Smart City Gnosys

Smart city article details

Title	Lightweight Contextual Llms For Iot Data Interpretation In Smart Cities
ID_Doc	35253
Authors	Kannadasan T.
Year	2025
Published	Proceedings of International Conference on Visual Analytics and Data Visualization, ICVADV 2025
DOI	http://dx.doi.org/10.1109/ICVADV63329.2025.10961648
Abstract	While the populations in cities are growing, the need for smart cities to optimize resources and life quality is increasingly a pressing matter. At the heart of this transformation lie the IoT devices creating massive volumes of data to be interpreted in real time. This paper introduces the Lightweight Contextual Large Language Models designed for efficient processing of IoT data in Smart Cities. In the approach proposed, advanced model pruning and knowledge distillation are used, keeping the solution computationally efficient with no sacrifice in interpretative power. We validate our model's effectiveness on the UrbanSensing dataset in anomaly detection, predictive maintenance, and analytics of engagement on tasks involving citizens. Our framework allows feasibility on edge devices with a 40% reduction in inference time and up to 50% decrease in computational overhead, thus guaranteeing scalability and sustainability in real-world smart city applications. The findings bridge the gap from computational efficiency to contextual understandings, thereby offering the premise for enhanced urban resilience and wiser decision-making. © 2025 IEEE.
Author Keywords	IoT Data Interpretation; Knowledge Distillation; Lightweight LLMs; Model Pruning; Real-Time Analytics; Smart Cities

Similar Articles

Id	Similarity	Authors	Title	Published
44384	0.893	Marripudugala M.	Real-Time Iot Data Analytics Using Advanced Large Language Model Techniques	2024 Global Conference on Communications and Information Technologies, GCCIT 2024 (2024)
58298	0.884	Lee J.; Song J.	Towards Semantic Smart Cities: A Study On The Conceptualization And Implementation Of Semantic Context Inference Systems	Sensors, 23, 23 (2023)
57866	0.884	Memon S.; Olaniyan R.; Maheswaran M.	Towards A Model For Intelligent Context-Sensitive Computing For Smart Cities	Handbook of Smart Cities: Software Services and Cyber Infrastructure (2018)
32187	0.88	Darwish D.	Integration Of Llms In Smart Cities For Sustainable Energy Solutions	Revolutionizing Urban Development and Governance With Emerging Technologies (2025)
1073	0.878	Aiello G.; Camillo A.; Del Coco M.; Giangreco E.; Pinnella M.; Pino S.; Storelli D.	A Context Agnostic Air Quality Service To Exploit Data In The Ioe Era	2019 4th International Conference on Smart and Sustainable Technologies, SpliTech 2019 (2019)
13791	0.877	Alsaig, A; Alagar, V; Chammaa, Z; Shiri, N	Characterization And Efficient Management Of Big Data In Iot-Driven Smart City Development	SENSORS, 19, 11 (2019)
57664	0.867	Moeini H.; Zeng W.; Yen I.-L.; Bastani F.	Toward Data Discovery In Dynamic Smart City Applications	Proceedings - 21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019 (2019)
34071	0.866	Sikeridis D.	Iot-Enabled Knowledge Extraction And Edge Device Sustainability In Smart Cities	Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020 (2020)
15913	0.862	Kamienski, CA; Borelli, FF; Biondi, GO; Pinheiro, I; Zyrianoff, ID; Jentsch, M	Context Design And Tracking For Iot-Based Energy Management In Smart Cities	IEEE INTERNET OF THINGS JOURNAL, 5, 2 (2018)
22927	0.86	Nizam M.K.; Goyal S.B.; Verma C.; Illés Z.	Empowering Smart Cities With Edge Computing-Based Iot Systems: A Focus On Data Analytics And Machine Learning Techniques	Lecture Notes in Electrical Engineering, 1194 (2024)