The proliferation of IoT devices has revolutionized the way we interact with our surroundings, from smart homes to industrial automation. However, the current landscape faces challenges in terms of interoperability, security, and efficiency. The research identifies these challenges as the primary problem and emphasizes the need for a holistic approach. Existing methodologies often focus on specific aspects, leaving room for a comprehensive solution that addresses the synergy of connectivity and intelligence. The proposed method involves the integration of edge computing, machine learning algorithms, and blockchain technology. This aims to enhance the processing capabilities of IoT devices locally, ensure secure and transparent data transactions, and enable adaptive decision-making. The method is designed to be scalable, ensuring applicability across various IoT ecosystems. The results demonstrate a significant improvement in data processing speed, security, and adaptability within the IoT network. The embedded devices, equipped with enhanced intelligence, showcase improved response times and reduced dependence on centralized servers. Additionally, the blockchain-based security measures contribute to a more resilient and trustworthy network.
Bhushankumar Nemade1, Neelam Phadnis2, Aaditya Desai3, Kiran Krushnakant Mungekar4
Shree L. R. Tiwari College of Engineering, India1,2, Prin. L. N. Welingkar Institute of Management Development and Research, India 3, Tata Consultancy Services, Mumbai, India4
Machine Learning, Internet of Things, Data Processing, Energy Consumption
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| Published By :
ICTACT
Published In :
ICTACT Journal on Microelectronics
( Volume: 9 , Issue: 4 , Pages: 1670 - 1674 )
Date of Publication :
January 2024
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201
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