Interactive System for Smart Workplace Management with Environmental Sensors and Employee Tracking

Resource efficiency and staff well-being may be considerably increased by using smart workplace management systems that integrate environmental sensors and personnel monitoring technology. Based on real-time data from sensors measuring temperature, humidity, CO2, and light, as well as movement data from RFID and Bluetooth devices connected to employees, it provides an intelligent system that dynamically modifies ambient conditions. For environmental management and real-time prediction, an adaptive model based on machine learning is used. The system achieves 92% CO2 level stability, 94% humidity maintenance, and 96% temperature control precision, according to the results. It also lowers system reaction time to 8 seconds and increases energy efficiency by 38%. The percentage of workers who felt comfortable at work rose from 6.5 to 9.1, and 89% of them used their workplace. These outcomes show how well the system balances energy savings with worker happiness. Future research will examine how to optimize the machine learning model for even greater energy savings and scale the technology to bigger office settings.

Authors

Institutions

Publication Details

Published
2025-05-23
DOI
https://doi.org/10.1109/incet64471.2025.11140066
Primary Topic
Transport and Logistics Innovations
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Interactive System for Smart Workplace Management with Environmental Sensors and Employee Tracking

G. Manikandan, Riya Garg, S. Jagan, S. Kaliappan et al.
Transport and Logistics Innovations
article

Interactive System for Smart Workplace Management with Environmental Sensors and Employee Tracking

G. Manikandan, Riya Garg, S. Jagan, S. Kaliappan, Pratik Singh, Ruchira Rawat
article en

Abstract

Resource efficiency and staff well-being may be considerably increased by using smart workplace management systems that integrate environmental sensors and personnel monitoring technology. Based on real-time data from sensors measuring temperature, humidity, CO2, and light, as well as movement data from RFID and Bluetooth devices connected to employees, it provides an intelligent system that dynamically modifies ambient conditions. For environmental management and real-time prediction, an adaptive model based on machine learning is used. The system achieves 92% CO2 level stability, 94% humidity maintenance, and 96% temperature control precision, according to the results. It also lowers system reaction time to 8 seconds and increases energy efficiency by 38%. The percentage of workers who felt comfortable at work rose from 6.5 to 9.1, and 89% of them used their workplace. These outcomes show how well the system balances energy savings with worker happiness. Future research will examine how to optimize the machine learning model for even greater energy savings and scale the technology to bigger office settings.

Lovely Professional University (IN), Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (IN), Noida International University (IN), Janardan Rai Nagar Rajasthan Vidyapeeth University (IN), Graphic Era University (IN), Saveetha University (IN)
Openalex Percentile: Top 67%
Transport and Logistics Innovations
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Interactive System for Smart Workplace Management with Environmental Sensors and Employee Tracking — G. Manikandan, Riya Garg, et al. · (2025) | TGRS Research Map | TGRS