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

Area of Science:

• Computer Science
• Information Security
• Artificial Intelligence

Background:

• Internet of Things (IoT) devices are vulnerable to security threats due to limited resources.
• Massive data generation by IoT poses challenges for processing, analysis, and pattern extraction.
• Existing platforms struggle to ensure data accountability, privacy, and accessibility for IoT environments.

Purpose of the Study:

• To present a secure fitness framework integrating IoT, blockchain, and machine learning.
• To enhance data security, privacy, and accessibility for IoT-enabled fitness applications.
• To extract hidden insights from user data for personalized health and fitness recommendations.

Main Methods:

• Developed a framework with a blockchain-based IoT network for data security and integrity.
• Integrated an enhanced smart contract and inference engine for data analysis and knowledge discovery.
• Implemented an intelligent fitness service using IoT devices, smart contracts, and a recommendation model.

Main Results:

• The framework securely acquires and processes personalized fitness data from IoT devices.
• The inference engine unearths hidden patterns, enabling personalized diet and workout recommendations.
• Performance analysis using Hyperledger Caliper demonstrates the architecture's applicability and extensibility.

Conclusions:

• The proposed IoT-enabled blockchain framework effectively addresses security and data processing challenges.
• The integrated machine learning approach provides valuable, personalized fitness insights and recommendations.
• The architecture is suitable for resource-constrained IoT environments and adaptable to various scenarios.