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

Haneen Fatima, Muhammad Ali Imran, Ahmad Taha, Lina Mohjazi

TL;DR

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 and finding 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.

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.

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

TL;DR

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 and finding 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.

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.
Paper Structure (16 sections, 6 figures, 1 table)

This paper contains 16 sections, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Design
  3. IoM Architecture
  4. Application Workload: Dental Smile Analysis Pipeline
  5. Computational Placement Strategies
  6. Experimental Setup
  7. Testbed Hardware
  8. Workload and Experimental Design
  9. Metrics
  10. Results and Discussion
  11. End-to-End Latency Breakdown
  12. Network Transfer Analysis
  13. Resource Utilisation
  14. Scalability Under Concurrent Load
  15. Deployment Implications
  16. ...and 1 more sections

Figures (6)

  • Figure 1: Computational placement strategies across the IoM tier hierarchy.
  • Figure 2: Physical IoM testbed configuration showing consumer, professional, and hub nodes
  • Figure 3: End-to-end latency decomposed by pipeline stage and network technology across the four computational placement strategies.
  • Figure 4: Network transfer overhead decomposed by inter-tier hop direction for each placement strategy.
  • Figure 5: Combined Resource utilisation (%) across IoM tiers per placement strategy.
  • ...and 1 more figures