IEEE Internet of Things Journal, 8(7), 5408–5415.

    Article  Google Scholar 

  65. Mozaffari, M., Saad, W., Bennis, M., Nam, Y. H., & Debbah, M. (2019). A tutorial on UAVs for wireless networks: Applications, challenges, and open problems. IEEE communications surveys and tutorials, 21(3), 2334–2360.

    Article  Google Scholar 

  66. Yang, Z., Xu, W., & Shikh-Bahaei, M. (2019). Energy efficient UAV communication with energy harvesting. IEEE Transactions on Vehicular Technology, 69(2), 1913–1927.

    Article  Google Scholar 

  67. Liu, C. H., Chen, Z., Tang, J., Xu, J., & Piao, C. (2018). Energy-efficient UAV control for effective and fair communication coverage: A deep reinforcement learning approach. IEEE Journal on Selected Areas in Communications, 36(9), 2059–2070.

    Article  Google Scholar 

  68. Wang, Q., Chen, Z., & Li, H. (2018). Energy-efficient trajectory planning for UAV-aided secure communication. China Communications, 15(5), 51–60.

    Article  Google Scholar 

  69. Miao, J., Li, H., Zheng, Z., & Wang, W. (2021). Secrecy energy efficiency maximization for UAV swarm assisted multi-hop relay system: Joint trajectory design and power control. IEEE Access, 9, 37784–37799.

    Article  Google Scholar 

  70. Li, Z., Wang, Y., Liu, M., Sun, R., Chen, Y., Yuan, J., & Li, J. (2019). Energy efficient resource allocation for UAV-assisted space-air-ground Internet of remote things networks. IEEE Access, 7, 145348–145362.

    Article  Google Scholar 

  71. Ahmed, S., Chowdhury, M. Z., & Jang, Y. M. (2020). Energy-efficient UAV relaying communications to serve ground nodes. IEEE Communications Letters, 24(4), 849–852.

    Article  Google Scholar 

  72. Sohail, M. F., Leow, C. Y., & Won, S. (2019). Energy-efficient non-orthogonal multiple access for UAV communication system. IEEE Transactions on Vehicular Technology, 68(11), 10834–10845.

    Article  Google Scholar 

  73. Zeng, Y., & Zhang, R. (2017). Energy-efficient UAV communication with trajectory optimization. IEEE Transactions on Wireless Communications, 16(6), 3747–3760.

    Article  Google Scholar 

  74. Yang, G., Dai, R., & Liang, Y. C. (2020). Energy-efficient UAV backscatter communication with joint trajectory design and resource optimization. IEEE Transactions on Wireless Communications, 20(2), 926–941.

    Article  Google Scholar 

  75. Yang, S., Deng, Y., Tang, X., Ding, Y., & Zhou, J. (2019). Energy efficiency optimization for UAV-assisted backscatter communications. IEEE Communications Letters, 23(11), 2041–2045.

    Article  Google Scholar 

  76. Shafique, T., Tabassum, H., & Hossain, E. (2019). End-to-end energy-efficiency and reliability of UAV-assisted wireless data ferrying. IEEE Transactions on Communications, 68(3), 1822–1837.

    Article  Google Scholar 

  77. Ruan, L., Wang, J., Chen, J., Xu, Y., Yang, Y., Jiang, H., Zhang, Y., & Xu, Y. (2018). Energy-efficient multi-UAV coverage deployment in UAV networks: A game-theoretic framework. China Communications, 15(10), 194–209.

    Article  Google Scholar 

  78. Pan, Y., Da, X., Hu, H., Zhu, Z., Xu, R., & Ni, L. (2019). Energy-efficiency optimization of UAV-based cognitive radio system. IEEE Access, 7, 155381–155391.

    Article  Google Scholar 

  79. Wu, J., Ma, J., Rou, Y., Zhao, L., & Ahmad, R. (2019). An energy-aware transmission target selection mechanism for UAV networking. IEEE Access, 7, 67367–67379.

    Article  Google Scholar 

  80. Liu, C., Feng, W., Wang, J., Chen, Y., & Ge, N. (2019). Aerial small cells using coordinated multiple UAVs: An energy efficiency optimization perspective. IEEE Access, 7, 122838–122848.

    Article  Google Scholar 

  81. Ahmed, S., Chowdhury, M. Z., & Jang, Y. M. (2020). Energy-efficient UAV-to-user scheduling to maximize throughput in wireless networks. IEEE Access, 8, 21215–21225.

    Article  Google Scholar 

  82. Nguyen, K. K., Vien, N. A., Nguyen, L. D., Le, M. T., Hanzo, L., & Duong, T. Q. (2020). Real-time energy harvesting aided scheduling in UAV-assisted D2D networks relying on deep reinforcement learning. IEEE Access, 9, 3638–3648.

    Article  Google Scholar 

  83. Zhang, X., & Duan, L. (2020). Energy-saving deployment algorithms of UAV swarm for sustainable wireless coverage. IEEE Transactions on Vehicular Technology, 69(9), 10320–10335.

    Article  Google Scholar 

  84. Mozaffari, M., et al. (2019). A tutorial on UAVs for wireless networks: Applications, challenges, and open problems. IEEE Communications Surveys and Tutorials, 21(3), 2334–2360.

    Article  Google Scholar 

  85. Asadpour, M., den Bergh, B. V., Giustiniano, D., Hummel, K. A., Pollin, S., & Plattner, B. (2014). Micro aerial vehicle networks: An experimental analysis of challenges and opportunities. IEEE Communications Magazine, 52(7), 141–149.

    Article  Google Scholar 

  86. Al-Hourani, A., & Gomez, K. (2017). Modeling cellular-to-UAV path-loss for suburban environments. IEEE Wireless Communications Letters, 7(1), 82–85.

    Article  Google Scholar 

  87. Lauridsen, M., et al. (2018) “An empirical NB-IoT power consumption model for battery lifetime estimation.” In 2018 IEEE 87th Vehicular Technology Conference (VTC Spring). IEEE.

Download references

Acknowledgements

The authors gratefully acknowledge the support provided by 5G and IoT Lab, SoECE, TBIC, TEQUIP-III at Shri Vaishno Devi University, Katra, Jammu and IIITDM Jabalpur department of ECE.

Funding

The work has carried out at 5G & IoT Lab, SMVDU.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Jammu & Kashmir, India

    Sakshi Popli

  2. Department of Electronics and Communication Engineering, IIITDM, Jabalpur, India

    Rakesh Kumar Jha

  3. Design and Manufacturing, Indian Institute of Information Technology, Jabalpur, India

    Sanjeev Jain

Authors
  1. Sakshi Popli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rakesh Kumar Jha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sanjeev Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

1Has written the survey paper with details; 2has proposed the architecture and done the mathematical analysis.

Corresponding author

Correspondence to Rakesh Kumar Jha.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Popli, S., Jha, R.K. & Jain, S. Green IoT: A Short Survey on Technical Evolution & Techniques. Wireless Pers Commun 123, 525–553 (2022). https://doi.org/10.1007/s11277-021-09142-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-09142-3

Keywords

Search

Navigation