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Performance Analysis of Zigbee based Wireless Sensor Network for Remote Patient Monitoring

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Abstract — Constant monitoring of patient’s health is essential to provide appropriate healthcare. With advances in electronic circuit miniaturization and micro-electromechanical systems (MEMS), wearable sensor nodes can be used to acquire
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  Performance Analysis of Zigbee based Wireless Sensor Network forRemotePatientMonitoring Rinki Sharma,Shreyas K. Gupta,Suhas K.K.andG. Srikanth KashyapDepartment of Computer Engineering,M.S.Ramaiah School of Advanced Studies,Bangalore, Indiarinki@msrsas.org,shrek06@gmail.com,suhas.best@gmail.comand kashyapg87@gmail.com  Abstract   —Constant monitoring of patient’s health is essentialto provide appropriate healthcare. With advances in electroniccircuit miniaturization and micro-electromechanical systems(MEMS), wearable sensor nodes can be used to acquirephysiological signals from patient’s body and transmit them toa remote location which can be accessed by the doctors.Wireless Sensor Networks (WSNs)are becoming an integralpart of healthcare systems, as they canovercome the need forpatients to be stationary while their vital parameters are beingchecked.In this paper we have studied and analyzed the effectof variation insenornodedensity, communication durationanddata transmission rate on network performancein termsof Packet Delivery Ratio (PDR), throughput, average network delay and energy consumed.Simulationsarecarried out usingns2.34. Obtainednetwork performanceis analyzed incomparison withtherequired performanceof variousphysiological signals used for patient healthmonitoring.Accomplished resultsprovide direction to choose appropriatenode density, data transmission rate and communicationduration for required performance while establishing Zigbeesensor based patient monitoring system in practical.  Keywords-Patient Monitoring;Wireless Sensor Networks(WSNs);Zigbee; PDR; Throughput; Latency; Energyconsumption, Network Simulation I.I  NTRODUCTION Constant monitoring ofhealth and vital parametersof  patientsisessentialto provideadequatehealthcare.As vital parameters and health of a patient can fluctuate over time,there should be a mechanism to constantly report them to thedoctors, nurses or caretakers, so that immediate care can begiven to the patient if required.In past, WSNs have beenusedto acquire data forindustrial monitoring and diagnostics[1,2]. Current trends in Telemedicine, Telecare, E-healthand E-medicine are aimed towards enhancing the presenthealthcare systems tocontinuously monitor the health of  patients through almost real-time updates of their medicalrecords [3, 4].Advances in electronic circuit miniaturization andMicro-electromechanical systems(MEMS)have provided smallsensor nodes which can be used to sense vital physiologicalsignals from patient’s body and transmit them to requireddestination over a wireless network[5].Sensor nodes presentinWireless Sensor Networks (WSNs)are resourceconstrained because they have limited battery power, processingpower and communication bandwidth.However,theyhave substantial data acquisition and distributedcomputing capabilities which make them crucial for sensingand monitoring applications.Patient health monitoring is one such application whereWSNs have found a key role [6, 7].Wearable physiologicalsensors can be placed on patient’s body to constantlymonitor vital parameters of the patient such as bodytemperature, blood pressure,respiratoryrate etc., andtransmittedto the doctor over a wireless network.With thehelp of such networks, doctors, nurses and care takerscanremotely and constantly monitor the health of the patients.Also, patients can move around withina given area whiletheir vital parameters are being checked.Zigbee is a low-cost,low-power standardforwirelessnetworks.Support of Zigbee for low powerconsumptionandlow data rate applications makesita vital choicefor sensor networks.Zigbee based sensing devices are widely beingused in healthcare for remote patient monitoring[8, 9].Zigbee based wearable physiological sensors can be used tomonitor the vital parameters of the patients and transmitthem to required destinationnode. Thedoctors can have a provision toaccess this informationandprovide requiredmedical careto the patients.Before establishing a network for patient healthmonitoring, it is essential to predict the behavior of thedesigned network in different network conditions with thehelp of different network scenarios.Network simulations play an important role in studying the networkperformanceunder different network conditions.Network simulator toolsgive provisionto perform such network analysis.ns2 isadiscrete event simulator, actively used in networkingresearch[10].In this paper, a Zigbeebased sensor network forpatient monitoring systemis simulatedin ns2.34. The performance of the simulated networkis analyzedin terms of PDR, averagenetworkdelay (latency), throughput andenergy consumption.The obtained performance isanalyzedin comparisonwiththerequired performance of  physiological signals used for patient health monitoring in practical.This paper is organized as follows. Section IIpresentssignificance of the studied parameters for patient monitoring.InSection IIItheaffectofvariation innode density(number of patients in a given area)and communication durationonnetwork performance is studiedthrough simulations.In 2014 Fourth International Conference on Communication Systems and Network Technologies 978-1-4799-3070-8/14 $31.00 © 2014 IEEEDOI 10.1109/CSNT.2014.2158   2014 Fourth International Conference on Communication Systems and Network Technologies 978-1-4799-3070-8/14 $31.00 © 2014 IEEEDOI 10.1109/CSNT.2014.2158  Section IV,the affect ofvariation innetwork loadand nodedensityis studied through simulations, andthenetwork  performanceis analyzed. Section V concludes the paper anddiscusses future work.II.SI GNIFICANCE OF S TUDIED P ARAMETERSFOR  P ATIENT M ONITORING In this paper, the performance of a Zigbee based WSNfor patient monitoringis analyzed, before establishing it in practical.Figure 1 depicts thescenario where vital parameters of patients can be transmittedover wirelessnetworkto the doctorpresent at a remote location,for constant monitoring.The patients can be either mobile or stationary.Each patient can beprovided with a wearable physiological sensor which measuresdifferentvital parameters and transmits them to a destination node, whichmakes them accessible to doctors, nurses or caretakers for constant patient health monitoring. Figure1: Patient Monitoring over Wireless Sensor Network  In the simulated network,the node at doctor’s end isrepresentedasPersonal Area Network (PAN)Coordinator,whilethepatients arerepresentedas mobile sensor nodes.Theperformance of the simulated network is studied interms of Packet Delivery Ratio (PDR), Throughput, Latencyand EnergyConsumption.Mobility of patients,leadstochange innetwork topologyaffectingPDR, network throughput and latency. Therefore,it isimportant to studythe variation of theseparametersthrough simulations,whileconsidering mobile nodes.Table Ipresentsperformance parameters forsome of the physiological signals which need to be monitored over WSN.This table is obtained from [11] T ABLE I:P ERFORMANCE P ARAMETERS FOR  P HYSIOLOGICAL S IGNALS In the simulation study, each node represents one BS(Body Sensor) node wearable by a patient, as shown inFigure 2.Therefore, the number of nodes in the network represents the number of BS nodes in the network, whichfurther represents each patient in the network.BS nodesconstantlymeasure physiological signalssuch as bodytemperature, blood pressure, pulse rate, etc.,andtransmitthemto thePAN coordinatorover long periods,so thatdoctors canextract clinically relevant informationfrom datacollected over time. Figure 2: Physiological Signals Aggregated at Body Sensor (BS) Node As the sensor nodes rely on limited battery power, energyconsumption over time is an importantcriterionto bestudied.Node density(number of patients in a given area)of a networkmay affect PDR, network throughput and latency,as the network bandwidthisshared among patients.Therefore,we have studied the effect of varying node densityin a given area.This studyhelps indecidingthe maximumnumber of patientsto be accommodatedin a given area toobtain requiredor acceptablenetwork performance.Sensor networks are primarily designed for low data rateapplications. However, depending on the operational needs,higherdata rates may be required. Therefore, wehave alsostudied the affect of variation indata rates on network  performance. This analysis can be used to identify theamountof traffic thenetwork can support while deliveringacceptable network performance.III.E FFECT OF V ARIATION IN  N ODE D ENSITYAND C OMMUNICATION D URATIONON  N ETWORK  P ERFORMANCE One of the importantissuesthat require attention duringsimulations is packet size.The physiological sensor data isquite small in size.However,sending multiple such smalldata packets increasesthe numberofpackets inthe wirelessnetwork, leading tonetwork congestion. In order to improvenetwork efficiency,the data from different physiologicalsensorsis aggregatedand 20 Bytes longdatapackets areformedat the Body Sensor (BS) node. Each BS noderepresents one patient. Network simulations are carried out in ns2.34 based onthe specifications given in Table II. In this set of simulations,the sensor node densityis variedas 9, 16, 25 and 35nodeswithinafixedterrain ofsize50m x50m, representing ahospital ward.This BS node aggregates physiological signalsreceived from other physiological sensorsonthepatient’s body,to produce 20 Bytes long packets. These 20 Bytes long packets arethen transmitted from BS to PAN coordinator. 59   59  In all the cases, one node actsasthePAN coordinator,while all the other nodes randomly transmit packets to thePAN coordinator.ThePAN coordinator is stationary,whileall the other nodes move around thePAN coordinator. T ABLE IIS CENARIO S PECIFICATIONS Simulation Parameters Routing ProtocolAODVMAC ProtocolIEEE 802.15.4Traffic TypeConstant Bit RateSimulation Time2000s, 4000s and6000sTerrain Size50m x50m Number of Nodes9, 16,25and35Mobility ModelRandom WaypointSpeed0.05ms -1 to 0.50ms -1 Pause Time60sPacket Size20BytesPacketTransmissionRate10packets per secondThroughout this paper, number of patients in a givenarea ismentionedas node densityorBSnode density.Allthe nodes (patients) move randomly in the network with anarea of 2500m 2 with wearable sensors. Node mobility withspeed of0.05ms -1 to 0.50ms -1 is considered to emulate patient mobility. Pause time of 60s is considered,assumingthat the patients may not move continuously. In order toextract clinically relevant information,doctors need tomonitor the vital parameters of the patients over long periods. Therefore, simulations are run for 2000, 4000 and6000 secondsfor all the node densities.In allthesimulations, node communication duration is same assimulation duration.Packets consisting of informationregarding vital parameters of the patient are 20 Bytes long,and are transmitted at a rate of 10 packets per second. Network performance is measured in terms ofPacketDelivery Ratio (PDR), throughput,latency andenergyconsumption.Figure 3 shows variation in these performance parameters with varying sensor node density and simulationduration.  A.Variation inPacket Delivery Ratio (PDR)with Sensor  Node DensityandSimulation Duration It is observedthat PDR remainsalmost same for  particular sensor node density over different simulationdurations.Therefore, it can be concluded thatPDR is notaffected overtime,but is affected onlybysensor nodedensity.PDR decreaseswith increase in node densityindicating deterioration innetwork performance. This could bemainly due to interferenceamong neighboringnodes.Interference leads to collisions and packet loss,which in turndeterioratesthePDR.  B.Variation in Average Network Delay (latency) withSensor Node Densityand Simulation Duration Average network delay is measured in seconds.It isobserved thataveragenetworkdelay remainsalmostsamefor a particular node density over different simulationdurations. Therefore, it can be concluded thataveragenetworkdelay is affectedwith change in sensornode density,while it remains almost constant overthesimulation durationfor a given node density.Averagenetworkdelay increaseswithincreasing node density, thus deteriorating network  performance.Increased node density leads to interferenceand collisions,leading to packet loss.The lost packets needto be retransmitted,leading to network delay. C.Variation inNetwork Throughputwith Sensor Node Densityand Simulation Duration From the obtained results,it is observed that,throughputremains almost constant for a particular sensor node densityover different simulation durations. However, it deteriorateswith increase in node density. As the node density increases,the number of nodes sending data and the amount of datasentalsoincreases.This leads to increased interference and packet collisions,thus deteriorating the network throughput.  D.Variation in Average Energy Consumption with Sensor  Node Densityand Simulation Duration Energy consumption is measured in Joules.It is observedfrom the obtained results that energyconsumed per nodeincreases with increase in node density.Also, energyconsumed increases with simulationduration,which is anexpected behavior.Increase in energy consumption withnode densityis mainly due to the increased collisions,whichleads to retransmissions and hence the amount of energyconsumedincreases.  E.Overall Observation It can beconcluded from the obtained resultsthat,number of nodes present in a given area havea major impacton network performance. As the node density is increasedfrom 9 to 25, PDR dropsapproximatelyby53%percent for all the node densities,while throughputdrops byaround4.3kbps for all the node densities.Average network delay(latency)increasesapproximatelyby0.7s for all nodedensities.Whilelatencydoesnot get much affected withincreasing node density,PDR get potentially affectedby thesame. Itis also observed that increased simulation duration,and node density have a negative impact on node energy.From the obtained results it is observed that for 9 nodes PDR is around 65%.Therefore,itcan be concluded that thenetwork must have less than 8patients in a 50m x 50m areafor better PDR.Maximum value of average network delay(latency) observed is0.38seconds for a networkconsistingof9patients.Based on the requirements mentioned in TableI,this is very much an acceptable value forpatientmonitoring system.Throughput of 6 kbps to 8 kbps isobtained for 9 to 16 patients in the network may be sufficientfor sending physiological data of the patient to thedestination node. However, throughput obtained with 25 patients in the networkmay not satisfy the requirements.Average energy consumption increases with simulationduration and node density,decidingthe lifetime of thenetwork. The lifetime of the patient monitoring systemmainly dependson configuration of batteries used in 60   60  wearable sensors and that of the PAN coordinator. Theseresults help us to decide upon appropriate node density toachieve required performance in terms of PDR, throughput,latency and energy consumption while establishing Zigbeesensor based patient monitoringsystems. Figure 3: Network Performance Analysis with Varying Node Densityand Simulation Duration IV.E FFECT OF V ARIATION IN D ATA R  ATEAND  N ODE D ENSITYON  N ETWORK  P ERFORMANCE Sensor networks are primarily designed for low data rateapplications. However, depending on the operational needs,high data rates may be required.In this section,the affect of varying data ratesfor different node densitieson network  performanceisstudied. T ABLE IIS CENARIO S PECIFICATIONS Simulation Parameters RoutingProtocolAODVMAC ProtocolIEEE 802.15.4Traffic TypeCBR Simulation Time2000sTerrain Size50m x50m Number of Nodes9, 16 and 25Mobility ModelRandom WaypointSpeed0.05ms -1 to 0.50ms -1 Pause Time60sPacket Size20BytesPacketTransmissionRate10-100 packets per second(increased in stepsof 10 ) TableIIprovides scenario specifications. The contents of TableIIare similar to those in TableIexcept SimulationTime and Data Transmission Rate.Theduration of simulation is kept constant to 2000s,while data transmissionrate is increased from 10 packets per second to 100packets per second, in steps of10 packets per second. Results areobtained for node densities of 9, 16 and 25 nodes in a givenarea.Data transmission rate is measuredin kbps. Size of each packet is 20 Bytes.Therefore, for 10 packets, we obtaindata transmission rate as10 x 20 x 8 = 1600 bps or 1.6 kbps(Number of Packets per second x Packet Size x 8).Figure 4shows variation in network throughput with varying datatransmission rate and node density.  A.Variation in Packet Delivery Ratio (PDR) withVarying  Data Transmission Rateand Node Density Value of PDR is calculated with varying datatransmission rate, for node density of 9, 16 and 25 nodes.Itis observed from the obtained results that there is almostexponential decrease in PDR with increased datatransmission rate.Also, as node density increases, PDR decreases.This is mainly due toincreased number of collisions ordelay in processing at the PAN coordinator,duetoincreased number of packets in the network.  B.Variation inAverage Network Delay (Latency)withVarying Data TransmissionRateand Node Density The obtained results show thataverage network delaydecreaseswithincrease indata transmission rate, while delayobserved at higher node density is more than that observed atlower node density.The obtained performance could be dueto node mobility. As the nodes move around a given area, atsome point of time,nodes may be closer to the PANcoordinator. Compared to low data rates, the number of  packets transmitted when the data rate increases is high.When the nodesarecloser, thepackets reach the destination(PAN coordinator) faster. Since more packets reach thedestination faster compared to lower data rates, the averagedelay decreases as the data transmission rate increases. C.Variation inThroughputwithVarying DataTransmission Rateand Node Density It is observed from the obtained resultsthat throughputincreases with increase in datatransmissionrates.This isanexpectedbehavior because,as the rate at which the packetsare sent is increased,the number of packets sentincreases.Due to this, the total number ofpackets received by the PANcoordinatoralso increases. This further leads to increasedthroughput. It is also observed that throughput increases withincrease in the data transmission rates for a given nodedensity.  D.Variation inAverage Energy ConsumptionwithVarying  Data Transmission Rateand Node Density As seen from the obtained results node energyconsumption increases with increased node density and datatransmission rate initially.However, at higher datatransmission rates consumption of node energy is notaffected much and remains roughly constant. This trend isobserved for all the node densities.  E.Overall Observation From the obtained results it can be concluded that, PDR and Throughput gets drastically affected with increased datatransmission rate and node density. For 9 nodes, PDR dropsfrom 65% at 1.6 kbps to 10% at 16 kbps. It deterioratesfurther with increase in node density. PDR of 10% is too lowfor any network, and it is an unacceptable value for patientmonitoring systems where critical information about patient’s health is exchanged. However, in patientmonitoring systems, where information about physiological parameters is sent, data transmission rate of 1.6 kbps may be 61   61  sufficient. But for PDR above 65% it may be required tokeep the number of patients in an area 50m x 50m to be lessthan 8. Amount of energy consumed increases over timewith increased data rate. Therefore, lifetime of the sensor nodes must be predicted (based on their specifications) for smooth operation of patient monitoring system. Figure4: Network Performance Analysis with VaryingData Rateand Node Density V.C ONCLUSIONS AND F UTURE W ORK  This paperpresentsthe performance of Zigbee basedsensor network for patient monitoring, throughsimulationscarried out using ns2.34 simulator tool.Simulationsarecarried out to study the effect ofvariation in node densityand communication duration on networkperformance, and tostudy the effect of data transmission rate and node density onnetwork performance (while keeping the duration of communication constant). Network performance is measuredin termsof PDR, average network delay or latency,throughput andenergy consumed. From the obtained resultsit is observed that PDR and throughput get drasticallyaffected with increase in node densityanddata transmissionrate, when compared to average network delay. Amount of energy consumed is an important parameter for WSNsandits variation is studied with respect to node density, durationof communication and datatransmission rate. As expected,energy consumed increases with communication duration.The performance analysis provided in this paper can be usedwhile establishing Zigbee sensor based patient monitoringsystems.Obtainedresults can be used to choose appropriatenode density, data transmission rate, amount of data totransmitand duration of communication, to achieve required performance in terms of PDR, throughput, latency andenergy consumption for the patient monitoring system.Infuture, we plan to establishthe sensor network for patientmonitoring in practical and study its performance in a livenetwork.VI.A CKNOWLEDGEMENTS Authors acknowledge Thriveni H.B., from theDepartment of Computer Engineering, M. S. RamaiahSchool of Advanced Studies, forhersupport.R  EFERENCES[1]S. Gupta, S. Varma, G.S. Tomar and R. K. Abrol, “Wireless Sensor  Network Based Industrial Monitoring and Diagnostic System ”. InIEEE Computational Intelligence, Communication Systems and Networks, pp 125-300,2009[2]S. Gupta, S. Varma, G.S. Tomar and R. K. Abrol, “IntelligentIndustrial Data Acquisition and Energy Monitoring using WirelessSensor Networks”, International Journal of Grid and HighPerformance Computing, Volume:2, Issue:3, pp. 44-59, 2010[3]P. Bauer, M. Sichitiu, R. Istepanian, K. Premaratne,“The mobile patient:wireless distributed sensor networks for patient monitoringand care”,Proc. IEEE EMBS International ConferenceonInformation TechnologyApplicationsin Biomedicine, Arlington,VA, pp. 17-21,2000[4]U. Varsheney, “Managing Wireless Health Monitoring for Patientswith Disabilities”, IEEE IT Professional, Volume:8, Issue:6, pp. 12-16, 2006[5]E. Jovanov, T. Martin and D.Raskovic, “Issues in waerablecomputing for medical monotoring applications: A case study of awearable ecg monitoring device”. In Digest of Papers.FourthInternational Symposium on Wearable Computers. IEEE Computer Society.2000[6]M.P.Rajasekaran, S. Radhakrishnan and P. Subbaraj, “Sensor gridapplications in patient monitoring”, Future Generation Computer Systems, Volume 26, Issue 4, pp. 569-575, 2010[7]A. Hadjidj, M. Souil, A. Bouabdallah, Y. Challal and H. Owen, “Wireless sensor networks for rehabilitation applications: Challengesand opportunities”,Journal of Network and Computer Applications,Volume 36, Issue 1, pp. 1-15, 2013[8]P. Barnoti, P. Pillai, V.W.C. Chook, S. Chessa, A. Gotta and Y. F.Hu, “ Wireless Sensor Networks: A survey on the state ofthe art andthe 802.15.4 and Zigbee standards”, Computer Communications,Volume 30, Issue 7, pp. 1655-1695[9]A. Kumar,C. Sekarani, A. Nwokafor, P. Johansson, K.D.M. Glaser,and I. Krueger, “ Zigbee sensor network for patient localization andair temperaturemonitoringduring emergency response to crisis”,IEEE Second International Conference on Sensor Technologies andApplication, pp: 233-238, 2008[10]The Network Simulator (ns-2) User Manual, Available Online:http://www. isi. edu/nsnam/ns/ns-documentation.html[11]J. Khan, M. R. Yuce and F. Karami, “Performance Evaluation of aWireless Body Area Sensor Network for Remote PatientMonitoring”,Engineering in Medicine and Biology Society,EMBS’08, 30 th Annual International Conference of the IEEE, pp.1266-1269, IEEE, 2008 62   62
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