# Intelligence of Things: A Spatial Context-Aware Control System for Smart Devices

arXiv: https://arxiv.org/abs/2504.13942 · HTML: https://mlsanity.com/paper/2504.13942/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 16 Apr 2025.

## Builds on (most cited first, 13 of 13)

- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities](https://mlsanity.com/paper/2401.12168/) (2024)
- [IoT-LLM: a framework for enhancing Large Language Model reasoning from real-world sensor data](https://mlsanity.com/paper/2410.02429/) (2024)
- [SAGE: Smart home Agent with Grounded Execution](https://mlsanity.com/paper/2311.00772/) (2023)
- [Efficient Prompting for LLM-based Generative Internet of Things](https://mlsanity.com/paper/2406.10382/) (2024)
- [When IoT Meet LLMs: Applications and Challenges](https://mlsanity.com/paper/2411.17722/) (2024)
- ["Get ready for a party": Exploring smarter smart spaces with help from large language models](https://mlsanity.com/paper/2303.14143/) (2023)
- [Integrating Large Language Models with Internet of Things Applications](https://mlsanity.com/paper/2410.19223/) (2024)
- [ROOT: VLM based System for Indoor Scene Understanding and Beyond](https://mlsanity.com/paper/2411.15714/) (2024)
- [Improving Vision-and-Language Reasoning via Spatial Relations Modeling](https://mlsanity.com/paper/2311.05298/) (2023)
- [IoT Device Labeling Using Large Language Models](https://mlsanity.com/paper/2403.01586/) (2024)
- [AutoIoT: Automated IoT Platform Using Large Language Models](https://mlsanity.com/paper/2411.10665/) (2024)

## Abstract (arXiv)

This paper introduces Intelligence of Things (INOT), a novel spatial context-aware control system that enhances smart home automation through intuitive spatial reasoning. Current smart home systems largely rely on device-specific identifiers, limiting user interaction to explicit naming conventions rather than natural spatial references. INOT addresses this limitation through a modular architecture that integrates Vision Language Models with IoT control systems to enable natural language commands with spatial context (e.g., "turn on the light near the window"). The system comprises key components including an Onboarding Inference Engine, Zero-Shot Device Detection, Spatial Topology Inference, and Intent-Based Command Synthesis. A comprehensive user study with 15 participants demonstrated INOT's significant advantages over conventional systems like Google Home Assistant, with users reporting reduced cognitive workload (NASA-TLX scores decreased by an average of 13.17 points), higher ease-of-use ratings, and stronger preference (14 out of 15 participants). By eliminating the need to memorize device identifiers and enabling context-aware spatial commands, INOT represents a significant advancement in creating more intuitive and accessible smart home control systems.