2011 Seventh International Conference on Mobile Ad-hoc and Sensor Networks
An environment monitoring system for precise agriculture based on wireless sensor networks Jianfa Xia, Zhenzhou Tang, *Xiaoqiu Shi, Lei Fan, Huaizhong Li College of physics and electronic information engineering Wenzhou University Wenzhou, China
[email protected],
[email protected],
[email protected],
[email protected],
[email protected]
growth of the product. Automatic data acquisition mainly uses sensor networks which are further classified as wired and wireless types based on the data transmission methods. Wired sensor networks use a wired network to connect all sensors and to transmit monitored data. The problems with wired sensor networks are that wiring, installation and testing is quite expensive, and that the network is difficult to be maintained. Besides, wired sensor networks have limited mobility and scalability which is often desired in precision agriculture. On the contrary, wireless sensor networks have the advantages that the sensors are compact, and do not rely on a wired network to transmit data. Thus, wireless sensor networks can be deployed in any greenhouse or sheds.
Abstract—To solve the problems occurring in the traditional precision agriculture such as poor real-time data acquisition, small monitoring coverage area, excessive manpower requirement etc., this paper designs and deploys an environment monitoring system for precise agriculture based on wireless sensor networks in a red bayberry greenhouse located on a hillside. This system can automatically collect the temperature, humidity, illumination, voltage and other parameters of the deployment zone, and transmit the data to the remote server via GPRS in real time. This system also includes a web-based platform integrated with Google Maps to release the greenhouse environmental status and provide real-time voice and SMS alarm service. Since the experimental area is lack of mains supply, the system is powered by solar and storage batteries. The experiment result shows that the low-cost system has strong scalability, and can provide real-time, stable and accurate service for precise agriculture.
Wireless sensor networks (WSNs) consist of large quantity of sensor nodes which are deployed in the monitored environment. WSNs carry the characteristics of random deployment, self-organizing network and multi-hops. A typical application of WSNs in precision agriculture is to deploy sensor nodes in greenhouse sheds or gardens to periodically collect temperature, humidity, illumination and carbon dioxide concentration. The acquired data is collected by the gateway nodes and then transmitted to a server via the internet. Users can check the product growth information through the WSNs, take appropriate management measures such as remote control for drip irrigation and fan facilities with the guidance of the expert system to improve micro-environment for the product.
Keywords- precise agriculture, WSN, GPRS
I.
INTRODUCTION
Precision agriculture is a new development in traditional agriculture. In precision agriculture, production environment is monitored, and the monitored data is used to derive the most suitable environment management decision which employs control and adjustment solutions to obtain better product yield. Greenhouse shed is one of the typical means in precision agriculture [1]. Naturally, in order to achieve precision control to the production environment, it is necessary to perform three tasks, namely, first monitoring parameters such as temperature, humidity and illumination which are associated with the production environment, as these parameters are the main influential factors for the product yield and quality. Secondly, control and management decision is determined based on the analysis of the collected data. Finally, based on the control decision, automatic or manual control mechanism is implemented to complete the required environment control and adjustment [2].
This paper describes the design and deployment of a production environment monitoring and service system based on wireless sensor network for a waxberry greenhouse shed. Waxberry is a type of Arbutus product. The best growth environment for wax berry should have sufficient illumination, right temperature and suitable humidity. Waxberry greenhouse shed can monitor and effectively control the illumination, temperature and humidity to gain better production and quality. This paper is organized as follows: section II introduces related work; section III provides the overall system infrastructure of the environment monitoring and warning system; section IV describes in detail the design method and deployment strategy for the system; section V illustrates the data publish and alarm sub-system; section VI shows the system operation data and result evaluation. Finally, a concluding section concludes the paper.
Data acquisition which can be done automatically or manually is fundamental for precision agriculture. Manual data acquisition is a labor intensive procedure which relies on periodic manual reading of thermometers and hydrometers, and thus is quite difficult to achieve real-time monitoring and good production field coverage. Besides, frequent manual reading of the distributed meters may bring inadequate interference to the *Corresponding author 978-0-7695-4610-0/11 $26.00 © 2011 IEEE DOI 10.1109/MSN.2011.16
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II.
RELATED WORK
III.
In recent years, as one of the most important applications for WSNs, a number of investigations have been conducted by scientists in realistic agricultural settings.
SYSTEM INFRASTRUCTURE
The overall system infrastructure is shown in Figure 1. The system is composed of four subsystems, namely, a data acquisition subsystem based on WSNs, a data collection and processing subsystem which adopts client server architecture, a web-based information dissemination and inquiring subsystem which is integrated with Google maps, and an alarm subsystem which is design for farmers and farming technicians.
Beckwith, Burrell et al. deployed a sensor network in an Oregon vineyard. 48 nodes were involved in the network and run over a period of more than 6 months, reporting temperature every five minutes [3, 4]. The collected data were converged in a centralized way and could be released on a map and retrieved on a per-sensor basis. Moreover, when the temperature decreased below 0, alarms could be sent out in time, indicating a risk of frost.
The data acquisition subsystem is deployed inside the shed. The subsystem consists of a number of TelosB wireless sensor nodes to acquire data. The onboard sensors include photosensitive, temperature and humidity sensors. The sensor nodes uses MSP 430F1611 and CC2420 chips to ensure versatility and to reduce power consumption of the nodes. The nodes form a self-organized and multi-hop network which relays the acquired data to the gateway node.
The NAV (Network Avanzato per il Vigneto – Advanced Vineyard Network) system was reported by A. Matese et al. This system was a wireless sensor network designed and developed with the aim of remote real-time monitoring and collecting of micro-meteorological parameters in a vineyard. “The system includes a base agrometeorological station (Master Unit) and a series of peripheral wireless nodes (Slave Units) located in the vineyard. The Master Unit is a typical single point monitoring station placed outside the vineyard in a representative site to collect agrometeorological data. It utilizes a wireless technology for data communication and transmission with the Slave Units and remote central server. The Slave Units are multiple stations placed in the vineyard and equipped with agrometeorological sensors for site-specific environmental monitoring, which store and transmit data to the Master Unit. Software was developed for setup and configuration functionality. A graphical user interface operating on the remote central server was implemented to collect and process data and provide real-time control. The devices were tested in a three-step process: hardware functionality and data acquisition, energy consumption and communication. The NAV system is a complete monitoring system that gave flexibility for planning and installation, which fully responded to the objectives of the work in terms of energy efficiency and performance.” [5]
Figure 1. System Infrastructure
The gateway node in the designed system is a composite node which consists of a sink node to collect data, a GPRS gateway module and a solar power system. The sink node collects data transmitted through the WSN and forwards the collected data to the GPRS module through a RS232 interface. The GRPS module then send the re-packed data using GPRS mobile network to a remote server located in the team’s laboratory. If there are several shed which are close-by, one gateway node can be shared by the several WSNs for the sheds. If the sheds are far apart, several gateway nodes have to be deployed.
“Phytophtora is a fungal disease which can enter a field because of a variety of sources. The climatological conditions within the field play a great part in the development and associated attack of the crop. Humidity is a crucial factor in the development of the disease as well as the temperature and whether the leaves are wet or not. [6]” For this reason, Baggio deployed a WSN to monitor humidity and temperature in order to better fight phytophtora in a potato field. However, only the pilot study was reported, and the full-size network has not been deployed yet.
The server uses a collection module which runs as a separate thread to perform batch collection of the data sent by the GPRS module. The collected packets are divided, and error checking is performed. Afterwards, the relevant data is stored in a relational database.
An in-fie1d soi1 moisture and temperature monitoring system was reported by Hui Liu et al [7]. This system consisted of the soi1 monitoring wire1ess sensor network and remote data center. The sensor node was deve1oped using JN5121 modu1eˈan IEEE 802.15.4/ZigBee wire1ess microcontroller. The sink node for data aggregating was based on ARM9 p1atform to meet the requirements of high-performance. And a GPRS gateway was used for long distance data transmission.
The web-based information dissemination and inquiring subsystem delivers shed essential information, network deployment and management information, production environment information and states, as well as user information, etc. Google maps technique is integrated into this subsystem to deliver location aware information dissemination and inquiring service.
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The alarm subsystem determines several alarm strategies in advance based on relevant production knowledge and experience. Alternative, alarm thresholds are set up for several key parameters, once the measured values of these parameters exceed the alarm thresholds, alarms in SMS or voice format are automatically sent by the system to the designated mobile phones normally owned by farmers or relevant farming technicians. IV.
deployment with 9 nodes should be able to cover the whole slope area inside No .1 shed.
DESIGN AND DEPLOYMENT OF THE WSNS
One of the important characteristics of the waxberry sheds discussed in this paper is that they are semi-natural sheds, namely, an enclosed area in the natural production environment is identified, and shedding facilities are set up in the designated area to form controllable production environment. Usually these sheds are located in a hillside; the area covered in a shed is accidented hillside surface which is completely different from other sheds in the same plane commonly seen in precision agriculture. In a waxberry shed, it is observed that humidity and temperature follow gradient change from hill top to the hill bottom. In the following, we will use No. 1 waxberry shed as an example to illustrate the network deployment.
Figure 2.
A. Node Deployment Size of the No. 1 shed is 64*24*51, the shed covers an area of 1500 square meters, as shown in Figure 2. The slope of the shed is 60 degree. As waxberry trees in the shed are quite dense, and there are many protruding rocks on the hill side surface, transmission quality of the wireless channels is greatly affected. Experiments show that the effective communication distance between two nodes is between 20 to 30 meters, though communication range for the TelosB nodes used in this system can reach 100 meters theoretically.
No. 1 shed
%
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(
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,
(a)
Coverage control is used to select as few active nodes as possible from all deployed sensor nodes such that sufficient coverage of the monitored area can be guaranteed; while reducing the energy consumption of each individual sensor node to prolong the network operation time[8]. The nodes are deployed in a chessboard array. Once the position for a node is determined, the node is hanged on a branch of a nearby waxberry tree. A sample chessboard deployment with 9 nodes is shown in Figure 3, where the matrix indicated by nodes AˈBˈCˈDˈEˈFˈGˈHˈI actually covers the slope surface from hilltop to the bottom in the shed(show in Figure 3(b)), sensor node A is in the middle. In order to ensure communication quality between the nodes and to actually monitor the shed environment, redundancy deployment is adopted, namely, the node A in the center can effectively communicate with all other nodes, while the nodes on the matrix edge can effectively communicate with at least 3 other nodes. In Figure 3(a), BC=AC ˈ AB=20m ˈ hence BC=10 2 m. The communication coverage of node A is 400 m2. If overlapped areas between nodes are removed, node A alone covers the shadow area shown in Figure 3(a) which is about 4 ⋅ 5 2 ⋅ 5 2 = 200 m2 DŽ Therefore, a chessboard
(b) Figure 3.
Node coverage area
B. Network Protocol Stack for the WSNs All sensor nodes use the same channel to communicate in the network, conflicts may occur when nearby nodes send data simultaneously. Therefore, similar to the IEEE 802.11 standard, MAC layer of the nodes adopts a technique which combines CSMA/CA with RTS/CTS hand shaking mechanism [10]. During the practical operation of the network, data collection interval for the nodes is quite long with a sampling frequency of once per 6 minutes. To reduce energy consumption caused by node listening during idle time, a solution based on the duty cycle mechanism is introduced, that is, the sensor node is periodically switched between working and sleeping states to achieve energy reservation by reducing
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idle listing. However, in order to successfully transmit data between neighboring nodes, their sleeping and working cycles have to be consistent. Therefore, FTSP protocol is used to perform time synchronization in the network. FTSP selects the node which has the minimum ID number as the root node, and uses flooding approach to broadcast root’s time to the whole network. As timestamp technique is used in the MAC layer, FTSP is robust, and can achieve high synchronization accuracy. Therefore, it is applicable in many applications [11].
time synchronization in FTSP, the sink node is the root node in FTSP and its ID is 1. Since the sink node broadcasts time information to the whole network using the flooding method, it is necessary to guarantee its power supply [13]. The sink node uses a message queue to store received data packets to send them in real time to the GPRS module in a FIFO way. D. The Sensor Nodes Figure 5 shows the work flow for a sensor node. After initialization of the node, it is checked to make sure that the node is time synchronized or wait until the node is time synchronized. Once the node is synchronized, data such as temperature, humidity, illumination and voltage of the node is acquired and packed into packets in every working cycle, and is sent to the sink node. If the data transmission is successful, all sensor nodes will be reset to ensure the correctness of the data acquisition in the next working cycle.
The data aggregation protocol of the network is CTP. CTP is a tree based aggregation protocol; its principle is to use Expected Transmissions (ETX) as the gradient routing. For example, ETX of the root node is 0 in CTP, and ETX of a nonroot node is the sum of ETX of its parent node and ETX of the link between this node and its parent node. If a node has several neighboring nodes, its parent node is selected as the one which has the minimum ETX value. Following this method, a CTP tree can be formed [12]. C. The Sink Node
START
Initialization
Wait for data
ReportReceiver
Yes
Broadcast Local time
UARTQueue
SendToComputer
No No
SerialSender
Yes TimeToSendBeacon Figure 4. Work Flow for the Sink Node
Figure 5. Work Flow for the Sensor Nodes
The sink node is responsible for the collection of data transmitted by the nodes and sends the collected data to the GPRS module. The sink node uses TinyOS-2.1.0 embedded operating system, its working principle is shown in Figure 4. As the node with minimum ID is selected as the root node for
A sensor node not only sends its own data, it also forwards packets sent by other sensor nodes. A node’s ID is added to the packets when the packets are forwarded by the node, therefore, Google maps can be used to display entire network topology using the routing information in the received packets. Various
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F. GPRS Gateway The waxberry shed in this application is located in a hill which is 5km away from the data storage server. There is no fundamental facility such as wired network and electricity in the shed. If multi-hop is used to directly transmit the acquired data back to the data storage server, it is very difficult to maintain the stability, security and reliability of the acquired data. Also, scalability is an issue as it is impossible to implement data transmission over multiple sheds and regions using this way.
timers are employed to control the working and sleeping time as well as sampling time for a sensor node [14]. E. Structure of a Data Frame A sensor node packs the acquired data and sends the packets to the sink node. The sink node then sends the data to the GPRS module which will re-packs and deliver the data to designated IP and ports. Table 1 shows the structure of a data frame which is received by the server. Obviously, this data frame contains necessary information for shed monitoring such as node ID, temperature, humidity, illumination, voltage, etc. Besides, assistant information such as neighboring nodes, relay node ID in the transmission path and CTP packet length is also packed into the frame in order to better comprehend the status of the network. For example, the relay node ID in the transmission path can be used to understand the network topology which is helpful in the management of the network. Besides data packets, the data frame also consists of state packets which can be used to identify the neighboring relation, and then use the neighboring relation to verify whether the acquired information is correct through comparing the information acquired by the connected neighboring nodes.
Figure 6. GPRS Gateway
Therefore, we propose to combine the sink node and a GPRS module to implement the data aggregation and transmission. As shown in Figure 6, the sink node is connected to the GPRS module through a RS232 interface. In the following, we refer the combination of the sink node and the GPRS module as the GPRS gateway. A 20W solar power system is selected to maintain the power supply to the combined gateway.
It is unlikely that all nodes can send the acquired data to the remote database within a working cycle. Therefore, in order to promptly reflect the temperature change in the shed, we set the sample period to 6 minutes so that alarm can be send out in time when the temperature rises rapidly.
The transmission rate using GPRS is normally 20-40Kb/s. In the deployment of the network, it is found that the selection of the baud rate is very important. Higher baud rate can improve communication and time synchronization efficiency, reduce working period to preserve node energy, and support data aggregation of larger network using a single sink node. However, it is found that higher baud rate also leads to higher data error rate and even paralyzes the GPRS gateway in the worst case. Through various experiments, finally the baud rate is selected as 38400 bauds.
TABLE I. STRUCTURE OF A DATA FRAME Field name
Length (Bytes)
Description
GPRS packet ID
3
TinyOS packet ID
1
TinyOS packet identification
ETX
2
Used in CTP to describe parameters of the node to Sink
Mote ID
2
ID of the node which sends the data
Type of Package
1
Types are: data packets, status packets, configuration packet, request packet
Timestamp
4
Records data transmission time
Temperature
2
Collection of temperature
Humidity
2
Collection of humidity
Light
2
Collection of light intensity
Voltage
2
Voltage of a node
ParentID
1
ParentID
1
The number of hops from source node to sink node ˄N˅
Hops Relay node ID CRC
2N 1
GPRS packet identification
V.
DATA PROCESSING, DISSEMINATION AND ALARM
A. Data Processing As shown in sub-section IV.E, the data frame transmitted back through the GPRS gateway cannot be used directly. A collection module on the server has to be used to process the received data. The collection module performs batch collection of the data sent by the GPRS gateway in every working cycle, and separates the collected packets to obtain information such as network topology and neighboring nodes. Furthermore, the collection module carries out error checking of the packets using CRC and packet lengths, and marks the erroneous packets to avoid false alarm in the system due to the wrong data. Useful information in the packets such as temperature, humidity, illumination, and voltage is extracted and stored in a relational database. If the value of a parameter exceeds the pre-
Relay node ID Check code
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determined threshold, the corresponding information is recorded in the alarm table which will be used by the alarm subsystem consequently.
Figure 7. Real Time Network Topology and Deployment on a Google Map
Figure 8 shows the data acquired by the sensor nodes within the above mentioned working cycle. The Y-axis represents temperature, while the X-axis represents the humidity. Size and color of points represent the illumination data and voltage data acquired by each node respectively. It is shown that 9 sensor nodes successfully acquire data, and the server receives 9 packets. There is 0 error packet. From the acquired data, it can be observed that the temperature in the shed is between 26 to 29 degrees after conversion of the raw data.
B. Data Dissemination using Google Maps Google maps are used to query the database and to implement the real time dissemination of data acquired by the WSNs. Google maps can be embedded in web sites and applications using APIs provided by Google. Superimposed user data can also be displayed on the embedded Google maps. In this application, visualized query and analysis functions are implemented using Google Vsualization API to present monitoring data for all sensor nodes in a working cycle and that for a particular node in all working cycles[14]. Figure 7 shows the network topology and node deployment based on the received data within the working cycle from 10:49:11 to 10:51:00 in June 21, 2011. The crossed dots on the map represent node locations which are roughly consistent with the true deployed node locations. A node’s acquired data such as temperature, humidity, illumination and voltage can be observed by clicking on the node.
Figure 8. Data Acquired by the Nodes
Figure 9 shows the historical temperature and illumination data over 5 days monitored by the No.15 nodes. It can be observed that there are 949 working cycles in the examined time interval, and the server receives 973 data packets. The reason to explain for more data packets is that the node failed to send the acquired data in some cycles; hence these data packets had to be sent in the next available cycle. it can inquiry the data for any node and any cycleˈeach point of the line represents light and temperature for a cycle and Y-axis represents the size of their values.
Figure 9. Historical data for the No. 15 Node
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C. User-Oriented Application Service Based on the acquired environment data for the shed, several user-oriented application services are provided. These services include web-based information dissemination and inquiry, SMS message, and voice alerting. The web-based information release and inquiring subsystem includes five functional modules, which are greenhouse information management module, user management module, greenhouse network management module, greenhouse planting management module, and knowledge management. The greenhouse information management module provides basic information such as the greenhouse location, size, crop varieties, owners etc. User Management module provides user-related management, including user accounts, permissions, customization and other management services. Network Management module provides the basic information of the sensor networks, such as network topology, node management and network exception management. Greenhouse planting management module can help administering planting information, such as temperature, humidity level and illumination. It provides the service of system parameters and data inquiry in the form of combined charts and tables.
•
A real time data dissemination and inquiry platform integrated with Google maps is provided to enable web-based remote information inquiry.
•
A real time alarm system is provided where the alarm rules can be pre-determined based on domain knowledge. The system can provide services such as SMS message and voice alerting. VII. CONCLUSION
Currently, further research is focused on expanding and improving the system. A couple of issues are being addressed to promote applicability and application value of the system, namely, how to design a system to implement large scale, multiple sheds and cross region applications; how to realize time synchronization, data fusion and node self-localization in the system under these application scopes; how to implement requirement-derived services such as shed management, expert system, alarm and intelligent decision assistance; and how to extend the system to other application situations in precision agriculture. ACKNOWLEDGMENT This work is supported by the National Natural Science Foundation of China (60970118) and Nonprofit Technology Application Research Projects of Zhejiang Province (2011C310290029).
The system checks all the data collected from sensor nodes and puts any suprathreshold data to a table called warning information table. The alarm system inquiries this table periodically to decide whether to send an alarm or not. And the alarm system also should decide the way of alarm, SMS or voice. In addition, the alarm voice and messages are stored in the warning information table in advance.
REFERENCES [1]
[2]
SYSTEM OPERATION RESULTS AND EVALUATION [3]
We did extensive prototype evaluation before the deployment of the application system. The prototype system was evaluated 3 times using 206 nodes over 15 months. The application system was then deployed to the waxberry shed and was run continuously over one month. Evaluation results based on the prototype and real system evaluation show that: •
The deployed system uses GPRS and solar power system, and it doesn’t rely on elemental facilities to be set up for the sheds. Therefore, deployment of the system doesn’t affect the production environment.
This paper presents an application system for precision agriculture based on wireless sensor network.
A planting rule table is maintained in the database designed for the system mentioned above. All these rules listed in the table are obtained from professional planting knowledge and experience accumulated. What’s more, in this database, there is table contains some alarm threshold values we concerned during waxberry growth process, such as the highest or lowest temperature, the highest or lowest humidity, the lowest power level, etc. The system will give a SMS or voice alarm to the peasants or owners once any parameter reaches the corresponding threshold.
VI.
•
[4]
[5]
The protocol stack for the sensor nodes and the application software achieves the design objectives. The system can automatically carry out data acquisition, transmission and processing for parameters such as temperature, humidity, illumination and voltage. The system virtually satisfies requirements for system stability, efficiency and energy preservation.
[6]
[7]
[8]
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