A Context-Aware Task Distribution (CATD) Framework for Internet of Mirrors (IoM) Applications

The Internet of Mirrors (IoM) is an Internet-of-Things (IoT) framework that interconnects smart mirrors with sensing and processing capabilities for distributed health and beauty applications. IoM systems employ a three-tier node hierarchy (Hub, Professional, Consumer) with heterogeneous computational capabilities, creating environments where efficient task distribution is critical. Existing approaches optimise individual dimensions like latency or resource availability but fail to integrate task requirements, node capabilities, network conditions, and proximity constraints simultaneously. We present the Context-Aware Task Distribution (CATD) framework that addresses this challenge through a multi-factor scoring mechanism combining resource availability, network quality, and geographic proximity into unified task-node compatibility evaluation. The framework implements hierarchical distribution with tier-based filtering, evaluated through discrete-event simulation modelling a 5×5 km urban deployment. Evaluation across eight test scenarios covering load scaling, fault tolerance, and dynamic adaptation demonstrates CATD's superior performance over other baseline algorithms.