[1] “Cisco Annual Internet Report (2018–2023)”, White Paper.
[2] W. Yang, N. Wang, Z. Guan, L. Wu, X. Du and M. Guizani, “A Practical Cross-Device Federated Learning Framework over 5G Networks”, IEEE Wireless Communications, pp. 1-8, 2022.
[3] C. Jiang, X. Cheng, H. Gao, X. Zhou, and J. Wan, “Toward Computation Offloading in Edge Computing: A Survey”, IEEE Access, vol. 7, pp. 131543–131558, 2019.
[4] آتوسا دقایقی و محسن نیک‌رای، ”ارائه‌ی یک مدل زمان‌بندی وظایف مبتنی بر الگوریتم ژنتیک چند هدفه با مرتب‌سازی نامغلوب برای برنامه‌های کاربردی شهر هوشمند در محیط ابر-مه“، مجله علمی رایانش نرم و فناوری اطلاعات، دوره 11، شماره 3، صفحات 64-82، 1401.
[5] L. Lei, H. Xu, X. Xiong, K. Zheng and W. Xiang, “Joint Computation Offloading and Multiuser Scheduling Using Approximate Dynamic Programming in NB-IoT Edge Computing System”, IEEE Internet of Things Journal, vol. 6, no. 3, pp. 5345-5362, 2019.
[6] F. Zantalis, G. Koulouras, S. Karabetsos, and D. Kandris, “A Review of Machine Learning and IoT in Smart Transportation”, Future Internet, vol. 11, no. 4, pp. 1-23, 2019.
[7] F. Al-Turjman, A. Malekloo, "Smart Parking in IoT-enabled Cities: A Survey", Sustainable Cities and Society, vol 49, pp. 1-20, 2019.
[8] H. Yang, Z. Wei, Z. Feng, X. Chen, Y. Li and P. Zhang, “Intelligent Computation Offloading for MEC-Based Cooperative Vehicle Infrastructure System: A Deep Reinforcement Learning Approach”, IEEE Transactions on Vehicular Technology, vol. 71, no. 7, pp. 7665-7679, 2022.
[9] F. Lu, H. Zhao, X. Zhao, X. Wang, A. Saleem, and G. Zheng, “Investigation of Near-Field Source Localization Using Uniform Rectangular Array”, Electronics, vol. 11, no. 12, pp. 1-15, 2022.
[10] S.K.u. Zaman, A.I. Jehangiri, T. Maqsood, Z. Ahmad, A.I. Umar, J. Shuja, E. Alanazi, W. Alasmary, “Mobility-aware Computational Offloading in Mobile Edge Networks: a Survey”, Cluster Computing, vol. 24, pp. 2735–2756, 2021.
[11] J. Plachy, Z. Becvar, E. C. Strinati and N. d. Pietro, “Dynamic Allocation of Computing and Communication Resources in Multi-Access Edge Computing for Mobile Users”, IEEE Transactions on Network and Service Management, vol. 18, no. 2, pp. 2089-2106, 2021.
[12] J. Shuja, K. Bilal, W. Alasmary, H. Sinky, E. Alanazi, “Applying Machine Learning Techniques for Caching in Next-generation Edge Networks: A Comprehensive Survey”, Journal of Network and Computer Applications, vol. 181, pp. 1-24, 2021.
[13] N. Abbas, Y. Zhang, A. Taherkordi and T. Skeie, “Mobile Edge Computing: A Survey”, IEEE Internet of Things Journal, vol. 5, no. 1, pp. 450-465, 2018.
[14] Y. Liao, L. Shou, Q. Yu, Q. Ai, and Q. Liu, “Joint Offloading Decision and Resource Allocation for Mobile Edge Computing Enabled Networks,” Computer Communication, vol. 154, pp. 361–369, 2020.
[15] F. Saeik, M. Avgeris, D. Spatharakis, N. Santi, D. Dechouniotis, J. Violos, A. Leivadeas, N. Athanasopoulos, N. Mitton, and S. Papavassiliou, “Task Offloading in Edge and Cloud Computing: A Survey on Mathematical, Artificial Intelligence and Control Theory Solutions”, Computer Networks, vol.195, pp. 1-26, 2021.
[16] سعدون عزیزی، ”یک مدل چندهدفه برای بهینه‌سازی زمان‌بندی وظیفه‌ها در محیط‌های رایانشی مه-ابر“، مجله علمی رایانش نرم و فناوری اطلاعات، دوره 10، شماره 4، صفحات 43-52، 1400.
[17] M. Deng, H. Tian and B. Fan, “Fine-granularity Based Application Offloading Policy in Cloud-enhanced Small Cell Networks”, IEEE International Conference on Communications Workshops (ICC), pp. 638-643, 2016.
[18] F. Farahbakhsh, A. Shahidinejad, and M. Ghobaei-Arani, “Multiuser Context-aware Computation Offloading in Mobile Edge Computing Based on Bayesian Learning Automata”, Transactions on Emerging Telecommunications Technologies, vol. 32, no. 1, pp. 1–26, 2021.
[19] F. Sufyan and A. Banerjee, “Computation Offloading for Distributed Mobile Edge Computing Network: A Multi-objective Approach”, IEEE Access, vol. 8, pp. 149915–149930, 2020.
[20] P. Match and Z. Becvar, “Mobile Edge Computing: A Survey on Architecture and Computation Offloading”, IEEE Communications Surveys and Tutorials, vol. 19, no. 3, pp. 1628-1656, 2017.
[21] A. Baktayan and I. Al-Balta, “Intelligent Computation Offloading and Pricing Strategies in UAV-enabled MEC Network for Utility Maximization: A Survey”, International Journal of Computing and Digital Systems, pp. 1–21, 2021.
[22] A. Shakarami, M. Ghobaei-Arani, A. Shahidinejad, “A Survey on the Computation Offloading Approaches in Mobile Edge Computing: A Machine Learning-based Perspective”, Computer Networks, vol. 182, pp. 1-24, 2020.
[23] A. Haydari and Y. Yılmaz, “Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey”, IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 1, pp. 11-32, 2022.
[24] L. Liu, C. Chen, Q. Pei, S. Maharjan and Y. Zhang, “Vehicular Edge Computing and Networking: A Survey”, Mobile Networks and Applications, vol. 26, pp. 1145–1168, 2021.
[25] م. دارچینی‌تبریزی و ر. انتظاری‌ملکی، ”مروری بر بارسپاری آگاه از کارایی وظایف در محاسبات لبه‌ای سیار“، بیست و هفتمین کنفرانس بین‌المللی کامپیوتر انجمن کامپیوتر ایران، 1400.
[26] G. Carvalho, B. Cabral, V. Pereira, and J. Bernardino, “Computation Offloading in Edge Computing Environments Using Artificial Intelligence Techniques”, Engineering Applications of Artificial Intelligence, vol.95, no.103840, pp. 1-19, 2020.
[27] S. Wu, W. Xia, W. Cui, Q. Chao, Z. Lan, F. Yan and L. Shen, "An Efficient Offloading Algorithm Based on Support Vector Machine for Mobile Edge Computing in Vehicular Networks", 10th International Conference on Wireless Communications and Signal Processing (WCSP), pp. 1-6, 2018.
[28] I. Goodfellow, Y. Bengio, and A. C. Courville, “Deep Learning”, Nature, vol. 521, pp. 436–444, 2015.
[29] S. Teerapittayanon, B. McDanel and H. T. Kung, “Distributed Deep Neural Networks Over the Cloud, the Edge and End Devices”, IEEE 37th International Conference on Distributed Computing Systems (ICDCS), pp. 328-339, 2017.
[30] C. Zhang, P. Patras and H. Haddadi, “Deep Learning in Mobile and Wireless Networking: A Survey”, IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2224-2287, 2019.
[31] K. Jiang, H. Zhou, D. Li, X. Liu and S. Xu, “A Q-learning Based Method for Energy-Efficient Computation Offloading in Mobile Edge Computing”, 29th International Conference on Computer Communications and Networks (ICCCN), pp. 1-7, 2020.
[32] H. Chen, S. Deng, H. Zhu, H. Zhao, R. Jiang, S. Dustdar and A. Y. Zomaya, “Mobility-Aware Offloading and Resource Allocation for Distributed Services Collaboration”, IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 10, pp. 2428-2443, 2022.
[33] C. Yang, Y. Liu, X. Chen, W. Zhong and S. Xie, “Efficient Mobility-Aware Task Offloading for Vehicular Edge Computing Networks”, IEEE Access, vol. 7, pp. 26652-26664, 2019.
[34] L. Huang, S. Bi and Y. -J. A. Zhang, “Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks”, IEEE Transactions on Mobile Computing, vol. 19, no. 11, pp. 2581-2593, 2020.
[35] H. Hu, Q. Wang, R. Q. Hu and H. Zhu, “Mobility-Aware Offloading and Resource Allocation in a MEC-Enabled IoT Network With Energy Harvesting”, IEEE Internet of Things Journal, vol. 8, no. 24, pp. 17541-17556, 2021.
[36] K. Li, “Heuristic Computation Offloading Algorithm for Mobile Users in Fog Computing”, ACM Transactions on Embedded Computing Systems, vol. 20, no. 11, pp. 1-28, 2021.
[37] J. Yan, S. Bi and Y. J. A. Zhang, “Offloading and Resource Allocation With General Task Graph in Mobile Edge Computing: A Deep Reinforcement Learning Approach”, IEEE Transactions on Wireless Communications, vol. 19, no. 8, pp. 5404-5419, 2020.
[38] Z. Ning, P. Dong, X. Kong and F. Xia, “A Cooperative Partial Computation Offloading Scheme for Mobile Edge Computing Enabled Internet of Things”, IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4804-4814, 2019.
[39] Z. Li, L. Shi, Y. Shi, Z. Wei and Y. Lu, “Task Offloading Strategy to Maximize Task Completion Rate in Heterogeneous Edge Computing Environment”, Computer Networks, vol. 210, no. 108937, pp. 1-14, 2022.
[40] U. Saleem, Y. Liu, S. Jangsher, Y. Li and T. Jiang, “Mobility-Aware Joint Task Scheduling and Resource Allocation for Cooperative Mobile Edge Computing”, IEEE Transactions on Wireless Communications, vol. 20, no. 1, pp. 360-374, 2021.
[41] D. Zhang, L. Tan, J. Ren, M. K. Awad, S. Zhang, Y. Zhang and P. J. Wan, “Near-Optimal and Truthful Online Auction for Computation Offloading in Green Edge-Computing Systems”, IEEE Transactions on Mobile Computing, vol. 19, no. 4, pp. 880-893, 2020.
[42] L. Qian, Y. Wu, F. Jiang, N. Yu, W. Lu and B. Lin, “NOMA Assisted Multi-Task Multi-Access Mobile Edge Computing via Deep Reinforcement Learning for Industrial Internet of Things”, IEEE Transactions on Industrial Informatics, vol. 17, no. 8, pp. 5688-5698, 2021.
[43] Z. Kuang, Z. Ma, Z. Li, X. Deng, “Cooperative Computation Offloading and Resource Allocation for Delay Minimization in Mobile Edge Computing”, Journal of Systems Architecture, vol. 118, pp. 1-9, 2021.
[44] Z. Ali, Z. H. Abbas, G. Abbas, A. Numani, and M. Bilal, “Smart Computational Offloading for Mobile Edge Computing in Next-generation Internet of Things Networks,” Computer Networks, vol.198, no.108356, pp. 1-9, 2021.
[45] H. Lu, X. He, M. Du, X. Ruan, Y. Sun and K. Wang, “Edge QoE: Computation Offloading With Deep Reinforcement Learning for Internet of Things”, IEEE Internet of Things Journal, vol. 7, no. 10, pp. 9255-9265, 2020.
[46] J. Li, H. Gao, T. Lv and Y. Lu, “Deep Reinforcement Learning Based Computation Offloading and Resource Allocation for MEC”, IEEE Wireless Communications and Networking Conference (WCNC), pp. 1-6, 2018.
[47] P. Zhao, H. Tian, C. Qin and G. Nie, “Energy-Saving Offloading by Jointly Allocating Radio and Computational Resources for Mobile Edge Computing”, IEEE Access, vol. 5, pp. 11255-11268, 2017.
[48] Y. Mao, J. Zhang, and K. B. Letaief, “Dynamic Computation Offloading for Mobile-edge Computing With Energy Harvesting Devices”, IEEE Journal on Selected Areas in Communications, vol. 34, no. 12, pp. 3590–3605, 2016.
[49] M. Chen, B. Liang and M. Dong, “Joint Offloading and Resource Allocation for Computation and Communication in Mobile Cloud With Computing Access Point”, IEEE Conference on Computer Communications, pp. 1-9, 2017.
[50] Z. Wang, B. Lin, L. Sun and Y. Wang, “Intelligent Task Offloading for 6G-Enabled Maritime IoT Based on Reinforcement Learning”, International Conference on Security, Pattern Analysis, and Cybernetics（SPAC), pp. 566-570, 2021.
