Computational energy efficiency; Industry 5.0; Joint optimization; Mobile edge computing; Partial offloading; Computer Science (all)
Abstract :
[en] Smart devices in Industry 5.0, such as sensors and robots, are often limited by low battery life and finite computational resources, hindering their ability to perform complex tasks. By offloading computation-intensive tasks to Mobile Edge Cloud Computing (MEC) servers at the network's edge, businesses can achieve real-time data processing and analysis, reducing communication latency, quicker response times, and improved system reliability. This work presents an integrated framework for MEC and Industry 5.0, aimed at enhancing the performance, efficiency, and flexibility of industrial processes. In particular, we propose a joint optimization problem that maximizes computational energy efficiency by optimally allocating resources, such as processing power and computational resources, as well as device association, in the most efficient manner possible. The problem is formulated as nonconvex/nonlinear, which is intractable and poses high complexity. To solve this challenging problem, we first transform and decouple the original optimization problem into a series of subproblems using the block coordinate descent method. Then, we iteratively obtain an efficient solution using convex optimization methods. In addition, our work sheds light on the fundamental trade-off between local computation and partial offloading schemes. The results show that for small data size requirements, the performance is comparable among different schemes. However, as data size increases, our proposed hybrid scheme, which includes a partial offloading scheme, outperforms others, highlighting the effectiveness of the proposed joint optimization scheme.
Disciplines :
Computer science
Author, co-author :
Nauman, Ali ; Department of Information and Communication Engineering, Yeungnam University, South Korea
KHAN, Wali Ullah ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Aldehim, Ghadah ; Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
Alqahtani, Hamed; Department of Information Systems, College of Computer Science, Center of Artificial Intelligence, Unit of Cybersecurity, King Khalid University, Abha, Saudi Arabia
Alruwais, Nuha; Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Saudi Arabia, Riyadh, Saudi Arabia
Duhayyim, Mesfer Al ; Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
Dev, Kapal ; Department of Computer Science and CONNECT Centre, Munster Technological University, Cork, Ireland ; Department of institute of intelligent systems, University of Johannesburg, South Africa ; Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
Min, Hong ; School of Computing, Gachon University, Gyeonggi-do, South Korea
Nkenyereye, Lewis ; Department of Computer and Information Security, Sejong University, South Korea
External co-authors :
yes
Language :
English
Title :
Communication and computational resource optimization for Industry 5.0 smart devices empowered by MEC
Original title :
[en] Communication and computational resource optimization for Industry 5.0 smart devices empowered by MEC
Publication date :
January 2024
Journal title :
Journal of King Saud University - Computer and Information Sciences
National Research Foundation of Korea Deanship of Scientific Research, King Khalid University Prince Sattam bin Abdulaziz University Ministry of Education
Funding text :
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through large group Research Project under grant number ( RGP2/ 159/ 44 ). Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R387), Princess Nourah bint Abdulrahman University , Riyadh, Saudi Arabia. Research Supporting Project number (RSPD2023R608), King Saud University, Riyadh, Saudi Arabia . This study is supported via funding from Prince Sattam bin Abdulaziz University, Saudi Arabia project number (PSAU/2023/R/1444). This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2021R1F1A1055408 ).The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through large group Research Project under grant number (RGP2/ 159/ 44). Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R387), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. Research Supporting Project number (RSPD2023R608), King Saud University, Riyadh, Saudi Arabia. This study is supported via funding from Prince Sattam bin Abdulaziz University, Saudi Arabia project number (PSAU/2023/R/1444). This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2021R1F1A1055408).
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