Mobile edge computing; Multi-agent reinforcement learning; Near field communication; Resource optimization; Communications systems; Contact less; Edge computing; Near fields; Near-field communication; Point of sale; Resources optimization; Sale services; Wireless communications; Computer Networks and Communications
Abstract :
[en] In the next-generation communication system, near-field communication (NFC) is a key enabler of contactless transactions, including mobile payments, ticketing, and access control. With the growing demand for contactless solutions, NFC technology will play a pivotal role in enabling secure and convenient payment experiences across various sectors. In contrast, Internet of Things (IoT) devices such as phones’ Point of Sale (PoS) constitute limited battery life and finite computational resources that act as a bottleneck to doing the authentication in a minimal amount of time. Because of this, it garnered considerable attention in both academic and industrial realms. To overcome this, in this work we consider the Multiple Mobile Edge Computing (MEC) as an effective solution that provides extensive computation to PoS connected to it. To address the above, this work considers the PoS-enabled multi-MEC network to guarantee NFC communication reliably and effectively. For this, we formulate the joint optimization problem to maximize the probability of successful authentication while minimizing the queueing delay by jointly optimizing the computation and communication resources by utilizing a multi-agent reinforcement learning optimization approach. Through extensive simulations based on real-world scenarios, the effectiveness of the proposed approach was demonstrated. The results demonstrate that adjusting the complexity and learning rates of the model, coupled with strategic allocation of edge resources, significantly increased authentication success rates. Furthermore, the optimal allocation strategy was found to be crucial in reducing latency and improving authentication success by approximately 9.75%, surpassing other approaches. This study highlights the importance of resource management in optimizing MEC systems, paving the way for advancements in establishing secure, efficient, and dependable systems within the Internet of Things framework.
Disciplines :
Computer science
Author, co-author :
Rehman, Ateeq Ur ; School of Computing, Gachon University, Seongnam, South Korea
Maashi, Mashael ; Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
Alsamri, Jamal; Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, Saudi Arabia
Mahgoub, Hany; Department of Computer Science, Applied College at Mahayil, King Khalid University, Saudi Arabia
Allafi, Randa; Department of Computers and Information Technology, College of Sciences and Arts, Northern Border University, Arar, Saudi Arabia
Dutta, Ashit Kumar; Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia
KHAN, Wali Ullah ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Nauman, Ali ; School of Computer Science and Engineering Yeungnam University, Gyeongsan, South Korea
External co-authors :
yes
Language :
English
Title :
Optimizing point-of-sale services in MEC enabled near field wireless communications using multi-agent reinforcement learning
Original title :
[en] Optimizing point-of-sale services in MEC enabled near field wireless communications using multi-agent reinforcement learning
The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/87/45 . Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R729), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia . Researchers Supporting Project number (RSPD2024R787), King Saud University, Riyadh, Saudi Arabia . The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research work through the project number NBU-FFR-2024-170-09. This work was supported by the Researchers Supporting Project Number (MHIRSP2024005) at Almaarefa University, Riyadh, Saudi Arabia .
Wang, H., Xiao, P., Li, X., Channel parameter estimation of mmwave MIMO system in urban traffic scene: A training channel-based method. IEEE Trans. Intell. Transp. Syst. 25:1 (2024), 754–762.
Ahmed, M., Shahwar, M., Khan, F., Ullah Khan, W., Ihsan, A., Sadiq Khan, U., Xu, F., Chatzinotas, S., NOMA-based backscatter communications: Fundamentals, applications, and advancements. IEEE Internet Things J. 11:11 (2024), 19303–19327.
Liu, J., Ahmed, M., Mirza, M.A., Khan, W.U., Xu, D., Li, J., Aziz, A., Han, Z., RL/DRL meets vehicular task offloading using edge and vehicular cloudlet: A survey. IEEE Internet Things J. 9:11 (2022), 8315–8338.
Wang, H., Xu, L., Yan, Z., Gulliver, T.A., Low-complexity MIMO-FBMC sparse channel parameter estimation for industrial big data communications. IEEE Trans. Ind. Inform. 17:5 (2021), 3422–3430.
Khan, W.U., Lagunas, E., Ali, Z., Javed, M.A., Ahmed, M., Chatzinotas, S., Ottersten, B., Popovski, P., Opportunities for physical layer security in UAV communication enhanced with intelligent reflective surfaces. IEEE Wirel. Commun. 29:6 (2022), 22–28.
Wang, H., Memon, F.H., Wang, X., Li, X., Zhao, N., Dev, K., Machine learning-enabled MIMO-FBMC communication channel parameter estimation in IIoT: A distributed CS approach. Digit. Commun. Netw. 9:2 (2023), 306–312.
Xu, B., Zhang, J., Du, H., Wang, Z., Liu, Y., Niyato, D., Ai, B., Letaief, K.B., Resource allocation for near-field communications: Fundamentals, tools, and outlooks. IEEE Wirel. Commun., 2024, 1–9.
Ahmed, M., Raza, S., Soofi, A.A., Khan, F., Khan, W.U., Abideen, S.Z.U., Xu, F., Han, Z., Active reconfigurable intelligent surfaces: Expanding the frontiers of wireless communication-A survey. IEEE Commun. Surv. Tutor., 2024, 1.
Cui, M., Wu, Z., Lu, Y., Wei, X., Dai, L., Near-field MIMO communications for 6G: Fundamentals, challenges, potentials, and future directions. IEEE Commun. Mag. 61:1 (2023), 40–46.
Pervez, F., Sultana, A., Yang, C., Zhao, L., Energy and latency efficient joint communication and computation optimization in a multi-UAV assisted MEC network. IEEE Trans. Wireless Commun., 2023, 1.
Coskun, V., Ok, K., Ozdenizci, B., Near Field Communication (NFC): From Theory to Practice. 2011, John Wiley & Sons.
Madlmayr, G., Langer, J., Kantner, C., Scharinger, J., NFC devices: Security and privacy. 2008 Third International Conference on Availability, Reliability and Security, 2008, IEEE, 642–647.
Mahmood, A., Hong, Y., Ehsan, M.K., Mumtaz, S., Optimal resource allocation and task segmentation in IoT enabled mobile edge cloud. IEEE Trans. Veh. Technol. 70:12 (2021), 13294–13303.
Ahmed, M., Xu, F., Wahid, A., Ali, K., Mirza, M.A., Khan, W., Dev, K., Hassan, S.A., Han, Z., A comprehensive survey of artificial intelligence advances in RIS-assisted wireless networks. 2024 [Online]. Available: http://dx.doi.org/10.36227/techrxiv.172349533.38675045/v1.
Mehrabi, M., You, D., Latzko, V., Salah, H., Reisslein, M., Fitzek, F.H., Device-enhanced MEC: Multi-access edge computing (MEC) aided by end device computation and caching: A survey. IEEE Access 7 (2019), 166079–166108.
Liu, J., Ahmed, M., Mirza, M.A., Khan, W.U., Xu, D., Li, J., Aziz, A., Han, Z., RL/DRL meets vehicular task offloading using edge and vehicular cloudlet: A survey. IEEE Internet Things J. 9:11 (2022), 8315–8338.
Mahmood, A., Ahmed, A., Naeem, M., Hong, Y., Partial offloading in energy harvested mobile edge computing: A direct search approach. IEEE Access 8 (2020), 36757–36763.
Wójtowicz, A., Chmielewski, J., Payment authorization in smart environments: security-convenience balance. Enterprise Information Systems: 20th International Conference, ICEIS 2018, Funchal, Madeira, Portugal, March 21-24, 2018, Revised Selected Papers 20, 2019, Springer, 58–81.
Ali, B., Gregory, M.A., Li, S., Multi-access edge computing architecture, data security and privacy: A review. IEEE Access 9 (2021), 18706–18721.
Mahmood, A., Ahmed, A., Naeem, M., Amirzada, M.R., Al-Dweik, A., Weighted utility aware computational overhead minimization of wireless power mobile edge cloud. Comput. Commun. 190 (2022), 178–189.
Zhang, W., Flores, H., Pan, H., Towards collaborative multi-device computing. 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops, 2018, IEEE, 22–27.
Fang, F., Xu, Y., Ding, Z., Shen, C., Peng, M., Karagiannidis, G.K., Optimal resource allocation for delay minimization in NOMA-MEC networks. IEEE Trans. Commun. 68:12 (2020), 7867–7881.
Hu, H., Wang, Q., Hu, R.Q., Zhu, H., Mobility-aware offloading and resource allocation in a MEC-enabled IoT network with energy harvesting. IEEE Internet Things J. 8:24 (2021), 17541–17556.
Liu, B., Liu, C., Peng, M., Resource allocation for energy-efficient MEC in NOMA-enabled massive IoT networks. IEEE J. Sel. Areas Commun. 39:4 (2021), 1015–1027.
Wang, J., Feng, D., Zhang, S., Liu, A., Xia, X.-G., Joint computation offloading and resource allocation for MEC-enabled IoT systems with imperfect CSI. IEEE Internet Things J. 8:5 (2021), 3462–3475.
Nauman, A., Khan, W.U., Aldehim, G., Alqahtani, H., Alruwais, N., Al Duhayyim, M., Dev, K., Min, H., Nkenyereye, L., Communication and computational resource optimization for industry 5.0 smart devices empowered by MEC. J. King Saud Univ.-Comput. Inf. Sci., 36(1), 2024, 101870.
Zhang, H., Shlezinger, N., Guidi, F., Dardari, D., Imani, M.F., Eldar, Y.C., Beam focusing for near-field multiuser MIMO communications. IEEE Trans. Wireless Commun. 21:9 (2022), 7476–7490.
Chen, J., Gao, F., Jian, M., Yuan, W., Hierarchical codebook design for near-field mmWave MIMO communications systems. IEEE Wirel. Commun. Lett. 12:11 (2023), 1926–1930.
Lu, H., Zeng, Y., Near-field modeling and performance analysis for multi-user extremely large-scale MIMO communication. IEEE Commun. Lett. 26:2 (2022), 277–281.
Dardari, D., Decarli, N., Guerra, A., Guidi, F., LOS/NLOS near-field localization with a large reconfigurable intelligent surface. IEEE Trans. Wireless Commun. 21:6 (2022), 4282–4294.
Myers, N.J., Heath, R.W., InFocus: A spatial coding technique to mitigate misfocus in near-field LoS beamforming. IEEE Trans. Wireless Commun. 21:4 (2022), 2193–2209.
Cao, Y., Lv, T., Lin, Z., Huang, P., Lin, F., Complex ResNet aided DoA estimation for near-field MIMO systems. IEEE Trans. Veh. Technol. 69:10 (2020), 11139–11151.
Z. Abu-Shaban, K. Keykhosravi, M.F. Keskin, G.C. Alexandropoulos, G. Seco-Granados, H. Wymeersch, Near-field Localization with a Reconfigurable Intelligent Surface Acting as Lens, in: ICC 2021 - IEEE International Conference on Communications, 2021, pp. 1–6.
Rinchi, O., Elzanaty, A., Alouini, M.-S., Compressive near-field localization for multipath RIS-aided environments. IEEE Commun. Lett. 26:6 (2022), 1268–1272.
Palmucci, S., Guerra, A., Abrardo, A., Dardari, D., Two-timescale joint precoding design and RIS optimization for user tracking in near-field MIMO systems. IEEE Trans. Signal Process. 71 (2023), 3067–3082.
Guerra, A., Guidi, F., Dardari, D., Djurić, P.M., Near-field tracking with large antenna arrays: Fundamental limits and practical algorithms. IEEE Trans. Signal Process. 69 (2021), 5723–5738.
Tadayon, N., Aissa, S., Modeling and analysis of cognitive radio based IEEE 802.22 wireless regional area networks. IEEE Trans. Wireless Commun. 12:9 (2013), 4363–4375.
Khan, W.U., Ali, Z., Lagunas, E., Mahmood, A., Asif, M., Ihsan, A., Chatzinotas, S., Ottersten, B., Dobre, O.A., Rate splitting multiple access for next generation cognitive radio enabled LEO satellite networks. IEEE Trans. Wireless Commun. 22:11 (2023), 8423–8435.
Nauman, A., Alshahrani, H.M., Nemri, N., Othman, K.M., Aljehane, N.O., Maashi, M., Dutta, A.K., Assiri, M., Khan, W.U., Dynamic resource management in integrated NOMA terrestrial–satellite networks using multi-agent reinforcement learning. J. Netw. Comput. Appl., 221, 2024, 103770.
Du, J., Kong, Z., Sun, A., Kang, J., Niyato, D., Chu, X., Yu, F.R., Maddpg-based joint service placement and task offloading in MEC empowered air-ground integrated networks. IEEE Internet Things J., 2023.
Xu, F., Hussain, T., Ahmed, M., Ali, K., Mirza, M.A., Khan, W.U., Ihsan, A., Han, Z., The state of AI-empowered backscatter communications: A comprehensive survey. IEEE Internet Things J. 10:24 (2023), 21763–21786.
J. Su, S. Adams, P. Beling, Value-decomposition multi-agent actor-critics, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, 2021, pp. 11352–11360, no. 13.
