تخلیه‌بار محاسباتی آگاه از تحرک و انرژی در رایانش لبه برای شبکه‌های مبتنی بر چند پهپاد
محورهای موضوعی : مهندسی برق و کامپیوتر
کیمیا قاسمی
1
,
زینب موحدی
2
*
1 - دانشكده مهندسي كامپيوتر، دانشگاه علم و صنعت ايران، تهران، ايران
2 - دانشكده مهندسي كامپيوتر، دانشگاه علم و صنعت ايران، تهران، ، ايران،
کلید واژه: تخلیه‌بار محاسباتی, رایانش لبه, شبکه‌های مبتنی بر چند پهپاد,
چکیده مقاله :
شبکه‌های ارتباطی و اینترنت اشیا با توسعه مداوم، به نیازمندی‌های مختلفی پاسخ می‌دهند. محدودیت‌های اندازه، توان محاسباتی و مصرف انرژی در دستگاه‌های اینترنت اشیا چالش‌های اساسی این فضا محسوب می‌شوند. این مقاله بر روی ترکیب پهپادها با رایانش لبه تاکید دارد به‌طوری‌که این یکپارچگی، پوشش پیشرفته و پشتیبانی محاسباتی کارآمد را فراهم می‌کند، به ویژه در شرایط نامعلوم مانند واکنش به حوادث. راه‌حل پیشنهادی، با لحاظ تحرک گره‌های اینترنت اشیا و با هدف بهبود کارایی انرژی کل سیستم، مسئله برنامه‌ریزی مسیر پهپاد و تخلیه‌بار محاسباتی به صورت توامان مدل‌سازی شده می‌کند. سپس، یک الگوریتم تخلیه‌بار محاسباتی و برنامه‌ریزی مسیر آگاه از تحرک و انرژی بر مبنای مجموعه پوششی کارآمد پهپادها ارائه شده است که با استفاده از ارتباطات و توافق‌های اجماعی بین گره‌های اینترنت اشیا به حداکثر رساندن کارایی انرژی سیستم کمک می‌کند. نتایج ارزیابی نشان می‌دهد که روش پیشنهادی کارایی انرژی را تا ۱۳۷ درصد و میزان مصرف انرژی را تا ۲۸ درصد نسبت به کارهای پیشین بهبود می بخشد.
Communication networks and the Internet of Things respond to various needs with continuous development. The limitations of size, computing power and energy consumption in Internet of Things devices are the main challenges of this space. This paper emphasizes combining UAVs with edge computing so that this integration provides advanced coverage and efficient computing support, especially in uncertain situations such as incident response. The proposed solution, in terms of the mobility of IoT nodes and with the aim of improving the energy efficiency of the whole system, the problem of UAV path planning and computation offloading is jointly modeled. Then, a mobility- and energy-aware computation offloading and path planning algorithm based on the efficient coverage set of UAVs is presented, which helps to maximize the energy efficiency of the system by using communication and consensus agreements between IoT nodes. The evaluation results show that the proposed method improves energy efficiency by 137% and energy consumption by 28% compared to previous works.
[1] Statista Research Department, Internet of Things - Number of Connected Devices Worldwide 2015-2025, https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/, Date Published: Nov 27, 2016, Last Access on Feb. 2024.
[2] C. Mateos, K. R. Choo, and A. Zunino, "Sharpening the edge: towards improved edge computing environment for mobile and IoT applications," Future Gener. Comput. Syst., vol. 107, pp. 1130-1133, Jun. 2020.
[3] W. Zhuang, Q. Ye, F. Lyu, N. Cheng, and J. Ren, "SDN/NFV-empowered future IoV with enhanced communication, computing, and caching," Proceedings of the IEEE, vol. 108, no. 2, pp. 274-291, Feb. 2020.
[4] T. Zhang, Y. Xu, J. Loo, D. Yang, and L. Xiao, "Joint computation and communication design for UAV-assisted mobile edge computing in IoT," IEEE Trans. Ind. Inform., vol. 16, no. 8, pp. 5505-5516, Oct. 2020.
[5] Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, "A survey on mobile edge computing: the communication perspective," IEEE Commun. Surv. Tut., vol. 19, no. 4, pp. 2322-2358, Jan. 2017.
[6] X. Xiong, K. Zheng, L. Lei, and L. Hou, "Resource allocation based on deep reinforcement learning in IoT edge computing," IEEE J. Sel. Areas Commun., vol. 38, no. 6, pp. 1133-1146, Apr. 2020.
[7] Z. Chang, L. Liu, X. Guo, and Q. Sheng, "Dynamic resource allocation and computation offloading for IoT fog computing system," IEEE Trans. Ind. Inform., vol. 17, no. 5, pp. 3348-3357, Jun. 2021.
[8] X. Hu, K. Wong, K. Yang, and Z. Zheng, "UAV-assisted relaying and edge computing: scheduling and trajectory optimization," IEEE Trans. Wirel. Commun., vol. 18, no. 10, pp. 4738-4752, Dec. 2019.
[9] M. Li, N. Cheng, J. Gao, Y. Wang, L. Zhao, and X. Shen, "Energy efficient UAV-assisted mobile edge computing: resource allocation and trajectory optimization," IEEE Trans. Veh. Technol., vol. 69, no. 3, pp. 3424-3438, Jan. 2020.
[10] L. D. Nguyen, A. E. Kalør, I. L. Mayorga, and P. Popovski, "Trusted wireless monitoring based on distributed ledgers over NB-IoT connectivity," IEEE Commun. Mag., vol. 58, no. 6, pp. 77-83, Apr. 2020.
[11] M. Pradhan and J. Noll, "Security, privacy, and dependability evaluation in verification and validation life cycles for military IoT systems," IEEE Commun. Mag., vol. 58, no. 8, pp. 14-20, Aug. 2020.
[12] Z. Tan, H. Qu, J. Zhao, S. Zhou, and W. Wang, "UAV-aided edge/fog computing in smart IoT community for social augmented reality," IEEE Internet Things J., vol. 7, no. 6, pp. 4872-4884, Feb. 2020.
[13] F. Zhou, Y. Wu, R. Q. Hu, and Y. Qian, "Computation rate maximization in UAV-enabled wireless-powered mobile-edge computing systems," IEEE J. Sel. Areas Commun., vol. 36, no. 9, pp. 1927-1941, Jun. 2018.
[14] L. Li, T. Q. S. Quek, J. Ren, H. H. Yang, Z. Chen, and Y. Zhang, "An incentive-aware job offloading control framework for multi-access edge computing," IEEE Trans. Mobile Comput., vol. 20, no. 1, pp. 63-75, Jan. 2021.
[15] K. Poularakis, J. Llorca, A. M. Tulino, I. J. Taylor, and L. Tassiulas, "Service placement and request routing in MEC networks with storage, computation, and communication constraints," IEEE/ACM Trans. Netw., vol. 28, no. 3, pp. 1047-1060, Apr. 2020.
[16] K. Poularakis, J. Llorca, A. M. Tulino, I. J. Taylor, and L. Tassiulas, "Service placement and request routing in MEC networks with storage, computation, and communication constraints," IEEE/ACM Trans. Netw., vol. 28, no. 3, pp. 1047-1060, Apr. 2020.
[17] T. Ouyang, Z. Zhou, and X. Chen, "Follow me at the edge: mobilityaware dynamic service placement for mobile edge computing," IEEE J. Sel. Areas Commun., vol. 36, no. 10, pp. 2333-2345, Sep. 2018.
[18] S. Wang, Y. Guo, N. Zhang, P. Yang, A. Zhou, and X. Shen, "Delayaware microservice coordination in mobile edge computing: a reinforcement learning approach," IEEE Trans. Mobile Comput., vol. 20, no. 3, pp. 939-951, Dec. 2021.
[19] Y. Liu, Y. Li, Y. Niu, and D. Jin, "Joint optimization of path planning and resource allocation in mobile edge computing," IEEE Trans. Mobile Comput., vol. 19, no. 9, pp. 2129-2144, Sept. 2020.
[20] F. Guo, H. Zhang, H. Ji, X. Li, and V. C. Leung, "Joint trajectory and computation offloading optimization for UAV-assisted MEC with NOMA," in Proc. IEEE Conf. Comput. Commun. Workshops, 6 pp., Paris, France, 29 Apr.-2 May 2019.
[21] S. Jeong, O. Simeone, and J. Kang, "Mobile edge computing via a UAV-mounted cloudlet: optimization of bit allocation and path planning," IEEE Trans. Veh. Technol., vol. 67, no. 3, pp. 2049-2063, Sept. 2018.
[22] Y. Qian, et al., "User association and path planning for UAV-aided mobile edge computing with energy restriction," IEEE Wirel. Commun. Lett., vol. 8, no. 5, pp. 1312-1315, Apr. 2019.
[23] X. Chen, et al., "Distributed computation offloading and trajectory optimization in multi-uav-enabled edge computing," IEEE Internet of Things, vol. 9, no. 20, pp. 20096-20110, May 2022.
[24] Q. Ye, et al., "Learningbased computing task offloading for autonomous driving: a load balancing perspective," in Proc. IEEE Int. Conf. Commun., 6 pp., Montreal, Canada, 14-23 Jun. 2021.
[25] I. A. Elgendy, S. Meshoul, and M. Hammad, "Joint task offloading, resource allocation, and load-balancing optimization in multi-UAV-aided MEC systems," Applied Sciences, vol. 13, no. 4, Article ID: 2625, Feb. 2023.
[26] B. Li, et al., "Robust computation offloading and trajectory optimization for multi-UAV-assisted MEC: a multi-agent DRL approach," IEEE Internet of Things J., vol. 11, no. 3, pp. 4775-4786, Aug. 2023.
[27] Z. Yu, Y. Gong, S. Gong, and Y. Guo, "Joint task offloading and resource allocation in UAV-enabled mobile edge computing," IEEE Internet Things J., vol. 7, no. 4, pp. 3147-3159, Jan. 2020.
[28] Y. Wang, Z. Ru, K. Wang, and P. Huang, "Joint deployment and task scheduling optimization for large-scale mobile users in multi-UAV-enabled mobile edge computing," IEEE Trans. Cybern., vol. 50, no. 9, pp. 3984-3997, Sept. 2020.
[29] A. Ranjha, G. Kaddoum, M. Rahim, and K. Dev, "URLLC in UAV-enabled multicasting systems: a dual time and energy minimization problem using UAV speed, altitude and beamwidth," Computer Communications, vol. 187, pp. 125-133, Apr. 2022.
[30] N. Lin, H. Tang, L. Zhao, S. Wan, A. Hawbani, and M. Guizani, "A PDDQNLP algorithm for energy efficient computation offloading in UAV-assisted MEC," IEEE Trans. on Wireless Communications, vol. 22, no. 12, pp. 8876-8890, Dec. 2023.
[31] M. Ma and Z. Wang, "Distributed offloading for multi-uav swarms in mec-assisted 5G heterogeneous networks," Drones, vol. 7, no. 4, Article ID: 226, Mar. 2023.