Integrating Large Language Models with Internet of Things Applications
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914988510150656 |
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| author | Zong, Mingyu Hekmati, Arvin Guastalla, Michael Li, Yiyi Krishnamachari, Bhaskar |
| author_facet | Zong, Mingyu Hekmati, Arvin Guastalla, Michael Li, Yiyi Krishnamachari, Bhaskar |
| contents | This paper identifies and analyzes applications in which Large Language Models (LLMs) can make Internet of Things (IoT) networks more intelligent and responsive through three case studies from critical topics: DDoS attack detection, macroprogramming over IoT systems, and sensor data processing. Our results reveal that the GPT model under few-shot learning achieves 87.6% detection accuracy, whereas the fine-tuned GPT increases the value to 94.9%. Given a macroprogramming framework, the GPT model is capable of writing scripts using high-level functions from the framework to handle possible incidents. Moreover, the GPT model shows efficacy in processing a vast amount of sensor data by offering fast and high-quality responses, which comprise expected results and summarized insights. Overall, the model demonstrates its potential to power a natural language interface. We hope that researchers will find these case studies inspiring to develop further. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19223 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Integrating Large Language Models with Internet of Things Applications Zong, Mingyu Hekmati, Arvin Guastalla, Michael Li, Yiyi Krishnamachari, Bhaskar Artificial Intelligence This paper identifies and analyzes applications in which Large Language Models (LLMs) can make Internet of Things (IoT) networks more intelligent and responsive through three case studies from critical topics: DDoS attack detection, macroprogramming over IoT systems, and sensor data processing. Our results reveal that the GPT model under few-shot learning achieves 87.6% detection accuracy, whereas the fine-tuned GPT increases the value to 94.9%. Given a macroprogramming framework, the GPT model is capable of writing scripts using high-level functions from the framework to handle possible incidents. Moreover, the GPT model shows efficacy in processing a vast amount of sensor data by offering fast and high-quality responses, which comprise expected results and summarized insights. Overall, the model demonstrates its potential to power a natural language interface. We hope that researchers will find these case studies inspiring to develop further. |
| title | Integrating Large Language Models with Internet of Things Applications |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2410.19223 |