Skip to main content

Advertisement

SpringerLink
Log in

Application of Green IoT in Agriculture 4.0 and Beyond: Requirements, Challenges and Research Trends in the Era of 5G, LPWANs and Internet of UAV Things

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Enabling technologies of Agriculture 4.0 such as IoT-driven Precision Agriculture (PA), Unmanned Aerial Vehicles (UAVs), and big data analytics are collaborating for the transformation of global agribusiness. In PA applications, IoT devices sense, collect, and transmit data to the cloud or edge for processing and processed data are stored in data centers. This exchange of a very large amount of information amongst billions of interconnected devices demands massive energy in PA applications. The growth of IoT devices is exponentially increasing directly or indirectly generating Green House Gases and causing energy deficiency in power-hungry IoT components like sensors. Thus, adopting green solutions is inevitable to promote energy-conserving, environment-friendly, and cost-effective IoT component designs. Inspired by achieving a green environment for IoT, we first give an overview of different data processing and energy-conserving strategies using machine learning, cloud computing, and edge computing. We then discuss and evaluate different Green IoT (GIoT) solutions that can be implemented for GIoT-based PA leveraging UAVs, Low Power Wide Area Networks (LPWANs), and 5G networks along with several implementation concerns. In addition, the sustainable progress towards Agriculture of 5.0 by integrating GIoT components is discussed to make the IoT greener using 5G networks and beyond. Based on the current survey, we have conceptualized a GIoT framework for designing energy-conserving, cost-effective, and environment-friendly PA applications while enabling ubiquitous connectivity. Finally, this paper systematically summarizes different security threats in GIoT layers and analyzes the measures that can be adopted to mitigate those threats in detail.

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
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26

Similar content being viewed by others

Data Availibility

Data sharing not applicable to this manuscript as no datasets were generated or analysed during the current study.

Code Availability

No codes are made available for sharing at present.

References

  1. Zhai, Z., Martinez, J. F., Beltran, V., & Martinez, N. L. (2020). Decision support systems for agriculture 4.0: Survey and challenges. Computers and Electronics in Agriculture, 170, 105256.

    Google Scholar 

  2. Shafi, M., Molisch, A. F., Smith, P. J., Haustein, T., Zhu, P., De Silva, P., Tufvesson, F., Benjebbour, A., & Wunder, G. (2017). 5G: A tutorial overview of standards, trials, challenges, deployment, and practice. IEEE Journal on Selected Areas in Communications, 35(6), 1201–1221.

    Google Scholar 

  3. Shafique, K., Khawaja, B. A., Sabir, F., Qazi, S., & Mustaqim, M. (2020). Internet of things (IoT) for next-generation smart systems: A review of current challenges, future trends and prospects for emerging 5G-IoT scenarios. IEEE Access, 8, 23022–23040.

    Google Scholar 

  4. Popli, S., Jha, R. K., & Jain, S. (2022). Green IoT: A short survey on technical evolution and techniques. Wireless Personal Communications, 123(1), 525–553.

    Google Scholar 

  5. Zhang, L., Dabipi, I. K., & BrownJr, W. L. (2018). Internet of things applications for agriculture. Internet of Things A to Z: Technologies and Applications, 507–528. https://doi.org/10.1002/9781119456735.ch18

  6. Alsamhi, S. H., Ma, O., Ansari, M. S., & Almalki, F. A. (2019). Survey on collaborative smart drones and internet of things for improving smartness of smart cities. IEEE Access, 7, 128125–128152.

    Google Scholar 

  7. Albreem, M. A., Sheikh, A. M., Alsharif, M. H., Jusoh, M., & Yasin, M. N. (2021). Green internet of things (GIoT): Applications, practices, awareness, and challenges. IEEE Access, 9, 38833–38858.

    Google Scholar 

  8. 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.

    Google Scholar 

  9. Hernandez-Vega, J.-I., Varela, E. R., Romero, N. H., Hernandez-Santos, C., Cuevas, J. L. S., & Gorham, D. G. P. (2018). Internet of things (IoT) for monitoring air pollutants with an unmanned aerial vehicle (UAV) in a smart city. In Smart Technology (pp. 108–120). Springer

  10. Ayoub, W., Samhat, A. E., Nouvel, F., Mroue, M., & Prevotet, J. C. (2018). Internet of mobile things: Overview of LoRaWAN, DASH7, and NB-IoT in LPWANS standards and supported mobility. IEEE Communications Surveys and Tutorials, 21(2), 1561–1581.

    Google Scholar 

  11. Ismail, D., Rahman, M., & Saifullah, A. (2018). Low-power wide-area networks: Opportunities, challenges, and directions. In Proceedings of the workshop program of the 19th international conference on distributed computing and networking (pp. 1–6).

  12. Mohamed, E. (2020). The relation of artificial intelligence with internet of things: A survey. Journal of Cybersecurity and Information Management, 1(1), 24–30.

    Google Scholar 

  13. Mughees, A., Tahir, M., Sheikh, M. A., & Ahad, A. (2020). Towards energy efficient 5G networks using machine learning: Taxonomy, research challenges, and future research directions. IEEE Access, 8, 187498–187522.

    Google Scholar 

  14. Nan, Y., Li, W., Bao, W., Delicato, F. C., Pires, P. F., Dou, Y., & Zomaya, A. Y. (2017). Adaptive energy-aware computation offloading for cloud of things systems. IEEE Access, 5, 23947–23957.

    Google Scholar 

  15. Popli, S., Jha, R. K., & Jain, S. (2016). A survey on energy efficient narrowband internet of things (NBIoT): architecture, application and challenges. IEEE Access, 7, 16739–16776.

    Google Scholar 

  16. Adam, A. H., Tamilkodi, R., & Valli, M. K. (2019). Low-cost green power predictive farming using IoT and cloud computing. In Proceedings of international conference on vision towards emerging trends in communication and networking (ViTECoN) (pp. 1–5). IEEE.

  17. Dhall, R., & Agrawal, H. (2018). An improved energy efficient duty cycling algorithm for IoT based precision agriculture. Procedia Computer Science, 141, 135–142.

    Google Scholar 

  18. Said, O., Zafer-Al, M., & Tolba, A. (2020). Ems: An energy management scheme for green IoT environments. IEEE Access, 8, 44983–44998.

    Google Scholar 

  19. Mekala, M. S., & Viswanathan, P. (2020). (t, n): Sensor stipulation with THAM index for smart agriculture decision-making IoT system. Wireless Personal Communications, 111(3), 1909–1940.

    Google Scholar 

  20. Cao, X., Song, Z., Yang, B., ElMossallamy, M. A., Qian, L., & Han, Z. (2019). A distributed ambient backscatter mac protocol for internet-of-things networks. IEEE Internet of Things Journal, 7(2), 1488–1501.

    Google Scholar 

  21. Sharma, V., You, I., & Kumar, R. (2016). Energy efficient data dissemination in multi-UAV coordinated wireless sensor networks. Mobile Information Systems, 2016, 1–13.

  22. Choi, D. H., Kim, S. H., & Sung, D. K. (2014). Energy-efficient maneuvering and communication of a single UAV-based relay. IEEE Transactions on Aerospace and Electronic Systems, 50(3), 2320–2327.

    Google Scholar 

  23. Bejiga, M. B., Zeggada, A., Nouffidj, A., & Melgani, F. (2017). A convolutional neural network approach for assisting avalanche search and rescue operations with UAV imagery. Remote Sensing, 9(2), 100.

    Google Scholar 

  24. Tuyishimire, E., Bagula, A., Rekhis, S., & Boudriga, N. (2017). Cooperative data muling from ground sensors to base stations using UAVs. In IEEE symposium on computers and communications (ISCC) (pp. 35–41).

  25. Quaritsch, M., Kruggl, K., Wischounig-Strucl, D., Bhattacharya, S., Shah, M., & Rinner, B. (2010). Networked UAVs as aerial sensor network for disaster management applications. Elektrotechnik Informationstechnik, 127(3), 56–63.

    Google Scholar 

  26. Ren, Y., Zhang, X., & Lu, G. (2020). The wireless solution to realize green IoT: Cellular networks with energy efficient and energy harvesting schemes. Energies, 13(22), 5875.

    Google Scholar 

  27. Brewster, C., Roussaki, I., Kalatzis, N., Doolin, K., & Ellis, K. (2017). Iot in agriculture: Designing a Europe-wide large-scale pilot. IEEE Communications Magazine, 55(9), 26–33.

    Google Scholar 

  28. Wang, S., Garg, H., Lin, G., Kaddoum, J., & Alhamid, M. F. (2021). An intelligent UAV based data aggregation algorithm for 5G-enabled internet of things. Computer Networks, 185, 107628.

    Google Scholar 

  29. Kouhdaragh, V., Verde, F., Gelli, G., & Abouei, J. (2020). On the application of machine learning to the design of UAV-based 5G radio access networks. Electronics, 9(4), 689.

    Google Scholar 

  30. Ray, P. P. (2017). Internet of things for smart agriculture: Technologies, practices and future direction. AIS, 9(4), 395–420.

    Google Scholar 

  31. Tzounis, A., Katsoulas, N., Bartzanas, T., & Kittas, C. (2017). Internet of things in agriculture, recent advances and future challenges. Biosystems Engineering, 164, 31–48.

    Google Scholar 

  32. Elijah, O., Rahman, T. A., Orikumhi, I., Leow, C. Y., & Hindia, M. H. (2018). An overview of internet of things (IoT) and data analytics in agriculture: Benefits and challenges. IEEE Internet of Things Journal, 5(5), 3758–3773.

    Google Scholar 

  33. Khanna, A., & Kaur, S. (2019). Evolution of internet of things (IoT) and its significant impact in the field of precision agriculture. Computers and Electronics in Agriculture, 157, 218–231.

    Google Scholar 

  34. Ruan, J., Wang, Y., Chan, F. T., Hu, X., Zhao, M., Zhu, F., Shi, B., Shi, Y., & Lin, F. (2019). A life cycle framework of green IoT-based agriculture and its finance, operation, and management issues. IEEE Communications Magazine, 57(3), 90–96.

    Google Scholar 

  35. Ferrag, M. A., Shu, L., Yang, X., Derhab, A., & Maglaras, L. (2020). Security and privacy for green IoT-based agriculture: Review, blockchain solutions, and challenges. IEEE Access, 8, 32031–32053.

    Google Scholar 

  36. 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.

    Google Scholar 

  37. Alsamhi, S. H., Ma, O., Ansari, M. S., & Meng, Q. (2018). Greening internet of things for smart everythings with a green-environment life: A survey and future prospects. arXiv. arXiv preprint arXiv:1805.00844

  38. Lyu, X., Tian, H., Jiang, L., Vinel, A., Maharjan, S., Gjessing, S., & Zhang, Y. (2018). Selective offloading in mobile edge computing for the green internet of things. IEEE Network, 32(1), 54–60.

    Google Scholar 

  39. Gupta, V., Tripathi, S., & De, S. (2020). Green sensing and communication: A step towards sustainable IoT systems. Journal of the Indian Institute of Science, 100(2), 383–398.

    Google Scholar 

  40. Foubert, B., & Mitton, N. (2020). Long-range wireless radio technologies: A survey. Future Internet Journal, 12(1), 13.

    Google Scholar 

  41. Malik, A., & Kushwah, R. (2022). A survey on next generation IoT networks from green IoT perspective. International Journal of Wireless Information Networks, 29(1), 36–57.

    Google Scholar 

  42. Lahmeri, M. A., Kishk, M. A., Alouini, M. S., Kishk, M. A., & Alouini, M. S. (2021). Artificial intelligence for UAV-enabled wireless networks: A survey. IEEE Open Journal of the Communications Society, 2, 1015–1040.

    Google Scholar 

  43. Alsamhi, S. H., Afghah, F., Sahal, R., Hawbani, A., Al-qaness, M. A., Lee, B., & Guizani, M. (2021). Green internet of things using UAVs in B5G networks: A review of applications and strategies. AdHoc Networks, 117, 102505.

    Google Scholar 

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

    Google Scholar 

  45. Dayarathna, M., Wen, Y., & Fan, R. (2016). Data center energy consumption modeling: A survey. IEEE Communications Surveys and Tutorials, 18(1), 732–794.

    Google Scholar 

  46. Azevedo, J., & Santos, F. (2012). Energy harvesting from wind and water for autonomous wireless sensor nodes. IET Circuits, Devices and Systems, 6(6), 413–420.

    MathSciNet  Google Scholar 

  47. Shaikh, F. K., & Zeadally, S. (2016). Energy harvesting in wireless sensor networks: A comprehensive review. Renewable and Sustainable Energy Reviews, 55, 1041–1054.

    Google Scholar 

  48. Wang, J., Hu, C., & Liu, A. (2017). Comprehensive optimization of energy consumption and delay performance for green communication in internet of things. Mobile Information Systems. https://doi.org/10.1155/2017/3206160

    Article  Google Scholar 

  49. Liu, X. F., Zhan, Z. H., & Zhang, J. (2017). An energy aware unified ant colony system for dynamic virtual machine placement in cloud computing. Energies, 10(5), 609.

    Google Scholar 

  50. Jayalath, J. M., Chathumali, E. J., Kothalawala, K. R., & Kuruwitaarachchi, N. (2019). Green cloud computing: a review on adoption of green-computing attributes and vendor specific implementations. In International research conference on smart computing and systems engineering (SCSE) (pp. 158–164).

  51. Bello, H., Xiaoping, Z., Nordin, R., & Xin, J. (2019). Advances and opportunities in passive wake-up radios with wireless energy harvesting for the internet of things applications. Sensors, 19(14), 3078.

    Google Scholar 

  52. Kozlowski, A., & Sosnowski, J. (2019). Energy efficiency trade-off between duty-cycling and wake-up radio techniques in IoT networks. Wireless Personal Communications, 107(4), 1951–1971.

    Google Scholar 

  53. Rawat, P., & Chauhan, S. (2021). Probability based cluster routing protocol for wireless sensor network. Journal of Ambient Intelligence and Humanized Computing, 12, 2065–2077.

    Google Scholar 

  54. Goldstein, A., Lior, F., Amit, M., Bohadana, S., Lutenberg, O., & Ravid, G. (2018). Applying machine learning on sensor data for irrigation recommendations: Revealing the agronomist’s tacit knowledge. Precision Agriculture Journal, 19(3), 421–444.

    Google Scholar 

  55. Kumar, A., Surendra, A., Mohan, H., Valliappan, K. M., & Kirthika, N. (2017). Internet of things based smart irrigation using regression algorithm. In Proceedings of international conference on intelligent computing, instrumentation and control technologies (ICICICT) (pp. 1652–1657). IEEE

  56. Mohapatra, A. G., Lenka, S. K., & Keswani, B. (2019). Neural network and fuzzy logic based smart DSS model for irrigation notification and control in precision agriculture. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 89(1), 67–76.

    Google Scholar 

  57. Keswani, B., Mohapatra, A., Keswani, P., Khanna, A., Gupta, D., & Rodrigues, J. (2020). Improving weather dependent zone specific irrigation control scheme in IoT and big data enabled self driven precision agriculture mechanism. Enterprise Information Systems Journal, 14(9–10), 1–22.

  58. Goap, A., Sharma, D., Shukla, A. K., & Rama-Krishna, C. (2018). An IoT based smart irrigation management system using machine learning and open source technologies. Computers and Electronics in Agriculture, 155, 41–49.

    Google Scholar 

  59. Vij, A., Singh, V., Jain, A., Bajaj, S., Bassi, A., & Sharma, A. (2020). Iot and machine learning approaches for automation of farm irrigation system. Procedia Computer Science, 167, 1250–1257.

    Google Scholar 

  60. Munir, M., Safdar, I., Sarwar, B., & Cheema, S. M. (2019). An intelligent and secure smart watering system using fuzzy logic and blockchain. Computers and Electrical Engineering Journal, 77, 109–119.

    Google Scholar 

  61. Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A., & DeFelice, F. (2020). Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions. Sustainability, 12(2), 492.

    Google Scholar 

  62. Remmert, H. (2020). Edge computing, artificial intelligence, machine learning and 5G. [https://www.digi.com/blog/post/edge-compute-artificial-intelligence-ml-5g]

  63. Mehmood, F., Hamza, M. A., Bukhsh, R., Javaid, N., Imran, M. I. U., Choudri, S., & Ahmed, U. (2020). Green fog: Cost efficient real time power management service for green community. In Proceedings of the 14th international conference on complex, intelligent and software intensive systems (pp. 142–155). Cham: Springer.

  64. Sakai, R., Saito, T., Nakamura, S., Enokido, T., & Takizawa, M. (2020). Software-oriented routing protocol for energy-efficient wireless communications. In Proceedings of the 14th international conference on complex, intelligent and software intensive systems (pp. 1–11). Cham: Springer.

  65. Saito, T., Nakamura, S., Enokido, T., & Takizawa, M. (2020). A topic-based publish/subscribe system in a fog computing model for the IoT. InProceedings of the 14th international conference on complex, intelligent and software intensive systems (pp. 12–21). Cham: Springer.

  66. Sheikhi, A., Rayati, M., & Ranjbar, A. M. (2015). Energy hub optimal sizing in the smart grid; machine learning approach. In IEEE power and energy society innovative smart grid technologies conference (ISGT) (pp. 1–5). IEEE.

  67. Mounce, S. R., Pedraza, C., Jackson, T., Linford, P., & Boxall, J. B. (2015). Cloud based machine learning approaches for leakage assessment and management in smart water networks. Procedia Engineering, 119, 43–52.

    Google Scholar 

  68. Lavassani, M., Forsstrom, S., Jennehag, U., & Zhang, T. (2018). Combining fog computing with sensor mote machine learning for industrial IoT. Sensors, 18(5), 1532.

    Google Scholar 

  69. Paris, L., & Anisi, M. H. (2019). An energy-efficient predictive model for object tracking sensor networks. In IEEE 5th World Forum on Internet of Things (WF-IoT) (pp. 263–268). IEEE.

  70. Liu, Y., Yang, C., Jiang, L., Xie, S., & Zhang, Y. (2019). Intelligent edge computing for IoT-based energy management in smart cities. IEEE Network, 33(2), 111–117.

    Google Scholar 

  71. Zhang, Q., Mozaffari, M., Saad, W., Bennis, M., & Debbah, M. (2018) Machine learning for predictive on-demand deployment of UAVs for wireless communications. In IEEE global communications conference (GLOBECOM) (pp. 1–9).

  72. Chen, J., Yatnalli, U., & Gesbert, D. (2017). Learning radio maps for UAV aided wireless networks: A segmented regression approach. In IEEE International Conference on Communications (ICC) (pp. 1–6).

  73. Zhang, Q., Saad, W., Bennis, M., Lu, X., Debbah, M., & Zuo, W. (2021). Predictive deployment of UAV base stations in wireless networks: Machine learning meets contract theory. IEEE Transactions on Wireless Communications, 20, 637–652.

    Google Scholar 

  74. Peng, H., Razi, A., Afghah, F., & Ashdown, J. (2018). A unified framework for joint mobility prediction and object profiling of drones in UAV networks. Journal of Communications and Networks, 20, 434–442.

    Google Scholar 

  75. Xiao, K., Zhao, J., He, Y., & Yu, S. (2019). Trajectory prediction of UAV in smart city using recurrent neural networks. In IEEE international conference on communications (ICC) (pp. 1–6).

  76. Kumari, R., & Kaushal, S. (2017). Energy efficient approach for applicationexecution in mobile cloud IoT environment. In Proceedings of the second international conference on internet of things, data and cloud computing (pp. 1–8).

  77. Alharbi, F., Tian, Y. C., Tang, M., Zhang, W. Z., Peng, C., & Fei, M. (2019). An ant colony system for energy-efficient dynamic virtual machine placement in data centers. Expert Systems with Applications, 120, 228–238.

    Google Scholar 

  78. Gharehpasha, S., Masdari, M., & Jafarian, A. (2021). Virtual machine placement in cloud data centers using a hybrid multi-verse optimization algorithms. Artificial Intelligence Review, 54, 2221–2257.

  79. Azar, J., Makhoul, A., Barhamgi, M., & Couturier, R. (2019). An energy efficient IoT data compression approach for edge machine learning. Future Generation Computer Systems, 96, 168–175.

    Google Scholar 

  80. Min, M., Xiao, L., Chen, Y., Cheng, P., Wu, D., & Zhuang, W. (2019). Learning based computation offloading for IoT devices with energy harvesting. IEEE Transactions on Vehicular Technology, 68(2), 1930–1941.

    Google Scholar 

  81. Ye, Y., Azmat, F., Adenopo, I., Chen, Y., & Shi, R. (2021). RF energy modelling using machine learning for energy harvesting communications systems. International Journal of Communication Systems, 34, 4688.

    Google Scholar 

  82. Khan, Z. A., Hussain, T., & Baik, S. W. (2022). Boosting energy harvesting via deep learning-based renewable power generation prediction. Journal of King Saud University-Science, 34, 101815.

    Google Scholar 

  83. Chu, M., Liao, X., Li, H., & Cui, S. (2019). Power control in energy harvesting multiple access system with reinforcement learning. IEEE Internet of Things Journal, 6, 9175–9186.

    Google Scholar 

  84. Zhang, Y., He, J., & Guo, S. (2018). Energy-efficient dynamic task offloading for energy harvesting mobile cloud computing. In 2018 IEEE international conference on networking, architecture and storage (NAS) (pp. 1–4).

  85. Singh, S., Sharma, P. K., Moon, S. Y., & Park, J. H. (2017). EH-GC: An efficient and secure architecture of energy harvesting green cloud infrastructure. Sustainability, 9, 673.

    Google Scholar 

  86. Kakati, S., Mazumdar, N., & Nag, A. (2022). Green cloud computing for IoT based smart applications. In Green mobile cloud computing (pp. 201–212). Cham: Springer International Publishing.

  87. Zhang, G., Zhang, W., Cao, Y., Li, D., & Wang, L. (2018). Energy-delay tradeoff for dynamic offloading in mobile-edge computing system with energy harvesting devices. IEEE Transactions on Industrial Informatics, 14, 4642–4655.

    Google Scholar 

  88. Lu, M., Fu, G., Osman, N. B., & Konbr, U. (2021). Green energy harvesting strategies on edge-based urban computing in sustainable internet of things. Sustainable Cities and Society, 75, 103349.

    Google Scholar 

  89. Tang, Q., Xie, R., Yu, F. R., Huang, T., & Liu, Y. (2020). Decentralized computation offloading in IoT fog computing system with energy harvesting: A dec-POMDP approach. IEEE Internet of Things Journal, 7, 4898–4911.

    Google Scholar 

  90. Kim, Y., & Lee, T. J. (2017). Service area scheduling in a drone assisted network. In International conference on computational science and its applications (pp. 161–171). Springer.

  91. Carrio, A., Parez, C. S., Ramos, A. R., & Campoy, P. (2017). A review of deep learning methods and applications for unmanned aerial vehicles. Journal of Sensors, 2, 1–13.

    Google Scholar 

  92. Yoo, S. J., Park, J. H., Kim, S. H., & Shrestha, A. (2016). Flying path optimization in UAV-assisted IoT sensor networks. ICT Express, 2(3), 140–144.

    Google Scholar 

  93. Hawbani, A., Wang, X., Kuhlani, H., Ghannami, A., Farooq, M. U., & Al-Sharabi, Y. (2019). Extracting the overlapped sub-regions in wireless sensor networks. Wireless Networks, 25(8), 4705–4726.

    Google Scholar 

  94. Moradi, M., Bokani, A., & Hassan, J. (2020). Energy-efficient and QoS-aware UAV communication using reactive RF band allocation. In 30th International telecommunication networks and applications conference (ITNAC) (pp. 1–6). IEEE.

  95. Ahmed, S., Chowdhury, M. Z., & Jang, Y. M. (2021). Energy-efficient UAV-to-user scheduling to maximize throughput in wireless networks. IEEE Access, 8, 21215–21225.

    Google Scholar 

  96. Li, M., Cheng, N., Gao, J., Wang, Y., Zhao, L., & Shen, X. (2020). Energy-efficient UAV-assisted mobile edge computing: resource allocation and trajectory optimization. IEEE Transactions on Vehicular Technology, 69(3), 3424–3438.

    Google Scholar 

  97. Nguyen, A. N., Vo, V. N., So-In, C., & Ha, D. B. (2021). System performance analysis for an energy harvesting IoT system using a DF/AF UAV-enabled relay with downlink NOMA under Nakagami-m fading. Sensors, 21(1), 285.

    Google Scholar 

  98. Namboodiri, V., & Gao, L. (2009). Energy-aware tag anticollision protocols for RFID systems. IEEE Transactions on Mobile Computing, 9(1), 44–59.

    Google Scholar 

  99. Choi, J. S., Son, B. R., Kang, H. K., & Lee, D. H. (2012). Indoor localization of unmanned aerial vehicle based on passive UHF RFID systems. In 9th international conference on ubiquitous robots and ambient intelligence (URAI) (pp. 188–189). IEEE.

  100. Hubbard, B., Wang, H., Leasure, M., Ropp, T., Lofton, T., Hubbard, S., & Lin, S. (2015). Feasibility study of UAV use for RFID material tracking on construction sites. In 51st ASC annual international conference proceedings.

  101. Allegretti, M., & Bertoldo, S. (2015). Recharging RFID tags for environmental monitoring using UAVs: A feasibility analysis. Wireless Sensor Network, 7(2), 13.

    Google Scholar 

  102. Hubbard, B., Wang, H., & Leasure, M. (2016). Feasibility study of UAV use for RFID material tracking on construction sites. In Presented at the Proc. 51st ASC annual international conference proceedings College Station, TX, USA.

  103. Greco, G., Lucianaz, C., Bertoldo, S., & Allegretti, M. (2015). A solution for monitoring operations in harsh environment: A rfid reader for small UAV. In International conference on electromagnetics in advanced applications (ICEAA) (pp. 859–862). IEEE.

  104. Malaver, A., Motta, N., Corke, P., & Gonzalez, F. (2015). Development and integration of a solar powered unmanned aerial vehicle and a wireless sensor network to monitor greenhouse gases. Sensors, 15(2), 4072–4096.

    Google Scholar 

  105. Ho, D. T., Grotli, E. I., Sujit, P., Johansen, T. A., & Sousa, J. B. (2015). Optimization of wireless sensor network and UAV data acquisition. Journal of Intelligent and Robotic Systems, 78(1), 159.

    Google Scholar 

  106. Moreno, C. A., Marin, R. B., Marco, A. M., & Nebra, R. C. (2017). Unmanned aerial vehicle based wireless sensor network for marine-coastal environment monitoring. Jornada de Jovenes Investigadores del, I3A, 5.

    Google Scholar 

  107. Zanjie, H., Hiroki, N., Nei, K., Fumie, O., Ryu, M., & Baohua, Z. (2014). Resource allocation for data gathering in UAV-aided wireless sensor networks. In Network infrastructure and digital content (ICNIDC), 4th IEEE international conference (pp. 11–16).

  108. Zhan, C., Zeng, Y., & Zhang, R. (2017). Energy-efficient data collection in UAV enabled wireless sensor network. IEEE Wireless Communications Letters, 7(3), 328–331.

    Google Scholar 

  109. Jawhar, I. H., Mohamed, N., Trabelsi, Z., & Al-Jaroodi, J. (2016). Architectures and strategies for efficient communication in wireless sensor networks using unmanned aerial vehicles. Unmanned Systems, 4(04), 289–305.

    Google Scholar 

  110. Horstrand, P., Guerra, R., Rodriguez, A., Diaz, M., Lopez, S., & Lopez, J. F. (2019). A UAV platform based on a hyperspectral sensor for image capturing and on-board processing. IEEE Access, 7, 66919–66938.

    Google Scholar 

  111. Bah, M. D., Dericquebourg, E., Hafiane, A., & Canals, R. (2018). Deep learning based classification system for identifying weeds using high-resolution UAV imagery (pp. 176–187). Cham: Springer.

    Google Scholar 

  112. Hassanein, M., & El-Sheimy, N. (2018). An efficient weed detection procedure using low-cost UAV imagery system for precision agriculture applications. In International archives of the photogrammetry: remote sensing & spatial information sciences.

  113. Spachos, P., & Gregori, S. (2019). Integration of wireless sensor networks and smart UAVs for precision viticulture. IEEE Internet Computing, 23(3), 8–16.

    Google Scholar 

  114. Carl, C., Landgraf, D., van der Maaten-Theunissen, M. T., Biber, M. P., & Pretzsch, H. (2017). Robinia pseudoacacia l. flowers analyzed by using an unmanned aerial vehicle (UAV). Remote Sensing, 9(11), 1091.

    Google Scholar 

  115. Faical, B. S., Costa, F. G., Pessin, G., Ueyama, J., Freitas, H., Colombo, A., Fini, P. H., Villas, L., Osorio, F. S., Vargas, P. A., & Braun, T. (2014). The use of unmanned aerial vehicles and wireless sensor networks for spraying pesticides. Journal of Systems Architecture, 60(4), 393–404.

    Google Scholar 

  116. Hassan, M. A., Yang, M., Rasheed, A., Yang, G., Reynolds, M., Xia, X., Xiao, Y., & He, Z. (2019). A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant Science, 282, 95–103.

    Google Scholar 

  117. Radoglou-Grammatikis, P., Sarigiannidis, P., Lagkas, T., & Moscholios, I. (2020). A compilation of UAV applications for precision agriculture. Computer Networks, 172, 107148.

    Google Scholar 

  118. Popescu, D., Stoican, F., Stamatescu, G., Ichim, L., & Dragana, C. (2020). A compilation of UAV applications for precision agriculture. Sensors, 20, 817.

    Google Scholar 

  119. Boursianis, A. D., Papadopoulou, M. S., Diamantoulakis, P., LiopaTsakalidi, A., Barouchas, P., Salahas, G., Karagiannidis, G., Wan, S., & Goudos, S. K. (2020). Internet of things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smartfarming: A comprehensive review. Internet of Things, 18, 100187.

    Google Scholar 

  120. Mekki, K., Bajic, E., Chaxel, F., & Fernand, M. (2019). A comparative study of LPWAN technologies for large-scale IoT deployment. ICT Express, 5(1), 1–7.

    Google Scholar 

  121. Islam, N., Ray, B., & Pasandideh, F. (2020). IoT based smart farming: Are the LPWAN technologies suitable for remote communication?. In IEEE international conference on smart internet of things (SmartIoT) (pp. 270–276).

  122. Valecce, G., Petruzzi, P., Strazzella, S., & Grieco, L. A. (2020). NB-IoT for smart agriculture: Experiments from the field. In International conference on control, decision and information technologies (pp. 71–75).

  123. Valente, A., Silva, S., Duarte, D., Cabral Pinto, F., & Soares, S. (2020). Low-cost LoRaWAN node for agro-intelligence IoT. Electronics, 9(6), 987.

    Google Scholar 

  124. Ramson, S. R. (2021). A self-powered, real-time, LoRaWAN IoT-based soil health monitoring system. IEEE Internet of Things Journal, 8, 9278–9293.

    Google Scholar 

  125. Fernandez-Ahumada, L. M., Ramirez-Faz, J., Torres-Romero, M., & Lopez-Luque, R. (2019). Proposal for the design of monitoring and operating irrigation networks based on IoT, cloud computing and free hardware technologies. Sensors, 19, 2318.

    Google Scholar 

  126. Dai, J., & Sugano, M. (2019). Low-cost sensor network for collecting real-time data for agriculture by combining energy harvesting and LPWA technology. In IEEE Global humanitarian technology conference.

  127. Ijaz, A., Zhang, L., Grau, M., Mohamed, A., Vural, S., Quddus, A. U., Imran, M. A., Foh, C. H., & Tafazolli, R. (2016). Enabling massive IoT in 5G and beyond systems: PHY radio frame design considerations. IEEE Access, 24(4), 3322–39.

    Google Scholar 

  128. Duan, L., & Xu, L. D. (2021). Data analytics in industry 4.0: A survey. Information Systems Frontiers. https://doi.org/10.1007/s10796-021-10190-0

    Article  Google Scholar 

  129. Li, S., Iqbal, M., & Saxena, N. (2022). Future industry internet of things with zero-trust security. Information Systems Frontiers. https://doi.org/10.1007/s10796-021-10199-5

    Article  Google Scholar 

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

    Google Scholar 

  131. 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.

    Google Scholar 

  132. 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.

    Google Scholar 

  133. Li, J., Liu, Y., Zhang, Z., Ren, J., & Zhao, N. (2017). Towards green IoT networking: Performance optimization of network coding based communication and reliable storage. IEEE Access, 5, 8780–8791.

    Google Scholar 

  134. Garzon, J., Acevedo, J., Pavon, J., & Baldiris, S. (2020). Promoting eco-agritourism using an augmented reality-based educational resource: a case study of aquaponics. Interactive Learning Environments, 30(7), 1–15.

  135. Skvortsov, E. A., Skvortsova, E. G., Sandu, I. S., & Iovlev, G. A. (2018). Transition of agriculture to digital, intellectual and robotics technologies. EoR, 14(3), 1014–1028.

    Google Scholar 

  136. Gandotra, P., Jha, R. K., & Jain, S. (2017). Green communication in next generation cellular networks: A survey. IEEE Access, 5, 11727–11758.

    Google Scholar 

  137. Buzzi, S., Chih-Lin, I., Klein, T. E., Poor, H. V., Yang, C., & Zappone, A. (2016). A survey of energy-efficient techniques for 5G networks and challenges ahead. IEEE Journal on Selected Areas in Communications, 34(4), 697–709.

    Google Scholar 

  138. Zhang, D., Zhou, Z., Mumtaz, S., Rodriguez, J., & Sato, T. (2017). One integrated energy efficiency proposal for 5G IoT communications. IEEE Internet of Things Journal, 3(6), 1346–1354.

    Google Scholar 

  139. 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 

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

    Google Scholar 

  141. Pan, C., Ren, H., Deng, Y., Elkashlan, M., & Nallanathan, A. (2019). Joint blocklength and location optimization for URLLC-enabled UAV relay systems. IEEE Communications Letters, 23, 498–501.

    Google Scholar 

  142. Anand, A., deVeciana, G., & Shakkottai, S. (2020). Joint scheduling of URLLC and eMBB traffic in 5G wireless networks. IEEE/ACM Transactions on Networking, 28, 477–490.

    Google Scholar 

  143. She, C., Liu, C., Quek, T. Q., Yang, C., & Li, Y. (2019). Ultra-reliable and low-latency communications in unmanned aerial vehicle communication systems. IEEE Transactions on Communications, 67(5), 3768–3781.

    Google Scholar 

  144. Riva, C., & Zaim, A. H. (2023). A comparative study on energy harvesting battery-free lorawan sensor networks. Electrica, 23(1), 40–47.

    Google Scholar 

  145. Gleonec, P. D., Ardouin, J., Gautier, M., & Berder, O. (2021). Energy allocation for lorawan nodes with multi-source energy harvesting. Sensors, 21, 2874.

    Google Scholar 

  146. Delgado, C., Sanz, J. M., & Famaey, J. (2019). On the feasibility of battery-less lorawan communications using energy harvesting. In Proceedings of IEEE global communications conference (GLOBECOM) (vol. 23, pp. 1–6). Waikoloa.

  147. Xu, J., Solmaz, G., Rahmatizadeh, R., Turgut, D., & Boloni, L. (2016). Internet of things applications: Animal monitoring with unmanned aerial vehicle. arXiv preprint arXiv:1610.05287

  148. Wang, X., Garg, S., Lin, H., Kaddoum, G., Hu, J., & Alhamid, M. F. (2021). An intelligent UAV based data aggregation algorithm for 5G-enabled internet of things. Computer Networks, 185, 107628.

    Google Scholar 

  149. Shi, L., Jiang, Z., & Xu, S. (2021). Throughput-aware path planning for UAVs in D2D 5G networks. AdHoc Networks, 116, 102427.

    Google Scholar 

  150. Dawit, M., & Frisk, F. (2019) Edge machine learning for energy efficiency of resource constrained IoT devices. In SPWID: The Fifth international conference on smart portable, wearable, implantable and disability oriented devices and systems.

  151. O’Grady, M. J., Langton, D., & O’Hare, G. M. (2019). Edge computing: A tractable model for smart agriculture? Artificial Intelligence in Agriculture Journal, 3, 42–51.

    Google Scholar 

  152. Baldi, M., & Ofek, Y. (2009). Time for a greener internet. In IEEE international conference on communications workshops, ICC Workshops (pp. 1–6). IEEE.

  153. Tahiliani, V., & Mavuri, D. (2018). Green IoT systems: An energy efficient perspective. In Eleventh international conference on contemporary computing (IC3). IEEE.

  154. Phalaagae, P., Zungeru, A. M., Sigweni, B., Chuma, J. M., & Semong, T. (2020). Security challenges in IoT sensor networks Green internet of things sensor networks (pp. 83–96). Cham: Springer.

    Google Scholar 

  155. Jabbar, W. A., Alsibai, M. H., Amran, N. S., & Mahayadin, S. K. (2018). Design and implementation of IoT-based automation system for smart home. In Proceedings of International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1–6).

  156. Bing, K., Fu, L., Zhuo, Y., & Yanlei, L. (2011). Design of an internet of things-based smart home system. In Proceedings of 2nd international conference on intelligent control and information processing (vol. 2, pp. 921–924).

  157. Lv, Z. (2020). Security of internet of things edge devices.

  158. Mahalakshmi, G., & Nadu, T. (2018). Denial of sleep attack detection using mobile agent in wireless sensors. International Journal for Research Trends and Innovation, 3(5), 139–149.

    Google Scholar 

  159. Gautam, S., Malik, A., Singh, N., & Kumar, S. (2019). Recent advances and countermeasures against various attacks in IoT environment. In 2019 2nd international conference on signal processing and communication (ICSPC (pp. 315–319).

  160. Cekerevac, Z., Dvorak, Z., Prigoda, L., & Cekerevac, P. (2017). Internet of things and the man-in-themiddle attacks–security and economic risks. MEST, 5(2), 15–25.

    Google Scholar 

  161. Singh, K. J., & Kapoor, D. S. (2017). Create your own internet of things: A survey of IoT platforms. IEEE Consumer Electronics Magazine, 6(2), 57–68.

    Google Scholar 

  162. Gupta, K. S., & Jayant, K. P. (2010). A review study on phishing attack techniques for protecting the attacks. Globus-An International Journal of Management and IT, 10(2), 22–25.

    Google Scholar 

  163. Kim, H., Kang, E., Broman, D., & Lee, E. A. (2018). Resilient authentication and authorization for the internet of things (IoT) using edge computing. ACM Transactions on Internet Things, 1, 1–27.

    Google Scholar 

  164. Quasim, M. T. (2021). Challenges and applications of internet of things (IoT) in Saudi Arabia. Easy Chair Preprint, 1–25. [https://easychair.org/publications/preprint_open/r2W4]

  165. Ravi, N., & Shalinie, S. M. (2020). Learning-driven detection and mitigation of DDoS attack in IoT via SDN-cloud architecture. IEEE Internet of Things Journal, 7(4), 3559–3570.

    Google Scholar 

  166. Zolanvari, M., Teixeira, M. A., Gupta, L., Khan, K. M., & Jain, R. (2019). Machine learning-based network vulnerability analysis of industrial internet of things. IEEE Internet of Things Journal, 6(4), 6822–6834.

    Google Scholar 

  167. Gupta, H., & Van-Oorschot, P. C. (2019). Onboarding and software update architecture for IoT devices. In 17th International conference on privacy, security and trust (PST), 8949023.

  168. Mahmoud, C., & Aouag, S. (2019). Security for internet of things: A state of the art on existing protocols and open research issues. In Proceedings of the 9th international conference on information systems and technologies (pp. 1–6).

  169. Hind, M., Noura, O., Amine, K. M., & Sanae, M. (2020). Internet of things: Classification of attacks using ctm method. In Proceeding series: In ACM international conference.

  170. Li, W., Logenthiran, T., Phan, V. T., & Woo, W. L. (2019). A novel smart energy theft system (SETS) for IoT-based smart home. IEEE Internet of Things Journal, 6(3), 5531–5539.

    Google Scholar 

Download references

Acknowledgements

The authors are grateful to all the staffs and Faculty Members of Computer Science and Engineering department of Tripura Institute of Technology, Agartala and National Institute of Technology, Agartala, India for providing smooth access to the computing resources under their custody.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Agartala, Jirania, Barjala, 799046, India

    Parijata Majumdar & Diptendu Bhattacharya

  2. Department of Computer Science and Engineering, Techno College of Engineering, Agartala, Maheshkhola, 799004, India

    Parijata Majumdar

  3. Department of Computer Science and Engineering, Tripura Institute of Technology, Agartala, Narsingarh, 799009, India

    Sanjoy Mitra

  4. Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh, 201310, India

    Bharat Bhushan

Authors
  1. Parijata Majumdar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Diptendu Bhattacharya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sanjoy Mitra
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bharat Bhushan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization of Idea of the Article: PM and SM. Literature Search and Data Analysis: SM and PM. Resources: DB and SM. Writing—Original Draft Preparation: PM and SM. Writing—Review and Editing: SM and PM and BB. Critical Revision of the Work: SM and BB. Visualization: PM and BB. Supervision: DB and SM. Overall Administration: DB and SM

Corresponding author

Correspondence to Sanjoy Mitra.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Human and Animals Rights

This review research is not having any involvement of Human Participants and/or Animals.

Informed Consent

Not applicable as no any involvement of human participants or any other living being.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Majumdar, P., Bhattacharya, D., Mitra, S. et al. Application of Green IoT in Agriculture 4.0 and Beyond: Requirements, Challenges and Research Trends in the Era of 5G, LPWANs and Internet of UAV Things. Wireless Pers Commun 131, 1767–1816 (2023). https://doi.org/10.1007/s11277-023-10521-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-023-10521-1

Keywords

Search

Navigation