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S5-HES Agent: Society 5.0-driven Agentic Framework to Democratize Smart Home Environment Simulation

Akila Siriweera, Janani Rangila, Keitaro Naruse, Incheon Paik, Isuru Jayanada

Abstract

The smart home is a key domain within the Society 5.0 vision for a human-centered society. Smart home technologies rapidly evolve, and research should diversify while remaining aligned with Society 5.0 objectives. Democratizing smart home research would engage a broader community of innovators beyond traditional limited experts. This shift necessitates inclusive simulation frameworks that support research across diverse fields in industry and academia. However, existing smart home simulators require significant technical expertise, offer limited adaptability, and lack automated evolution, thereby failing to meet the holistic needs of Society 5.0. These constraints impede researchers from efficiently conducting simulations and experiments for security, energy, health, climate, and socio-economic research. To address these challenges, this paper presents the Society 5.0-driven Smart Home Environment Simulator Agent (S5-HES Agent), an agentic simulation framework that transforms traditional smart home simulation through autonomous AI orchestration. The framework coordinates specialized agents through interchangeable large language models (LLMs), enabling natural-language-driven end-to-end smart home simulation configuration without programming expertise. A retrieval-augmented generation (RAG) pipeline with semantic, keyword, and hybrid search retrieves smart home knowledge. Comprehensive evaluation on S5-HES Agent demonstrates that the RAG pipeline achieves near-optimal retrieval fidelity, simulated device behaviour and threat scenarios align with real-world IoT datasets, and simulation engine scales predictably across home configurations, establishing a stable foundation for Society 5.0 smart home research. Source code is available under the MIT License at https://github.com/AsiriweLab/S5-HES-Agent.

S5-HES Agent: Society 5.0-driven Agentic Framework to Democratize Smart Home Environment Simulation

Abstract

The smart home is a key domain within the Society 5.0 vision for a human-centered society. Smart home technologies rapidly evolve, and research should diversify while remaining aligned with Society 5.0 objectives. Democratizing smart home research would engage a broader community of innovators beyond traditional limited experts. This shift necessitates inclusive simulation frameworks that support research across diverse fields in industry and academia. However, existing smart home simulators require significant technical expertise, offer limited adaptability, and lack automated evolution, thereby failing to meet the holistic needs of Society 5.0. These constraints impede researchers from efficiently conducting simulations and experiments for security, energy, health, climate, and socio-economic research. To address these challenges, this paper presents the Society 5.0-driven Smart Home Environment Simulator Agent (S5-HES Agent), an agentic simulation framework that transforms traditional smart home simulation through autonomous AI orchestration. The framework coordinates specialized agents through interchangeable large language models (LLMs), enabling natural-language-driven end-to-end smart home simulation configuration without programming expertise. A retrieval-augmented generation (RAG) pipeline with semantic, keyword, and hybrid search retrieves smart home knowledge. Comprehensive evaluation on S5-HES Agent demonstrates that the RAG pipeline achieves near-optimal retrieval fidelity, simulated device behaviour and threat scenarios align with real-world IoT datasets, and simulation engine scales predictably across home configurations, establishing a stable foundation for Society 5.0 smart home research. Source code is available under the MIT License at https://github.com/AsiriweLab/S5-HES-Agent.
Paper Structure (29 sections, 12 equations, 9 figures, 3 tables)

This paper contains 29 sections, 12 equations, 9 figures, 3 tables.

Table of Contents

  1. INTRODUCTION
  2. Related Works
  3. Preliminary
  4. Smart Home Research Scenario
  5. Problem Definition
  6. Proposed Method
  7. System Architecture
  8. Configurable Home Builder: Solution to Definition 1.1
  9. Multi-Inhabitant Behavior: Solution to Definition 1.2
  10. Threat Injection Engine: Solution to Definition 1.3
  11. RAG pipeline: Solution to Definition 1.4
  12. Deterministic Simulation: Solution to Definition 1.5
  13. Multi-Agent Orchestration: Solution to Definition 1.6
  14. Evaluation
  15. Evaluation Metrics
  16. ...and 14 more sections

Figures (9)

  • Figure 1: S5-HES: Normative System architecture
  • Figure 2: Set-based and rank-weighted performance (Solid lines for S5-HES variants and dashed lines for baselines. Shaded regions indicate ±1 std over 100 queries.)
  • Figure 3: Position-based metrics comparison (Error bars show the std of per-query scores across the 100 queries.)
  • Figure 4: Category-wise generation quality
  • Figure 5: Response length vs quality correlation
  • ...and 4 more figures