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

TL;DR

This study evaluates computation placement strategies in the Internet of Mirrors ecosystem, revealing trade-offs between latency, resource use, and network overhead under real network conditions, and emphasizing adaptive solutions.

Contribution

First physical IoM testbed assessment of computational placement strategies, providing empirical insights into latency-resource trade-offs and guiding adaptive task placement.

Findings

Offloading to higher-tier nodes reduces latency and resource load.

Network overhead increases with payload size and hop count.

No single placement strategy is optimal; context-dependent decisions are necessary.

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.