An ASABE Meeting Presentation Paper Number: 08
Development of a Wireless Soil Sensor Network Jing Huang Iowa State University, Ames, IA 50011
Ratnesh Kumar Iowa State University, Ames, IA 50011
Ahmed E. Kamal Iowa State University, Ames, IA 50011
Robert J. Weber Iowa State University, Ames, IA 50011
Written for presentation at the 2008 ASABE Annual International Meeting Sponsored by ASABE Rhode Island Convention Center Providence, Rhode Island June 29 – July 2, 2008
Abstract: There are currently few sensor systems capable of measuring soil parameters at multiple points in space over time consistently and economically, and without disrupting activities at the land surface. We have developed prototype nodes of a wireless sensor network for agricultural monitoring and management. The underground nodes are capable of transmitting soil measurements of moisture at scheduled intervals. Reliable communication can be established over 20-30 meters range at the rate of 256 bits/second that is more that adequate for the application. The calculated battery life based on the energy usage per transmission is estimated over 3 years which are sufficient for long-term real-time monitoring. The data from a network of these sensors could be used to vastly improve agriculture management and environmental protection practices through a more accurate modeling of crop-growth, hydrologic flow, and carbon-nutrient cycling processes. Keywords: Wireless soil sensor networks, Precision agriculture
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Introduction Over the last decade, technological advances such as the emergence of global positioning systems (GPS), geographical information system (GIS), and variable rate control has led to increased interest and adoption of the concept of precision agriculture. The term “precision agriculture” means an integrated information- and production-based farming system that is designed to increase long-term, site-specific, and whole farm production efficiencies, productivity, and profitability while minimizing unintended impacts on wildlife and the environment (USC, 2006). Thus, a well-managed agriculture not only is vital to the economy and the food supply, but also significantly impacts environmental protection through the accumulation of atmospheric carbon into biomass and reductions in the amount of nutrient losses to waterways. According to the environmental protection agency (EPA), there are more than 20,000 U.S. lakes, streams and rivers that fail to meet current water quality standards, as defined by the clean water act of 1972. Among the sources of pollutants are run-offs from farm field fertilizers (IPTV, 2000). Although precision agriculture has tremendous potential, there are some barriers preventing the full benefits of precision agriculture being realized (Zhang et al., 2002). A major impediment to widespread implementation of precision agriculture is gathering the requisite information to adequately describe the spatial and temporal variations of key variables. There is a tremendous need for sensing technologies which will allow automated collection of soil, crop, climactic, pest, and bio-safety data. The development of such technologies is critical for widespread adoption of precision agriculture. The use of information technologies in agriculture has the potential to provide significant economic, security, and environmental benefits to society by judicious use of agricultural inputs. The full benefit of precision agriculture will only be realized if the spatial and temporal variations across the field are accurately determined and the relationship between inputs (water/nitrate/pesticide/herbicide) and yield, environmental impact, and food security are correctly identified. This requires data collection on a finer spatial resolution than is economically feasible with manual and/or laboratory methods. Remote sensing of soil parameters is promising but problems such as timeliness, cost and poor spatial resolution have limited its widespread use (Zhang et al., 2002). For example, passive or active radiometry can avoid direct measurements by providing area-wide indications of surface soil water content (Bogena et al., 2007). However, the received signal is strongly influenced by the vegetation and surface structure, and the sampling depth is restricted to the uppermost soil (2-5cm) (Walker et al., 2004). Consequently, direct measurements are still indispensable in areas with significant vegetation and little cover (Bogena et al., 2007). Many sensors and monitors already exist for in situ and on-the-go measurement for a variety of crop, soil and climatic variables (Taylor and Whelan, 2005). Technological advances in wireless communication and microelectronics have enabled the development of small, low cost and in situ soil sensors, and their wireless networks. With traditional methods, analysis error is relatively low; however, sampling error can be substantial due to the limited sampling intensity. In situ sensors can provide a sampling intensity several orders of magnitude greater than what is attainable with traditional methods. Therefore, in situ sensor based monitoring can tolerate much higher analysis errors while providing greater overall accuracy in mapping temporal and spatial variability. In situ sensor networks have an additional advantage that not only the spatial
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variation can be accurately quantified, but also the temporal variation can be continuously monitored. Despite the fact that great potentials of wireless sensor technology have been recognized, the adoption of this technology has not been as fast as one would image (Wang et al., 2006). Currently, there are few low-cost, continuously deployable monitoring systems capable of generating spatial averages of soil parameters such as moisture and temperature. In (Tiusanen, 2007), wireless underground soil scout prototypes are presented and used for remote soil monitoring during five months in real conditions. However, since all scouts were trying to broadcast sensing data directly to a central receiving antenna, this approach limits the area the sensor system can cover and is not optimized for power consumption. Instead, our approach is to let sensor nodes “talk” to their neighbors and use multi-hop communications to relay sensing data. By employing power conserving and balancing routing strategies, and efficient medium access protocols, our sensor network will be able to cover much larger area and last much longer. Some of the salient contributions of the work will be: (i) establishment of an underground sensor and communication network; and (ii) development of a network-based framework for researchers to better understand properties of soils and the environment over a large area over time. Besides the above intended application, the soil sensor system will be useful in non-agricultural and military applications requiring underground sensing such as: 1. Soil moisture saturation levels in roads/embankments/levees to predict and prevent impending failure; 2. Surface motion sensing for military and/or homeland security applications; and 3. Calibration/validation of remote sensing equipment/algorithms.
Objective The goal of this work is the development of in situ soil sensors and their underground wireless sensor network for the monitoring of spatio-temporally varying physical parameters needed to support fertilization prescription in precision farming to improve efficiency and minimize the environmental impact. Our application requires that sensors be embedded in ground at 30cm depth where the moisture level is more stable and available for root uptake. Such sensors can have antennas protruding out of ground so that they can communicate to each other over the ground. However, having protruding antennas will make the sensor nodes vulnerable to any above-ground farming activities. This necessitates developing sensor nodes capable of underground wireless communication. There are two critical challenges to the implementation of sensor networks in agricultural fields: (i) The development of inexpensive sensor nodes that withstand harsh conditions and survive field operations, and (ii) Robust and power conserving communication between sensor nodes that are buried in the soil. Soil moisture sensors are being used as an initial proof of concept of the sensor network. For one, soil water stress is a critical factor in crop development and therefore vitally important to understanding yield limiting factors and plant nitrogen demands. Moreover, a number of established soil moisture measurement methods based on dielectric measurements exist, and could be integrated as part of the sensor node subject to further refinement and calibration. If an underground sensor network is developed, the potential applications of the sensor network will
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significantly increase as different soil sensors are added. These include the ones that sense temperature, soil organic matter (Sudduth and Hummel, 1996; Hummel et al., 2001) and soil nutrients (Artigas et al., 2001; Birrell and Hummel, 2001).
Approach For the initial proof of concept, we have integrated off-the-shelf components to form a pair of “sensor nodes” to test basic sensing and point-to-point underground communication. Each sensor node consists of a microcontroller and a wireless transceiver. The “sender” node also has a moisture sensor. An illustration of the sender and receiver pair with above configuration is shown in Figure 1.
Figure 1. Illustration of sensor nodes Below we discuss our implementation details, experiment results and power consumption estimation for these sensor nodes.
Hardware For microcontroller, an ATmega128L low-power CMOS 8-bit microcontroller has been used. It is based on the AVR enhanced RISC architecture and features 128K bytes of In-System Programmable Flash with Read-While-Write capabilities, 4K bytes EEPROM, 4K bytes SRAM, 53 general purpose I/O lines, 2 USARTs and an 8-channel, 10-bit Analog-to-Digital converter (Atmel, 2006). The ATmega128L is mounted on a STK300 development board (Kanda, 2006), populated with 8 buttons, 10 LED's and 2 RS232 ports. For wireless transceiver, an Elsema FMTR-27R transceiver has been used. It is a half-duplex radio data module, capable of transmitting and receiving data with rates from 300 to 4800 bauds
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(Elsema, 2006). The transceiver is operating at ISM frequency 27.145MHz. This band is rarely used in and around farming areas. This frequency choice greatly minimizes underground signal scattering. For moisture sensor, a Decagon Echo2 EC20 probe has been used. The Echo2 probe measures the dielectric constant of the soil in order to find its volumetric water content. It does this by finding the rate of change of voltage applied to the sensor. The Echo2 probe is the only one of its kind to have a comparatively low sensitivity to saline and temperature effects in the soil. Furthermore, it has a very low power requirement (Decagon, 2007). As mentioned above, our application requires that sensor nodes be buried underground at a depth of about 30cm. For this reason, we experimented with a simple antenna made of twisted wire of one quarter wavelength (about 2.8 meters) for underground wireless communication. In addition, two battery packs are used to supply the power to the STK300 development board and the FMTR-27R transceiver, respectively, due to their different input voltage requirements. Note that it is possible to use one battery pack with some additional circuitry to power both development board and the transceiver, but for the initial proof of concept, it is more convenient to just use two battery packs instead. All hardware components have been sealed in a plastic box.
Software The software has been coded with C language, compiled by an open-source compiler and then programmed into the flash memory of the microcontroller. It realizes the following functions: Sensor reading: The microcontroller uses an I/O line to send an excitation voltage in the range of 2 to 5 volts to the moisture sensor input. The sensor produces an output voltage that depends on the dielectric constant of the medium surrounding the sensor. Then the microcontroller reads the output voltage of the sensor, which ranges between 10% and 50% of the excitation voltage. The reading is then converted via the on-board ADC, stored in the EEPROM and available for further processing and transmission. Transmitting and receiving: The microcontroller uses its I/O lines to transmit and receive data via the wireless transceiver. We designed and implemented our own bit-oriented protocol for the wireless communication, which has the following features: •
Framing All data being transmitted is broadcast in a single data frame. The beginning and end of each frame is identified by using a frame delimiter, which is a unique sequence of bits that is guaranteed not to be seen inside a frame. In our case, we choose the commonly used sequence of “01111110”. Since actual binary data could easily have a sequence of bits that is the same as the frame delimiter, zero-insertion technique is applied to distinguish them. Specifically, one bit of “0” is appended to any data bit sequence of “011111” to make it “0111110”, so that a data bit sequence of “0111111” and “0111110” turns into “01111101” and “01111100”, respectively. This ensures that no more than 5 consecutive “1”-bits will be sent. The receiver reverses this procedure, i.e., after seeing 5 “1”-bits in a row, a following 0-bit is discarded from the received data. In this way, any data bit sequence will not include a section that appears to be a frame delimiter.
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•
Structure The contents of a data frame, including the preamble, delimiter, check sequence, etc. are listed in Table 1. Table 1. Structure of Data Frame Preamble
Delimiter
Payload
Check Sequence
Delimiter
10101010…11
01111110
Data
CRC16
01111110
7 bytes of 10101010, ends with “11”
1 byte
Variable bytes
2 bytes
1 byte
Aside from the above-mentioned frame delimiter, a frame includes a preamble and a check sequence. The preamble is used for synchronization of transmitter and receiver at the start of each frame while the check sequence is used to detect errors in the transmission of the frame. In our implementation, we use 16-bit cyclic redundancy check (CRC16) for the transmission error detection. •
Encoding We also use Manchester format to encode each bit being transmitted. Manchester encoding is a form of binary phase-shift keying (BPSK) that defines the binary states of “1” and “0” to be transitions rather than static values. It has gained wide acceptance as the modulation scheme for low-cost radio-frequency (RF) transmission of digital data. For one, it is a simple method to encode digital serial data of arbitrary bit patterns without having any long strings of continuous zeros or ones. Furthermore, it has the encoding clock rate embedded within the transmitted data. These two characteristics enable low-cost data recovery that can decode transmitted data with variable signal strengths from transmitters with imprecise clocks (Maxim, 2004).
Control and monitoring: The microcontroller monitors the button status on the STK300 development board. By pushing the buttons in a specific manner, we can not only control the baud rate of the wireless transmission but also specify the actual codes being transmitted. At the same time, the microcontroller also flashes the LED's on the development board to give users the visual feedback of what is being received and what is being transmitted. The microcontroller also relays the data being received to a laptop computer for monitoring and logging via a Universal Synchronous and Asynchronous serial Receiver and Transmitter (USART). This connection to PC is relayed through a MAX202 chip on the STK300 development board to give RS232 level signals. This function facilitates not only the real-time monitoring of experiments but also the subsequent analysis of the wireless transmission quality.
Experiments We have carried out the field tests for both above-ground and underground transmissions. For the former, sender and receiver were placed in line of sight. Reliable communications could be
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established up to a distance of 120 yards at a baud rate of 256 bits/second. The wireless signal strength was measured under different distances via a spectrum analyzer and is shown in Table 2. Table 2. Signal Strength vs. Distance (above-ground) Distance (yards) 0 30 60 90 120 Strength (dBm) -28 -68 -80 -80 -90 For the underground transmission tests, both sender and receiver were buried at the depth of about 30cm. Received data were logged to check the communication quality. With baud rate of 256 bits/second, the frame error rates were found to be 17.14% and 35.14% at the distance of 66 feet and 91 feet, respectively.
Power The life span is without doubt a critical issue for the sensor nodes. We have calculated power consumption based on the specifications of components, application requirements and the data frame structure for the wireless transmission. Note that compared with other tasks, communications are much more power-consuming, so we could estimate the life span of a sensor node based on the power consumption of communications. We assume that 1. The energy required to switch transceiver modes can be neglected; 2. The transceiver is completely shut down without power consumption during idle time; 3. The energy consumed for the additional bits due to zero-insertion can be neglected; and 4. No energy loss for the battery exists other than that consumed by the transceiver. In our implementation, the nominal transmitting current and receiving current are 400mA and 12mA, respectively. Powered by a battery with capacity of 2000 mA-hours, with the baud rate as 256 bits/second, 10 data bytes per payload, and the idle time between consecutive transmissions as one hour, the life time of the sensor node is estimated to 27106 hours, or approximately 3 years.
Conclusion Off-the-shelf components have been assembled to form prototypes of sensor nodes to collect real-time data for in situ soil moisture monitoring. The prototype nodes are able to collect sensor readings and communicate with each other while buried underground. Field tests have been carried out to validate the implementation of the prototypes. A network of such nodes could be used to vastly improve agriculture management and environmental protection practices through a more accurate modeling of crop-growth, hydrologic flow, and carbon-nutrient cycling processes. Further work is underway to (i) expand prototype functions, (ii) improve medium access control and routing to minimize energy of communication, and (iii) enhance sensor function and quality.
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