Optimizing point-of-sale services in MEC enabled near field wireless communications using multi-agent reinforcement learning

IF 4.5 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications Pub Date : 2024-10-10 DOI:10.1016/j.comcom.2024.107962

Ateeq Ur Rehman , Mashael Maashi , Jamal Alsamri , Hany Mahgoub , Randa Allafi , Ashit Kumar Dutta , Wali Ullah Khan , Ali Nauman

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引用次数: 0

Abstract

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.

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利用多代理强化学习优化支持 MEC 的近场无线通信中的销售点服务

在下一代通信系统中，近场通信（NFC）是实现移动支付、票务和门禁等非接触式交易的关键因素。随着人们对非接触式解决方案的需求日益增长，NFC 技术将在各行各业实现安全、便捷的支付体验方面发挥关键作用。相比之下，手机销售点（PoS）等物联网（IoT）设备的电池寿命和计算资源有限，成为在最短时间内完成身份验证的瓶颈。正因为如此，它在学术界和工业界都引起了相当大的关注。为了克服这一问题，我们在这项工作中考虑将多重移动边缘计算（MEC）作为一种有效的解决方案，为与其连接的 PoS 提供大量计算资源。为解决上述问题，本研究考虑了支持 PoS 的多 MEC 网络，以确保 NFC 通信的可靠性和有效性。为此，我们提出了一个联合优化问题，即利用多代理强化学习优化方法，通过联合优化计算和通信资源，最大化成功验证的概率，同时最小化排队延迟。通过基于真实世界场景的大量模拟，证明了所提方法的有效性。结果表明，调整模型的复杂度和学习率，再加上边缘资源的战略性分配，大大提高了认证成功率。此外，研究还发现最优分配策略在减少延迟和提高认证成功率方面发挥了至关重要的作用，成功率提高了约 9.75%，超过了其他方法。这项研究强调了资源管理在优化 MEC 系统中的重要性，为在物联网框架内建立安全、高效、可靠的系统铺平了道路。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computer Communications 工程技术-电信学

CiteScore

14.10

自引率

5.00%

发文量

397

审稿时长

66 days

期刊介绍： Computer and Communications networks are key infrastructures of the information society with high socio-economic value as they contribute to the correct operations of many critical services (from healthcare to finance and transportation). Internet is the core of today''s computer-communication infrastructures. This has transformed the Internet, from a robust network for data transfer between computers, to a global, content-rich, communication and information system where contents are increasingly generated by the users, and distributed according to human social relations. Next-generation network technologies, architectures and protocols are therefore required to overcome the limitations of the legacy Internet and add new capabilities and services. The future Internet should be ubiquitous, secure, resilient, and closer to human communication paradigms. Computer Communications is a peer-reviewed international journal that publishes high-quality scientific articles (both theory and practice) and survey papers covering all aspects of future computer communication networks (on all layers, except the physical layer), with a special attention to the evolution of the Internet architecture, protocols, services, and applications.