2014 IEEE Biennial Congress of Argentina (ARGENCON)
Low-Power Wireless Sensor Network for Frost Monitoring in Agriculture Research Ana Laura Diedrichs†1 , Germán Tabacchi†2 , Guillermo Grünwaldt†3 , Matías Pecchia†4 , Gustavo Mercado†5 and Francisco González Antivilo∗6 † Laboratorio
GridTICs, Dpto. Electrónica, Universidad Tecnológica Nacional Facultad Regional Mendoza Rodríguez 273, Ciudad de Mendoza, Mendoza, Argentina 1
[email protected] [email protected] 3
[email protected] 4
[email protected] 5
[email protected] 2
∗ Cátedra
de Fisiología Vegetal, Facultad de Ciencias Agrarias, Universidad Nacional de Cuyo Alte Brown 500, Chacras de Coria, Lujan de Cuyo, Mendoza, Argentina 6
[email protected]
Abstract—This work presents the development of a wireless sensor network (WSN), based on IEEE-802.15.4, in order to be used for frost characterization in precision agriculture by measuring temperature. Our key objective is to reduce the power consumption of the network to the minimum, allowing several measurement points per node and the remote monitoring of the sensors behaviour. For the communication interface between a WSN node and the sensors, we have developed a serial protocol inspired in SDI-12. Preliminary results show a low-cost and low-power WSN. The user can access and use the data for agronomic research. Resumen– Este trabajo presenta el desarrollo de una red inalámbrica de sensores (WSN), basada en IEEE-802.15.4, para ser usada en la caracterización de las heladas en agricultura de precisión mediante la medición de temperatura. Nuestro principal objetivo es reducir el consumo energético de la red al mínimo, permitiendo varios puntos de medición por nodo y el monitoreo remoto del comportamiento de los sensores. Para la interfaz de comunicación entre el nodo de la WSN y los sensores, desarrollamos un protocolo de comunicación serie inspirado en SDI-12. Los resultados preliminares muestran una WSN de bajo costo y bajo consumo. El usuario puede acceder a los datos y utilizarlos para la investigación agronómica. Index Terms—Wireless Sensor Network, Precision Agriculture, Remote Sensing, Micro-climate, low-power WSN, WPAN, IEEE 802.15.4
I. I NTRODUCTION Precision agriculture (PA) uses decision support systems to manage the crops. The ambiental parameters needed (e.g, temperature, humidity) can be measured using sensors. Wireless sensor networks [1] (WSN) consist of random or planned spatially distributed nodes, also called motes, each of which is equipped with sensors, memory for storage, microprocessor for computing their decisions, batteries for energy supply and are able to communicate wirelessly with other nodes in a short-range. Thanks to the different technology advances, today it is possible to build multifunctional sensor devices which are smaller, cheaper, able to communicate to each other and can operate with low-power, c 978−1−4799−4269−5/14/$31.00 2014 IEEE
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in contrast with the traditional data loggers. Using WSN as telemetry system instead of data loggers for agronomic applications has many advantages [2], [3]. An important issue is the spatial and temporal resolution that WSN allow to increase. The agronomic industry and research take advantage of empirical models, created using visual observation, farmers’ intuition and sensor data. The lack of spatial resolution, given by a few sensors or meteorological stations distributed in the land, is one of the limits for the construction of models with more precision and accuracy. Previous works [4] on PA show different kinds of WSN deployment on the field in several use cases, such as animal monitoring [5], crops management, viticulture [6], videosurveillance [7] and so on. Some of them use Zigbee [8], which operates over IEEE 802.15.4, as the network protocol stack for the communication between the nodes. Many of them have chosen SDI-12 [9], e.g [10], as serial interface between mote and different kinds of sensor probes, instead of others which have worked with IEEE 1451, e.g [6], a standard for smart transducers. Our network design focuses on achieving a local requirement of environmental parameter monitoring in times of frost [11]. The damage caused by the frost takes place when the temperatures are below than a tolerable limit for the plants. Each phenological state, e.g flowering, has a variable cold hardiness, so the lethal temperature is also variable. Freezing climatic events are the most dangerous, because they affect a large land surface. Mendoza is not an exception. According to the Instituto Nacional de Vitivinicultura (INV), in 2013 the loss of the vine crop reached up to 27% [12]. Big part of that lost of yield was during the early spring. In order to study the micro-climate phenomenon of frost in Mendoza, a WSN was placed between the vineyards, where the sensors are distributed vertically as well horizontally. Increasing the spatial sensor resolution is so important because the air temperatures change vertically as horizontally, and the plant has also different cold hardiness in the organs like trunk, flowers, shoots. For a better understanding of the phenomenon a precise measurement is necessary.
As our motes are battery-powered devices, it is critical to face the energy consumption in order to increase the network lifetime. There are many energy conservation schemes [13]. The chosen approach to energy saving in our WSN is to optimize the duty-cycle of the nodes, which are the different tasks that the motes perform during their lifetime, e.g transmiting and receiving data, getting data from the temperature probes, and so on. Section V-C deals with the low-power functionality of the motes showing results and section V-B specifies how the WSN work. Our deployed WSN was designed according to the requirements of agronomic engineers and researchers team, which are: taking and storing periodic temperatures measurements with their context information, such as sensor location and timestamp; to provide information about the network performance; customizable measurement interval; and remote access to sensor status reporting tools. The following section II introduces the network architecture of our deployed WSN, which achieves these requirements for the users. II. N ETWORK ARCHITECTURE The protocol used for communication in the WSN is IEEE 802.15.4 [14] , which covers physical layer and MAC (medium access control) sublayer and allow only peer to peer or star as possible topologies in a wireless personal area network. Motes can be reduced function devices (RFD) or full function devices (FFD). The RFD is the sensor node and has a low power duty-cycle and the FFD is the coordinator of the network. Carrier sense multiple access with collision avoidance (CSMA-CA) is used to control medium access. For the first prototype, the star topology was chosen. Fig 1 shows the topology of the network: some motes are placed in the greenhouse and the others in the vineyard. The following sections give details about the communication between the motes V-B4. Radiation shields protect the temperature probes from weather contingencies. Details about the temperature probes are in the next section III. The motes are protected by an IP-65 enclosure box (the boxes of the fig 1). Each RFD can have connected many temperature probes. Section IV explains how the mote can communicate with many temperature probes. A gateway device was built using a Rabbit 3700 board [15] and an FFD to convert from IEEE 802.15.4 frames to TCP packages that are forwarded to the server. The gateway is connected to the powerline, and it has a 12 V Gel Cell battery with a capacity of 7 Ah as backup to face power outages. In case of network connectivity loss, the gateway also has capacity to store frames and try to reconnect with the server to send the data. The server has a Mysql database installed and a web server based on Java technologies. The end user, can access to the web application and download status reports of the network and sensor data. III. T EMPERATURE PROBE The temperature probe was built using Microchip devices: a PIC12F683 [16] microcontroller, a TC1047A [17] integrated silicon temperature sensor and an MCP1525 [18] 526
Fig. 1.
Topology of the wireless sensor network
voltage reference. These components were chosen for their low power features. We designed a dual layer printed circuit board (PCB), which has a solder mask, in order to facilitate manual soldering, taking into account the size of the SMD components. We used an insulating spray to protect from corrosion due to humidity. The probes have a connector that allows a fast and easy replacement. We used anti-corrosion gel to avoid galvanic corrosion between the connectors of the probe and the wire.
Fig. 2. Temperature probes. In the bottom one from left to right: the connector, the voltage reference, the microcontroller, and the sensor.
For practical reasons, we programmed the probes to send raw data. Due to dispersion of the sensor [17], the data conversion and calibration is done by the server, where the previously measured calibration parameters for each unit can be found. To improve the noise tolerance, an oversampling technique [19] was implemented, increasing the resolution of the ADC from 10 to 14 bits. While working with 10 bits, the minimum fluctuation caused by the noise would represent a variation of 0.244 ◦ C in the temperature measured, but on working with 14 bits, that value would be of only 0.015 ◦ C.
IV. M OTE - PROBE COMMUNICATION
RFD
We have chosen digital multiplexing for mote-probe communication for two main reasons: to reduce the physical distortion, e.g interferences or thermal noise, which could affect the measurement, and to increase communication distances using the necessary electrical adaptation. We decided to use a serial protocol that achieves good energetic autonomy with low cost of the materials needed. We developed an ad-hoc simple protocol inspired by SDI-12 [9] for future interoperability with commercial SDI-12 sensors, eg. anemometers [20] and humidity [21]. Comparing with other protocols, like Dallas/Maxim’s 1-Wire [22] and standarized ones as IEEE1451.001-1999 [23], this one is simpler to implement in firmware. It shares the following characteristics with SDI12 [9]: • Low system cost. • Probes can be interchanged without reprogramming the mote. • Power and data are available through the same connector. • The use of a standard serial interface significantly eliminates complexity in the design of data recorders via hardware UART. The serial bus is shared by all the sensors connected to the mote in a master-slave scheme. The serial bus must be used by only one sensor at once. • Motes can be designed and produced independently of future probes development. • In future development, motes might interface with a variety of sensors. • Frame built using ASCII encoding, following this format: 1 start bit, 7 data bits (least significant bit transmitted first), 1 parity bit (even parity) and 1 stop bit. • Addressing up to 62 probes by mote, since SDI-12 is an ASCII reduced protocol. Some SDI-12 specifications are not met, as the following: • Only Start Measurement Command (aM!) and Send Data Command (aD0!) commands: the probes take the data in less than one second. The other commands were not implemented for simplifying the design. • Instead of 12 volt Power Line we use 3 volt, since this is battery voltage used in the motes. • All the electrical recommendations and indications: the mote-probe distance will be not higher than 3 meters. By the way we simplified the design model. Fig. 3 shows the mote polling system and probe responses via SDI-12 commands. To access the data of probe 3, for example, the mote sends a 12 ms. break and a “3M!” ASCII text; this is the measure command. All the probes in the channel leave the sleep state, only the probe with address 3 responds with “30001\r\n” text, where the first character is its own address; the next three characters indicate the time measurement, that means it will be ready in zero seconds, and the last one, that there is only one value to transfer. Then the mote sends a “3D0!” text, this is the data access command to the probe with address 3 and the 0 gets the only available data. The probe with address 3 responds instantly with the string “3+#####\r\n”, where the ##### is the raw acquired value in decimal representation. 527
PROBE 3
to
PROBE n
Break
Break
3M!
3M!
n=3? ttt
1\r\n 3000
30001\r
\n
Not a Break sleep( )
ttt
3D0! \n ###\r
3+##
n=3? sleep( )
3D0! 3+####
Not a Break sleep( )
#\r\n
Not a Break sleep( ) sleep( )
Fig. 3.
Commands scheme of the probe-mote protocol
Using an industrial specification like SDI-12 makes the interoperability possible between different kinds of sensors with the sensor node, in our case the mote. Installing other commercial sensors with any further special requirements is viable simply adding the necessary commands in the mote and the electrical interface. V. S ENSOR NETWORK A. Mote
Fig. 4.
Mote RCB230 Radio Controller Board V3.2
The prototype WSN deployment uses motes of type RCB230 Radio Controller Board V3.2 [24] (Fig. 4), which are 2.4 GHz radio modules that have an AVR AT86RF230 transceiver [25] according to the IEEE 802.15.4 standard specifications [14] for wireless personal area networks and low data transfer rate, and a low-power ATmega1281V microcontroller [26]. We have done a base board to connect the mote with its batteries and the sensor probes. Each RFD was supplied with lithium iron disulfide (LiF eS2 ) batteries, designed to operate in low temperatures [27].
B. Application module For the application module of the mote, a group of commands have been developed to manage the most important functions such as delivering data, manage synchronization and getting status information (e.g. the battery level). 1) Starting the network: When a node restarts, it sends a MOTE_START command to the FFD. The MOTE_START package gives information about the MAC address (64 bits), short address (16 bits), number of sensors connected and the sensors addresses. When FFD receives a MOTE_START, it forwards the package to the gateway, to resend it to the server where the software application stores the data in the database. Thanks to his function, new motes with their sensors can be easily added to the WSN. 2) Taking sensor measurement: The RFD application takes care of querying each temperature probe to get the sensor data, then it puts it in a package with its timestamp and sends it to the FFD. We call this DATA_IND command. The RFD clock interruption (RFD clock tick) checks if it is time to take the measurement. Every Meas-Int minutes, being this variable a measurement interval configurable at deployment time, the RFD queries through the serial bus the probes, as we can see in Fig 5. Once the RFD has all the data, the temperature values for all the sensors, sends a DATA_IND package to the FFD with all the sensors values, the timestamp and the Link Quality Indicator (LQI). When the data comes to the server side, it will be stored in its database. The actual implementation sets the Meas-Int in 5 minutes, as the agronomic engineer suggests for frost monitoring.
Fig. 5.
outdoor deployed and deals with many signal interference causes. In that way, we can infer if we have data loss because the motes can not communicate with the FFD, checking the LQI history. 3) Getting the battery level: Taking the battery level of each RFD is an important issue because we can calculate how much power it has spent and how much time the RFD will be operating without battery change. Fortunately, the AVR AT86RF230 transceiver [25] has a battery monitor (BATMON) that detects a low supply voltage. This is done by comparing the voltage on the external supply with a programmable internal threshold voltage. The programmable voltage threshold range is from 1.7 V to 3.675 V. The signal bit BATMON_OK of register BATMON indicates with a 0 that the battery voltage is lower than the threshold voltage, and with a 1 that it is higher. Looping through the different thresholds and checking BATMON_OK, the approximate battery level is calculated. The application of the RFD sends the BATTERY_STATUS command to the FFD twice a day, which is a configurable interval. 4) Synchronization issues: It is essential to know the time the data were taken. The motes does not have a real time clock (RTC) embedded. A tiny synchronization module was developed in order to face this issue, creating an RTC command (RTC_CMD). Once a day, the server synchronizes with a Network Time Protocol (NTP) server using the NTP protocol. Then, it sends the current timestamp T to the gateway, which forwards it to the FFD. Every 10 min, which is a configurable interval (sync_interval), the RFD asks for synchronization information sending an RTC_CMD to the FFD. As we can see in Fig. 6, the FFD answers sending a sync_response package that contains: its actual clock, a 32 bits timestamp given in seconds and the time to tick (T tT ) in microseconds. The T tT gives information about when comes the next timestamp info.
Query bus mote-probes
The IEEE 802.15.4 standard defines the LQI measurement as a characterization of the strength and/or quality of a received package. LQI values shall be an integer ranging from 0 to 255. The minimum and maximum LQI values (0 and 255) should be associated with the lowest and highest quality compliant signals, respectively, and LQI values in between should be uniformly distributed between these two limits. The AT86RF230 tranceiver [25] determines the LQI using correlation results of multiple symbols within a frame to determine the LQI value. This is done for each received frame. The use of the LQI value by the network or application layer is not specified in this standard. We consider important to store the LQI values, because the WSN is 528
Fig. 6.
Synchronization
5) Low-power RFD state: The rest of the time, the node is in sleep state, spending less power than in others states. The RFD wakes up from the sleep state with every clock tick, and if there is nothing to do, it returns to sleep immediately. Section V-C gives more details about the power consumption of the nodes in their duty-cycle.
A+S T The table I shows the results for each state. C=
(3)
The measured average current consumption using the values on table I was of approximately 315 µA. TABLE I C URRENT DRAW, ACTIVE TIME AND POWER CONSUMPTION OF EACH STATE .
Fig. 7.
Synchronization
C. Low-power consumption The setup for measuring the power consumption was an RFD with four temperature probes associated with a coordinator. To measure the power consumption it is necessary to distinguish the different states of the application. There is a low-power Sleep state and there are also the following active states: • Status: that sends a BATTERY_STATUS to the FFD. • Sync: that synchronize the clock with the FFD. • Clock Tick: that wakes up the mote periodically and returns to sleep if it doesn’t have pending tasks. • Sensing: that reads the probes and sends a DATA_IND to the FFD. The total active consumption A is given by equation (1): A=
N X i=1
F ai ∗ T ai ∗ Cai
(1)
Considering that: • N : the total number of active states. • Ca: the average current draw in each active state given in mA. • T a: how long an RFD remains in that active state in ms. • F a: the frequency of occurrence of the state per day. The measurement of the Ca for each active state was performed by measuring the voltage drop with an oscilloscope across a 10Ω shunt resistor in the positive power rail in series with the RFD. The average current draw in each state is then computed by dividing the average voltage over the 10Ω resistor. As most of the lifetime of the RFD stays in sleeping state, the total consumption of the sleep state S was calculated as shown in (2). S = [T − (
N X i=1
F ai ∗ T ai )] ∗ Sc
(2)
The measurement of sleep state power consumption Sc, in mA, was taken using an ammeter capable of taking microampere measurements in series with the mote. Considering a total time T of 24 hours to compute the frequencies, the average current consumption C was calculated using (3). 529
State
Freq.
Status Sync Clock Tick Sensing Sleep
2 144 86400 288 -
Active Time [ms] 4.500 6.690 1.540 931.9 8.60×107
Current Draw [mA] 19.47 23.09 13.78 14.54 0.25
Current Consumption [mA · ms] 175 2.22×104 1.83×106 3.90×106 2.15×107
1) Battery life estimation: Taking into account the total battery capacity Bc and the average current draw of the device C, the estimated battery life L is Bc (4) C Thus, for 2 AA lithium batteries [27] with a total capacity of 6000 mAh and a current draw of 0.315 mA we get a battery life of approximately 19000 hours, a little more than 2 years. These batteries can deliver approximately full rated capacity in temperatures as low as -40◦ C while discharged at a continuous 25 mA rate. Considering that our maximum power consumption state reaches 23.09 mA during only 6.69 ms as seen on Table I, the batteries give comparable performance over the entire operating range. L=
VI. C ONCLUSIONS The WSN was recently deployed for agriculture research with temperature probes, developed to achieve low cost and precision measurement requirements. The users can also access to a website for monitoring and reporting. Taking into account that these are our first results, which were taken in the lab, the preliminary power consumption measurements show a promising lifetime of two years. It is also possible to study the battery measurements on the field using the BATTERY_STATUS levels, correlating them with the temperature values, in order to learn the effects of temperature on battery health and to obtain the real consumption of the network. There are still many issues to improve, some of which are ongoing now. In future works, we plan to add an energy harvesting module using solar panels, a routing layer in the WSN to reach an scalable network, and other types of sensors, as well as the development of a frost prediction system. VII. ACKNOWLEDGMENT This project was supported by PICT2010-22 RED SIPIA - Red de Sensores inalámbricos para investigación Agronómica, PID 25/J071 LIVRES: Análisis y evaluación de características relevantes de las redes de sensores inalámbricos aplicadas al manejo y sensado en agricultura de
precisión, both from GridTICs, Facultad Regional Mendoza, Universidad Tecnológica Nacional and project Mecanismo de resistencia a temperaturas subcero en tejidos leñosos de Vitis vinifera cv Malbec from Ing. Agr. González Antivilo, Facultad de Ciencias Agrarias de la UNCuyo. Ana Diedrichs thanks her doctoral fellowship granted by Universidad Tecnológica Nacional and the direction of her advisor Dr. Facundo Bromberg. R EFERENCES [1] J. Yick, B. Mukherjee, and D. Ghosal, “Wireless sensor network survey,” Computer networks, vol. 52, no. 12, pp. 2292–2330, 2008. [2] R. Beckwith, D. Teibel, and P. Bowen, “Report from the field: results from an agricultural wireless sensor network,” in Local Computer Networks, 2004. 29th Annual IEEE International Conference on. IEEE, 2004, pp. 471–478. [3] N. Wang, N. Zhang, and M. Wang, “Wireless sensors in agriculture and food industry. recent development and future perspective,” Computers and electronics in agriculture, vol. 50, no. 1, pp. 1–14, 2006. [4] L. Ruiz-Garcia, L. Lunadei, P. Barreiro, and I. Robla, “A review of wireless sensor technologies and applications in agriculture and food industry: state of the art and current trends,” Sensors, vol. 9, no. 6, pp. 4728–4750, 2009. [5] R. N. Handcock, D. L. Swain, G. J. Bishop-Hurley, K. P. Patison, T. Wark, P. Valencia, P. Corke, and C. J. O’Neill, “Monitoring animal behaviour and environmental interactions using wireless sensor networks, GPS collars and satellite remote sensing,” Sensors, vol. 9, no. 5, pp. 3586–3603, 2009. [6] M. A. Fernandes, S. G. Matos, E. Peres, C. R. Cunha, J. A. López, P. Ferreira, M. Reis, and R. Morais, “A framework for wireless sensor networks management for precision viticulture and agriculture based on ieee 1451 standard,” Computers and Electronics in Agriculture, vol. 95, pp. 19–30, 2013. [7] A.-J. Garcia-Sanchez, F. Garcia-Sanchez, and J. Garcia-Haro, “Wireless sensor network deployment for integrating video-surveillance and data-monitoring in precision agriculture over distributed crops,” Computers and Electronics in Agriculture, vol. 75, no. 2, pp. 288– 303, 2011. [8] T. Kalaivani, A. Allirani, and P. Priya, “A survey on Zigbee based wireless sensor networks in agriculture,” in Trendz in Information Sciences and Computing (TISC), 2011 3rd International Conference on. IEEE, 2011, pp. 85–89. [9] “SDI-12 A Serial-Digital Interface Standard for MicroprocessorBased Sensors Version 1.3,” Tech. Rep., Jan. 1999. [10] J. Lopez, F. Soto, J. Suardiaz, P. Sanchez, A. Iborra, and J. Vera, “Development of a mote for horticulture based on the SDI-12 standard,” in Industrial Electronics, 2009. IECON’09. 35th Annual Conference of IEEE. IEEE, 2009, pp. 2630–2635. [11] R. L. Snyder and J. P. Melo-Abreu, Frost protection: fundamentals, practice and economics. Volume 1. FAO, 2005. [12] “El INV presentó el ajuste de la Estimación de Cosecha 2014,” Instituto Nacional de la Vitivinicultura, Mendoza, Argentina, online news, February 2014. [Online]. Available: http://www.inv.gov.ar/noticias.php?ind=1&id_nota=514 [13] G. Anastasi, M. Conti, M. Di Francesco, and A. Passarella, “Energy conservation in wireless sensor networks: A survey,” Ad Hoc Networks, vol. 7, no. 3, pp. 537–568, 2009. [14] “IEEE Std 802.15.4-2003: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (LR-WPANs),” IEEE Standard, 2003. [15] Rabbit 3700 models Datasheet. [Online]. Available: http://ftp1.digi.com/support/documentation/019-0136_L.pdf [16] PIC12F683 Datasheet 8-Pin Flash-Based, 8-Bit CMOS Microcontrollers with nanoWatt Technology, Microchip Technology Inc., 2007. [Online]. Available: http://www.microchip.com/downloads/en/DeviceDoc/41211D_.pdf [17] TC1047/TC1047A Precision Temperature-toVoltage Converter Datasheet, Microchip Technology Inc., 2001-2012. [Online]. Available: http://www.microchip.com/downloads/en/DeviceDoc/21498D.pdf [18] MCP1525/41 2.5V and 4.096V Voltage References, Microchip Technology Inc., 2001-2012. [Online]. Available: http://ww1.microchip.com/downloads/en/DeviceDoc/21653C.pdf [19] “AVR121: Enhancing ADC resolution by oversampling,” Tech. Rep., 2005. [Online]. Available: http://www.atmel.com/Images/doc8003.pdf [20] “Sonic Wind Sensor, SDI-12,” last visit: 15/08/2012. [Online]. Available: http://www.campbellsci.com/windsonic4-overview
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