A Hybrid Genetic-Reinforcement Learning Framework for Edge-Enabled Social IoT
TL;DR
A novel Coevolutionary Hybrid Intelligence system that combines Genetic Algorithms, Reinforcement Learning, and Edge AI to enable adaptive and collaborative SIoT systems is introduced and shows that CHI is an effective and scalable solution for privacy-preserving and low-latency personalized SIoT applications.
Abstract
The Social Internet of Things (SIoT) enables socially connected devices to deliver context-aware and personalized services. However, traditional SIoT systems are typically cloud-based, leading to high latency, high bandwidth consumption, privacy concerns, and limited adaptability to user preferences. To address these issues, this study introduces a novel Coevolutionary Hybrid Intelligence (CHI) system that combines Genetic Algorithms (GAs), Reinforcement Learning (RL), and Edge AI to enable adaptive and collaborative SIoT systems. The proposed framework is built using a distributed edge architecture that continuously updates device policies and user preference models by incorporating: on-device intelligence, local inference, user feedback, and a coevolutionary optimization engine. Experimental testing in a simulated smart-home environment demonstrates that CHI can deliver a response time of 123 ms, a bandwidth utilization of 84 MB/day, an energy consumption of 8.8 W, and a user satisfaction score of 4.7/5. The proposed model is compared to cloud-centric and edge-only baselines, and the results indicate better responsiveness and lower communication overhead. The results show that CHI is an effective and scalable solution for privacy-preserving and low-latency personalized SIoT applications.