Earticle

초록

영어

This study proposes an AI-based wireless network temperature measurement system designed to capture and monitor fine-grained temperature variations within a grid-partitioned indoor environment. Unlike conventional thermostats that provide only a few point measurements, the proposed system employs a dense array of wireless sensor nodes distributed across a spatial grid to achieve high-resolution spatial coverage. Sensor readings are transmitted in real time to a centralized data aggregation server, which performs data cleaning, anomaly filtering, and integration with historical records. The core of the approach is an LSTMbased prediction model capable of short-horizon forecasting of temperature dynamics in each grid cell by leveraging temporal dependencies across multi-sensor data streams. By combining dense sensing, robust preprocessing, and predictive analytics, the system enhances spatial resolution and improves both the accuracy and responsiveness of indoor climate control. Early detection of anomalies such as localized hotspots or cooling inefficiencies allows proactive HVAC adjustments that reduce energy consumption, mitigate thermal discomfort, and prevent operational faults. Field experiments demonstrate that the proposed framework delivers real-time forecasting with low latency and high detection sensitivity, supporting adaptive decision-making in dynamic indoor environments. This integrated sensing-and-prediction paradigm represents a scalable and energy-aware solution for next-generation smart buildings, contributing to improved occupant comfort, sustainable energy management, and overall operational efficiency.

목차

Abstract
1. INTRODUCTION
2. SYSTEM ARCHITECTURE
3. DATA COLLECTION AND PREPROCESSING
3.1 Dataset and Deployment
3.2 Data Quality Metrics
3.3 Preprocessing Pipeline
3.4 Windowing Strategy
3.5 Feature Augmentation
3.6 Labeling for Anomaly Study
4. EXPERIMENTS
5. PREDICTIVE MODEL
5.1 Model architecture
5.2 Training protocol
5.3 Operational flow
5.4 Anomaly detection
5.5 Control integration
6. RESULTS AND DISCUSSION
6.1 Predictive accuracy
6.2 Stability and latency
6.3 Residual-distribution analysis
6.4 Spatial insights from heatmaps
6.5 Discussion
7. COMPARATIVE ANALYSIS
8. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• GeonUk Kang [ Ph. Student, Department of Smart Factory Convergence, Sungkyunkwan University, Suwon, Korea ] Corresponding Author
• JongPil Jeong [ Professor, Department of Smart Factory Convergence, Sungkyunkwan University, Suwon, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380