Ambient Intelligence for Adaptive Smart Home Environments Using 5G Networks

Abstract

Ambient Intelligence (AmI) emphasizes the construction of intelligent environments that explicitly behave in ways that benefit human capital by improving comfort, effectiveness, and safety. As the 5G network improves with minimal latency, high-speed communication enables seamless connections among IoT devices, sensors & user interfaces in adaptive home automation. Modern smart home systems are often rule-based or designed with basic automations, resulting in delayed responses, excessive energy consumption, and inflexible adaptation to user behavior and the environment. To overcome these constraints, in this study, we proposed to use a Context-Aware Reinforcement Learning (CARL) framework for Smart Home Adaptation, wherein the data obtained through an array of sensors is offered continuously so that user behavior contexts and environmental factors correlate at runtime, coalesce to learn home automation policies via repeated upgradation. The framework utilizes 5G-distributed communication to ensure prompt interaction between user devices and the central learning agent, thereby enabling speed-sensitive decision-making and real-time adaptation. Using the proposed CARL framework for applications such as climate control, lighting adjustment, and appliance management can enable homes to automatically respond to people's habits while conserving energy. Experimental simulations show that the proposed system yields significantly greater user comfort and energy management than classical rule-based/non-adaptive systems.