Modelling Proactive wide Area Health care UsingWireless Sensors in NS2

Izuchukwu Benedict OkoyeDepartment of Computer Science, University of Greenwich

London, UKOi103@gre.ac.uk

Abstract - Advances in technology haveprovided the ability to equip the homeenvironment with a layer of technology toprovide a truly ‘Smart Home’. These homesoffer improved living conditions and levels ofindependence for the population who requiresupport with both physical and cognitivefunctions. At the core of the Smart Home is acollection of sensing technology which is usedto monitor the behaviour of the inhabitant andtheir interactions with the environment. Avariety of different sensors measuring light,sound, contact and motion provide sufficientmulti-dimensional information about theinhabitant to support the inference of activitydetermination. A problem which impingesupon the success of any information analysis isthe fact that sensors may not always providereliable information due to faults, operationaltolerance levels or corrupted data. There existstheories (e.g. Dempster-Shafer theory) that helpus to gather reliable information from sensorsthrough weighted sum operator and evidentialcontextual information which is analysed andmerged to achieve a consensus inautomatically inferring activities of daily livingfor inhabitants in Smart Homes.

Key words: (Body Sensor Networks, ClusterHead (CH), Base Stations, Motes, Handoff, ECG,Wireless Sensor Network (WSN), CoronaryHeart Disease (CHD), Body Area Network (BAN),Wireless BAN, Body Sensor/Central Unit(BS/CU), Personal Area Network, AdaptiveResponse by Attractor Selection (ARAS), Sink,Agent).

I. IntroductionWhile most computer networksare designed to optimallyfunction under controlledconditions, biologicallyinspired networks rely on

fluctuations and randomness.Due to the probabilistic natureof centrally controlledcomputer systems, they shouldbe considered less reliable oncritical entities. Such assingle point of failure, totalconsumption of bandwidth etc.several other mitigations maybe utilized to permitacceptable quality ofoperation.

However with biologicallycontrolled systems, it does notinstantly reach theiroperational performance levelbut will slowly do byfluctuations. Thesefluctuations may be hundreds orthousands of tiny leveldifferences which are adaptedbefore reaching an acceptablelevel of performance. AdaptiveResponse by Attractor Selection(ARAS) is a concept where anode searching forneighbourliness adapts tochanges in the dynamic networkorganization based on theavailability of paths for whichno machinery is available forsignal path selection. Anattractor can be likened tothat region where a particularnode normally returns toregardless of initialconditions. e.g. sick bed. Evenif the node position isdynamically altered because of

needs and other factors, itwill be attracted back to thatposition of rest. Byformulating the desired outcomeas a stochastic differentialequation system, and anappropriate mapping of theactivities of the system, ARAScan be viewed as an adaptivecontrol mechanism with manypossible applications.It can be used to ascertainbest path in the route set-upof multiple routing scenario,for instance by broadcastingroute request in a conventionalrouting methods e.g AODVrouting.Consider that there are N pathsfrom source to destination andfor each packet delivered; thepath is i therefore having theprobability of Ni which can bedetermined by Stochasticdifferential equation system

(1)Where i = 1…

m

Note that function f determinesthe locations, and the dynamicsof each Ni depends on the termα which represents the activityof the system. Activity isdirectly influenced by theenvironment which in the caseof routing scenario isexpressed by the path metrics.The random noise term (ŋi)becomes negligible as αincrease [4]. This correlationof modern computers andbiologically inspired networksimply points to thedestination of technology as,we are yet to build computersthat can do one millionth ofnetwork functions performed bybiological cells like in thebrain, nervous systems etc with

even very negligible amount ofpower.

II. Literature ReviewAs the world’s populationexpectedly grows, the number ofelderly around us willtremendously increase and soalso will the diseases theysuffer. In-home smart networksmay assist residents byproviding memory enhancement,intelligent control of homeappliances, medical data lookupand emergency communication. Inconspicuous, wearable sensorswill allow vast amounts of datato be collected and mined fornext generation clinicaltrails. Data will be collectedand reported automatically,reducing the cost andinconvenience of regular visitsto the physician [3].

“Distributedmicrosensing” is one example ofa situation well beyond thehuman/machine/networkbreakpoint. Future embeddeddevices are likely to havemultimodal environmentalsensing abilities, includingacoustic and seismic sensing,even miniature cameras.Networking these sensors—empowering them to coordinatethemselves on a larger sensingtask—will revolutionizeinformation gathering andprocessing in many of thesesituations. Large-scale,dynamically changing, robustsensor colonies would then bedeployable in such inhospitablephysical environments as remotegeographic regions and toxicurban locations. They will alsoenable low-maintenance sensingin more benign, but lessaccessible, environments,

including smart homes, largeindustrial plants, aircraftinteriors, and more [1].

In the era of informationtechnology, the elderly anddisabled can be monitored withnumerous intelligent devices.Sensors can be implanted intotheir home for continuousmobility assistance and non-obtrusive disease prevention.Modern sensor-embedded houses,or smart houses, cannot onlyassist people with reducedphysical functions but helpresolve the social isolationthey face. They are capable ofproviding assistance withoutlimiting or disturbing theresident's daily routine,giving him or her greatercomfort, pleasure, and well-being. This article presents aninternational selection ofleading smart home projects, aswell as the associatedtechnologies ofwearable/implantable monitoringsystems and assistive robotics.The latter are often designedas components of the largersmart home environment. Thepaper will conclude bydiscussing future challenges ofthe domain [2][16][18][19][20].

Wireless medical bodysensor networks enable a newway of continuous patientmonitoring, extending the reachof current healthcaresolutions. They improve caregiving by a flexibleacquisition of relevant vitalsign data, and by providingmore convenience for patients. Applying the general concept ofsensor networks to the medicaldomains requires development ofreliable, distributed micro-systems with secure andfailsafe system set-up andefficient operation. The systemhas to work automatically, but

has also to be always underexplicit control of anyclinician.

III. Simulation AnalysisThis section tries to explainand illustrate a typical activescenario of proactive medicarewith all communication patternsrecorded and examined forreliability, consistency andaccuracy.

Table: Definition of variable andparametersParameter ValueTopology size 500m X 500mSimulation time 200minsNumber of nodes 21Number ofsources

18

Number of sinks 3Traffic type TCPPacket size 512bytesPacket rate 4

packets/secSpeed Dynamic

Topology of SimulationIn quest to adopt a realistictopology for this paper, asection of London came to mind(Woolwich area) which wasfurther divide it into arealistic population densitythat can apply to thisresearch. I look up for thepopulation of London which wasapproximately 7.7 millionpeople living on a land area ofabout 1610square kilometres.

Mathematically the populationdensity becomes 7.7million/1610= 4783 person/sqkm.

1km = 1000m = 2 x 500m1sqkm = 1000sqm = 4 x 500sqm density in500sqm = 4783 / 4 = 1196 persons per500sqm.

If 5% of this people are hospital patients (Indoor and outdoor), then 5% of 1196 = 60patients.

Assuming 1/3 of those are inelderly home, 1/3 are indoorhospital patients and the last1/3 are mobile patient within a500sqm.Therefore, we now have 20 nodesper 500sqm for the mobile nodes(patients).This shall be the basis of oursimulation.

Figure 1: Scenery of aMetropolitan Area with patientsand hospitals in Cyan/Blue andRed respectively.

Smart Sensor Network SimulationHere, this work tries to have acritical look at the resultsfrom the simulations and thedirect implication of theiranalysis in real lifeapplications especially as itconcern the handover/roamingtechnique which has asignificant success in cellularand GSM systems.

Handoff Requirements andProcedureHandoff is the process by whicha mobile node (patient)switches or transfers itscommunication from onestationary base station (HealthCentre) to another for becausethe mobile node is moving away

from the coverage area of aparticular base station or thesignal strength of itsassociated base station hasdropped below certain thresholdwhich suggests a handoff toretain and maintain quality andefficient communication ofsensed data between the twodevices.

In order to achieve this, ahandoff procedure and decisioncode was scripted in theOptical Script (TCL) for thenetwork animator (NAM) tofollow as a model for handoff.

Consider the piece of codebelow:

proc handoff {X_ 250 Y_ 250} {for {set i 3} {$i == $val(nn)} &

if {set X_ 250} {$x => 250} &{set Y_ 250} {$Y => 250} {Set TCP prams for Sink A}else{Set TCP params for Sink B}}

This code simply says toconsider the position of themobile nodes at any particularpoint in time of thesimulation. If during thedynamic movements of the nodes,any one of them moves beyondthe boundary region describedby {set X_ 250} {$x => 250} and{set Y_ 250} {$Y => 250}, thena communication pattern thattalks only to the base station(Sink A) in that region iscalled otherwise, the nodescontinuously communicates itregistered base station (SinkB) which rather supportcommunication for areas notoutlined in the describedboundary as shown in the pieceof code above.

Results and Outputs AnalysisIt is necessary to write ascript that is capable ofextracting, computing andcomparing parameters ofinterest in order to make alogical judgement on theperformance based on therequired expectations of theexperiment.To achieve this, three analysistools were used to compare andanalyse the results of thiswork simulations.A perl script to analyze thetrace file, an NS2 virtualtrace analyser and a Tgraphanalysis tool to produce thefollowing statistics of thetrace files:

AODV broadcast sent, AODVbroadcast received, Data sent,Data received, Router drop,Delivery ratio, Delay andJitter, End to End throughputand Connectivity.

Data Sent and ReceiveThis describes the totaltraffic data exchanged in thecommunications between thepatients and the hospitalstations. It includes all theTCP sent and ACK receivedrespectively. From the flowinformation, it can be deducedthat over 12MB of data wereexchanged with a loss of about2KB. The TCP data sending showsabout 91.5% success rate withall other losses resulting fromlink failures summing up toabout 0.02%.This statistic is good for thisexperiment but, othershortcomings resulting fromdelays, throughputs andconnectivity should be factoredin, to provide a holistic viewof the performance of thismodel experiment.Route Drop and Delivery ratio

The TCP packets showed verynegligible drop ratio of about0.04% while the ACK packetsshows more losses of about14.6%. This is an issue that iscritical to this experimentsince, failure ofacknowledgement packets canclog the network, cause delaysand make the entire system lesseffective. A better ACK packetsdelivery ratio is desirablesince we are dealing with adata sensitive network. This high drop ratio isexperienced with theacknowledgement packet could beattributed to high trafficarriving at the sink nodes. Inreality, sink nodes (basestation) are designed to handlevoluminous traffic flow hence,this will not be a seriouschallenge.

Delivery ratio is the measureof the generated and sentpackets against received andacknowledged packets express inpercentage.To ascertain D/R in thissimulation, we shall extractthe total packetacknowledgement transferredwhich equals 23686Pkts anddivide by the total packetstransferred which equal27737Pkts

Delivery Ratio = (D/R)*100 =(23686/27737* 100 = 85.395%.

Transmission DelayDelay is the amount of time ittakes for the packet header ofa signal to travel from thesource to the destinationdesired. Because the scenariois dynamic and the networktopology continuously changes,the time for packets destinedfor same destination from samesource have a very high

probability of changing. Due tothe random motion of thepatients, proximity of ad-hocnodes is continuously changing.There are three parameters thatinterplay to affect the delayof packet in transit. Speed,distance and time.Relationships between them;

Speed = Distance/Time

Time = Distance/Speed

Distance = Speed * Time

Jitter = time0 – time1 = ∆twhere time0 = ∆t0s and time1 =∆t1s for each packet transfer.

One should note that, variationof any one of the parametersabove on any packet in transitwill produce jitter. This maybe inevitable in this type ofimplementation but it isnecessary to keep it minimaland ensure it that end to endthroughput is not sacrificed byjitter caused delays.Jitter is the undesireddeviation from the periodictransit time of signal(packets) which can be causedby any variations in patients’speed or intermediate ad-hocnodes dynamic distances.

My statistics from the figurebelow shows TCP and ACK packetaverage delays of about 0.068sand 0.32s respectively as wellas average packet jitters of0.018s and 0.0065scorrespondingly.I can argue that this is a finestatistics for the experimentbut even better is moredesirable and achievable.

In a not shell, lower delaysand even absence of jitter is

most desirable but, thisoccurrences can only beminimized reasonably.

Figure 2: Delay Statisticsfor TCP and ACK packets fromscenario 1 simulation.Shows Cluster Head graph ofDelay against Packetsstatistics for simulation withcluster model and withoutcluster model respectively

Note: If printed in black and white, the topgraph signifies the ACK packets while thelower graph represents TCP packets at thebottom along the Packet Id axis (X-axis).

The Figure 2a graph above showseffective delay spread in thescenario1 of this experiment.It is obvious that until thepacket volume reached about25000, the average delay forboth TCP and ACK packets wereunder 0.8 milliseconds. Thegraph however showed seriousvariation from the 25000packetsmark to the end of thesimulation with a proportionateincrease in delay as thepackets transfer increased.But this is not the case withFigure 2b where, the packettransmission shows a noisypattern which definitely leadsto increased packet drops,retransmission and delay. Theseparameters put together resultsin unnecessary waste ofbandwidth and energy which arevery crucial to thesustainability of this

technology and itsimplementation.One can infer from the graph inFigure 2a that, due toincreased traffic arriving atthe sink, the buffer wasstretched to capacity as thequeue built up henceconstituting delay as thesinking rate of node_0 couldnot match the rate at whichpackets were arriving.In reality, this means that thebase station, cluster head orserver was overwhelmed bytraffic. With increase RAM,buffer memory and CPU powerthis challenged will be reducedsignificantly.However for that in 2b, it issimply not feasible for thisimplementation as energywastage due to chaotictransmission resulting incollisions, delays and packetdrops is not tolerable.Since energy transfer can beused to do work, and power canbe viewed as the rate at whichthis work is performed, thenpower is the rate at whichenergy is transferred, used, ortransformed.

Figure 3a & 3b: Shows the graphof Jitter per Packet duringtransmission in paper clusterbased model and normal (withoutCH model) respectively.

Similar trend as seen in delay(Figure 2a & 2b) played out inthe jitter as well. The

variation in delay for thepackets remained relativelystable until the 25000th packetbefore it increasedsignificantly and continuouslyin Figure 3a. The solution fordelay will also alleviate thejitter effects.Also, the jitter resulting fromrunning the scenario withoutcluster model as suggested inthis paper shows that, it issimply not workable consideringthe energy constraints ofwireless sensor nodesespecially BSNs as applied inthis experiment.

End to End ThroughputThroughput or networkthroughput is the average rateof successful message deliveryover a communication channel.In this process, data packetsmay be transferred over aphysical or logical link, orpass through a certain networknode. The throughput is usuallycalculated in bits per second(bit/s or bps), andoccasionally in data packetsper second or data packets pertime slot. The aggregatethroughput is the sum of thedata rates that are deliveredto all nodes in a network.Throughput is essentiallysynonymous to digital bandwidthconsumption.Looking at the figure below, wecan ascertain that thethroughput for the network onthe average is about 59kbps forTCP packets and about 5kbps forthe ACK packets. Higherthroughput values can beachieved in reality due to moreefficient modern technologythat is available to us.ConnectivityThe statistics below shows aconnectivity of over 99.5%which is excellent and a

prerequisite for successfulimplementation of the idea inthis experiment.Figure 4: Global view of theinternetwork connectivity ofsensor and devices for thiswork

Data Packet Transmission EnergyModel AnalysisFurther evaluation of energyconsumption as it relates tothis work data properties, isoutlined below.

Energy consumptions based onData rateIf we further evaluate energyconsumption in terms of networkinterface activities ratherthan electronics properties asabove, then we shall consideredthe scope as a dispersednetwork resource, since energyhas distinctive propertieswhich differentiate it fromother resources. Energy is non-renewable and a mobile node hasa limited, single source anddiminishing energy store. Incontrast with bandwidth, theenergy cost of a packetrequires separate calculationswith respect to the sender, theanticipated receiver(s) andother nodes which overhear themessage. This makes itessential to distinguishbetween broadcast (general)traffic, which is processed byall nearby nodes, and point-to-point traffic, which is

processed by the intendedreceiver(s) and discarded byother nearby nodes.

The cost of sending orreceiving a network-layerpacket via anode is modelled aslinear. There is a set costconnected with channelacquisition and an incrementalcost proportional to the sizeof the packet:

Cost = massage * size + b(2)

Where m = massages, size refersto packet size and b = channelaccess cost.

The total cost of a packet isthe sum of the costs in-curredby the sending node (S), andall receivers. Potentialreceivers include the intendeddestination node (D), all nodeswithin wireless range of S andD. Note that S and D areextremely dynamic since we aredealing with mobile nodesnetwork [5].

Broadcast trafficTaking into account the abovelinear equation adopted from[5], the total broadcasttraffic can be calculated as Broadcast Cost = massagesend * size +bsend + ∑(n∈S)(massagerecv × size +brecv) (3)

This means that, energyconsumed at each broadcastincludes all energies used insending the massage andreceiving same at all the nodeswithin the wireless coverage(cluster) of the sending node.

Point to point traffic

To ascertain P2P traffic energyconsumption, we must put intoaccount, the channel accesssend and receive controlmassages, the massage properand then the channel accessacknowledgment control massage.If translated into equation, ittake the form:

P2P Sender Cost = bsendctrl + brecvctrl +massagesend * size + bsend + brecvctrl

(4)

And at the receiving end wehave:

P2P Receiver Cost = brecvctrl + bsendctrl +massagerecv * size + brecv + bsendctrl

(5)

Discard trafficThis refers to the effect ofexposed terminal whichoverhears transmissions notmeant for them and eventuallydiscards the massages. Thetotal costs of the behaviour ina wireless network particularlyad hoc or sensor networks isreflected in the equationbelow.

Discard Cost = ∑(n∈S)bdiscardctrl +∑(n∈D)bdiscardctrl + ∑(n∈S) (massagediscard

* size + bdiscard)+ ∑(n∈D)bdiscardctrl

(6)

Since massages can be lost dueto collisions or otherfailures, and layers 2 & 3 ofthe protocol stack provides forretransmission mechanisms, theneach component of the equationabove should include animplicit factor of(1+Nretransmissions/duplicates) in orderto augment for the energy costof retransmissions andduplications.

Sample calculation fortransmission energy costThis is actually indeterminatein this work as no real devicewas used as opposed tomeasurements from thesimulation since some aspectswould be based on inferences.Broadcast Cost = massagesend × size +bsend + ∑(n∈S)(massagerecv × size +brecv) (7)Evaluation of transmissionpattern for the sensed andtransmitted data Figure 5: No Cluster head

Figure 6: Cluster head based

Examination of the figuresabove show that, more packetsof data were successfullytransmitted and acknowledged inthe simulation with ClusterHead (CH) model than whenallowed to operated asindependent sensors asreflected in figures 6 and 7respectively.

Another close look onfigures shows excessive loss ofsensitive data while trying tocomplete acknowledgementcommunication sequence. A 38%loss of total packet sentduring acknowledgement process

is not tolerable as it implies38% retransmissions, 38%bandwidth clogging, processorcycle wastage and above allinvaluable energy wastage.

IV. ConclusionParticularly in the Ambient

Assistive Living there is asignificant amount of basicresearch in progress andnumerous prototyping and lab-type pilots. More emphasisneeds to be placed onintegrated services i.e. notjust standalone ‘one off’ typesolutions. This is why thispaper is looking beyond astandalone system to anintegrated service prototypethat can be considered a goodidea for commercializationafter exhaustive testing. I amconfident that this idea willwork very well and benefit theentire human populace leadingto a better Medicare whichidentifies patient as citizenof the world.

Health care without boundaryis what the paper envisage bymaximizing the cellular hand-off technique that has alreadyproved efficient and effective.While hospitals today use avery limited number of wirelessdevices to monitor patients,they are most often used onlyfor the most critical cases, asthe equipment is expensive andadds data traffic to alreadyclogged Wi-Fi networks.In almost all cases, patientsare still tethered tomonitoring devices by wiresthat can take five to 10minutes to detach in order toallow them to move, and thatcan cause infection if theybecome contaminated. Otherpatients, outside of intensivecare units, often aren'tmonitored at all because of the

issues surrounding the devices.MBANs provide a cost-effectiveway to monitor every patient,so clinicians can provide real-time and accurate data,allowing them to intervene andin some cases save lives. Andin certain cases, the monitorsare cheap enough that they'redisposable [6][7].A future where everybody has asafe healthy life and anarticulate monitoring of samenoninvasively is what thispaper seeks to highlight andproffer.

References[1] Chan, M. Estève, D. Escriba, C. & Campo, E. 2008.

Computer Methods and Program inBiomedicine

Volume 91, Issue 1, Pages 55-81,July 2008.

[2] J. A. Stankovic, Q. Cao, T. Doan,L. Fang, Z. He, R. Kiran, S. Lin,S. Son, R. Stoleru, A. Wood,Wireless Sensor Networks for In-Home Healthcare: Potentials andChallenges.Department of Computer Science,University of Virginia.

[3] Harbor Research, 2012. Designingthe Future of Smart ConnectedThings. [Online] Available at:http://www.theinternetofthings.eu/sites/default/files/Rob%20van%20Kranenburg/HRI%20paper.pdf[Viewed: 13/06/12].

[4] Mahmoud, H.Q. ed., 2007. CognitiveNetworks, Towards Self-AwareNetworks. Chichester, England ;Hoboken, NJ : John Wiley & SonsLtd.

[5] Laura, M.F. 2001. An EnergyConsumption Model for PerformanceAnalysis of Routing Protocols forMobile Ad Hoc Networks. MobileNetworks and Applications 6, 239–249, 2001.Kluwer AcademicPublishers. Manufactured in TheNetherlands.

[6] Baldus, H. Klabunde, K. & Müsch,G., 2004. Reliable Set-Up ofMedical Body-Sensor Networks.Wireless Sensor Networks LectureNotes in Computer Science, 2004,Volume 2920/2004, 353-363, DOI:10.1007/978-3-540-24606-0_24

[7] Computerworld.com. 2012. Body AreaNetworks: should free HospitalsBandwidth, untether Patients.

[Online] Available at:http://www.computerworld.com/s/article/9227683/_Body_Area_Networks_should_free_hospital_bandwidth_untether_patients?source=CTWNLE_nlt_emgtech_2012-06-06 [Accessed: 29 June 2012].

[8] Issariyakul, T. Hossain, E., 2009.Introduction to Network SimulatorNS2. New York : Springer. ISBN 978-0-387-71759-3, pages 27,31.

[9] Smart Homes. [Online] Available at:www.tiresias.org/orgresearch/guidelines/smart_home.htm [Accessed: 20September 2012].

[10] D. Estrin, R. Govindan, J.Heidemann. Communications of theACM, May 2000/Vol. 43, No.5, Pg 38.

[11] Benny, P.L. Lo, S.T. Rachel, K &Guang-Zhong, Y. 2005. Body SensorNetwork – A Wireless SensorPlatform for Pervasive HealthcareMonitoring.

[11] Desai, U.B. Jain, B.N. &Merchant S.N. Wireless SensorNetworks: Technology Roadmappage 9. A Project supported byDepartment of InformationTechnology, Ministry ofInformation and CommunicationTechnology, India.

[12] Bharathidasan, A. Ponduru, V.A.S2002 Sensor Networks: AnOverview. Department of ComputerScience University ofCalifornia, Davis, CA 95616.

[13] Capsil.org, 2012. Wireless BodySensor Networks. [Online]Available at:http://www.capsil.org/system/files/Chapter+5.pdf [Accessed: 12August 2012].

[15] Chee-Yee, C. Kumar, S.P. 2003.Sensor Networks: Evolution,Opportunities and Challenges. Aproceedings of the ieee, vol.91, no. 8, august 2003.

[16] Chiara, B. Conti, A. Dardari, D.& Verdone, R. 2009. An Overviewon Wireless Sensor NetworksTechnology and Evolution.Received: 25 July 2009; inrevised form: 24 August 2009 /

Accepted: 25 August 2009/Published: 31 August 2009. ISSN1424-8220.

[17] Chung, W.Y. Walia, G. Young-DongLee, Y.D. & Myllyla, R. 2007.Design Issues and Implementationof Query-Driven HealthcareSystem Using Wireless Sensor Ad-hoc Network. Pages 99-104(1,Volume 13).

[18] Desai, U.B. Jain, B.N. &Merchant S.N. Wireless SensorNetworks: Technology Roadmappage 9. A Project supported byDepartment of InformationTechnology, Ministry ofInformation and CommunicationTechnology, India.

[19] Dong, Z. Xiang, H. & He, W.2010. Remote Diagnosis inTraditional Chinese MedicineUsing Wireless Sensor Networks aproceeding of thirdInternational Symposium onInformation Processing, 2010.Pages 255-257, ISBN: 978-0-7695-4261-4.

[20] Guest Editorial Body SensorNetworks: From Theory toEmerging Applications. IEEE Transactions on InformationTechnology in Biomedicine, vol.13, no. 6, November 2009. Pages859 - 863.

[21] Heile, B. Gifford, I. & Siep, T.1999. “The IEEE P802.15 workinggroup for wireless personal areanetworks,” IEEE Netw., vol. 13,no. 4, pp. 4–5, Jul. 1999.

[22] Hu, F. Cao, X. 2010. WirelessSensor Networks: Principles andPractice. Boca Raton, Fla.:Auerbach ; London : Taylor &Francis [distributor] ISBN 978-1-4200-9215-8.