CN201666950U - Wireless sensor network farmland pollution area tracking system - Google Patents

Abstract

The utility model relates to a wireless sensor network farmland pollution area tracking system which includes an intelligent sensor node collecting farmland parameter indicators, a gateway node receiving the collected farmland parameter indicator data and the ID number of the intelligent sensor node as well as a remote data center receiving and processing the collected farmland parameter indicator data, the ID number of the intelligent sensor node and the IP address of the gateway node; and the remote data center implements temporal analysis on the information of the intelligent sensor node and displays the information through a user interface. The wireless sensor network farmland pollution area tracking system locates precisely, can realize locating arithmetic with self regulation under a movable network environment, and can resist various interferences of external environments.

Description

Farmland pollution area following system by wireless sensor network

Technical field

The utility model belongs to wireless sensor network, relates in particular to a kind of farmland pollution area following system by wireless sensor network.

Background technology

Wireless sensor network is the network that is made of the mode of a large amount of small nodes that is integrated with sensor, data processing unit and wireless communication module by self-organization (Ad hoc).It combines sensor technology, embedded computing technique, modern network and wireless communication technology, distributed information processing, has very wide application prospect.It all has potential practical value at many key areas such as military and national defense, industrial or agricultural, city management, biomedicine, environmental monitoring, rescue and relief work, anti-terrorism, hazardous location Long-distance Control simultaneously.

Agriculturally, agricultural is the basis of national economy, and the sustainable development of agricultural will directly influence the expanding economy of China entire society.At present, the security situation of agricultural products in China and farm environment is severe just day by day.Along with industrialization; modernized and socioeconomic fast development; the sharp increase of population; and it is human to the resource irrational utilization; the environmental pollution that the discharging of industry " three wastes " and domestic refuse causes; chemical fertilizer; agricultural chemicals; growth hormone; a large amount of uses of chemical substances such as agricultural plastic film; the fowl poultry discarded object that the development of large-scale cultivation industry brings increases; the agricultural use of industrial waste pollutant and the unreasonable disposal of farmland discarded object etc.; caused water body-soil-biology-atmosphere in the agricultural system directly; compound; intersect and cyclic polluting (being " agricultural is three-dimensional to be polluted " that the relevant expert proposes); greatly influenced the performance of the stable and function of Agro-ecological System, thus the safety in production of serious threat agricultural products in China.So according to wireless sensor network wide application prospect on agricultural, foundation is based on the farmland pollution monitoring technique of wireless sensor network, make up farmland system three-dimensional prevention and cure of pollution Information Technology Development system, raising is to three-dimensional monitoring and the prediction ability that pollutes the condition of a disaster of farmland system, and is very necessary.

Yet when being implemented in the farmland pollution supervisory system of wireless sensor network, gathering the three-dimensional basic data information of polluting of agricultural system must be in conjunction with its positional information in measurement coordinate system, otherwise can't determine the zone of the generation of pollution source, influence the analysis and decision of system.So set up farmland pollution area following system by wireless sensor network, determine and follow the trail of the position of Polluted area, be further to the prerequisite of farmland systemic contamination monitor and predict.Farmland pollution area following system by wireless sensor network belongs to outdoor wireless sensor network positioning system, present outdoor wireless sensor network location technology still exists the more effectively accurate distance-finding method of shortage, accurately determines the wireless sensor network location.

The utility model content

At the problem that prior art exists, the utility model provides a kind of farmland pollution area following system by wireless sensor network that can effectively improve bearing accuracy.

For achieving the above object, the technical solution of the utility model is: a kind of farmland pollution area following system by wireless sensor network, it comprises the intelligence sensor node that the farmland parameter index is gathered, the remote data center of the farmland parameter index data that farmland parameter index data that reception collects and intelligence sensor node ID number gateway node and reception and processing collect, intelligence sensor node ID number and gateway node IP address, remote data center carries out space-time analysis by user interface with the information of intelligence sensor node and shows.

This intelligent sensing node mainly partly is made up of single-chip microcomputer, wireless radio frequency modules, sensor probe unit and power supply unit, the sensor probe unit is gathered each simulating signal of farmland parameter index, and simulating signal changed into digital signal, after the single-chip microcomputer processing, data are sent to gateway node by wireless radio frequency modules.

This gateway node comprises microprocessor, wireless radio frequency modules, embedded database and GPRS wireless communication module, this gateway node receives the data that near all intelligence sensor nodes send over, and uploads to remote data center by the GPRS wireless communication module.

The farmland parameter index comprises farming district air nitrogen dioxide, carbon dioxide, the total zinc of soil, total nickel, total salt, total nitrogen, total phosphorus, fluoride and irrigation water coliform group count, total zinc, total selenium, nitrogen, phosphorus, fluoride.

Compared with prior art, the utility model location is more accurate, can realize having the location algorithm of self-adjusting under mobile network environment, and can resist the various interference of external environment.

Description of drawings

Fig. 1 is based on the dynamically process flow diagram of the farmland pollution area following system by wireless sensor network of adjustment of anchor node.

Fig. 2 is based on the dynamically process flow diagram of the wireless sensor network node location algorithm of adjustment of anchor node.

Fig. 3 is intelligent sensing node structure figure.

Fig. 4 is the gateway node structural drawing.

Fig. 5 is the remote data center user interface.

Fig. 6 is based on a network model of the dynamic adjustment of anchor node among the utility model embodiment.

Fig. 7 is the relation of signal intensity RSSI and distance among the utility model embodiment.

Fig. 8 is that anchor node C is the coordinate system of initial point among the utility model embodiment.

Fig. 9 is the coordinate diagram that anchor node obtains after dynamically adjusting among the utility model embodiment.

Embodiment

Fig. 1 is the process flow diagram based on the dynamic farmland pollution area following system by wireless sensor network of adjusting of anchor node.This system comprises following step:

1, the intelligent sensing node is gathered the farmland parameter index;

2, intelligence sensor node with the data that collect with and send to for ID number near gateway node;

3, gateway node utilizes the GPRS radio communication the farmland parameter index data that collect, and the IP address of intelligence sensor node ID number and gateway node sends to remote data center;

4, remote data center arrives the SQL2000 database to the data storage that sends over of gateway node, and data are handled;

5, whether the farmland parameter index that collects of interpretation is greater than the safety index of farmland system; If less than the safety index of farmland system, repeating step 1;

If 6 safety indexs greater than the farmland system utilize the wireless sensor network node location algorithm of dynamically adjusting based on anchor node to determine the position of intelligent sensing node;

7, system utilizes user interface that the regional location of intelligent sensing node and contamination index's parameter are shown.

Wherein, as shown in Figure 2, can be divided into following step based on the flow process of the dynamic wireless sensor network node location algorithm of adjusting of anchor node:

1, adopt based on the relevant method of the distance of signal intensity (RSSI), by the control of node emissive power, near 3 the best anchor nodes Dynamic Selection node;

2,, the signal intensity (RSSI) of 3 anchor nodes that obtain is converted into measuring distance according to the logarithmic signal attenuation model;

3, select by dynamic anchor node, determine two to unknown node apart from the anchor node position of minimum and they distance to node;

4, set up one to be the coordinate axis of initial point, obtain the unknown node coordinate by the solving equation group to the shortest anchor node of unknown node distance;

5, coordinate axis is transferred to original state again, according to leg-of-mutton corner relation obtain unknown node actual position (x, y).

The farmland pollution area following system by wireless sensor network of dynamically adjusting based on anchor node comprises the farmland pollution areas monitoring platform and dynamically adjusts location algorithm based on anchor node.Wherein the farmland pollution areas monitoring platform has intelligence sensor node (shown in Figure 3), and gateway node (as shown in Figure 4) and remote data center (shown in Figure 5) three parts are formed.Dynamically adjusting location algorithm based on anchor node is described by setting up a sensing model (shown in Figure 6), algorithm is by adopting the relation (shown in Figure 7) based on the distance of signal intensity (RSSI), control by the node emissive power, near 3 the best anchor nodes Dynamic Selection node, realization is apart from local measurement, piecewise approximation; Utilize both sides to measure the method for (shown in Figure 8) and anchor node coordinate adjustment (shown in Figure 9) then, finally determine the position of node.

Farmland pollution areas monitoring platform structure is divided into the Monitoring Data acquisition system, remote data center two parts.The Monitoring Data acquisition system is made up of intelligence sensor node and gateway node two parts.The Monitoring Data acquisition system adopts sensor network technique, the data fusion technology, the GPRS wireless communication technology, utilize the intelligent sensing node to realize, the collection of parameter indexs such as the total zinc of soil, total nickel, total salt, total nitrogen, total phosphorus, fluoride and irrigation water coliform group count, total zinc, total selenium, nitrogen, phosphorus, fluoride to farming district air nitrogen dioxide, carbon dioxide.All nodes are collected the data final route to gateway node, by gateway node total data is forwarded to remote data center by GPRS wireless communication transmissions mode at last.Wherein the intelligent sensing node comprises two kinds of unknown node and anchor nodes, and anchor node can be known they position itself by GPS or artificial setting; Other intelligent sensing node is called unknown node, and they do not know the position of itself, needs anchor node to help the location.

Remote data center utilizes infotech to formulate or continue to use unified data standard and metadata standard, uniform data transmission mode, host-host protocol and coded system, structure is realized reception, storage and the space-time analysis of data based on the exploitation of VS2005.NET, with the data integration and the management system of SQL2000 as the data access basis.

Fig. 3 is intelligent sensing node structure figure, and it is mainly by the microprocessor MSP430 single-chip microcomputer of low-power consumption, less radio-frequency CC2420 module, and the sensor probe unit is formed with power supply unit 4 parts.

The MSP430F microcontroller of microcontroller circuit TI company wherein, MSP430F is 16 risc instruction set single-chip microcomputers of super low-power consumption Flash type of the up-to-date release of American TI Company, is the high single-chip microcomputer of a cost performance; Radio-frequency communication module adopts less radio-frequency CC2420 module.It adopts the SmartRF03 technology of Chipcon company, uses the CMOS explained hereafter, and operating voltage is low, energy consumption is low, volume is little, has characteristics such as output signal strength and transmitting-receiving frequency be able to programme.This chip only needs crystal oscillator and outer members seldom such as load capacitance, I/O matched element and power supply coupling capacitor to get final product operate as normal, can guarantee the validity and the reliability of short haul connection, and its maximum transmitting-receiving speed is up to 250kbps; The sensor probe unit can realize that to farming district air nitrogen dioxide, carbon dioxide farmland parameter indexs such as the total zinc of soil, total nickel, total salt, total nitrogen, total phosphorus, fluoride and irrigation water coliform group count, total zinc, total selenium, nitrogen, phosphorus, fluoride are gathered.

Fig. 4 is the gateway node structural drawing, and it is by processor S3C2410, GPRS module, less radio-frequency CC2420 module, compositions such as embedded database.

Wherein processor selection have a resource on the abundant sheet ARM9 microprocessor S3C2410 be core, this chip is based on the ARM920T kernel, adopt 5 level production lines and Harvard structure, adopt 0.18 μ m CMOS standard block structure, maximum operation frequency can reach 203MHz, not only performance is good, low in energy consumption, integrated level is high, and resource is also very abundant in the sheet.

The GPRS radio communication connects GPRS communication module Siemens MC35i by an asynchronous serial port, and realization GPRS remote data communication is finished the design of Drive Layer, protocol layer and application layer from bottom to top.Choosing when the configuration embedded Linux kernel supports serial equipment to realize the driving to the MC35i module; Embedded Linux kernel is supported PPP (Point to Point Protocol) agreement and ICP/IP protocol, chooses when the compiling linux kernel and supports these options; Application layer is after network connects foundation, and specific implementation is transmitted the function of data to remote data center.Also can select short message mode or GPRS mode to carry out the transmission of information simultaneously according to user's requirement.

Embedded database is placed in the Nand Flash, support two file system formats, with combining of read-only file system cramfs and read-write file system yaffs, cramfs is used for storing kernel and system file, the data that the read-write file system stores of yaffs is gathered can guarantee the integrality of data behind the system cut-off like this.When the intelligent sensing node when gateway node is uploaded data, system merges data, like this can reduce data redundancy, improves the accuracy of data and the loss of saving energy.

Fig. 5 is the user interface of remote data center, and remote data center has selected Visual Studio 2005 as developing instrument, adopts SQL2000 database realization node data to store and read.The fixed-point data that realization is gathered the monitoring node that is distributed in the farmland is carried out space-time analysis, if greater than the safety index of farmland system, the user interface of remote data center shows intelligent sensing node region position and the contamination index's parameter that the wireless sensor network node location algorithm that utilizes based on the dynamic adjustment of anchor node obtains.

Fig. 6 is the wireless sensor network model based on the dynamic adjustment of anchor node, supposes to have in the network five nodes, and a unknown node X is wherein arranged, and its coordinate is that (x, y), other four is respectively A as anchor node, B, C, D.Their coordinate be (xa, ya), (xb, yb), (xc, yc), (xd, yd), X is to A, B, C, the distance of D is respectively ra, rb, rc, rd.Their value is unknown, needs to estimate by signal intensity (RSSI).

In the range observation, unknown node is at first by the different power of emission, and dynamically searching is nearest from it, the anchor node that signal is best.The purpose of launching different power is in order to improve distance measuring precision, because transmission frequency is big more, the scope that signal covers is big more, but signal fluctuation is big more, and error is big more.So, when unknown node to the distance of anchor node hour, signal attenuation is very fast, uses low power transmitting can reduce the error of range finding significantly; When both distances increased, the emissive power that needs to improve transmitting node obtained enough anchor nodes.For the sensor network model of Fig. 6, unknown node X can emissive power be the signal of P1 at first.The coverage of P1 signal is RP1, is the center of circle if anchor node is in X, and radius is in the circle of RP1, and it can receive the signal that X sends.If the anchor node number in circle is less than 3, it is the signal of P2 that X continues emissive power, and P2＞P1 is if the anchor node number in the RP2 circle is still less than 3, in order to save energy consumption, the X node with transmitting node the peak power P3 that can launch obtain 3 nearest anchor nodes of distance X.Reliability for proof theory, adopt two intelligent sensing nodes to experimentize, a node is as transmitting node, and another is as recipient node, the transmitting node invariant position obtains the relation of its acknowledge(ment) signal intensity (RSSI) and distance by the position that changes recipient node.Transmitting node is launched-0DBm at every turn ,-10DBm, and three signals of-20DBm, recipient node is accepted to adopt a secondary data for per 0.1 meter, and measurement range is 6 meters.The relation of acknowledge(ment) signal intensity (RSSI) and distance as shown in Figure 7.

After selecting 3 best anchor nodes, next step is exactly that the signal intensity (RSSI) of 3 anchor nodes that obtain is changed into distance.Along with the principle that the increase of distance reduces, can set up the logarithmic signal attenuation model according to the signal under the true environment:

$\mathrm{Pr}\left(d\right)=P\left(\mathrm{do}\right)-10n{\log }_{10}\left(\frac{d}{\mathrm{do}}\right)+{\mathrm{\ϵ}}_{\mathrm{dB}}$

The signal that receives for reference distance do of P (do) wherein.It can be obtained by the free space path loss equation.The loss index n in path is by environmental variance and the decision of result on every side.ε dB is a zero-mean, and it is the Gaussian distributed random variable that changes with (σ 2, N (0, σ 2)).Can obtain 3 different emissive power correspondences and 3 different logarithmic signal attenuation model curves, each model is just effective in specific regional extent.Range estimation based on the logarithmic signal attenuation model is then:

$d=\mathrm{do}10\frac{-(P_r\left(d\right)-P\left(\mathrm{do}\right))}{10n}$

Obtain distance according to formula (2), suppose ra＞rb＞rc from three nearest anchor nodes of unknown node:

$\mathrm{rc}=\mathrm{do}10\frac{-(P_r\left(\mathrm{rc}\right)-P\left(\mathrm{do}\right))}{10n}---\left(3\right)$

$\mathrm{rb}=\mathrm{do}10\frac{-(P_r\left(\mathrm{rb}\right)-P\left(\mathrm{do}\right))}{10n}---\left(4\right)$ $\mathrm{ra}=\mathrm{do}10\frac{-(P_r\left(\mathrm{ra}\right)-P\left(\mathrm{do}\right))}{10n}$

(5)

System sends to remote data center to these three distance values and anchor node ID number by near gateway node, remote data center is selected by dynamic anchor node, determine two to unknown node apart from the anchor node position of minimum and they distance to node.To be initial point, rebulid a new coordinate axis then to the shortest anchor node of unknown node distance.Can find the solution the position of unknown node at last by system of equations.As in the model of Fig. 6, obtained the relation of 4 anchor nodes, rc＜rb＜ra＜rd to the unknown node distance.

So can set up one is the coordinate axis of initial point with a C, as shown in Figure 8, the coordinate of unknown node X is that (xn is B to the shortest anchor node of X distance in other anchor nodes yn), and the coordinate that B is new is: xbn=xb-xa, ybn=yb-ya, the coordinate that C is new is: xcn=xc-xa, ycn=yc-ya.The range estimation of these two anchor nodes of C and D does not need quantitative test, only need know the magnitude relationship ra＜rd of these two distances.

Can obtain a system of equations:

x _n ²+y _n ²＝rc ²

(6)

(x _n-x _bn) ²+(y _n-y _bn) ²＝rb ² (7)

Solving equation group (6), (7) can obtain the coordinate of unknown node X:

$x_n=\frac{({x_{\mathrm{bn}}}^2+{y_{\mathrm{bn}}}^2)+({\mathrm{rc}}^2-{\mathrm{rb}}^2)-2y_n{y}_{\mathrm{bn}}}{{2x}_{\mathrm{bn}}}---\left(8\right)$

$y_n{=}_{-}^{+}\sqrt{{\mathrm{rc}}^2-{x_n}^2}---\left(9\right)$

Final step is exactly a coordinate axis from newly transferring to original state, according to leg-of-mutton corner relation obtain unknown node actual position (x, y).

$x=x_c+a\×\mathrm{rc}\×\sin (\frac{\mathrm{\π}}{2}-\arctan \left(\frac{x}{y}\right)-\arctan \left(\right|\frac{(y_c-y_b)}{(x_c-x_b)}\left|\right))---\left(10\right)$

$y=y_c+a\×\mathrm{rc}\×\cos (\frac{\mathrm{\π}}{2}-\arctan \left(\frac{x}{y}\right)-\arctan \left(\right|\frac{(y_c-y_b)}{(x_c-x_b)}\left|\right))---\left(11\right)$

The coordinate that so just can obtain node to (XRG1, YRG1), (XRG1, YRG2) }, as shown in figure 10, wherein when coefficient a=1, X=RG1,, y=YRG1.When coefficient a=0, x=XRG2,, y=YRG2.

The user interface of last remote data center shows intelligent sensing node region position and the contamination index's parameter that the wireless sensor network node location algorithm that utilizes based on the dynamic adjustment of anchor node obtains.

Claims (4)

1. farmland pollution area following system by wireless sensor network, it is characterized in that comprising the intelligence sensor node that the farmland parameter index is gathered, the remote data center of the farmland parameter index data that farmland parameter index data that reception collects and intelligence sensor node ID number gateway node and reception and processing collect, intelligence sensor node ID number and gateway node IP address, remote data center carries out space-time analysis by user interface with the information of intelligence sensor node and shows.

2. farmland pollution area following system by wireless sensor network according to claim 1, it is characterized in that: this intelligent sensing node mainly partly is made up of single-chip microcomputer, wireless radio frequency modules, sensor probe unit and power supply unit, the sensor probe unit is gathered each simulating signal of farmland parameter index, and simulating signal changed into digital signal, after the single-chip microcomputer processing, data are sent to gateway node by wireless radio frequency modules.

3. farmland pollution area following system by wireless sensor network according to claim 2, it is characterized in that: this gateway node comprises microprocessor, wireless radio frequency modules, embedded database and GPRS wireless communication module, this gateway node receives the data that near all intelligence sensor nodes send over, and uploads to remote data center by the GPRS wireless communication module.

4. according to each described farmland pollution area following system by wireless sensor network of claim 1 to 3, it is characterized in that: the farmland parameter index comprises farming district air nitrogen dioxide, carbon dioxide, the total zinc of soil, total nickel, total salt, total nitrogen, total phosphorus, fluoride and irrigation water coliform group count, total zinc, total selenium, nitrogen, phosphorus, fluoride.

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