International Journal of Computer Science and Communication Vol. 2, No. 1, January-June 2011, pp. 183-189

ABSTRACT

TRAFFIC AND PERFORMANCE MANAGEMENT FOR BIOMEDICAL SENSOR NETWORK

Dheerendra S. Gangwar1 Department of ECE, G.L.A. University Mathura-281406, U.P. India, E-mail: dsgangwar@gmail.com.

This work examines the performance statics of event based data delivery model for a Biomedical Sensor Network(BSN) designed in accordance with IEEE 802.15.4/ZigBee wireless communication technology. A BSN consists of 5 to10 invasive or noninvasive sensor nodes acquiring physiological signals from the subject body and transmitting itto the network coordinator through wireless channel. All the sensor nodes in the network share the same mediumwith different traffic characteristics and different Quality of Service (QoS) requirements. This work presents asimulation model for channel access mechanism and on demand awakening in Biomedical Sensor Network. On thebasis of the BSN traffic specifications, we have used Castalia framework to validate traffic and performance parametersof prototype BSN model in order to meet desired QoS requirements. The performance metrics of the BSN architectureincludes energy consumption, packet reception ratio, network capacity, connectivity and packet transmission delay.Keywords: IEEE 802.15.4, ZigBee, Physiological Signals, Sensor Node, Network Traffic.

1. INTRODUCTIONBiomedical Sensor Network (BSN) is a new class ofwireless networks which offers opportunities to newservices for monitoring health, fitness and wellness ofindividuals. It offer prompt feedback for efficient andreliable patient monitoring, disease management andpromotes self care [1]. A typical BSN consists of numberof sensor nodes with different resource requirements likedata processing capability, power requirements,bandwidth requirements and reliability features. Specialdesign characteristics of sensor and their human centricapplication make such networks different fromconventional wireless networks [2]. These characteristicspose different challenges for system architecture andprotocol design. For example BSN nodes require lowcomplexity computational resources and energy efficientcommunication to support efficient and reliabletransmission of physiological parameters.

A critical design issue for Biomedical Sensor Networksis limited availability of hardware resources within BSNnodes (shown in Fig. 1). Most of the sensor nodes areReduced Functional Devices (RFD) having limitedresources and few of them may be Full Functional Devices(FFD). Therefore making good use of these resources is animportant design issue. These nodes are supposed tooperate for longer duration, as in case of implanted nodesexpected life span ranges from 5 to 10 years. Low powerconsumption and QoS requirements for reliabletransmission of acquired parameters is must [4].

To satisfy all these requirements nodes are designedto operate with energy saving communication and dataprocessing hardware resources [5] that places sensornodes in sleep mode. On demand awakening and event

driven data delivery mechanisms are beneficial to obtainlonger life span for sensor node [6]. This model is designedon the basis of CC2420 base radio transceivers, operatingon IEEE 802.15.4/ZigBee communication standard helpin achieving these endeavors [7]. The proposed model usessimilar design features for simulation as they aresupported by the CC2420 transceiver.

Remaining paper is organized as follows. Previouswork is presented in Section 2, which describes somesimulation studies carried out in this field. BSN systemarchitecture is introduced in Section 3 followed by trafficand performance management issues of the BSN networksin Section 4. A simulation model for prototype system isdescribed in Section 5. Performance analysis is presentedin Sections 6 and 7 concludes entire discussion.

2. RELATED WORKMany of the researchers are putting their collective effortstowards the development of Health Monitoring Systems

Fig. 1: Biomedical Sensor Network

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(HMS) to provide cost effective and efficient medicalassistance to the people [8]. Technological advancementsin the field of Biomedical Sensors and Wireless Communi-cation have made possible development of newapplications in the field of health care monitoring [9].Protocol stack model for BSN network is shown in Fig. 2,depicting various aspects related to communication,process management, mobility management and powermanagement. Biomedical Sensor Network based heathmonitoring systems are no more the subject of curiosityfor academic and research community. ZigBee enabledhealthcare solutions have moved from the soft academicenvironment to the harsher world of commercialapplications [10]. As power efficiency is very importantissue to design a practical BSN solution and adequateattention has been paid in this direction. Chang andHuang [11], Lo and Yang [2], Chen and Dressler [12] andC. Mallanda [13] are major contributors for power efficienttechniques for such type of systems.

advance Digital Signal Processors and other integratedapplication software platforms. Critical alarm signals andperiodic diagnosis information is sent to medical professi-onals for further actions using extra-BSN communicationlink. This link is facilitated by existing public communi-cation network. However this work is focused only forintra-BSN communication of physiological information.

BSN network consists of three modules namely SensorNode, Physical Process and Wireless Channel. Howeversensor node has some sub modules down the hierarchy.These modules are Application Module, Network InterfaceModule, Node Resource Manager, Mobility Module andSensor Device Manager. Network Interface Module (shownin Fig. 4) is further divided into Network, Medium AccessControl and Radio Module. This paper describes designissues associated with each design entities of BSN Network.

Out of all the sub-modules of the sensor node,communication or network interface module is acompound one that comprises of three different modulesrepresenting communication protocol stack. Thesemodules are Radio Module (corresponding to the PhysicalLayer), MAC Module (corresponding to the MediumAccess Control Layer) and Network Module (corres-ponding to the network routing Layer).

For modeling of this network prototype Castaliaframework is used [14]. It is a simulation framework forWireless Sensor Networks consist of low power embeddeddevices and uses OMNeT++ as basic simulation platform[15]. Castalia is a very good research vehicle for simulationprotocols in realistic wireless channel and radio models,with a realistic node behavior especially relating to accessof the radio link.

Radio module is a simple module which is definedwith the help of C++ and NED files incorporating entirefunctional behavior of Physical Layer as defined for IEEE802.15.4 wireless communication standard. The radiomodule tries to capture many features of a real genericlow power radio, one that is likely to be used in wirelesssensor network platforms. As such, it supports multiplestates (transmit, receive/listen, sleep) with different powerconsumption and delays for transitions from one state toanother. It supports multiple transmission power levels.It also supports carrier sensing (with help from thewireless channel module). The user can play with thedata rate and other parameters that affect the probabilityof packet reception given a Signal to Interference Ratio. Inthe proposed model radio parameters for radio modulecorresponds to the Chipcon CC2420 transceiver [16].

The Medium Access Control protocol is an importantpart of the node’s behavior; therefore in the proposed modelthere is a separate MAC module that defines it. The trafficbased MAC protocol for BSN accommodates the entire BSNtraffic in a reliable manner. A beacon enabled IEEE 802.15.4

Fig. 2: Protocol Stack for BSN Network

3. BSN ARCHITECTUREA prototype BSN model is shown in Fig. 3, illustrating allphysiological parameters and sensor nodes. Thisproposed architecture is supported by five physiologicalsensors acquiring Electrocardiogram (ECG), BloodPressure (BP), Blood Oxygen Saturation, Temperature andBody Movement. This multi-parametric and multidi-mensional time series information is transmitted by eachindividual sensor node to the BSN coordinator. Alldetection decisions are taken at this stage with the help of

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MAC protocol based on slotted CSMA-CA satisfies BSNtraffic requirements [17]. IEEE 802.15.4 MAC protocol isthe standard for low power wireless networks. ProposedBSN model describes following features of MAC protocol:

• CSMA-CA functionality (slotted);• Beacon-enabled network architecture;• Direct data transfer mode; and• Guaranteed time slots (GTS).

BSN communication is supported by single hop starnetwork topology and therefore routing is not a big issue.The network layer of BSN coordinator is responsible forstart up of network and assigning network addresses tonewly associated devices [1]. The underlying MAC layeradapts the BSN network address as 16 bit short address.Addresses are unique to a particular network and areassigned by BSN coordinator to the BSN nodes. ZigBeerouting algorithm is designed to enable reliable, costeffective and low power monitoring and control operations.

To make sensing operation more relativistic physicalprocess model is required which corresponds to realworld physical parameters. Castalia supports a physicalprocess model that is flexible enough yet have corres-pondence to real processes (e.g., spatial correlation of data,variability over time). The wireless channel is anotoriously difficult medium to model, especially whentaking into account mobile nodes, a changing environment(e.g., in the BSN case: the body moving) and broadbandcommunications [14].

Fig. 3: BSN Architecture Model for OMNeT++

Fig. 4: BSN Communication Module

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4. TRAFFIC AND PERFORMANCE MANAGEMENTTraffic management is required to offer sustainable endto end Quality of Service (QoS) support for efficient, reliableand cost effective data transport from the sensor node tothe BSN coordinator [18]. Traffic management takes careof lost packets due to buffer overfolow and gaurantiesreliability for packets flowing through BSN. Ammountand type of clinical data is different for each sensor nodeassociated with a specific physiological variablel [4].Table 1 summarises BSN traffic specifications requiredfor physiological signals [19]. Traffic management anddata delivery methods are different for BSN in comparisionto conventional wireless sensor netowrks, these networkssupport event based data delivery model ratrher thancontinuous or query based delivery model. Effective trafficmanagement enhances network performance in terms of:

• Throughput;• Message delay;• Energy efficiency; and• Buffering and bandwidth requirements.

PRR =Number of ACK packetsNumber of PRBpackets ... (1)

Single hop average delay using CSMA-CA MACprotocol is calculated with the help of timestampdifference between Probe (PRB) packet and Acknow-ledgement (ACK) packet received

Delay =2

ACK PRBTimestamp Timestamp−

... (2)

5. BSN SIMULATION MODELBSN model, shown in Fig. 5, consists of modules thatcommunicate by passing messages. BSN network consistsof three modules namely Sensor Node, Physical Process andWireless Channel. However sensor node has some submodules down the hierarchy. These modules areApplication Module, Network Interface Module, Node ResourceManager Module and Sensor Device Manager Module. NetworkInterface Module is the most significant module that playsa pivotal role in modeling of various communication anddata handling protocols and processes. It is further dividedinto Network Layer Module, MAC Layer Module and RadioModule. For the sake of simplicity without compromisinggeneral functional behavior of the proposed model onlynecessary functions are taken into account.

Table 1BSN Traffic Specifications

Signal Parameter Range Traffic (kbps)

ECG 0.5 –4.0 mV 8Blood Pressure 10 –400 mmHg 0.96

Oxygen Tension 80%–100% 1.2Body Temperature 33 –40 � C 0.32

Body Motion Ankle Movement 0.32

Another key design challenge is low power which isessential to prolong sensor life time and it depends on QoSrequirements for reliable data delivery [4], [9], [ 11] and [17].Energy is wasted in case of any packet discard or packetcollission. Energy efficient MAC protocols [17] andtransport protocols help a lot in power saving process inBSN communication. Zigbee offers Link Quality Indication(LQI) mechanism to manage efficient and reliablecommunication link by measuring signal strength andquality of received packet. The Link Quality Information isexchanged with the help of Probe (PRB) packet.

Performance metrics for BSN traffic includesthroughput, latency, network connectivity and powerconsumption. In Biomedical Sensor Networks eventreliability is used as a measurement to show accuracy oftransmission of event from source to sink. For most of theapplications, having tolerance for packet loss reliabilityis defined as Packet Reception Ratio (PRR). It is given asthe ratio of successfully received packet over the totalnumber of packets transmitted. Packet Reception Ratio(PRR) of BSN Network is given as

Fig. 5: BSN Simulation Test Bed

Modeling style for Reduced Functional Device (RFD)and Full Functional Device (FFD) coordinator isaccomplished keeping all the design and power constraintsin mind. Communication process and channel access iscontrolled by Medium Access Control Module. NetworkLayer Module is responsible for managing networkresources for communication process. Node Sensor DeviceManager Module takes care for sensing device and physicalprocess associated with the target application. NodeResource Manager Module handles data processing and

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power supply to all node modules and applications. NodeApplication Module is accountable for handling ofapplication it is involved and makes it application specific.This Module divided lager data packets into smaller onesand interacts withNetwork Layer Module of Communicationor Network Interface Module interfacing of various modulesis also very important feature of this simulation model thatprovides a set of rules for interaction among all networkentities for better device and process management.

Traffic simulation parameters are given in Table 2,where each node corresponds to a physiological infor-mation signal. Simulation of this model (described in .nedfile) after providing various simulation parameters foreach node and module in the initialization file (*.ini file)results into the performance statistics is shown in Table 3.The file omnetpp.ini includes information regardingvarious simulation parameters. The ECG signal infor-mation is the most critical out of all these parameters and

Table 2BSN Traffic Simulation Parameters

Parameter Value

BSN.node[1].nodeApplication.packet_rate 8 kbps

BSN.node[2].nodeApplication.packet_rate 960 bps

BSN.node[3].nodeApplication.packet_rate 1.2 kbps

BSN.node[4].nodeApplication.packet_rate 320 bps

BSN.node[5].nodeApplication.packet_rate 320 bps

Table 3Delay and PRR for BSN Network

Sensor node Transmitted Received Packet reception Average Powerpackets packets ratio (%) delay (ms) consumption (mW)

ECG Node (1) 400 395 98.75000 17.7143 0.0143556Blood Pressure Node (2) 48 47 97.91667 17.7143 0.0144865SpO2 Node (3) 60 54 90.00000 17.7143 0.013255Temperature Node (4) 16 15 93.75000 17.7143 0.013255Body Motion Node (5) 16 15 93.75000 17.7143 0.0139856

need to be given highest priority for packet delivery fromECG sensor node (Node (1)) to the BSN Coordinator (Node(0)). This model simulates only reception of physiologicalinformation for intra BSN communication and this modeldoes not consider extra BSN communication and datamining for detection decisions.

6. PERFORMANCE ANALYSISThis paper presents an empirical investigation on theperformance of BSN network model using IEEE 802.15.4/ZigBee wireless communication. Simulation is requiredto validate proposed algorithms and protocols beforephysical implementation to save engineering resourcesand time involved in the process. BSN traffic verificationusing OMNeT++ simulation environment is carried outwith the help of various simulation parameters andsimulation class libraries.

Functional validation of BSN traffic and performanceis based on IEEE 802.15.4/ZigBee protocol stack. Somerelated medium access control parameters are given as

BSN.node[*].networkInterface.macModuleName =Mac802154Module.

BSN.node[0].networkInterface.MAC.isFFD = True.BSN.node[0].networkInterface.MAC.isPANCoordinato

= Truer.BSN.node[1].networkInterface.MAC.requestGTS = 3.After performing the simulation various performance

statistics are generated. Packet delay, delay histogram,packet reception information, loss of packets caused byinterference, low sensitivity and non Rx state are major to

quote here. This information helps in calculation ofaverage delay for network nodes and overall throughput.Average packet delivery delay is calculated as

_Delay avg =__

Total DelayPacket recived ... (3)

Average delay for simulation is found as 17.71429 ms.The information for Total_Dealy and Packets_Received isdrawn from the delay histogram generated for applicationlevel latency as shown in Fig. 6. Different packets reachthe BSN coordinator with different packet delay profile

Fig. 6: Application Level Latency for Data Packets

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ranging from as low as 1ms and up to 600 ms. This Figureillustrates histogram values for packet delivery delay.

Throughput for network is expressed as

Throughput =_ _

_Total Data Bits

Simulation runtime ... (4)

Total_Data_Bits can be calculated from number ofnumber of Packets_receive and packet size. For the givensimulation model packet size is 1024 bits and forsimulation, run time of 50 seconds. If number of packetsreceived is 525 then BSN network throughput is10.50 kbps. Packet Reception Ratio of all sensor nodetransmitting information to Node (0) or BSN coordinatoris shown in the Fig. 7. Node (1) has highest PRR whereasnode (3) is having the lowest one. Figure 8 presentsanalysis for power consumption at every node trans-mitting packets to the Node (0). Node (2) is consuminghighest power as it is handling the greatest numberof packets. Energy consumption for Node (0) is0.158175 mW, which is considerably high in comparisonto any of the sensor node.

transmission reliability and power efficiency is managedby connectivity of the nodes with the BSN coordinator.

This simulation model also presents fading profilefor the wireless channel. Fed depth distribution is shownin Fig. 10 that illustrates histogram for the range of –50dBto 10 dB values for different points. Channel characteri-zation is a very critical issue because human body itselfaffects channel characteristics. An accurate estimate offade depth is also of great importance for design of reliablecommunication. Simulation tracks record for packet id oftransmitting node and received signal strength indicator(RSSI) values. The receiver sensitivity for BSN model isset as –87 dBm.

Fig. 7: Packet Reception Ratio for BSN Nodes

Fig. 8: Power Consumption for BSN Nodes

Network connectivity is an important performancemetrics therefore Fig. 9 presents a statistical overview forconnectivity information for all sensor nodes. The wake-up scheduling schemes at the MAC layer which wakesup sleeping nodes when they need to transmit/receive,thus avoiding degradation in network connectivity orquality of service provisioning. The tradeoff between

Fig. 9: Fraction of Time without PAN Connection

Fig. 10: Fade Depth Distribution for Wireless Channel

7. CONCLUSIONTechnological advancements in the field of medical sensors,artificial intelligence along with information andcommunication technologies are opening new paradigmin the field of health monitoring process. A prototype BSNmodel is simulated in OMNet++ simulation environmentto validate traffic characteristics that emphasizes channelaccess control to serve many purposes like packet lossreduction, sleep mode operation, power efficiency andpacket delivery latency. Results for traffic characterizationand performance evaluation are discussed. The prototypesystem uses power efficient event based data delivery modelto report the physiological signal for reduction ofcommunication power. Vital signs are acquired and passedon to the base unit on the basis of ‘on demand awakening’of sensor nodes for transmission of sensor information.Power efficiency of the system was the major concernthroughout entire discussion, which was addressed by on

Traffic and Performance Management for Biomedical Sensor Network 189

demand awakening, single hop star topology and ZigBeewireless communication protocol stack.

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