Research on Dynamic Cost Monitoring and Linear Programming Optimization for Old Residential Area Renovation Projects Based on Internet of Things and Big Data

Shu Zong (), Peng Liu (), Yu Su () and Junhui Che ()
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Shu Zong: Kunming University of Science and Technology, Faculty of Architectural Engineering Oxbridge College
Peng Liu: Kunming University of Science and Technology, Faculty of Architectural Engineering Oxbridge College
Yu Su: Kunming University of Science and Technology, Faculty of Architectural Engineering Oxbridge College
Junhui Che: Kunming University of Science and Technology, Faculty of Architectural Engineering Oxbridge College

A chapter in Proceedings of the 2025 Seminar on Modern Property Management Talent Training Enabling New Productive Forces (MPMTT 2025), 2025, pp 50-66 from Springer

Abstract: Abstract This study focuses on the dynamic cost monitoring and linear programming optimization of old residential area renovation projects based on the Internet of Things (IoT) and big data. By deploying IoT devices in the renovation projects to collect data in real time and utilizing big data technology for storage, management, and analysis, a dynamic cost monitoring system is established. Simultaneously, a linear programming model is constructed to achieve reasonable cost allocation and optimal control, thereby enhancing the economic and social benefits of the projects. The innovation of this study lies in combining IoT, big data, and linear programming optimization methods to address the insufficient integration of intelligent renovation and cost optimization in existing research. The effectiveness of the proposed method is validated through five practical cases, and the results indicate that it can effectively reduce costs and improve resource utilization efficiency, providing support for the sustainable development of old residential area renovation projects.

Keywords: Old residential area renovation; Internet of Things; Big data; Cost dynamic monitoring; Linear programming optimization (search for similar items in EconPapers)
Date: 2025
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DOI: 10.2991/978-94-6463-778-6_8

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