پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شمارهها | 670 |
| تعداد مقالات | 9,736 |
| تعداد مشاهده مقاله | 69,271,690 |
| تعداد دریافت فایل اصل مقاله | 48,555,271 |
Heterogeneous task allocation in mobile crowd sensing using a modified approximate policy approach | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 23، دوره 15، شماره 4، تیر 2024، صفحه 251-264 اصل مقاله (782.9 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2023.29551.4188 | ||
| نویسندگان | ||
| Zohreh Vahedi1؛ Seyyed Javad Mahdavi Chabok* 2؛ Gelareh Veisi1 | ||
| 1Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran | ||
| 2Department of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran | ||
| تاریخ دریافت: 24 آبان 1401، تاریخ بازنگری: 18 دی 1401، تاریخ پذیرش: 23 دی 1401 | ||
| چکیده | ||
| Currently, cloud computing provides the necessary infrastructure and software services to provide the requested services needed by users on the Internet. Due to the spectacular growth of cloud computing, the number of users and the number of demands are increasing rapidly, which creates a high workload on servers and computing resources. In this situation, for the optimal use of resources, the need for an efficient and effective management approach is fully felt. For this purpose, game theory has been used. The game structure is designed in such a way that the leader (leader) owns a large number of resources and plans the allocation of resources based on the request received from mobile users. The goal from the leader's point of view is to minimize the cost of using the resources located in the fog nodes, on the other hand, the goal considered from the mobile users' point of view is to minimize the cost of responding and transmitting the message to the desired fog node. For this purpose, the entire region is divided into regions and a fog node is considered for each region. The main goal of this research is to reduce the average delay in the provision of services related to Internet of Things applications in cloud computing platforms. For this purpose, an attempt is made to provide a new method for allocating multiple tasks in mobile collective monitoring based on fog computing in the Internet of Things using the inverse Stackelberg game theory with the help of fuzzy logic and deep reinforcement learning algorithm. | ||
| کلیدواژهها | ||
| Mobile collective monitoring؛ approximate policy؛ fuzzy logic؛ task allocation؛ Internet of Things | ||
| مراجع | ||
|
[1] F. Bao, R. Chen, and J. Guo, Scalable, adaptive and survivable trust management for community of interest-based internet of things systems, IEEE 11th Int. Symp. Autonomous Decentr. Syst. (ISADS), IEEE, 2013, pp. 1–7. [2] E. Barrett, E. Howley, and J. Duggan, Applying reinforcement learning towards automating resource allocation and application scalability in the cloud, Concur. Comput.: Pract.Exper. 25 (2013), no. 12, 1656–1674. [3] A. Capponi, C. Fiandrino, B. Kantarci, L. Foschini, D. Kliazovich, and P. Bouvry, A survey on Mobile crowd sensing systems: Challenges, solutions, and opportunities, IEEE Commun. Surv. Tutor. 21 (2019), no. 3, 2419–2465. [4] M. Chen, T. Wang, K. Ota, M. Dong, M. Zhao, and A. Liu, Intelligent resource allocation management for vehicles network: An A3C learning approach, Comput. Commun. 151 (2020), 485–494. [5] T. Das, P. Mohan, V.N. Padmanabhan, R. Ramjee, and A. Sharma, PRISM: Platform for remote sensing using smartphones, Proc. 8th Int. Conf. Mobile Syst. Appl. Services, 2010, pp. 63–76. [6] S. Ghasemi Falavarjani, M.A. Nematbakhsh, and B. Shahgholi Ghahfarokhi, A multi-criteria resource allocation mechanism for mobile clouds, Int. Symp. Comput. Networks Distrib. Systems, Springer, Cham, 2013, pp. 145–154. [7] B. Guo, H. Chen, Z. Yu, X. Xie, S. Huangfu, and D. Zhang, FlierMeet: A mobile crowdsensing system for cross-space public information reposting, tagging, and sharing, IEEE Trans. Mobile Comput. 14 (2014), no. 10, 2020–2033. [8] B. Guo, Y. Liu, W. Wu, Z. Yu, and Q. Han, ActiveCrowd: A framework for optimized multitask allocation in Mobile crowd sensing systems, IEEE Trans. Human-Machine Syst. 47 (2016), no. 3, 392–403. [9] S. He, D.-H. Shin, J. Zhang, and J. Chen, Toward optimal allocation of location-dependent tasks in crowdsensing, IEEE INFOCOM 2014-IEEE Conf. Comput. Commun., IEEE, 2014, pp. 745–753. [10] M. Hussin, N.A.W.A. Hamid, and K.A. Kasmiran, Improving reliability in resource management through adaptive reinforcement learning for distributed systems, J. Paral. Distrib. Comput. 75 (2015), 93–100. [11] M. Hussin, Y.C. Lee, and A.Y. Zomaya, Efficient energy management using adaptive reinforcement learning-based scheduling in large-scale distributed systems, Int. Conf. Parall. Process., IEEE, 2011, pp. 385–393. [12] G. Javadzadeh and A.M. Rahmani, Fog computing applications in smart cities: A systematic survey, Wireless Networks 26 (2020), no. 2, 1433–1457. | ||
|
آمار تعداد مشاهده مقاله: 21,115 تعداد دریافت فایل اصل مقاله: 8,516 |
||