AQUASENSOR - UMa

AQUASENSORAQUASENSORAQUASENSORAQUASENSORWireless Sensor Network for Water Resources Optimization

Joaquim Amândio AzevedoDionísio Simões Barros

Maurício Rodrigues Filipe Santos

OutlineOutlineOutlineOutline

UgMO™ – Underground Monitoring

Ranch Systems – Vineyard wireless solutions for data collection and irrigation control

Introduction

W ire le s s Se n so r Ne tw o r kf o r Wa te r Re sou rc e s

Optimization

irrigation control

Camalie Networks – Wireless Vineyard Monitoring

SENSORNET – Wireless Sensor Networks for the Ornamental Industry

Rainforest rehabilitation underpinned by smart sensor network technology

Wireless sensor systems for irrigation management in container growncrops

Wireless Sensor Networks Protocols and Radios 2

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Optimization

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Optimization

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Optimization

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Optimization

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C C N sc a n be c o n ne c te d to p o w e r g r i d o r to b a t te r ie s.�

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Optimization

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T he m a i n g o a l o f t he a l l sy s te m i s to m a i n t a i n t he re a d i ng s i n t he Ug MO zo ne . Ug MOzo ne de f i ne s t he i de a l va lu e s o f te mp e r a tu re , hu m i d i ty a n d s a l i n i ty f o r e a c h se n so r n o de .

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Optimization

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Optimization

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T he Sy s te m p r o v i de s w ire le s s i nf o r m a t i o n a bo u t hu m i d i ty , te mp e r a tu re a n d s a l i n i ty o fso i l ;

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Optimization

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U se r sc a n w a tc h re a l t i me i nf o r m a t i o n a bo u t me a su re me n t s tr ou g h a w e b br o w se r ;�

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C C N sc a n ’ t be b a t te r y p o w e re d , p re ve n t i ng t he m to s le e p ;�

T he Sy s te m d o e s n o t p e r f o r m a n y ty p e o f a c tu a t i o n o n ir r ig a t i o n sy s te m ;�

D a t a mu s t be se n t to Ug MO ’ s d a t a c e n te r ;�

M o n t h l y f e e s a re c h a r g e d ; 1 0

Ranch Systems – Vineyard wireless solutions for data collection and irrigation control.

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R a nc h Sy s te m s i s b a se d o n a w ire le s s se n so r ne tw o r k to m o n i to rv i ne y a r d s a n d m ic r o c l i m a te s. T he sy s te m a l so p r o v i de s c o n tr o l o nir r ig a t i o n p r o c e s s.W ire le s s Se n so r Ne tw o r kf o r Wa te r Re sou rc e s

Optimization

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So me o f t he p o s s i b le me a su re me n t s a re: te mp e r a tu re , hu m i d i ty ,lu m i n o s i ty , w i n d sp e e d a n d d ire c t i o n , w a te r f l o w a n d w a te rp re s su re . T he sy s te m c a n a l so a c tu a te o n so le n o i d va l ve s o r o t he re le c tr ic e qu ip me n t tr o u g h a re l a y .�

T he s i mp le s t se n so r n o de s a re t he m o de l R S1 0 0 o p e r a t i ng o n 4 3 3MH z . T he se n o de s su p p o r tu p to tw o a n a l o g o r d ig i t a l se n so r s. T he ya re p o w e re d b y a 3 . 6 V l i t h iu m b a t te r y w i t h t he c a p a c i ty o f 1 A h . RS 10 0 s e ns o r no d e. 1 1


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T he o t he r se n so r n o de i s t he R S 20 0 . I t su p p o r t 5 a n a l o g o rd ig i t a l se n so r s a n d tw o se r i a l p o r t s. T h i s m o de l a l so su p p o r t su pto tre e re l a y / va l ve c o n tr o l le r s.W ire le s s Se n so r Ne tw o r kf o r Wa te r Re sou rc e s

Optimization

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R S 20 0 i s p o w e re d b y a 2 W So l a r P a ne l a n d a 9. 6 V N i M h- 2 40 0m A h b a t te r y . T h i s b a t te r y gu a r a n te e s a bo u t 2 o r 3 w e e k s w i t h o u tsu n .

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T he c o m mu n ic a t i o n be tw e e n R S 20 0 n o de s i s b a se d o n a me s hto p o l o g y . T he se n o de s o p e r a te a t 2. 4 GH z o r 90 0 MH z a n d t a l k toe a c h o t he r a n d a l so w i t h t he b a se s t a t i o n . T he y a l so a c t a s are p e a te r f o r R S 20 0 a n d R S1 0 0 se n so r n o de s. RS 20 0 s e ns o r no d e. 1 2

Ranch Systems – Vineyard wireless solutions for data collection and irrigation control.�

B a se S t a t i o n s f o r m t he c o re o f t he ne tw o r k. T he y ’rere sp o n s i b le f o r c o l le c t i ng a l l t he d a t a se n t b y se n so r n o de sa n d r o u t i ng i t b a c k to R a nc h Sy s te m ’ s d a t a c e n te r , v i a w if i o rG S M . B a se S ta t i o n sc a n be u se d a s a w ire le s s me s h ne tw o r kW ire le s s Se n so r Ne tw o r kf o r Wa te r Re sou rc e s

Optimization

G S M . B a se S ta t i o n sc a n be u se d a s a w ire le s s me s h ne tw o r kc o o r d i n a to r , a w e a t he r s t a t i o n , ir r ig a t i o n c o n tr o l le r o r ju s tse n so r n o de ;�

E a c h B a se S t a t io nc a n m a n a g e u p to 3 5 se n so r n o de s. T he ya re p o w e re d b y a 20 W So l a r P a ne l a n d a 1 2 V le a d a c i db a t te r y .�

B a se S t a t i o n s a l so h a ve a d i sp l a y a n d ke y bo a r d , t h a t a l l o w sf o r l o c a l d a t a v ie w i ng a n dc o nf igu r a t i o n . Ba s e s ta tio n a c ti ng a s aw e a t h e rs ta tio n.Ba s e s ta tio n a c ti ng a s aw e a t h e rs ta tio n. 1 3


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R a nc h Sy s te m s d a t a c e n te r s to re s d a t a f r o m a l li n s t a l l a t i o n s. T he d a t a i s m a de a va i l a b le v i a a w e ba p p l ic a t i o n .W ire le s s Se n so r Ne tw o r kf o r Wa te r Re sou rc e s

Optimization

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B a se d o n t he c o l le c te d i nf o r m a t i o n t he a p p l ic a t i o nc a lc u l a te se va p o tr a n sp ir a t i o n , me d iu m te mp e r a tu re s a n dh y dr ic p r o f i le s o f so i l s.�

Re m o te a c t i va t i o n o f va l ve s o r e qu ip me n t i s p o s s i b leu s i ng t he w e b a p p l ic a t i o n . T he sc he du l i ng so f tw a rea l l o w s mu l t ip le ir r ig a t i o n se t s p e r d a y , a n d le t s t he u se rde f i ne “f e e d b a c k se n so r s ” (e . g . f l o w se n so r s ) to p r o v i dea le r t s if ir r ig a t i o n i s n o t f l o w i ng a s sc he du le d . RS 20 0 s e ns o r no d e a c ti n g a swi re l e s s va l v e c o n t ro l l e r.RS 20 0 s e ns o r no d e a c ti n g a swi re l e s s va l v e c o n t ro l l e r. 1 4

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Optimization

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Optimization

S c r e e n- c a p tu re, s o i l p ro fi l e g ra p h. 1 6

St re ng t hs

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Optimization

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Sy s te m i sc a p a b le o f a c tu a t i ng o n se ve r a le le c tr ic e qu ip me n t s l i ke e le c tr ic va l ve s a n dw a te r p u mp s. I r r ig a t i o n sc he du le dc a n be se t v i a w e b a p p l ic a t i o n ;�

We b a p p l ic a t i o n p r o v i de se va p o tr a n sp ir a t i o nc a lc u lu s;�

R S 20 0 se n so r n o de s su p p o r tu p to 6 s i x se n so r s p lu s 2 se r i a l p o r t se n so r s.�

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R S 20 0 a n d B a se S t a t i o n su se So l a r re c h a r g e a b le b a t te r ie s;L i m i ta t io ns�

Sy s te m i s n o t fu l l au to n o m o u s i n t he ir r ig a t i o n p r o c e s s ,u se r ne e d s to sc he du le i t

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W ire le s s ne tw o r k i s l i m i te d to 3 5 se n so r n o de s;�

R S1 0 0 h a ve o n l y 1 y e a r b a t te r y au to n o m y ;�

M o n t h l y f e e s a re c h a r g e d ; 1 7


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C a m a l ie Ne tw o r k s o f f e r s a w ire le s s se n so r ne tw o r k c a p a b le o f m o n i to r i ng v i ne y a r d sir r ig a t i o n a n dc l i m a t ic c h a ng e s;�

T he c o mp le te ne tw o r k i s b a se d o n se ve r a l w ir e le s s se n so r n o de s , o ne b a se s t a t i o n a n do ne G a te w a y .W ire le s s Se n so r Ne tw o r kf o r Wa te r Re sou rc e s

Optimization

N e t wo r k a rc h i te c tu re fo r C a m a l i e n e t wo r ks .N e t wo r k a rc h i te c tu re fo r C a m a l i e n e t wo r ks . 1 8


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T he n o de s a re e Ko Pr o Se r ie s. T h i s n o de i s p o w e re d b y as m a l l so l a r p a ne l ( 3 . 2 x 6. 3 c m ) a n d 3 A A N i MHre c h a r g e a b le b a t te r ie s. T he l if e e xp e c t a nc y i s 3 m o n t h sw i t h n o so l a r re c h a r g i ng , a n d m o re t h a n 5 y e a r s w i t h so l a rre c h a r g i ngW ire le s s Se n so r Ne tw o r kf o r Wa te r Re sou rc e s

Optimization

re c h a r g i ng .

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W ire le s s n o de s f o r m a me s h ne tw o r k b a se d o n X me s hl o w - p o w e r ne tw o r k i ng p r o to c o l . T he se n so r s me a su re so i lm o i s tu re , so i l te mp e r a tu re , so l a r r a d i a t i o n , c a n o p yte mp e r a tu re hu m i d i ty a n d le a f w e t ne s s w i t h a p e r i o d ic i t yo f 20 m i nu te s. Se nso r No d e mo u n te d i n av i n e ya r d.Se nso r No d e mo u n te d i n av i n e ya r d. 1 9


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D a t a c o l le c te d b y w ire le s s se n so r n o de s i s r ou te d v i a me s h ne tw o r k to B a se S t a t i o n .�

B a se S t a t i o n i s a ne m be d de d Se n so r Ne tw o r k G a te w a y de v ic e . I t se n d s d a t a c o l le c te do n f ie l d to C a m a l ie Ne tw o r k ’ s h o s t i ng se r v ic eW ire le s s Se n so r Ne tw o r kf o r Wa te r Re sou rc e s

Optimization

o n f ie l d to C a m a l ie Ne tw o r k s h o s t i ng se r v ic e .�

We b a p p l ic a t i o n o n l y s h o w s re a d i ng s f r o m se n so r n o de s. I t d o e s n o t a l l o w to se t a l a r m so r sc he du le ir r ig a t i o n p r o c e s s.

20



Optimization

S c re e n- c a p tu r e Ca ma li e N e t wo r k’s w e ba p p lic a tio n.S c re e n- c a p tu r e Ca ma li e N e t wo r k’s w e ba p p lic a tio n. S c r e e n- c a p tu re Ca ma li e N e t wo r k’s w e ba p p lic a tio n. S o i lMo i s tu re a n d t e m p e ra tu re g ra p hsS c r e e n- c a p tu re Ca ma li e N e t wo r k’s w e ba p p lic a tio n. S o i lMo i s tu re a n d t e m p e ra tu re g ra p hs 21

St re ng t hs

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T he Sy s te m p r o v i de s w ire le s s i nf o r m a t i o n a bou t so i l m o i s tu re , so i l te mp e r a tu re , so l a rr a d i a t i o n a n d le a f w e t ne s s;�

W ire le s s se n so r n o de s w o r k su n de r a fu l l me s h- ne tw o r k;�

Se n so r n o de s a re so l a r p o w e re d ;



Optimization

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Se n so r n o de s a re so l a r p o w e re d ;�

T he sy s te m i s i n de p e n de n t , be i ng a b le to fu nc t i o n w i t h o u t I n te r ne tc o n ne c t i o n ;�

N o m o n t h l y f e e s;L i m i ta t io ns�

Sy s te m d o e s n o t p r o v i de c o n tr o l o n t he ir r ig a t i o n sy s te m o r a n y o t he r ty p e o fa c tu a to r ;�

Se n so r n o de s o n l y su p p o r te Ko c o mp a t i b le se n so r ( Cr o s s bo w E S B p r o to c o l ) i n a n a t i vew a y , o t he r se n so r s m a y be u se d w i t h E S B i n te r f a c e c a r d ; 2 2

SENSORNET – Wireless Sensor Networks for theOrnamental Industry�

S E N SO R N E T i s a p a r t ne r s h ip be tw e e n se ve r a l U n i ve r s i t ie s a n di n s t i tu te s w i t h M a r y l a n d U n i ve r s i ty . T he m a i n g o a l o f t h i sp r o je c t i s to p r o v i de g r o w e r s w i t h t he a b i l i ty to p re c i se l ym o n i to r a n d c o n tr o l t he p r o c e s s o f ir r ig a t i o n a n d f e r t i l i z a t i o nW ire le s s Se n so r Ne tw o r kf o r Wa te r Re sou rc e s

Optimization

m o n i to r a n d c o n tr o l t he p r o c e s s o f ir r ig a t i o n a n d f e r t i l i z a t i o nb a se du p o n d a i l y p l a n t re qu ire me n t s.�

T hre e W ire le s s Se n so r Ne tw o r k s h a ve be e n de p l o y e d du r i ng20 0 8 a n d 20 0 9. La s t g e ne r a t i o n o f se n so r s u se d w e rede ve l o p e d b y I n s t i tu te o f Ro bo t ic s a t C a r ne g ie Me l l o nU n i ve r s i ty . E a c h se n so r n o de h a s 2 to 6 m o n t h s l if e e xp e c t a nc yw i t h 4 Dc e l l s n o t re c h a r g e a b le .�

T he se n so r n o de su p p o r tu p to 5 a n a l o g se n so r s a n d 1 0 d ig i t a lse n so r s. T he n o de i s a l so c a p a b le o f a c t i ng o n a so le n o i d va l ve . S e ns o r No d e c o n t ro l li n g as o l e no i d va l v e .S e ns o r No d e c o n t ro l li n g as o l e no i d va l v e . 2 3

SENSORNET – Wireless Sensor Networks for theOrnamental Industry


Optimization

S e ns o r No d e d e v e lo p e d by Ca r n e g i e M e l lo nRo bo tic s I ns ti tu t e.S e ns o r No d e d e v e lo p e d by Ca r n e g i e M e l lo nRo bo tic s I ns ti tu t e. S e ns o r No d e d e v e lo p e d by Ca r n e g i e M e l lo n Ro bo ti c sI ns ti tu t e.S e ns o r No d e d e v e lo p e d by Ca r n e g i e M e l lo n Ro bo ti c sI ns ti tu t e. 2 4


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Re a d i ng s f r o m se n so r n o de s a re r ou te d be tw e e n n o de s to t he b a se s t a t i o n .�

B a se S t a t i o n h a s a so f tw a re c a l le d “ We b Se n so r ” t h a t i s ve r y c u s to m i z a b le a n di b i h l l f d h i h iW ire le s s Se n so r Ne tw o r kf o r Wa te r Re sou rc e s

Optimization

i n te g r a te s a w e b- ve r s i o n t h a t a l l o w s f o r d a t a s h a r i ng o ve r t he i n te r ne t.�

Se n so r s n o de s w e re c o nf igu re d to a c tu a te o ve r so le n o i d s va l ve s b a se d o n t he re a de dva lu e s f r o m t he se n so r s.�

Re a d i ng s f r o m se n so r s n o de s a re a l so se n t to C a r ne g ie Me l l o n U n i ve r s i ty to a n a l y s i s v i aI n te r ne t. 2 5



Optimization

S c re e n- c a p tu re fo r We b Se nso r a p p l i c a t io n.S c re e n- c a p tu re fo r We b Se nso r a p p l i c a t io n. 2 6

St re ng t hs

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Se n so r n o de s su p p o r t s se ve r a l a n a l o g o r d ig i t a l se n so r s s i mu l t a ne a l y ;�

Se n so r n o de s a re c a p a b le o f a c tu a t i ng o n so le n o i d va l ve s;�

Se n so r n o de s w o r ku n de r a fu l l me s h- ne tw o r k;SENSORNET – Wireless Sensor Networks for theOrnamental Industry


Optimization

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Se n so r n o de s w o r ku n de r a fu l l me s h- ne tw o r k;�

We b a p p l ic a t i o n p r o v i de s re a l t i me i nf o r m a t i o n ;L i m i ta t io ns�

Se n so r n o de s h a ve l o w l if e e xp e c t a nc y , o n l y 6 m o t h s w i t h 4 D b a t te r ie s , s i nc e n oh a r ve s t i ng e ne r g y sy s te me x i s t s.

2 7

Rainforest rehabilitation underpinned by smartnetwork technology�

E ve r y 5 m i nu te s s i nc e 20 0 8 a w ire le s s se n so r ne tw o r k h a sbe e n m o n i to r i ng te mp e r a tu re , a ir hu m i d i ty , le a f w e t ne s s ,w i n d sp e e d a n d d ire c t i o n a t M t Sp r i ng br o o k – so u t h- e a s tQu e e n s l a n d Au s tr a l i a .W ire le s s Se n so r Ne tw o r kf o r Wa te r Re sou rc e s

Optimization

Qu e e n s l a n d Au s tr a l i a .�

Se n so r n o de s a re b a se d o n t he F le c k p l a tf o r m de ve l o p e d b yC S I RO – Co m m o n w e a l t h Sc ie n t if ic a n d I n du s tr i a l Re se a rc hO r g a n i z a t i o n . T he y a re p o w e re d b y a 90 0 m W so l a r p a ne l a n d3 A A N i MH b a t te r ie s.�

D a t a i s r ou te d o n a me s h ne tw o r k tr o u g h se n so r n o de s toG a te w a y . T he n d a t a i s se n t to a d a t a se r ve r , t h a t m a ke s i ta va i l a b le tr o u g h a br o w se r o n c o mp u te r o r m o b i le p h o ne .T he sy s te m d o e s n o t p r o v i de a n y ty p e o f re m o te a c tu a t i o n . F l e c k s e nso r no d e, i ns ta l l e da t M t. S p r i ng b ro o k.F l e c k s e nso r no d e, i ns ta l l e da t M t. S p r i ng b ro o k. 2 8

Rainforest rehabilitation underpinned by smartnetwork technology�

Re a d i ng s f r o m w ire le s s se n so r n o de s i s a va i l a b le to g e ne r a l p u b l ic i n a w e b a p p l ic a t i o n .


Optimization

S c re e n- c a p tu re o f we b a p p l i c a t io n. 2 9

Rainforest rehabilitation underpinned by smartnetwork technology


Optimization

S c re e n- c a p tu re o f we b a p p l i c a t io n, s ho w i ng a i r te m pe ra tu re fo ro n e o f t h e no d e s 3 0

St re ng t hs

�

Se n so r n o de s a re p o w e re d b y re c h a r g e a b le b a t te r ie s a n d a so l a r p a ne l ;�

Se n so r n o de s w o r ku n de r a fu l l me s h- ne tw o r k;�

Se n so r n o de s m o n i to r mu l t ip le p a r a me te r s o f a ir a n d so i l ;Rainforest rehabilitation underpinned by smartnetwork technology


Optimization

�

Se n so r n o de s m o n i to r mu l t ip le p a r a me te r s o f a ir a n d so i l ;L i m i ta t i o ns�

Se n so r n o de s d o n o t p r o v i de a n y ty p e o f a c tu a t i o n ;31

Wireless Sensor systems for irrigation managementin container grown crops

�

T h i s p r o je c t w a s de ve l o p e d b y Wa g e n i ng e n U n i ve r s i ty & Re se a rc hCe n tre , Ne t he r l a n d s a n d i t c o n s i s t s i n a w ire le s s se n so r ne tw o r ku se d to m o n i to r te mp e r a tu re a n d hu m i d i ty i n c o n t a i ne r g r o w nc r o p s;W ire le s s Se n so r Ne tw o r kf o r Wa te r Re sou rc e s

Optimization

c r o p s;

�

Tw o sy s te m s h a ve be e n te s te d . O ne w i t h So w ne t n o de s a n d o t he rw i t h Cr o s s bo w e Ko Pr o Se r ie s n o de s. T he se n so r su se d w e re S M 20 0( d ie le c tr ic so i l m o i s tu re ) a n d Wa te r m a r k se n so r ( so i l m a tr i xp o te n t i a l ) ;�

So w ne t n o de s w o r k o n a se m i- me s h ne tw o r k a t 8 6 6 M H zf re qu e nc y . T h i s n o de s a re p o w e re d b y 1 Dc e l l . So w n e t no d e .So w n e t no d e . 3 2


�

Cr o s s bo w n o de s w o r k o n 2. 4 GH z a n d a re p o w e re d b y a so l a rp a ne l a n d a re c h a r g e a b le b a t te r y .�

E xp e r i me n t s w e re m a de du r i ng 6 m o n t h s;W ire le s s Se n so r Ne tw o r kf o r Wa te r Re sou rc e s

Optimization

�

E xp e r i me n t s w e re m a de du r i ng 6 m o n t h s;�

e Ko Se r ie s n o de s w e re ve r y re l i a b le du r i ng t he e xp e r i me n t , w i t hm a x i mu m r a ng e > 20 0 m p e r h o p . Re c h a r g e a b le b a t te r ie s w o r ke df i ne w i t h so l a r p a ne l s. F u l l au to n o m y .�

So w ne t n o de s h a d a m a x i mu m r a ng e o f 1 0 0 m , a n d so me o ft he m f a i lu re du r i ng t he e xp e r i me n t.�

Co r re c t p l a c e me n t a n d c a l i br a t i o n o f t he se n so r s f o r c o n t a i ne rc r o p i sc ru c i a l . C ro s s bo w no d e .C ro s s bo w no d e . 3 3



Optimization

F i e l d i ns t a l l a t io n.F i e l d i ns t a l l a t io n. 3 4

St re ng t hs

�

Se n so r n o de s p r o v i de s i nf o r m a t i o n a bo u t te mp e r a tu re a n d hu m i d i ty i n s i de c o n t a i ne rg r o w nc r o p s;

�

e Ko Pr o Se r ie s n o de s w o r ke d w i t h re c h a r g e a b le b a t te r ie s a n d so l a r p a ne l s re su l t i ng i nWireless Sensor systems for irrigation managementin container grown crops


Optimization

�

e Ko Pr o Se r ie s n o de s w o r ke d w i t h re c h a r g e a b le b a t te r ie s a n d so l a r p a ne l s , re su l t i ng i nfu l l au to n o m y du r i ng t he te s t s;L i m i ta t io ns�

Sy s te m d o n ’ t a c tu a te o n ir r ig a t i o n sy s te m s;�

T he te s t s o n l y to o k 6 m o n t h s;

3 5

Wireless Sensor Network Protocols and RadiosI n t he p a s t w e h a ve s tu d ie d T m o te S ky , M ic a Z a n d X be e . F r o m t he se s tu d ie s w e c a n s a yt h a t:

�

M ic a Z de ve l o p me n t sy s te m i s a re a d y to u se te s t p l a tf o r m t h a t a l l o w su se r to u se va r i ou s


Optimization

�

M ic a Z de ve l o p me n t sy s te m i s a re a d y to u se te s t p l a tf o r m t h a t a l l o w su se r to u se va r i ou sse n so r s , a n d to c o n tr o l so me ne tw o r k p a r a me te r s , a l t h o u g h w e w e re l i m i te d top o s s i b i l i t ie s o f f e re d b y t he sy s te m .�

T m o te S ky i s a n o p e n sou rc e w ire le s s se n so r ne tw o r k p l a tf o r m . T he h a r d w a re i s ve r ys i m i l a r to M ic a Z , bu t i t w a s ne c e s s a r y so me e xp e r t i se to w o r k w i t h t he n o de .�

T he w o r k w i t h X be e de m o n s tr a te d to be ve r y r o bu s t , t a k i ng s h o r t t i me f o r t hei mp le me n t a t i o n . W i t h t he Z ig Be e p r o to c o l t he r o u te r s mu s t be a l w a y sc o n ne c te d to t hef e e d i ng sy s te m . U s i ng t he D ig i Me s h p r o to c o l , t h i s se n so r n o de se e m s to be ve r y a t tr a c t i vef o r me s h ne tw o r k a p p l ic a t i o n s. 3 6

Zigbee

�

Z ig be e i s a n o p e n s t a n d a r d p r o to c o l ,c o mp l i a n t w i t h I E E E 80 2.1 5. 4 sp e c if ic a t i o n s.�

Z ig be e de f i ne s t hre e n o de ty p e s: Co o r d i n a to r , Rou te r a n d Te r m i n a l N o de .�

Co o r d i n a to r i s r e sp o n s i b le f o r e s t a b l i s h i ng a ne tw o r k. T he re i s o n l y o ne c o o r d i n a to r i n


Optimization

�

Co o r d i n a to r i s r e sp o n s i b le f o r e s t a b l i s h i ng a ne tw o r k. T he re i s o n l y o ne c o o r d i n a to r i ne a c h ne tw o r k.

�

Ro u te r a c t a s i n te r me d i a te n o de , re l a y i ng d a t a f r o m o t he r de v ic e s.�

Te r m i n a l N o de s c a n be b a t te r y p o w e re d . O n l y t h i s ty p e o f n o de i s a b le to s le e p .Te r m i n a l N o de s o n l y t a l k w i t h t he ir p a re n t s (r o u te r s o r c o o r d i n a to r s ).�

Rou te r s a n dc o o r d i n a to r sc o m mu n ic a te i n a me s h to p o l o g y , w h i le t he te r m i n a l n o de sc o m mu n ic a te w i t h r ou te r s i n a s t a r to p o l o g y .�

F o r be i ng a n o p e n s t a n d a r d Z ig be e i s ve r y i n te r o p e r a b le be tw e e n ve n d o r s. 3 7

Zigbee


Optimization

Z ig be e n e t wo r k a rc h i te c tu re . 3 8

DigiMesh

�

D ig i Me s h i s a p r o p r ie t a r y p o w e r- o p t i m i ze d p r o to c o l f o r u se i n w ire le s s se n so rne tw o r k s.

�

D ig i Me s h a l l o w s a l l n o te s to s le e p , i nc lu d i ng r ou te r s.W ire le s s Se n so r Ne tw o r kf o r Wa te r Re sou rc e s

Optimization

D ig i m e s h n e t wo r k a rc h i te c tu re .�

T he re i s n o p a re n t- c h i l d re l a t i o n s h ip s.�

D ig i me s h su p p o r t s h ig h p a y l o a d s a n d me s s a g ef r a g me n t a t i o n .�

D ig i me s h re qu ire s a l o ng e r w a ke t i me totr a n s m i t d a t a t h a n Z ig be e .�

g p , g�

D ig i me s h p r o v i de s o n l y o ne ty p e o f n o de s , w h ic h me a n s s i mp le r c o nf igu r a t i o n a n di n s t a l l a t i o n .

3 9

Z-Stack

�

Z- S t a c k i s a n o p e n s t a n d a r d p r o to c o l , de ve l o p e d b y Te xa s I n s tru me n t sc o mp l i a n t w i t hI E E E 80 2.1 5. 4 sp e c if ic a t i o n s.�

Z S t a c k su p p o r t s H o me Au to m a t i o n a n d S m a r t E ne r g y p r o f i le sW ire le s s Se n so r Ne tw o r kf o r Wa te r Re sou rc e s

Optimization

�

Z- S t a c k su p p o r t s H o me Au to m a t i o n a n d S m a r t E ne r g y p r o f i le s.�

Z- S t a c k A P Ie n a b le s qu ic k de ve l o p me n t f o r ne w u se r s.�

Z- S t a c k o f f e r s h a r d w a re b a se d l o c a t i o n w i t hc o mp a t i b le m o du le s.�

O ve r t he a ir d o w n l o a d i s su p p o r te d .�

S i nc e Z- S t a c k i s c o mp l i a n t w i t h Z ig be e a n d 80 2.1 5. 4 , i t d o e s n o t a l l o w f o r s le e p i ngr o u te r s. Ne tw o r k to p o l o g y i s t he s a me a s f o r Z ig be e . 40

CC2531

�

C C 2 5 31 i s a tru e Sy s te m- O n- C h ip f o r w ire le s s se n so r ne tw o r k i ng , w i t h U S B i n te r f a c e f o rqu ic k de ve l o p me n t.

CC2531Operating Voltage Sensitivity Max Power TX

2-3.6 V -97 dBm 4 dBm


Optimization

2-3.6 V -97 dBm 4 dBmTX Current RX Current Sleeping modes/

current29 mA 24 mA Mode 1: 0.2 mA

Mode 2: 1 uA

Mode 3: 0.4 uASupported Stacks Antenna compatibility

Z-Stack,Zigbee, Wirelesshart, Simpliciti

RemoteTI,6LOWPAN

PCB Antenna 41

CC2431

�

C C 2 4 31 i s a tru e Sy s te m- O n- C h ip f o r w ire le s s se n so r ne tw o r k i ng .CC2431

Operating Voltage Sensitivity Max Power TX2-3.6 V -91 dBm 0.6 dBm


Optimization

2-3.6 V -91 dBm 0.6 dBmTX Current RX Current Sleeping modes/

current28 mA 27 mA Power down: 0.5 uA

Stand-by mode: 0.3 uASupported Stacks Antenna compatibilityZ-Stack,Zigbee,

Wirelesshart, Simpliciti

RemoteTI,6LOWPAN

SMA, with developmentkit.

Needs balun adaptation forSoC 4 2

Xbee

�

X be e i s a r o bu s t a n d e f f e c t i ve so lu t i o n w i de l y u se d .XBEE

Operating Voltage Sensitivity Max Power TX2.8-3.4 V -92 dBm 3 dBm


Optimization

2.8-3.4 V -92 dBm 3 dBmTX Current RX Current Sleeping modes/

current45 mA 50 mA Pin Ibernate < 10 uA

Pin Doze < 50 uA

Cyclic Sleep < 50 uASupported Stacks Antenna compatibility Notes:DigiMesh , Zigbee RPSMA, UFL, Chip antenna

and whip antenna.Sleep mode is onlyavailable when configuredas a terminal node inZigbee 4 3

Research directionsResearch directionsResearch directionsResearch directions

�

B a se d o n t he a n a l y se s p e r f o r me d to c o m me rc i a l s sy s te m s a n d p r o je c t s t h a tu se w ire le s sse n so r ne tw o r k to m o n i to r so i l a n d a ir c o n d i t i o n s w e s h a l l de ve l o p a sy s te m t h a t o f f e r s a l lp o s s i b i l i t ie s o f c o n tr o l a n d d a t a v i su a l i z a t i o n tr o u g h a w e b i n te r f a c e .W ire le s s Se n so r Ne tw o r kf o r Wa te r Re sou rc e s

Optimization

p o s s i b i l i t ie s o f c o n tr o l a n d d a t a v i su a l i z a t i o n tr o u g h a w e b i n te r f a c e .�

T he i n te r f a c e s h o u l d be o p t i m i ze d f o r m o b i le de v ic e s a n d a l l t he so f tw a re s h o u l d ru n o nc l ie n tc o mp u te r s.�

I n sp i t o f t he m a j o r i ty o f sy s te m su se r so l a r p o w e r to re c h a r g e b a t te r ie s , w e i n te n t to s tu d yo t he r p o s s i b i l i t ie s to re c h a r g e b a t te r ie s m a x i m i z i ng b a t te r ie s l if e e xp e c t a nc y .�

T he sy s te m s h o u l dc o n tr o l i n a n au to n o m ou s w a y a l l t he ir r ig a t i o n sy s te m . 4 4

ReferencesReferencesReferencesReferences[1 ] Ug MO . ( 20 0 9 ) Ug M o Te c h n o l o g y . [O n l i ne ] . h t tp: / /u g m o .c o m / te c h n o l o g y /[ 2 ] J . H u lf a c h o r . ( 20 1 0 , J a n . ) A p e r f e c t tu r f . Su n Se n t i ne l [O n l i ne ] .h t tp: / /w w w . su n- se n t i ne l .c o m / br o a d b a n d / t he e dg e / ne w s i l lu s tr a te d / sf l- e dg e - n i- l a n d- s h a r ku r f ,0 , 6 6 6 4 31 6. h t m lp a g eW ire le s s Se n so r Ne tw o r kf o r Wa te r Re sou rc e s

Optimization

u r f ,0 , 6 6 6 4 31 6. h t m lp a g e[ 3 ] R ie g e r , " W ire le s s V i ne y a r d M o n i to r i ng Te c h n o l o g y , " V in ey ar d a n d W in er y M a n ag em e n t ,20 0 7.[ 4 ] R a nc h Sy s te m s L L C , RS2 0 0F a m il y D a t as h e e t. C a l if o r n i a , 20 0 8.[ 5 ] R a nc h Sy s te m s L L C , RS1 0 0 fA MIL Y D a t as h e e t. C a l if o r n i a , 20 0 8.[ 6 ] R a nc h Sy s te m s L L C , R M2 1 0F am il y D a t as h e e t. C a l if o r n i a , 20 0 8.[ 7 ] R a nc h Sy s te m v i ne y a r d w ire le s s so lu t i o n s f o r d a t a c o l le c t i o n a n d ir r ig a t i o nc o n tr o l .[O n l i ne ] . h t tp: / /w w w . r a nc h sy s te m s.c o m / s s i te / i n de x. s h t m l[ 8 ] C a m a l ie Ne tw o r k So lu t i o n s. [O n l i ne . h t tp: / /c a m a l ie ne tw o r k s.c o m / 4 5

[ 9 ] U n i ve r s i ty o f M a r y l a n d . We lc o me to Se n so r Ne t. [O n l i ne ]h t tp: / /w w w . se n so r ne t.u m d .e du / i n de x. h t m l[1 0 ] C S I RO . W ire le s s Se n so r Ne tw o r k s- F le c k. [O n l i ne ] .h t tp: / /w w w . se n so r ne t s.c s ir o . au /f le c k1 . h t mW ire le s s Se n so r Ne tw o r kf o r Wa te r Re sou rc e s

Optimization

h t tp: / /w w w . se n so r ne t s.c s ir o . au /f le c k1 . h t m[1 1 ] De p l o y me n t Sp r i ng br o o k. [O n l i ne ] . h t tp: / /1 50 . 2 2 9. 9 8. 6 8 / de p l o y me n t s / 6 3[1 2 ] J . H e m m i ng ,F . Ke m ke s , J . B a le n d o nc k " W ire le s s se n so r sy s te m s f o r ir r ig a t i o nm a n a g e me n t i nc o n t a i ne r g r o w nc r o p s , " 20 0 9.[1 3 ] Cr o s s Bo w ,e Ko B a se S t a t i o n D a t a s he e t.[1 4 ] Cr o s s Bo w ,e Ko N o de D a t a s he e t.[1 5 ] Cr o s s Bo w ,e Ko O u t d o o r W ire le s s Sy s te m D a t a s he e t.[1 6 ] U n i ve r s i ty o f M a r y l a n d . We lc o me to Se n so r Ne t. [O n l i ne ]h t tp: / /w w w . se n so r ne t.u m d .e du / i n de x. h t m l 4 6

[1 7 ] J . Le a - Co x , A. R i s t ve y ,F . Ar gu e d a s , D. Ro s s , a n d G . K a n to r , " De p l o y me n t o f W ire le s sSe n so r Ne tw o r k s f o r re a l- t i me ir r ig a t i o n Sc he du l i ng i n Nu r se r y a n d Gre e n h o u sePr o du c t i o n E n v ir o n me n t s " .[1 8 ] J . Le a - Co x , A. R i s t ve y ,F . Ar gu e d a s , D. Ro s s , a n d G . K a n to r , " A w ire le s s se n so r ne tw o r k


Optimization

[1 8 ] J . Le a Co x , A. R i s t ve y ,F . Ar gu e d a s , D. Ro s s , a n d G . K a n to r , A w ire le s s se n so r ne tw o r kf o r t he Nu r se r y a n d g re e n h o u se I n du s tr y , " 20 0 7[1 9 ] Te xa s I n s tru me n t s , Z- s t a c k De ve l o p e r ' s Gu i de ;[ 20 ] Te xa s I n s tru me n t s , C C 2 4 31 D a t a s he e t.[ 21 ] Te xa s I n s tru me n t s , C C 2 5 31 D a t a s he e t.[ 2 2 ] D ig i , D ig i me s h D a t a s he e t[ 2 3 ] D ig i , XB e e ™ / XB e e- P RO ™ O E M RF Mo du l es D a t a s h e e t[ 2 4 ] D ig i , Z ig b e ev s D ig im es h . 4 7


Optimization

The end

4 8

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