Optimization of Sensor Deployment in WSN for Precision Irrigation using Spatial Arrangement of Permanent Crop Hema N
Krishna Kant
Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida, India
[email protected]
Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida, India
[email protected]
irrigation, automated irrigation, evaporation-transpiration reduction etc., can be used for efficient water management.
Abstract—Precision irrigation is the best practice for efficient water management. Wireless Sensor Networks (WSN) are widely used in environment monitoring especially in the precision irrigation. One of the fundamental issues of WSN is optimum senor deployment. Type of radio communication, number, and location of sensor deployment will have direct impact on the coverage, connectivity, cost and life of the sensors in WSN.This paper explores the problem of optimal WSN deployment by considering the unique way of spatial arrangement of the permanent crop during transplantation. Instead of conventional horizontal or linear row farming we propose the hexagonal pattern farming for the suitable permanent crops. This modified spatial arrangement will increase the coverage of the WSN without losing the specification of growing conditions. There is an improvement in total number of trees covered by 13 percentage in this method. Mathematical model is proposed, which justify the increase in coverage by using this modified spatial arrangement.
Crop irrigation scheduling varies in time with weather, soil conditions and growing stage. Precision irrigation[2] provides a means for evaluating a crop’s water requirement and means for applying the right amount at the right time without wasting a single drop of water. Precision irrigation scheduling is based on environmental data, whether that data comes from local field sensors or from more global sources such as regional meteorological information. Using global meteorological data, precision irrigation may not be accurate if the distance between remote irrigation area and remote weather station is more than 50 KM. Wireless Sensor Networks are best used to gather local weather data, which are more accurate for the precision irrigation.[3],[4] and [5] shows one such application of wireless sensor network in precision irrigation.
Keywords—Precision irrigatio;Wireless Sensor Network;Sensor Deployment; Spatial Arrangement of the Permanent Crop
Fig.1 shows the precision irrigation scheduler using Wireless Sensor Network. This model consists of sensors modules, microcontrollers, wireless module and irrigation control module. Sensor module consists of various sensors like temperature, humidity, moisture and GPS for measuring climatic conditions for measuring water requirement. Wireless module like zigbee works with 2.4 GHz used for communication between microcontroller, central coordinator and irrigation module. Irrigation control module controls the valve of the water tank to start and stop the irrigation on demand bases.For the calculations of ETo rate on sites, climate and weather sensor’s dataare required and depending upon ETo the drip irrigation ratecan be adjust automatically. To calculate ETo FAO Penman-Monteith [6] method is very popularas the evapotranspiration is with respect to grass level.
I. INTRODUCTION Water is the scarce resource in arid and semi-arid region of the country. India has more than 17% of the world’s population, but has only 4% of world’s renewable water resources with 2.6% of world land area [1]. With growing population, urbanization and climate change, water scarcity further increases. Crop irrigation is the major sector in which water consumption is more. Any mismanagement of water resources will lead into a critical situation in arid and semi-arid region. Efficient water management in irrigation is the need of the hour. Every drop of water has to be saved to accommodate growing population, rapid urbanization, rapid industrialization and economic development.
In wireless sensor network, the success of the network mainly dependents on the sensor’s position, referred to as the deployment of the network. Network coverage mainly depends on the way the sensors are deployed. In our investigation of precision irrigation schedulingusing WSN on permanent crop, we have crop atpredetermined position and hence the
The main sources of water for irrigations are river, rainfall and groundwater. Declining ground water levels in overexploited areas need to be arrested by introducing improved technologies of water use. Some of the method like aligning cropping pattern with natural resources endowment, micro-
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cost is low. Optimal deployment also helps in development of heuristic algorithms for topology control and sensor scheduling. X. Bai, Z.yun and W. Jia [14] suggest asymptotic optimality of a deployment pattern such as Triangular pattern, Diamond pattern, Square pattern and Double-strip pattern in deterministic deployment that achieves four-connectivity and full coverage.
deployment will be in static position. A permanent crop is one produced from plants which last for many seasons, rather than being replanted after each harvest. Every crop has spacing pattern for growing. Horizontal or linear row farming is more conventional patterns followed in farming. Precision irrigation using WSN for permanent crops is more economical. Instead of row pattern spacing for permanent crop, we are proposing the hexagonal pattern spacing which increases the coverage area of the deployed sensors. This modified spacing does not compromise with required spacing for growing permanent crop.
In sensor deployment, both coverage and connectivity are critical requirements. As explored by X. Bai, Z.yun and W. Jia [14] Triangular pattern remains optimal when rcr/rsr ξ͵ , where rcr and rsr are sensors communication range and sensing range. In practice, the value of rcr/rsr has wide range, not necessarily greater thanξ͵.The optimal deployment in diamond pattern is rcr/rsr>ξʹ. The optimal deployment in regular hexagon is when rcr/ rsrൌ ξ͵. The optimal deployment in square pattern isrcr/rsrൌ ξʹǤThe optimal deployment in double-strip pattern isrcr/rsr< 16/17. Knowing the appropriate number of sensors to deploy is critical to ensuring that a desired quality of monitoring is achieved at the lowest possible cost. If deterministic deployment fails in proper positioning, that connectivity and coverage is still an issue and random deployment plays a dominating role as sensor deployment. C. Random deployment One important class of WSNs is wireless ad-hoc sensor networks (WASN), characterized by an “ad-hoc” or random sensor deployment method, where the sensor location is not known a priori. This feature is required when individual sensor placement is infeasible, for example battlefield or disaster areas of environmental monitoring applications. The characteristics of a WASN include limited resources, large and dense networks, and dynamic topology [15].
Fig. 1.Precision Irrigation Scheduling using Wireless Sensor Network.
This paper summarizes asfollows: In section 2, discussion on various issues in the sensor deployment in WSN. In section 3, elaborate difference between conventional row spacing of the permanent crops and hexagonal spacing. Further analyzed, howa modified spacing of the permanent crops will optimize the sensor deployment in WSN for precision irrigations. In section 4, shows the computation of totalnumber of trees covered underhexagonalpattern spacing and square pattern spacing for the same area of coverage. In section 5, concludes the paper. II.
Many investigation and proposal have been done about coverage, connectivity and lifetime for randomlydistributed large scale WSN. Uniform randomly distributed WSN has more focus as it has good option for different sensing task and also for well-known energy hole problem. Generally, more sensors are deployed than required (compared with the optimal placement) to perform the proposed task; this compensates for the lack of exact positioning and improves the fault-tolerance. The size of a WASN may reach hundreds or even thousands of sensor nodes.
WIRELESS SENSOR NETWORK DEPLOYMENT AND ISSUES
A. Deployment ways In wireless sensor network, sensor deployment has two ways [7],[8], one is deterministic and other is random deployment. In deterministic deployment, sensors are placed at planned, predetermined locations. In random deployment, sensors are placed at random position depending upon sensing locations requirement. In WSN application, sensors should not only cover the entire area but also requires connectionwith the communication network. Therefore coverage and connectivity are required and different ratio of this represents the different optimality. Lot of researchesare going on for optimum deployment of sensors in various conditions [9][10][11][12][13].
Generally, random deployment need more sophisticated algorithmlike shelf organizing, greedy, k-neighborhood and genetic [16][17][18] etc., are required to check the desired coverage and connectivity. These algorithms consumes more energy for computation which creates energy hole in wireless sensor network There are many research efforts in the design of algorithms that efficiently organize or schedule sensors that have been previously deployed (especially in randomly deployment) to achieve certain degree of coverage and connectivity.
B. Determistic deployment In deterministic deployment, it is desirable that the pattern requires the minimum number of sensors so that deployment
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III.
DEPLOYMENT OF SENSOR
USING HEXAGONAL ARRANGEMENT OF PERMANENT CROP
SPATIAL
full coverage, when the ratio of communication range with sensing range is equal to the square root of three. Here, not only the sensor but also permanent crop is placed in regular hexagonal pattern so the no extra cost is involved in the deploying the sensors.
It is assumed that the sensing and communication range of every node are perfect circle as well as the ability to place the sensors in exact location. It is also assuming that the sensors are fixed at particular height so that there is no interference with crop leaf and braches.
We are able to mathematically prove the validity of their pattern, also this pattern for the practical deployment.
Generally, in sensor deployment one will always concentrate on the place of the sensor to be deployed but in practical scenario, one has to take environment constrain also in sensor deployment. Sensor deployment for precision irrigation is even more challenge as there may be interference from stem, branch and leaf. Sensor communications have many obstacles and coverage reduces. In such application not only the sensor deployment is critical but also the crop placement which play significant role in the coverage. Precision irrigation is more economical in permanent crop like sugarcane, palm, cotton, mango, apple trees etc., these permanent crops are placed with row spacing as show in below Fig.2 and Fig. 3 as a traditional method of spacing between crops. A. Row pattern spacing Row pattern spacing is followed in the tradition irrigation as the farmers use to have small canals for flooding of water to irrigate. Fig. 2 and Fig. 3 shows the row spacing current used in many crops as the spacing pattern. In modern irrigation techniques like automatic micro-irrigation using wireless sensor network this traditional spacing will not help much as our focus is not just scheduling irrigation but also increase the coverage and connectivity of the wireless sensor network.
Fig. 3.Row Spacing of Coconut Trees
Fig.4.Hexagonal Spacing pattern
Fig. 2. Row Spacing of Palm Trees
In general, square organization of planting with a variable spacing is practiced in different permanent crops. As a monocrop Edward and Craig 2006 [19] suggest coconut trees can also be planted with a spacing of 7.5m to 9m in a triangular pattern. Triangular pattern are consideredto bean efficient design. By combining 6 equilateral triangles one can form a
B. Hexagonal Pattern As a sophisticated deterministic deployment method, we propose to arrange the sensors in a regular hexagonal pattern which would correspond with Voronoi polygon. This pattern achieves four-way connectivity from each of the nodes with
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regular hexagonal where 7 trees can be planted. A low cost Zigbee series can cover Indoor/Urban range of 40m and outdoor/Line-of-sight 120m i.e., about ~60-70 trees can be covered within a multiple, honeycomb hexagonal structure, with at least 7 trees per hexagon as shown in Fig.4. Advantage of using regular hexagon is that careful placement of Zigbee modules under line-of-sight will increase the number of trees coverage and hence reduces over all installation cost. For this design, star topology of sensors network is more suitable
deploy a sensor at the center of each grid. Relative position of k1 and k2are as follows: ఝ
݇ଵ ൌ ʹݎ௦ ܿ ݏඥʹሺͳ െ ܿ߮ݏሻ, ଶ and ఝ
݇ଶ ൌ ʹݎ௦ ܿ ݏඥʹሺͳ ܿ߮ݏሻ, ଶ Where, ߮ ൌ ݉ܽݔሺʹܽݏܿܿݎሺ
IV. SIMULATION RESULT A square spacing of 9m, which is generally practiced, can cover a total numbers of trees as show in the equation 1. ݊݉ ݈݊ ݀,
(2)
ݎܿݎ ʹݎݏݎ
(3)
ሻǡ ςȀ͵ሻǤ
In this pattern, the coverage contribution of each individual sensor is k1k2/2.The area of regular hexagonal is ൌ ݇ଵ ݇ଶ Ȁʹ ൌ ͵ξ͵ݎ௦ଶ ȀʹǤ
(1)
(4)
where, n is rows and m is columns in the square.l is constant which is equal to two,indicating 2 intersection pre side of the squaring spacing.n and mshould be as close as possible for maximum coverage and row should be one greater than or equal tocolumn.d will be 0 or 1 depending upon weather n>m or n=m respectively. Table 1 show the total number of trees covered under square spacing. From the data we analyze that when the numbers of squares are less we have nearly double number of the total trees covered per square. As the number of square increase i.e., more than 500 squares the total number of trees covered will be almost same as that of number of square. Fig. 5 shows the placement of trees in every edge of square. TABLE I.
TOTAL NUMBER OF TREES COVERED UNDER DIFFERENT ROW AND COLUMNS Fig. 5.Tree placement at square spacing with 9m apart.
Sr. No 0
No. of rows 2
No. of columns 1
No. of squares 2
Total no. of trees covered under square spacing 4
1 2 3 4 5 6 7 8 9 10 11 12 13
3 4 5 6 7 8 9 10 11 12 13 14 15
2 3 4 5 6 7 8 9 10 11 12 13 14
6 12 20 24 35 56 72 90 110 132 156 182 210
12 20 30 42 56 72 90 110 132 156 182 210 240
14 15
16 17
15 16
240 272
272 306
16
18
17
306
342
Average
120
142
Fig. 6: Sensor Deployment in Hexagonal Pattern
In our design, we have considered regular hexagon which can cover a total no. of trees as shown by the below equation: ܶ ݀݁ݎ݁ݒܿݏ݁݁ݎൌ ܽ σே ୀ ሺܾ ܿሺܰ െ ݅ሻ݀ሻሻǡ
(5)
where, N is number of layers required for the coverage, a is constant assigned with 7 as the innermost layer as total number of trees equal to 7, b is again constant which adds 6 hexagon with 4 trees per hexagon with connected layer-0 i.e., total no. of trees is 24 . c is constant equal to 6, for each layer multiple of 6 hexagons will be added. d is constant equal to 3, for each added hexagon 3 trees will be added.
In deployment of sensors using regular hexagonal pattern, we need rcr/ rsras square root of three [14].Fig.6 illustrates the relative position of sensors in this pattern. At the end points of each grid sensor are employed using k1 and k2, and finally
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Fig.7showsLayer inner-most, Layer-0, Layer-1,Layer-2 and so on. Cell a, b and c show the connected hexagons with layer0.Fig.8shows the placement of tree with 9m spacing.
In the same time we can also see for the same coverage area hexagonal spacing is covering(100%)as compared to square spacing which is covering maximum of 87%.
Below table 2 shows the different layers with no. of trees covered under each layer.From the table it is clear that in the first few layers the number of trees covered is almost double in every next layer and become almost constant difference between layers as more layers are added.By taking different range of sensing range rsrand analysis is done for total trees covered by square spacing and hexagonal spacing of 9m.As the radius of sensing range increasing, the number of trees covered also increasing due to the increase in the area under that circumference. Total area covered by hexagonal spacing is same as that of the square spacing area.
TABLE II. DIFFERENT LEVEL OF LAYERS AND TOTAL NO. OF TREES COVERED UNDER EACH LAYER
Sr. No
Fig 7: Different layers of hexagonal spacing
No.of Layers
No. Of hexagon
Total no. of trees under each layer
1
Layer-0
7
31
2
Layer-1
19
73
3
Layer-2
37
133
4
Layer-3
61
211
5
Layer-4
91
307
6
Layer-5
127
421
7
Layer-6
169
553
8
Layer-7
217
703
9
Layer-8
271
871
10
Layer-9
331
1057
11
Layer-10
397
1261
12
Layer-11
469
1483
13
Layer-12
547
1723
14
Layer-13
631
1981
15
Layer-14
721
2257
16
Layer-15
817
2551
Average
307
976
V. CONCLUSIONS In precision irrigation using wireless sensor network, proposed designed hexagonal model for spacing of trees which covers more clusters of trees per wireless sensor node and also covers maximum sensing range compared to square spacing of trees at low cost. The difference in total number of trees within the same radius 109m for both square & hexagonal spacing is 1148 trees that is hexagonal spacing is covering 13% more than that of square spacing. Using climate, weather and soil moisture sensor information, the irrigation need is calculated and corresponding valve will be controlled for micro-irrigation. Only one time infrastructure is needed, as the sensor deployment is stationary with respect to permanent crop whose life can vary from 40 to 80 years. Equipped with solar batteries, design is more cost effective and permanent with respect to sensor node lifetime.
Fig 8: Hexagonal spacing with 9m each from edge and center.
Table 3 shows the difference in both spacing whereas Fig. 9 shows the graphical analysis of these.In this table ratio of rcr/ rsrissquare root of three.The difference in total number of trees within the same average radius of 109m for square & hexagonal spacing is 1148 trees that is hexagonal spacing is covering 13% more than that of square spacing.In the graph the value of rsr is increasing from 3 % (initially) to 1% (middle on ward), the values of total number of trees covered under both the hexagonal(4 to 1 %) and square(9 to 1%) is also increasing.
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TABLE III.
DIFFERENCE BETWEEN ROW SPACING AND COLUMN SPACING
Sr . N o.
rsr
rcr
Area of hexgo nal spacin g with 9m
1
9
16
210
No. of Hex ago n 1
No . Of lay ers Inn er
No. of trees Cove red unde r hexa gon
No. of squar es with 9m spaci ng
No. of tree s Cov ered und er squ are
7
2
6
2
24
42
1473
7
0
31
18
28
3
39
68
3998
19
1
73
49
64
4
55
95
7785
37
2
133
96
117
5
70
121
12834
61
3
211
158
185
6
86
149
19146
91
4
307
236
268
7
101
175
26721
127
5
421
329
367
8
117
203
35558
169
6
553
438
481
9
133
230
45657
217
7
703
563
612
10
148
256
57018
271
8
871
703
758
11
164
284
69642
331
9
1057
859
918
12
179
310
83529
397
10
1261
1031
1096
13
195
338
98678
469
11
1483
1218
1289
14
210
364
115089
547
12
1723
1420
1497
8834
7120
7686
631
509
549
sum Avera ge
109
189
41238
196
6
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[3]
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[11] 2000 1800
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800 600
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0 rsr No. of trees Covered under hexagon No. of trees Covered under square
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Fig. 9. Comparisons of Square Spacing with Hexagonal Spacing
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