In the ubiquitous power Internet of things, all kinds of growing power terminal equipment and business applications will generate massive data, which will cause huge pressure to the master station, and high delay and security cannot meet the requirements of new business forms. Edge computing organically integrates computing, storage, and other resources on the edge of the network and responds to the task request of the network edge node timely and effectively according to the principle of nearest service. Due to the limited resources of edge nodes, such as power monitoring camera capability, resources, bandwidth, energy, etc., computing offload is a key problem of edge computing. To solve this problem, this paper proposes a method of edge computing offload based on genetic algorithm. Firstly, in the edge-computing scenario of the power Internet of things, we analyze the computing unloading problem model under the time sequence condition. Then, aiming at the optimal decision-making problem of energy consumption and time delay of terminal equipment, we creatively transform the problem of computational offload into the problem of multi-objective optimization. In the genetic algorithm, we use NSGA-II to achieve the multi-objective optimization of the decision-making. Through conversion, time delay and energy consumption, the optimization can be achieved. Finally, we designed a simulation experiment. The results show that the unloading decision of NSGA-II can reach the best. The results show that the results of NSGA-II can be distributed in a wider range.
| Published in | Internet of Things and Cloud Computing (Volume 9, Issue 1) |
| DOI | 10.11648/j.iotcc.20210901.11 |
| Page(s) | 1-9 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Edge Computing Offload, NSGA-II, Power Internet of Things
| [1] | A Ahmed and E Ahmed. A survey on mobile edge computing. 2016 10th International Conference on Intelligent Systems and Control (ISCO), 2016. |
| [2] | Y M Cai, S Y Feng, H W Du, M X Liu, X H Ding, W L Ji. Novel Edge-ware Adaptive Data Processing Method for the Ubiquitous Electric Power Internet of Things. High Voltage Engineering, 2019. |
| [3] | E Cuervo, A Balasubramanian, D Cho, et al. MAUI: making smartphones last longer with code offload. Proceedings of the 8th international conference on Mobile systems, applications, and services. ACM, 2010. |
| [4] | K Deb, A Pratap, S Agarwal, et al. A fast and elitist multi objective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 2002, 6 (2): 182-197. |
| [5] | M Dorigo, V Maniezzo, A Colorni. Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, man, and cybernetics, Part B: Cybernetics, 1996, 26 (1): 29-41. |
| [6] | R Eberhart and J Kennedy. A new optimizer using particle swarm theory. MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. Ieee, 1995: 39-43. |
| [7] | H Flores, P Hui, S Tarkoma, et al. Mobile code offloading: from concept to practice and beyond. IEEE Communications Maga-zine, 2015, 53 (3): 80-88. |
| [8] | S Hagen and A Kemper. Model-based planning for state related changes to infrastructure and software as a service instances in large data centers. Proceedings of 2010 IEEE 3rd International Conference on Cloud Computing (CLOUD). Miami, USA: IEEE, 2010. |
| [9] | J H Holland. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press, 1992. |
| [10] | K He. The Key Technologies of IOT with Development and Applications. Radio frequency world, 2010, (1): 33-35. |
| [11] | L Jiao, R Friedman, X Fu, et al. Cloud-based computation of-floading for mobile devices: State of the art, challenges and opportunities. Future Network and Mobile Summit, 2013. |
| [12] | X C Jiang, Y D Liu, X F Fu, P Xu, S Q Wang, G H Sheng. Construction Ideas and Development Trends of Transmission and Distribution Equipment of the Ubiquitous Power Internet of Things. High Voltage Engineering, 2019. |
| [13] | J Q Li. National Grid Corporation fully deploys ubiquitous power Internet of Things recommendations. 2019. |
| [14] | F M Liu, P Shu, H Jin, et al. Gearing resource-poor mobile devices with powerful clouds: architectures, challenges, and applications. IEEE Wireless Communications, 2013. |
| [15] | Q Long, C Wu, T Huang, et al. A genetic algorithm for unconstrained multi-objective optimization. Swarm and Evolutionary Computation, 2015, 22: 1-14. |
| [16] | Y J Lv. The origin and development trend of the Internet of Things. Information and communication technology, 2010, 4 (2): 4-8. |
| [17] | P Mach and Z Becvar. Mobile edge computing: A survey on architecture and computation offloading. IEEE Communications Surveys and Tutorials, 2017, 19 (3): 1628-1656. |
| [18] | Y Mao, C You, J Zhang, et al. A survey on mobile edge computing: the communication perspective. IEEE Communications Surveys and Tutorials, 2017, PP (99): 1-1. |
| [19] | G May, B Stahl, M Taisch, et al. Multi-objective genetic algorithm for energy-efficient job shop scheduling. International Journal of Production Research, 2015, 53 (23): 7071-7089. |
| [20] | M Mukherjee, L Shu, D Wang. Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges. IEEE Communications Surveys and Tutorials, 2018. |
| [21] | T Murata and H Ishibuchi. MOGA: Multi-objective genetic algorithms. IEEE international conference on evolutionary computation, 1995, 1: 289-294. |
| [22] | J H Park, K H Lee, G J Jin, et al. Loading/unloading decision system of ship block in the shipyard. Journal of the Institute of Electronics Engineers of Korea CI, 2010, 47 (6): 40-46. |
| [23] | W Y Poe and J B Schmitt. Node deployment in large wireless sensor networks: coverage, energy consumption, and worst-case delay. Asian Internet Engineering Conference. ACM, 2009: 77-84. |
| [24] | W Shi, J Cao, Q Zhang, et al. Edge computing: Vision and challenges. IEEE Internet of Things Journal, 2016, 3 (5): 637-646. |
| [25] | State Grid Internet Department. Ubiquitous power Internet of Things construction overall plan. 2019. |
| [26] | C Wang, F R Yu, C Liang, et al. Joint computation offloading and interference management in wireless cellular networks with mobile edge computing. IEEE Transactions on Vehicular Technology, 2017, 66 (8): 7432-7445. |
| [27] | R C Xie, X F Lian, Q M Jia, T Huang, Y J Liu. Survey on computation o_ loading in mobile edge computing. Journal on Communications, 2018. |
| [28] | X Yang. Building a ubiquitous power Internet of Things to meet more individual needs. State Grid News, 2019. |
| [29] | K Zhang, Y Mao, S Leng, et al. Energy-effcient offloading for mobile edge computing in 5G heterogeneous networks. IEEE access, 2016, 4: 5896-5907. |
| [30] | J Zhang, X Hu, Z Ning, et al. Energy-latency trade on for energy-aware offloading in mobile edge computing networks. IEEE Internet of Things Journal, 2017, 5 (4): 2633-2645. |
| [31] | Y Zhen, X Z Li, H Q Ou, L K Zeng. Internet of Things and Smart Grid. Engineering Applications, 2012. |
APA Style
Yue Ma, Xin Li, Jianbin Li. (2021). An Edge Computing Offload Method Based on NSGA-II for Power Internet of Things. Internet of Things and Cloud Computing, 9(1), 1-9. https://doi.org/10.11648/j.iotcc.20210901.11
ACS Style
Yue Ma; Xin Li; Jianbin Li. An Edge Computing Offload Method Based on NSGA-II for Power Internet of Things. Internet Things Cloud Comput. 2021, 9(1), 1-9. doi: 10.11648/j.iotcc.20210901.11
AMA Style
Yue Ma, Xin Li, Jianbin Li. An Edge Computing Offload Method Based on NSGA-II for Power Internet of Things. Internet Things Cloud Comput. 2021;9(1):1-9. doi: 10.11648/j.iotcc.20210901.11
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.iotcc.20210901.11,
author = {Yue Ma and Xin Li and Jianbin Li},
title = {An Edge Computing Offload Method Based on NSGA-II for Power Internet of Things},
journal = {Internet of Things and Cloud Computing},
volume = {9},
number = {1},
pages = {1-9},
doi = {10.11648/j.iotcc.20210901.11},
url = {https://doi.org/10.11648/j.iotcc.20210901.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.iotcc.20210901.11},
abstract = {In the ubiquitous power Internet of things, all kinds of growing power terminal equipment and business applications will generate massive data, which will cause huge pressure to the master station, and high delay and security cannot meet the requirements of new business forms. Edge computing organically integrates computing, storage, and other resources on the edge of the network and responds to the task request of the network edge node timely and effectively according to the principle of nearest service. Due to the limited resources of edge nodes, such as power monitoring camera capability, resources, bandwidth, energy, etc., computing offload is a key problem of edge computing. To solve this problem, this paper proposes a method of edge computing offload based on genetic algorithm. Firstly, in the edge-computing scenario of the power Internet of things, we analyze the computing unloading problem model under the time sequence condition. Then, aiming at the optimal decision-making problem of energy consumption and time delay of terminal equipment, we creatively transform the problem of computational offload into the problem of multi-objective optimization. In the genetic algorithm, we use NSGA-II to achieve the multi-objective optimization of the decision-making. Through conversion, time delay and energy consumption, the optimization can be achieved. Finally, we designed a simulation experiment. The results show that the unloading decision of NSGA-II can reach the best. The results show that the results of NSGA-II can be distributed in a wider range.},
year = {2021}
}
TY - JOUR T1 - An Edge Computing Offload Method Based on NSGA-II for Power Internet of Things AU - Yue Ma AU - Xin Li AU - Jianbin Li Y1 - 2021/03/22 PY - 2021 N1 - https://doi.org/10.11648/j.iotcc.20210901.11 DO - 10.11648/j.iotcc.20210901.11 T2 - Internet of Things and Cloud Computing JF - Internet of Things and Cloud Computing JO - Internet of Things and Cloud Computing SP - 1 EP - 9 PB - Science Publishing Group SN - 2376-7731 UR - https://doi.org/10.11648/j.iotcc.20210901.11 AB - In the ubiquitous power Internet of things, all kinds of growing power terminal equipment and business applications will generate massive data, which will cause huge pressure to the master station, and high delay and security cannot meet the requirements of new business forms. Edge computing organically integrates computing, storage, and other resources on the edge of the network and responds to the task request of the network edge node timely and effectively according to the principle of nearest service. Due to the limited resources of edge nodes, such as power monitoring camera capability, resources, bandwidth, energy, etc., computing offload is a key problem of edge computing. To solve this problem, this paper proposes a method of edge computing offload based on genetic algorithm. Firstly, in the edge-computing scenario of the power Internet of things, we analyze the computing unloading problem model under the time sequence condition. Then, aiming at the optimal decision-making problem of energy consumption and time delay of terminal equipment, we creatively transform the problem of computational offload into the problem of multi-objective optimization. In the genetic algorithm, we use NSGA-II to achieve the multi-objective optimization of the decision-making. Through conversion, time delay and energy consumption, the optimization can be achieved. Finally, we designed a simulation experiment. The results show that the unloading decision of NSGA-II can reach the best. The results show that the results of NSGA-II can be distributed in a wider range. VL - 9 IS - 1 ER -
Information
Email: