an estimation of qos for classified based approach and...

Research ArticleAn Estimation of QoS for Classified Based Approachand Nonclassified Based Approach of Wireless AgricultureMonitoring Network Using a Network Model

Ismail Ahmedy Hisham A Shehadeh and Mohd Yamani Idna Idris

Department of Computer System and Technology Faculty of Computer Science and Information Technology University of MalayaKuala Lumpur Malaysia

Correspondence should be addressed to Ismail Ahmedy ismailahmedyumedumyand Mohd Yamani Idna Idris yamaniumedumy

Received 22 February 2017 Revised 4 May 2017 Accepted 30 May 2017 Published 5 July 2017

Academic Editor Stefano Savazzi

Copyright copy 2017 Ismail Ahmedy et alThis is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Wireless Sensor Network (WSN) can facilitate the process of monitoring the crops through agriculture monitoring networkHowever it is challenging to implement the agriculture monitoring network in large scale and large distributed area Typicallya large and dense network as a form of multihop network is used to establish communication between source and destinationThis network continuously monitors the crops without sensitivity classification that can lead to message collision and packets dropRetransmissions of drop messages can increase the energy consumption and delay Therefore to ensure a high quality of service(QoS) we propose an agriculture monitoring network that monitors the crops based on their sensitivity conditions wherein thecrops with higher sensitivity are monitored constantly while less sensitive crops are monitored occasionally This approach selectsa set of nodes rather than utilizing all the nodes in the network which reduces the power consumption in each node and networkdelayTheQoS of the proposed classified based approach is compared with the nonclassified approach in two scenarios the backoffperiods are changed in the first scenario while the numbers of nodes are changed in the second scenario The simulation resultsdemonstrate that the proposed approach outperforms the nonclassified approach on different test scenarios

1 Introduction

Wireless Sensor Network (WSN) is a network consisting ofsensing devices connected via wireless communication [1ndash3] These devices are known as motes or sensor nodes thatconvert the raw signals into information through four mod-ules (1) sensing unit senses and detects events from targetedenvironment and then converts the physical measurementto digital signals (2) processing unit controls all sensorfunctions and manages the communication between sensorsby changing the sensors status from sleep mode to idealmode or start mode (3) transceiver unit sends the physicalmeasurement from the sensor to the base station (sink node)(4) energy unit (battery) is power source of the sensors andallows the sensors to operate for years or months [3ndash7]Thesemake WSN suitable in many applications such as health caremonitoring wildlife monitoring security monitoring firedetection and agriculture monitoring [8 9]

In this work we focus on wireless network for agriculturemonitoring as depicted in Figure 1 [10] Figure 1 shows thearchitecture of agriculture sensors connected with sink nodeThe aggregated data are sent to remote server to be processedanalyzed and stored This network is used to monitorand analyze crops health from the impact of unpredictableclimate change or other conditionsTheWireless AgricultureMonitoring Network (WAMN) is widely considered as newgeneration of agriculture monitoring network to monitordifferent types of crops [11 12] WAMN mainly controlledgreenhouse system via interconnected network that deliversdata from greenhouse to control center These data areacquired from multiple sensors such as temperature sensorand humidity sensor However it is challenging to implementthe existing WAMN architecture [13ndash20] in large scale andlarge distributed area This is because the sensors are sus-ceptible to high delays and high energy consumption duringsensing processing and transmitting data over the dense

HindawiWireless Communications and Mobile ComputingVolume 2017 Article ID 3626571 14 pageshttpsdoiorg10115520173626571

2 Wireless Communications and Mobile Computing

Sink node

Agriculturenode 4

Agriculturenode 2

Agriculturenode 1

Agriculturenode 3

Remote server

Web interface

Figure 1 Agriculture monitoring network

and Wide Area Network Furthermore the characteristic ofa monitoring system also contributes to the aforementionedproblem as large data from continuous monitoring inducemessage collision and packet drop Retransmission of thedrop messages increases the energy consumption and delay

Implementing the existing WAMN architecture in largescale and large distributed area can be further challengingas the monitoring and data transmission are done contin-uously without sensitivity classification Consequently theunnecessary data transmission from all nodes in a networkcan lead to inefficient use of energy Therefore we proposea classified based approach to classify crops to a set ofclusters based on their sensitivity of agricultural conditionsSensitivity levels of 5∘C and 10∘C are introduced in this paperThe two levels of sensitivity are chosen based on the optimaltemperature condition that will be discussed further in latersection

To evaluate the effectiveness of our proposed methodtwo tests are conducted These tests investigate the effects ofbackoff period and number of nodes Energy consumptionend-to-end delay and jitter are thenmeasured and comparedwith the nonclassified based approach

This paper is organized as follows Section 2 presentsthe challenges and related works of agriculture monitoringnetwork Section 3 shows a Smart Agriculture MonitoringNetwork (SAMN) architecture model The classified basedtechnique and the proposed methodology are presented inSection 4 Section 5 describes the details of the experiment

and reports the results Section 6 presents the discussion andwe conclude the work in Section 7

2 Literature Review

This section presents the challenges and related works ofagriculture monitoring network We organize this section asfollows

21 Challenges of GreenhousesMonitoring Network Thechal-lenges of applying agriculture monitoring network in real lifeare summarized as follows

(i) Network scalability the nature of agriculturemonitor-ing network based on the wide area of deploymentthis makes the network very sensitive to failure andreduces the performance of network [12 22 23] Todiminish this challenge hierarchical architecture stylewould perform better than the simple style (flat singletier network architecture)

(ii) Simplicity we should consider that the end usershave limited knowledge of WSN Thus the designedsolutions and platforms should be user-friendly andeasy to use [24]

(iii) Real-time monitoring most of previous works donot focus on the issue of real-time monitoring ofthe crops [24] This factor has a strong relationshipwith network metrics that measure the quality of

Wireless Communications and Mobile Computing 3

the network such as end-to-end delay [25] For thisreason delay or network latency should be examined

(iv) Energy harvesting and energy management energymanagement is crucial in WSN as the power sourceof the sensor is limited Thus energy consumptionshould be considered in the designing of the systemalgorithms and components Alternatively the renew-able energy resources [26] such as wind power [27]and solar power [28] can be used as unlimited powersource for the sensor For this reason we propose anetwork model using nonconventional resources topower the network components

22 State of the Art This subsection presents the relatedworks ofWAMNinprecision agriculturewith various sensorsdeployments and data transmission scenarios over IEEE802154 communication technology Most WAMN agricul-tural deployments [13ndash20 29ndash31] transmit different types ofinformation via devices that located up to several kilometresapart from control center These existing works give thedetailed description of the devices employed and describethe design on how the deployment of WAMN is performedThough the method proposed is able to work accordinglyenergy consumption or network life time of sensing devicesremains a challenge in these works Extending the networklife time of sensing devices is important since agriculturecycle usually takes several weeks or months until the harvesttime Another important aspect that is not considered pre-viously is number of nodes Typically the number of nodesset in the experiment is small (eg less than ten sensornodes) and may not be suitable for implementation of denseWAMN

WAMNare commonly implemented through short rangecommunication technologies such as ZigBee and WiFi andlong distance communications such as multihop network[32] The costs to transmit the sensed data via these com-munication technologies are important in energy manage-ment strategy Since many of the existing works will beactively sensing data almost all the time (keeping attentionto the channel by listening and transmitting or receivinginformation) researchers devise various methods to reducenetwork complexity in order to simplify data transmissionFor exampleAyday and Safak [33] proposed a network designthat consists of gateways deployed between end sensors andcontrol center which help to reduce the network complexityby centralizing and storing sensorsrsquo data in the gatewaysGarcia-Sanchez et al [34] look into further details evaluationby studying different test scenarios by changing the numberof events and the parameter of Beacon Interval (BI) ofsynchronization scheme (the value of BO and SO) They alsostudy the end-to-end delay of the network by changing thenumber of sensor nodes

23 Nonclassified Based Approach TheWAMN implementa-tion as discussed in the prior section shows many promisingbenefits for agriculture monitoring However all crops mon-itored are considered equally sensitive even if there is timewhere less (or close) monitoring is needed In real agricultureimplementation monitoring frequency is flexible and can be

Start

Farmers plantinggreenhouses

Crops needmonitoring

Sensors join network

Send data

End

NoYes

Figure 2 Nonclassified based technique

based on the environment conditions We call this commonWAMN implementation as nonclassified based approach andthe implementation is illustrated in Figure 2 Nonclassifiedbased approach has limitation in managing energy usageThis is because monitoring is done continuously withoutconsidering other factors to adjust the sensitivity of themonitoring frequency

3 A Smart Agriculture Monitoring Network(SAMN) Architecture Model

We propose the usage of a Smart Agriculture MonitoringNetwork (SAMN) instead of multihop network for largegeographical greenhouses monitoring network SAMN is acommunications infrastructure model that consists of manyparts (hierarchical architecture style) Furthermore SAMNhelps to prolong across the whole greenhouses to the neigh-borhood area and the wide area (monitoring center) which isused to provide many features that cannot be achieved withmultihop network such as flexibility stability good quality ofservices andmaintainability Figure 3(a) shows a greenhousenetwork model of multilayer structure The previous featurescannot be achieved by WAMN because it operates based onmultihop network to reach the destination This makes thenetwork very sensitive to failure as in case of any dead hopthe whole network will crash Moreover WAMN has lowerflexibility than SAMN as the process of adding or deleting


Greenhouse

Distribution

GAN

Transmission

NANWAN

Control center

Control center

(a)

GAN cognitive gateway(GGW)


NAN cognitive gateway(NGW)


Base station

Control center

Base stationGreenhouse area network

(GAN)Neighborhood area network

(NAN)Wide area network

(WAN)





Base station

Control center

Base station

(b)

Figure 3 Hierarchical communications architecture in Smart Agriculture Monitoring Network

node from the network in WAMN can be complicated Inaddition SAMN is easier to maintain than WAMN becauseSAMN deal with part of network rather than the wholenetwork

To support data transfer and management of agriculturalequipment in SAMN various wireless network architecturescan be applied as shown in Figure 3(b) Three main net-works used in SAMN are varied in locations and sizes arethe greenhouse area network (GAN) neighborhood area

network (NAN) and Wide Area Network (WAN) Thesenetworks are summarized as follows

(i) Greenhouse area network (GAN) GAN broadcasts onthe local area wireless or short range transmission(eg ZigBee orWiFi) to support real-time data trans-formation controlling different types of actuatorslike irrigation system and cooling and heating sys-tems actuators Wireless technologies are the popular


choices for GANs due to their flexibility low cost andbetter control For example ZigBee is an appropriatetechnology for GANs in terms of interoperability Ina GAN the greenhouse gateway or GAN gatewayis used to transmit data to the external entity AGANgateway can be integrated into some greenhousedevices like programmable thermostat

(ii) Neighborhood area network (NAN) NAN relates mul-tiple GANs together As we can notice in Figure 3(b)GAN gateway transfers sensor data to Data Aggre-gation Unit (DAU) through NAN The DAU com-municates with the GAN gateway using short rangenetwork technologies such as ZigBee FurthermoreDAU can act as the NAN gateway to transmit datato NAN servers As we can notice in Figure 3(b) wepropose the usage of renewable energy to support thispart of the network such as using solar power or windpower to provide the transmission devices by power

(iii) Wide area network (WAN) WAN is used to relateremote systems (NAN servers) together Further-more the WAN is used to collect and manage datatransmission and after that to do different tasks likemeasurement and control purposes WAN in thiscase should provide a backhaul connection whichcan adopt different technologies (eg broadbandwireless access or cellular network) to transmit thedata from DAU in a NAN to the control center WANgateway can broadcast over broadband connection(eg WiMAX satellite and 3G satellite) to collectthe required data Indeed in this part of network assame as NANpart we propose the usage of renewableenergy to support this part of the network such asusing solar power or wind power to provide thetransmission devices with power Figure 3(b) showsthe WAN area network with the needed equipment

4 Classified Based Technique andthe Proposed Methodology

Various ideas are proposed to solve the challenges of agri-culture monitoring network Some of these ideas are basedon topology typesize and sensors types to reduce the com-munication cost in WSN Konstantinos et al [32] proposedmultihop network to solve the problem of a long distancecommunications but the system failed in powermanagementas the routers should be active almost all the time On theother hand some of researches used a smart gateway betweensensors and control center to reduce the communication costas the gateways are used to increase the network efficacy bystoring a copy of sensorsrsquo data Garcia-Sanchez et al [34]controlled the power consumption in agriculture monitoringnetwork by determining the optimal value of node eventsBI of the network and number of nodes in the networkThese studies as same as many other studies such as in [13ndash20] used nonclassified based system (traditional agriculturalmonitoring system) to monitor the crops Nonclassifiedbased system treats all crops equally without classificationThis reduces the network lifetime which leads to a rapid

Table 1 Optimal temperatures for crops [36ndash39]

Type of crops Optimal temperature [∘C]Potato 15minus20Corn 22minus25Soybean 25minus28Wheat 20minus25Tomato 21minus24Cucumber 24minus27Carrots 15minus18

node death For these reasons we propose an approachfor a traditional greenhouses monitoring system (nonclas-sified based system) that depends on the crops sensitivityprofile We classified crops into three clusters based ontheir sensitivity Clusters include crops which have sensitiveagricultural conditions for transmission of their agriculturalparameters continually and crops that have less sensitiveand nonsensitive agricultural conditions for transmission ofagricultural parameters partially This enables the lands tobe monitored for a long time by selecting a set of nodesrather than utilizing all sensors in the network Consequentlythis reduces the power consumption in each node andincreases the network efficiency by reducing network delayFurthermore this helps to manage communications betweendifferent types of agriculture sensorsrsquo and monitor centerThe main idea of the selection process is by determining theoptimal temperature for each crop and after that comparingit with environment temperature to determine the sensitivityof each crop Figure 4 depicts the flow chart of classified basedtechnique Mainly the greenhouse plantation is affected bythe temperature which strongly related to humidity and CO2For example the greenhouse with high temperature has a lowrelative humidity Therefore humidity and other influencingfactors can be inferred from a known temperature [35] Forthis reason temperature sensor is integrated with humiditysensor in one microcontroller chip The optimal temperaturefor crops is varied between types and sensitivity as thereare crops that grow inside the soil (root crops) such ascarrots and potatoes and crops grow over the soil such astomato and corn The optimal temperatures for these cropsare summarized in Table 1 [36ndash39]

Sensitivity defined in (1) is the difference between optimaltemperature of the crops and actual temperature (environ-ment temperature) Hatfield and Prueger [40] found thatthe productions of crops were reduced when the actualtemperature changes over 5∘C above the optimal temperatureof the crops Therefore we choose 5∘C as the first thresholdvalue in this procedure [40] Additionally the production ofphotosynthetic pigments is affected when the temperatureis 10∘C above the optimal growth temperature of the cropsThis can limit the photosynthesis and leads to crops damageTherefore 10∘C is set as the second threshold value in thisprocedure [41]

Sensitivity = 1003816100381610038161003816optimal temperature

minus environment temperature1003816100381610038161003816 (1)


Determine the optimaltemperature for each crop

Calculate the sensitivity

Put the sensor on sensitivecondition

Put the sensor on less sensitivecondition

Put the sensor on nonsensitivecondition

Maximum number of crops End

YesNo

Yes

No

YesNo

Sensitivity gt 10

ensitivity gt 5Sensitivity lt 10 amp S

Figure 4 Flow chart of classified based technique

5 Experimentation and Results

In this experiment the proposed method is implementedin Ns2 and simulated on two scenarios First the size ofnetwork topology is fixed and other network parameterssuch as amount of transmission rate and size of data packetsare constant We distribute 20 sensors of type S05-TH in20 greenhouses where each greenhouse has one sensor Inaddition we use cluster tree topology to connect GAN withNAN area networks Each sensor such as Reduced FunctionalDevice (RFD) senses and transmits environment data to arouter Then the router transmits the data to a GreenhousesArea Network Gateway (GGW) of Full Functional Devices(FFD) type to perform all network management tasks Thecognitive gateway is responsible for delivering the gathereddata to NAN server for broadcasting these data over WANMoreover it is used to store a copy of these data to make thenetwork more reliable In the second scenario network sizesare varied from 7 through 14 to 21 sensors These sensorsare formed using tree topology which send the data to a setof routers After that the routers resend the data to GGWFigure 5 shows the components of greenhouses area networkThe topology size was 100 times 105 Agricultural parametersin simulations are set according to standard values and in[20] as listed in Table 2 We determine the transmissiontime for each cluster and these intervals are summarized inTable 2

51 NetworkMetrics We focus on a set of important networkmetrics that are used to determine the quality of services(QoS) of any network We organized them as follows

511 Energy Consumption This parameter is important fordetermining the quality of networkThe energy consumptionis calculated in each node at fourmodes (receivemode trans-mit mode sleep mode and idle mode) Energy consumptionis summarized in the following equation [42]

Energy (120583J) = Current sdot Voltage sdot Time (2)

where

(i) current consumption is in Amperes(ii) voltage is in Volts(iii) time is in seconds

512 End-to-End Delay This parameter measures the timetaken to successfully deliver a data packet from sensornode to coordinator node including transmission time ofpacket turnaround time of transceiverrsquos (119879TA) backoff time(119879bo) interframe space time (119879IFS) and acknowledgmenttransmission time (119879ACK) End-to-end delay can be expressedin the following equation [43]

119879119897 = 119879packet + 119879bo + 119879TA + 119879IFS + 119879ACK (3)


Greenhouse area network(GAN)



GAN router

GAN router


Figure 5 Components of greenhouses area network (GAN)

119879packet is a transmission time of any data packet It can beexpressed as follows

119879packet =119871PHY + 119871MHR + payload + 119871MFR

119877data (4)

where(i) LPHY is size of physical header (byte)(ii) LMHR is size of MAC header (byte)(iii) payload is size of data in the packet (byte)(iv) LMFR is size of MAC footer (byte)

Now we should take into consideration the equation that isused to measure the backoff periods for each node in thenetwork This model can be determined by calculating thedevice probability (119875119904) of accessing the medium in successfulway 119875119904 can be measured by the following equation

119875119904 =119886=119887

sum119886=1

119875119888 (1 minus 119875119888)(119886minus1) (5)

where 119887 is the number of maximum backoff periods and 119875119888 isthe node probability to assess the idle channel at the end ofbackoff period

119875119888 = (1 minus 119902)119899minus1 (6)

where 119902 is the node probability to transmit at any time and 119899is the number of nodes that operate on the network

The average of backoff period (119877) is given as

119877 = (1 minus 119875119904) 119887 +119886=119887

sum119886=1

119886119875119888 (1 minus 119875119888)(119886minus1) (7)

Thus the total of backoff time (119879bo) can be measured as

119879bo = FractionalPart [119877] 119879bop (IntegerPart [119877] + 1)

+119886=IntegerPart[119877]

sum119886=1

119879bop (119886) (8)

where 119879bop is the average backoff period it is given as

119879bop (119886) =2macMinBe+119886minus1 minus 1119877data

119879boslot (9)

where

(i) macMinBE is initial value of backoff(ii) 119879boslot is backoff time at one slot duration equal to

duration of 20 symbols in IEEE 802154ZigBee


Table 2 Simulation parameters

Parameter ValuesSimulator Ns2Sensing area 105m times 100mNumber of greenhouses (nodes) in firstscenario 20

Number of routers in first scenarios 4Number of greenhouses (nodes) insecond scenario 7 14 21

Number of routers in second scenario 1 2 3Number of gateways in both scenarios 1Simulation time 1000 secRadio type IEEE 802154Frequency band 24GHzThe distances between sensors routerand gateway 10 meters

Antenna model Omni AntennaEnergy model MicaZTopology type Cluster treeItem to send 0Item size 16 bytesChannel access mechanism CSMA enabled

Traffic Constant Bit Rate(CBR)

(BO SO) Shown in Table 3Start time 15 secEnd time 1000 secTransmission time for sensitive cluster 1000 secTransmission time for less sensitivecluster

(23) sdotSimulation time

Transmission time for nonsensitivecluster


513 Average Jitter or Packet Delay Variation (PDV) PDVmeasures the variance of end-to-end delay value of packetsflow in single flow direction PDV can be expressed bymeasuring the difference in delay values for successfullyreceived packets summarized in the equation [44]

119869119894 =1003816100381610038161003816(119877119894+1 minus 119877119894) minus (119878119894+1 minus 119878119894)

1003816100381610038161003816 (10)

or by using

119869119894 =1003816100381610038161003816(119877119894+1 minus 119878119894+1) minus (119877119894 minus 119878119894)

1003816100381610038161003816 (11)

where

119878119894 is time when packet 119894 is sent from sender

119877119894 is time when packet 119894 is received from receiver

Through the simulation 119873 packets are sent from sender toreceiver for that we use the above definition to calculate jitterand then get the average

52 Backoff Period and Superframe Structure in IEEE 802154Backoff period is a chosen period that checks for channelclearance before packet transmission In WSN coordinatoris responsible for determining the tasks for each node whichallows the nodes to accomplish their tasks simultaneouslyFull Functional Devices (FFDs) such as coordinator areauthorized to send beacon frame Beacon frame from ZigBeeis a new technique to let coordinator identify and synchronizesensor of type Reduced Functional Devices (RFDs) BeaconInterval (BI) consists of two parts as summarized in Figure 6

(i) Active period is divided into 16 time slots and deter-mined by SuperframeDuration (SD) SD is composedof Contention Access Period (CAP) and ContentionFree Period (CFP) In CAP all RFDs try to accessthe channel simultaneously in ideal mode while inCFP all the packets owned by a specific node areguaranteed to transmit on the channel this way iscalled Guaranteed Time Slot (GTS) [3 4]

(ii) Inactive period all nodes and their coordinator are insleep mode [3 4]

Coordinator is responsible for choosing BI periodwherein Beacon Order (BO) determines the Beacon Intervalwhile Superframe Duration is expressed in terms of Super-frameOrder (SO)The duty cycle of each node in activemodecan be identified by the values of (BO SO) Both CFP andCAP are referred to as the Active Period which is the timewhen the active nodes use the channel and is referred to asSuper Frame Duration (SD) We measure the BI value byusing BO value and SD value can be measured by using SOvalue [45ndash48]

BI = aBaseSuperframeDuration sdot 2BO

SD = aBaseSuperframeDuration sdot 2SO(12)

where 0 le SO le BO le 14

53 Results Wemake two scenarios to measure the previousmetrics as follows

531 By Changing the Backoff Period In this scenario we testthe network by changing the backoff period for each testThevalues of (BO SO) are changed from (1 1) to (5 5) Thesevalues are used by coordinator to synchronize between sen-sors which is a chosen period that checks channel clearancebefore packet transmission

By examining this period we determine the value of thisparameter that gives the best synchronization between nodesto reduce delay and power consumption The test is repeated10 times to ensure the quality of results Table 3 summarizesthe comparison between classified based approach and non-classified based approach in terms of energy consumptionaverage end-to-enddelay and average jitter including various(BO SO) values The simulation results show that classifiedbased approach outperforms nonclassified based approachSpecifically the energy consumption is reduced by 294end-to-end delay is improved by 416 and average jitter isimproved by 399


Transmitted by network coordinator containing network informationframe structure and notification of pending node messages

Space reserved for beacon growth due to pending node messages

Access by any node using CSMA-CS

Network beacon

Beacon extensionperiod

Contentionperiod

GuaranteedTime Slot

GTS2 GTS1

Contention Access Period Contention Free Period

Where 0 ge n ge 1415 ms lowast 2n

Reserved for nodes requiring guaranteed bandwidth [n = 0]

Figure 6 Superframe structure of IEEE 802154 MAC [21]

Table 3 Comparison between classified based approach and nonclassified based approach

(BO SO)Energy consumption Average delay (S) Average jitter (S)

Nonclassifiedbased approach

Classified basedapproach

Nonclassified basedapproach




(1 1) 5430595 382021 0080376 0011147 0016831 0010891(2 1) 386659 2970488 0507429 005402 0496642 0048795(2 2) 4414995 305634 0007193 0006783 000714 0006717(3 1) 3425723 257706 0012856 0012528 0012565 0012169(3 2) 341323 258846 0353966 0010849 0340354 0010489(3 3) 3817103 272464 000779 0006512 0007687 0006485(4 1) 3299546 2423932 0126403 0072692 0125846 0071958(4 2) 3372698 2506478 0043967 0019212 0043512 0018852(4 3) 3362108 263793 0019911 0012546 0013207 0011956(4 4) 3513385 2446568 0019227 001082 0018857 0010439(5 1) 3423613 247945 3631231 3086479 3630403 3065488(5 2) 3112943 2325575 0374811 0119828 0369399 011773(5 3) 3045893 239331 03783 0130883 0374485 0127778(5 4) 298724 2355568 027799 013272 0273567 0129005(5 5) 3479038 2371425 1311405 0173336 1311013 0169969

The energy consumed for both classified and nonclassi-fied based approaches including various BO and SO valuesis shown Figure 7The classified based approach outperformsnonclassified based approach as (1 1) has the largest amountof energy consumption between (BO SO) values while (5 4)has consumed the lowest amount of energy between (BO SO)values The rest of (BO SO) values have the medium energyconsumption values

Figure 8 shows information about the average end-to-end delay for both classified and nonclassified basedapproaches including various BO and SO values We cannotice that classified based approach outperforms nonclas-sified based approach as (5 1) has the largest amountof delay between (BO SO) values while (2 2) has had

the lowest amount of delay between (BO SO) valuesThe rest of (BO SO) values have the medium delayvalues

Figure 9 shows information about the average jitter forboth classified and nonclassified based approaches includingvarious BO and SO valuesWe can notice that classified basedapproach outperforms nonclassified based approach as (5 1)has the largest amount of jitter between (BO SO) valueswhile (2 2) has had the lowest amount of jitter between(BO SO) valuesThe rest of (BO SO) values have themediumjitter values

532 By Changing the Number of Nodes In this scenariothe number of nodes in each test is changed to examine the


Ener

gy co

nsum

ptio

n (m

Wh)

Nonclassified based approachClassified based approach

543

0595

382

021

386

659

297

0488

441

4995

342

5723

257

706

341

323

258

846

305

5634

381

7103

329

9546

242

3932 337

2698

250

6478

272

464

263

793

336

2108

351

3385

244

6568 3

4236

132

4793

5

233

5575

311

2943

304

5893

239

331

298

724

235

5568

237

1425347

9038

(BO SO)

0

1

2

3

4

5

6

(2 1) (2 2) (3 1) (3 2) (3 3) (5 4)(4 2) (4 3) (4 4) (5 1) (5 2) (5 3) (5 5)(1 1) (4 1)

Figure 7 Classified based approach versus nonclassified based approach in terms of energy consumption for first scenario

363

1231

308

6479

007

79 037

83

008

0376

050

7429

001

1147

000

7193

005

402

001

2856

000

6783

035

3966

001

2528

001

0849

012

6403

000

6512

004

3967

007

2692

001

9911

001

9212

001

9227

001

2546

001

082

037

4811

011

9828

027

799

013

0883

131

1405

013

272

017

336

(BO SO)

0

05

1

15

2

25

3

35

4

Aver

age e

nd-to

-end

del

ay (S

)


(1 1) (2 1) (2 2) (3 1) (3 2) (3 3) (4 1) (4 2) (4 3) (4 4) (5 1) (5 3) (5 4) (5 5)(5 2)

Figure 8 Classified based approach versus nonclassified based approach in terms of end-to-end delay for first scenario

stability of the network and this is repeated 10 times to ensurethe quality of results

(1) GAN contains seven nodes which are used to send thedata to single router after that router resends the datato one gateway

(2) GAN contains 14 nodes to send the data to tworouters after that routers resend the data to onegateway

(3) GAN contains 21 nodes to send the data to threerouters after that routers resend the data to onegateway

Figure 10 shows information about the energy consumedfor both classified and nonclassified based approaches underthe condition of changing the number of nodes (from 7nodes through 14 nodes to 21 nodes) We can notice thatthe classified based approach consumed less power thannonclassified based approach in the different conditions

Figure 11 shows information about the average end-to-end delay for both classified and nonclassified basedapproaches under the condition of changing the number ofnodes (from 7 nodes through 14 nodes to 21 nodes) We cannotice that the classified based approach has less delay thannonclassified based approach in the different conditions

Figure 12 shows information about the average jitter forboth classified and nonclassified based approaches under


001

6831

001

0891

360

1403

306

5488

049

6642

000

714

004

8795

001

2565

000

6717

034

0354

001

2169

000

7687

001

0489

012

5846

000

6485

004

3512

007

1958

001

3207

001

8852

001

8857

001

1956

001

0439

036

9399

037

4485

011

773

027

3567

012

778

131

1013

012

9005

016

9969

(BO SO)

0

05

1

15

2

25

3

35

4Av

erag

e jitt

er (S

)


(1 1) (2 1) (2 2) (3 1) (3 2) (3 3) (4 1) (4 2) (4 3) (4 4) (5 1) (5 2) (5 3) (5 4) (5 5)

Figure 9 Classified based approach versus nonclassified based approach in terms of average jitter for first scenario

343

902

353

5433

368

6067

263

197

288

0097

319

166


14 217Number of nodes

0

05

1

15

2

25

3

35

4

Ener

gy co

nsum

ptio

n (m

Wh)

Figure 10 Classified based approach versus nonclassified basedapproach in terms of energy consumption for second scenario

the condition of changing the number of nodes (from 7nodes through 14 nodes to 21 nodes) We can notice thatthe classified based approach has less average jitter thannonclassified based approach in the different conditions

6 Discussion

The nature of cultivation in agriculture areas is based onwide area of crop deployments The traditional agriculture

003

0715

005

2149 005

7866

002

3598 003

0372

003

4572



0

001

002

003

004

005

006

007

Aver

age e

nd-to

-end

del

ay (S

)

Figure 11 Classified based approach versus nonclassified basedapproach in terms of end-to-end delay for second scenario

monitoring network operates on dense network by formingmultihop network to cover a large area and connect thegreenhouses with control center However WAMN suffersfrom energy consumption and packet collision as all thenodes continuously monitor the crops In case of droppedmessages retransmission can cause more energy consump-tion and higher delays Therefore we proposed a classifiedbased approach to reduce energy consumption and network


002

8295

004

9153 0

0549

29

002

0787

002

8714

003

2738

Aver

age j

itter

(S)



0

001

002

003

004

005

006

Figure 12 Classified based approach versus nonclassified basedapproach in terms of average jitter for second scenario

delay in WAMN Our approach selects a set of sensors inthe network according to the sensitivity of the crops Thesensitivity is calculated by measuring the difference betweenoptimal temperature of the crops and actual temperature(environment temperature) Additionally we proposed amultilayer architecture network model that enables the landsto be controlled and monitored remotely The proposedapproach is compared with nonclassified based approachin two scenarios by changing the backoff periods and thenumber of nodes in first and second scenarios respectivelyThe simulation results indicate that the proposed classifiedbased approach outperforms nonclassified based approach byreducing energy consumption by 294 improving end-to-end delay by 416 and average jitter by 399

7 Conclusion

From literature review most researchers focus on buildingagricultural monitoring network but the quality of servicesand stability of the network are ignored Furthermore theprior works monitor all crops equally without classifyingThis consumes more battery power and reduces the life timeof the network Additionally the prior works are untested inlarge scale agriculture monitoring network

In this paper we proposed a classified based approachfor large scale agriculture monitoring network We examinethe factors affecting the QoS of the proposed approach suchas energy consumption and end-to-end delay Our findingsdemonstrate that utilizing a set of sensors rather than allthe sensors in the network reduced the power consumptionand delay This provides a high quality of services for the

agriculture monitoring network Furthermore the proposedapproach improves the traditional approach by 294 foraverage energy consumption 416 for an average end-to-end delay and 399 for average jitter

In future we will apply the proposed approach in real-lifeagricultural monitoring network through integration withcloud computing to facilitate monitoring accessibility andthe process of storing the data

Disclosure

The work was deduced from Hishamrsquos PhD thesis as DrIsmail Ahmedy and Dr Mohd Yamani Idna Idris supervisedhim along his study

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

The authors acknowledge University of Malaya for thefinancial support (UMRG Grants BK043-2015 and RP036A-15AET) and facilitating carrying out the work

References

[1] I F Akyildiz W Su Y Sankarasubramaniam and E Cayirci ldquoAsurvey on sensor networksrdquo IEEE Communications Magazinevol 40 no 8 pp 102ndash105 2002

[2] L Selavo A Wood Q Cao et al ldquoLUSTER wireless sensornetwork for environmental researchrdquo in Proceedings of the 5thACM International Conference on Embedded Networked SensorSystems (SenSys rsquo07) pp 103ndash116 ACM November 2007

[3] A Koubaa M Alves and E Tovar ldquoA comprehensive simu-lation study of slotted CSMACA for IEEE 802154 WirelessSensor Networksrdquo in Proceedings of the IEEE InternationalWorkshop on Factory Communication Systems (WFCS rsquo06) pp183ndash192 June 2006

[4] A Koubaa Promoting Quality of Service in Wireless Sensor Net-works vol 3 Habilitation Qualification in Computer ScienceNational School of Engineering Sfax Tunisia 2011

[5] G Anastasi M Conti M Di Francesco and A PassarellaldquoEnergy conservation in wireless sensor networks a surveyrdquoAdHoc Networks vol 7 no 3 pp 537ndash568 2009

[6] P Park ldquoProtocol design for control applications using wirelesssensor networksrdquo KTH Universitetsservice US-AB vol 10 no 1p 40 2009

[7] MAMVieira C N Coelho D C Da Silva and JMDaMataldquoSurvey on wireless sensor network devicesrdquo in Proceedings ofthe 2003 IEEE Conference on Emerging Technologies and FactoryAutomation ETFA 2003 pp 537ndash544 September 2003

[8] V Potdar A Sharif and E Chang ldquoWireless sensor networksa surveyrdquo in Proceedings of the International Conference onAdvanced Information Networking and Applications Workshops(WAINA rsquo09) pp 636ndash641 2009

[9] M Ilyas ldquoEmerging applications of sensor networksrdquo in Pro-ceedings of the 2nd Symposium on Wireless Sensors and CellularNetworks pp 13ndash17 Jeddah Saudi Arabia 2013


[10] L Bencini A Manes D Di Palma G Manes and G CollodiWireless Sensor Networks for On-Field Agricultural ManagementProcess INTECH Open Access Publisher 2010

[11] M Mancuso and F Bustaffa ldquoA wireless sensors network formonitoring environmental variables in a tomato greenhouserdquoin Proceedings of the WFCS 2006 2006 IEEE InternationalWorkshop on Factory Communication Systems pp 107ndash110 June2006

[12] J Gutierrez J F Villa-Medina A Nieto-Garibay and M APorta-Gandara ldquoAutomated irrigation system using a wirelesssensor network and GPRS modulerdquo IEEE Transactions onInstrumentation and Measurement vol 63 no 1 pp 166ndash1762014

[13] A Awasthi and S Reddy ldquoMonitoring for precision agricultureusing wireless sensor network-a reviewrdquo Global Journal ofComputer Science and Technology vol 13 no 7 pp 22ndash28 2013

[14] C Akshay N Karnwal K A Abhfeeth et al ldquoWireless sens-ing and control for precision Green house managementrdquo inProceedings of the 2012 6th International Conference on SensingTechnology ICST 2012 pp 52ndash56 ind December 2012

[15] W Qiu L Dong F Wang and H Yan ldquoDesign of intelligentgreenhouse environment monitoring system based on ZigBeeand embedded technologyrdquo in Proceedings of the 2014 IEEEInternational Conference on Consumer Electronics China ICCE-C 2014 April 2014

[16] Z Zhang and H Zhang ldquoDesign of wireless monitoring andwarning system for protected agriculture environmentrdquo inProceedings of the 2010 6th International Conference on WirelessCommunications Networking and Mobile Computing WiCOM2010 September 2010

[17] Y Zhou X Yang X Guo M Zhou and L Wang ldquoA design ofgreenhousemonitoringamp control systembased onZigBeewire-less sensor networkrdquo in Proceedings of the International Con-ference on Wireless Communications Networking and MobileComputing (WiCOM rsquo07) pp 2563ndash2567 Shanghai ChinaSeptember 2007

[18] S M Saad L M Kamarudin K Kamarudin et al ldquoA real-timegreenhouse monitoring system for mango withWireless SensorNetwork (WSN)rdquo in Proceedings of the 2014 2nd InternationalConference on ElectronicDesign ICED2014 pp 521ndash526 August2014

[19] N Pang ldquoZigBeemesh network for greenhousemonitoringrdquo inProceedings of the 2011 International Conference on MechatronicScience Electric Engineering and Computer MEC 2011 pp 266ndash269 August 2011

[20] WWang and S Cao ldquoApplication research on remote intelligentmonitoring system of greenhouse based on ZIGBEE WSNrdquo inProceedings of the 2009 2nd International Congress on Image andSignal Processing CISP rsquo09 October 2009

[21] X Li C J Bleakley and W Bober ldquoEnhanced beacon-enabledmode for improved IEEE 802154 low data rate performancerdquoWireless Networks vol 18 no 1 pp 59ndash74 2012

[22] T Ojha S Misra and N S Raghuwanshi ldquoWireless sensornetworks for agriculture the state-of-the-art in practice andfuture challengesrdquoComputers and Electronics in Agriculture vol118 pp 66ndash84 2015

[23] M Moghaddam Y Goykhman M Liu et al ldquoA wirelesssoil moisture smart sensor web using physics-based optimalcontrol concept and initial demonstrationsrdquo IEEE Journalof Selected Topics in Applied Earth Observations and RemoteSensing vol 3 no 4 pp 522ndash535 2010

[24] S E Dıaz J C Perez A C Mateos M-C Marinescu andB B Guerra ldquoA novel methodology for the monitoring ofthe agricultural production process based on wireless sensornetworksrdquo Computers and Electronics in Agriculture vol 76 no2 pp 252ndash265 2011

[25] I Demirkol and C Ersoy ldquoEnergy and delay optimized con-tention for wireless sensor networksrdquo Computer Networks vol53 no 12 pp 2106ndash2119 2009

[26] N G Shah U B Desai I Das N Merchant and S SYadav ldquoIN-field wireless sensor network (WSN) for estimatingevapotranspiration and leaf wetnessrdquo International AgriculturalEngineering Journal vol 18 no 3-4 pp 43ndash51 2009

[27] C Park and P H Chou ldquoAmbiMax autonomous energyharvesting platform for multi-supply wireless sensor nodesrdquo inProceedings of the 3rd Annual IEEE Communications Societyon Sensor and Ad Hoc Communications and Networks (SECONrsquo06) pp 168ndash177 Reston Va USA September 2006

[28] F I Simjee and P H Chou ldquoEfficient charging of superca-pacitors for extended lifetime of wireless sensor nodesrdquo IEEETransactions on Power Electronics vol 23 no 3 pp 1526ndash15362008

[29] A Matese S F Di Gennaro A Zaldei L Genesio and F PVaccari ldquoA wireless sensor network for precision viticultureTheNAV systemrdquoComputers and Electronics in Agriculture vol69 no 1 pp 51ndash58 2009

[30] R Morais M A Fernandes S G Matos C Serodio P J S GFerreira and M J C S Reis ldquoA ZigBee multi-powered wirelessacquisition device for remote sensing applications in precisionviticulturerdquo Computers and Electronics in Agriculture vol 62no 2 pp 94ndash106 2008

[31] J Panchard P Papadimitratos J-P Hubaux P R S Rao MS Sheshshayee and S Kumar ldquoWireless sensor networking forrain-fed farming decision supportrdquo in Proceedings of the ACMSIGCOMM 2008 Conference on Computer Communications -2nd ACM SIGCOMM Workshop on Networked Systems forDeveloping Regions NSDR rsquo08 pp 31ndash36 Seattle WashingtonDC USA August 2008

[32] K Konstantinos X Apostolos K Panagiotis and S GeorgeldquoTopology optimization in wireless sensor networks for preci-sion agriculture applicationsrdquo in Proceedings of the 2007 Inter-national Conference on Sensor Technologies and ApplicationsSENSORCOMM 2007 pp 526ndash530 October 2007

[33] C Ayday and S Safak ldquoApplication of wireless sensor networkswith GIS on the soil moisture distribution mappingrdquo in Pro-ceedings of the 16th International Symposium GIS Ostrava 2009- Seamless Geo-information Technologies pp 123ndash132 2009

[34] A-J Garcia-Sanchez F Garcia-Sanchez and J Garcia-HaroldquoWireless sensor network deployment for integrating video-surveillance and data-monitoring in precision agriculture overdistributed cropsrdquoComputers and Electronics in Agriculture vol75 no 2 pp 288ndash303 2011

[35] J Zhang Z Xie J Zhang et al ldquoHigh temperature PEM fuelcellsrdquo Journal of Power Sources vol 160 no 2 pp 872ndash891 2006

[36] D Pimentel ldquoClimate changes and food supplyrdquo Forum forApplied Research amp Public Policy vol 8 no 4 pp 54ndash60 1993

[37] S Sato M M Peet and J F Thomas ldquoPhysiological factorslimit fruit set of tomato (Lycopersicon esculentumMill) underchronic mild heat stressrdquo Plant Cell and Environment vol 23no 7 pp 719ndash726 2000

[38] M Karlsson Growing Cucumbers in Greenhouses University ofAlaska FairbanksCooperative Extension Service in cooperationwith the United States Department of Agriculture 2014


[39] T K Lim Edible Medicinal and Non Medicinal Plants SpringerNetherlands Dordrecht 2014

[40] J L Hatfield and J H Prueger ldquoTemperature extremes effect onplant growth and developmentrdquoWeather and Climate Extremesvol 10 pp 4ndash10 2015

[41] M Hasanuzzaman K Nahar and M Fujita Extreme Temper-ature Responses Oxidative Stress And Antioxidant Defense inPlants INTECH Open Access Publisher 2013

[42] M G Torres Energy Consumption in Wireless Sensor NetworksUsing GSP [PhD thesis] Doctoral Dissertation at University ofPittsburgh 2006

[43] M Hamdan H A Shehadeh and Q Y Obeidat ldquoMulti-Objective Optimization of ElectrocardiogramMonitoring Net-work for Elderly Patient inHomerdquo International Journal of OpenProblems in Computer Science andMathematics vol 8 no 1 pp82ndash95 2015

[44] V N Talooki and K Ziarati ldquoPerformance comparison ofrouting protocols for mobile ad hoc networksrdquo in Proceedingsof the 2006 Asia-Pacific Conference on Communications APCCSeptember 2006

[45] H Lee K Lee S Ryu S Lee K Song and Y Shin ldquoAn efficientslotted CSMACA algorithm for the IEEE 802154 LR-WPANrdquoin Proceedings of the International Conference on InformationNetworking 2011 ICOIN 2011 pp 488ndash493 January 2011

[46] L Krishnamurthy R Adler P Buonadonna et al ldquoDesign anddeployment of industrial sensor networks experiences froma semiconductor plant and the North Seardquo in Proceedings ofthe 3rd ACM International Conference on Embedded NetworkedSensor Systems (SenSys rsquo05) pp 64ndash75 ACM San Diego CalifUSA November 2005

[47] P Park C Fischione and K H Johansson ldquoAdaptive IEEE802154 protocol for energy efficient reliable and timely com-municationsrdquo in Proceedings of the 9th ACMIEEE InternationalConference on Information Processing in Sensor Networks IPSN2010 pp 327ndash338 April 2010

[48] B AM Bouman S Peng A R Castaneda and RM VisperasldquoYield and water use of irrigated tropical aerobic rice systemsrdquoAgriculturalWater Management vol 74 no 2 pp 87ndash105 2005

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Active and Passive Electronic Components

Control Scienceand Engineering

Journal of


International Journal of

RotatingMachinery


Hindawi Publishing Corporation httpwwwhindawicom

Journal of

Volume 201

Submit your manuscripts athttpswwwhindawicom

VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

Acoustics and VibrationAdvances in



Electrical and Computer Engineering

Journal of

Advances inOptoElectronics


Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation



DistributedSensor Networks



Sink node

Agriculturenode 4

Agriculturenode 2

Agriculturenode 1

Agriculturenode 3

Remote server

Web interface

Figure 1 Agriculture monitoring network

and Wide Area Network Furthermore the characteristic ofa monitoring system also contributes to the aforementionedproblem as large data from continuous monitoring inducemessage collision and packet drop Retransmission of thedrop messages increases the energy consumption and delay

Implementing the existing WAMN architecture in largescale and large distributed area can be further challengingas the monitoring and data transmission are done contin-uously without sensitivity classification Consequently theunnecessary data transmission from all nodes in a networkcan lead to inefficient use of energy Therefore we proposea classified based approach to classify crops to a set ofclusters based on their sensitivity of agricultural conditionsSensitivity levels of 5∘C and 10∘C are introduced in this paperThe two levels of sensitivity are chosen based on the optimaltemperature condition that will be discussed further in latersection

To evaluate the effectiveness of our proposed methodtwo tests are conducted These tests investigate the effects ofbackoff period and number of nodes Energy consumptionend-to-end delay and jitter are thenmeasured and comparedwith the nonclassified based approach

This paper is organized as follows Section 2 presentsthe challenges and related works of agriculture monitoringnetwork Section 3 shows a Smart Agriculture MonitoringNetwork (SAMN) architecture model The classified basedtechnique and the proposed methodology are presented inSection 4 Section 5 describes the details of the experiment

and reports the results Section 6 presents the discussion andwe conclude the work in Section 7

2 Literature Review

This section presents the challenges and related works ofagriculture monitoring network We organize this section asfollows

21 Challenges of GreenhousesMonitoring Network Thechal-lenges of applying agriculture monitoring network in real lifeare summarized as follows

(i) Network scalability the nature of agriculturemonitor-ing network based on the wide area of deploymentthis makes the network very sensitive to failure andreduces the performance of network [12 22 23] Todiminish this challenge hierarchical architecture stylewould perform better than the simple style (flat singletier network architecture)

(ii) Simplicity we should consider that the end usershave limited knowledge of WSN Thus the designedsolutions and platforms should be user-friendly andeasy to use [24]

(iii) Real-time monitoring most of previous works donot focus on the issue of real-time monitoring ofthe crops [24] This factor has a strong relationshipwith network metrics that measure the quality of







Start




Send data

End

NoYes






Greenhouse

Distribution

GAN

Transmission

NANWAN

Control center

Control center

(a)





Base station

Control center




(WAN)





Base station

Control center

Base station

(b)

























YesNo

Yes

No

YesNo

Sensitivity gt 10








where








GAN router

GAN router





119877data (4)



119875119904 =119886=119887

sum119886=1

119875119888 (1 minus 119875119888)(119886minus1) (5)


119875119888 = (1 minus 119902)119899minus1 (6)



119877 = (1 minus 119875119904) 119887 +119886=119887

sum119886=1

119886119875119888 (1 minus 119875119888)(119886minus1) (7)




sum119886=1

119879bop (119886) (8)



119879boslot (9)

where
















1003816100381610038161003816 (10)

or by using


1003816100381610038161003816 (11)

where


















Network beacon


Contentionperiod

GuaranteedTime Slot

GTS2 GTS1













(1 1) 5430595 382021 0080376 0011147 0016831 0010891(2 1) 386659 2970488 0507429 005402 0496642 0048795(2 2) 4414995 305634 0007193 0006783 000714 0006717(3 1) 3425723 257706 0012856 0012528 0012565 0012169(3 2) 341323 258846 0353966 0010849 0340354 0010489(3 3) 3817103 272464 000779 0006512 0007687 0006485(4 1) 3299546 2423932 0126403 0072692 0125846 0071958(4 2) 3372698 2506478 0043967 0019212 0043512 0018852(4 3) 3362108 263793 0019911 0012546 0013207 0011956(4 4) 3513385 2446568 0019227 001082 0018857 0010439(5 1) 3423613 247945 3631231 3086479 3630403 3065488(5 2) 3112943 2325575 0374811 0119828 0369399 011773(5 3) 3045893 239331 03783 0130883 0374485 0127778(5 4) 298724 2355568 027799 013272 0273567 0129005(5 5) 3479038 2371425 1311405 0173336 1311013 0169969







Ener

gy co

nsum

ptio

n (m

Wh)


543

0595

382

021

386

659

297

0488

441

4995

342

5723

257

706

341

323

258

846

305

5634

381

7103

329

9546

242

3932 337

2698

250

6478

272

464

263

793

336

2108

351

3385

244

6568 3

4236

132

4793

5

233

5575

311

2943

304

5893

239

331

298

724

235

5568

237

1425347

9038

(BO SO)

0

1

2

3

4

5

6

(2 1) (2 2) (3 1) (3 2) (3 3) (5 4)(4 2) (4 3) (4 4) (5 1) (5 2) (5 3) (5 5)(1 1) (4 1)


363

1231

308

6479

007

79 037

83

008

0376

050

7429

001

1147

000

7193

005

402

001

2856

000

6783

035

3966

001

2528

001

0849

012

6403

000

6512

004

3967

007

2692

001

9911

001

9212

001

9227

001

2546

001

082

037

4811

011

9828

027

799

013

0883

131

1405

013

272

017

336

(BO SO)

0

05

1

15

2

25

3

35

4

Aver

age e

nd-to

-end

del

ay (S

)


(1 1) (2 1) (2 2) (3 1) (3 2) (3 3) (4 1) (4 2) (4 3) (4 4) (5 1) (5 3) (5 4) (5 5)(5 2)










001

6831

001

0891

360

1403

306

5488

049

6642

000

714

004

8795

001

2565

000

6717

034

0354

001

2169

000

7687

001

0489

012

5846

000

6485

004

3512

007

1958

001

3207

001

8852

001

8857

001

1956

001

0439

036

9399

037

4485

011

773

027

3567

012

778

131

1013

012

9005

016

9969

(BO SO)

0

05

1

15

2

25

3

35

4Av

erag

e jitt

er (S

)


(1 1) (2 1) (2 2) (3 1) (3 2) (3 3) (4 1) (4 2) (4 3) (4 4) (5 1) (5 2) (5 3) (5 4) (5 5)


343

902

353

5433

368

6067

263

197

288

0097

319

166



0

05

1

15

2

25

3

35

4

Ener

gy co

nsum

ptio

n (m

Wh)



6 Discussion


003

0715

005

2149 005

7866

002

3598 003

0372

003

4572



0

001

002

003

004

005

006

007

Aver

age e

nd-to

-end

del

ay (S

)




002

8295

004

9153 0

0549

29

002

0787

002

8714

003

2738

Aver

age j

itter

(S)



0

001

002

003

004

005

006



7 Conclusion





Disclosure




Acknowledgments


References



















































RoboticsJournal of





Journal of



RotatingMachinery



Journal of

Volume 201


VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation















Start




Send data

End

NoYes






Greenhouse

Distribution

GAN

Transmission

NANWAN

Control center

Control center

(a)





Base station

Control center




(WAN)





Base station

Control center

Base station

(b)

























YesNo

Yes

No

YesNo

Sensitivity gt 10








where








GAN router

GAN router





119877data (4)



119875119904 =119886=119887

sum119886=1

119875119888 (1 minus 119875119888)(119886minus1) (5)


119875119888 = (1 minus 119902)119899minus1 (6)



119877 = (1 minus 119875119904) 119887 +119886=119887

sum119886=1

119886119875119888 (1 minus 119875119888)(119886minus1) (7)




sum119886=1

119879bop (119886) (8)



119879boslot (9)

where
















1003816100381610038161003816 (10)

or by using


1003816100381610038161003816 (11)

where


















Network beacon


Contentionperiod

GuaranteedTime Slot

GTS2 GTS1













(1 1) 5430595 382021 0080376 0011147 0016831 0010891(2 1) 386659 2970488 0507429 005402 0496642 0048795(2 2) 4414995 305634 0007193 0006783 000714 0006717(3 1) 3425723 257706 0012856 0012528 0012565 0012169(3 2) 341323 258846 0353966 0010849 0340354 0010489(3 3) 3817103 272464 000779 0006512 0007687 0006485(4 1) 3299546 2423932 0126403 0072692 0125846 0071958(4 2) 3372698 2506478 0043967 0019212 0043512 0018852(4 3) 3362108 263793 0019911 0012546 0013207 0011956(4 4) 3513385 2446568 0019227 001082 0018857 0010439(5 1) 3423613 247945 3631231 3086479 3630403 3065488(5 2) 3112943 2325575 0374811 0119828 0369399 011773(5 3) 3045893 239331 03783 0130883 0374485 0127778(5 4) 298724 2355568 027799 013272 0273567 0129005(5 5) 3479038 2371425 1311405 0173336 1311013 0169969







Ener

gy co

nsum

ptio

n (m

Wh)


543

0595

382

021

386

659

297

0488

441

4995

342

5723

257

706

341

323

258

846

305

5634

381

7103

329

9546

242

3932 337

2698

250

6478

272

464

263

793

336

2108

351

3385

244

6568 3

4236

132

4793

5

233

5575

311

2943

304

5893

239

331

298

724

235

5568

237

1425347

9038

(BO SO)

0

1

2

3

4

5

6

(2 1) (2 2) (3 1) (3 2) (3 3) (5 4)(4 2) (4 3) (4 4) (5 1) (5 2) (5 3) (5 5)(1 1) (4 1)


363

1231

308

6479

007

79 037

83

008

0376

050

7429

001

1147

000

7193

005

402

001

2856

000

6783

035

3966

001

2528

001

0849

012

6403

000

6512

004

3967

007

2692

001

9911

001

9212

001

9227

001

2546

001

082

037

4811

011

9828

027

799

013

0883

131

1405

013

272

017

336

(BO SO)

0

05

1

15

2

25

3

35

4

Aver

age e

nd-to

-end

del

ay (S

)


(1 1) (2 1) (2 2) (3 1) (3 2) (3 3) (4 1) (4 2) (4 3) (4 4) (5 1) (5 3) (5 4) (5 5)(5 2)










001

6831

001

0891

360

1403

306

5488

049

6642

000

714

004

8795

001

2565

000

6717

034

0354

001

2169

000

7687

001

0489

012

5846

000

6485

004

3512

007

1958

001

3207

001

8852

001

8857

001

1956

001

0439

036

9399

037

4485

011

773

027

3567

012

778

131

1013

012

9005

016

9969

(BO SO)

0

05

1

15

2

25

3

35

4Av

erag

e jitt

er (S

)


(1 1) (2 1) (2 2) (3 1) (3 2) (3 3) (4 1) (4 2) (4 3) (4 4) (5 1) (5 2) (5 3) (5 4) (5 5)


343

902

353

5433

368

6067

263

197

288

0097

319

166



0

05

1

15

2

25

3

35

4

Ener

gy co

nsum

ptio

n (m

Wh)



6 Discussion


003

0715

005

2149 005

7866

002

3598 003

0372

003

4572



0

001

002

003

004

005

006

007

Aver

age e

nd-to

-end

del

ay (S

)




002

8295

004

9153 0

0549

29

002

0787

002

8714

003

2738

Aver

age j

itter

(S)



0

001

002

003

004

005

006



7 Conclusion





Disclosure




Acknowledgments


References



















































RoboticsJournal of





Journal of



RotatingMachinery



Journal of

Volume 201


VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Greenhouse

Distribution

GAN

Transmission

NANWAN

Control center

Control center

(a)





Base station

Control center




(WAN)





Base station

Control center

Base station

(b)

























YesNo

Yes

No

YesNo

Sensitivity gt 10








where








GAN router

GAN router





119877data (4)



119875119904 =119886=119887

sum119886=1

119875119888 (1 minus 119875119888)(119886minus1) (5)


119875119888 = (1 minus 119902)119899minus1 (6)



119877 = (1 minus 119875119904) 119887 +119886=119887

sum119886=1

119886119875119888 (1 minus 119875119888)(119886minus1) (7)




sum119886=1

119879bop (119886) (8)



119879boslot (9)

where
















1003816100381610038161003816 (10)

or by using


1003816100381610038161003816 (11)

where


















Network beacon


Contentionperiod

GuaranteedTime Slot

GTS2 GTS1













(1 1) 5430595 382021 0080376 0011147 0016831 0010891(2 1) 386659 2970488 0507429 005402 0496642 0048795(2 2) 4414995 305634 0007193 0006783 000714 0006717(3 1) 3425723 257706 0012856 0012528 0012565 0012169(3 2) 341323 258846 0353966 0010849 0340354 0010489(3 3) 3817103 272464 000779 0006512 0007687 0006485(4 1) 3299546 2423932 0126403 0072692 0125846 0071958(4 2) 3372698 2506478 0043967 0019212 0043512 0018852(4 3) 3362108 263793 0019911 0012546 0013207 0011956(4 4) 3513385 2446568 0019227 001082 0018857 0010439(5 1) 3423613 247945 3631231 3086479 3630403 3065488(5 2) 3112943 2325575 0374811 0119828 0369399 011773(5 3) 3045893 239331 03783 0130883 0374485 0127778(5 4) 298724 2355568 027799 013272 0273567 0129005(5 5) 3479038 2371425 1311405 0173336 1311013 0169969







Ener

gy co

nsum

ptio

n (m

Wh)


543

0595

382

021

386

659

297

0488

441

4995

342

5723

257

706

341

323

258

846

305

5634

381

7103

329

9546

242

3932 337

2698

250

6478

272

464

263

793

336

2108

351

3385

244

6568 3

4236

132

4793

5

233

5575

311

2943

304

5893

239

331

298

724

235

5568

237

1425347

9038

(BO SO)

0

1

2

3

4

5

6

(2 1) (2 2) (3 1) (3 2) (3 3) (5 4)(4 2) (4 3) (4 4) (5 1) (5 2) (5 3) (5 5)(1 1) (4 1)


363

1231

308

6479

007

79 037

83

008

0376

050

7429

001

1147

000

7193

005

402

001

2856

000

6783

035

3966

001

2528

001

0849

012

6403

000

6512

004

3967

007

2692

001

9911

001

9212

001

9227

001

2546

001

082

037

4811

011

9828

027

799

013

0883

131

1405

013

272

017

336

(BO SO)

0

05

1

15

2

25

3

35

4

Aver

age e

nd-to

-end

del

ay (S

)


(1 1) (2 1) (2 2) (3 1) (3 2) (3 3) (4 1) (4 2) (4 3) (4 4) (5 1) (5 3) (5 4) (5 5)(5 2)










001

6831

001

0891

360

1403

306

5488

049

6642

000

714

004

8795

001

2565

000

6717

034

0354

001

2169

000

7687

001

0489

012

5846

000

6485

004

3512

007

1958

001

3207

001

8852

001

8857

001

1956

001

0439

036

9399

037

4485

011

773

027

3567

012

778

131

1013

012

9005

016

9969

(BO SO)

0

05

1

15

2

25

3

35

4Av

erag

e jitt

er (S

)


(1 1) (2 1) (2 2) (3 1) (3 2) (3 3) (4 1) (4 2) (4 3) (4 4) (5 1) (5 2) (5 3) (5 4) (5 5)


343

902

353

5433

368

6067

263

197

288

0097

319

166



0

05

1

15

2

25

3

35

4

Ener

gy co

nsum

ptio

n (m

Wh)



6 Discussion


003

0715

005

2149 005

7866

002

3598 003

0372

003

4572



0

001

002

003

004

005

006

007

Aver

age e

nd-to

-end

del

ay (S

)




002

8295

004

9153 0

0549

29

002

0787

002

8714

003

2738

Aver

age j

itter

(S)



0

001

002

003

004

005

006



7 Conclusion





Disclosure




Acknowledgments


References



















































RoboticsJournal of





Journal of



RotatingMachinery



Journal of

Volume 201


VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation




























YesNo

Yes

No

YesNo

Sensitivity gt 10








where








GAN router

GAN router





119877data (4)



119875119904 =119886=119887

sum119886=1

119875119888 (1 minus 119875119888)(119886minus1) (5)


119875119888 = (1 minus 119902)119899minus1 (6)



119877 = (1 minus 119875119904) 119887 +119886=119887

sum119886=1

119886119875119888 (1 minus 119875119888)(119886minus1) (7)




sum119886=1

119879bop (119886) (8)



119879boslot (9)

where
















1003816100381610038161003816 (10)

or by using


1003816100381610038161003816 (11)

where


















Network beacon


Contentionperiod

GuaranteedTime Slot

GTS2 GTS1













(1 1) 5430595 382021 0080376 0011147 0016831 0010891(2 1) 386659 2970488 0507429 005402 0496642 0048795(2 2) 4414995 305634 0007193 0006783 000714 0006717(3 1) 3425723 257706 0012856 0012528 0012565 0012169(3 2) 341323 258846 0353966 0010849 0340354 0010489(3 3) 3817103 272464 000779 0006512 0007687 0006485(4 1) 3299546 2423932 0126403 0072692 0125846 0071958(4 2) 3372698 2506478 0043967 0019212 0043512 0018852(4 3) 3362108 263793 0019911 0012546 0013207 0011956(4 4) 3513385 2446568 0019227 001082 0018857 0010439(5 1) 3423613 247945 3631231 3086479 3630403 3065488(5 2) 3112943 2325575 0374811 0119828 0369399 011773(5 3) 3045893 239331 03783 0130883 0374485 0127778(5 4) 298724 2355568 027799 013272 0273567 0129005(5 5) 3479038 2371425 1311405 0173336 1311013 0169969







Ener

gy co

nsum

ptio

n (m

Wh)


543

0595

382

021

386

659

297

0488

441

4995

342

5723

257

706

341

323

258

846

305

5634

381

7103

329

9546

242

3932 337

2698

250

6478

272

464

263

793

336

2108

351

3385

244

6568 3

4236

132

4793

5

233

5575

311

2943

304

5893

239

331

298

724

235

5568

237

1425347

9038

(BO SO)

0

1

2

3

4

5

6

(2 1) (2 2) (3 1) (3 2) (3 3) (5 4)(4 2) (4 3) (4 4) (5 1) (5 2) (5 3) (5 5)(1 1) (4 1)


363

1231

308

6479

007

79 037

83

008

0376

050

7429

001

1147

000

7193

005

402

001

2856

000

6783

035

3966

001

2528

001

0849

012

6403

000

6512

004

3967

007

2692

001

9911

001

9212

001

9227

001

2546

001

082

037

4811

011

9828

027

799

013

0883

131

1405

013

272

017

336

(BO SO)

0

05

1

15

2

25

3

35

4

Aver

age e

nd-to

-end

del

ay (S

)


(1 1) (2 1) (2 2) (3 1) (3 2) (3 3) (4 1) (4 2) (4 3) (4 4) (5 1) (5 3) (5 4) (5 5)(5 2)










001

6831

001

0891

360

1403

306

5488

049

6642

000

714

004

8795

001

2565

000

6717

034

0354

001

2169

000

7687

001

0489

012

5846

000

6485

004

3512

007

1958

001

3207

001

8852

001

8857

001

1956

001

0439

036

9399

037

4485

011

773

027

3567

012

778

131

1013

012

9005

016

9969

(BO SO)

0

05

1

15

2

25

3

35

4Av

erag

e jitt

er (S

)


(1 1) (2 1) (2 2) (3 1) (3 2) (3 3) (4 1) (4 2) (4 3) (4 4) (5 1) (5 2) (5 3) (5 4) (5 5)


343

902

353

5433

368

6067

263

197

288

0097

319

166



0

05

1

15

2

25

3

35

4

Ener

gy co

nsum

ptio

n (m

Wh)



6 Discussion


003

0715

005

2149 005

7866

002

3598 003

0372

003

4572



0

001

002

003

004

005

006

007

Aver

age e

nd-to

-end

del

ay (S

)




002

8295

004

9153 0

0549

29

002

0787

002

8714

003

2738

Aver

age j

itter

(S)



0

001

002

003

004

005

006



7 Conclusion





Disclosure




Acknowledgments


References



















































RoboticsJournal of





Journal of



RotatingMachinery



Journal of

Volume 201


VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation













GAN router

GAN router





119877data (4)



119875119904 =119886=119887

sum119886=1

119875119888 (1 minus 119875119888)(119886minus1) (5)


119875119888 = (1 minus 119902)119899minus1 (6)



119877 = (1 minus 119875119904) 119887 +119886=119887

sum119886=1

119886119875119888 (1 minus 119875119888)(119886minus1) (7)




sum119886=1

119879bop (119886) (8)



119879boslot (9)

where
















1003816100381610038161003816 (10)

or by using


1003816100381610038161003816 (11)

where


















Network beacon


Contentionperiod

GuaranteedTime Slot

GTS2 GTS1













(1 1) 5430595 382021 0080376 0011147 0016831 0010891(2 1) 386659 2970488 0507429 005402 0496642 0048795(2 2) 4414995 305634 0007193 0006783 000714 0006717(3 1) 3425723 257706 0012856 0012528 0012565 0012169(3 2) 341323 258846 0353966 0010849 0340354 0010489(3 3) 3817103 272464 000779 0006512 0007687 0006485(4 1) 3299546 2423932 0126403 0072692 0125846 0071958(4 2) 3372698 2506478 0043967 0019212 0043512 0018852(4 3) 3362108 263793 0019911 0012546 0013207 0011956(4 4) 3513385 2446568 0019227 001082 0018857 0010439(5 1) 3423613 247945 3631231 3086479 3630403 3065488(5 2) 3112943 2325575 0374811 0119828 0369399 011773(5 3) 3045893 239331 03783 0130883 0374485 0127778(5 4) 298724 2355568 027799 013272 0273567 0129005(5 5) 3479038 2371425 1311405 0173336 1311013 0169969







Ener

gy co

nsum

ptio

n (m

Wh)


543

0595

382

021

386

659

297

0488

441

4995

342

5723

257

706

341

323

258

846

305

5634

381

7103

329

9546

242

3932 337

2698

250

6478

272

464

263

793

336

2108

351

3385

244

6568 3

4236

132

4793

5

233

5575

311

2943

304

5893

239

331

298

724

235

5568

237

1425347

9038

(BO SO)

0

1

2

3

4

5

6

(2 1) (2 2) (3 1) (3 2) (3 3) (5 4)(4 2) (4 3) (4 4) (5 1) (5 2) (5 3) (5 5)(1 1) (4 1)


363

1231

308

6479

007

79 037

83

008

0376

050

7429

001

1147

000

7193

005

402

001

2856

000

6783

035

3966

001

2528

001

0849

012

6403

000

6512

004

3967

007

2692

001

9911

001

9212

001

9227

001

2546

001

082

037

4811

011

9828

027

799

013

0883

131

1405

013

272

017

336

(BO SO)

0

05

1

15

2

25

3

35

4

Aver

age e

nd-to

-end

del

ay (S

)


(1 1) (2 1) (2 2) (3 1) (3 2) (3 3) (4 1) (4 2) (4 3) (4 4) (5 1) (5 3) (5 4) (5 5)(5 2)










001

6831

001

0891

360

1403

306

5488

049

6642

000

714

004

8795

001

2565

000

6717

034

0354

001

2169

000

7687

001

0489

012

5846

000

6485

004

3512

007

1958

001

3207

001

8852

001

8857

001

1956

001

0439

036

9399

037

4485

011

773

027

3567

012

778

131

1013

012

9005

016

9969

(BO SO)

0

05

1

15

2

25

3

35

4Av

erag

e jitt

er (S

)


(1 1) (2 1) (2 2) (3 1) (3 2) (3 3) (4 1) (4 2) (4 3) (4 4) (5 1) (5 2) (5 3) (5 4) (5 5)


343

902

353

5433

368

6067

263

197

288

0097

319

166



0

05

1

15

2

25

3

35

4

Ener

gy co

nsum

ptio

n (m

Wh)



6 Discussion


003

0715

005

2149 005

7866

002

3598 003

0372

003

4572



0

001

002

003

004

005

006

007

Aver

age e

nd-to

-end

del

ay (S

)




002

8295

004

9153 0

0549

29

002

0787

002

8714

003

2738

Aver

age j

itter

(S)



0

001

002

003

004

005

006



7 Conclusion





Disclosure




Acknowledgments


References



















































RoboticsJournal of





Journal of



RotatingMachinery



Journal of

Volume 201


VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation






















1003816100381610038161003816 (10)

or by using


1003816100381610038161003816 (11)

where


















Network beacon


Contentionperiod

GuaranteedTime Slot

GTS2 GTS1













(1 1) 5430595 382021 0080376 0011147 0016831 0010891(2 1) 386659 2970488 0507429 005402 0496642 0048795(2 2) 4414995 305634 0007193 0006783 000714 0006717(3 1) 3425723 257706 0012856 0012528 0012565 0012169(3 2) 341323 258846 0353966 0010849 0340354 0010489(3 3) 3817103 272464 000779 0006512 0007687 0006485(4 1) 3299546 2423932 0126403 0072692 0125846 0071958(4 2) 3372698 2506478 0043967 0019212 0043512 0018852(4 3) 3362108 263793 0019911 0012546 0013207 0011956(4 4) 3513385 2446568 0019227 001082 0018857 0010439(5 1) 3423613 247945 3631231 3086479 3630403 3065488(5 2) 3112943 2325575 0374811 0119828 0369399 011773(5 3) 3045893 239331 03783 0130883 0374485 0127778(5 4) 298724 2355568 027799 013272 0273567 0129005(5 5) 3479038 2371425 1311405 0173336 1311013 0169969







Ener

gy co

nsum

ptio

n (m

Wh)


543

0595

382

021

386

659

297

0488

441

4995

342

5723

257

706

341

323

258

846

305

5634

381

7103

329

9546

242

3932 337

2698

250

6478

272

464

263

793

336

2108

351

3385

244

6568 3

4236

132

4793

5

233

5575

311

2943

304

5893

239

331

298

724

235

5568

237

1425347

9038

(BO SO)

0

1

2

3

4

5

6

(2 1) (2 2) (3 1) (3 2) (3 3) (5 4)(4 2) (4 3) (4 4) (5 1) (5 2) (5 3) (5 5)(1 1) (4 1)


363

1231

308

6479

007

79 037

83

008

0376

050

7429

001

1147

000

7193

005

402

001

2856

000

6783

035

3966

001

2528

001

0849

012

6403

000

6512

004

3967

007

2692

001

9911

001

9212

001

9227

001

2546

001

082

037

4811

011

9828

027

799

013

0883

131

1405

013

272

017

336

(BO SO)

0

05

1

15

2

25

3

35

4

Aver

age e

nd-to

-end

del

ay (S

)


(1 1) (2 1) (2 2) (3 1) (3 2) (3 3) (4 1) (4 2) (4 3) (4 4) (5 1) (5 3) (5 4) (5 5)(5 2)










001

6831

001

0891

360

1403

306

5488

049

6642

000

714

004

8795

001

2565

000

6717

034

0354

001

2169

000

7687

001

0489

012

5846

000

6485

004

3512

007

1958

001

3207

001

8852

001

8857

001

1956

001

0439

036

9399

037

4485

011

773

027

3567

012

778

131

1013

012

9005

016

9969

(BO SO)

0

05

1

15

2

25

3

35

4Av

erag

e jitt

er (S

)


(1 1) (2 1) (2 2) (3 1) (3 2) (3 3) (4 1) (4 2) (4 3) (4 4) (5 1) (5 2) (5 3) (5 4) (5 5)


343

902

353

5433

368

6067

263

197

288

0097

319

166



0

05

1

15

2

25

3

35

4

Ener

gy co

nsum

ptio

n (m

Wh)



6 Discussion


003

0715

005

2149 005

7866

002

3598 003

0372

003

4572



0

001

002

003

004

005

006

007

Aver

age e

nd-to

-end

del

ay (S

)




002

8295

004

9153 0

0549

29

002

0787

002

8714

003

2738

Aver

age j

itter

(S)



0

001

002

003

004

005

006



7 Conclusion





Disclosure




Acknowledgments


References



















































RoboticsJournal of





Journal of



RotatingMachinery



Journal of

Volume 201


VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation













Network beacon


Contentionperiod

GuaranteedTime Slot

GTS2 GTS1













(1 1) 5430595 382021 0080376 0011147 0016831 0010891(2 1) 386659 2970488 0507429 005402 0496642 0048795(2 2) 4414995 305634 0007193 0006783 000714 0006717(3 1) 3425723 257706 0012856 0012528 0012565 0012169(3 2) 341323 258846 0353966 0010849 0340354 0010489(3 3) 3817103 272464 000779 0006512 0007687 0006485(4 1) 3299546 2423932 0126403 0072692 0125846 0071958(4 2) 3372698 2506478 0043967 0019212 0043512 0018852(4 3) 3362108 263793 0019911 0012546 0013207 0011956(4 4) 3513385 2446568 0019227 001082 0018857 0010439(5 1) 3423613 247945 3631231 3086479 3630403 3065488(5 2) 3112943 2325575 0374811 0119828 0369399 011773(5 3) 3045893 239331 03783 0130883 0374485 0127778(5 4) 298724 2355568 027799 013272 0273567 0129005(5 5) 3479038 2371425 1311405 0173336 1311013 0169969







Ener

gy co

nsum

ptio

n (m

Wh)


543

0595

382

021

386

659

297

0488

441

4995

342

5723

257

706

341

323

258

846

305

5634

381

7103

329

9546

242

3932 337

2698

250

6478

272

464

263

793

336

2108

351

3385

244

6568 3

4236

132

4793

5

233

5575

311

2943

304

5893

239

331

298

724

235

5568

237

1425347

9038

(BO SO)

0

1

2

3

4

5

6

(2 1) (2 2) (3 1) (3 2) (3 3) (5 4)(4 2) (4 3) (4 4) (5 1) (5 2) (5 3) (5 5)(1 1) (4 1)


363

1231

308

6479

007

79 037

83

008

0376

050

7429

001

1147

000

7193

005

402

001

2856

000

6783

035

3966

001

2528

001

0849

012

6403

000

6512

004

3967

007

2692

001

9911

001

9212

001

9227

001

2546

001

082

037

4811

011

9828

027

799

013

0883

131

1405

013

272

017

336

(BO SO)

0

05

1

15

2

25

3

35

4

Aver

age e

nd-to

-end

del

ay (S

)


(1 1) (2 1) (2 2) (3 1) (3 2) (3 3) (4 1) (4 2) (4 3) (4 4) (5 1) (5 3) (5 4) (5 5)(5 2)










001

6831

001

0891

360

1403

306

5488

049

6642

000

714

004

8795

001

2565

000

6717

034

0354

001

2169

000

7687

001

0489

012

5846

000

6485

004

3512

007

1958

001

3207

001

8852

001

8857

001

1956

001

0439

036

9399

037

4485

011

773

027

3567

012

778

131

1013

012

9005

016

9969

(BO SO)

0

05

1

15

2

25

3

35

4Av

erag

e jitt

er (S

)


(1 1) (2 1) (2 2) (3 1) (3 2) (3 3) (4 1) (4 2) (4 3) (4 4) (5 1) (5 2) (5 3) (5 4) (5 5)


343

902

353

5433

368

6067

263

197

288

0097

319

166



0

05

1

15

2

25

3

35

4

Ener

gy co

nsum

ptio

n (m

Wh)



6 Discussion


003

0715

005

2149 005

7866

002

3598 003

0372

003

4572



0

001

002

003

004

005

006

007

Aver

age e

nd-to

-end

del

ay (S

)




002

8295

004

9153 0

0549

29

002

0787

002

8714

003

2738

Aver

age j

itter

(S)



0

001

002

003

004

005

006



7 Conclusion





Disclosure




Acknowledgments


References



















































RoboticsJournal of





Journal of



RotatingMachinery



Journal of

Volume 201


VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Ener

gy co

nsum

ptio

n (m

Wh)


543

0595

382

021

386

659

297

0488

441

4995

342

5723

257

706

341

323

258

846

305

5634

381

7103

329

9546

242

3932 337

2698

250

6478

272

464

263

793

336

2108

351

3385

244

6568 3

4236

132

4793

5

233

5575

311

2943

304

5893

239

331

298

724

235

5568

237

1425347

9038

(BO SO)

0

1

2

3

4

5

6

(2 1) (2 2) (3 1) (3 2) (3 3) (5 4)(4 2) (4 3) (4 4) (5 1) (5 2) (5 3) (5 5)(1 1) (4 1)


363

1231

308

6479

007

79 037

83

008

0376

050

7429

001

1147

000

7193

005

402

001

2856

000

6783

035

3966

001

2528

001

0849

012

6403

000

6512

004

3967

007

2692

001

9911

001

9212

001

9227

001

2546

001

082

037

4811

011

9828

027

799

013

0883

131

1405

013

272

017

336

(BO SO)

0

05

1

15

2

25

3

35

4

Aver

age e

nd-to

-end

del

ay (S

)


(1 1) (2 1) (2 2) (3 1) (3 2) (3 3) (4 1) (4 2) (4 3) (4 4) (5 1) (5 3) (5 4) (5 5)(5 2)










001

6831

001

0891

360

1403

306

5488

049

6642

000

714

004

8795

001

2565

000

6717

034

0354

001

2169

000

7687

001

0489

012

5846

000

6485

004

3512

007

1958

001

3207

001

8852

001

8857

001

1956

001

0439

036

9399

037

4485

011

773

027

3567

012

778

131

1013

012

9005

016

9969

(BO SO)

0

05

1

15

2

25

3

35

4Av

erag

e jitt

er (S

)


(1 1) (2 1) (2 2) (3 1) (3 2) (3 3) (4 1) (4 2) (4 3) (4 4) (5 1) (5 2) (5 3) (5 4) (5 5)


343

902

353

5433

368

6067

263

197

288

0097

319

166



0

05

1

15

2

25

3

35

4

Ener

gy co

nsum

ptio

n (m

Wh)



6 Discussion


003

0715

005

2149 005

7866

002

3598 003

0372

003

4572



0

001

002

003

004

005

006

007

Aver

age e

nd-to

-end

del

ay (S

)




002

8295

004

9153 0

0549

29

002

0787

002

8714

003

2738

Aver

age j

itter

(S)



0

001

002

003

004

005

006



7 Conclusion





Disclosure




Acknowledgments


References



















































RoboticsJournal of





Journal of



RotatingMachinery



Journal of

Volume 201


VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










001

6831

001

0891

360

1403

306

5488

049

6642

000

714

004

8795

001

2565

000

6717

034

0354

001

2169

000

7687

001

0489

012

5846

000

6485

004

3512

007

1958

001

3207

001

8852

001

8857

001

1956

001

0439

036

9399

037

4485

011

773

027

3567

012

778

131

1013

012

9005

016

9969

(BO SO)

0

05

1

15

2

25

3

35

4Av

erag

e jitt

er (S

)


(1 1) (2 1) (2 2) (3 1) (3 2) (3 3) (4 1) (4 2) (4 3) (4 4) (5 1) (5 2) (5 3) (5 4) (5 5)


343

902

353

5433

368

6067

263

197

288

0097

319

166



0

05

1

15

2

25

3

35

4

Ener

gy co

nsum

ptio

n (m

Wh)



6 Discussion


003

0715

005

2149 005

7866

002

3598 003

0372

003

4572



0

001

002

003

004

005

006

007

Aver

age e

nd-to

-end

del

ay (S

)




002

8295

004

9153 0

0549

29

002

0787

002

8714

003

2738

Aver

age j

itter

(S)



0

001

002

003

004

005

006



7 Conclusion





Disclosure




Acknowledgments


References



















































RoboticsJournal of





Journal of



RotatingMachinery



Journal of

Volume 201


VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










002

8295

004

9153 0

0549

29

002

0787

002

8714

003

2738

Aver

age j

itter

(S)



0

001

002

003

004

005

006



7 Conclusion





Disclosure




Acknowledgments


References



















































RoboticsJournal of





Journal of



RotatingMachinery



Journal of

Volume 201


VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation


















































RoboticsJournal of





Journal of



RotatingMachinery



Journal of

Volume 201


VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









an estimation of qos for classified based approach and...

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