toposense_bench

TopoSense-Bench: Semantic-Spatial Sensor Scheduling

TopoSense-Bench is a large-scale, rigorous benchmark designed to evaluate Large Language Models (LLMs) on the Semantic-Spatial Sensor Scheduling (S³) problem.

Originating from the ACM MobiCom '26 paper "IoT-Brain: Grounding LLMs for Semantic-Spatial Sensor Scheduling", this benchmark tests an agent's ability to translate high-level natural language user intents (e.g., "Find my backpack lost between the library and the gym") into precise physical sensor activation plans within a large-scale digital twin.

📊 Overview

• Source: Hosted on Hugging Face (Seamlessly integrated via the datasets library).
• Scale:
  • 5,250 Natural Language Queries.
  • 2,510 Sensors (Cameras).
  • 161 Floor Plans across 33 Buildings.
• Problem Domain: Embodied AI, IoT, Spatial Reasoning, and RAG (Retrieval-Augmented Generation).

🎯 Task Taxonomy

The benchmark categorizes queries into three tiers of complexity based on the spatial scope and reasoning difficulty:

• Tier 1: Intra-Zone Perception
  • Simple queries focused on specific rooms or focal areas (e.g., "Check the entrance of the conference hall").
• Tier 2: Intra-Building Coordination
  • Complex queries requiring navigation across multiple floors within a single building (e.g., "Track the path from the 4th-floor lab to the ground floor exit").
• Tier 3: Inter-Building Coordination
  • Long-horizon queries involving transitions between outdoor spaces and multiple buildings (e.g., "I walked from the Library to the Gym, check cameras along the way").

⚙️ Evaluation Methodology

Unlike standard QA benchmarks, TopoSense-Bench employs a Retrieval-Augmented Generation (RAG) workflow to simulate realistic sensor scheduling:

1. Context Retrieval: The system dynamically retrieves the relevant topological map data (textual representation of buildings/floors) based on the user's query using a heuristic TopologyManager.
2. Reasoning: The LLM acts as a scheduler. It must analyze the provided map data and the user's intent to identify the specific sensor node ID that best satisfies the request.
3. Scoring: The evaluation uses a parsing-based exact match metric. It compares the core identifier in the LLM's output against the ground truth sensor ID (e.g., teaching_building_1_camera_03).

🚀 Quick Start

1. Installation

Ensure you are in the benchmarks/toposense_bench directory, then install the required dependencies:

pip install -r requirements.txt

2. Configuration

Create or edit env.toml to configure your LLM provider. This benchmark uses litellm for model calls.

[llm]
# Example for OpenAI
OPENAI_API_KEY = "sk-..."

# Example for DeepSeek (OpenAI-Compatible)
# OPENAI_API_KEY = "sk-..."
# OPENAI_API_BASE = "https://api.deepseek.com"

3. Run Evaluation

Run the evaluation script. You must specify the model name.

Note: If using a non-OpenAI provider (like DeepSeek or Qwen) via the OpenAI-compatible endpoint, please add the openai/ prefix to the model name.

# Run with GPT-4o
bash run.sh "gpt-4o"

# Run with DeepSeek-Chat
bash run.sh "openai/deepseek-chat"

4. Results

After the run completes, results will be saved in the outputs/ directory:

• summary.json: Overall accuracy and breakdown by task tier.
• results.jsonl: Detailed logs including retrieval status, model input/output, and correctness for every query.

📚 Citation

If you use this benchmark in your research, please cite our MobiCom '26 paper:

@inproceedings{iotbrain2026,
  title={IoT-Brain: Grounding LLMs for Semantic-Spatial Sensor Scheduling},
  author={Anonymous Author(s)},
  booktitle={Proceedings of the 32nd Annual International Conference on Mobile Computing and Networking (MobiCom '26)},
  year={2026}
}