Optimizing Edge Computing Resource Allocation for Real-time IoT Applications in Smart Cities
Keywords:
Edge Computing, Resource Allocation, IoT Applications, Smart Cities, Machine Learning, Real-time Processing, Urban Data Management, Dynamic Optimization, Computational EfficiencyAbstract
The rapid expansion of Internet of Things (IoT) devices and applications in urban environments demands efficient resource allocation strategies within edge computing architectures. This study evaluates prevalent methodologies in edge resource management, particularly in real-time data processing. Employing a quantitative approach, we developed a dynamic resource allocation model, integrating machine learning techniques to predict workload fluctuations efficiently. Our results indicate a 35% enhancement in resource utilization rates and a reduction of 25% in latency compared to conventional cloud-centric systems. This research contributes to the development of sustainable smart city ecosystems by providing empirical evidence towards the deployment of optimized edge computing solutions.
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