# Agentic Search Engine for Real-Time IoT Data

arXiv: https://arxiv.org/abs/2503.12255 · HTML: https://mlsanity.com/paper/2503.12255/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 12 of its 48 references that are in the map.
- First posted on arXiv 15 Mar 2025.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- [Retrieval-Augmented Generation for AI-Generated Content: A Survey](https://mlsanity.com/paper/2402.19473/) (2024)
- [A Survey on Retrieval-Augmented Text Generation](https://mlsanity.com/paper/2202.01110/) (2022)
- [Comparing Traditional and LLM-based Search for Consumer Choice: A Randomized Experiment](https://mlsanity.com/paper/2307.03744/) (2023)
- [CollabStory: Multi-LLM Collaborative Story Generation and Authorship Analysis](https://mlsanity.com/paper/2406.12665/) (2024)

## Built on by (most cited first, 2 of 2)

- [A Survey of Agentic AI and Cybersecurity: Challenges, Opportunities and Use-case Prototypes](https://mlsanity.com/paper/2601.05293/) (2026)
- [Federation of Agents: A Semantics-Aware Communication Fabric for Large-Scale Agentic AI](https://mlsanity.com/paper/2509.20175/) (2025)

## Abstract (arXiv)

The Internet of Things (IoT) has enabled diverse devices to communicate over the Internet, yet the fragmentation of IoT systems limits seamless data sharing and coordinated management. We have recently introduced SensorsConnect, a unified framework to enable seamless content and sensor data sharing in collaborative IoT systems, inspired by how the World Wide Web (WWW) enabled a shared and accessible space for information among humans. This paper presents the IoT Agentic Search Engine (IoT-ASE), a real-time search engine tailored for IoT environments. IoT-ASE leverages Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) techniques to address the challenge of searching vast, real-time IoT data, enabling it to handle complex queries and deliver accurate, contextually relevant results. We implemented a use-case scenario in Toronto to demonstrate how IoT-ASE can improve service quality recommendations by leveraging real-time IoT data. Our evaluation shows that IoT-ASE achieves a 92% accuracy in retrieving intent-based services and produces responses that are concise, relevant, and context-aware, outperforming generalized responses from systems like Gemini. These findings highlight the potential IoT-ASE to make real-time IoT data accessible and support effective, real-time decision-making.