Recently, the development of the Internet of Things (IoT) provides plenty of opportunities and challenges in various fields. As an essential part of IoT, machine-to-machine (M2M) communications open a novel way that the machine-type communication devices (MTCDs) are connected and communicated without any human intervention. Meanwhile, delay-tolerant data play an important role in M2M communications-based IoT, and it puts more emphasis on powerful data caching, computing, and processing, as well as the security and stability of data transmission. To meet these requirements in M2M communications networks, in this article, we introduce some promising technologies, such as edge computing and blockchain, and propose a joint optimization framework about caching, computation, and security for delay-tolerant data in M2M communications networks based on dueling deep Q-network (DQN). According to the dynamic decision process by DQN, the optimal selection and decision of caching servers, computing servers, and blockchain systems can be made to achieve maximum system rewards, which includes higher efficiency of data processing, lower network costs, and better security of data interaction. Extensive simulation results with different system parameters show that our proposed framework can effectively improve the system performance for blockchain-enabled M2M communications compared to the existing schemes.

中文翻译：

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通过边缘计算支持区块链的物联网中延迟容忍数据的资源优化：一种深度强化学习方法


近年来，物联网（IoT）的发展给各个领域带来了大量的机遇和挑战。作为物联网的重要组成部分，机器对机器 (M2M) 通信开辟了一种新颖的方式，使机器类型通信设备 (MTCD) 在无需任何人工干预的情况下进行连接和通信。同时，容延迟数据在基于M2M通信的物联网中发挥着重要作用，它更强调强大的数据缓存、计算和处理以及数据传输的安全性和稳定性。为了满足 M2M 通信网络中的这些要求，在本文中，我们介绍了一些有前途的技术，例如边缘计算和区块链，并提出了一种基于 M2M 通信网络中延迟容忍数据的缓存、计算和安全性的联合优化框架决斗深度 Q 网络 (DQN)。根据DQN的动态决策过程，可以对缓存服务器、计算服务器和区块链系统进行最优选择和决策，以获得最大的系统奖励，包括更高的数据处理效率、更低的网络成本和更好的数据安全性相互作用。不同系统参数的广泛仿真结果表明，与现有方案相比，我们提出的框架可以有效提高基于区块链的 M2M 通信的系统性能。