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Towards Secure Fitness Framework Based on IoT-Enabled Blockchain Network Integrated with Machine Learning Algorithms

机译：基于启用IOT的区块链网络与机器学习算法集成的安全健身框架

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摘要

Blockchain technology has recently inspired remarkable attention due to its unique features, such as privacy, accountability, immutability, and anonymity, to name of the few. In contrast, core functionalities of most Internet of Things (IoT) resources make them vulnerable to security threats. The IoT devices, such as smartphones and tablets, have limited capacity in terms of network, computing, and storage, which make them easier for vulnerable threats. Furthermore, a massive amount of data produced by the IoT devices, which is still an open challenge for the existing platforms to process, analyze, and unearth underlying patterns to provide convenience environment. Therefore, a new solution is required to ensure data accountability, improve data privacy and accessibility, and extract hidden patterns and useful knowledge to provide adequate services. In this paper, we present a secure fitness framework that is based on an IoT-enabled blockchain network integrated with machine learning approaches. The proposed framework consists of two modules: a blockchain-based IoT network to provide security and integrity to sensing data as well as an enhanced smart contract enabled relationship and inference engine to discover hidden insights and useful knowledge from IoT and user device network data. The enhanced smart contract aims to support users with a practical application that provides real-time monitoring, control, easy access, and immutable logs of multiple devices that are deployed in several domains. The inference engine module aims to unearth underlying patterns and useful knowledge from IoT environment data, which helps in effective decision making to provide convenient services. For experimental analysis, we implement an intelligent fitness service that is based on an enhanced smart contract enabled relationship and inference engine as a case study where several IoT fitness devices are used to securely acquire user personalized fitness data. Furthermore, a real-time inference engine investigates user personalized data to discover useful knowledge and hidden insights. Based on inference engine knowledge, a recommendation model is developed to recommend a daily and monthly diet, as well as a workout plan for better and improved body shape. The recommendation model aims to facilitate a trainer formulating effective future decisions of trainee’s health in terms of a diet and workout plan. Lastly, for performance analysis, we have used Hyperledger Caliper to access the system performance in terms of latency, throughput, resource utilization, and varying orderer and peers nodes. The analysis results imply that the design architecture is applicable for resource-constrained IoT blockchain platform and it is extensible for different IoT scenarios.
机译：由于其独特的功能，例如隐私，问责制，不可变性和匿名，少数人的名称，BlockChain技术最近引起了显着的关注。相比之下，大多数事情互联网（物联网）资源的核心功能使他们容易受到安全威胁的影响。如智能手机和平板电脑，在网络，计算和存储方面的IOT设备（如智能手机）有限，这使得它们更容易易受伤害的威胁。此外，IOT设备产生的大量数据，这仍然是用于处理，分析和解除潜在模式以提供便利环境的现有平台的开放挑战。因此，需要一种新的解决方案来确保数据责任，提高数据隐私和可访问性，并提取隐藏的模式和有用知识以提供适当的服务。在本文中，我们提出了一种安全的健身框架，该框架是基于与机器学习方法集成的IOT的SlinkChain网络。所提出的框架由两个模块组成：基于区块链的IoT网络，提供安全性和完整性的传感数据以及增强的智能合同支持的关系和推理引擎，以发现来自IOT和用户设备网络数据的隐藏洞察和有用的知识。增强型智能合同旨在支持用户使用实际应用程序，该应用程序提供在多个域中部署的多个设备的实时监控，控制，轻松访问和不可变日志。推理引擎模块旨在从物联网环境数据中解除潜在的模式和有用知识，这有助于提供便捷的服务。对于实验分析，我们实现了一个基于增强型智能合同的智能健身服务，使能的关系和推理引擎作为一种案例研究，其中用于安全地获取用户个性化健身数据的案例研究。此外，实时推理引擎调查用户个性化数据以发现有用的知识和隐藏的见解。基于推理引擎知识，开发了推荐模型以推荐每日和每月饮食，以及锻炼计划，以便更好地改善身体形状。建议模式旨在促进培训师在饮食和锻炼计划方面制定实习生健康的有效未来决策。最后，对于性能分析，我们使用过度遍及的Caliper来在延迟，吞吐量，资源利用率和不同订购者和对等体节点方面访问系统性能。分析结果意味着设计架构适用于资源受限的IOT区块链平台，并且对于不同的IOT方案，它是可扩展的。

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