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Optimizing Data Forwarding from Body AreaNetworks in the Presence of Body Shadowing with

Dual Wireless Technology NodesAntonios Argyriou, Member, IEEE, Alberto Caballero Breva, and Marc Aoun

Abstract—In this paper we are concerned with the problemof data forwarding from a wireless body area network (WBAN)to a gateway when body shadowing affects the ability of WBANnodes to communicate with the gateway. To solve this problem wepresent a new WBAN architecture that uses two communicationtechnologies. One network is formed between on-body nodes, andis realized with capacitive body-coupled communication (BCC),while an IEEE 802.15.4 radio frequency (RF) network is used forforwarding data to the gateway. WBAN nodes that have blockedRF links due to body shadowing forward their data throughthe BCC link to a node that acts as a relay and has an activeRF connection. For this architecture we design a network layerprotocol that manages the two communication technologies andis responsible for relay selection and data forwarding. Next, wedevelop analytical performance models of the medium accesscontrol (MAC) protocols of the two independent communicationlinks in order to be used for driving the decisions of the previousalgorithms. Finally, the analytical models are used for furtheroptimizing energy and delay efficiency. We test our system underdifferent configurations first by performing simulations and nextby using real RF traces.

Index Terms—Wireless body area network (WBAN), bodysensor network (BSN), IEEE 802.15.4, body shadowing, mediumaccess control (MAC), capacitive body-coupled communication,cooperative communications, relay, performance analysis, delay,energy, optimization.

I. INTRODUCTION

IN several applications where wireless body area networks(WBAN) are deployed around the human body, reliable and

low delay communication is of paramount importance becauseof the critical nature of the collected data (e.g. heart rate, bloodpressure). Energy consumption is also key for the prolongedoperation of the devices attached to the human body. For theircommunication needs these WBANs usually employ radiofrequency (RF) technologies that operate in the industrial,scientific and medical (ISM) radio band. One of the dominantsolutions is the IEEE 802.15.4 standard that is engineeredspecifically for low power devices [1], [2]. However, it is alsopossible to use the widely popular wireless LAN (WLAN)standard IEEE 802.11 [3], [4] because of the readily availableaccess point (AP) infrastructure. Regardless of the specificwireless communication technology, when multiple sensors aredeployed in the human body, a WBAN in a star topology isusually created so that all the on-body nodes communicatewith a gateway for forwarding the collected data. Fig. 1 (left)depicts the organization of a WBAN in a star topology that

Antonios Argyriou is with the Department of Electrical and ComputerEngineering, University of Thessaly, Greece (anargyr@ieee.org), AlbertoCaballero Breva is with the University of Seville, Spain, Marc Aoun is withPhilips Research, Eindhoven, The Netherlands.

C

RF

gateway

CBCC

gateway

C

D

AB

C

D

AB

BCC

Fig. 1. Nodes in a WBAN maybe unable to communicate to a gateway dueto body shadowing (left). In the proposed system the BCC link is used forforwarding data to the nodes that have the optimal RF link (right).

is employed by practical systems [2]. Although RF is theonly practical mechanism to forward data in this scenario, stillseveral significant problems remain. RF signals suffer consid-erably from body shadowing in a highly variable way withrespect to human body [5]–[7]. This makes communicationbetween on-body nodes, and also off-body, very unreliable.

This inherent unreliability of RF communication aroundthe human body is a critical problem for several real-lifeapplications. We discuss two emerging application scenarioshere to motivate our system design. One example is vitalsign monitoring where multiple nodes need to be deployedin several different places of the human body [8]. Traffic isusually flowing in the uplink direction, i.e. from the WBANnodes to the gateway. Consider for example a human thathas a node on the torso and one on the wrist for monitoringthe heart rate. If this human is a patient lying in bed, thenthe RF link of the sensor on the torso might be completelyblocked. However, it is possible that the node on the wrist cancommunicate perfectly with the gateway. Or it can happen theother way around as Fig. 1(a) indicates. Since humans usuallymove frequently, any sensor can be potentially blocked and thedata may not be communicated on-time to the gateway (or itcan be lost completely). Besides health monitoring, the otherimportant application is real-time media entertainment. Withthe rising popularity of wearable devices,1 it is even possiblethat high data rate video streams need to be forwarded fromthe human body (uplink). One specific application examplewith downlink traffic, is audio streaming from a WiFi accesspoint (AP) to on-body earphones without wires [9]. In thesetwo examples RF connectivity between the AP and the ear-phones/glasses might be unreliable leading to problems in the

1E.g. the Google glass project: www.google.com/glass

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audio/video transmission. Therefore, the optimal node on thebody must be found and act as a relay in order to transmit thedata to and from the WiFi AP. This node can be for examplea smartphone.

To address the problems identified in the aforementionedscenarios, in this paper we propose a novel WBAN archi-tecture, a network layer (NWK) protocol that exploits theproposed architecture, and an optimization framework sup-ported by an analytical performance model of the system. Inour architecture that is depicted in Fig. 1(b), all the WBANnodes on the human body are equipped with both RF andbody-coupled communication (BCC) transceivers. When theRF link of a node is unreliable the node uses the BCC linkfor forwarding its data through the human body to a nodewith a better RF link. The rationale of this design choice isbased on the extremely power-efficient BCC technology thatcan be used instead of RF communication. Prototype BCCtransceivers use simple baseband communication and have avery small form factor (1mm2 dye in [10]). Thus, the proposedWBAN architecture with two transceivers on board a WBANnode is an economically and technically viable option for real-life applications.

Contributions. The contributions of this paper are:

C1 First, we propose a new cooperative WBAN architecturethat employs two communication technologies namelyBCC and RF. This architecture is orchestrated by a novelNWK relay selection protocol that identifies the optimalrelay for forwarding the data off the body. The proposedscheme is unlike any existing relay selection protocolssince two different communication technologies are used.

C2 Second, for each medium access control (MAC) protocolof the RF and BCC subnetworks we develop accurateexpressions of the delay and energy consumption asa function of specific protocol parameters. Our novelcontributions here include first the performance modelingof a NWK that concurrently uses two wireless MAC/PHYtechnologies. Second, our model is a cross-technologymodel, i.e. it quantifies the impact of a specific parametersetting for one MAC on the second and on the completesystem.

C3 Third, we propose the use of the analytical performancemodels for further optimization of the duty cycling of theBCC transceivers and the retransmission strategy of theRF transceivers so that energy consumption and delay areminimized.

Benefits. The first advantage is that devices with a blockedRF link can use the services of the remaining nodes forforwarding their data to a gateway and improve thus thereliability of the data delivery. This idea is demonstrated withthe help of Fig. 1(b) while we have also outlined this genericarchitecture in [11]. This is accomplished with C1. Second,RF transmissions are reduced to the absolute minimum sinceWBAN nodes do not communicate with each other through theRF link. The benefit is that the RF link is carrying a reducedload and the interference to surrounding RF devices is alsominimized [12], [13]. This is accomplished with C1. The thirdadvantage is that a node with an RF link that is not completely

blocked, but operates inefficiently (higher number of requiredretransmissions that increase delay and energy consumption),can communicate with other nodes through BCC and selectanother node for data forwarding. This is accomplished withC2 and C3. Our performance evaluation focuses on high-lighting these benefits. Performance is evaluated both throughsimulations and also through the use of real RF traces. Resultswith IEEE 802.15.4 RF traces interestingly support even morethe need for our system in real scenarios due to the severityof human body shadowing.

Paper Organization. The rest of this paper is organized asfollows. Related work is presented in Section II. In Section IIIwe provide an overview of the system architecture and inSection IV we delve into the details of the relay selectionand packet forwarding algorithm. Section V presents themotivation and an overview of our performance modelingapproach. In Section VI we present the performance model ofthe RF network while in Section VII we present the analyticalmodel of the BCC network. The MAC protocol optimizationthat further exploits the previous analysis is described inSection VIII. The performance of our system is evaluated withsimulations and real measurements in Section IX. Finally, inSection X we conclude this paper.

II. RELATED WORK

In this paper we deal with the problem of optimizingdata delivery from a WBAN to a gateway. The problem ischallenging because communication between on-body and off-body nodes is very unreliable [14], [15]. The most promisingway to attack the problem is through cooperation betweenWBAN nodes [2]. With cooperation the nodes that aid inforwarding the data of other nodes are the relays. Thereis a plethora of works that focus on selecting the optimalrelay in the general case of wireless sensor networks (WSN)by considering different optimization objectives, while fewerworks have focused in WBANs.

The majority of research works investigated cooperation inWSNs/WBANs with the objective to minimize power consump-tion. In [16] the authors consider relay selection in WSNsfor minimizing the power consumption of individual nodes.They evaluate the impact of the sensor wake up scheduleson the consumed power, and then they propose incentivemechanisms for participating in the cooperative network. Thisis an optimization approach that perceives the nodes as in-dependent competing entities that may help each other forrelaying data to a gateway. A number of authors investigatedthe impact of cooperation on the power consumption andlifetime of the complete WBAN introducing thus a moreholistic approach [17], [18]. In these two works the coop-erating nodes communicate with each other through the RFlinks opportunistically when they cannot reach the gateway.However, a particular node may still not be able to reachanother on-body node. To combat these intermittent problems,another parameter that was also investigated together withrelay selection was power control. Power control effectivelymeans topology control in the WBAN [19], i.e. we can allocatemore power to nodes that need it so that they can reach the

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rest of the network or the gateway. Even though this approachensures optimal power allocation for given channel conditions,power may be wasted in nodes with poor RF links. In thecategory of research efforts that target power minimizationwe cannot identify solutions that eliminate the problem of acompletely blocked RF link, but rather state-of-art solutionsthat allocate optimally the system resources to combat the RFinefficiencies. This is still unacceptable in scenarios where datafrom a specific node must be transmitted in real-time to thegateway.

Another significant number of research efforts focused onthe problem of reliable communication and was motivatedfrom experimental results. In [20] the authors first focus onobtaining an experimental characterization of the channel.From the obtained results, the use of statically assigned relaynodes in specific body locations was proposed. Nevertheless,specific on-body nodes may still fail or be occluded dueto body shadowing. Another aspect that affects the WBANperformance is the mobility of the user. Mobility models forWBANs driven from experiments were studied in [21]. Inthis work the authors study the problem of user mobility andposture changes and how they affect WBAN performance. Thesolution is a multi-hop protocol that is aided by a single relay.

In contrast to all the previous works that look into eachspecific node as a potential relay, there is an option to usemultiple WBAN nodes for improving the reliability and/orenergy/power. This class of protocols, that also targets gen-eralized wireless cooperative networks, proposes the creationof clusters across groups of nodes in order to improve thediversity gain in the RF link by simultaneously transmittingthe same information over different wireless paths [22]. Nev-ertheless, there is the major problem of node synchronizationin this case. In cooperative schemes that involve many nodeswe can also add recent works that apply advanced techniqueslike network coding [23], [24].

Despite the architectural and protocol differences, all thepreviously described schemes share one common characteristicwhich is the use of a single RF communication technology.To alleviate the problems of a single technology two differentones can be used. The number of works in this area is quitelimited. A communication protocol that uses two technologieswas presented in [25]. In that work the authors proposedthe use of two different RF bands namely the 433MHz and2.4GHz. The 433MHz band is used for data aggregationwhereas the 2.4GHz band is used for data forwarding to thegateway. Since the range of the 433MHz band is limited to ap-proximately 2 meters around the node it is possible to improvethe reliability and energy consumption by reducing the numberof nodes competing for the same channel. Still, both linksuse RF for on-body communications and two independent RFbands are required. Thus, all the techniques we discussed inthe last three paragraphs cannot address completely the RFreliability problem because of the fundamental problems of thewireless channel.

Finally, we should mention that none of the previous worksemployed an in-depth analysis of the impact of the MACprotocol parameters on energy/delay/reliability of the completecooperative network. We could only trace a limited number

t

cca cycle

packet arrivalbackoff cca

data ack

attbackoff cca

Fig. 2. Packet transmission with the IEEE 802.15.4 MAC protocol.

of works for non-cooperative systems that use analyticalprotocol performance models for further optimization. Theseapproaches use the MAC model of IEEE 802.15.4 for powerminimization at an individual node [26], [27].

When compared to all the related works, our approachis significantly different. It focuses on using simultaneouslythe most reliable and most efficient RF link (in terms ofdelay/energy) for data forwarding since these two aspects areintertwined. Our solution consists of a new WBAN architec-ture and a novel cooperative protocol design that is supportedby an analytical performance model. With our system evenwhen at least one node has an active RF link the data fromevery WBAN node will reach the gateway. To the best of ourknowledge no other WBAN system can guarantee that.

III. SYSTEM ARCHITECTURE

We consider a WBAN that consists of N nodes whereeach node is equipped with an RF transceiver and a BCCtransceiver. These two hardware components use differentMAC/PHY protocols and are both controlled from a special-ized NWK protocol. The nodes are organized into a single-hop BCC network, and a subset Nr of them form a single-hop RF network as shown in Fig. 1. All the nodes thatare part of the RF network can connect to a gateway forforwarding the collected data. Each wireless link has differentdelay/energy characteristics due to channel variations. Whileonly Nr nodes are part of the RF network, all the N nodesparticipate in the BCC network. Non-relay nodes transmit theirpackets through the BCC link to one of the relays, while theselected relay transmits wirelessly to the gateway the locallygenerated and forwarded data packets. Nodes that do not havea relay responsibility are allowed to put into sleep mode theRF transceiver for reducing the energy consumption. However,since it is possible that in the future they assume the roleof an RF relay, they wake up periodically, they transmit anRF packet, and after they receive a response they update thereceived signal strength indication (RSSI) value of their RFlink.

A. The IEEE 802.15.4 RF Network

The IEEE 802.15.4 is used for RF communication. A non-beacon-enabled and un-slotted carrier sense multiple accesswith collision avoidance (CSMA/CA) algorithm is assumed forchannel access. All the nodes sense the channel status duringthe clear channel assessment (CCA) slots. The basic idea ofthe un-slotted CSMA/CA algorithm is that backoff and packettransmissions are not aligned to specific slot boundaries. Theun-slotted CSMA/CA operates as follows. Each time a device

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t

t

node A

node B

RTR

Preamble

Rl

Rs

data

Ta

Tb

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Fig. 3. Low power listening and transmission of a preamble when the BCCchannel is free. The time period Tb indicates that the BCC node is awake andchecks the channel for the presence of preambles.

generates a packet for transmission it waits for a randomnumber of slots, called the backoff counter, ranging from 0to 2BE−1, where BE denotes the backoff exponent. In IEEE802.15.4, the backoff counter is decremented to zero regardlessof the CCA result and by default BE is initialized to 3. Whenthe backoff counter reaches zero, the device performs CCAonly once in order to check whether the channel is busy or not.If the channel is idle during the CCA period that has durationTcca (Fig. 2), the device transmits its data packet. When thechannel is busy during the period Tcca, the backoff exponentBE is increased by 1 and the random backoff procedure isrepeated. The backslash-shaped blocks in Fig. 2 depict theincrease in the backoff time after a failed CCA. More detailsregarding its operation are provided later while the completealgorithm is available in [1].

B. The BCC PHY and MAC

BCC without requiring skin contact can be realized withtwo electrode transmitter/receiver devices capacitively coupledto the human body [12]. The transmitter generates a variableelectric field while the receiver senses the variable potential ofthe body with respect to the environment. A signal attenuationof less than 70dB has been measured between devices placedat various positions of the human body [13]. The human bodychannel is especially affected by interference below 1MHzwhile for higher frequencies the interference level is below -75dBm. In this paper a BPSK modulation scheme is assumedwhere the digital pulses are directly transmitted to the humanbody through the capacitive plates [10]. Digital pulses of1.2V for transmission are used, and an receiving band of 1-30MHz was chosen for improving the BER in the presence ofinterference. The BER was measured to be lower than 10−6

for the aforementioned conditions. The energy efficiency ofthe transceiver was measured at 0.32nJ/bit. Since the designof the PHY is not the focus of this paper we do not delveinto further details. The interested reader can find more detailsabout a typical prototype transceiver in [10].

Regarding the MAC layer it is a protocol that employsthe well-known concept of low power listening (LPL) [28].LPL exploits a specialized wake up receiver hardware thatis always in active mode and consumes very little energywhile the main receiver is deactivated. Fig. 3 presents thechannel access scheme for a single packet. A preamble packetis always transmitted before the transmission of an actual datapacket which means that nodes contend with preambles. The

TABLE IMAIN FUNCTIONS USED IN THE PROTOCOL STACK OF THE COMPLETE

SYSTEM. NOTATION tx X Y MEANS THAT THIS FUNCTION IS EXECUTEDWHEN A MESSAGE OF TYPE X IS TRANSMITTED/RECEIVED IN

LAYER/TECHNOLOGY Y .

Main functions Message contentstx STATUS BCC(src s) RF/BCC param. for node stx TOKEN BCC(src s,dst d) Release token from node stx DATA BCC(src s,dst d,data k) TX data in the BCC linktx DATA RF(src s,dst d,data k) TX data in the RF linktx DATA NWK(src s,dst d,data k) Data from APPL to NWKrx BCC(src s) Message received at the BCCrx STATUS BCC(src s) RF stats received at the BCCrx TOKEN BCC(src s) Token received at the BCCrx DATA BCC(src s) Data received at the BCC

contention for the transmission of preambles follows the samebackoff procedure with the RF MAC. Nodes that are asleepcan wake up with the preamble and prepare for the actualdata reception. The preambles contain the destination addressso that they wake up the appropriate node. When a preambleis successfully received, the node that wakes up sends anacknowledgment packet that is named ready-to-receive (RTR)since it also serves as an indication that the receiver can acceptthe actual data packet. After this transmission the receiverwaits for the data packet to arrive and transmits the final ACK.

IV. RELAY SELECTION

The first and main task of our distributed WBAN system isto calculate the optimal subset of Nr RF relays from all theavailable nodes, while the second task is to forward the datathrough the optimal relay if needed. To accomplish the firsttask, our system is designed as follows. Each WBAN nodei calculates the average delay and energy of the direct RFtransmission and then it informs the remaining nodes in thenetwork. The random variables of the delay and energy aredenoted as Drf (i) and Erf (i) respectively. This functionalitywill be analyzed in subsections IV-A and IV-B. How thisinformation is used for selecting a node as a relay will beexamined in subsection IV-C. The process of making the actualforwarding decision is described in IV-D. All this functionalityis concisely captured in the pseudo-code of Fig. 4.

In the pseudo-code of Fig. 4 we introduce two specificcontrol messages at the NWK, namely STATUS BCCfor broadcasting the parameter estimates, and secondTOKEN BCC that is used for relay selection. A summaryof the most important functions that are used by our protocolstack are provided in Table I. Finally, our protocol definesthree data structures at each node in the form of linked lists,and are denoted as NodesD,NodesE,Relays while their usewill be explained in the next subsections.

A. Parameter Estimation and Information Exchange

The parameter estimation and information exchange func-tionality is captured in the function param est node() in thepseudo-code of Fig. 4 and is executed periodically every Testseconds by every node. When an RF node is not a relay, the

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param est node(Test sec)1: Read latest local RF and BCC RSSI measurements2: Estimate E[Drf (i)],E[Dbcc],E[Erf (i)],E[Ebcc]3: tx STATUS BCC(E[Drf (i)],E[Erf (i)]2/Erem(i))4: rx STATUS BCC()

——————————————————————————rx STATUS BCC()

1: Update linked lists NodesD,NodesE2: Sort ascending NodesD,NodesE3: better = 04: for relay n ∈ Nodes do5: if E[Drf (i)] > NodesD(n) then6: better ++7: end if8: end for9: if better ≥ Nr&&I am relay == TRUE then

10: // Drop relay status11: new relay ⇐ best non relay node from NodesD12: tx TOKEN BCC(i,new relay)13: end if14: if better < Nr&&I am relay == FALSE then15: // I should be a relay, waiting for TOKEN16: end if17: if better ≥ Nr&&I am relay == FALSE then18: power down BCC()19: end if——————————————————————————rx TOKEN BCC()

1: if waiting for token == TRUE then2: //I am the new relay, I have the TOKEN3: end if4: //Update list Relays

——————————————————————————tx DATA NWK(data k)

1: for relay n ∈ Relays do2: if E[Erf (i)] > E[Ebcc] +NodesE(n) then3: //Forward pkt k to the best relay node n4: tx DATA BCC(i,n,k); exit;5: else6: tx DATA RF(i,gway,k); exit; //Direct RF TX7: end if8: end for

Fig. 4. Pseudo-code for the cooperative protocol at the WBAN node i.

RF transceiver is deactivated by entering an idle state [29] andso it has to enter an active state to execute this function. Thechannel estimation itself is a local task at each RF node thatis executed by default in IEEE 802.15.4 for every receivedpacket and so no extra overhead is introduced by our scheme(i.e. when param est node() is invoked it reads the latestRSSI measurement from the related register of the micro-controller). Next, this measurement is used for calculating theaverage delay and energy that are denoted as E[Drf (i)] andE[Erf (i)] respectively (the expectation of these random vari-ables). This is accomplished by using the RSSI measurement

in the analytical model that we develop in Sections VI and VII.Next, node i broadcasts the RF link performance estimatesexpressed though the average delay E[Drf (i)] and averageenergy cost E[Erf (i)]2/Erem(i),2 with the special messageSTATUS BCC. This message is transmitted at the BCC linkand is broadcast which means that all the BCC transceivers ofall the nodes will receive it. This concludes the descriptionof the core functionality of the parameter estimation andinformation exchange functionality of our system. Next wediscuss some important details.

Robustness of STATUS BCC transmissions. The BCCPHY offers a proven reliable link since it transmits withBPSK modulation and in addition it uses channel codingfor eliminating bit errors [10]. Furthermore, STATUS BCCmessages are sent with a constant period Test even when thechannel does not change. In case of a packet loss, this periodicpropagation of information allows all nodes to reach the sameconsistent state.

Communication Overhead. The forwarding of this infor-mation through the BCC link consumes a fraction of the BCCdata rate. Here we provide a simple analysis to demonstratethat this communication overhead is negligible for the BCClink. Every Test the N nodes transmit a STATUS BCCmessage of length L bits. And in the (unrealistic) worstcase every Test all the current Nr relays will also send amessage TOKEN BCC in order to change their role. Thenthe BCC data rate overhead is (N + Nr)L/Test bps. For atypical update frequency of Test=1 sec, and a payload of L=20bytes for these short control messages,3 N=10, and Nr=5, wehave an overhead 2.4Kbps which is negligible for the BCCPHY of 10Mbps. The advantage of our system is that theBCC link offers highly reliable, low-energy, and high-data ratecommunication [12], [13]. Therefore, the control messages thatare exchanged across the different nodes and pass through theBCC link are more efficient in terms of both energy/bit anddelay/bit when compared to RF.

Frequency of Channel Estimate Propagation. When theRF channel changes rapidly, more frequent updates will berequired since they will affect the relay selection. Instead,when the channel is fairly static, the relay nodes will change inlarger time scales. This is an important aspect that is a generalproblem that presents itself in wireless communication sys-tems. In this paper we evaluate this aspect in our performanceevaluation section.

B. Processing Information Updates

While collecting the information and broadcasting it is animportant task, it is also critical to ensure that the incominginformation at a node is properly processed. This functionalityis implemented in the rx STATUS BCC() function of thepseudo-code in Fig. 4. This function is invoked when anew STATUS BCC message is received. In this case this

2For the energy cost we use the ratio E[Erf (i)]2/Erem(i). This is a

simple metric but it captures the fact that the cost is increased as theremaining energy is reduced after continuous utilization, while if the energyof a single transmission is low then this has an impact on lowering the cost.Our framework can support different and potentially better cost functions.

3IEEE 802.15.4 with 16-bit addressing uses a 13-byte header.

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25314

tx status BCCNr = 2⇒ ⇒ 5

3214

(broadcasted)

NodesD NodesD

current state: next state:

Fig. 5. Example that shows the ordered list NodesD at all nodes. We assumethat node 2 will stop being a relay. The transmission of STATUS BCCthat originates from node 2 will update NodesD at every node.

function first re-sorts the two linked lists named NodesD andNodesE that contain all the N network nodes according to thedelay and energy estimates of their RF links. Next we explainwhy this simple message processing approach is enough.

Exploiting the MAC for Serializing Information Up-dates. The STATUS BCC messages are transmitted andprocessed sequentially by all nodes and this ensures consistentand reliable flow of information in the network. Fig. 5 has anexample with five nodes that will clarify how these messagesare processed. Assume that the nodes start from the sameconsistently order lists NodesD and NodesE shown in theleft of Fig. 5. Assume now that node 2 measures a local changein the RF performance. From the last known state shown in theleft of Fig. 5, it knows its new position in the list NodesD thatit now updates locally. Then, it broadcasts a STATUS BCC.The transmission of this message is naturally serialized by theBCC MAC in the sense that if there are more updates fromother nodes they will be transmitted sequentially. Thus, this listwill be updated only once for every STATUS BCC messagetransmission and all nodes make this update simultaneously.

C. Relay Selection AlgorithmAt this stage of our system description we have explained

how all the necessary information is being updated. Now theactual relay selection decision is carried out in the second partof the function rx STATUS BCC(). In lines 4-8 of the pseudo-code node i checks how many nodes perform better than itselfin terms of delay and energy costs. A relay node drops the roleof being a relay if it estimates from these measurements thateither its delay or energy performance of the RF link is worsefrom more than Nr (nodes that are allowed to be relays at alltimes). If this is the case it means that at least Nr nodes havebetter performance characteristics depending on the delay orenergy optimization objective. The node releases the token bytransmitting a message TOKEN BCC to the currently bestnon-relay node in list NodesD (lines 11-12). Similarly, in thecase that a node estimates that it should become a relay, butcurrently it is not a relay, it waits for the broadcasted token thatwill be released in the way we explained before (lines 14-16).The rx TOKEN BCC() function is responsible for processingthe TOKEN BCC messages. When this message is receivedby the appropriate node, it starts this new relay role.

Robustness of TOKEN BCC transmissions.TOKEN BCC messages are transmitted reliably, i.e.

all nodes ACK the packet.4 Since a token is acknowledgedby the new relay, the ”old” relay that releases it, and theremaining network nodes, know that the transaction wasexecuted. Thus, all nodes know exactly which node is a relayand which is not. Note also that the transmission of tokens isalso serialized in the BCC network which means that a newtoken is released when the last transaction is successfullycompleted.

D. Packet Transmission and Forwarding

At this stage in describing our system we have explainedhow it creates a list of the best relays. Now the task of node iis to check if packet forwarding through relay node n shouldtake place and this task is executed at the network layer bythe function tx DATA NWK(). When the application layer(APPL) wants to send a packet it does so with this function thatis executed on demand. This is where the algorithm determinesif the BCC or the RF links will be used. In the pseudo-codein Fig. 4, the condition for forwarding data through a relayn is that the expectation of the energy cost of the local RFtransmission denoted as E[Erf (i)], must be higher than theenergy cost of the combined use of the BCC link and the RFlink of the relay n:

E[Erf (i)] ≥ E[Ebcc] + E[Erf (n)]2/Erem(n) (1)

The forwarding condition can also be formulated in terms ofthe delay and be expressed as

E[Drf (i)] ≥ E[Dbcc] + E[Drf (n)], (2)

or the forwarding decision can be based on a combined metric(e.g forward only when both the delay and energy of therelayed transmission is better). In the previous expressions,E[Ebcc] and E[Dbcc] denote the expectation of the randomvariables of the energy and delay for one packet transmissionin the BCC network.

Expressions (1) and (2) are evaluated by the main loopof the packet forwarding algorithm depicted in functiontx DATA NWK(k) of Fig. 4. In this function, the loop checksfirst the best relay node that is contained in the list Relays, andif the condition is satisfied, then the message is forwarded toit. Otherwise, if the condition is not satisfied by the best relaythen we employ direct RF transmission since all the remainingrelays will have worse energy transmission cost.

V. CROSS-TECHNOLOGY MAC PERFORMANCEMODELING

Motivation. In the previous section we explained that theRF relays are selected based on their delay and/or energyefficiency by using information that is distributed in theWBAN. If a node simply measures the average delay in theRF link and reports it to the WBAN, this does not accountfor the time consumed in retransmissions and channel accessfailures. The same is true for the BCC link. Thus, a node

4Recall that the transmission of any BCC packet is preceded by a preamblethat wakes up the desired set of destination nodes. This preamble contains thisordered list of nodes, and after the data is transmitted they send sequentiallyACKs. This is a mechanism of aggregating MAC protocol data units similarto IEEE 802.11n [30].

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needs to precisely account for the previous parameters inorder to decide if it will use forwarding through BCC or not.Therefore, before an actual forwarding decision is made by anode it must know that this is indeed the optimal course ofaction. To accomplish that we develop not a cross-layer, buta same-layer and cross-technology performance model of thetwo MAC/PHYs that are controlled by the NWK.

Traffic Assumptions. Throughout this section we considerapplications where a node n generates locally and asyn-chronously data packets with an Poission rate of λn packetsper second. Thus, we adopt an M/G/1 queueing model formodeling the random variables of the Drf and Dbcc for theRF and BCC networks respectively. Several works that wereference in our subsequent analysis use a similar approachfor capturing the service behavior of CSMA/CA algorithm.We also assume that the total load λ =

∑Nn=1 λn can be

forwarded even by a single RF node. The rate of packetsthat are locally generated and forwarded directly from therelays is denoted as λd =

∑Nr

n=1 λn. We also denote withλf the total network load5 that nodes receive through theBCC link and must forward through their RF links, i.e. it isλf =

∑Nn=Nr+1 λn. By decomposing the total load into the

two components we have that λ =∑Nr

n=1 λn +∑Nn=Nr+1 λn

RF Channel Assumptions. Similar to other works, thatfocus primarily on accurate modeling of IEEE 802.15.4, weadopt the following assumptions for modeling the radio net-work [31]. The wireless channel is a frequency-flat Rayleighfading wireless link that remains invariant per PHY frame,but may vary between frames. This means that the receivedsignal-to-noise ratio (SNR) γ per frame is a random variable.SNR estimation is performed from the RSSI at the receiver.Finally, we assume additive white Gaussian noise (AWGN).

VI. PERFORMANCE OF THE RF NETWORK

Modeling the delay and energy cost of wireless RF transmis-sions is important for making the optimal decisions. However,existing models cannot be directly used [31]–[34]. The reasonis that we consider the aggregate traffic load that is flowingfrom the WBAN to the gateway and at the same time thistraffic goes though the different packet erasure links of theinvolved RF relay nodes. Thus, the basic idea of our modelis that the packet loss probability is calculated by consideringfirst the different packet erasure probability of each RF link,and second by using the aggregate traffic load for deriving theprobability of having a CCA failure.

A. Packet Erasures

Now according to the IEEE 802.15.4 PHY coding scheme,the code that is used is a pseudo-orthogonal scheme where 4bits are encoded together into a 32 chip signal. The modulationscheme is offset quadrature phase shift keying with half sineshaping (OQPSK-HSS) at a rate of 2Mchip/s. The result is thatthe 1/8th rate coding scheme achieves a throughput of 250kb/s.

5Note that although the total load that will be transmitted in the BCCnetwork will be λf , it is not distributed equally to all the relay nodes sincetheir link quality may be different instantaneously resulting into differencesin (1) and (2).

Now if Q(x) is the Gaussian Q function, then for OQPSK ina Rayleigh channel, the instantaneous bit error rate (BER) is:

ε = Q(√

3γ)

(3)

The packet error probability can be calculated as

peb = 1− (1− ε)L, (4)

where L is the length of the packet in bits. Since ACKsare used the probability that a PHY frame transmission failstowards the gateway from relay node n is:

πe(n) = 1− [1− pebdata][1− peback], (5)

where pebdata and peback are the packet error probability fordata packets and acknowledgments respectively.

B. CCA Failures

Let now πcca denote the probability that the RF channelis busy when CCA is performed. The probability that thechannel is free after u − 1 failed CCA attempts is simplyπυ−1cca (1 − πcca). The value of πcca depends only on Nr, i.e.

how many nodes contend for the RF channel. This probabilityis independent at each attempt while such an approximationis very accurate for saturated traffic as shown in [32], andhas been widely adopted in the literature also for unsaturatedtraffic [33]. We provide the methodology for the derivation ofπcca below.

Let Srf denote the number of served/transmitted packets ina busy period of the M/G/1 RF queueing system. Then it is

ρ =λ

µrf, E[Srf ] =

1

1− ρ . (6)

Note that for the M/G/1 queuing system we consider thetotal load that is transmitted in the RF network with anaverage service rate µrf . This is because there is no needto differentiate the traffic from each node since the aggregateis what matters for the model as along as the arrival rate ateach node is Poisson. Now for each packet it will be that theaverage service rate is:

µrf = 1/(E[DHOL] + Td + Tatt + Tack) (7)

In the above expression DHOL is the random variable ofhead of line (HOL) delay while Td and Tack are the timedurations of the data packets and the ACKs. Tatt is therequired turnaround time to switch from receiving (RX) totransmitting (TX) mode as specified in the standard [1]. TheHOL delay is the duration from the time instant that the packetarrives at the head of the RF transmission queue to the timeinstant just before its transmission or final discard.

To obtain the expression for πcca we have to divide the timeperiod that the channel is occupied by the remaining Nr − 1devices versus the total duration of the M/G/1 system busyperiod (see Fig. 2). The term 1

λf+λd+E[Srf ]E[DHOL] is the

total duration of the busy period in the RF network. So wehave that:

πcca =(Nr − 1)(1− πloss)E[Srf ](Tcca + Td + TATT + Tack)

1λf+λd

+ E[Srf ]E[DHOL](8)

In the above equation we can replace E[Srf ] that can bederived as a function of E[DHOL] from (6) and (7). In the

8

last equation, πloss is the average packet loss probability inthe RF network that occurs both because of erasures and CCAfailures. Since these two events are independent, we have that

πloss =1

Nr

Nr∑n=1

Mr−1∑k=0

πke (n)(1−πe(n))(1−

Mc−1∑υ=0

πvcca(1−πcca)),

(9)where Mr and Mc are the maximum number of transmissionsand CCA attempts respectively. Note in the last equation thatwe account for the different πe(n) that an RF relay mighthave. This is an important difference with related works onMAC performance modeling [31]–[34] that consider a singlepacket erasure probability but instead we consider the differentimpact of all the used relays.

Now we will also derive a second equation that alongwith (8) can be solved numerically for the derivation of πccaand E[DHOL].

C. Delay

For calculating the delay DHOL we have to model thebehavior of the backoff algorithm. We denote the value ofthe contention window as Wi = 2BEmin+i. For the successfulCCA after Mc − 1 failed ones, the HOL delay that accountsonly for CCAs will be:

E[DccaHOL,rf ] =

Mc−1∑υ=0

πvcca(1− πcca){ υ∑i=0

Wi − 1

2Ts

+(υ + 1)Tcca

}+ πMc

cca

{Mc−1∑i=0

Wi − 1

2Ts + (Mc + 1)Tcca

}(10)

The previous equation covers the case of v failed CCAattempts and a last successful one. In the case that there were vtimes a failed CCA, the total delay is attributed to the backoffalgorithm that was executed at the sender, plus there is thetime spent for the v CCAs and is denoted as Tcca (plus onesuccessful CCA). The parameter Ts is the duration of the slottime. The second summation term covers the case of reachingthe maximum limit of Mc failed CCAs.

If there is an erasure and the number of allowed transmis-sions attempts is Mr, then the average HOL delay becomes:

E[DHOL,rf ] =1

Nr

Nr∑n=1

Mr−1∑k=0

πke (n)(1− πe(n)){E[DccaHOL,rf ]

+ k(Tatt + Td + Tack)} (11)

The term πke (n)(1 − πe(n)) is similarly with before theprobability that a packet was erased k times before a finalsuccessful transmission. If the data packet is lost then the lackof an ACK incurs a delay Tatt+Tack. The transmitter observesthe lack of an ACK packet and assumes the packet was lost andproceeds with backoff and another round of possible multipleCCAs (depicted in Fig. 2). Now the average per packet delayof the RF link is:

E[Drf ] = E[DHOL,rf ] + Td + Tatt + Tack (12)

D. Energy of RF Transmissions

In this subsection we characterize the average energy con-sumption of the RF transmissions. If the power consumption of

the RF subsystem in active, CCA, transmit, and receive modesare Pact, Pcca, Ptx, and Prx respectively, then the energyrequired for the RF transmission is calculated as follows:

E[Erf ] = (E[DHOL,rf ]−AccaTcca)Pact + PccaAccaTcca

+ TdPtx + TattPact + TackPrx (13)

The first term is the energy consumed during contention whilethe node was simply in active mode, but we subtract the totaltime duration that CCAs were executed during the total timethat the packet was in the HOL position. Acca is the averagenumber of CCAs performed during this period and it is equalto

Acca =1

Nr

Nr∑n=1

(Mr−1∑k=0

kπke (n)(1−πe(n))Mc−1∑u=0

uπucca(1−πcca)).

(14)

VII. PERFORMANCE OF THE BCC NETWORK

Although the transmission through the BCC network isvery reliable, an increased number of contending nodes willeventually translate to an increase in the delay and energyconsumption of the BCC subsystem. Therefore, in this sectionwe analyze the MAC layer performance of the BCC networkin order to calculate the impact of the different parameters onthe packet delay and energy.

A. Delay

We distinguish the delay to transmit a packet successfullyon the BCC network as three random components. FromFig. 3 we can identify those three components easily. First,E[DHOL,bcc] is the expectation of the random HOL delayspent by the transmitter node from the moment it decidesto send a data packet, until the time instant it transmits apreamble packet. This delay does not include the processingtime and the transmission time of the preamble. Then T2 is therandom delay spent by the transmitter node until the receivernode is in the listening state and an RTR packet arrives atthe transmitter node. Finally, T3 is the random delay spentby the transmitter node from the instant of the RTR receptionuntil the transmission of the data packet. This component alsoincludes the processing time, the transmission time of the datapacket, and the transmission of the ACK.

HOL Delay Before Preamble Transmission. With theused MAC protocol, the preamble transmission mechanism isbased on a binary exponential backoff algorithm similar to theIEEE 802.15.4 RF MAC. As we explained earlier, once theBCC channel is obtained through this preamble contentionscheme, data packets are transmitted contention-free. If thechannel is busy, a random backoff is executed before a furtherpreamble transmission attempt. Let Mmp be the maximumnumber of CCA attempts for a preamble. Also Tcca is therequired channel sensing time from the BCC hardware andTs is the slot time. Let also σcca be the probability to sensethe BCC channel busy, and Sbcc is the number of transmittedpackets in a busy period of the BCC queueing system. Theparameter σcca in this case is calculated in the same way with

9

the RF network:

σcca =(N −Nr − 1)(1− πloss)E[Sbcc](Tcca + Td + Tatt + Tack)

1∑Nn=Nr+1 λn

+ E[Sbcc]E[DHOL,bcc]

(15)Recall that in this case there are no packet erasures sincereliable transmission is assumed once the channel is occupiedand so πloss is calculated from a simpler version of (9).Equation (15) demonstrates another novel aspect of our modelsince it incorporates this cross-technology interaction: It quan-tifies the impact of different number of RF relays Nr on theprobability that there is a channel access failure in the BCClink. This expression also quantifies the impact of a differentforwarding load λf on the BCC network. Similar with the RFcase we can write for the transmission of the preambles:

E[DHOL,bcc] =

Mmp−1∑υ=0

σucca(1− σcca){ υ∑i=0

Wi − 1

2Ts

+ (υ + 1)Tcca

}+ σMmp

cca

{Mmp−1∑i=0

Wi − 1

2Ts

+ (Mmp + 1)Tcca

}(16)

Since the BCC MAC is also modeled as an M/G/1 queue,we can have a similar expression with (8) for σcca andE[DHOL,bcc]. This second equation is solved jointly with (16),expressions for the HOL delay and channel busy probabilityσcca for the BCC network are obtained.

Preamble and RTR Transmission Delay T2. Now wecalculate T2, i.e. the random delay the transmitter node waitsfrom the start of transmitting a preamble until the RTRtransmitted by the receiving node is received by the transmitternode. Fig. 3 depicts this important detail for the calculationof T2. An important characteristic of the used BCC MACprotocol is that the selection of the sleeping time Rs is alwayssuch that the target sleeping node can be awake only fromthe successful transmission of a single preamble packet. Thelistening time Rl on the other hand depends on the hardware.Since the preamble has a fixed duration, the receiver willalways wait until the end of the preamble [28]. Therefore itwill be:

T2 = Tpream + Tatt + Trtr (17)

Although T2 was derived with simple reasoning, there is aneed to calculate another parameter that will be needed for theenergy estimation in a later subsection. To this aim we defineas Tw the random time to wait until the wake up receiverand the actual receiver wakes up completely. In our systemthis time duration can be easily modeled since it falls withuniform distribution in the range [0, Rs]. This is because such atime duration is computed from the beginning of the preambletransmission of the transmitting node that may uniformly fallsomewhere in the interval [0, Rs]. Therefore, the average valuefor Tw will be E[Tw]=Rs/2. We also denote with Ta therandom time to wait from the beginning of the preambletransmission until the start of the listening period (see Fig. 3).Now if the node is awake it is easy to see that Ta=0, otherwiseit will be E[Tw] on average. So for the average value of Ta

we can write that:

E[Ta] = E[Tw] Pr{asleep} =Rs2

RsRs +Rl

(18)

Also from Fig. 3, Tb is the delay from the wake up moment ofthe receiver due to the preamble, until the end of the preamble.This time duration will be simply

E[Tb] = Tpream − E[Ta]. (19)

Data Transmission Delay T3 and Total Delay. After thenode is awakened and the RTR is sent, the transmission ofthe data packet with the associated ACK consume a constantamount of time that depends only on the length of the datapacket L. Since the probability of packet errors at the BCCPHY is assumed to be negligible the BCC transmission isalways successful and T3 is constant. This constant delaycomponent is

T3 = Td + 2Tatt + Tack, (20)

where Tatt is the time required for the BCC transceiver toswitch from RX to TX mode, and Td is the transmission timeof the data packet of length L. Thus, we have that the totalaverage delay from the moment a packet becomes the HOLpacket until it is ACKed is:

E[Dbcc] = E[DHOL,bcc] + T2 + T3 (21)

B. Energy of BCC Transmissions

The total energy consumption over a listening-sleeping timeduration Rs+Rl, is calculated by adding the energy consumedby a transmitting node to send a data packet (E[Esend,bcc]), andthe energy consumed by the receiving node to receive the datapacket (E[Erecv,bcc). The calculations use the value of powerconsumption for the BCC transceiver and the time durationthat this power drainage occurred, while we again remove thesubscript that indicates the BCC system for avoiding cloggingthe presentation. From Fig. 3 we derive the time instants thatthe sender uses its transceiver in RX, and TX modes.6 Soit will be just for the contention round for a single packettransmission:

E[E1,bcc] = E[DHOL,bcc(act)]Pact + E[DHOL,bcc(cca)]Pcca(22)

Similarly we calculate E[E2,bcc],E[E3,bcc] from (17) and (20).The average energy consumption E[Esend,bcc] is the sum ofthese three expectations.

At the receiver we calculate similarly the time instants thatthe hardware is in TX/RX mode, active, and in sleep mode.During the reception of a preamble we calculated that theaverage duration that the receiver is awake is E[Tb]. So wehave that for a single packet transmission:

E[Erecv,bcc] = RsPsleep + E[Tb]Pact + TrtrPtx

+ TdPrx + TackPtx (23)

The average BCC energy E[Ebcc] for a single packet trans-mission is the sum of (23) and E[Esend,bcc] that is derived aswe explained a few lines above.

6Note that for the used BCC hardware it is Pcca ' Prx [10].

10

VIII. OPTIMIZATION

From our derivations we observe from (10), (11), (22)and (23) that the total average energy consumption dependson several parameters. If we notice more carefully we see thatfor constant N and Nr the energy and delay expressions forthe RF and BCC links are independent. This means that theenergy for the RF link can be further optimized individuallyand locally by each node depending on the local RF channelwhile the parameters of the BCC link are optimized in thesame way by all nodes. Many optimization objectives exist forsuch a system. As a representative case, we examine energyminimization subject to an average packet delay constraint.

A. Energy Minimization

For the RF part two parameters can be optimized for agiven Nr and packet length L (this usually depends on theapplication), and these are the maximum number of transmis-sions Mr and CCA attempts Mc. We use the RF averageenergy expression in (13) for optimally selecting the twoaforementioned parameters at relay node n:

minMc,Mr

E[Erf ](Mc,Mr, L,Nr, πe(n)) (24)

subject to πloss(n) ≤ 15%

The constraint in the above is the packet loss rate (PLR) for aspecific node n and is given from (9) before the averaging overall the relays. An important note is that in theory the aboveoptimization problem must be solved on-line every time a nodecalculates a different πe with the help of (3), (4), (5) and thelocal RSSI.

For the energy of the BCC link more parameters can beoptimized while the optimization problem has to be solvedonce for a given nework configuration. The reason is that fora given number of nodes N , relays Nr, and locally generatedload by each node λn, the BCC load λf that must be forwardedis constant regardless of who is the RF node that actuallyforwards it. This also means that σcca is constant for a givennetwork configuration. With this logic we can easily see thatthe following optimization problem indeed needs to be solvedonce:

minRl,Rs,Mmp

E[Ebcc](Rs, Rl,Mmp, L, λf , σcca, N,Nr) (25)

subject to E[Drf ] + E[Dbcc] ≤ τThe constraint denotes the average estimate of the delay thatmust be less that the maximum allowed delay τ . The reasonthat we need the delay constraint here unlike (24), is thatthe energy in the case of the BCC link can be minimizedby increasing Rl, Rs which will incur higher delay.

B. Solving the Optimization Problems Online

Regarding the solution and the implementation, for theparameters πcca, σcca, DHOL,bcc, DHOL,rf , we produce off-line several analytical expressions for a given Nr, N , and L.The result is that for the delay and energy of the RF networka linear expression is produced that only has as optimizationparameters Mc, Mr, and accepts as input πe(n) from the realmeasurements of each node n. A linear equation with two

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rma

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d e

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ba

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System load λ

Nr=1,N=4

Nr=2,N=4

Nr=3,N=4

Nr=N=4

(a) τ=1s

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Proposed Baseline

(b) τ=1s, Nr=N=4

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Nr=N=4

(c) τ=50ms

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Pa

cke

t lo

ss r

ate

System load λ

Proposed Baseline

(d) τ=50ms, Nr=N=4

Fig. 6. Results for the relative energy consumption and the PLR of theproposed system versus the baseline system for different λ and Scenario 1.

unknowns can be solved in real-time even by underpoweredmicro-controllers. Also for the BCC network the energy anddelay formulas are similarly calculated off-line for specific setsof input parameters, while Mmp,Rs,Rl, are the optimizationparameters. After the selection of the optimal values is made,they are enforced in next cycle of the node.7

IX. PERFORMANCE EVALUATION

A. Simulation

Evaluated Systems. In the first part of our evaluation ofthe proposed system we used simulation due to the lack ofhardware devices equipped with RF and BCC transceiversthat are in a size that can be deployed in a human. Forsimulating our system we implemented the NWK protocoland the RF and BCC MAC protocols. For the BCC PHYsystem we used parameters available from the literature andare presented shortly, while for the RF link we considereda Rayleigh channel and the IEEE 802.15.4 PHY that wedescribed earlier in Section III. We compare our system with abaseline system that employs only RF communication. In thissystem when a node is completely blocked from the gateway, itconnects opportunistically with an on-body node that can reachthe gateway. For all systems we measured energy consumption,average per packet delay, and PLR.

Parameters and Topology. The raw data rate of the BCCtransceiver is approximately 8.5Mbps with a BER of 10−6

and a voltage setting of 1.2V, the power consumption is2.1mW in RX mode, and 0.6mW in TX mode [10]. Fora typical IEEE 802.15.4 RF transceiver the CC2420 fromTexas Instruments that operates with the same voltage, thecurrent consumption is at 19.7mA for the TX mode and

7A further optimization was employed in our actual implementation sinceRs, Rl are the same for the complete network. Only one node is needed inpractice to make the previous optimization and it informs the remaining onesabout the result.

11

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0 5 10 15 20 25 30

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rma

lize

d e

ne

rgy (

Ep

rop/E

ba

se)

SNR

Nr=1,N=4

Nr=2,N=4

Nr=3,N=4

Nr=N=4

(a) Varying RF channel SNR for Sce-nario 1

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

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rma

lize

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ne

rgy (

Ep

rop/E

ba

se)

System load λ

S1,Mr=1,Mc=3

S1,Mr=2,Mc=3

S1,Mr=1,Mc=6

S1,Mr=2,Mc=6

S2,Mr=1,Mc=3

S2,Mr=2,Mc=3

(b) Nr=2,N=4, varying Mr and Mc,for Scenarios 1,2

Fig. 7. Results for the relative energy consumption of the proposed systemversus the baseline system.

17.4mA for the RX mode [29]. For this simulation everynode starts with the same energy budget and there is a fixednumber of 1000 packets that must be transmitted from eachnode to the gateway. We also set L=100 bytes, Ts,bcc=23us,Tack,bcc=Trtr,bcc=0.1ms, Tatt,bcc=0.1ms, Td,bcc=0.2ms,Tatt,bcc=0.384ms, Ts,rf=0.192ms, Tcca,rf=0.25ms,Td,rf=1.12ms, and Tack,rf=0.352ms. Regarding the topologyof the network, we consider Scenario 1 where a human sitsin a static position with randomly placed nodes in the bodybut with more than 50% of the nodes within line-of-sight(LOS) of the gateway. For Scenario 2 we considered a morechallenging case where a human has half of the nodes in thetorso (within LOS) and half in the back while lying in bed(no LOS).

1) Results for Energy Minimization: Fig. 6(a) depicts theratio of the consumed energy of the proposed system (Eprop),versus the energy of the baseline system (Ebase), for a differentsystem load λ and Scenario 1. The delay constraint is veryrelaxed and equal to τ=1s while Mr=Mc=3. The maximumload corresponds to 250Kbps which is the maximum data rateat the PHY of the IEEE 802.15.4 RF link. It is interesting tosee from these results that with the proposed system the energyreduction is significant regardless of how many relays (Nr)are used. The dominant factor that determines the reductionis the load which as it is increased, the performance gainbecomes higher even with a single relay. This can be explainedbecause for a higher load with the baseline method nodes withpoor RF links are still used for transmission and successivefailures (allowed by the high τ ) result in a higher numberof retransmissions. The higher number of packets in the RFmedium increases also the contention that has minor impactin the overall PLR as Fig. 6(b) shows. However, when thedelay constraint is more tight, and equal to 5ms, the energydifferences as we can see in Fig. 6(c) are reduced, but PLRincreases dramatically as Fig. 6(d) indicates. For a high load,PLR becomes nearly 10% for the baseline case while it isincreased slightly in the proposed system. This is becausethe average number of retransmissions and CCA attempts arereduced significantly for the baseline case, which eventuallyleads to lower energy wastage but still high PLR. This is aresult that demonstrates that the proposed scheme optimizesthe performance of each node individually and also jointly theperformance of the complete network.

Fig. 7(a) depicts the impact of the channel conditions

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Pa

cke

t lo

ss r

ate

System load λ

Proposed Baseline

Fig. 8. Results for the PLR and the relative delay of the proposed systemversus the baseline system for τ=1s and Scenario 1.

on the energy reduction for λ=0.5 and Scenario 1. This isalso an interesting result since it shows that the energy isreduced drastically as the SNR of the RF channel improves.However, at a certain point around 20dB, we see clearlythat the energy reduction is minimized. This is because thechannel is improved and direct transmissions succeed withoutrequiring retransmissions. Therefore, relaying starts becomingless useful in this case since every node can optimize its owntransmissions. On the other hand for poor channel quality,relaying cannot help considerably because all the RF links areaffected.

In Fig. 7(b) we present results for Nr=2 and N=4, whiledifferent fixed values of maximum RF transmissions Mr, CCAattempts Mc, and loads were simulated. Here, τ was set to200ms. As the load is increased we observe that there is aneed for more CCA attempts than the standard maximum 3while the value of Mr is not that important for Scenario 1.With the proposed scheme and Scenario 1, one or at mosttwo transmissions are enough. However, for the cases withMr=1,Mc=3, and Mr=2,Mc=3 of Scenario 2, we notice thatthe ratio is reduced even more. The reason is that for higherMr the baseline method is very energy inefficient since halfof the nodes suffer from high PLR and several retransmissionsare needed. On the other hand the proposed system does notuse the maximum Mr=2 since packets are forwarded throughother RF links.

2) Results for Delay Minimization: For this experiment, wereversed the optimization objective and the constraints of prob-lem formulations explained in Section VIII. More specificallythe objective in this case was delay minimization subject toan energy constraint. The results correspond to different loadλ and number of relays Nr. The total energy constraint wasset to 100 Joule that can cover in theory the transmission of1000 100-byte packets with the selected RF transceiver. Forthe optimization problem a proportional energy budget wasassigned to each packet depending on the remaining energyof the node. The improvement in the average delay and PLRof the transmitted packets can be seen in Fig. 8. When everynode operates with the baseline system the average number ofretransmissions reaches a value between three and four. Weobserved that this difference is more significant if there is noenergy constraint.

B. Results with Real RF Traces

RF Traces and Setup. For this part of our evaluationwe used the real RSSI traces from two mobile experiments

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Fig. 9. Results for different systems with real RF traces.

that used the IEEE 802.15.4 and involved two users [34].According to the scenario in [34], a single node is placedin each user and both users walk towards each other startingfrom a distance of 50m. They continue walking after theycross each other for an additional distance of 10m. In thefirst experiment both RF nodes are placed in the torso andin the second experiment the node is located in the pocketof a user’s trousers. These measurements reflect exactly whatwe want to test and what [34] also showed: for the largerduration of the experiment connectivity between the two torsonodes is very good and better than the experiment where nodesare placed in the pocket. However, when the two users crosseach other, connectivity between nodes in the torso is lostcompletely, while for the experiment with nodes placed in thepocket connectivity is still good. Note also that these tracescorrespond to low mobility scenarios that is generally the focusof WBAN monitoring applications.

Energy Model of the Complete System. In this casewe considered additional sources of power consumption. Weinclude first the power consumption for powering-up/down theRF transceiver as specified in the CC2420 datasheet [29].We also approximated the computational overhead of therelay selection algorithm by compiling and profiling in termsof executed instructions our code in the CC2420 simulator.Based on the number of required execution cycles, we usedthe additional delay and power consumption of the micro-controller in that mode. So in this way we include bothcommunication and computational energy and delay costs.

Results. In our results presented in Fig. 9 we see theaverage packet delay for different Mr. It is interesting tonote that even when both nodes are assumed to be usedsimultaneously, helping each other as the baseline system wedefined earlier (this system here is denoted as ”RF relay”), thisis not always the optimal case even when compared with just”Torso” and ”Pocket”. The reason is that there is significantbody shadowing between the node in torso and the node in

the pocket and so there is significant packet loss that hasto be compensated with higher number of retransmissions.Energy and delay results follow a similar trend in Fig. 9(b).Regarding the PLR, when Mr is increased this is decreased asFig. 9(c) indicates. The proposed system reaches perfect PLRbecause the best link is selected and there is no need for manyretransmissions.

Finally, we also evaluated the impact of a slower frequencyfor propagating RF channel estimates. We measured all theprevious parameters and we only present delay in Fig. 9(d).This higher update frequency mainly affects the ”RF relay”system because it makes suboptimal RF relay decisions. Oursystem is affected with a difference that is barely noticeable.Of course for an even slower update frequency of more than10 seconds, we observed that similarly the performance of allsystems was becoming proportionally worse.

An interesting detail is that the used traces consider back-logged traffic at each node which is inconsistent with thePoisson assumption used in our performance model. Thus, theperformance improvement clearly shows that our approach isnot limited by the traffic model and it can work under moregeneral settings.

X. CONCLUSIONS

In traditional RF-based WBANs in the face of body shad-owing there is a fundamental tradeoff: Either consume moreenergy for retransmissions or channel coding in order toincrease reliability and suffer also higher delay, or reducedelay and energy at the cost of reduced reliability. How-ever, we demonstrated that the above does not have to bethe case if nodes cooperate through a delay/energy-efficientsecondary link (in this case BCC) in order to select the mostefficient RF link for forwarding WBAN data to a gateway.The performance gains are materialized not only with thenovel WBAN architecture, but also with a NWK protocolthat uses an algorithm for RF relay selection and packetforwarding driven by a performance model. Furthermore, thecross-technology performance models are exploited for localoptimization of the MAC parameters of the protocols thatoperate in the BCC and RF subnetworks. The performanceresults indicate that the proposed system is more efficient thana state-of-the-art scheme in terms of energy and delay underdifferent realistic channel conditions, application loads, andperformance constraints.

The potential concern regarding the proposed system is theneed for two different technologies. However, existing IC tech-nology already allows the integration of multiple transceiversin a single chip. Our future work will be focused first onthe optimal configuration of other system parameters like thenumber of used relays and the content of the transmittedpackets. Next, we intend to investigate the potential benefits ofthe proposed system when it is used with an IEEE 802.11 RFcommunication link for multimedia transmission scenarios.

ACKNOWLEDGMENT

The authors would like to acknowledge the anonymousreviewers for their thorough comments that helped improvethis manuscript considerably.

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Antonios Argyriou received the Diploma in electrical and computer engineer-ing from Democritus University of Thrace, Greece, in 2001, and the M.S. andPh.D. degrees in electrical and computer engineering as a Fulbright scholarfrom the Georgia Institute of Technology, Atlanta, USA, in 2003 and 2005,respectively.

Currently, he is a tenure-track faculty member at the department ofelectrical and computer engineering, University of Thessaly, Greece. From2007 until 2010 he was a Senior Research Scientist at Philips Research,Eindhoven, The Netherlands. From 2004 until 2005, he was a Senior Engineerat Soft.Networks, Atlanta, GA. Dr. Argyriou currently serves in the editorialboard of the Journal of Communications. He has also served as guest editorfor the IEEE Transactions on Multimedia Special Issue on Quality-DrivenCross-Layer Design, and he was also a lead guest editor for the Journalof Communications, Special Issue on Network Coding and Applications. Dr.Argyriou serves in the TPC of several international conferences and workshopsin the area of communications, networking, and signal processing. His currentresearch interests are in the areas of wireless communication systems andnetworks, and video communications. He is a member of IEEE.

Alberto C. Breva received the Diploma in electrical and computer engineer-ing from the University of Seville, Spain, in 2008, and the M.S. degree in2010 from the same University. He was an intern at Philips Research during2009-2010 where he worked on the topic of body coupled communication.

Marc Aoun received his Bachelor degree in Computer and CommunicationsEngineering from the American University of Beirut, Lebanon, in 2004, andhis Masters degree in Communications Engineering from RWTH Aachen,Germany, in 2007. Since 2007 he is a Research Scientist at Philips Research,The Netherlands. His current research interests are in the areas of wirelesscommunication systems, wireless sensor networks, real-time systems andsegmentation of large-scale networks.