IoT-based Indoor Occupancy Estimation Using Edge Computing

Rastogi, Krati; Lohani, Divya

doi:10.1016/j.procs.2020.04.208

Cited by 7 publications

(7 citation statements)

References 24 publications

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“…In contrast, there are studies in which the area is larger than 100 m

, and fewer sensors are deployed. For example, Rastogi et al [ 6 ] installed one CO

, temperature, RH, and infrared proximity sensor in a 524.5 m

classroom. Their models included Multiple Linear Regression (MLR) and Quantile Linear Regression, and the coefficient of determination (

) for each model was 0.88 and 0.91, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…People YES YES CA [ 66 ] 6 D Detection, Num. People NO YES 1 min NFA Accuracy 98.3% [ 7 ] 30 D Detection, Levels NO YES 1 min Bayes filter Accuracy 85.57% [ 2 ] 11 H Num.People NO YES 1 seg DFE Recall 91.93–96.80% [ 6 ] Num.People NO YES 1 min MLR, QL

88–91% [ 12 ] 13 D Levels YES YES 10 seg, 30 seg, 1 min K-NN, SVM, DT Accuracy 88.39–99.67% …”

Section: Table A1mentioning

confidence: 99%

“…Figure 1 presents the commonly used sensors for CO

, temperature, RH, and barometric pressure. The available CO

sensors are MQ135 [ 6 ], CL11 (also measure temperature and RH) [ 7 , 8 , 9 ], SenseAir S8 [ 10 ], and HOBO MX1102 Zhou2020, among others. To measure temperature, RH, and barometric pressure, some commercial sensors are the SENSIRION STS31 [ 11 ], BME280 [ 2 , 12 ], and MHB-382SD [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

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Measuring Indoor Occupancy through Environmental Sensors: A Systematic Review on Sensor Deployment

Mena

Ceballos

Alvarado-Uribe

2022

Sensors

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The COVID-19 pandemic has changed our common habits and lifestyle. Occupancy information is valued more now due to the restrictions put in place to reduce the spread of the virus. Over the years, several authors have developed methods and algorithms to detect/estimate occupancy in enclosed spaces. Similarly, different types of sensors have been installed in the places to allow this measurement. However, new researchers and practitioners often find it difficult to estimate the number of sensors to collect the data, the time needed to sense, and technical information related to sensor deployment. Therefore, this systematic review provides an overview of the type of environmental sensors used to detect/estimate occupancy, the places that have been selected to carry out experiments, details about the placement of the sensors, characteristics of datasets, and models/algorithms developed. Furthermore, with the information extracted from three selected studies, a technique to calculate the number of environmental sensors to be deployed is proposed.

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“…In contrast, there are studies in which the area is larger than 100 m

, and fewer sensors are deployed. For example, Rastogi et al [ 6 ] installed one CO

, temperature, RH, and infrared proximity sensor in a 524.5 m

classroom. Their models included Multiple Linear Regression (MLR) and Quantile Linear Regression, and the coefficient of determination (

) for each model was 0.88 and 0.91, respectively.…”

Section: Discussionmentioning

confidence: 99%

88–91% [ 12 ] 13 D Levels YES YES 10 seg, 30 seg, 1 min K-NN, SVM, DT Accuracy 88.39–99.67% …”

Section: Table A1mentioning

confidence: 99%

“…Figure 1 presents the commonly used sensors for CO

, temperature, RH, and barometric pressure. The available CO

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Measuring Indoor Occupancy through Environmental Sensors: A Systematic Review on Sensor Deployment

Mena

Ceballos

Alvarado-Uribe

2022

Sensors

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“…Occupancy monitoring is a substantial component of building management. The occupancy information obtained through monitoring an area in the building is beneficial for estimating hourly sales rates in commercial buildings, improving building security services, and providing opportunities to implement automated Heating, Ventilation, and Air Conditioning (HVAC) systems in the building [1].…”

Section: Introductionmentioning

confidence: 99%

Classroom Occupancy Monitoring System using IoT Device and the k-Nearest Neighbors Algorithm

Fidarliyan,

Prasetijo,

Eridani

2022

Jurnal Teknik Elektro

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The occupancy monitoring system is one of the substantial aspects of building management. Through monitoring the occupancy in the area in a building, the obtained information can be used for building management purposes such as controlling indoor area air quality and improving building security. Some technologies such as video surveillance cameras, Radio Frequency Identification (RFID), and motion sensors have been used in the occupancy monitoring system. However, those technologies pose several disadvantages including privacy concerns and limited information generated. A classroom occupancy monitoring system using an Internet of Things (IoT) device and the k-Nearest Neighbors (k-NN) algorithm was built to monitor classroom occupancy by classifying the number of occupants based on classroom environmental data into occupancy levels by using the k-NN classifier model. By utilizing IoT devices, CO2, temperature, and humidity data in a naturally ventilated classroom were recorded using the MQ-135 and BME280 sensors, as well as WiFi-based NodeMCU, was used to distribute data to the cloud. The collected data were trained and tested by the k-NN algorithm to produce a k-NN classifier model. From the tests conducted, the performance of the k-NN classifier model in classifying the number of occupants into occupancy levels resulted in an accuracy of 88%. In addition, the proposed system also produces a web-based classroom occupancy monitoring application that has been integrated with the k-NN classifier model so the classification can be done for real-time data and monitored directly.

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“…(1)(2)(3)(4)(5)(6)(7) In recent years, low-cost IoT systems (8,9) have been used for detection systems including for gas detection and environment monitoring. (10)(11)(12) In addition, the low-cost ESP8266 Wi-Fi module has been used by many researchers in their IoT systems, (13)(14)(15) in which the accuracy and long-term working modes of the sensors and Wi-Fi module have been demonstrated.…”

Section: Introductionmentioning

confidence: 99%

Cost-effective Evaluation, Monitoring, and Warning System for Water Quality based on Internet of Things

Truong¹

2021

Sensors and Materials

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This paper proposes an evaluating, monitoring, and warning system for water quality for taking care of fish that includes an Arduino Mega2560 board and a sensor system with wireless communication technologies based on IoT technology. Not only the temperature but also the pH and dissolved oxygen (DO) content of water are acquired through the Arduino Mega2560 board. These measurement data are transferred to the server through ESP8266 and SIM800A modules integrated on the board. The stored temperature, pH, and DO values are displayed on a ThingSpeak server. Furthermore, the fault detection of water quality under real working conditions is handled and displayed on the platform or a cellphone-based web service and short messaging service (SMS). The accuracy and reliability of the system for the functions of monitoring, evaluation, and fault detection were demonstrated. Compared with established systems, the proposed system has the advantages of (1) reduced cable use for the monitoring system, (2) a warning system via a cellphone-based SMS cellphone or web service, (3) costeffectiveness, and (4) portability.

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IoT-based Indoor Occupancy Estimation Using Edge Computing

Cited by 7 publications

References 24 publications

Measuring Indoor Occupancy through Environmental Sensors: A Systematic Review on Sensor Deployment

Measuring Indoor Occupancy through Environmental Sensors: A Systematic Review on Sensor Deployment

Classroom Occupancy Monitoring System using IoT Device and the k-Nearest Neighbors Algorithm

Cost-effective Evaluation, Monitoring, and Warning System for Water Quality based on Internet of Things

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