Toward Real-Time Mirrors Intelligence: System-Level Latency and Computation Evaluation in Internet of Mirrors (IoM)

2026-03-08

AI Summary

This study addresses the computation task allocation problem in the three-tier architecture of the Internet of Mirrors (IoM) by constructing the first physical IoM testbed and empirically evaluating four task placement strategies under real-world Wi-Fi and 5G network conditions. The work systematically characterizes, for the first time, the trade-off space between computation and communication in IoM, revealing that no single strategy is universally optimal; instead, effective placement must dynamically adapt to network conditions, node proximity, and concurrent workload. Experimental results demonstrate that offloading classification tasks to higher-tier nodes significantly reduces end-to-end latency and terminal-side computational load, though this benefit is constrained by payload size and hop count. These findings provide critical design insights for resource scheduling in IoM systems.

Technology Category

• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The Internet of Mirrors (IoM) is an emerging IoT ecosystem of interconnected smart mirrors designed to deliver personalised services across a three-tier node hierarchy spanning consumer, professional, and hub nodes. Determining where computation should reside within this hierarchy is a critical design challenge, as placement decisions directly affect end-to-end latency, resource utilisation, and user experience. This paper presents the first physical IoM testbed study, evaluating four computational placement strategies across the IoM tier hierarchy under real Wi-Fi and 5G network conditions. Results show that offloading classification to higher-tier nodes substantially reduces latency and consumer resource load, but introduces network overhead that scales with payload size and hop count. No single strategy is universally optimal: the best choice depends on available network, node proximity, and concurrent user load. These findings empirically characterise the computation-communication trade-off space of the IoM and motivate the need for intelligent, adaptive task placement responsive to application requirements and live ecosystem conditions.

Problem

• Internet of Mirrors
• computation placement
• system-level latency
• resource utilisation
• task offloading

Innovation

• Internet of Mirrors
• computation offloading
• latency evaluation
• adaptive task placement
• edge computing