LEVERAGING COMPUTER SCIENCE IN THE DESIGN AND OPTIMIZATION OF INTERNET OF THINGS SYSTEMS
DOI:
https://doi.org/10.59743/jbs.v37i2.312Keywords:
Internet of Things (IoT), Computer Science Integration, Resource Optimization, Machine Learning, Blockchain, Edge ComputingAbstract
The Internet of Things (IoT) connects billions of devices globally, transforming industries with intelligent solutions. Despite its potential, IoT systems face critical challenges such as resource constraints, high latency, security vulnerabilities, and energy inefficiency. This study investigates how integrating computer science principles can address these issues. It focuses on advanced algorithms for resource optimization, machine learning, artificial intelligence for decision-making, and emerging technologies like edge computing and blockchain.Heuristic algorithms and optimization techniques have been shown to improve resource allocation and efficiency in IoT networks significantly. AI and machine learning enhance real-time decision-making and operational optimization, particularly in industrial applications. The integration of blockchain and edge computing strengthens security, ensures data integrity, and reduces latency. This research also presents comprehensive empirical evaluations, demonstrating measurable improvements in throughput, latency reduction, and energy efficiency.The findings emphasize implementing these computational strategies to optimize IoT systems, improve scalability, enhance security measures, and promote sustainable, energy-efficient designs. Future directions include exploring quantum computing for scalability and cross-industry validation of proposed frameworks.
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