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. 2017 Feb 14;17(2):365.
doi: 10.3390/s17020365.

A Wearable Wireless Sensor Network for Indoor Smart Environment Monitoring in Safety Applications

Diego Antolín  1 , Nicolás Medrano  2 , Belén Calvo  3 , Francisco Pérez  4
Affiliations

A Wearable Wireless Sensor Network for Indoor Smart Environment Monitoring in Safety Applications

Diego Antolín et al. Sensors (Basel). .

Abstract

This paper presents the implementation of a wearable wireless sensor network aimed at monitoring harmful gases in industrial environments. The proposed solution is based on a customized wearable sensor node using a low-power low-rate wireless personal area network (LR-WPAN) communications protocol, which as a first approach measures CO₂ concentration, and employs different low power strategies for appropriate energy handling which is essential to achieving long battery life. These wearables nodes are connected to a deployed static network and a web-based application allows data storage, remote control and monitoring of the complete network. Therefore, a complete and versatile remote web application with a locally implemented decision-making system is accomplished, which allows early detection of hazardous situations for exposed workers.

Keywords: CO2 smart detection; remote web application; safety applications; wearable sensor node; wearable wireless sensor network (W-WSN).

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Wearable sensor node block diagram.
Figure 2
Figure 2
Wearable sensor node photograph: (Left) without XBee; (Right) with XBee.
Figure 3
Figure 3
Sensor board and node connection: (a) block diagram; (b) photograph.
Figure 4
Figure 4
Flowchart of sensor node software operation. ISR: Interrupt service routine.
Figure 5
Figure 5
Indoor wearable wireless sensor network (W-WSN) deployment in the application test scenario (dimensions: 50 m × 15 m).
Figure 6
Figure 6
(a) Relative humidity acquired during laboratory sessions; (b) CO2 concentration acquired during these same periods.
Figure 6
Figure 6
(a) Relative humidity acquired during laboratory sessions; (b) CO2 concentration acquired during these same periods.
Figure 7
Figure 7
Mobile node current consumption. Nodes are biased by a nominal 3.7 V battery, charged to 5 V (green), 4.2 V (red) and 3.4 V (blue).
Figure 8
Figure 8
Experimental battery voltage (V) vs. time (min) for a constant 60 mA discharge.
Figure 9
Figure 9
Experimental (blue) and polynomial (red) fit for battery discharge (mAmin) vs. battery voltage.
Figure 10
Figure 10
Lifetime estimation. Red line indicates the useful energy limit stored in the battery.
Figure 11
Figure 11
Interconnection of web application elements. JSON: JavaScript Object Notation.
Figure 12
Figure 12
Abstraction layers from the operating system to the application code. API: Application program interface.
Figure 13
Figure 13
(a) Class hierarchy modelling the XBee according to the datasheet; (b) Class hierarchy modelling the XBee request according to the datasheet; (c) Class hierarchy modelling the XBee response according to the datasheet.
Figure 14
Figure 14
Web application screenshot.
See this image and copyright information in PMC

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