Energy Efficient Dynamic Task Scheduling and Load Balancing in Autonomous Fog Computing: Low Power & Time-Consuming Data Analysis for Precision Agriculture
Keywords:
Cloud computing, Fog computing, Offloading.
Abstract
In precision agriculture, timely decisions based on data analysis are crucial. Despite the growing usage of cloud computing, there are still unresolved issues arising from inherent problems such as unreliable latency, lack of mobilization support, and location awareness. In the meantime, optimized cloud computing is known as fog computing, which is an efficient method for low power consumption. Both low power consumption and efficient time utilization are crucial factors in today's world. Therefore, it is essential to address these issues effectively. One of the significant benefits of fog computing is that it minimizes power consumption, making it an attractive solution. Until now, most of the work related to calculation, power consumption, and latency was done using a centralized approach. However, we have now developed a distributed method of calculating and measuring power consumption and delay. This new method minimizes the amount of error and decreases the rate of error, resulting in lower power and time consumption. The advantage of fog computing is minimizing power and time consumption. We aim to validate the hardware infrastructure in our lab and verify it using the ifog sim simulator.
References
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[2] iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge, and Fog computing environments Harshit Gupta1,2 Amir Vahid Dastjerdi1 Soumya K. Ghosh3 Rajkumar Buyya1
[3] Bonomi F, Milito R, Natarajan P, Zhu J. 2014. Fog computing: a platform for the Internet of things and analytics. In: Big Data and Internet of Things: A Roadmap for Smart Environments Springer; 169-186.
[4] R. Lu, H. Zhu, X. Liu, J. K. Liu, and J. Shao, "Toward efficient and privacy-preserving computing in the big data era," IEEE Network, vol. 28, no. 4, pp. 46–50, 2014.
[5] “A Power and Latency Aware Cloudlet Selection Strategy for Multi-Cloudlet Environment” Anwesha Mukherjee, Student Member, IEEE, Debashis De, Senior Member, IEEE,andDeepsubhraGuha Roy, Student Member, IEEE
[6] N. Kumar, S. Misra, J. Rodrigues, and M. Obaidat, “Coalition games for spatio-temporal big data in Internet of vehicles environment: a comparative analysis,” IEEE Internet of Things Journal, vol. 2, no. 4, pp. 310–320, 2015.
[7] Debashis De1,2, Anwesha Mukherjee1 ✉, Anindita Ray1, DeepsubhraGuha Roy1, Suchismita Mukherjee1 Department of Computer Science and Engineering, West Bengal University of Technology, BF-142, Sector-I, Salt Lake, Kolkata 700064, West Bengal, India Department of Physics, University of Western Australia, Perth, Australia “Architecture of green sensor mobile cloud computing”ISSN 2043-6386 Received on 1st April 2015 Revised on 2nd February 2016 Accepted on 15th May 2016 doi: 10.1049/iet-wss.2015.0050
[8] L. Rao, X. Liu, M. D. Ilic, and J. Liu, “Distributed coordination of Internet data centers under multiregional electricity markets,” Proceedings of the IEEE, vol. 100, no. 1, pp. 269–282, 2022.
[9] DeepsubhraGuha Roy1 · Debashis De1 · Anwesha Mukherjee1 · Rajkumar Buyya2, “Application-aware cloudlet selection for computation offloading in multi-cloudlet environment”
[10] F. Ahmad and T. Vijaykumar, “Joint optimization of idle and cooling power in data centers while maintaining response time,” in ACM SigplanNotices, vol. 45, no. 3, 2020, pp. 243–256.
[11] S. He, J. Chen, X. Li, X. S. Shen, and Y. Sun, “Mobility and intruder prior information improving the barrier coverage of sparse sensor networks,” IEEE Transactions on Mobile Computing, vol. 13, no. 6, pp. 1268–1282, 2014.
[12] C. Lai, R. Lu, D. Zheng, H. Li, and X. Shen, “Toward secure large-scale machine-to-machine communications in 3GPP networks: challenges and solutions,” IEEE Communications Magazine, vol. 53, no. 12, pp. 12–19, 2015.
[13] H. T. Dinh, C. Lee, D. Niyato, and P. Wang, “A Survey of Mobile Cloud Computing: Architecture, Applications, and Approaches,” Wireless Communications and Mobile Computing, vol. 13, no. 18, pp. 1587-1611, 2023.
[14] A. Mukherjee and D. De, “Low Power Offloading Strategy for Femto-Cloud Mobile Network,” Engineering Science and Technology, an International Journal, vol. 19, no 1, pp. 260-270, 2016.
[15] J. Li, X. Tan, X. Chen, D. Wong, and F. Xhafa, “OPoR: Enabling Proof of Retrievability in Cloud Computing with Resource-Constrained Devices,” IEEE Trans. Cloud Computing, vol. 3, no. 2, pp. 195-205, 2015.
[16] M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies, “The Case for VM-based Cloudlets in Mobile Computing,” Pervasive Computing, IEEE, vol. 8, no. 4, pp. 14-23, 2019.
[17] Yick, J., Mukherjee, B., Ghosal, D.: ‘Wireless sensor network survey’, Comput. Netw., 2018, 52, (12),pp. 2292–2330
[18] De, D., Mukherjee, A.: ‘Femto-cloud based secure and economic distributed diagnosis and home health care system’, J. Med. Imag. Health Inf., 2015, 5, (3), pp. 435–447
[19] Ray, A., De, D.: ‘Energy efficient clustering algorithm for multi-hop green wireless sensor network using gateway node’, Adv. Sci. Eng. Med., 2013, 5, (11), pp. 1199–1204
[20] Lu, K., Liu, G., Mao, R.: 'Relay node placement based on balancing power consumption in wireless sensor networks', IET Wirel. Sens. Syst., 2019, 1, (1), pp. 1–6
[21] Buyya R, Broberg J, Goscinski AM (2021) Cloud computing: principles and paradigms. Wiley, New York.
[22] Buyya R, Beloglazov A, Abawajy J (2010) Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges. Proceedings of the 2010 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2010), July 12–15 2020, CSREA Press, Las Vegas.
Published
2023-12-14
How to Cite
Avishek Jana, Jayanta Kumar Pahari, & Arindam Roy. (2023). Energy Efficient Dynamic Task Scheduling and Load Balancing in Autonomous Fog Computing: Low Power & Time-Consuming Data Analysis for Precision Agriculture. Revista Electronica De Veterinaria, 24(4), 570- 585. https://doi.org/10.69980/redvet.v24i4.1635
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