Future Internet Architecture for IoT Frameworks Implementing the Synapse Algorithm for Intelligent Energy Management in Smart Home Automation

This paper proposes a novel Internet architecture for IoT frameworks that integrates the Synapse Algorithm for intelligent energy management in smart homes. Current IoT energy management systems often face challenges such as inefficient energy consumption, poor adaptability to user preferences, and limited scalability in managing multiple interconnected devices. These issues are exacerbated by the inability to process large datasets effectively and in real-time. To address these challenges, the proposed framework, Intelligent Energy Management using the Synapse Algorithm (IEM-SA), offers a sophisticated solution. The framework leverages the Synapse Algorithm to optimize energy consumption by analysing data from interconnected devices in real- time, learning user behaviour patterns, and dynamically adjusting energy usage based on predicted demand. This not only enhances the system's energy efficiency but also enables seamless control and adaptability across different environments and devices. The proposed method improves energy efficiency, scalability, and adaptability within smart home environments. By integrating real-time data processing and machine learning, it supports the seamless operation of multiple IoT devices while significantly reducing energy waste. Findings indicate that the IEM-SA framework outperforms existing methods in terms of energy savings, system responsiveness, and scalability. Through simulation results, the proposed framework demonstrates its potential to optimize energy usage while improving the overall smart home automation experience.