WSN Issues Implemantation

Embed Size (px)

Citation preview

  • 8/9/2019 WSN Issues Implemantation

    1/13

    Wireless sensor networks for personal health monitoring: Issues andan implementation

    Aleksandar Milenkovic *, Chris Otto, Emil Jovanov

    Electrical and Computer Engineering Department, The University of Alabama in Huntsville, 301 Sparkman Drive, Huntsville, AL 35899, USA

    Available online 6 March 2006

    Abstract

    Recent technological advances in sensors, low-power integrated circuits, and wireless communications have enabled the design of low-cost, miniature, lightweight, and intelligent physiological sensor nodes. These nodes, capable of sensing, processing, and communicatingone or more vital signs, can be seamlessly integrated into wireless personal or body networks (WPANs or WBANs) for health monitor-ing. These networks promise to revolutionize health care by allowing inexpensive, non-invasive, continuous, ambulatory health moni-toring with almost real-time updates of medical records via the Internet. Though a number of ongoing research efforts are focusingon various technical, economic, and social issues, many technical hurdles still need to be resolved in order to have flexible, reliable,secure, and power-efficient WBANs suitable for medical applications. This paper discusses implementation issues and describes theauthors prototype sensor network for health monitoring that utilizes off-the-shelf 802.15.4 compliant network nodes and custom-builtmotion and heart activity sensors. The paper presents system architecture and hardware and software organization, as well as theauthors solutions for time synchronization, power management, and on-chip signal processing. 2006 Elsevier B.V. All rights reserved.

    Keywords: Wireless sensor networks; Health monitoring; Hardware; Software; Signal processing; Time synchronization; Energy efficiency

    1. Introduction

    Current health care systems structured and optimizedfor reacting to crisis and managing illness are facing newchallenges: a rapidly growing population of elderly and ris-ing health care spending. According to the U.S. Bureau ofthe Census, the number of adults age 6584 is expected todouble from 35 million to nearly 70 million by 2025 whenthe youngest Baby Boomers retire. This trend is global, so

    the worldwide population over age 65 is expected to morethan double from 357 million in 1990 to 761 million in2025. Also, overall health care expenditures in the UnitedStates reached $1.8 trillion in 2004 with almost 45 millionAmericans uninsured. In addition, a recent study foundthat almost one third of U.S. adults, most of whom heldfull-time jobs, were serving as informal caregivers mostly

    to an elderly parent. It is projected that health care expen-ditures will reach almost 20% of the Gross Domestic Prod-uct (GDP) in less then 10 years, threatening the wellbeingof the entire economy [1]. All these statistics suggest thathealth care needs a major shift toward more scalable andmore affordable solutions. Restructuring health care sys-tems toward proactive managing of wellness rather than ill-ness, and focusing on prevention and early detection ofdisease emerge as the answers to these problems. Wearable

    systems for continuous health monitoring are a key tech-nology in helping the transition to more proactive andaffordable healthcare.

    Wearable health monitoring systems allow an individualto closely monitor changes in her or his vital signs and pro-vide feedback to help maintain an optimal health status. Ifintegrated into a telemedical system, these systems can evenalert medical personnel when life-threatening changesoccur. In addition, patients can benefit from continuouslong-term monitoring as a part of a diagnostic procedure,can achieve optimal maintenance of a chronic condition,

    0140-3664/$ - see front matter 2006 Elsevier B.V. All rights reserved.

    doi:10.1016/j.comcom.2006.02.011

    * Corresponding author.E-mail addresses:[email protected](A. Milenkovic),chrisaotto@

    yahoo.com(C. Otto),[email protected](E. Jovanov).

    www.elsevier.com/locate/comcom

    Computer Communications 29 (2006) 25212533

    mailto:[email protected]:chrisaotto@%20yahoo.commailto:chrisaotto@%20yahoo.commailto:[email protected]:[email protected]:chrisaotto@%20yahoo.commailto:chrisaotto@%20yahoo.commailto:[email protected]
  • 8/9/2019 WSN Issues Implemantation

    2/13

    or can be supervised during recovery from an acute eventor surgical procedure. Long-term health monitoring cancapture the diurnal and circadian variations in physiologi-cal signals. These variations, for example, are a very goodrecovery indicator in cardiac patients after myocardialinfarction[2]. In addition, long-term monitoring can con-

    firm adherence to treatment guidelines (e.g., regular cardio-vascular exercise) or help monitor effects of drug therapy.Other patients can also benefit from these systems; forexample, the monitors can be used during physical rehabil-itation after hip or knee surgeries, stroke rehabilitation, orbrain trauma rehabilitation.

    During the last few years there has been a significantincrease in the number of various wearable health monitor-ing devices, ranging from simple pulse monitors[3,4], activ-ity monitors [5,6], and portable Holter monitors [7], tosophisticated and expensive implantable sensors [8]. How-ever, wider acceptance of the existing systems is still limitedby the following important restrictions. Traditionally, per-

    sonal medical monitoring systems, such as Holter moni-tors, have been used only to collect data. Data processingand analysis are performed offline, making such devicesimpractical for continual monitoring and early detectionof medical disorders. Systems with multiple sensors forphysical rehabilitation often feature unwieldy wiresbetween the sensors and the monitoring system. Thesewires may limit the patients activity and level of comfortand thus negatively influence the measured results [9]. Inaddition, individual sensors often operate as stand-alonesystems and usually do not offer flexibility and integrationwith third-party devices. Finally, the existing systems are

    rarely made affordable.Recent technology advances in integration and minia-

    turization of physical sensors, embedded microcontrollers,and radio interfaces on a single chip; wireless networking;and micro-fabrication have enabled a new generation ofwireless sensor networks suitable for many applications.For example, they can be used for habitat monitoring[10], machine health monitoring and guidance, traffic pat-tern monitoring and navigation, plant monitoring in agri-culture [11], and infrastructure monitoring. One of themost exciting application domains is health monitoring[12,13]. A number of physiological sensors that monitorvital signs, environmental sensors (temperature, humidity,and light), and a location sensor can all be integrated intoa Wearable Wireless Body/Personal Area Network(WWBAN) [14]. The WWBAN consisting of inexpensive,lightweight, and miniature sensors can allow long-term,unobtrusive, ambulatory health monitoring with instanta-neous feedback to the user about the current health statusand real-time or near real-time updates of the usersmedical records. Such a system can be used for comput-er-supervised rehabilitation for various conditions, andeven early detection of medical conditions. For example,intelligent heart monitors can warn users about impedingmedical conditions [15] or provide information for a spe-

    cialized service in the case of catastrophic events [16].

    Accelerometer-based monitoring of physical activity withfeedback can improve the process of physical rehabilitation[17]. When integrated into a broader telemedical systemwith patients medical records, the WWBAN promises arevolution in medical research through data mining of allgathered information. The large amount of collected phys-

    iological data will allow quantitative analysis of variousconditions and patterns. Researchers will be able to quan-tify the contribution of each parameter to a given conditionand explore synergy between different parameters, if anadequate number of patients is studied in this manner.

    In this paper, we describe a general WWBAN architec-ture as well as our prototype WWBAN designed usingTelos motes[18] and application-specific signal condition-ing modules. The prototype consists of several motion sen-sors that monitor the users overall activity and an ECGsensor for monitoring heart activity. This paper detailsour hardware and software platforms for medical monitor-ing, discusses open issues, and introduces our solutions for

    time-synchronization, efficient on-sensor signal processing,and an energy-efficient communication protocol.

    Section 2 describes the general WWBAN architecture, itsintegration into a telemedical system, and possible deploy-ment configurations. Section3describes our WWBAN pro-totype, including hardware design of the sensor platformActiS, as well as corresponding software modules. Section4describes our implementation of a time-synchronizationprotocol. Section5 describes power measurements and theresulting power profiles of our WWBAN prototype. Section6concludes the paper.

    2. WWBAN architecture

    WWBANs are a pivotal part of a multi-tier telemedicinesystem as illustrated inFig. 1. Tier 1 encompasses a num-ber of wireless medical sensor nodes that are integratedinto a WWBAN. Each sensor node can sense, sample,and process one or more physiological signals. For exam-ple, an electrocardiogram sensor (ECG) can be used formonitoring heart activity, an electromyogram sensor(EMG) for monitoring muscle activity, an electroencepha-logram sensor (EEG) for monitoring brain electrical activ-ity, a blood pressure sensor for monitoring blood pressure,a tilt sensor for monitoring trunk position, and a breathingsensor for monitoring respiration; and motion sensors canbe used to discriminate the users status and estimate her orhis level of activity.

    Tier 2 encompasses the personal server (PS) applicationrunning on a Personal Digital Assistant (PDA), a cellphone, or a home personal computer. The PS is responsiblefor a number of tasks, providing a transparent interface tothe wireless medical sensors, an interface to the user, andan interface to the medical server. The interface to theWWBAN includes the network configuration and manage-ment. The network configuration encompasses the follow-ing tasks: sensor node registration (type and number of

    sensors), initialization (e.g., specify sampling frequency

    2522 A. Milenkovicet al. / Computer Communications 29 (2006) 25212533

  • 8/9/2019 WSN Issues Implemantation

    3/13

    and mode of operation), customization (e.g., run user-spe-cific calibration or user-specific signal processing procedure

    upload), and setup of a secure communication (keyexchange). Once the WWBAN network is configured, thePS application manages the network, taking care of chan-nel sharing, time synchronization, data retrieval and pro-cessing, and fusion of the data. Based on synergy ofinformation from multiple medical sensors the PS applica-tion should determine the users state and his or her healthstatus and provide feedback through a user-friendly andintuitive graphical or audio user interface. Finally, if acommunication channel to the medical server is available,the PS establishes a secure link to the medical server andsends reports that can be integrated into the users medical

    record. However, if a link between the PS and the medicalserver is not available, the PS should be able to store thedata locally and initiate data uploads when a link becomesavailable.

    Tier 3 includes a medical server(s) accessed via the Inter-net. In addition to the medical server, the last tier mayencompass other servers, such as informal caregivers, com-mercial health care providers, and even emergency servers.The medical server typically runs a service that sets up acommunication channel to the users PS, collects thereports from the user, and integrates the data into theusers medical record. The service can issue recommenda-tions, and even issue alerts if reports seem to indicate anabnormal condition. More details about this architectureand services can be found in [14].

    2.1. Deployment scenarios

    Fig. 2illustrates three typical scenarios using WWBAN.The configuration on the left can be deployed at home, inthe workplace, or in hospitals. Wireless medical sensorsattached to the user send data to a PDA, forming ashort-range wireless network (e.g., IEEE 802.15.1 or802.15.3/4). The PDA equipped with a WLAN interface(e.g., IEEE 802.11a/b/g) transmits the data to the home

    (central) server. The home server, already connected to

    the Internet, can establish a secure channel to the medicalserver and send periodic updates for the users medical

    record.The modified configuration in the middle is optimized

    for home health care. The sensor network coordinator(nc inFig. 2) is attached to the home personal server thatruns the PS application. The medical sensor nodes andthe network coordinator form a wireless personal area net-work. By excluding the PDA, we can reduce system cost.However, this setting is likely to require more energy spentfor communication due to an increased RF output powerand lower Quality of Service (QoS), requiring frequentretransmissions.

    The configuration on the right illustrates ambulatory

    monitoring applicable any time and everywhere the PSapplication runs on a Wireless Wide Area Network(WWAN) enabled PDA/cell phone (e.g., 2G, 2.5G, and3G) that connects directly to the medical server. To illus-trate, we will follow a user (lets call him Sam) who has apredisposition for heart disorders. Sam continues his nor-mal daily activities, but now equipped with several non-in-vasive medical sensors applied in his clothes or as tinypatches on his skin. The PS application can discriminateSams state (walking, running, lying down, sitting, riding,etc.) using the data from the motion sensors, and can rec-ognize an arrhythmic event, analyzing the data from theECG sensor. If Sams activity, heart rate, and personalizedthresholds indicate an abnormal condition, he will receive awarning. In addition, a precise incident report can be sentto the medical server at Sams hospital or doctors office.

    2.2. Requirements for wireless medical sensors

    Wireless medical sensors should satisfy the mainrequirements such as wearability, reliability, security, andinteroperability.

    2.2.1. Wearability

    To achieve non-invasive and unobtrusive continuous

    health monitoring, wireless medical sensors should be

    nc

    nc

    InternetInternet

    Tier 1:

    WWBAN

    WLAN

    or

    WWAN

    A A

    AE

    Tier 2:

    PS

    Tier 3:

    MS

    Fig. 1. WWBAN integrated into a telemedical system for health monitoring.

    A. Milenkovicet al. / Computer Communications 29 (2006) 25212533 2523

  • 8/9/2019 WSN Issues Implemantation

    4/13

  • 8/9/2019 WSN Issues Implemantation

    5/13

    3.1. Hardware platform

    The ActiS sensor node features a hierarchical organi-zation employed to offer a rich set of functions, benefitfrom the open software system support, and performcomputation and communications tasks with minimalpower consumption. Each ActiS node utilizes a commer-cially available wireless sensor platform Telos fromMoteiv [18] and a custom intelligent signal processingdaughter card attached to the Telos platform (Fig. 4).The daughter boards interface directly with physical sen-

    sors and perform data sampling and in some cases preli-minary signal processing. The pre-processed data is thentransferred to the Telos board. The Telos platform cansupport more sophisticated real-time analysis and canperform additional filtering, characterization, featureextraction, or pattern recognition. The Telos platformis also responsible for time synchronization, communica-tion with the network coordinator, and secure datatransmission.

    Fig. 5shows a block diagram of an ActiS with a Telosplatform and a customIntelligent Signal Processing Module(ISPM). Telos is powered by two AA batteries and featuresan ultra-low power Texas Instruments MSP430 microcon-troller [20]; a Chipcon CC2420 radio interface in the2.4 GHz band [21]; an integrated onboard antenna with50 m range indoors/125 m range outdoors; a USB portfor programming and communication; an external flashmemory; and integrated humidity, temperature, and lightsensors. The MSP430 microcontroller is based around a16-bit RISC core integrated with RAM and flash memo-

    ries, analog and digital peripherals, and a flexible clocksubsystem. It supports several low-power operating modesand consumes as low as 1 lA in a standby mode; it also hasvery fast wake up time of no more than 6 ls. Telos Revi-sion A features a MS430F149 microcontroller with 2 KBRAM and 60 KB flash memory; Telos Revision B (nowTmoteSky) features a MSP430F1611 with 10 KB ofRAM and 48 KB of flash memory. The CC2240 wirelesstransceiver is IEEE 802.15.4 compliant and has program-mable output power, maximum data rate of 250 Kbs, andhardware support for error correction and encryption.The CC2240 is controlled by the MSP430 microcontrollerthrough the Serial Peripheral Interface (SPI) port and a ser-ies of digital I/O lines with interrupt capabilities. The Telosplatform features a 10-pin expansion connector with oneUART (Universal Asynchronous Receiver Transmitter)and one I2C interface, two general-purpose I/O lines, andthree analog input lines.

    We developed two custom boards specifically for healthmonitoring applications, an ISPM and an IAS (IntelligentActivity Sensor). The ISPM board extends the capabilitiesof Telos by adding two perpendicular dual axis accelerom-eters (Analog Devices ADXL202), a bioamplifier with sig-nal conditioning circuit, and a microcontrollerMSP430F1232 (Fig. 5). The ISPMs two ADXL202 accel-

    erometers cover all three axes of motion. One ADXL202 is

    105 105.5 106 106.5 107 107.5 108 108.5 1090

    500

    1000

    1500

    2000

    Step

    105 105.5 106 106.5 107 107.5 108 108.5 1090.5

    1

    1.5x 10

    acc

    105 105.5 106 106.5 107 107.5 108 108.5 1091000

    2000

    3000

    4000

    Time [sec]

    ECG

    accX

    accYaccZ

    Fig. 3. ECG, acceleration, and foot switch data collected by the WWBAN prototype during normal walking.

    Fig. 4. Photo of two ActiS sensors with customized daughter boards.

    A. Milenkovicet al. / Computer Communications 29 (2006) 25212533 2525

  • 8/9/2019 WSN Issues Implemantation

    6/13

  • 8/9/2019 WSN Issues Implemantation

    7/13

  • 8/9/2019 WSN Issues Implemantation

    8/13

    One of the key protocols for time synchronization inWSNs is the Flooding Time Synchronization Protocol(FTSP) developed at Vanderbilt University[29]. It featuresMAC layer time stamping for increased precision and skewcompensation with linear regression to account for clockdrift. The FTSP generates time synchronization with peri-

    odic time sync messages. The network can dynamicallyelect a root node. Whenever a node receives a time syncmessage, it rebroadcasts the message, thus flooding the net-work with time sync messages. The message itself containsa very precise timestamp of when the message was sent.The receiving node takes an additional local timestampwhen it receives the message. Because the timestamps aretaken deep in the radio stack, they eliminate non-determin-istic error sources and only include highly deterministicevents such as air propagation time, radio transmission,and radio reception time. Comparing the timestamps fromthe last several messages received, the node computes asimple linear regression to allow it to account for the offset

    difference in its clock from global time as well as the rela-tive difference in frequency of local oscillators. This enableseach node to maintain an accurate estimate of global timedespite the fact that its local clock may be running slightlyfaster or slower than the global clock source.

    We modified the original FTSP for WWBAN settings[30]. Our modification exploits the ZigBee[31]star networktopology to further minimize resources needed for timesynchronization. The prototype features a masterslavehierarchy where the network coordinator periodicallytransmits a beacon message to its slave nodes to maintain

    the synchronized communication link; a slave node receivesthe beacon without re-transmission. This highly accuratetime synchronization allows a slave node to disable itsradio and enter a low power sleep mode, waking up justbefore the next message is due. In addition, we allowedan original implementation where a root can be dynamical-

    ly chosen and the network flooded by the sync messages inthe case that the network coordinator fails or is turned off.For time synchronization to work, there must be a fixed

    point in time from which both sender and receiver can ref-erence the timestamp in a given message. For a ZigBeemessage, this point is at the end of the Start of FrameDelimiter (SFD). Fig. 7 shows the interface between theChipcons CC2420 radio transceiver and the microcontrol-ler at the top, and the IEEE 802.15.4 physical frame formatand corresponding pin activity at the bottom. On the Telosplatform, the wireless transceiver provides a signal to themicrocontroller to indicate when the SFD byte has beenreceived or transmitted. As soon as the SFD byte is trans-

    mitted, the radio transceiver raises the SFD pin that initi-ates a timer capture and an interrupt request on themicrocontroller. This allows the microcontroller to get atimestamp immediately after the SFD byte is transmitted.This timestamp is then inserted into the current time syn-chronization message (Fig. 8). It should be noted that themessage has already begun transmission when the time-stamp is made and added to the message. In a similarway, the receiver makes a local timestamp when it receivesthe SFD and stores it with the time synchronization mes-sage (Fig. 8). Later, the microcontroller will compare the

    FIFOFIFOP

    CCA

    SFD

    CSn

    SISO

    SCLK

    GIO1

    Interrupt

    GIO0

    SPI

    GIO2MOSI

    MOSO

    SCLK

    Data transmitted

    over RFMAC Protocol DataLengthSFDPreamble

    SFD Pin

    Automaticallygenerated

    preamble and SFD

    Data fetched from TxFIFO CRC

    Data received

    over RFMAC Protocol DataLengthSFDPreamble

    SFD Pin

    FIFO

    Timer Capture

    SPI

    Fig. 7. Interfacing CC2420 RF transceiver (top). IEEE 802.15.4 frame format and pin activity (bottom).

    2528 A. Milenkovicet al. / Computer Communications 29 (2006) 25212533

  • 8/9/2019 WSN Issues Implemantation

    9/13

    two timestamps to determine the offset between the localtime and the global time, and adjust the degree to whichthe local clock is running faster or slower than the globalclock (the network coordinator global time).

    This is a platform-specific implementation of the origi-nal FTSP mechanism, and it results in very accurate time-stamps deep in the radio stack [30]. Unlike the originalimplementation of the FTSP on Mica2 boards where theprocessor directly controls the wireless bit-stream, the

    Teloss wireless controller offloads channel access fromthe main processor. In addition, the CC2420 provides flex-ible receive and transmit FIFO buffers and communicateswith the main processor using a synchronous peripheralinterface (SPI). Generally, when a message is being trans-mitted, the processor loads up the transmit FIFO withthe entire message and then enables transmission. Howev-er, the FTSP messages contain a timestamp that is generat-ed after the message has begun transmission. To implementthis on the Telos platform, most of the message is placed inthe FIFO and transmission is enabled. When the SFDinterrupt occurs, the captured timer value is retrieved andconverted to a global timestamp. The timestamp is insertedinto the message and the rest of the message is placed in theFIFO. Assuming this can all be done quickly enough, theentire message is transmitted properly. If however the pro-cess is too slow, the FIFO will under-run and the messagetransmission will fail. Therefore, the speed must be checkedfor each individual application since ZigBee specifies a fair-ly speedy effective bit rate of 250 kbps.

    The SPI interface between the microcontroller and thewireless controller on the Telos platform runs at 500 kbps,twice as fast as the message is transmitted over the radio.Experimental time measurements were taken to ensure ade-quate margin and instill confidence that our protocol can

    run reliably without FIFO under-run. It takes about

    700 ls to transmit a time sync message and about 150 lsto calculate and insert the timestamp. It takes another300 ls to send the second part of the message over theSPI and finish filling the FIFO. All of this means that theentire message makes it into the FIFO with about 250 lsto spare, which is adequate for our application.

    Software support for time synchronization is imple-mented as a nesC interface that provides applicationsaccess to the time synchronization information. It can give

    the current global time, convert a local timestamp to globaltime, or calculate wait period until a future global time.This last facility is useful for an application that wants todo something at a specific global time. For example, weuse it to put the radio transceiver in the sleep mode untilthe next communication window. If the next window willoccur at a particular global time, the node can find outhow long it will have to wait and then set a timer accord-ingly so it can wake up and enable the radio.

    All timestamps and clocks were based on the 32768 Hzcrystal on the Telos board. The crystal exhibits good short-term stability, an essential quality for our WWBAN proto-type to work properly. Though the low frequency crystaldoes not allow a very high-resolution synchronization, theperiod of 30.5 ls is quite satisfactory for typical WWBANapplications. It should be noted that the MSP340 microcon-troller has a sophisticated clock module with a digitally con-trolled oscillator (DCO) with clock frequencies up to 5 MHz.Though a DCOs clock short-term stability is rather poor, anonline modulationand clock stabilizationcan be employed ifhigher resolution is needed.

    For testing of time synchronization protocol, we devel-oped a test bed where the network coordinator andWWBAN slave sensors are all connected to a commonwired signal connected to an MSP430s digital I/O port

    with timer capture capability. All nodes are configured to

    Propagation

    Data transmitted

    over RFMAC Protocol DataLengthSFDPreamble

    SFD Capture Timer

    Timestamp

    MAC Protocol DataLengthSFDPreamble TimestampData received

    over RF

    SFD Capture Timer

    Synchronize local time(TinyOS)

    Network

    Coordinator

    Process Send

    NC

    BAN

    EE

    AAAA

    AA

    Fig. 8. Time synchronization in WBAN prototype.

    A. Milenkovicet al. / Computer Communications 29 (2006) 25212533 2529

  • 8/9/2019 WSN Issues Implemantation

    10/13

    report their respective global timestamps every time thecommon signal changes its state. For each event, the mas-ters timestamp was compared to the slaves timestamp todetermine the absolute error of the slaves timekeeping.

    Testing the prototype indicated very good time synchro-nization. A number of experiments have been run varying

    the frequency of synchronization messages and test dura-tion. In most cases the slave nodes error was within 1tick (30.5ls), and an average error was approximately0.1* Tcycle or 3 ls. It is expected that the more frequentlytime sync messages are sent, the better the network nodeswill be able to maintain synchronization. Somewhat sur-prisingly, we discovered that increasing the time betweenmessages improves time synchronization, because nodesare better able to estimate clock skew. However, the costof larger inter-beacon periods is an increase in algorithmconversion time. Consequently, we implemented a hybridscheme where messages are sent more often. During con-vergence, nodes will process every beacon. After a coarse

    convergence is achieved, nodes will begin to process everyN-th beacon, allowing for a more precise skew estimation.

    5. Energy efficiency

    Energy consumption is a first class design constraint inwireless sensor networks since they are battery operated.To extend each nodes lifetime, it is necessary to reducepower dissipation as much as possible; dissipation below100 lW will enable operation on energy scavenged fromthe environment. Various design trade-offs between com-munication and on-sensor computation, collaborative pro-

    tocols, and hierarchical network organization can yieldsignificant energy savings. Once the sensor network isdeployed, dynamic power management techniques can beemployed in order to maximize battery life.

    In WWBAN systems, reducing total power consump-tion is crucial for several reasons. Size and weight of sen-sors are predominantly determined by the size and weightof the batteries. On the other hand, a batterys capacityis directly proportional to its size. Consequently, WWBANsensor nodes need to be extremely energy efficient, sincereducing energy requirements will allow designers to use

    smaller batteries. Smaller batteries will result in furtherminiaturization of physiological sensors and, in turn, anincreased level of users comfort. Second, an extended peri-od of operation without battery changes is desirable,because frequent battery changes on multiple sensors arelikely to hamper users acceptance. In addition, longer bat-

    tery life will decrease WWBAN operational costs.We have designed a custom, application-specific proto-col according to 802.15.4 recommendations [27]. In orderto satisfy medical application requirements, the networkprotocol specifies a 1-s super frame cycle (TSFC= 1 s) andeach slave node has its reserved time slot of 50 ms to trans-mit the data (Fig. 9). A super frame cycle starts with a bea-con message sent by the network coordinator; the beaconmessage carries time synchronization information. Eachsensor node wakes its radio interface up in a receive modeimmediately before the next expected beacon.

    Fig. 10shows the power profiles recorded for a motionsensor using an environment for real-time power monitor-

    ing[32]. We can clearly identify three distinct states: Listen,Transmit, and Inactive modes. As described in Section 3,the ultra-low power microcontroller on the daughter boardsamples 3-axes of acceleration with frequency of 200 Hz.The data is filtered and buffered. The processor on theTelos board wakes up every 40 ms (25 Hz) and raises aninterrupt requiring the data from the daughter card. Inthe Inactive mode the wireless transceiver on the Telosplatform is turned off, and the average current drawn forthe whole sensor is 1.53 mA. The motion sensor can beconfigured to send raw accelerometer data or detectedsteps. If raw accelerometer data is required, the amount

    information to be sent per one super frame (1 s) is 3axes*40 Hz*2 bytes = 240 bytes, which is equivalent to12 TinyOS packets. If the step detection is performed onthe sensor only, information about that event and corre-sponding timestamp are sent (1 or 2 packets). The motionsensor draws on average 20.1 mA in the Transmit modeand 20.8 mA in the Listen mode. The main contributorto this figure is the wireless radio that draws 17.4 mA whenit is transmitting and 19.7 mA when it is receiving. Basedon these parameters, we can calculate the average currentas follows:

    Fig. 9. Super frame cycle.

    2530 A. Milenkovicet al. / Computer Communications 29 (2006) 25212533

  • 8/9/2019 WSN Issues Implemantation

    11/13

    Iavg TListen

    TSFC

    IListen TTransmit

    TSFC

    ITransmit

    TSFC TListen TTransmit

    TSFC

    IInactive.

    The average current can be used to estimate battery life.If only two messages are sent per super frame cycle(TListen= 50 ms, TTransmit= 15 ms), the average current is2.77 mA. Two AA batteries on the Telos platform have2900 mAh capacity, so the expected operating time of themotion sensor is 1046 h or over 6 weeks. However, witha tiny 120 mA rechargeable battery, the operating time willbe slightly less than 2 days. Further optimizations are alsopossible: depending on the WWBAN deployment scenario,

    we could decrease the output power during transmission, asuper cycle can be extended (the node would spend lesstime in the listen mode), or data can be stored locally ina compressed format and then later transmitted.

    6. Conclusions

    This paper demonstrates the use of WWBANs as a keyinfrastructure enabling unobtrusive, continual, ambulatoryhealth monitoring. This new technology has potential tooffer a wide range of benefits to patients, medical person-nel, and society through continuous monitoring in the

    ambulatory setting, early detection of abnormal condi-

    tions, supervised rehabilitation, and potential knowledgediscovery through data mining of all gathered information.

    We have described a general WWBAN architecture,important implementation issues, and our prototypeWWBAN based on off-the-shelf wireless sensor platformsand custom-designed ECG and motion sensors. We haveaddressed several key technical issues such as sensor nodehardware architecture, software architecture, network timesynchronization, and energy conservation. Further effortsare necessary to improve QoS of wireless communication,reliability of sensor nodes, security, and standardizationof interfaces and interoperability. In addition, further stud-ies of different medical conditions in clinical and ambulato-ry settings are necessary to determine specific limitations

    and possible new applications of this technology.

    Acknowledgments

    We thank John Gober for his help in the ISPM and IASprinted circuit board development and Dennis Cox for hishelp with time synchronization protocol. We also thankPiet de Groen from Mayo Clinic for inspiring exchangeof ideas about WWBAN.

    References

    [1] National Coalition on Health Care, in: , Accessed: June 2005.

    0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

    5

    10

    15

    20

    25

    Time [sec]

    ]Am[I

    0.65 0.7 0.75 0.8 0.85 0.9

    0

    5

    10

    15

    20

    Time [sec]

    ]Am[I

    Listen Transmit

    Idle

    Fig. 10. Actis current consumption during 5 s of operation. Red arrow marks one super frame cycle. (For interpretation of the references to color in thisfigure legend, the reader is referred to the web version of this paper.)

    A. Milenkovicet al. / Computer Communications 29 (2006) 25212533 2531

    http://www.nchc.org/facts/cost.shtmlhttp://www.nchc.org/facts/cost.shtmlhttp://www.nchc.org/facts/cost.shtmlhttp://www.nchc.org/facts/cost.shtml
  • 8/9/2019 WSN Issues Implemantation

    12/13

    [2] P. Binkley, Predicting the potential of wearable technology, IEEEEngineering in Medicine and Biology Magazine 22 (3) (2003)2324.

    [3] Seiko Pulse Graph Wrist Watch, Available at: (Japanese), Accessed: July 2005.

    [4] Polar, Available at: , Accessed: July2005.

    [5] Digi-Walker step counter, Available at: , Accessed: July 2005.

    [6] Actigraph, Available at: ,Accessed: July 2005.

    [7] Holter Systems, Med-electronics Inc., Available at: , Accessed: July 2005.

    [8] Cardio Micro Sensor, Available at: ,Accessed: July 2005.

    [9] T. Martin, E. Jovanov, D. Raskovic, Issues in wearable computingfor medical monitoring applications: a case study of a wearable ECGmonitoring device, in: International Symposium on Wearable Com-puters ISWC 2000, Atlanta, October 2000.

    [10] R. Szewczyk, E. Osterweil, J. Polastre, M. Hamilton, A. Mainwaring,D. Estrin, Habitat monitoring with sensor networks, Commun. ACM47 (6) (2004) 3440.

    [11] J. Burrell, T. Brooke, R. Beckwith, Vineyard computing: sensornetworks in agricultural production, IEEE Pervasive Computing 03(1) (2004) 3845.

    [12] E. Jovanov, J. Price, D. Raskovic, K. Kavi, T. Martin, R. Adhami,Wireless personal area networks in telemedical environment, in:Proceedings of the Third International Conference on Informationtechnology in Biomedicine(ITAB-ITIS2000), Arlington, VA, 2000, pp.2227.

    [13] E. Jovanov, A. Lords, D. Raskovic, P. Cox, R. Adhami, F. Andrasik,Stress monitoring using a distributed wireless intelligent sensorsystem, IEEE Engineering in Medicine and Biology Magazine 22(3) (2003) 4955.

    [14] E. Jovanov, A. Milenkovic, C. Otto, P.C. de Groen, A wireless bodyarea network of intelligent motion sensors for computer assistedphysical rehabilitation, Journal of NeuroEngineering and Rehabili-tation, 2:6, March 1, 2005. Available at: .

    [15] J. Welch, F. Guilak, S.D. Baker, A wireless ECG smart sensor forbroad application in life threatening event detection, in: Proceedingsof the 26th Annual International Conference of the IEEE Engineeringin Medicine and Biology Society, San Francisco, CA, September2004, pp. 34473449.

    [16] D. Malan, T.R.F. Fulford-Jones, M.Welsh, S. Moulton, CodeBlue:an ad hoc sensor network infrastructure for emergency medical care,in: Proceedings of the MobiSys 2004 Workshop on Applications ofMobile Embedded Systems (WAMES 2004), Boston, MA, June,2004, pp. 1214.

    [17] M.J. Mathie, B.G. Celler, A system for monitoring posture andphysical activity using accelerometers, in: Proceedings of the 23rdAnnual International Conference of the IEEE Engineering in

    Medicine and Biology Society, 2001, pp. 36543657.[18] Moteiv: Wireless Sensor Networks, Available at: , Accessed: June 2005.

    [19] C. Otto, J.P. Gober, R.W. McMurtrey, A. Milenkovic, Emil Jovanov,An implementation of hierarchical signal processing on a wirelesssensor in TinyOS environment, in: Proceedings of the 43rd ACMSoutheastern Conference, Kennesaw, GA, vol. 2, March 2005, pp.4953.

    [20] MSP430 MCUs, Available at: ,Accessed: January 2005.

    [21] Chipcon RF Tranceivers, Available at: ,Accessed: June 2005.

    [22] TinyOS: an open-source operating system for sensor networks,Available at: , Accessed: June 2005.

    [23] D. Gay, P. Levis, R. von Behren, M. Welsh, E. Brewer, D. Culler, ThenesC language: a holistic approach to networked embedded systems,in: Proceedings of Programming Language Design and Implementa-tion (PLDI) 2003, June 2003.

    [24] J. Elson, D. Estrin, Time synchronization for wireless sensornetworks, in: Proceedings of the 2001 International Parallel andDistributed Processing Symposium (IPDPS 01), pp. 19651970.

    [25] J. Elson, L. Girod, D. Estrin, Fine-grained network time synchroni-zation using reference broadcasts, in: Proceedings of the fifthsymposium OSDI 02, pp. 147163.

    [26] S. Ganeriwal, R. Kumar, M.B. Srivastava, Timing-Sync Protocol forSensor Networks, in: Proceedings of the 1st International Conferenceon Embedded Network Sensor Systems (SenSys 03), pp. 138149.

    [27] 802.15.4-2003 IEEE Standard for Information Technology-Part 15.4:Wireless Medium Access Control (MAC) and Physical Layer (PHY)specifications for Low Rate Wireless Personal Area Networks(LR-WPANS), 2003.

    [28] M. Maroti, B. Kusy, G. Simon, A. Ledeczi, Robust multi-hop timesynchronization in sensor networks, in: Proceedings of the Interna-tional Conference on Wireless Networks (ICWN 04), vol. 1,pp. 454460.

    [29] M. Maroti, B. Kusy, G. Simon, A. Ledeczi, The flooding timesynchronization protocol, in: Proceedings of the 2nd InternationalConference on Embedded Network Sensor Systems (SenSys 04),pp. 3949.

    [30] D. Cox, E. Jovanov, A. Milenkovic, Time synchronization for ZigBeenetworks, in: Proceedings of the 37th IEEE Southeastern Symposiumon System Theory (SSST05), Tuskegee, AL, March 2005,pp. 135138.

    [31] ZigBee Alliance, Available at: , AccessedJune 2005.

    [32] A. Milenkovic, M. Milenkovic, E. Jovanov, D. Hite, An environmentfor runtime power monitoring of wireless sensor network platforms,in: Proceedings of the 37th IEEE Southeastern Symposium on SystemTheory (SSST05), Tuskegee, AL, March 2005, pp. 406410.

    Aleksandar Milenkovicis an assistant professor inthe Department of Electrical and ComputerEngineering at the University of Alabama inHuntsville. He currently directs the LaCASALaboratory (http://www.ece.uah.edu/~lacasa/).His research interests include advanced archi-tectures for the next generation computingdevices, low-power VLSI, reconfigurable com-puting, embedded systems, and wireless sensornetworks. Dr. Milenkovic received his Dipl. Ing.,MSc, and PhD degrees in Computer Engineering

    and Science from the University of Belgrade, Serbia, in 1994, 1997, and1999, respectively.

    Chris Otto is an embedded software engineer atLewis Innovative Technologies, Inc. in Hunts-ville, Alabama. His interests include wirelesssensor networks, real-time systems, embeddedsystems and packet voice technologies. Mr. Ottoreceived his BS in Computer Engineering at theUniversity of Alabama in Huntsville in 1999 andis expected to receive his MS in 2006.

    2532 A. Milenkovicet al. / Computer Communications 29 (2006) 25212533

    http://www.seiko-pgt.or.jp/http://www.seiko-pgt.or.jp/http://www.polarusa.com/http://www.digiwalker.com/http://www.digiwalker.com/http://www.mtiactigraph.com/http://med-electronics.com/http://med-electronics.com/http://www.cardiomems.com/http://www.jneuroengrehab.com/content/2/1/6http://www.jneuroengrehab.com/content/2/1/6http://www.moteiv.com/http://www.moteiv.com/http://www.ti.com/msp430http://www.chipcon.com/http://www.tinyos.net/http://www.zigbee.org/http://www.ece.uah.edu/~lacasa/http://www.ece.uah.edu/~lacasa/http://www.zigbee.org/http://www.tinyos.net/http://www.chipcon.com/http://www.ti.com/msp430http://www.moteiv.com/http://www.moteiv.com/http://www.jneuroengrehab.com/content/2/1/6http://www.jneuroengrehab.com/content/2/1/6http://www.cardiomems.com/http://med-electronics.com/http://med-electronics.com/http://www.mtiactigraph.com/http://www.digiwalker.com/http://www.digiwalker.com/http://www.polarusa.com/http://www.seiko-pgt.or.jp/http://www.seiko-pgt.or.jp/
  • 8/9/2019 WSN Issues Implemantation

    13/13

    Dr. Emil Jovanovis an Associate Professor in theElectrical and Computer Engineering Depart-ment at the University of Alabama in Huntsville.He received the Dipl. Ing., MSc, and PhDdegrees in electrical and computer engineeringfrom the University of Belgrade. Dr. Jovanov hasbeen developing wireless intelligent sensors forpersonal health monitoring and mobile comput-

    ing for more than five years. He was principalinvestigator or co-investigator on several grantsfrom NSF and industry in the field of wireless

    sensor networks, wireless intelligent sensors, and wearable health moni-tors. His research interests include ubiquitous and mobile computing,biomedical signal processing, and telemedical health monitoring.Dr. Jovanov serves as an Associate Editor of IEEE Transaction onInformation Technology in Biomedicine and Applied Psychophysiologyand Biofeedback.

    A. Milenkovicet al. / Computer Communications 29 (2006) 25212533 2533