A Low Power Wireless Sensor For Online Ambient Monitoring.pdf

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A Low-Power Wireless Sensor for Online Ambient Monitoring Silviu C. Folea, Member, IEEE, and George Mois, Member, IEEE

Abstract— This paper presents the development of a compact battery-powered system that monitors the carbon dioxide level, temperature, relative humidity, absolute pressure, and intensity of light in indoor spaces, and that sends the measurement data using the existent wireless infrastructure based on the IEEE 802.11 b/g standards. The resulted device’s characteristics and performance are comparable with the ones provided by recognized solutions, such as ZigBee-based sensor nodes. By combining Wi-Fi connectivity with ambient sensors, this solution can be used for the remote gathering and further processing of measurement data. Testing revealed that the system can operate continuously for up to three years on a single 3 V small battery. Index Terms— Sensor systems, wireless sensor networks, reconfigurable architectures, Internet.

I. I NTRODUCTION

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NDOOR air quality (IAQ) represents an important factor affecting the comfort, the health and also the safety of building occupants. IAQ problems lead to a set of symptoms, including headaches, dizziness, difficulties in concentration and others, referred to as “sick building syndrome” (SBS). Basic measurements, such as temperature, relative humidity and CO2 , can provide information useful in solving such problems [1]. The present paper presents the development of a compact battery-powered system, that monitors the temperature, relative humidity, the carbon dioxide level, the absolute pressure and the intensity of light in indoor spaces, and that sends the measurement data using the existent wireless infrastructure based on the IEEE 802.11 b/g standards. This provides the possibility of the remote gathering and further processing of data from a large number of such wireless sensing systems. Furthermore, by combining wireless connectivity with ambient sensors, this solution can be used for reducing the overall energy consumption of an entire building [2]. The characteristics of the developed device, namely reduced dimensions, low power consumption, high flexibility and robustness, make it suitable for its use as a node in a wireless sensor network (WSN) or in an Internet of Things (IoT) scenario. The reduced energy profile is achieved by the use of a low power core microcontroller, and of an nondispersive Manuscript received June 20, 2014; accepted August 12, 2014. Date of publication August 22, 2014; date of current version November 20, 2014. The associate editor coordinating the review of this paper and approving it for publication was Dr. M. R. Yuce. The authors are with the Department of Automation, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, Cluj-Napoca 400114, Romania (e-mail: [email protected]; [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSEN.2014.2351420

infrared sensor (NDIR) for CO2 measurements, having the lowest power consumption on the market. The temperature and relative humidity sensor has a power consumption that is comparable to the one of the gas sensor (1 mA), while the other attached sensors, measuring pressure and light intensity, are less power hungry than these, consuming 5 µA and 0.24 mA, respectively. Moreover, a Wi-Fi module with an advanced API software, named WiFly, which allows efficient power management, was chosen for data transmission. These, combined with suitable power saving strategies, and depending on preset measurement rates, lead to the achievement of a battery life between one month and several years. Although the acquired ambient data can be displayed locally on the attached LCD, for testing the most probable usage scenario, they were visualised using a commercial solution, provided by Xively, a “Public Cloud for the Internet of Things” [3]. The use of gas sensors in general, and of CO2 sensors, in particular, in small battery powered devices was not possible until recently because of their large power consumption and dimensions. The low power NDIR sensors field is at the beginning, with the Cozir® Ambient CO2 sensor dominating the market. The solution presented in this paper employs this sensor, achieving satisfactory accuracy and battery lifetime. The other attached sensors, namely the ones measuring temperature and relative humidity, pressure and light intensity, do not pose as many problems as the CO2 sensor and can be efficiently included in a portable device. As far as the authors know, there are no other devices with the same performance and features. Several solutions are presented in the literature or are present on the market, but they provide a limited set of functionalities and a reduced number of attached sensors. A similar device represents the subject of paper [4], but the power consumption here is not calculated. Based on the analysis of the used chips and on the general description, the design in this case cannot achieve low power consumption and, therefore, the device cannot be a mobile one. Another solution, this time closer to the one presented here, comes from the company Point SixTM , and employs an NDIR sensor and Wi-Fi connectivity [5]. However, this system does not include humidity, atmospheric pressure and light measurement capabilities. The third monitoring system is a self-powered one and is developed by EnOcean Alliance [6]. It is also based on the Cozir® sensor, but it allows only a reduced number of measurement rates, between 9 and 2 per hour, depending on light intensity. The device presented in this paper is a small battery powered ambient (temperature, relative humidity, CO2 , absolute pressure and light intensity) wireless sensor allowing

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Fig. 2. Fig. 1.

Ambient sensor front view.

measurement rates between one and 60 samples per hour. When taking a single measurement per hour, it can run for up to three years without requiring maintenance. The tests showed a battery lifetime of up to three years, comparable with the one of the device presented in paper [7], based on 802.15.4/ZigBee communication, which consumes less energy than Wi-Fi. The rest of the paper is structured as follows. Section II presents the hardware architecture of the developed wireless sensor, highlighting the system components and their features. Section III details the software architecture of the application running on the device, along with several usage scenarios. The specification of message components and some examples of sent values are also given here. Section IV presents the power consumption of the entire system, along with scenarios and mechanisms for reducing it, while the final section gives the concluding remarks. II. H ARDWARE A RCHITECTURE A. General Overview The developed ambient wireless sensor, shown in Figure 1, is a stand-alone device, which measures the CO2 level in the air, the temperature, humidity, absolute pressure and light intensity and which sends the acquired information using the IEEE 802.11 b/g standards to a preset IP address. The acquired data can be displayed locally on an LCD with backlight, showing text on 2 rows and 16 columns, by pressing a button, or at a remote location, by using a specialized application or a web page. The Internet of Things scenario offers the possibility of remotely visualizing numerical and graphical values over time, setting triggers, sending short text messages using the Short Message Service (SMS) in case of alarms triggering and so on. All these alarms and triggers are implemented in the software running on a remote computer used for data gathering and visualization. B. Internal Structure The device’s core is represented by a PSoC 3, a programmable system on chip microcontroller. This is the central part of the ambient sensor, initiating all the main actions that have to be performed for its proper operation. The components that make up the system can be divided, in the same way as

Wireless sensor hardware architecture.

for a wireless sensor node in a wireless sensor network, into four main groups: the sensing unit, the processing and storage unit, the transceiver and the power supply. The sensing unit consists of a CozirTM CO2 Ambient Sensor, a DHT22 digital temperature and humidity sensor, an MPL115A2 barometer sensor and a TSL2561 light sensor. The sensors were chosen to respect satisfactory range and accuracy requirements, while achieving the smallest power consumption. Another important criterion was represented by the cost of the sensors, a reasonable price being achieved for small quantities. This makes the system very competitive and suitable for mass-production. The processing and storage unit is represented by the core microcontroller, while data transmission is implemented by the RN-131C/G wireless LAN module, from Roving Networks. A 3 V CR123A battery and a DC/DC converter form the power supply unit. The architecture is presented in Figure 2, where the main components of the measurement system are highlighted. As it can be seen in Figures 2 and 3, all the sensors are attached to the core microcontroller and operate at the same time. C. The PSoC 3 Core The advances in semiconductor industry, the smaller process technologies and the maximized circuit densities lead to a continuously increasing number of System-on-Chip solutions in a large number of applications. These circuits integrate signal acquisition and conversion functions, data storage and processing capabilities and I/O, providing significant advantages, the most important consisting in low power consumption, reduced dimensions and low costs. By including a wide range of system components into the chip, the number of parts on the printed circuit board (PCB) is reduced, directly affecting the power consumption and production costs of the digital system. Such a device is the PSoC, the acronym for programmable system on chip, produced by Cypress Semiconductor [8]. It integrates discrete analog and programmable logic along with memory and a microcontroller, being suitable for the design of embedded systems. These are the reasons why a PSoC 3 microcontroller, namely CY8C3246PVI-147, was chosen as the data processing unit of the wireless sensor presented in this paper. It has an 8-bit single cycle pipelined 8051 processor running at 24 MHz, as core, 64 kB of flash memory, an 8 kB SRAM and an on-chip EEPROM for storing nonvolatile data. This chip was chosen because it

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offers enough memory for implementing the application. The program occupies 62% of flash memory and 3.5% of SRAM. D. Wireless Module A standalone embedded wireless 802.11 b/g networking module, the RN-131C/G Wireless LAN Module from Roving Networks, was chosen for sending measurement data through UDP to a specific IP address [9]. The transmission mechanism along with the message format will be presented in Section III. The sensor’s Wi-Fi communication capability makes use of the existent wireless infrastructure and provides high transfer rates even when encryption is employed (WPA2), but it also limits the battery lifetime because of the increased power consumption. This effect was countered by the use of several mechanisms for reducing the power consumption of the entire system. E. CO2 , Temperature and Humidity Sensors The ultra low power CozirTM CO2 Ambient Sensor, specially developed for battery powered applications, was selected for measuring the carbon dioxide level in the air. It can measure CO2 concentrations between 0 and 2000 ppm. Its average power consumption is less than 3.5 mW, but the power supply of the measurement system must generate a peak of 33 mA for a short period of time. The noise is higher than ±50 ppm, but by activating the digital filter, so that an average between 2 . . . 32 instant measurements is computed, its value can be attenuated. There is also a drawback to this action, the filter value affecting the warm-up period of the sensor, 3 to 32 seconds being necessary for allowing the response to reach a final value. This period has an impact over the overall power consumption profile and the user must carefully choose an appropriate filter value. The atmospheric pressure is one of the factors affecting CO2 monitoring and altitude compensation is required. It can be set manually in the system configuration step or it can be computed depending on the information given by the absolute pressure sensor. Another important issue that has to be addressed is the auto-calibration routine because carbon-dioxide sensors are widely used as part of demand-control ventilation (DCV) systems. Therefore, the performance of the CO2 sensors can significantly affect energy use as well as indoor air quality in these cases. Overestimation of the CO2 concentration leads to increased outdoor air usage and increased energy costs, while underestimation may lead to poor IAQ and SBS [10]. Fresh air calibration is implemented by the core microcontroller, since the CO2 sensor is powered down after each reading. This is performed at intervals specified by the user in the measurement system configuration phase, and can also be disabled if desired. The operating environment in which the system operates is of great importance also, because testing revealed that the carbon dioxide sensing component is very sensitive to the dew point, where the digital output can decrease down to zero. The temperature and relative humidity values are acquired by a very low cost digital temperature and humidity sensor, the DHT22. It consists of several electronic components on

IEEE SENSORS JOURNAL, VOL. 15, NO. 2, FEBRUARY 2015

a small PCB, encased in a plastic box: a capacitive humidity sensor, a 10 kohms thermistor as the temperature transducer and a small package microcontroller, STM8S103F3, used for signal processing. This sensor’s accuracy is acceptable in many applications with values of ±2 % (with a maximum of ±5 %) for humidity and of ±0.5 °C for temperature. The power consumption value is 1 mA in active mode and 40 µA in sleep mode. This high value during sleep mode is one of the reasons for implementing a separate power supply for sensors, which can be switched off by the central processing unit. The temperature and humidity ranges are given by the CO2 sensor’s specifications, its operation conditions allowing temperatures between 0°C and 50°C and relative humidities between 0% and 95% (non-condensing). F. Absolute Pressure and Light Sensors An absolute pressure sensor, MPL115A2, with an I2 C interface, was chosen for measuring the atmospheric pressure and for compensating the CO2 deviation, if required. The initial accuracy is of ±1 kPa, which translates to an error of approximately 100 m in altitude. The absolute pressure range of this sensor is between 50 kPa and 115 kPa. The power consumption is 5 µA in active mode and only 1 µA in shutdown mode. The light sensor, TSL2561, which also communicates through an I2 C interface, was chosen for determining the light intensity. In this case, the power consumption is 0.24 mA in active mode and 3.2 µA in power down mode. The need for polling the sensor can be removed by programming it with an interrupt function. The sensor outputs a digital value from which illuminance, or the ambient light level, in lux is derived using an empirical formula to approximate the human eye response. G. Sensors Power Supply and Reverse Battery Protection The power consumption in sleep mode for all the sensors does not allow a long battery utilization period. This is why a separate power supply was developed and included in the design. The chip used offers an output disconnected from the input, high efficiency while using small amounts of power, a range up to 140 mA at +3.3 V, from an 1.8 V input, and a current consumption in shutdown mode, which is lower than 1 µA. All these characteristics maximize the lifetime of the battery in mobile applications. A CR123A 3 V lithium battery represents the main power supply. A reverse protection is implemented for accomplishing safe operation even when changing the battery. This type of battery has a capacity of 1500 mAh and is only slightly influenced by temperature variations and by loads [11]. H. PCB The device’s PCB (Fig. 3) is double sided, all components being populated on the top layer; the bottom layer is used only for traces and for the ground plane. The components that make up the user interface and that can be accessed by the users, namely the LCD, the buttons and the LED,

FOLEA AND MOIS: LOW-POWER WIRELESS SENSOR FOR ONLINE AMBIENT MONITORING

Fig. 3.

Wireless sensor printed circuit board.

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interface (Fig. 1) pressed. Configuration is performed through the serial interface, by using an RS232 cable. The menu allows entering and displaying the parameters needed for the correct operation of the measurement system. These consist of the period between measurements, which can be set to have a value between one minute and 60 minutes; the information for connecting to wireless LANs, namely the channel used, the SSIDs and passwords; the data server information, which includes the server port, IP, gateway and the subnet mask; the node IP and the CO2 sensor’s data. The latter is composed of filter value, altitude value and number of days for autocalibration using fresh air, if this feature is activated. The next step is the configuration of the RN-131C/G wireless module by the core microcontroller using the data previously set and saved in the EEPROM. Communication with this component is performed serially, using the UART. WiFly commands that make the Wi-Fi module automatically connect to a specific access point and act as a pipe sending serial information over UDP, when reset, are sent. After these actions are completed, the period values are set in such a way that a first measurement is taken when entering the main application loop. The button on the interface has a single functionality here, namely the display of the last values read by the attached sensors. For minimizing the power consumption, the LCD, the sensors and the Wi-Fi module are powered only when they need to perform actions. After each measurement, the Wi-Fi module is woken up, and specially formatted messages are sent to the previously set IP address. The other important action performed inside the application main loop is the CO2 sensor auto-calibration, taking place at previously set time intervals (after days of continuous operation), using fresh air. During this action, the sensor’s “fresh air” concentration value, considered to be 400 ppm, is replaced with the minimum recorded value, provided the fact that it had sensed fresh air at some point in time. A. Usage Scenarios

Fig. 4.

Software application flow diagram.

are accessible from outside. Special attention was paid to creating a PCB as compact as possible, ensuring that all of the sensors are exposed correctly and that ease of access for connecting programming and configuration cables is provided. III. S OFTWARE A RCHITECTURE The block diagram of the main routines that make up the wireless sensor firmware is presented in Figure 4. Its main component is represented by the main loop, where all the sensors are powered up and read, and data messages are sent using UDP. When it is first used, the wireless sensor needs to be configured. This action is initiated by powering up or resetting the device with the button on the

Every ambiental sensor is associated to an AP and can measure the received signal strength indicator (RSSI) for determining if the network is proper for communication with low energy costs. It is possible that from time to time the sensor scans the network, trying to determine whether an AP is closer than the one to which it is associated. If the result is favourable, the sensor will associate to a closer AP in case it has the required security data stored in the EEPROM. This scenario also applies when the sensor tries to associate to an AP for a predetermined number of times and fails, leading to the conclusion that the access point is no longer active One of the most common applications employing wireless sensors is represented by wireless sensor networks (WSNs). These consist of a large number of sensor nodes, communicating in a wireless fashion among each other or to an external base-station [12]. The first field where they had been used and where the potential is huge is represented by environmental monitoring, this being the primary purpose of deploying sensor networks [12]. Other domains include, but are not limited to military, health, home and other commercial

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Fig. 5.

Sensors in a wireless sensor network.

Fig. 7. Front panel and block diagram of the application implemented in LabVIEWTM .

Fig. 6.

Data visualization using web application.

applications [13], [14]. By satisfying the requirements for use in WSNs, namely low cost, low power consumption, multifunctionality, small dimensions and wireless communication capabilities, the sensor presented in this paper represents a wireless sensor node (Fig. 5). Recently, a new generation of digital systems, called cyberphysical systems (CPSs) [15], emerged. These use a wide range of sensors for collecting information about the physical world and exploit the information collected by WSNs to bridge real and cyber spaces [16]. Furthermore, the vision of Internet of Things calls for connectivity not only to consumer electronics and home appliances, but also to small battery powered devices which cannot be recharged [17]. The device presented in this paper can operate as an active component in CPSs or in the IoT. In this direction and for validating the proposed solution, an application running on a personal computer was developed. This gets data from the device and sends them to a web application server for public display (Fig. 6). The site presenting the measured data is www.xively.com, a “Public Cloud for the Internet of Things,” which displays data from sensors connected to the Internet from around the world [3]. The software for displaying the data is unchained, residing on the Internet. The data from the sensors are processed by a LabVIEWTM application running on a PC or a

server and are sent to the Xively web-site. For being correctly interpreted by the web-site, the data have to be bundled into an EEML (Extended Environments Markup Language) script. The advantage of using an application running on a PC consists in the ability to read data from multiple devices and to send a reduced number of packets to the Internet without performing a large number of accesses. For a low cost solution, the server and the application that runs on the server can be omitted, including the enclosing of the data in an proper format into the sensor. This scenario has a major disadvantage, the use of the TCP/IP protocol, which leads to an increase in the overall power consumption. An advantage of the solution presented in Figure 7 is the fact that data preprocessing takes place in the application, the firmware being simplified, and the connections with the Internet being reduced. B. Data Transmission The Wi-Fi standard was chosen for communication because the number of sensors used in the scenario of indoor environmental monitoring is not large and there is no need for complex routing protocols. The access point, or the router, covers, in this case, the entire area of the house and the wireless sensor nodes can associate and send messages directly to it. Furthermore, environmental sensors do not have critical real time constraints which can be met only by protocols such as ISA100 or WirelessHART. The major advantage of using Wi-Fi technology consists in the use of the existing infrastructure, which can be found in almost every home, where Internet connectivity or digital television is present. The major disadvantage lies in the increased power consumption, which directly influences the node lifetime. However, as the next section will show, this drawback can be overcome. The protocol chosen for data transmission is UDP, instead of TCP/IP, offering lower package sizes, increased speeds, low

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TABLE I M ESSAGE C OMPONENTS

Fig. 8.

latency and connectionless communication. The messages consist of fixed-size numerical codes, which are called operation codes or opcodes. These describe the settings of the sensor or measurement results. As it can be seen in Table I, the opcodes represent pairs of hexadecimal numbers associated to a specific function, with the first element being the function code, and the second its associated value. For avoiding overhearing problems, an unidirectional scheme was chosen, the device only sending short messages separated by the specified interval [18]. Because household environmental information is not sensitive from the security and privacy points of view, standard WPA2 encryption is used. The possibility of changing the security protocol, depending on the one used in the wireless computer network to which the sensor node connects, is also available. IV. P OWER C ONSUMPTION E STIMATION The entire system has a CR123A 3 V battery as the main power supply. This is the reason why several mechanisms for ensuring the low-power operation of the device, were implemented. They lead to the achievement of a period between one and three years of operation using a single commercial off-the-shelf battery. The configuration menu allows for values between one minute and 60 minutes to be set as the period between two consecutive measurement and data transmission actions. The system is a duty cycled one, spending most of the time in sleep mode. The ratio between wakeup and sleep times can take values between 1:8 and 1:500. This alternation leads to a power consumption between one and two hundred microwatts. Wakeup time lasts for only a few seconds, depending on the value of the CO2 sensor’s digital filter,

Fig. 9.

Complete wakeup periods.

Detail of the normal measurement and data transmission.

period in which the consumed current lies between 14 mA and 26 mA (medium values), depending on the completed tasks: read data from the attached sensors (temperature and humidity, CO2 , pressure and light) or send data. Three complete wakeup cycles are shown in Figure 8: 1. boot, Wi-Fi and CO2 sensor setup (the other sensors do not require a setup action), measurement of temperature, humidity, CO2 , pressure and light intensity, and data transmission; 2. awakening at the pressing of the user button and displaying data on LCD; and 3. awakening from the sleep period, measurement of the five physical quantities and data transmission via Wi-Fi. The first actions are executed in 28 seconds, the average current consumption being 19.89 mA, the second period is 10 seconds long, with an average current of 14.01 mA, while the last one depends on the value of the digital filter (for 10, wakeup time is 13 seconds), with a current of 25.68 mA. For further reducing the power consumption, a DC/DC converter, which can be turned off, was used. This way, during sleep, when only the PSoC microcontroller and the DC/DC source are active, the entire system consumes 10 µA. Another choice motivated by the battery lifetime requirements is represented by the unidirectional communication scheme and by the use of short opcodes. The device only sends short messages between previously set time intervals, after which it goes to sleep mode. Figure 9 presents a single wakeup period, consisting in temperature, humidity, CO2 , pressure and light intensity

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Fig. 10.

Measurement setup diagram. TABLE II BATTERY L IFETIME

measurements, association to the access point and message transmission. In sleep mode, the power consumption is lower than 30 µW and for a complete cycle (measurement, transmission – association and transmission of results), the power consumption is between 42 and 78 mW. These power consumption measurements were performed using the setup presented in Figure 10, consisting of an INA138 circuit (current shunt monitor) and an integrating function for computing the consumed current during a wakeup period. The energy consumed during a complete cycle was computed and then, based on the sleep/wakeup ratio and on the total capacity of the battery, the node lifetime was estimated. The operating time of a measurement system with wakeup time of 7 seconds (the filter is set 2) and with varying sleep intervals is presented in Table II. Several devices were tested for a measurement cycle of 1 minute, each of them performing around 30,000 measurements and transmissions with the same small 3V battery. When using a measurement cycle of one hour (1 hour between two consecutive measurements and transmissions), 30,000 cycles stretch over more than three years. The devices are currently tested for long operation periods, by using a sleep time of 60 minutes. The experimental setup consisted of a system similar with the one presented in Figure 5, where several sensors send UDP messages, which also include the battery voltages, to a server posting the data on www.xively.com. The results are comparable with the ones presented in [7], where the authors estimate a node lifetime of almost three years for a smart gas monitoring system, organized as a IEEE 802.15.4/ZigBee network, in a clustertree configuration. The battery chosen for being used by the device is a CR-123A, which is not a high performance one. However, it provides the advantage of reduced dimensions and can be bought at a relatively low price. If the design included a type C or D battery instead of the CR123A one, the sensor node’s lifetime would be doubled or even tripled with the cost of an increase in volume of the entire system. The battery voltage for a sensor node during testing is shown in

Fig. 11.

Battery voltage over time.

Figure 11. It has the same characteristic as the one given by the manufacturer for a given load. It can be replaced by a photovoltaic cell and a supercapacitor [19]. By using the information from the light intensity sensor and by periodically checking the voltage on the capacitor, the device can compute the right moment for data transmission. The device is designed in such a way that it can operate properly on a voltage starting from 2.0 V, making energy harvesting viable. V. C ONCLUSION The development of a compact battery-powered system, that monitors the temperature, relative humidity, the carbon dioxide level, the absolute pressure and the intensity of light in indoor spaces, and that sends the measurement data using the existent wireless infrastructure based on the IEEE 802.11b/g standards, was presented. Its power consumption was tested in a real environment, with a rate of one transmission per minute, indicating a battery lifetime close to one month. Further, tests and simulations revealed that the system can operate continuously for up to three years without requesting battery replacement. The device automatically self-calibrates the attached CO2 sensor and offers the possibility of operation without maintenance for a long time. It can be used in a wide range of monitoring applications as a component in a WSN, in the IoT or in a cyber-physical system. The replacement of the battery with an accumulator, a photovoltaic cell and a charging circuit represents the subject of future work. By carefully selecting the board components and sensors, a reasonable price of the developed system was achieved even for small quantities. ACKNOWLEDGEMENT The authors would like to thank Synchro Comp S.R.L, Craiova, Romania, and especially Mr. Vio Biscu, for supporting this research. R EFERENCES [1] United States Environmental Protection Agency. (1991). “Indoor air facts no. 4 (revised) sick building syndrome,” Air and Radiation (6609J), Research and Development (MD-56), Tech. Rep. [Online]. Available: http://www.epa.gov/iaq/pdfs/sick_building_factsheet.pdf [2] S. Sharma, V. N. Mishra, R. Dwivedi, and R. R. Das, “Quantification of individual gases/odors using dynamic responses of gas sensor array with ASM feature technique,” IEEE Sensors J., vol. 14, no. 4, pp. 1006–1011, Apr. 2014. [3] Xively. Xively Is the Public Cloud Specifically Built for the Internet of Things. [Online]. Available: https://xively.com/whats_xively/

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[4] H. Yang, Y. Qin, G. Feng, and H. Ci, “Online monitoring of geological CO2 storage and leakage based on wireless sensor networks,” IEEE Sensors J., vol. 13, no. 2, pp. 556–562, Feb. 2013. [5] PointSix. WiFi 2000 ppm CO2 and Temperature Transmitter 3008-40-V6. Point Six Wireless, Data Sheet. [Online]. Available: http://www.pointsix.com/PDFs/3008-40-V6.pdf [6] Enocean Alliance. Self-Powered CO2 Sensor Moves Into Volume Production. [Online]. Available: http://www.enocean-alliance.org/ en/gss-seamless-sensing-co2-sensor-moves-into-volume-production/ [7] V. Jelicic, M. Magno, D. Brunelli, G. Paci, and L. Benini, “Contextadaptive multimodal wireless sensor network for energy-efficient gas monitoring,” IEEE Sensors J., vol. 13, no. 1, pp. 328–338, Jan. 2013. [8] Cypress Semiconductor. (May 21, 2014). Programmable System-onChip (PSoC). PSoC® 3: CY8C32 Family Data Sheet, document 00156955. [Online]. Available: http://www.cypress.com/?docID=49257 [9] Roving Networks. (2012). RN-131G & RN-131C 802.11 b/g Wireless LAN Module. [Online]. Available: http://www.rovingnetworks.com [10] S. S. Shrestha, “Performance evaluation of carbon-dioxide sensors used in building HVAC applications,” Ph.D. dissertation, Dept. Mech. Eng., Iowa State Univ., Ames, IA, USA, 2009. [Online]. Available: http://lib.dr.iastate.edu/etd/10507 [11] S. Folea, G. Mois, L. Miclea, and D. Ursutiu, “Battery lifetime testing using LabVIEW,” in Proc. 9th Int. Conf. Remote Eng. Virtual Instrum. (REV), Jul. 2012, pp. 1–6. [12] D. Larios, J. Barbancho, G. Rodríguez, J. Sevillano, F. Molina, and C. León, “Energy efficient wireless sensor network communications based on computational intelligent data fusion for environmental monitoring,” IET Commun., vol. 6, no. 14, pp. 2189–2197, Sep. 2012. [13] J. Ko, C. Lu, M. B. Srivastava, J. A. Stankovic, A. Terzis, and M. Welsh, “Wireless sensor networks for healthcare,” Proc. IEEE, vol. 98, no. 11, pp. 1947–1960, Nov. 2010. [14] C. H. See, K. V. Horoshenkov, R. A. Abd-Alhameed, Y. F. Hu, and S. Tait, “A low power wireless sensor network for gully pot monitoring in urban catchments,” IEEE Sensors J., vol. 12, no. 5, pp. 1545–1553, May 2012. [15] T. Sanislav and L. Miclea, “An agent-oriented approach for cyberphysical system with dependability features,” in Proc. IEEE Int. Conf. Autom. Quality Testing Robot. (AQTR), May 2012, pp. 356–361. [16] F.-J. Wu, Y.-F. Kao, and Y.-C. Tseng, “From wireless sensor networks towards cyber physical systems,” Pervasive Mobile Comput., vol. 7, no. 4, pp. 397–413, 2011. [17] S. Tozlu, M. Senel, W. Mao, and A. Keshavarzian, “Wi-Fi enabled sensors for internet of things: A practical approach,” IEEE Commun. Mag., vol. 50, no. 6, pp. 134–143, Jun. 2012.

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Silviu C. Folea (M’08) received the degree in control systems and the Ph.D. degree from the Technical University of Cluj-Napoca (TUC-N), Cluj-Napoca, Romania, in 1995 and 2005, respectively. He is currently an Associate Professor with the Department of Automation, TUC-N. His research interests include hardware and software embedded systems, reconfigurable systems, data acquisition, wireless networks, and low-power sensors. He has authored nine books and book chapters, edited one book, and authored about 84 conference and journal publications; he was involved in over 33 research contracts, and two U.S. patents resulted from the research contracts he participated in.

George Mois (M’14) received the Degree in control systems and the Ph.D. degree from the Technical University of Cluj-Napoca (TUC-N), Cluj-Napoca, Romania, in 2008 and 2011, respectively. He is currently a Lecturer with the Department of Automation, TUC-N. His research interests include embedded system design, digital design, FieldProgrammable Gate Array (FPGA)-based systems, and fault-tolerant and error-tolerant systems.