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Energy Efficient System-on-chip Design for WirelessBody Area Sensor NetworkChi-Chou Kaoa & Wen-Lin Yanga

a Department of Computer Science and Information Engineering, National University ofTainan, Tainan, TaiwanPublished online: 24 Apr 2014.

To cite this article: Chi-Chou Kao & Wen-Lin Yang (2014) Energy Efficient System-on-chip Design for Wireless Body Area SensorNetwork, Electric Power Components and Systems, 42:7, 737-745, DOI: 10.1080/15325008.2014.890973

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Electric Power Components and Systems, 42(7):737–745, 2014Copyright C© Taylor & Francis Group, LLCISSN: 1532-5008 print / 1532-5016 onlineDOI: 10.1080/15325008.2014.890973

Energy Efficient System-on-chip Design for WirelessBody Area Sensor NetworkChi-Chou Kao and Wen-Lin YangDepartment of Computer Science and Information Engineering, National University of Tainan, Tainan, Taiwan

CONTENTS

1. Introduction

2. Energy-efficient Master–slave Design

3. Cluster Algorithm

4. Hardware Implementation

and Experimental Results

5. Conclusion

References

Keywords: power, protocol, system on chip, master–slave architecture,body sensor network

Received 21 June 2013; accepted 18 January 2014

Address correspondence to Chi-Chou Kao, Department of ComputerScience and Information Engineering, National University of Tainan, 33,Sec. 2 Shu-Lin St, Tainan 700, Taiwan. E-mail: cckao@mail.nutn.edu.twColor versions of one or more of the figures in the article can be found onlineat www.tandfonline.com/uemp.

Abstract—This article presents a novel energy-efficient on-chip de-sign for wireless body area sensor networks focused towards per-vasive healthcare applications. The network adopts a master–slavearchitecture, where the body-worn slave nodes periodically send sen-sor readings to a central master node. Unlike traditional peer-to-peerwireless sensor networks, the nodes in this wireless body area sen-sor networks are not deployed in an ad hoc fashion. The network iscentrally managed and all communications are single-hop. A clus-ter algorithm is also presented so that all slave nodes are within thetransmission range of the master nodes. It pretends that all slave nodescan share resources and information over the internet to reduce en-ergy consumption. The design on system-on-chip platform has beensimulated for some experiments and implemented. Compared to pub-lished and industrially used schemes, the power consumption of theproposed design is over 30 and 99% lower in the simulation andplatform implementation, respectively.

1. INTRODUCTION

The long-distance medical technology improves patient healthstatus under reducing the number of times which patients goto hospitals. By using body area sensor networks (WBASNs),physiological body signals can be monitored to control chronicdiseases, conditions, and contingencies, such as heart rate,muscle contraction, brainwave activity, eye movements, etc.The data collected by wireless transmission will be transportedto the hospital, thus doctors and medical personnel can imme-diately grasp the condition of patients. Especially, if some ofthe important physiological signals exceed the safety value,the system can send the patient location signal to automati-cally call ambulances for help [1–8].

Because the sensing elements must be configured in the hu-man body for WBASNs, compared with traditional sensors setin wireless sensor networks (WSNs), the sensors in WBASNsneed more power saving, low cost, and small size. In the WSNs,the primary structure of sensors is composed of low-powercomputing equipment, sensing modules, memory, communi-cation devices, positioning devices, and analog/digital con-verter with built-in power supply device. Most of the power

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source is a battery in WSNs. Although the power consumptionof such sensors is low, it cannot be applied to WBASNs. It isbecause if the battery needs to be replaced in the body, the risknot only is the pain and infection, but also may bring a veryhigh mortality rate. Moreover, the maintenance work will alsobe time-consuming. In fact, because of different applications,WSN and WBASN have many differences. Table 1 gives abrief comparative analysis [9]. Obviously, previous WSN de-signs [10–17] are not well suited for WBASN applications.

Many studies have been implemented in the WBASN en-vironment. Ekstrom [18] implemented a small electrocardio-gram (ECG) sensor system that can be wirelessly connected toa personal digital assistant (PDA) via Bluetooth. Park et al. [19]proposed an ultra-wearable wireless low-power ECG monitor-ing system that uses wearable ultra-low-power wireless sen-sor nodes. It communicates using a 2.4-GHz transceiver and aGaussian frequency-shift keying (GFSK) modulator. Lee et al.[20] proposed a biomedical sensor node that communicatesusing ZigBee and a code division multiple access (CDMA)modem for integration with PDAs and mobile phones. Sakaueand Makikawa [21] developed a wireless biosignal monitoringdevice which consists of a micro-processing unit, a three-axisacceleration sensor, an ECG amplifier, and a Bluetooth wire-less communication module. Even though the above designsmay have the advantage of low power consumption, the highcost is still a problem for WBASN.

The article first presents a novel master–slave architecturefor WBASNs. In the architecture, the slave nodes are the ac-tual WBASN nodes that acquire sensor data and transmit tothe master node for processing. Each individual master–slavenetwork is referred to as a cluster. The proposed architectureis centrally managed to achieve low power consumption. Next,a cluster algorithm is proposed to generate the master nodes

WSN WBASN

Traffic Specific, sporadic/cyclic, modest data rate

Topology Random, dynamic DynamicConfiguration/

maintenanceSelf-configurable

unattended operationSome flexibility,

specialists areneeded

Network size Unlimited number(typically 102–106)

Dense distributionlimited by body size

Node low/modest complexity

Overall designgoals

Energy efficiency,self-operability, costoptimization

Limited electromagneticexposure, energyefficiency

TABLE 1. Comparisons WSN with WBASN [9]

in high density so that all slave nodes are within the trans-mission range of the master nodes. Hence, all slave nodes canshare resources and information over the internet to achievelow power purposes. Finally, considering the hardware im-plementation cost, the system-on-chip (SoC) method is usedto incorporate a central-processing unit, memory, and a radiotransceiver into a single custom chip for the proposed WBASN.Compared to published and industrially used schemes, thepower consumption of the proposed design is over 30 and99% lower in the simulation and platform implementation,respectively.

The remainder of the article is organized as follows. In Sec-tion 2, an energy-efficient master–slave design for WBASNsis introduced. Section 3 identifies the cluster algorithm in de-tail. The hardware implementation and experimental resultsare presented in Section 4. Finally, Section 5 provides the con-clusions.

2. ENERGY-EFFICIENT MASTER–SLAVE DESIGN

In this section, a master–slave design for WBASNs will bepresented. Unlike traditional peer-to-peer WSNs, the networkis centrally managed and all communications are single-hopsuch that the energy consumption can be reduced. The networkarchitecture is first proposed. In defining the architecture, someframeworks are used to reduce energy consumption. Next, aprotocol in accordance with the master–slave architecture isdescribed. For the proposed design, a cluster step is necessary.Hence, a cluster algorithm is described in the next section.Before presenting the design, the following assumptions canbe deduced about the WBASN.

1. The wireless sensor nodes are attached to the body.Hence, the proposed design might not be applica-ble in real-time scenarios where the nodes are dy-namic and could be removed and replaced in denseenvironments.

2. Assume that the transmitting frequency of the monitor-ing data is low and when some of the important physio-logical signal exceeding the safety value, the monitoringdata are transmitted.

3. If any node becomes feeble and dies, the network doesnot need to respond immediately.

4. Sensors monitor a range of vital signs which are typicallyat a low data rate, such as temperature, pressure, andheart rate. However, some higher data rate applicationsmust also be catered to, such as the streaming of ECGsignals.

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Kao and Yang: Energy Efficient System-on-chip Design for Wireless Body Area Sensor Network 739

FIGURE 1. Master–slave network topology (S = slave node,M = master node).

5. The nodes are miniature. They need to run ideas for daysfrom very low power.

6. Sensor nodes are resource constrained; i.e., they havelow processing power and limited storage.

2.1. Network Architecture

According to the above assumptions, a point to multi-pointnetwork architecture was proposed. As shown in Figure 1, thecentral node acts as the master while the other nodes are slaves.Each individual master–slave network is referred to as a cluster.The slave nodes are the actual WBASN nodes and the masternodes are hospital server nodes. In the master–slave environ-ment, the slave nodes first acquire sensor data and transmit tothe hospital server node for processing. Although it is possibleto form hierarchical networks of a “central master” with othermaster nodes, this article concentrates on the network whosedepth is one. In other words, all master nodes must be on thesame stage in the network so that the data is transmitted di-rectly to the nearest hospital servers from the actual WBASNnodes. If necessary, master nodes can communicate to transmitdata to the IP network. The hospital servers are significantlyless resourced and power constrained relative to the wirelesssensors. The transmission ranges of the slave nodes are shortdue to their limited power supply. In this architecture, the com-plexity of the master nodes is increased. However, because themaster nodes have significantly more power and processingresources, it is not a problem for the design.

In this architecture, a time division multiple accesses withclear channel assessment is used (TDMA/CCA) [22, 23] net-work to access the scheme between slave nodes and masternodes. Data is sent from a master node to the other masternodes using persistent carrier sense multiple access (CSMA)protocol [24]. The proposed network access scheme signifi-cantly reduces the likelihood of collision and idle listening,speed up data transmission and power savings. In addition,time-slot allocation is dynamically controlled by TDMA/CCA,

FIGURE 2. Relationship between the demand on the WBASNand many existing wireless communications protocol [25].

so a slave time slot could be changed each time it communi-cates with the master.

2.2. Protocol Operation

The communication protocol for WBASN design is an es-sential issue. Figure 2 depicts the relationship between thedemand on the WBASN and many existing wireless commu-nication protocols [25]. Obviously, compared with the existingwireless communication protocols, WBAN need to generate ahigher data transmission rate in the same power or the requiredpower lower in the same data transmission rate. Therefore, aprotocol is needed that is in accordance with the master–slavearchitecture.

In the master–slave protocol design, the whole process isdivided into several rounds; each round is separated into twophases, namely the set-up phase and the steady-state phase,as shown in Figure 3. The main action in the setup phase iscluster setup. In the proposed cluster algorithm, the number ofmaster nodes is first determined by the master node selectionalgorithm. Subsequently, a cluster formation algorithm is usedto determine which master node the slave node belongs to. Thecluster algorithm is further discussed in Section 3.

After all nodes have been partitioned into clusters, the dataacquired by slave nodes in a cluster must be sent to the masternode. The slave nodes aggregate the collected data and thensend the data to the master node in the steady-state phase.In the proposed design, the steady-state phase is divided intomany frames. In each frame, the slave nodes send data to theirmaster node according to slot time. In this steady-state phase,all slave nodes collect information from the environment andsend packets to the master node. The master node may combinesome data to reduce the amount of redundant data to increaseenergy efficiency. This procedure is replicated in each rounduntil accomplishing data transmission.

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FIGURE 3. Communication protocol: (a) process stages, (b)setup phase, and (c) steady-state phase.

2.3. Energy-efficient Mechanism

To reduce energy dissipation, each slave node uses power con-trol to adjust the amount of transmission power based on thereceived strength of the master node advertisement. Further-more, the radio of each slave node is turned off by its allocatedtransmission time. The time division multiple access with clearchannel assessment media access control (TDMA/CCA MAC)protocol is used when the slave nodes have data to transmit tothe master node. TDMA is a channel access method for sharedmedium networks. It allows several slave nodes to share thesame frequency channel by dividing the signal into differenttime slots. The users transmit in rapid succession, one afterthe other, each using its own time slot. This allows multiplestations to share the same transmission medium while usingonly a portion of its channel capacity. The purpose of CCAis to detect the presence of ongoing transmissions reliably soas to enable the sending node to decide whether to proceedwith channel access. The master node must receive all theinformation from the slave nodes in the cluster. Once the mas-ter node receives all the data, it performs data aggregationto enhance the common signal and to reduce the uncorre-lated noise among the signals. This process is repeated in eachround, thus the high energy dissipation of communication withthe master is spread to all sensor nodes and energy is saveddue to data aggregation in a cluster.

Recollect that the slave nodes are the actual WBASN nodesattached to a human body in WBASN. To ensure power saving,when patients move, the established master–slave architecturecan be redeployed. In some existing clustering protocol flow

[26–31], if the architecture needs to be redeployed, the protocolmust repeat all phases. However, it thus increases latency andenergy consumption in the redeployment process.

In the proposed clustering protocol flow, if a patient moves,the slave nodes s, which is set in the patient’s body, send the mo-tion distance information to its master node. The master nodejudges whether the patient moves in the transmission range ofthe cluster according to the motion distance information. If thepatient lives beyond the cluster transmission range, the masternode immediately broadcasts advertisement messages to theother master nodes through the IP network. The distance dis(s)between each master node and s is recalculated. A master nodewhose distance is shortest can be selected in dis(s) as the newmaster node of s. By using the above clustering protocol flow,the master nodes can be speedily resolved. The master nodesform clusters in densely deployed regions. The set-up phasedoes not need to be repeated, reducing the energy needed forthe redeployment procedure.

Data is transferred from a master node to the other masternodes using persistent CSMA protocol [24]. CSMA is a prob-abilistic MAC protocol in which a node verifies the absenceof other traffic before transmitting on a shared transmissionmedium. “Carrier sense” describes the fact that a transmit-ter uses feedback from a receiver that detects a carrier wavebefore trying to send. That is, it tries to detect the presenceof an encoded signal from another station before attempt-ing to transmit. If a carrier is sensed, the station waits forthe transmission in progress to complete before initiating itsown transmission. “Multiple access” describes the fact thatmultiple stations send and receive along the medium. Trans-missions by one node are generally received by all other sta-tions using the medium. In the proposed protocol, when amaster node receives data to transmit to the other masternodes, it must sense the channel to determine whether an-other node is carried on the IP network. If so, the master nodewaits to transmit the data. Otherwise, the master node sendsthe data. A flowchart of the proposed protocol is shown inFigure 4.

In conclusion, the proposed protocol has the following mainadvantages:

1. The necessary function and power of the actual WBASNnodes is small then the scheme is easily implemented inhardware.

2. The access scheme significantly reduces the likelihoodof collision and idle listening, speed up data transmissionand power savings.

3. The redeployment process is energy efficient.

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Kao and Yang: Energy Efficient System-on-chip Design for Wireless Body Area Sensor Network 741

FIGURE 4. Flowchart of proposed protocol.

3. CLUSTER ALGORITHM

According to the above descriptions, a cluster algorithm isneeded in set-up phase. Master node selection is an impor-tant consideration for the proposed communication protocol.Randomly selecting master nodes leads to an improper numberof clusters in each round, which in turn leads to excess energyconsumption. A large number of master nodes will increasethe energy consumed for transmission between slave nodesand master nodes. A small number of master nodes will in-crease the energy consumed of master nodes. To mitigate thesedrawbacks, a master node selection algorithm that generatesthe proper number of master nodes is proposed.

The proposed master node selection algorithm uses a dou-ble selection method to select the master nodes. In the firstselection, the number of optimal clusters, nopt, is first foundby adapting the simulated annealing algorithm [32, 33] ac-cording to the location information of all slave nodes [34].Initially, the distribution of vectors over clusters is randomlyassigned. At each iterative step, a randomly selected vectoris taken out of its cluster and reassigned to another randomlychosen cluster. A new sum of distances within clusters is com-puted and compared with the previous sum of distances withinclusters. If the previous sum of distances within clusters islarger than the new sum of distances within clusters, the newdesignation of the vector is unconditionally accepted and usedas the starting point for the next iteration. Otherwise, anothernew assignment is generated with probability. The iterativescheme is based on standard approaches used in regular sta-tistical tests. The basic idea is to organize the search of theoptimal number of clusters simultaneously with the optimiza-

tion of the distribution of genes over clusters. The simulatedannealing algorithm guarantees to find the globally optimaldistribution of genes over clusters. Notice that nopt is the num-ber of optimal clusters for location information but not energyinformation.

Afterwards, some nodes are selected as the candidates ofmaster nodes with a given parameter n and the expected num-ber of master nodes, n, is set larger than the optimal number ofmaster nodes, nopt. In this algorithm, n is set to 2nopt. The slavenode sends its energy information to the candidates of masternodes in the second selection. If there is any candidate, m,of master nodes whose energy can not load the server nodesenergy, m must be wiped out from the candidates of masternodes. After deleting the under-capacity candidates of masternodes, the candidates of master node n are decreased as n′.In the second step, if n′ is larger than nopt, nopt master nodeswhose residual energy is the largest among the candidates areselected. If the n′ is equal or less than nopt, all candidates areselected as master nodes. Through the selection, all masternodes are generated.

The distance between the master node and the slave node isthe central factor in the cluster formation algorithm. For slavenode s, a set of the distances between s and each master nodeis denoted as dis(s). If the distance between the master node mand s is shortest in dis(s), slave node s belongs to the clusterwhose master node is m.

4. HARDWARE IMPLEMENTATIONAND EXPERIMENTAL RESULTS

This section describes the experiments carried out to verify theproposed master–slave protocol. The simulation experimentsare described first. Next, based on the considerations of thecomplexity and cost of the hardware implementation, the pro-posed scheme is integrated into a chip to regulate the physicalpower efficiency.

4.1. Simulations

The energy and time spent probing the medium was measured,starting a transmission, sending one packet, stopping a trans-mission after a successful or failed transmission, and receivinga packet. Every scenario was reconstructed with MATLAB(The MathWorks, Natick, Massachusetts, USA) by adding theenergy expended during each performance. Measurements in-dicate that it takes a little under 32 μs to send one byte, a sourcenode sends 40 Bs (bytes) packets at a rate of 1/2 ptk.s−1, andthe time separating two frames in a stream is 1.351 ms.

Broadly speaking, the delay for data gathering can be de-composed into queuing delay, channel access delay, delay forwaking up sleeping nodes, and the packet transmission delay

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in WSNs. Based on the following several reasonable descrip-tions, the packet transmission delay is significant energy forthe proposed WBASN. First, in the proposed WBASN, eachslave node is turned off by its allocated transmission time andwhen some of the important physiological signal exceeding thesafety value, the monitoring data are transmitted. Due to theapplication-specific design of WBASNs, most traffic through-out the network is due to transporting the gathered data to themaster nodes. In such a light traffic scenario, queuing delay isnot as a major concern. Second, the TDMA/CCA MAC andCSMA MAC protocols are used so that channel access delaydue to collision detection and avoidance is negligible. Third, itis assumed that a full duty cycle, ultra-low power wakeup radio[35] is available for each sensor node. Thus, sleeping sensornodes can be woken up for packet transmission with almostno delay and energy penalties. Based on the above observa-tions, the time of packet transmission in proposed WBASNsconstitutes a significant portion of the overall delay. Packettransmission delay is defined as the time between the first at-tempt to send a packet and the successful reception of thispacket.

In wireless communications, shadow fading is the deflec-tion of the attenuation that a carrier-modulated telecommu-nication signal experiences over certain propagation media.The fading may vary with time, geographical position and/orradio frequency, and is often modeled as a randomprocess.In wireless systems, fading may either be due to multipath-propagation, referred to as multipath induced fading, or dueto shadowing from obstacles affecting the wavepropagation.However, the shelter range of patients’ motion will not gener-ate a large number of changes in WBASNs. Hence, the signalstrength of slave nodes suffered shadow fading should not gen-erate big change and it is then assumed that the sensor nodeshave the ability to adjust their transmission power according tothe length of the receiving node. The power model presentedin [36] is assumed. The amount of power consumed over adistance is given by:

PT x (l, d) = lPT xelec + lεampdn,

PRx (l, d) = lP Rxelec,

where PT xelec and P Rx

elec are the amounts of power being dissipatedto run the transmitter’s or and receiver’s circuitry, respectively,εamp is the power consumed by the transmission amplifier fora receiver with a distance d. L is the length of the transmitted/received message in bits, and n is the path loss exponent whichis limited by the radio propagation model.

Based on the above explanations, Table 2 gives the averagepower consumption and packet transmission delay (the delay)for the proposed protocol and WiseMAC [37]. Compared tothe existing protocol, the proposed protocol consumes 30%

Proposed WiseMAC

Power (J) Delay (sec) Power (J) Delay (sec)

1.8 1.74 2.6 1.521 1 +30.8% −14.5%

TABLE 2. Power consumption and packet delay

less power. However, the delivery delay of regular packets forthe proposed protocol is larger than that for WiseMAC.

The lifetime of WSNs is the time span from deploymentto the instant when the network is considered non-functional.However, the point at which a network is considered non-functional is application-specific. It can be, for example, theinstant when the first node dies, a certain percentage of sensorsdie, the network partitions, or loss of coverage occurs. In somecases it is necessary that all nodes stay alive as long as possible,since network quality decreases considerably as soon as onenode fails. In these scenarios, it is important to know when thefirst node dies. In other cases, sensors can be placed in proxim-ity to each other. Thus, adjacent sensors could record relates oridentical data. Hence, the loss of a single or few nodes does notautomatically diminish the quality of service of the network.In this study, the latter is considered as the network lifetime.Consequently, the network lifetime is considered as the totalavailable power (Ptotal) divided by total power consumptionduring a round (Pround); i.e., Ptotal

Pround. The lifetime was evaluated

accurately by measuring the energy consumed under variousbasic operations using a fast data acquisition board. To verifythe accuracy of the reconstruction technique for determiningnode lifetime, several scenarios were ran for which node life-time was measured directly for initial energy equal to 1 J;the results are shown in Table 3. The results show that theproposed protocol can reduce power consumption, prolongingsystem lifetime.

4.2. Platform Implementation

General wireless sensor node contains four units: Processingunit, sensor unit, the wireless transmission unit, and power

Time first Time half Time lastsensor node of sensor sensor node

Protocol dies nodes die dies

ZigBee 3 6 7802.11 10 12 18Proposed 42 46 50

TABLE 3. Sensor node life (days)

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Kao and Yang: Energy Efficient System-on-chip Design for Wireless Body Area Sensor Network 743

supply unit [38, 39]. The processing unit is responsible forcontrolling the operation of the sensor node hardware andits surroundings. The processing unit also enforces specifiedfunctions in system code, controls the operation of the sensornodes, and sends an order to start the node in the sendingunit to collect environmental information, which sends backafter processing. The sensor unit is mainly constructed by avariety of micro-sensors, such as temperature, humidity, lightintensity, acceleration, pressure, sound, and so on. The sensorunit is responsible for collecting environmental parameters andmeasurement information and converts the collected analoginformation into digital signals. The wireless transmission unitis used to link sensor nodes to a wireless network and receivedata through a variety of wireless transmission ways of wirelesssuch as infrared and radio waves, etc. The power supply unitis mainly used to supply the energy required for sensor node.Such a wireless sensor node design needs highly complex andcostly and cannot be used in WBASN.

Because the wireless body area network that was designedis a master–slave structure, the sensor node does not re-quire sophisticated processing unit. Some complex process-ing operations can be performed from the back-end masternodes through a network. Understandably, if the wireless bodyarea network is constructed in the master–slave environment,the design of slave nodes can be simpler than in the otherenvironments to reduce greatly the cost and complexity ofdesign.

The proposed slave sensor architecture design is based onSoC. As the semiconductor manufacturing process advance-ment, system chip is the main trend of future growth. In thisdesign, the proposed protocol was implemented as a key part ofa custom SoC using the SensiumTM platform [40] for biomedi-cal WBASN applications. Sensium is a low-power sensor inter-face and transceiver platform for a wide range of applicationsin healthcare and lifestyle management. The device includes areconfigurable sensor interface, digital block with an 8051 pro-cessor, and a Radio Frequency (RF) transceiver block. Togetherwith an appropriate external sensor, the Sensium platform canbe used for low-power monitoring in biomedical applications.It can also interface with three-axis accelerometers and pres-sure sensors, and includes a temperature detector on a chip.In the implementation, one or more Sensium-enabled digitalplasters continuously monitor key physiological parameters ofthe body and report to hospital servers. The data can be furtherfiltered and processed by the server software. The Sensiumsystem block diagram is shown in Figure 5 [40].

The power consumption of the proposed platform imple-mentation was compared with that of a previously publishedscheme [41], which uses Bluetooth technology and point-to-point architecture to connect a PDA and general wireless sen-

FIGURE 5. SensiumTM SoC block diagram [39].

sor nodes, for wireless ECG sensors. The requirements ofECGs are set as follows:

• sampling rate: 400 samples/sec

• compression rate: 1.92

• sensor data bits per sample: 12 bits

• average transfer time per sample: 6.25 μs

• setup and control overhead time per sample: 12.5 μs (thesetup and control overhead time is the time taken to ini-tialize the RF transceiver, modulator, and demodulator,and executing the control signals)

• switch overhead time per sample: 1.56 μs (the switchoverhead time is the time required to change the trans-mitter or receiver in a transceiver)

• total transfer time: 1.525 ms

• sleep time: 0.995 sec

• duty cycle: 0.488% (duty cycle = TATS

(1 + NR), where theTA is active time, TS is allocating sleep time, and NR isthe number of retransmissions [42, 43])

The power consumption of the ECG sensor system [40]is 170 mW, and that of the proposed scheme is 765.6 μW,for 400 ECG values per second. This shows that the powerconsumption of the proposed work is 99% lower. Attentionmust be given to the 30% less power than WiseMAC [39] forsimulation. The difference of the two results is because thesimulation results only compare the master–slave architectureand the proposed protocol with the point-to-point architecturewith Bluetooth protocol in Table 2. When the SoC implemen-tation method is added to the design, it generates more energysavings.

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FIGURE 6. Proposed wireless body area network diagram[42].

5. CONCLUSION

In this article, an energy-efficient MAC protocol was presentedfor WBASN focused on pervasive healthcare applications. Theproposed protocol uses the attributes of the master–slave net-work to implement a very low power architecture which iscapable of fast reaction to sporadic alarm events. Using thecluster algorithm in the proposed communication protocol toselect master nodes and generate clusters, the energy dissi-pation is reduced and the lifetime of the wireless body areasensor network is disclosed. A prototype healthcare monitor-ing system has been developed to implement the design by SoCtechnology as shown in Figure 6 [43]. The measured resultshave validated the effective operation of the new invention.

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BIOGRAPHIES

Chi-Chou Kao received his B.S. in engineering science fromNational Cheng-Kung University, Taiwan, in 1990, and hisM.S. and Ph.D. in electrical engineering from National Cheng-Kung University, Taiwan, in 1996 and 2002, respectively. Heis currently an associate professor in the Department of Com-puter Science and Information Engineering, National Univer-sity of Tainan, Taiwan. He received the Long-Term Distin-guished Paper Award, Acer Foundation, in 2003. His researchinterests include graph algorithms, combinatorial optimiza-tion, and circuit design.

Wen-Lin Yang received his Ph.D. and M.S. in computer sci-ence from Pennsylvania State University and State Universityof New York, in 1993 and 1988, respectively. Currently, heserves as Dean of College of Science and Engineering as wellas a professor in the Department of Computer Science andInformation Engineering at National University of Tainan,Taiwan. His primary research interests include routing pro-tocols, quality of service, wireless networks, and distributedcomputing.

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