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    IEEE SENSORS JOURNAL, VOL. 15, NO. 4, APRIL 2015 2255 Design and Application of a VOC-Monitoring System Based on a ZigBee Wireless Sensor Network Changhai Peng, Kun Qian, Member, IEEE, and Chenyang Wang Abstract— Monitoring volatile organic compound (VOC) pollution levels in indoor environments is of great importance for the health and comfort of individuals, especially considering that people currently spend >80% of their time indoors. The primary aim of this paper is to design a low-power ZigBee sensor network and internode data reception control framework to use in the real-time acquisition and communication of data concerning air pollutant levels from VOCs. The network consists of end device sensors with photoionization detectors, routers that propagate the network over long distances, and a coordinator that communicates with a computer. The design is based on the ATmega16 microcontroller and the Atmel RF230 ZigBee module, which are used to effectively process communication data with low power consumption. Priority is given to power consumption and sensing efficiency, which are achieved by incorporating various smart tasking and power management protocols. The measured data are displayed on a computer monitor through a graphical user interface. The preliminary experimental results demonstrate that the wireless sensor network system can monitor VOC concentrations with a high level of accuracy and is thus suitable for automated environmental monitoring. Both good indoor air quality and energy conservation can be achieved by integrating the VOC monitoring system proposed in this paper with the residential integrated ventilation controller. Index Terms— VOCs, monitoring, ZigBee, wireless sensor networks, photoionization detector. I. INTRODUCTION V OLATILE organic compounds (VOCs) are emitted as gases from certain solids and liquids. VOCs include a variety of chemicals, some of which can cause short- and long-term adverse health effects. Many VOCs are found at consistently higher (up to ten times higher) concentrations Manuscript received October 10, 2014; revised November 18, 2014; accepted November 19, 2014. Date of publication November 24, 2014; date of current version February 5, 2015. The work was supported in part by the Open Project Program of the Key Laboratory of Urban and Architectural Heritage Conservation through the Southeast University, in part by the Ministry of Education under Grant KLUAHC1212, in part by the National Natural Science Foundation of China under Grant 51278107, in part by the Research and Development Program, Ministry of Housing and Urban-Rural Development, China, under Grant 2011-K1-2, and in part by the Key Program of the Natural Science Foundation of Jiangsu Province under Grant BK2010061. The associate editor coordinating the review of this paper and approving it for publication was Prof. Subhas C. Mukhopadhyay. C. Peng and C. Wang are with the School of Architecture, Southeast University, Nanjing 210018, China (e-mail: pengchanghai@gmail.com; wangchenyang109@foxmail.com). K. Qian was with the School of Automation, Southeast University, Nanjing 210018, China. He is now with the School of Architecture, Southeast University, Nanjing 210018, China (e-mail: kqian@seu.edu.cn). 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.2014.2374156 indoors compared to outdoors [1]. In addition, VOCs are emitted by thousands of products, including paints and lacquers, paint strippers, cleaning supplies, pesticides, building materials and furnishings, office equipment (such as copiers, printers, correction fluids, and carbonless copy paper), and graphics and craft materials (including glues and adhesives, permanent markers, and photographic solutions) [2], [3]. Long-term exposure to VOCs can cause damage to the liver, kidneys, and central nervous system. Short-term exposure to VOCs can cause eye and respiratory tract irritation, headaches, dizziness, visual disorders, fatigue, loss of coordination, allergic skin reactions, nausea, and memory impairment [4]–[7]. Standards have yet to be set for VOCs in non-industrial settings [8]. The Occupational Safety and Health Administration (OSHA) of the United States regulates formaldehyde, which is a VOC, as a carcinogen. OSHA has adopted a permissible exposure level (PEL) of 0.75 ppm and an action level of 0.5 ppm. The U.S. Department of Housing and Urban Development has established a level of 0.4 ppm for mobile homes. Based on current information, addressing the presence of formaldehyde if it is present at levels higher than 0.1 ppm is advisable [2]. Data on VOCs that are normally found in low concentrations in indoor air are highly dependent on how the VOCs are measured. All available measurement methods are selective in what they can accurately measure and quantify, and none are capable of measuring all of the VOCs present in an area. For example, benzene and toluene are measured using different methods than those used to measure formaldehyde and other similar compounds. A wide range of measurement methods and analytical instruments are available, and thus, the sensitivity of the measurements varies greatly and exhibits a large selectivity and bias. For this reason, any statement about the VOCs present in a given environment must be accompanied by a description of how the VOCs were measured so that the results can be correctly interpreted by a professional. In the absence of such a description, the statement would have limited practical meaning [9]. Compared with traditional digital data loggers that provide monitoring only at a single point, recent advances in low-power wireless sensor networks (WSNs) allow for the monitoring of spatially varying phenomena, such as the VOC concentrations in buildings [10]. These WSNs allow environmental information to be measured and collected at distributed monitoring points, which 1530-437X © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. 2256 IEEE SENSORS JOURNAL, VOL. 15, NO. 4, APRIL 2015 provides awareness of the environmental conditions that affect the overall uptime, safety, and compliance in built environments and enables agile and flexible monitoring and control systems [11], [12]. ZigBee-based WSNs are an emerging technology with a wide range of potential applications, including environmental monitoring and measurements. ZigBee-based WSNs consist of several nodes that are equipped with processing, communication and sensing capabilities. These nodes are smaller and less expensive than regular sensor devices. Such networks consist of a large number of distributed sensor nodes that organize themselves into a multi-hop wireless network [13]. These networks collect critical data from the system or the environment and display these data on a monitoring server. Thus, supervisors can interpret the data and, if necessary, take immediate action in real time. Using the collected data, the operational team can gain a better understanding of the environment and can thus increase efficiency or prevent accidents while reducing the total cost of data acquisition. These benefits provide a distinct competitive advantage in the new industrial revolution [14], [15]. This paper proposes a ZigBee WSN-based monitoring system for measuring VOC concentrations. First, the system hardware, which consists of a gateway, a base station, and sensors, is designed. Specifically, a new hardware platform for VOC sensor nodes is developed to collect both local and remote VOC concentration measurements. Second, system software is designed to enable data acquisition, processing and transmission, and energy conservation strategies for the entire system. Third, a preliminary experiment is performed to verify the relative accuracy of the WSN sensor. Finally, automatic action strategies are addressed to reduce VOC levels and conserve energy. II. RELATED WORK A. VOC Monitoring System With Sensor Network Several studies have focused on improving the technologies of wireless sensor networks and monitoring systems to enhance environmental monitoring [16], [17]. Monitored data that are transmitted by the wireless sensor network are analyzed by a backend server, and the analysis results are passed to the monitoring area if the air quality characteristics exceed acceptable levels. In [18], an air pollution monitoring system was implemented in an outdoor environment. Several sensor devices were set up, and the sensor-monitored data were transmitted to the server. The server de-capsulated and sorted the data, performed calculations according to the domain knowledge and management rules in the database, and transmitted the data to the alert system based on the results. The alert system sent an alarm message to warn operators that the concentration of a given gas was over the limit at that location. In [19], a low-cost, low-complexity, convenient and reliable system was designed for the implementation of a real-time indoor VOC gas sensor network. After analysis and comparison with other related studies, we selected semiconductor sensors for sensing VOCs in our system. The disadvantages of this type of sensor are that the target gas is non-selective and easily influenced by temperature and humidity, and thus, the resistance sensor (RS) value of clean air must be known prior to the measurements (it must be measured for a period of time in normal outdoor air). In [20], the sensor’s hardware and software were integrated and used in tests in a real environment. Instead of simultaneously setting up different sensors, the authors integrated the different sensors (e.g., humidity, temperature and VOC sensors) on the same board. The different types of monitored air quality data were transmitted to the monitoring system for further analysis through a wireless sensor network. In the software implementation, hop-by-hop communication was adopted with medium access control (MAC), and efficient routing protocols were used to forward the data. From source to sink, this process adopted a tree-based method to establish the routing table. The drivers of the devices used RETOS (Resilient, Expandable and Threaded Operating System). Three components were included in the applications: the multi-hop relay was responsible for transmitting data, the sensor controller controlled the sensors and accessed the monitored data, and the data controller interpreted the monitored data to measure harmful gases. A wireless sensor network for monitoring the indoor air quality (IAQ) in buildings was presented in [21]. This network was composed of various IAQ sensors and was capable of simultaneously measuring the IAQ levels at several locations within a building. The network was integrated with the building’s heating, ventilation, and air-conditioning systems to ensure acceptable air quality in interior spaces. Using the sensor network, various indoor air pollutants (CO, CO2, VOCs, and airborne particles) can be measured in spaces that contain potential sources of pollutants. When the measured pollutant levels were above acceptable limits, the network notified indoor occupants using an alarm and activated the building’s climate control equipment to exhaust the polluted air and bring in fresh air from outside. B. Energy Efficiency in Wireless Networks A WSN may consist of thousands of small and fully autonomous nodes that make measurements, perform data processing, and communicate with each other. The nodes route data via multiple low-energy hops to sink nodes, which act as gateways to other networks. Network management is fully decentralized, which eliminates the need for a fixed controller and infrastructure and prevents a single point of failure [22]. In most envisioned applications, nodes must operate with a low energy budget and may have to scavenge supply energy solely from their operational environment or operate for up to several years on small batteries [23]. Hence, energy consumption is a key performance metric for WSN realizations. To satisfy the energy budget, WSN nodes operate with constrained communication and computational resources. Thus, a typical node processes data using a Micro-Controller Unit (MCU) with a processing speed of several million instructions per second (MIPS) and tens of kilobytes of program and data memories [24]. Although advances in radio frequency (RF) circuits have been significant, the radio transceiver has the highest power consumption in a WSN node. The power consumption of current radios is nearly the same in the transmission and reception modes, and energy PENG et al.: DESIGN AND APPLICATION OF A VOC MONITORING SYSTEM 2257 is conserved only in sleep mode, during which the radio circuitry is completely switched off. The nodes should come out of sleep mode only to transmit or receive packets that are vital for node operation, which avoids idle listening to unnecessary traffic [13]. Therefore, a pair of nodes should be simultaneously active. Because global synchronization is difficult to achieve in large networks using low-power and low-cost components [25], nodes can reduce the amount of idle listening and energy consumption by forming locally synchronized communication links with their neighbors. The WSNs are usually considered to be stationary in a manner such that the established communication links are unaltered for the entire network lifetime. In practice, even an immobile network exhibits dynamic behavior [26] due to random node failures and due to a dynamic operating environment caused by the opening and closing doors, moving objects, changing weather conditions, and interference with other networks, all of which affect RF propagation. In addition, numerous envisioned WSN applications (i.e., access control, asset tracking, and interactive games) require mobile nodes, which drive dynamic network topologies [13], [27]. Thus, WSN nodes should be able to recognize topology changes and update communication links rapidly, in an energy-efficient manner, and with minimum interruptions in application data routing. In current WSN proposals, new neighbors are typically discovered by listening on available RF channels for their transmissions (network scan), which last up to tens of seconds [12], [29]. As calculated in [29], each second of a network scan consumes energy equal to 2800 frame transmissions using a typical low-power transceiver. In dynamic networks, scans can increase the node energy consumption by one order of magnitude. Thus, we need an energy-efficient neighbor discovery process to substitute for network scans. WSNs are often characterized by highly dense and largescale deployments in resource-constrained environments. The constraints include limited capacity for processing, storage and, especially, energy because the sensors are primarily powered using batteries. Battery recharging in sensor networks is sometimes impossible because of the number of nodes but more often for the simple reason that it is practically or economically unattainable. It is widely accepted that energy limitations are an inevitable obstacle in the design of WSNs because they impose strict constraints on network operations. Moreover, the energy consumption of sensors plays an important role in the network lifetime and has become the dominant performance criterion in this area. If we want the system to operate in a satisfactory mode for as long as possible, these energy constraints require a compromise between several activities at both the node and network levels [30]. Several studies have focused on optimizing node energy consumption through the use of innovative conservation techniques to improve network performance and maximize sensor lifetimes. In general, energy conservation ultimately must find the best trade-off between different energy-consuming activities. The literature on WSNs recognizes that the radio is a prominent consumer of energy [31], [32]. Minimizing energy consumption is a key goal in many multi-hop wireless networking systems, especially if the nodes of the network are battery powered. This requirement has become increasingly important for WSNs. WSNs differ from other types of multi-hop wireless networks because in most cases, the sensor data must be delivered to a single sink or base station (BS). Clearly, one of the primary concerns is the lifetime of the network. Although there are several definitions of the lifetime [33], a sensor network must be considered to be dead whenever it is no longer able to forward any data to the BS. We use a definition in which the network lifetime is the time span from deployment to the instant at which the network is considered to be nonfunctional. However, the moment at which a network can be considered nonfunctional is application-specific, e.g., at the instant that the first sensor dies, a percentage of sensors die, or a loss of coverage occurs [34]. To design solutions that improve energy efficiency in wireless networks, it is essential to first identify the main sources of energy consumption in wireless devices and understand how wireless protocols and operations affect the energy demand [35]. Several papers in the literature argue that network infrastructure is often unnecessarily powered on, especially when the number of users and their traffic loads are low [36]. This common practice guarantees high degrees of coverage and service availability but is responsible for a large amount of energy consumption and could be addressed by intelligent sleep policies. Similar considerations apply at the client side, where cellular phones could go into sleep mode by switching their transceivers off during inactive periods [37]. Similarly, although at different time scales, sleep mode operation could be enabled in WLAN (wireless local area network) personal devices that are engaged in lightweight communications [38] or in wireless sensors that will only be requested to propagate information after certain triggering events [39]. A second important cause of energy consumption is rooted in the hardware design. Hardware inefficiencies have been noted by many authors [40]. This body of work addresses factors such as power-hungry processors, poor power amplifier designs, and inefficient heat dissipation that demands intense cooling schemes. Unfortunately, overcoming hardware inefficiencies involves a complete redesign of the wireless equipment, which is costly and time-consuming and is thus subject to practical considerations. Although hardware inefficiencies are difficult to address, the various layers of a networking stack of wireless devices have different degrees of flexibility. Several studies indicate that most of these components also lack an energy-aware design. For example, the physical (PHY) layer is frequently implemented with sub-optimal rate selection and transmission power control algorithms [41]. The MAC operation uses inappropriate contention parameters and yields non-optimal and avoidable collision rates or unsatisfactory and unfair transmission schedules [42]. Similarly, routing protocols for wireless networks neither use energy as a path metric nor leverage the node density to minimize energy consumption [43]. In addition, coordination between the PHY, MAC and routing layers usually does not occur [44]. These papers demonstrate the intensive efforts that have been made toward reducing the power consumption of WSNs 2258 IEEE SENSORS JOURNAL, VOL. 15, NO. 4, APRIL 2015 TABLE I PERFORMANCE OF THE PID-AH Fig. 1. Schematic of the WSNs. and improving the accuracy of gas sensors. Using state-of-theart technology and with the expectations of using commercial off-the-shelf photoionization detector (PID) gas sensors, we develop our system with PID gas sensors. We also focus on reducing the power consumption of the gas-monitoring WSN by managing the sleep state of the nodes. This feature is essential for obtaining sufficiently low average power consumption for the network to operate for a substantial amount of time. A favorable WSN network should be able to operate continuously for several months, or even a year, if it operates at a low capture rate. We evaluated the theoretical work lifetime of a sensor node and tested the performance of its actual power consumption using an experimental circuit for several days as a preliminary attempt to quantify the energy consumption of the designed WSN node. III. DESIGN OF A VOC MONITORING SYSTEM BASED ON A ZIGBEE WSN A. System Hardware The monitoring system consists of a gateway, a base station, and sensors, as shown in Figure 1. The gateway, which is also known as the client, connects the sensor nodes to an existing Ethernet network. The base station provides the connection between the sensor nodes and the gateway. The sensors monitor the VOC concentrations in the building and transmit data to the base station. 1) Gateway: The gateway, which is a model SQ120 in this implementation (depicted as “Client” in Figure 1), is based on an Intel IXP420 XScale processor running at 266 MHz and features one wired Ethernet port and two USB 2.0 ports. The device is also equipped with 8 MB of programmable FLASH, 32 MB of random-access memory (RAM), and a 2 GB USB 2.0 system disk. The SQ120 runs the Debian Linux operating system, which is a full-fledged standard Linux distribution for the ARM architecture and comes preloaded with Crossbow’s sensor network management and data visualization software packages XServe and MoteExplorer. These programs are automatically started during the boot time of the SQ120. To set up a sensor network gateway configuration, a base station must be plugged into the secondary USB port of the SQ120. The sensor network management tool (XServe) can automatically identify the types of sensor boards that are plugged into the Fig. 2. Linearity of the PID-AH sensor for VOC concentrations. nodes of the WSN and instruct MoteExplorer to display the data [45]. 2) Base Station: The base station, which is a full function device (FFD), consists of the mote processor/radio platforms (XM2110) and a gateway (MIB520CB) that are connected via a 51-pin expansion connector. Thus, the base station is configured as a ZigBee coordinator (ZC) of the WSNs. A base station enables the aggregation of sensor network data onto a PC or other computer platform. In particular, the MIB520 provides a serial/USB interface for both programming and data communications. Therefore, the base station receives the data sent by all of the nodes in the network and sends a message across the USB connection to the computer. 3) Sensor Node: The PID sensor is a typical detector for measuring VOCs and other gases in concentrations from less than one part per billion to 10,000 parts per million (ppm). PID sensors are efficient and inexpensive detectors for many gas and vapor analyses. They can operate continuously and produce instantaneous readings. For this implementation, we use the PID-AH sensor from Apollosense Ltd. The performance of the PID-AH is shown in Table 1. Figure 2 shows the linearity measured by the PID-AH for VOC concentrations, and Figure 3 shows the PID-AH response to 1 ppm of isobutylene [46]. The sensor node is composed of a mote processor/radio platform (M) and a sensor (S) that are connected via a 51-pin and a 3-pin expansion connector, as shown in Figure 4. Each node is a fully integrated and rugged sensor package that uses energy-efficient radios and sensors to ensure an extended battery life and performance. Each node is an FFD and integrates Crossbow’s IRIS/IMOTE2 family processor/radio board and antenna, which are powered by rechargeable batteries and are configured as ZigBee end devices (ZEDs) or ZigBee PENG et al.: DESIGN AND APPLICATION OF A VOC MONITORING SYSTEM 2259 Fig. 3. Response of the PID-AH sensor to 1 ppm of isobutylene. Fig. 4. Circuit structure of the sensor node. consumption by the radio. For example, energy depletion is caused by the power consumption of the circuit and of the transmitted signal. The consumption by the circuit is greater than the transmission power at short distances, but the signal power becomes dominant at longer ranges [48]. Transmission Power Control (TPC) has been investigated for enhancing energy efficiency at the physical layer by adjusting the radio transmission power [49]. In Cooperative Topology Control with Adaptation (CTCA) [50], regular adjustments of the transmission power of every node are proposed to consider the uneven energy consumption profile of the sensors. Therefore, a node with a greater amount of remaining energy may increase its transmission power, which will potentially enable other nodes to decrease their transmission power and save energy. However, the TPC strategy affects not only the energy but also delays, link quality, interference and connectivity [47]. Directional antennas allow signals to be sent and received in one direction at a time, which improves the transmission range and throughput. Directional antennas may require localization techniques for orientation, but multiple communications can occur in close proximity, which results in the spatial reuse of bandwidth. In contrast to omni-directional motes, which transmit in unwanted directions, directional antennas limit overhearing and require less power for a given range. Thus, these devices can improve network capacity and lifetime while positively influencing delay and connectivity [51]. routers (ZRs). A node has a radio range of 10 to 80 m (indoors) and 50 to 300 m (outdoors) depending on its deployment. The nodes form a wireless mesh network, and the range of coverage can be expanded by adding additional wireless mesh networks. A node comes pre-programmed and configured with Crossbow’s XMesh low-power networking protocol. The circuit of the sensor node is shown in Figure 4. 4) ZigBee Module: The ZigBee module is composed of mote processor/radio platforms (XM2110) and uses the Atmel RF230, IEEE 802.15.4 compliant, ZigBee-ready radio frequency transceiver integrated with an Atmega16 microcontroller. These enhancements provide up to three times the radio range and twice the programmable memory of previousgeneration MICA motes [45]. In the sensor node, the XM2110 connects with the sensor board via a 51-pin expansion connector, whose structure is shown in Figure 4. ZigBee is a wireless network protocol that is owned by the ZigBee Alliance and is adapted from the IEEE 802.15.4 standard, which defines the media layer and objective layer. ZigBee provides a low transmission speed at low cost, low power consumption, and high security and supports a large number of web node operations. The radio module is usually the main component that causes the depletion of a sensor node’s battery. To reduce energy dissipation due to wireless communications, we attempt to optimize radio parameters such as coding and modulation schemes, power transmission and antenna direction [47]. Modulation optimization attempts to find the optimal modulation parameters that result in the minimum energy B. System Software 1) ZigBee Sensor Node Firmware: The firmware on the sensor nodes sets the sensors in a wireless mesh network and provides the algorithms that are necessary to operate the radio and route the messages. The firmware is programmed and compiled on a desktop computer using the nesC programming language and is transferred to the on-board microprocessor of a node [52]. The firmware on the sensor nodes primarily performs the following three tasks: a) Initialization of each hardware device: To prepare an operating system for operation, each hardware device must initialize the hardware platform and software architecture after powering on. The initialization primarily includes the initialization of each hardware module, stack, system clock, flash memory, several nonvolatile variables, MAC layer, application framework layer and operating system and formation of the terminal’s MAC address [53]. b) Data acquisition and processing: The TinyOS implementation of the ZigBee protocol stack enables the efficient processing of events, such as sensor data acquisition, data processing, and radio transmission. The TinyOS platform can be configured to ensure that the node uses little energy when no action is required by the sensor or radio [52]. To save energy, the amount of data to be delivered to the sink must be reduced. Generally, aggregation and adaptive sampling can be adopted jointly during the data acquisition and processing stages. In data aggregation schemes, nodes along a path toward the sink perform data fusion to reduce the amount of forwarded data. For example, a node could 2260 IEEE SENSORS JOURNAL, VOL. 15, NO. 4, APRIL 2015 Fig. 6. ZED data transmission transaction sequence. Fig. 5. Workflow of the sensor nodes. retransmit only the average or the minimum of the received data. Moreover, data aggregation may reduce the latency because it reduces traffic, thus reducing delays. However, data aggregation techniques can reduce the accuracy of the collected data. Depending on the aggregation function, the original data may not be recovered by the sink, and thus, information precision can be lost [47], [54], [55]. The sensing task can be energy-consuming and may generate unneeded samples that affect the communication resources and processing costs. Adaptive sampling techniques adjust the sampling rate at each sensor while ensuring that the application needs are met in terms of coverage or information precision. For example, in a supervision application, low-power acoustic detectors can be used to detect an intrusion. When an event is reported, powerhungry cameras can be switched on to obtain more detailed information [47], [56]. The firmware of the sensor node runs through a sequence of states during its lifecycle. A finite-state machine (FSM) is designed with three different states: Sleeping, Wakeup, and Execute. The transition between the states of the sensor node is controlled by the FSM following the flowchart shown in Figure 5 [57]. In the Sleeping state, the processor is halted, but the static random access memory (SRAM), serial peripheral interface (SPI), and interrupt system continue to run, and the wireless module receives data at a low current. When the wireless communication module of a sensor node receives commands from the gateway node or from other neighbor nodes, it transitions to the Wakeup state. If the object of the commands is the current sensor node, the Execute state is activated; otherwise, the sensor node enters the Sleeping state after forwarding the commands [57]. Idle states are major sources of energy consumption in the radio component. Sleep/wakeup schemes manage the node activity to save energy by putting the radio into sleep mode. Duty cycling schemes schedule the node radio state based on network activity to minimize idle listening and favor the sleep mode [47]. These schemes are usually divided into three categories: on-demand, asynchronous and scheduled rendezvous [56]. Duty-cycle-based protocols are the most energy efficient, but they suffer from sleep latency because a node must wait for the receiver to be awakened. Moreover, Fig. 7. Example of network coding. in certain cases, it is not possible for a node to broadcast information to all of its neighbors because they are not active simultaneously. Finally, fixing parameters such as the listen and sleep periods, preamble length and slot time becomes a problematic issue because these parameters influence the network performance. For example, a short duty cycle saves a large amount of energy but can drastically increase communication delays. Thus, protocol parameters can be specified prior to deployment for simplicity, although this process leads to a lack of flexibility, or they can be set up dynamically for improved adaptation to traffic conditions. Some work has been performed on duty cycling in terms of managing the active period of nodes online to optimize power consumption as a function of the traffic load, buffer overflows, delay requirements or harvested energy [58], [59]. Although duty cycling wastes energy due to unnecessary wake-ups, lowpower radios are used to awaken a node only when it needs to receive or transmit packets, and a power-hungry radio is used for data transmission [47]. c) Data transmission: As shown in Figure 6, the ZigBee end device (ZED) sends the ZED command request for the ZED information (INFO) and then sends a notification that the radio frequency (RF) receiver is ON. After the parent node receives the ZED command request, it sends an acknowledgment (ACK) and the ZED INFO.request to the ZED, which tells the RF receiver the time period during which it is ON. The ZED sends the ZED INFO.reply, which states the time period during which the RF receiver of the ZED is ON. The parent node now knows the time period in which the RF receiver of the ZED is ON; if it has any data to send to the ZED, it checks the corresponding address of the ZED and the time period during which each RF receiver of the ZED is ON [60]. Network coding (NC) and data compression can be used together during the data transmission phase. NC is used to reduce the traffic in broadcast scenarios by sending a linear combination of several packets instead of a copy of PENG et al.: DESIGN AND APPLICATION OF A VOC MONITORING SYSTEM 2261 Fig. 8. Overall workflow of the gateway. each packet. To illustrate network coding, Figure 7 shows a five-node topology in which node 1 must broadcast two items of data, a and b. If the nodes simply store and forward the packets that they receive, six packet transmissions will be generated (two each for nodes 1, 2 and 3). With the NC approach, nodes 2 and 3 can transmit a linear combination of data items a and b such that they will need to send only one packet. Nodes 4 and 5 can decode the packet by solving the linear equations. Therefore, two packets are saved in this example. Network coding exploits the trade-off between computation and communication because communications are slow compared to computations and consume more energy [61]. Data compression encodes information to reduce the number of bits that are required to represent the initial message. This process is energy efficient because it reduces transmission times due to the use of smaller packets [62]. Our methodology of coding combines n packets into one coded packet using a linear combination. In node t, we generate n coefficients at,1 ∼ at,n and compute bt = at,1 · p1 + at,2 · p2 + at,n · pn, where pk is the k-th packet and bt is the coded packet. The n coefficients are included in the header of the coded packet. Therefore, a node that receives n coded packets can easily solve ap = b by Gaussian elimination. 2) Design of the Base Station Software: The base station is primarily responsible for communications between the WSNs and the PC gateway and acts as a bridge between wireless and wired communications. The base station collects data from sensor nodes and relays all of the network radio messages from the wireless network to the gateway, where the USB interface is used for data transfer between the base radio and the application software that runs inside the gateway. The base station uploads data to the gateway through a USB port according to the following steps. After the base station saves the data in the serial buffer, it calls the function HalUARTWrite() to transfer the data to the USB module through UART and waits for the USB module to upload the data to the gateway through the USB port. 3) Software Design of the Gateway: When a set of sensors is deployed within a building, a monitoring gateway is developed to gather and store sensing data that are captured by the network of sensors and to deliver data through its Ethernet interface. Figure 8 shows the overall workflow of the gateway that stores and forwards data from the sensor network. Fig. 9. Flowchart for addressing two types of data packets. Fig. 10. Flowchart for processing sensor data packets. The gateway processes two types of data packets that are received from the base station, sensor data packets and health packets, according to the flowchart shown in Figure 9. Figure 10 and Figure 11 show the steps used to process sensor data packets and health packets, respectively. The health packet contains per-node health statistics, which include the current battery voltage of the node and the number of network hops from the node to the base station. The health packet also contains the load information that indicates the number of packets forwarded by this node over the number of packets generated. After the gateway successfully receives the data from the base station, it stores and manages the data in a local SQLite database. The system offers multiple types of data interfaces that can publish data packets to client applications that interact with the wireless mesh network. In particular, the gateway is pre-configured to send data packets as XML data streams through an XML socket connection in which XServe implements a simple communication protocol over TCP/IP to send XML documents back and forth via a specified communication 2262 IEEE SENSORS JOURNAL, VOL. 15, NO. 4, APRIL 2015 Fig. 13. BEEMS system architecture. Fig. 11. Flowchart for processing health packets. Fig. 14. Data table for environmental monitoring parameters. Fig. 15. VOC sensor data storage and transmission format. Fig. 12. XML RPC document for forwarding VOC data. port (9003). The first 4 bytes over the socket indicate the length of the payload. Figure 12 shows an example of an XML RPC document that formats the VOC data output in the implemented system. 4) Data Management and Web Application: To implement web-based monitoring of the IAQ to ensure the health and comfort of building occupants, the WSN-based VOC-sensing function is further integrated with a previously developed energy consumption monitoring system [63], which results in a complete Built Energy and Environment Management System (BEEMS). The BEEMS is constructed with a browser/server multi-layer structure, as shown in Figure 13 and is deployed at the campus of Southeast University to monitor the energy consumption of the entire campus and the built environment. The VOC monitoring function is integrated with the BEEMS by implementing an application that listens to the 9003 port of the gateway SQ120. The application parses the wrapped XML documents and inserts each data item into the database. In the BEEMS server, data are stored and managed in an SQL server database. The data table in the SQL server 2008 database is designed according to the category of the computed statistical information and the different spatial scales of the campus, which include the room level, floor level, building level and campus level. Figure 14 shows the data table that was designed to manage the environmental parameters, which include temperature, humidity, carbon dioxide, and VOC. Figure 15 shows the format of the VOC sensor data formulated as xt = (x, t), in which X is the measured data, and t is the timestamp. The monitoring application software follows a browser/server structure, which allows users to access both real-time and historic monitoring data via the campus network or the Internet. Rich environmental monitoring functions are provided, including the data profile, data comparison/ranking, and data analysis, as shown in Figure 16 and Figure 17. As a result, a remote client user can easily gain access to the monitoring results and formulate proper policies to improve the IAQ. PENG et al.: DESIGN AND APPLICATION OF A VOC MONITORING SYSTEM 2263 Fig. 16. Web-based monitoring user interface. Fig. 18. Sensor network deployment. battery conservation, and data reduction approaches can affect the accuracy of the collected information. Additionally, energy-saving mechanisms will remain essential if improved recharging capabilities are not developed in the future [47]. Fig. 17. Data analysis user interface. C. Discussion The design of sustainable WSNs is a highly challenging issue. Energy-constrained sensors are expected to run autonomously for long periods of time. However, it may be cost-prohibitive or even impossible to replace exhausted batteries in hostile environments. Unlike other networks, WSNs are designed for specific applications that range from small-size healthcare surveillance systems to large-scale environmental monitoring. Thus, any WSN deployment must satisfy a set of requirements that differs from one application to another [47]. ZigBee-based WSNs are particularly well-suited to VOC monitoring because wired deployment can be expensive and inefficient. The main requirements for VOC monitoring are hard real-time delays, quality of service, security, mobility and lifetime prolongation. The sensors are generally expected to operate autonomously for long periods of time ranging from weeks to months. However, VOC monitoring is energyconstrained due to the scarce battery resources of the sensors, which limits the network lifetime. It may not always be possible to manually replenish the motes because of their number, the maintenance cost or the inaccessibility of the monitored regions. Energy-efficient routing protocols and sleep/wakeup schemes directly influence network latency. Similarly, radio optimization presents a tradeoff between signal quality and IV. EXPERIMENTAL RESULTS The goal of this study is to construct a VOC monitoring system using WSN technology. The sensor node used in this system consists of core components, a power management unit, a temperature and humidity sensor unit, and a processing unit. The sensor node must connect to the gas sensor so that the sensor node can measure gas concentrations. To verify the reliability and relative accuracy of this WSN monitoring system, we use this system and a traditional standard instrument to measure VOC concentrations in a bamboo house in Nanjing, China. The sensor network deployment is shown in Figure 18. The experimental conditions of the tested rooms are as follows: a) Temperature: 28 ± 0.5 °C b) Relative humidity: 50-60% c) All windows and doors are closed. Air sampling was conducted from 10:00 to 14:00 daily in 30-minute intervals for three consecutive days. Sorbent tubes with 200 mg of Tenax-TA resin (Markes International Ltd., UK) were used to collect VOCs. The sorbent tube incorporated a 0.5-m-long copper tube with a coated inner surface and potassium iodine as an ozone trap. Personal sampling pumps (224-PCXR 4, SKC Inc., USA) were used to draw air at a flow rate of 40 ml min−1. Flows were measured using a thermal mass flowmeter (TSI4100, TSI Inc., USA), and the variation in the sampling flow between the starting and ending periods was less than 10%. After collecting the air samples, the tubes were wrapped with aluminum foil, packed in a Ziploc bag, and stored at 4 °C until the analysis could be performed. The samples were quantitatively analyzed for individual VOCs using thermal desorption (UNITY 2, Markes International Ltd., UK) and gas chromatography/mass spectrometry (QP500, Shimadzu Co., Japan). Six-point calibrations were performed with toluene d-8 as an internal standard. 2264 TABLE II COMPARISON OF MEASURED VALUES BETWEEN THE ZED AND THE TRADITIONAL INSTRUMENT (PPM) IEEE SENSORS JOURNAL, VOL. 15, NO. 4, APRIL 2015 Fig. 19. GP regression for estimating the distribution of the VOC field. (a) Sensor nodes measurements. (b) Predicted and measured GP. TABLE III NORMAL POWER CONSUMPTION SPECIFICATION We calculated the relative errors of the readings from the instrument and from the wireless gas sensor from 10:00 to 14:00 daily in 30-minute intervals for three consecutive days. The formula for calculating the relative error is R E = |AB S|/T R, in which R E is the relative error, AB S is the absolute error, AB S = Z E D − T R, T R is the reading from the traditional instrument, and Z E D is the reading from the Z E D. The experimental results in Table 2 show that after correction, the AVERAGE of the relative error of the wireless gas sensor is 2.9% (standard gas), the MAX is 4.56%, the MIN is 0.14%, and the STDEV (standard deviation) is 1.18%. Therefore, the measurement accuracy of this WSN monitoring system is acceptable, and this system can be used to monitor VOC concentrations in buildings. The system also provides VOC field computations, which reduce the number of sensor nodes that must be deployed. In the monitoring system, a limited number of environmental sensor nodes are placed in the rooms and measure the VOC concentrations at a limited number of locations. The spatial distribution of the indoor VOC field in the system is computed based on these sampling points, and the VOC concentrations at points without sensors can be inferred from the distribution model. An effective Gaussian Process (GP) representation [64] is used for the data regression to establish a VOC field distribution model. A GP is a natural generalization of linear regression that allows us to consider the uncertainty of the predictions. Figure 19(a) shows an example of a sensor’s measurements in a room in which 100 points that represent different locations are recorded. Figure 19(b) compares the prediction of the VOC field distributions with the measured values at the testing points, which are marked as crosses. The results show that the function provides reliable and accurate VOC monitoring with fewer sensors, especially if it is deployed in large-scale environments. The energy consumption of the wireless senor network is an important practical consideration. According to the normal power consumption specifications of the processor and the RF transceiver (Table 3) [65], [66], the power consumptions of the processor and RF transceiver vary significantly when the modules operate in different modes. To evaluate the theoretical lifetime of the sensor node, we use the equation T = max( Is end , Battery Irec, Iidle , Volume(m Ah) Isleep, Icpu_acti ve , Icpu_sleep ) , in which Isend , Irec, Iidle and Isleep are the currents of the RF transceiver module when it operates in the send, receive, idle and sleep modes, respectively, and Icpu_active, PENG et al.: DESIGN AND APPLICATION OF A VOC MONITORING SYSTEM 2265 Fig. 20. Testing the energy consumption of a wireless node. TABLE IV AVERAGE VOLTAGE AND CURRENT and Icpu_sleep are the currents of the processor module when it operates in the active and sleep modes, respectively. According to Table 3 and assuming a battery volume of 800 mAh, the estimated theoretical lifetime of the sensor node is 41 h if the node maintains constant activity. In our implementation, the RF transceiver module completes one data packet transmission in approximately 1.5 ms, which is estimated based on the instruction period of the processor and on the number of code lines employed for data acquisition and transmission. We also designed a lifetime management scheme that controls the sensor node to transmit one data packet (1.5 ms) and maintain activity (10 s) every 900 seconds. According to the active duty cycle of 90 seconds, the sensor node is expected to operate for 3690 hours, or approximately 153 days. We also attempted to measure the energy consumption of the wireless transceiver module in a simple manner under practical conditions because the current of the RF transceiver module is higher than that of the processor module. Figure 20 shows the energy consumption diagram used in an experiment in which the load is a 20 register. We designed a 5 day × 24 hour operational test in which two NANFU LR6 AA batteries are used and the active duty cycle of the sensor node is 180 seconds. Table 4 lists the results of the average voltage and current measurements. The average power consumption during the test is estimated as 2.8896 V*0.7628 mA = 2.204 mW. V. APPLICATION Most people spend more than two-thirds of their lifetime inside buildings [67]–[70], and indoor air pollutants represent a considerable health risk, especially for the young, the elderly and those with respiratory problems [71], [72]. Two factors can play a key role in the degradation of air quality in indoor environments [73]. First, many buildings contain synthetic materials and furnishings (such as walls, carpets, and air conditioning systems), which all can emit a wide variety of pollutants such as VOCs [74]. Second, to save energy, the airtightness of buildings has been increased, and the supply of fresh air has been reduced [75]. For example, many newer homes are now required to be well-insulated to improve energy efficiency by reducing heating and cooling requirements. Fig. 21. Schematic of the integration of the VOC monitoring system and RIVEC in Solark. The red dotted lines indicate that each ZED in the home communicates with the ZC, which corresponds with RIVEC. The green dotted lines show that each fan in the home communicates with the controller, which adjusts the operation of the whole-house fan as necessary. However, without provisions for ventilation, the airtightness requirements to meet such mandated energy efficiencies can allow contaminants to build up within a house to unacceptable levels [76]. In older homes, ventilation is typically achieved via windows, envelope leakage (i.e., infiltration), and lowefficiency ventilation fans. However, these simple solutions cannot be relied upon for adequate ventilation because most new insulated dual-pane windows are usually kept shut to retain heat during the winter months, to keep heat out during summer months, or for reasons related to noise or security. Retrofit sealing of older homes can exacerbate the problem by reducing the contribution of infiltration to home ventilation; therefore, infiltration alone cannot be relied upon to provide an adequate exchange of air [76]. The effects of these two factors have significantly increased indoor air pollutant levels. A series of long-term studies of human exposure to air pollutants indicated that indoor levels of many pollutants may be 25 times, and occasionally more than 100 times, higher than outdoor levels [77]. As a result, there is a greater need for newer or recently weatherized homes to apply a whole-house mechanical ventilation system to ensure safe and healthy IAQ. Although the continuous circulation of outdoor air using a dedicated primary house fan is an effective way to eliminate the buildup of harmful pollutants, it is not energy-efficient because excessive ventilation reduces the effectiveness of heating and cooling systems that attempt to control the indoor temperature [78]. Fortunately, these problems can be properly addressed by integrating the VOC monitoring system proposed in this paper with the RIVEC (Residential Integrated VEntilation Controller). This system has been applied in Solark, the contribution of TEAM SEU to the Solar Decathlon China 2013, as shown in Figure 21. The RIVEC, which is based on the work by Sherman et al., is a dynamic control system for whole-house ventilation fans that uses the fundamental relationships between airflow and IAQ with knowledge of the airflows in passive stacks and other exogenous 2266 IEEE SENSORS JOURNAL, VOL. 15, NO. 4, APRIL 2015 mechanical systems to reduce the energy that is required to meet ventilation standards [79]. The RIVEC aims to address the IAQ/energy trade-off and peak demand problems associated with ventilation while maintaining compliance with ventilation standards such as the ASHRAE Standard 62.2-2010 [80]. The RIVEC coordinates the operation of a whole-house exhaust or supply fan with the operation of other fans or devices in the house that increase the building ventilation rate (such as bathroom and kitchen fans) by implementing the concepts of efficacy and intermittent ventilation to allow time-shifting of ventilation [81]. Using this approach, ventilation can be shifted away from periods of high cost or high outdoor pollution to times when it is cheaper and more effective to run the system. A. Control Approach A dynamic controller would ideally be able to manage any mechanical ventilation system that is installed to meet the whole-house mechanical ventilation requirements at a minimum energy cost. The controller can do this by shifting the ventilation load of the whole-house HVAC system off peak and by considering the exogenous mechanical ventilation of other systems (such as bathroom and kitchen fans) [82]. To accomplish this task, the controller must be able to regulate the state of the installed mechanical ventilation system and sense the status of all significant exogenous mechanical ventilation systems. To prevent rapid cycling or valving of the whole-house ventilation fan, the controller makes decisions at fixed time steps or intervals, such as every 10 min. To perform the necessary calculations, the controller must be programmed with a variety of specific house and system parameters. B. Controller Logic A reasonable controller logic that achieves the intent of the ASHRAE Standard 62.2 while minimizing energy costs and operations during peak times would use the following set of actions at each time step [82]: 1) Determine the Current Mechanical Ventilation Rate: The controller monitors the status of the whole-house mechanical system and all exogenous mechanical ventilation systems. It then totals all exhaust flows separately, all supply flows separately, and all balanced flows separately. The current mechanical ventilation rate will be the balanced flow that is added to the larger value of the supply flow or exhaust flow. 2) Estimate the Current IAQ: Using the sensors and equations of IAQ in ASHRAE 62.2, determine the relative exposure (instantaneous), R, and the relative dose (24 hour integrated exposure), d. These values will be used in the control algorithm. 3) Modify the Whole-House Mechanical Ventilation: Based on the control algorithm, the whole-house mechanical ventilation will be turned on or off for the next period. The control algorithm aims to keep the dose at or below unity and to shut off the whole-house mechanical ventilation system during a designated peak period. To accomplish this aim, each day is broken up into four periods. There is a (four-hour) peak period during which the whole-house system is off. There are also pre- and post-peak shoulder periods (4 h each) and a 12-h base period. VI. CONCLUSION The primary aim of this paper is to design a low-power ZigBee WSN and to control the inter-node data reception for use in real-time acquisition and communication of levels of air pollutants from VOCs. The network consists of end devices with PID sensors, routers that propagate the network over long distances, and a coordinator that communicates with a computer. The design is based on the ATmega16 microcontroller and the Atmel RF230 ZigBee module, which effectively process and communicate data with a low power consumption. Priority was given to power consumption and sensing efficiency by incorporating various smart-tasking and power management protocols. The preliminary experimental results demonstrate that the WSN system can monitor VOC concentrations with a high level of accuracy and thus can be used for automatic environmental monitoring. The system was deployed with the RIVEC at the campus of Southeast University and performed favorably in practice. The measurement nodes are small and easy to deploy in buildings, and the structure of the multi-layer system allows the incorporation of sensor nodes distributed at large scales in one or more buildings. 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S. Walker, and D. J. Dickerhoff, “EISG final report: Residential integrated ventilation controller,” California Energy Commission, Sacramento, CA, USA, EISG-PIER Rep. 55044A, Jun. 2009. [80] Ventilation and Acceptable Indoor Air Quality in Low-Rise Residential Buildings, ASHRAE Standard 62.2., 2010. [81] M. H. Sherman, J. M. Logue, and B. C. Singer, “Infiltration effects on residential pollutant concentrations for continuous and intermittent mechanical ventilation approaches,” HVAC&R Res., vol. 17, no. 2, pp. 159–73, 2011. [82] M. H. Sherman and I. S. Walker, “Meeting residential ventilation standards through dynamic control of ventilation systems,” Energy Buildings, vol. 43, no. 8, pp. 1904–1912, 2011. Changhai Peng received the Ph.D. degree in building sciences and technologies from Southeast University, Nanjing, China, in 2003, where he is currently an Associate Professor with the School of Architecture. His research interests include intelligent buildings, building energy, and environment monitoring and management. Kun Qian received the Ph.D. degree in control theory and control engineering from Southeast University, Nanjing, China, in 2010, where he is currently a Lecturer with the School of Automation. His research interests are intelligent robotic system and smart sensing. Chenyang Wang is currently pursuing the M.S. degree at Southeast University, Nanjing, China. Her current research interests include building energy and environment monitoring and management.

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