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首页> 外文期刊>Future generation computer systems >To chain or not to chain: A reinforcement learning approach for blockchain-enabled IoT monitoring applications
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To chain or not to chain: A reinforcement learning approach for blockchain-enabled IoT monitoring applications

机译：链条或不连锁：支持区块链的IOT监控应用程序的加强学习方法

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摘要

Traceability and autonomous business logic execution are highly desirable features in IoT monitoring applications. Traceability enables verifying signals' history for security or analytical purposes. On the other hand, the autonomous execution of pre-defined rules establishes trust between parties involved in such applications and improves the efficiency of their workflow. Smart Contracts (SCs) firmly guarantee these two requirements due to the blockchain's immutable distributed ledger and secure cryptographic consensus rules. Thus, SCs emerged as an appealing technology for monitoring applications. However, the cost of using public blockchains to harvest these guarantees can be prohibitive, especially with the considerable fluctuation of coin prices and different use case requirements. In this paper, we introduce a general SC-based IoT monitoring framework that can leverage the security features of public blockchains while minimizing the corresponding monetary cost. The framework contains a reinforcement learning agent that adapts to users' needs and acts in real-time to smartly set the data submission rate of IoT sensors. Results based on the Ethereum protocol show significant potential cost saving depending on users' preferences.
机译：可追溯性和自主业务逻辑执行是IOT监控应用中的非常理想的功能。可追溯性使验证信号的历史为安全或分析目的。另一方面，预定义规则的自主执行建立了在此类应用程序中涉及的各方之间的信任，并提高了工作流程的效率。智能合同（SCS）由于区块链的不变分布式分区和安全的加密共识规则，牢固保证这两个要求。因此，SC被出现为监测应用的吸引力技术。但是，使用公共区块收获的成本这些保证可能是持久的，特别是对于硬币价格的相当波动和不同的用例要求。在本文中，我们介绍了一般的基于SC的IOT监控框架，可以利用公钥的安全功能，同时最小化相应的货币成本。该框架包含一个强化学习代理，它适应用户的需求，并实时行动，以便巧妙地设置IOT传感器的数据提交率。根据用户偏好，基于Etereum协议的结果显示出显着的潜在成本节约。

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