DESIGN AND OPTIMIZATION OF ENERGY-EFFICIENT EDGE COMPUTING ARCHITECTURES FOR SCALABLE IOT-BASED APPLICATIONS

Abstract

This research presents the design together with optimization strategies for building an energy-efficient edge computing framework capable of handling scalable IoT-based applications. Modern computer systems require greater energy efficiency combined with scalability because IoT devices grow rapidly. The presentation of real-time energy optimization strategies with adaptive resource management allows dynamic computing resource assignment to meet present needs effectively. The proposed framework achieves energy reduction of 33% over typical edge computing systems and resulted in substantial energy savings according to our tests. The method delivers 25% performance improvement for latency thus improving IoT application real-time responsiveness. At the same time throughput demonstrates 25% higher processing capacity to manage remote data quantities effectively. Beyond its excellent use of available resources the proposed framework optimizes accessibility by at least ninety percent of available computing resources. The proposed edge computing structure demonstrates enhanced performance capability for dynamic IoT conditions with concurrent delivery of energy-efficient operations and scalability solutions. This research establishes sustainable data processing strategies that achieve effective energy reduction thus providing an excellent foundation for future IoT applications.