UrbanInsight: A Distributed Edge Computing Framework with LLM-Powered Data Filtering for Smart City Digital Twins

• URL: http://arxiv.org/abs/2509.00936v1
• Date: Sun, 31 Aug 2025 17:10:31 GMT
• Title: UrbanInsight: A Distributed Edge Computing Framework with LLM-Powered Data Filtering for Smart City Digital Twins
• Authors: Kishor Datta Gupta, Md Manjurul Ahsan, Mohd Ariful Haque, Roy George, Azmine Toushik Wasi,
• Abstract summary: Cities generate enormous streams of data from sensors, cameras, and connected infrastructure.<n>Most existing systems struggle with scale, latency, and fragmented insights.<n>This work introduces a framework that blends physics-informed machine learning, multimodal data fusion, and knowledge graph representation.
• Score: 5.477477311297089
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cities today generate enormous streams of data from sensors, cameras, and connected infrastructure. While this information offers unprecedented opportunities to improve urban life, most existing systems struggle with scale, latency, and fragmented insights. This work introduces a framework that blends physics-informed machine learning, multimodal data fusion, and knowledge graph representation with adaptive, rule-based intelligence powered by large language models (LLMs). Physics-informed methods ground learning in real-world constraints, ensuring predictions remain meaningful and consistent with physical dynamics. Knowledge graphs act as the semantic backbone, integrating heterogeneous sensor data into a connected, queryable structure. At the edge, LLMs generate context-aware rules that adapt filtering and decision-making in real time, enabling efficient operation even under constrained resources. Together, these elements form a foundation for digital twin systems that go beyond passive monitoring to provide actionable insights. By uniting physics-based reasoning, semantic data fusion, and adaptive rule generation, this approach opens new possibilities for creating responsive, trustworthy, and sustainable smart infrastructures.

Related papers

• Forging Spatial Intelligence: A Roadmap of Multi-Modal Data Pre-Training for Autonomous Systems [75.78934957242403]
  Self-driving vehicles and drones require true Spatial Intelligence from multi-modal onboard sensor data.<n>This paper presents a framework for multi-modal pre-training, identifying the core set of techniques driving progress toward this goal.
  arXiv  Detail & Related papers  (2025-12-30T17:58:01Z)
• Interpretable Hybrid Deep Q-Learning Framework for IoT-Based Food Spoilage Prediction with Synthetic Data Generation and Hardware Validation [0.5417521241272645]
  The need for an intelligent, real-time spoilage prediction system has become critical in modern IoT-driven food supply chains.<n>We propose a hybrid reinforcement learning framework integrating Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for enhanced spoilage prediction.
  arXiv  Detail & Related papers  (2025-12-22T12:59:48Z)
• Wireless Traffic Prediction with Large Language Model [54.07581399989292]
  TIDES is a novel framework that captures spatial-temporal correlations for wireless traffic prediction.<n> TIDES achieves efficient adaptation to domain-specific patterns without incurring excessive training overhead.<n>Our results indicate that integrating spatial awareness into LLM-based predictors is the key to unlocking scalable and intelligent network management in future 6G systems.
  arXiv  Detail & Related papers  (2025-12-19T04:47:40Z)
• PercepTwin: Modeling High-Fidelity Digital Twins for Sim2Real LiDAR-based Perception for Intelligent Transportation Systems [3.1508266388327324]
  This paper introduces a rigorous methodology for creating large-scale, high-quality synthetic datasets using High-Fidelity Digital Twins (HiFi DTs)<n>The proposed workflow outlines the steps, tools, and best practices for digitally replicating real-world environments.
  arXiv  Detail & Related papers  (2025-09-03T00:12:15Z)
• Predicting Large-scale Urban Network Dynamics with Energy-informed Graph Neural Diffusion [51.198001060683296]
  Networked urban systems facilitate the flow of people, resources, and services.<n>Current models such as graph neural networks have shown promise but face a trade-off between efficacy and efficiency.<n>This paper addresses this trade-off by drawing inspiration from physical laws to inform essential model designs.
  arXiv  Detail & Related papers  (2025-07-31T01:24:01Z)
• Spatial Knowledge Graph-Guided Multimodal Synthesis [78.11669780958657]
  We introduce a novel multimodal synthesis approach guided by spatial knowledge graphs, grounded in the concept of knowledge-to-data generation.<n>In experiments, data synthesized from diverse types of spatial knowledge, including direction and distance, enhance the spatial perception and reasoning abilities of MLLMs markedly.<n>We hope that the idea of knowledge-based data synthesis can advance the development of spatial intelligence.
  arXiv  Detail & Related papers  (2025-05-28T17:50:21Z)
• Open-Source LLM-Driven Federated Transformer for Predictive IoV Management [1.8024397171920885]
  Federated Prompt-d Traffic Transformer (FPoTT) is a novel framework that leverages open-source Large Language Models for predictive IoV management.<n>FPoTT introduces a dynamic prompt optimization mechanism that iteratively refines textual prompts to enhance trajectory prediction.<n>The architecture employs a dual-layer federated learning paradigm, combining lightweight edge models for real-time inference with cloud-based LLMs to retain global intelligence.
  arXiv  Detail & Related papers  (2025-05-01T16:54:21Z)
• Physics Informed Constrained Learning of Dynamics from Static Data [8.346864633675414]
  A physics-informed neural network (PINN) models the dynamics of a system by integrating the governing physical laws into the architecture of a neural network.<n>Existing PINN frameworks rely on fully observed time-course data, the acquisition of which could be prohibitive for many systems.<n>In this study, we developed a new PINN learning paradigm, namely Constrained Learning, that enables the approximation of first-order derivatives or motions using non-time course or partially observed data.
  arXiv  Detail & Related papers  (2025-04-17T06:06:53Z)
• Conservation-informed Graph Learning for Spatiotemporal Dynamics Prediction [84.26340606752763]
  In this paper, we introduce the conservation-informed GNN (CiGNN), an end-to-end explainable learning framework.<n>The network is designed to conform to the general symmetry conservation law via symmetry where conservative and non-conservative information passes over a multiscale space by a latent temporal marching strategy.<n>Results demonstrate that CiGNN exhibits remarkable baseline accuracy and generalizability, and is readily applicable to learning for prediction of varioustemporal dynamics.
  arXiv  Detail & Related papers  (2024-12-30T13:55:59Z)
• Scalable Offline Reinforcement Learning for Mean Field Games [6.8267158622784745]
  Off-MMD is a novel mean-field RL algorithm that approximates equilibrium policies in mean-field games using purely offline data. Our algorithm scales to complex environments and demonstrates strong performance on benchmark tasks like crowd exploration or navigation.
  arXiv  Detail & Related papers  (2024-10-23T14:16:34Z)
• Explainable Spatio-Temporal Graph Neural Networks [16.313146933922752]
  We propose an Explainable Spatio-Temporal Graph Neural Networks (STGNN) framework that enhances STGNNs with inherent explainability. Our framework integrates a unified-temporal graph attention network with a positional information fusion layer as the STG encoder and decoder. We demonstrate that STExplainer outperforms state-of-the-art baselines in terms of predictive accuracy and explainability metrics.
  arXiv  Detail & Related papers  (2023-10-26T04:47:28Z)
• Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly [62.473245910234304]
  This paper takes a hardware-centric approach to explore how Large Language Models can be brought to modern edge computing systems. We provide a micro-level hardware benchmark, compare the model FLOP utilization to a state-of-the-art data center GPU, and study the network utilization in realistic conditions.
  arXiv  Detail & Related papers  (2023-10-04T20:27:20Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.