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.

翻译：镜像物联网（IoM）是一种新兴的物联网生态系统，由互联的智能镜子构成，旨在通过覆盖消费者节点、专业节点和中心节点的三层节点层级提供个性化服务。确定计算应位于该层级结构中的何处是一个关键的设计挑战，因为放置决策直接影响端到端延迟、资源利用率和用户体验。本文首次提出了物理IoM测试床研究，在真实的Wi-Fi和5G网络条件下，评估了跨IoM层级结构的四种计算放置策略。结果表明，将分类任务卸载到更高层级的节点可显著降低延迟和消费者节点的资源负载，但会引入随有效载荷大小和跳数增加的网络开销。没有单一策略是普遍最优的：最佳选择取决于可用网络、节点邻近度和并发用户负载。这些发现从实证角度刻画了IoM中计算与通信的权衡空间，并推动了对能够响应应用需求和实时生态系统条件的智能自适应任务放置的需求。