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Farmani, M., Farnam, S., Khani, M., Torabi, Z., Shirmohammadi, Z.. (1402). Comprehensive Review of Modern Computing Paradigms Architectures for Intelligent Agriculture. فناوری آموزش, 12(1), 99-114. doi: 10.22061/jecei.2023.9682.648
M. Farmani; S. Farnam; M. J. Khani; Z. Torabi; Z. Shirmohammadi. "Comprehensive Review of Modern Computing Paradigms Architectures for Intelligent Agriculture". فناوری آموزش, 12, 1, 1402, 99-114. doi: 10.22061/jecei.2023.9682.648
Farmani, M., Farnam, S., Khani, M., Torabi, Z., Shirmohammadi, Z.. (1402). 'Comprehensive Review of Modern Computing Paradigms Architectures for Intelligent Agriculture', فناوری آموزش, 12(1), pp. 99-114. doi: 10.22061/jecei.2023.9682.648
Farmani, M., Farnam, S., Khani, M., Torabi, Z., Shirmohammadi, Z.. Comprehensive Review of Modern Computing Paradigms Architectures for Intelligent Agriculture. فناوری آموزش, 1402; 12(1): 99-114. doi: 10.22061/jecei.2023.9682.648

Comprehensive Review of Modern Computing Paradigms Architectures for Intelligent Agriculture

Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 7، دوره 12، شماره 1، فروردین 2024، صفحه 99-114    اصل مقاله (1.39 M)
نوع مقاله: Review paper
شناسه دیجیتال (DOI): 10.22061/jecei.2023.9682.648
نویسندگان
M. Farmani1؛ S. Farnam1؛ M. J. Khani1؛ Z. Torabi2؛ Z. Shirmohammadi* 2
1Faculty of Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.
2Department of Computer Systems Architecture, Faculty of Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.
تاریخ دریافت: 22 فروردین 1402،  تاریخ بازنگری: 19 تیر 1402،  تاریخ پذیرش: 01 مرداد 1402 
چکیده
Background and Objectives: With the increase of population in the world along with the decrease of natural resources, agricultural land and the increase of unpredictable environmental conditions, it causes concerns in the field of food supply, which is one of the serious concerns for all countries of the world. Therefore, the agricultural industry has moved towards smart agriculture. Smart agriculture using the Internet of Things, which uses different types of sensors to collect data (such as temperature, humidity, light, etc.), a communication network to send and receive data, and information systems to manage and analyze data. Smart agriculture deals with a huge amount of data collected from farms, which has fundamental challenges for analysis using old systems such as lack of storage space, processing delay. Computational paradigm is a key solution to solve the problems of time delay, security, storage space management, real-time analysis. Computing paradigms include cloud, fog and edge computing, which by combining each of them in smart agriculture has caused a great transformation in this industry. The purpose of this article is to provide a comprehensive review of the architecture of computing paradigms in smart agriculture applications.
Methods: To achieve the goals of this article, the methodology is divided into two parts: article selection and review of the selected articles. The computational paradigms used in the selected articles are from 2019 to 2022. Each selected paper is then reviewed in detail in terms of categories of computing paradigms, architectures, key points, advantages, and challenges.
Results: Computational paradigms have significant advantages. Combining these paradigms with each other in a complementary way covers many challenges. The architecture based on the combination of edge-fog-cloud computing is one of the best architectures combined with smart agriculture.
Conclusion: By combining computing paradigms and smart agriculture, the challenges based on traditional and old systems are overcome. Combining these paradigms complement each other's challenges.
کلیدواژه‌ها
Intelligent Agriculture؛ Computing Technology؛ Cloud Computing؛ Fog Computing؛ Edge Computing
مراجع
[1] U. Nations, growing at a Slower Pace, World Population is Expected to Reach 9.7 billion in 2050 and Could Peak at Nearly 11 billion around 2100.

[2] N. Alexandratos, J. Bruinsma, World Agriculture towards 2030/2050: The 2012 Revision. 2012. (accessed on 1 September 2021).

[3] M. S. Farooq, S. Riaz, A. Abid, T. Umer, Y. Bin Zikria, “Role of IoT technology in agriculture: a systematic literature review,” Electronics, 9(2): 319, 2020.

[4] V. K. Quy, N. V. Hau, D. V. Anh, N. M. Quy, N. T. Ban, S. Lanza, A.  Muzirafuti, “IoT-Enabled smart agriculture: architecture, applications, and challenges,” Appl. Sci., 12(7): 3396, 2022.‏

[5] Y. Kalyani, R. Collier, “A systematic survey on the role of cloud, fog, and edge computing combination in smart agriculture,” Sensors, 21(17): 5922, 2021.‏

[6] S. Singh, I. Chana, R. Buyya, “Agri-Info: cloud based autonomic system for delivering agriculture as a service,” Internet Things, 9: 100131, 2020.‏

[7] M. De Donno, K. Tange, N. Dragoni, "Foundations and evolution of modern computing paradigms: Cloud, IoT, edge, and fog," IEEE Access, 7: 150936-150948, 2019.

[8] The NIST Definition of Cloud Computing, NIST Special Publication 800-145, 2011.

[9] E. Symeonaki, K. G. Arvanitis, D. D. Piromalis, “Review on the trends and challenges of cloud computing technology in climate-Smart agriculture,” in Proc. HAICTA: 66–78, 2017.

[10] F. Bonomi, R. Milito, J. Zhu, S. Addepalli, “Fog computing and its role in the internet of things,” in Proc. the first edition of the MCC Workshop on Mobile Cloud Computing: 13–16, 2012.

[11] R. K, Naha, S. Garg, A. Chan. “Fog computing architecture: Survey and challenges,” arXiv preprint arXiv:1811.09047, 2018.

[12] R. Deng, R. Lu, C. Lai, T. H. Luan, H. Liang, "Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption," IEEE Internet Things J., 3(6): 1171-1181, 2016.

[13] A. Yousefpour, C. Fung, T. Nguyen, K. Kadiyala, F. Jalali, A. Niakanlahiji, J. Kong, J. P. Jue, “All one needs to know about fog computing and related edge computing paradigms: A complete survey,” J. Syst. Archit., 98: 289–330, 2019.

[14] S. Dhifaoui, C. Houaidia, L. A. Saidane, “Cloud-Fog-Edge computing in smart agriculture in the Era of drones: a systematic survey,” in Proc. 2022 IEEE 11th IFIP International Conference on Performance Evaluation and Modeling in Wireless and Wired Networks (PEMWN): 1-6, 2022.

[15] M. S. Farooq, S. Riaz, A. Abid, K. Abid, M. A. Naeem, “A survey on the role of IoT in agriculture for the implementation of smart farming,” IEEE Access, 7: 156237-156271, 2019.

[16] L. Ruiz-Garcia, L. Lunadei, P. Barreiro, I. Robla, “A review of wireless sensor technologies and applications in agriculture and food industry: State of the art and current trends,” Sensors, 9: 4728–4750, 2009.

[17] M. Bacco, A. Berton, A. Gotta, L. Caviglione, “IEEE 802.15.4 air-ground UAV communications in smart farming scenarios,” IEEE Commun. Lett., 22: 1910–1913, 2018.

[18] X. Wang, J. Zhang, Z. Yu, S. Mao, S. C. G. Periaswamy, J. Patton, “On remote temperature sensing using commercial UHF RFID tags,” IEEE Internet Things J., 6: 10715–10727, 2019.

[19] S. Popli, R.K. Jha, S. Jain, “A survey on energy efficient Narrowband Internet of Things (NBIoT): Architecture, application & challenges,” IEEE Access, 7: 16739–16776, 2019.

[20] T. Ojha, S. Misra, N. S. Raghuwanshi, “Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges,” Comput. Electron. Agric., 118: 66-84, 2015.

[21] O. Ali, M. K. Ishak, M. K. L. Bhatti, I. Khan, K. I. Kim, “A comprehensive review of internet of things: Technology stack, middlewares, and fog/edge computing interface,” Sensors, 22: 995, 2022.

[22] F. A. Almalki, B. O. Soufiene, S. H. Alsamhi, H. Sakli, “A low-cost platform for environmental smart farming monitoring system based on IoT and UAVs,” Sustainability, 13(11): 5908, 2021.‏

[23] M. Saban, O. Aghzout, A. Rosado-Muñoz, "Deployment of a LoRa-based network and web monitoring application for a smart farm," in Proc. 2022 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT): 424-427, 2022.

[24] J. K. Park, E. Y. Park “Monitoring method of movement of grazing cows using cloud-based system,” ECTI Trans. Comput. Inf. Technol., 15(1): 24-33, 2021.‏

[25] C. S. M. Babou, B. O. Sane, I. Diane, I. Niang, “Home edge computing architecture for smart and sustainable agriculture and breeding,” in Proc. the 2nd International Conference on Networking, Information Systems & Security: 1-7, 2019.‏

[26] X. Li, Z. Ma, J. Zheng, Y. Liu, L. Zhu, N. Zhou, “An effective edge-assisted data collection approach for critical events in the SDWSN-based agricultural internet of things,” Electronics, 9(6): 907, 2020.‏

[27] M. A. Uddin, U. Kumar Dey, M. Akter, "Proposing a cloud and edge computing based decision supportive consolidated farming system by sensing various effective parameters using IoT," in Proc. 2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS): 1-6, 2022.

[28] M. Baghrous, A. Ezzouhairi, N. Benamar, “Smart farming system based on fog computing and lora technology,” Embedded Systems and Artificial Intelligence, 1076: 217-225, 2020.

[29] K. Lee, B. N. Silva, K. Han, “Deep learning entrusted to fog nodes (DLEFN) based smart agriculture,” Appl. Sci., 10(4): 1544, 2020.‏

[30] A. Tsipis, A. Papamichail, I. Angelis, G. Koufoudakis, G. Tsoumanis, K. Oikonomou, “An alertness-adjustable cloud/fog IoT solution for timely environmental monitoring based on wildfire risk forecasting,” Energies, 13(14): 3693, 2020.‏

[31] T. N. Gia, L. Qingqing, J. P. Queralta, Z. Zou, H. Tenhunen, T. Westerlund, “Edge AI in smart farming IoT: CNNs at the edge and fog computing with LoRa,” in Proc. 2019 IEEE AFRICON: 1-6, 2019.

[32] U. Sakthi, J. D. Rose, “Smart agricultural knowledge discovery system using IoT technology and fog computing,” in Proc. Third International Conference on Smart Systems and Inventive Technology (ICSSIT): 48-53, 2020.

[33] E. Symeonaki, K. G. Arvanitis, D. D. Piromalis, “Review on the trends and challenges of cloud computing technology in climate-smart agriculture,” in Proc. HAICTA: 66-78, 2017.

 

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