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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 17, NO. 8, AUGUST 2018 5637

Performance Analysis of IEEE 802.15.6-BasedCoexisting Mobile WBANs With Prioritized

Traffic and Dynamic InterferenceXiaoming Yuan , Student Member, IEEE, Changle Li , Senior Member, IEEE, Qiang Ye , Member, IEEE,

Kuan Zhang, Member, IEEE, Nan Cheng , Member, IEEE, Ning Zhang , Member, IEEE,and Xuemin Shen, Fellow, IEEE

Abstract— Intelligent wireless body area networks (WBANs)have entered into an incredible explosive popularization stage.WBAN technologies facilitate real-time and reliable health mon-itoring in e-healthcare and creative applications in other fields.However, due to the limited space and medical resources, deeplydeployed WBANs are suffering severe interference problems.The interference affects the reliability and timeliness of datatransmissions, and the impacts of interference become moreserious in mobile WBANs because of the uncertainty of humanmovement. In this paper, we analyze the dynamic interferencetaking human mobility into consideration. The dynamic interfer-ence is investigated in different situations for WBANs coexistence.To guarantee the performance of different traffic types, a healthcritical index is proposed to ensure the transmission privilegeof emergency data for intra- and inter-WBANs. Furthermore,the performance of the target WBAN, i.e., normalized throughputand average access delay, under different interference intensityare evaluated using a developed three-dimensional Markov chainmodel. Extensive numerical results show that the interferencegenerated by mobile neighbor WBANs results in 70% throughputdecrease for general medical data and doubles the packet delayexperienced by the target WBAN for emergency data comparedwith single WBAN. The evaluation results greatly benefit thenetwork design and management as well as the interferencemitigation protocols design.

Manuscript received October 16, 2017; revised March 19, 2018 andJune 4, 2018; accepted June 5, 2018. Date of publication June 25, 2018; dateof current version August 10, 2018. This work was supported in part by theNational Natural Science Foundation of China under Grant 61571350, in partby the Key Research and Development Program of Shaanxi under Contracts2017KW-004, 2017ZDXM-GY-022, and 2018ZDXM-GY-038, and in partby the 111 Project under Grant B08038. The associate editor coordinatingthe review of this paper and approving it for publication was Z. Dawy.(Corresponding author: Changle Li.)

X. Yuan and C. Li are with the State Key Laboratory of Inte-grated Services Networks, Xidian University, Xi’an 710071, China (e-mail:[email protected]; [email protected]).

Q. Ye, N. Cheng, and X. Shen are with the Department of Elec-trical and Computer Engineering, University of Waterloo, Waterloo,ON N2L 3G1, Canada (e-mail: [email protected]; [email protected];[email protected]).

K. Zhang is with the Department of Electrical and ComputerEngineering, University of Nebraska-Lincoln, Omaha, NE 68182 USA(e-mail: [email protected]).

N. Zhang is with the Department of Computing Sciences, TexasA&M University–Corpus Christi, Corpus Christi, TX 78412 USA (e-mail:[email protected]).

Color versions of one or more of the figures in this paper are availableonline at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TWC.2018.2848223

Index Terms— Wireless body area network (WBAN),interference, CSMA/CA, IEEE 802.15.6, Markov chain model.

I. INTRODUCTION

W IRELESS Body Area Networks (WBANs) arehuman-centered, highly reliable short range wireless

communication networks [1]. With flexibility, scalabilityand low-cost characteristics, WBANs have been widelyused to provide real-time and continuous care for healthmonitoring in e-healthcare [2]. The application of WBANalleviates the conflicts between limited healthcare resourcesand increasing needs of aging population. WBAN technologyhas been envisioned as one of the primary technologies forthe ubiquitous Internet-of-Things (IoT) to enhance the qualityof people’s life [3]–[5].

WBANs are widely deployed in densely populated sce-narios, for example, wards and waiting rooms in hos-pitals, resident-centered nursing homes, and even publictransports. The communication zones of adjacent WBANsare close or even overlapped. Coexisting densely deployedWBANs inevitably suffer severe intra- and inter- WBANinterference. Intra-WBAN interference occurs when nodes ina WBAN transmit data at the same time. Inter-WBAN inter-ference is caused by simultaneous data transmission amongtwo or more adjacent WBANs. Inter-WBAN interferencedecreases the reliability and timeliness of physiological datatransmission, increasing the network management and eco-nomic cost paid on healthcare. Particularly, interference maylead to catastrophic incomplete or overdue data in emergencymedical diagnosis and threaten the life safety of people.Therefore, interference problem of coexisting multi-WBANsneeds in-depth study.

Many researchers have paid attention to the interference ofcoexisting WBANs. The inter-user interference among adja-cent WBANs is investigated in [6]. An interference distributionmodel [7] is proposed by using a geometrical probabilityapproach. The inter-WBAN interference is related to the activ-ities of users carrying them and is different from other types ofwireless networks [8]. The traditional interference mitigationapproaches are not suitable for WBANs. The inter-WBAN

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5638 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 17, NO. 8, AUGUST 2018

coexistence and interference mitigation schemes are surveyedand discussed in [9]. A Bayesian game based power controlscheme [10] is introduced to mitigate the impact of inter-WBAN interference. A beacon interval shifting scheme [11]and a beacon scheduling scheme [12] are developed to executecarrier sensing before beacon transmission to reduce inter-WBAN interference.

However, in the study of interference analysis and interfer-ence mitigation, there are few work considering the dynamicinterference of mobile neighbor WBANs. Mobility of WBANsaffects the service-oriented as well as the application-orientednetwork performance [13]. Interference intensity varies withhuman movements. Interference lasts for a short period whenneighbor WBAN just passes by or lasts long when thereis no relative movement between inter-WBANs. Networkperformance, i.e., throughput and delay, fluctuate with inter-ference variation and become severe when adjacent WBANsare deeply overlapped with each other [14]. Therefore, ana-lyzing the dynamic interference in coexisting multi-WBANsis required, which satisfies the practical scenario well and cangreatly benefit network design and management.

On the other hand, different types of traffic in the WBANhave different Quality-of-Service (QoS) requirements. Forexample, life-critical emergency electroencephalogram (EEG)and electrocardiogram (ECG) data require high reliabilityand low transmission delay while regular temperature dataare delay-tolerant. It is necessary to differentiate the trafficpriorities to satisfy the diversified performance requirements.In addition, some monitoring physiological data in medicalservices exceed the normal range and trigger other abnormalchanges occasionally. The variation message and the abnormaldata have higher priority than usual, and they should be trans-mitted to the hub or data center timely for further processing.Both of the general data and the abnormal data are requiredto be considered in multi-WBANs coexistence.

In this paper, we analyze the interference of coexistingWBANs by considering human mobility and traffic differen-tiation. The network topology changes when human moves.Different nodes with different traffic priorities in the net-work have diverse performance requirements. In this case,the asynchronous IEEE 802.15.6-based Carrier Sense MultipleAccess with Collision Avoidance (CSMA/CA) protocol [15]is employed rather than the scheduled Time Division MultipleAddress (TDMA). The CSMA/CA scheme is adaptive to thenetwork topology and network load changes, and it does notrequire complex time synchronization compared to TDMAmethod. We develop the CSMA/CA Medium Access Control(MAC) mechanism in this paper to improve the flexibility andscalability of a WBAN. We investigate the MAC performancewith interference dynamics from neighboring WBANs andguarantee the required QoS for different types of traffic. Ourmain contributions is summarized as follows.

• Firstly, we analyze dynamic inter-WBAN interferencewith the movement of neighbor WBANs in rectilinearmotion and curvilinear motion cases. We also investigatethe interference dynamics in different situations for amulti-WBANs coexistence scenario (e.g. different dis-tances between inter-WBANs, varying number of nodes

of each WBAN, different number of coexisting WBANs,etc.). Furthermore, we consider the interference dynamicsin developing the performance analytical model, whichfacilitates the practicability and credibility of the modelin WBANs.

• Secondly, we propose a health critical index to guaranteethe privilege of time sensitive emergency and burst med-ical data traffic. The definition of the health critical indextakes into account service types, traffic differentiation,and data severity. The burst abnormal data enjoy higherhealth critical index value to ensure the channel accessprivilege than other normal data. In addition, an algorithmis designed to manage the service order of all the nodesin multi-WBANs coexistence scenario.

• Finally, we develop a three-dimensional Markov chainperformance analytical model to investigate the impactof interference on throughput and packet access delay ofprioritized traffic in the target WBAN. Extensive numeri-cal results demonstrate the accuracy and effectiveness ofour proposed performance analytical framework.

The remainder of paper is organized as follows. Section IIsummarizes the related work. The network model and trafficmodel is presented in Section III. In Section IV, the changinginterference and the proposed severity index are described.Performance on normalized throughput and average packetaccess delay are analyzed in Section V. Section VI demon-strates analytical results and discussions. Finally, concludingremarks and future work outlines are given in Section VII.

II. RELATED WORK

Multiple WBANs coexist in one place in order to accessto healthcare services for different patients. The interferencefrom neighbor WBANs is inevitable in dense area, degrad-ing network performance and increasing network manage-ment cost [16]. Interference in multiple WBANs include themutual or inter-user interference incurred by simultaneoustransmissions in the vicinity of multiple WBANs, and cross-technology interference caused by the utilization of differenttransmission technologies operating in the same spectrumrange [17]. More and more researchers have paid their atten-tion on interference analysis and mitigation in coexistingWBANs.

The coexistence and interference management issues weresummarized [18], exploring a wide variety of communicationstandards and methods deployed in WBAN. Sun et al. [19]presented a stochastic geometry analysis model to analyzethe inter-user interference in IEEE 802.15.6 based WBANsand derived the optimal interference detection range totradeoff between outage probability and spatial throughput.Jameel et al. [20] analyzed the impact of co-channel inter-ference on WBANs under generalized fading. The progressof interference generation between two deeply overlappedWBANs [21] was introduced. Three heuristic solutions [22]were presented to solve the problems of mutual interferenceand cross-technology interference. The nodes of intra-WBANemployed the IEEE 802.15.4 protocol [23] with not distin-guishing the traffic priorities. Zhang et al. [24] described the

YUAN et al.: PERFORMANCE ANALYSIS OF IEEE 802.15.6-BASED COEXISTING MOBILE WBANs 5639

TABLE I

BASIC NOTATIONS

necessity of classifying different types of health data for aWBAN.

Moreover, different analytical models and proposed algo-rithms are used in the MAC performance analysis for WBANs.In the presence of inter-user interference, a stochastic geom-etry model [25] were proposed to analyze the effects ofIEEE 802.15.6 MAC. The outage probability and spatialthroughput of WBANs were derived on the spatial distribu-tion of the interfering WBANs. An analytical model for theIEEE 802.15.4 MAC protocol [26] was proposed to explore thetime-varying feature of the MAC statistics under periodic traf-fic. Based on Bianchi’s work [27], many authors contributedto the Markov analytical models for WBANs [28]–[31].A criticality index was introduced [28] to identify the criticalphysiological parameter, using a Markov chain-based ana-lytical approach to maximize the reliability of the criticalnode. Nevertheless, the different User Priorities (UPs) ofvarious traffic in a WBAN should be considered. A fourdimensional Markov chain [30] was used to evaluate the powerconsumption, throughput and average delay concerned thetime spent by a node awaiting the acknowledgement frame.The IEEE 802.15.6 CSMA/CA mechanism with different UPswas analyzed employing a three-dimensional Markov chainmodel [31]. The proposed scheduling techniques [32] wereevaluated within various traffic rates and time slot lengths toimprove WBAN reliability and energy efficiency. However,these works are more comprehensive if they considered thetraffic critical conditions out of safety range and the influenceof interference from adjacent WBANs.

Several approaches were proposed to mitigate the intra- andinter- WBAN interference [33]–[39]. A superframe overlap-ping scheduling method [33] based on beacon shifting andsuperframe interleaving interference mitigation techniques wasdeveloped for coexisting WBANs. A two-layer interferencemitigation MAC protocol (2L-MAC) was proposed based onIEEE 802.15.6 for WBANs [34]. However, the scheme eval-uated the long-term interference in non-adaptive and sched-uled one node for transmission regardless of the interferencelevel. Cheng and Huang [36] employed TDMA method toreduce mutual interference of nearby WBANs. A horse racingscheduling scheme [37] was raised to decrease single-WBANinterference. But the resource allocation scheme might degrade

once the topology of a WBAN changes with human move-ments. Therefore, the random adaptive CSMA/CA method ismore suitable in the analysis of interference dynamics withhuman movements and diversity traffic types. The authors [39]proposed an adaptive CSMA/CA MAC protocol by adjust-ing the length of each polling period/MAC frame based onits perceived interference level to mitigate the inter-WBANinterference.

III. SYSTEM MODEL

In this section, we present the network model as well as thetraffic model. Some important notations used in the paper aresummarized in Table I.

A. Network Model

The WBAN usually consists of a centralized entity hub anda number of sensor nodes to monitor the vital signs of humanbody. The Hub is usually performed by a smartphone or aPersonal Digital Assistant (PDA), which has greater comput-ing capability with power-rechargeable. Sensor nodes collectEEG, ECG, motion signals, and general physiological data.There is a set of coexisting WBANs {Nm|m = 1, 2, . . . , M}in the system, as shown as in Fig. 1, where M is the number ofWBANs and Nm stands for WBAN m. The hub is denoted by{Nmh|m = 1, 2, . . . , M} and several numbered physiologicalsensor nodes are denoted by {sy

m|y = 1, 2, . . . , Y }.There are four communications types in WBANs, namely

in-body, on-body, off-body and body-to-body [40]. Usually,the collected data traffic is transmitted from nodes to hubthrough on-body channel and from hub to the data centerthrough free space wireless channel. The body-to-body typerepresents communications from one person’s body to anotherperson’s body. In general, the distance between WBANs islarge enough such that there is no inter-WBAN interference,just as the non-intrusive distribution of N3 and N4. With themovement of human body, the distance between WBANs isconstantly changing. As the inter-WBANs distance decreases,the communication zones are overlapped partially. The inter-ference occurs between the adjacent WBANs if there are senornodes in the communication area of neighbor WBANs, as thedistribution of N1, N2 and N3 in Fig. 1. The data transmissions


Fig. 1. Network model.

TABLE II

UP MAPPING AND CW BOUNDS

of sensor node s41 in the overlapped area of N1 and N2 intrude

all the data transactions of N2, since s41 is in the interference

range of hub N2h. s53 in N3 also interferes data transactions

in N2, increasing the data collision probability. If the inter-WBAN distance becomes smaller, the overlapped area as wellas the number of interference nodes increases. The worstcase is that two or more communication zones are completelyoverlapping. The interference seriously aggravates the networkperformance.

B. Traffic Model

Sensor nodes in each WBAN collect and transmit realtimesensing data to the hub. Different sensing data types havedifferent reliability and delay requirements, especially thelife-critical emergency data in medical applications whichrequire high reliability and minimum transmission delay. Thesensing health data are classified into different data or userpriorities to satisfy the diversified performance requirements.In IEEE 802.15.6, eight User Priorities (UPs) are predefinedin Table II. The higher user priority has privilege to access thechannel. The user priorities of medical data traffic are from5 to 7, guaranteeing the severer traffic in high priority to betransmitted timely. We consider each node in the WBAN onlycollects one kind of user priority traffic and the WBAN is

TABLE III

WBAN PRIORITY OF SERVICE

saturated that every node in the network always has packetsto contend to access the channel. Data with higher user priorityenjoy small Contention Window (CW) size to guarantee thelow access delay.

The definition of user priority is based on the collected data.The collected data are used to provide various applications andservices (medical or non-medical services). The definition ofBAN priority is from the perspective of applications. In multi-WBANs coexistence scenario, it is necessary to classify theBAN priority with different services among inter-WBANs.Different applications may collect the same kind of data.For example, both interactive motion sensing games andmedical applications for disability assistance are required tocollect the motion data. According to the traffic designationin Table II, the motion data have the same kind of userpriorities and contention window. The collision probabilityincreases when the same UP data are transmitted at the sametime in neighbor interference area. But the motion data requirelower delay and higher reliability for medical service thanthat for entertainment. Therefore, we differentiate the BANpriority besides the user priority, as shown in Table III. Themedical application takes precedence over other non-medicalapplications. The BAN priority guarantees the privilege ofnodes accessing the channel for health monitoring, especiallyin the condition of existing few interference nodes in multipleWBANs coexistence scenario.

IV. INTERFERENCE OF COEXISTENCE MOBILE WBANS

The mutual interference of multi-WBAN coexistence iscaused by the concurrent data transactions of sensor nodesfrom neighbor WBANs. In other words, the body-to-bodycommunications generate interference. Human, as the wearerof WBAN, is not static. The slow or quick movement leadsto channel dynamics. The body-to-body WBAN interferencechannel modeling with movement can be found in [41]and [42]. In this section, we first calculate the changing inter-fering nodes and discuss the dynamic interference of multiplecoexisting WBANs in different cases. Then we propose ahealth critical index to guarantee the transmission privilegeof emergency data of a particular node or WBAN in multi-WBAN coexistence scenario.

A. Dynamic Interference of Coexisting Mobile WBANs

The human mobility is reflected by the velocity and direc-tion angle of WBAN movement. The interference channel gainis dominated mostly by human movements [43]. For very slowmovement, the channel coherence time is relative long, and theinterference channel gain could be considered unchanged. The


Fig. 2. The relative position of N1 and Nm.

channel fading and path loss of the movement is negligible.In this paper, we pay our attention to the influence of changinginterference intensity related to human mobility on networkperformance, instead of how human movement manners affecttime varying interference channel.

The number of sensor nodes attached on a human bodyvaries depending on the individual requirements. We assumeall the WBANs are in same configuration. The topology ofa WBAN can be modeled as a hub centered circle. Thehub is located near the center of a human body with thecommunication range Υ. We consider that the interferencerange of sensor nodes is the same as communication range Υ.The projections of all the nodes are relative uniformly randomdistribution within the circle. The modeled topology of aWBAN is two dimensional for the tractable analysis insteadof the three-dimensional distribution in reality.

Assuming N1 as the target WBAN, we firstly pay ourattention to the impact of one existing neighbour WBAN Nm.Every hub knows its coordinate in the network. The coordi-nates of N1h and Nmh are (x1, y1) and (xm, ym), respectively.The inter-WBAN distance d between N1 and Nm is definedas the distance from N1h to Nmh. The distance d can becalculated as

d =√

(xm − x1)2 + (ym − y1)

2 (1)

WBAN movements lead to changing relative distance ofinter-WBANs. If an arbitrary WBAN Nm moves in the direc-tion of target WBAN N1, there are three relative positions ofthe two WBANs, as shown in Fig. 2. If d > 2Υ, there isno inference between them. Nm keeps moving from positionD to position C, then it reaches the edge that can interfereN1. We mark this critical point d = 2Υ as the momentt = 0. Supposing that Nm continues moving to N1 tillposition B where d is less than 2Υ resulting in an overlapof the communication zones. Sensor nodes of Nm in theoverlapped area become interference sources, increasing thecollision probability of data transactions of the target WBANand degrading the network performance on throughput andpacket delay at the same time. The interference duration endsat the moment when the inter-WBAN distance d equals 2Υthe next time.

The moving direction of Nm at next moment is uncertainsince people have a variety of options. In this paper, the move-ment of Nm could be classified into two cases: rectilinearmotion and redirection curvilinear motion. The velocity ofNmh is denoted by vm. The direction angle between the

Fig. 3. The relative motion of Nm.

leaving and the approaching direction of Nmh is α. Therectilinear motion of Nm means the directional angle α iszero or π, as shown in Fig. 3(a). Nmh moves from position Bto position E. Section 1 is the original overlapping area whilesection 2 is the overlapping area at the next moment. Thecurvilinear motion refers that the value of α ranges in (0, π),as shown in Fig. 3(b). Nmh moves from position B at momentt1 to position F at moment t2. The value of α could be acquiredin the law of cosines from Eq. (2).

cosα =d2

t1 + d2mΔt − d2

t2

2dt1dmΔt(2)

where dt1 and dt2 are the inter-WBAN distance at momentt1 and t2, respectively. dmΔt is the straight-line distanceof Nmh in the time duration Δt (Δt = t2 − t1). WhenNmh is in continuous movements, we can get dmΔt =√

(xmt2 − xmt1)2 + (ymt2 − ymt1)

2 = vm(t2 − t1). Thedirectional angle α and the value of vm affect the interferenceduration.

The interference intensity is directly decided by the numberof interference nodes nI , which is related to the inter-WBANdistance d. The intersection area of the target WBAN N1 andNm is twice of the difference between sector area (SXAY ) andtriangle area (S�XAY ), as shown in Fig. 2. Therefore, the nI

can be obtained from Eq. (3).

nI =2Y

πΥ2×(

arccosd

2ΥΥ2 − d

2

√Υ2 − d2

4

)(3)

where Y is the number of sensor nodes in a WBAN. Themaximum nI can be reached at d = 0 where the twoWBANs are totally overlapped, the value of which is Y . Theinterference nodes continuously affect the performance of N1

until Nm leaves.For the coexistence of more than two WBANs, the distance

between any WBAN and the target WBAN affects the numberof interference nodes. It makes no great difference to thetarget WBAN whether the other WBANs have overlap or not.In other words, we only focus on the number of nodes in theinterference range of target WBAN and ignore the interferenceamong any other WBANs. One case that any two of the threeWBANs have an overlap is described in Fig. 4. The numberof interference nodes to the target WBAN N1 equals the sumof interfering nodes from Np and Nq in the interference range


Fig. 4. The relative position of three WBANs.

and can be obtained using Eq. (4).

nI =2Y

π

(arccos

dp

2Υ+ arccos

dq

2Υ

)

− Y

πΥ2

(dp

√Υ2 − d2

p

4+ dq

√Υ2 − d2

q

4

)(4)

where dp =√

(xp − x1)2 + (yp − y1)

2 and dq =√(xq − x1)

2 + (yq − y1)2, respectively.

Given that there are M WBANs in the WBAN coex-istence scenario, the potential interference sources set tothe target WBAN at moment t can be represented as{NK |0 ≤ K ≤ M ; ∀K, dk < 2Υ}. Calculating the wholeintersection area of the target WBAN N1 and interferenceWBAN NK(0 ≤ K ≤ M), we can get the total number ofinterference nodes by Eq. (5).

nI =2Y

πΥ2

∑m∈K

(arccos

dm

2ΥΥ2 − dm

2

√Υ2 − d2

m

4

)(5)

where dm is the distance from an arbitrary WBAN m inthe interference source sets to the target WBAN. dm and nI

are affected by the directional angle and the velocity vm ofWBAN m.

B. Health Critical Index

The definition of critical index indicate the ratio of deviationin sensed physiological data and the normal value of thatphysiological parameter is proposed in [44]. The critical indexis used to assist a data-rate tuning mechanism for resourcesharing [44]. The mechanism is IEEE 802.15.4-based, withoutdifferentiating the traffic priority and the access privilegeof the nodes as well as the interference problem. A fuzzyinferencing-based health criticality assessment approach fol-lowed by an efficient decision making model based on theconcepts of Markov decision process [45] is proposed tooptimize the energy consumption in WBAN. It takes bodytemperature and human age as the influential criteria. However,the work in [45] focuses on the individual instead of the multi-WBANs coexistence. To guarantee the transmission delay oftime sensitive emergency and burst data, the heath criticalindex (H) is introduced to determine the order of nodesenjoying service and transmission in multi-WBAN coexistencewith interference.

TABLE IV

PAIRWISE COMPARISON MATRIX

The heath critical index H in our paper considers the trafficuser priority, WBAN priority (BP ) and the severity condition.The H helps select the critical data among multiple nodesin the WBANs coexistence scenario. Then the node withemergency data performs the CSMA/CA contention progressto access the channel. The traffic priority UPk and WBANpriority BPm can be found in Table II and Table III. We usethe severity index (SI) denotes the degree of data deviatingfrom the safety range. The details of this severity index areavailable in the original paper [46]. The value of SI satisfies0 ≤ SI ≤ 1. The values of H can be obtained through Eq. (6).

H =ρ1×UPk

7+ρ2×SI+ρ3×BPm

3, ρ1+ ρ2+ ρ3 =1 (6)

where ρ1 is the weight factor of the user priority, ρ2 isthe weight factor of the data critical condition while ρ3 iscorresponding to the BAN priority. The values of the weightfactors could be calculated according to the Analytic HierarchyProcess (AHP) method [47], [48].

The AHP method provides an effective solution to quan-tify qualitative problems through pairwise comparisons. Thecomparisons are made using a scale of numbers that indicates,how much more, one element dominates another in regard tothe criterion or property. The fundamental scale of absolutenumbers is from 1 to 9. The minimum value 1 means thetwo attributes contribute equally to the objective while themaximum value 9 means one attributes is extreme importantover another.

Three attributes are considered in the paper, i.e. WBANpriority, user priority, and severity condition. The WBANpriority is strongly important over data deviation. Data severityis more important than user priority. In other words, nodes formedical applications have privilege than non-medical applica-tions. Nodes in abnormal condition have higher heath criticalindex value than that of normal, which gets preferential orderto access to services. For example, the monitoring data of aUP5 node in a serious fluctuation are more important thana UP6 node in normal condition. The pairwise comparisonmatrix of the three attributes with respect to the H is shownin Table IV. The attributes listed on the left are matched one byone to each attribute listed on top according to their importancewith respect to the health critical index. The weight factorsare obtained by Asymptotic Normalization Coefficient (ANC).The derived weight factor vector (ρ1 ρ2 ρ3)T based onthe judgements in the matrix is (0.15 0.51 0.34)T . Withregard to nodes in disparate WBANs of interfering sourcessets, the proposed H guarantees their service quality morethoroughly.

The service order algorithm is described in Algorithm 1 fordifferent nodes in multi-WBANs coexistence scenario. Hstart

m

is the health severity index value of the pending data packet


Algorithm 1 Algorithm for Determining the Service Order ofNodesInput: Physiological data from sensor nodesOutput: Service priority of different data types1: There are M WBANs in the system while each WBAN

has Y sensor nodes.2: for m=1:M do3: compute Hm according to Eq. 64: sort H= [Hm] ↓5: if Hstart

m ≥ Hstartm+1 then

6: serve WBAN m7: for y=1:Y do8: compute Hy

m according to Eq. 69: sort H= [Hy

m] ↓10: if Hy

m > Hy+1m then

11: serve node y in WBAN m12: else13: serve node y + 1 in WBAN m14: end if15: end for16: end if17: end for

located at the head of data queue in hub Nmh. Hym is the health

severity index value of node y in WBAN m. According toEq. 6, the values of health critical index of different WBANsand different nodes could be acquired. Then we sort theWBANs or nodes by the value of H from large to small.Anyone with the higher H value is conferred the higherprivilege to access the channel. The introduced H helps toensure that nodes in emergency condition could be processedin lower delay and better services for health monitoring.

V. PERFORMANCE ANALYSIS

Multiple concurrent WBANs share the same operatingchannel. Sensors in different WBANs may collide overthe channels. We investigate the slow movement scenario.The channel coherence time is relative long. The interferencechannel gain could be considered unchanged. The channelfading and path loss of the movement is neglected. In thissection, we analyze the normalized throughput and averagepacket delay by employing a three-dimensional Markov model.

A. Superframe Structure

In the light of IEEE 802.15.6, two Exclusive Access Phases(EAPs), two Random Access Phases (RAPs), two ManagedAccess Phases (MAPs), and a Contention Access Phase (CAP)are placed in order in the superframe. The seven access phasesphases are optional. In this paper, only the contention phasesEAP and RAP are selected. All the nodes in the networkare synchronized to support the contention-based mechanismwhile the hub establishes the time reference operating in thebeacon mode with superframes. Each superframe is boundedby a maximum number of 255 equal length allocation slots.The layout of superframe is shown in Fig. 5.

Fig. 5. The superframe structure.

Fig. 6. Markov model for a node with UPk .

The Beacon (B) frame is transmitted by the hub at thebeginning of each superframe to notify network managementinformation such as BAN identification, synchronization andcoordination of medium access. If there is highest priority data,the hub and nodes could acquire a contended allocation ShortInterframe Space (SIFS) after the beginning of EAP (EAP1 orEAP2) without performing the CSMA/CA. In RAP, all UPk

nodes perform CSMA/CA to contend for available slots totransmit management or data frames. If there are no morepending transmissions in data queues, all nodes including hubturn into inactive state over some time intervals for energysaving.

B. Markov Chain Model for A UPk Node With Interference

Nodes contend to access the channel in CSMA/CA mecha-nism for intra-WBAN. In light of the random characteristics ofcontention progress of the nodes, the access attempts of nodehaving pending data to be transmitted can be analyzed witha three-dimensional discrete time Markov chain. The Markovchain model for the contention progress is shown as Fig. 6.All the notations used in the analysis are presented in Table I.

The state of a node at moment t in the contention progresscan be described by three variables {k, BT (t), CW (t)}, wherek is the user priority of a node, BT is the backoff times, and


CW is the contention window value in Backoff Counter (BC).Prioritized access for different types of traffic with differentUPs shall be attained through the predefined CW values.The relation between contention window bounds CWmax andCWmin and the corresponding data traffic is shown in Table II.

Nodes which have data to be transmitted will switch theirstates between back-off and data transmission, or nodes willturn into inactive state to save energy. At the initial state,a node with user priority k denoted by UPk senses thechannel having been idle for at least a short interframe spacing(pSIFS) time interval. Then it selects a random integer fromthe interval [1, CWUPk

] as the BC values, where CWUPk∈

(CWmin[UPk], CWmax[UPk]). The values of CWmin[UPk] andCWmax[UPk] can be obtained from Table II. Furthermore,the BC is decreased by one for each idle CSMA/CA slot,and nodes will switch to transmission state once the BCreaches zero meanwhile the channel is idle. Otherwise, the BCwill be locked if the channel is busy or the remaining timeinterval is not long enough for completing a frame transaction.The BT increases by one in case of an unsuccessful mediumaccess due to collisions. Nodes conduct the backoff progressuntil the frame is transmitted successfully or initiate a newdata transmission when the BT value reaches the maximumbackoff retry limits BTmax.

We suppose that there are M WBANs in a dense deploy-ment scenario. There are one hub and nk nodes of UPk, where7∑

k=0

nk = Y , k = 0, . . . , 7, operates in the beacon mode with

superframe boundaries in a random selected target WBAN.Assuming that the access probability of node UPk in a randomCSMA/CA slot is τk. The access probability of interferencenodes is considered sharing the same probability with a UPk

node. The probability that channel is idle during the contentionintervals of the target WBAN is

Pidle =7∏

k=0

(1 − τk)nk (7)

The backoff counter is locked when the channel is busy andthe remainder time is not long enough completing a frametransaction during its backoff stage. The probabilities that thebackoff counter is locked due to the insufficient remainingtime for a frame transaction from the current CSMA contendedallocation slot to the end of the EAP and RAP period are

PUPk−inlock =TR − Tdata − TkCW

TR, RAP (8)

PUP7−inlock =TE+TR−Tdata−T7CW

TE+TR, EAP and RAP

(9)

where TE and TR is the number of CSMA slots allocated toUPk in EAP and RAP access phases, respectively. Tdata is thesuccessful data transmission time. The backoff times is markedas l and the mean backoff value is approximately calculated asCWmin[UPk ]+CWmax[UPk]

4 [31]. TkCW is the backoff time and

can be calculated as

TkCW =BTmax∑

l=0

Tc−slot

(CWmin[UPk]+CWmax[UPk]

)4

(10)

Tc−slot = TCCA+ TMP (11)

where Tc−slot is the CSMA slot length with a fixed duration.TCCA is the Clear Channel Assessment (CCA) time andTMP is the time cost that MAC transfers a frame to thePhysical (PHY) layer.

For the highest user priority data, the contention attempts areexclusively allowed in EAP. A hub or a node may regard thecombined EAP (EAP1 or EAP2) and RAP (RAP1 or RAP2)as a single EAP1 or EAP2 to allow continual invocationof CSMA/CA and improve channel utilization. Therefore,the probability of the channel is idle for UP7 in a CSMAslot during EAP and RAP is different [49]. The idle channelprobability P7idle of UP7 node in EAP and RAP can beobtained from Eq. (12). Then the random selected UP7 nodelock its backoff counter owing to the busy channel can beobtained from P7b = 1 − P7idle.

P7idle =TE(1−τ7)

n7I+n7−1

TE + TR+

TRPidle(1−τ7)n7I−1

TE+TR(12)

where n7I is the number of interference nodes with UP7 frominter-WBANs. The value of n7I can be obtained from Eq. (5).n7 is the number of UP7 nodes from intra-WBAN. Pidle is theprobability that channel is idle during the contention intervalsof the target WBAN.

In RAP, a node with UPk locks its backoff counter whenit finds the channel is busy in its backoff counter count-down. In other words, at least one of the remaining nodesof intra-WBAN or interference nodes of inter-WBAN sendspackets or ACK packets, leading to the collision. There arenkI interference sources from inter-WBAN affecting the datatransactions. Supposing that the node finds the channel is idlein probability Pkidle during its backoff period, the Pkidle isdescribed as

Pkidle =

(1 − τk)nkI+nk∏i�=k

(1 − τi)ni

(1 − τk)

= (1 − τk)nkI−17∏

i=0

(1 − τk)nk (13)

The packet collision probability Pb for each UPk node atany backoff stage equals the probability of busy channel andcould be expressed as

Pb = 1 − Pkidle = 1−(1 − τk)nkI−17∏

i=0

(1 − τk)nk (14)

According to Eq. (7)-Eq. (14), the probability Pu thatthe backoff counter is unlock can be obtained in Eq. (15).Eq. 15 (a) is particularly for UP7 nodes to get the unlockedprobability in EAP and RAP. The probability of the otherUPk(k = 0, . . . , 6) data unlock its back counter in RAP is


indicated in Eq. 15 (b).

Pu = (1 − Pb)(1 − PUPk−inlock)

=

⎧⎪⎨⎪⎩

P7idle(Tdata + TCW

TE + TR), (a)

Pkidle(1 − τk)nI−1

(Tdata + TkCW

TR

), (b)

(15)

If the collision occurs, the node resets the CW valueof backoff counter according the regulations aforementioned,selects a random integer from the interval [1, CWUPk

] andturns into the next backoff stage. Thereafter, the changingCWUPk

value Wk,j during the jth backoff period for a nodeof UPk is predicted as following,

Wk,0 = CWmin[UPk], j = 0Wk,j = Wk,j−1, for j is an odd number, 1 ≤ j ≤ BTmax

Wk,j = min{2Wk,j−1, CWmax[UPk]}, for j is an evennumber, 1 < j ≤ BTmax

The first time CW value reaches CWmax[UPk] at its sthbackoff stage and remains unchanged in the following back-off retry procedure. The stationary distribution probabilityof the Markov chain is Mk,j,i, where k is the user pri-ority (k = 0, . . . , 7), j is the value of back off stages(j = 0, 1, . . . , BTmax), and i is the BC values(i = 0, 1 . . . , Wk,j). The state transition probabilities are givenby

P {k, j + 1, Wk,j |k, j, 0} =Pb

Wk,j, ∀j ∈ [0, BTmax − 1]

(16)

P {k, j + 1, 0 |k, j, 0} = Pb (17)

P {k, j, Wk,j − 1 |k, j, Wk,j } = Pu (18)

P {k, j, Wk,j |k, j, Wk,j } = 1 − Pu (19)

Nodes will discard the packet if the packet fails in accessingthe channel. The failure data transmission probability Pf ofUPk node is expressed as

Pf = PBTmax+1b (20)

In steady state, we can get the following relations accordingto the Markov chain regularities. Eq. (25) shows the sum ofall the state probabilities is equal to one.

Mk,j,0 = P jb Mk,0,0, ∀j ∈ [0, BTmax] (21)

Mk,BTmax,0 =PBTmax

b

1 − PbMk,0,0 (22)

M0,0,0 =Pb

1 − PbMk,0,0 (23)

Mk,j,i =Wk,j − i + 1

Wk,jPuMk,j,0, i ∈ [1, Wk,j ] (24)

1 = M0,0,0 +BTmax∑

j=0

Wk,j∑i=0

Mk,j,i (25)

All the probabilities have a strong correlation with accessprobability τ . Once the value of backoff counter is zero,the node initiates an attempt to access the channel. Therefore,the access probability of node UPk in a random CSMA slot τk

is the sum all the steady states probabilities τk=BTmax∑

j=0

Mk,j,0.

C. Normalized Throughput and Average Access Delay

We define the normalized throughput as the ratio of timecost of average successful transmitted payloads to the totalintervals between two consecutive transmissions. A successfulpacket transmission time is Tpk and can be obtained from

Tpk =Npacket

Rs

=Npreamble+Nheader×Sheader+ Ntotal×SPSDU

log2M

Rs(26)

where Rs is the sysmbol rate. Npreamble and Nheader

stand for the length of Physical Layer Convergence Protocol(PLCP) preamble and header, respectively. Sheader denotesthe spreading factor for the PLCP header. SPSDU denotes thespreading factor for Physical Service Data Unit (PSDU). Mis the cardinality of the constellation of a given modulationscheme. Ntotal is the total bits which flow in the PHY layerand can be derived in Eq. 27.

Ntotal = NPSDU + log2M

⌈NPSDU + (n − k)NCW

log2M

⌉

+ (n− k)NCW − [NPSDU + (n − k)NCW ] (27)

where NCW denotes the number of Bose, Ray-Chaudhuri,Hocquenghem code (BCH) codewords in a frame. In IEEE802.15.6, NCW =

⌈NPSDU

k

⌉, where n and k are selected by

BCH encoder to achieve the desired code rate k/n. NPSDU

is the length of the PSDU. This component is formed byconcatenating the MAC header with the MAC payload andframe check sequence (FCS). Given the length of MACheader NMHeader , MAC payload NMPayload and FCS NFCS,the NPSDU can be acquired from Eq. (28).

NPSDU = 8 × (NMHeader + NMPayload + NFCS) (28)

TMPayload =NMPayload × SPSDU

log2M × Rs(29)

The normalized throughput with the interference is calcu-lated as

Sk =τkPkidleTMPayload

PidleTc−slot+τkPkidleTpk+τk (1−Pkidle)Tfpk(30)

where Tc−slot is the contended CSMA/CA slot length. Pkidle

is the probability that the channel is idle during the backoffperiod of node UPk. Pkidle is influenced by the number ofinterfering nodes, the value of which can be obtained fromEq. (12) and Eq. (13). Ts in Eq. (31) is the mean time of asuccessful transmission.

Ts = Tpk + TpSIFS + TI−ACK (31)

While the occupied slot length for a failure packet deliveryTfpk is described as

Tfpk = Tpk + TpSIFS + Ttimeout (32)

An absent I-ACK in an interval Ttimeout will be treated asa failure transmission. The Tfail is the total time cost beforea packet has been successfully transmitted.

Tfail =BTmax∑

l=0

lPbl(Tpk+TpSIFS+Ttimeout) + TkCW (33)


TABLE V

SYSTEM PARAMETERS

where l is the failure times before the packet is successfullytransmitted. The average packet delay is defined as the timeinterval from the packet that is ready to be transmitted tothe moment that the I-ACK frame of this packet is received.In Eq. (34), the average delay Dk is given as

Dk=Ts + Tfail (34)

By substituting Eqs. (31) and (33) into Eq. (34), the aver-age access delay can be calculated. Both the normalizedthroughput and the average access delay consider the trafficdifferentiation and interference dynamics.

VI. PERFORMANCE EVALUATION

In this section, we evaluate the normalized throughput andthe average access delay of coexisting WBANs based onIEEE 802.15.6 CSMA/CA mechanism. Numerical results ofthe performance evaluation based on the proposed interferencemodel are implemented and verified by the simulation toolMATLAB. One-hop star topology structure in a WBAN isconsidered in the monitoring scenario. The channel fadingand path loss of the movement could be neglected in slowmovements. The channel condition is ideal during the datatransmission. Each hub operates in the beacon mode withsuperframes and each node produces one UP traffic in the net-work with all the data fluctuation in normal range. The healthcritical index value H is in corresponding to their UP valuesof each node. The length of an allocation slot Tslot is equal topAllocationSlotMin+L×pAllocationSlotResolution [15].The maximum MAC payload size is 250 bytes in the analysis.The other relevant simulation parameters are listed in Table V.

A. Normalized Throughput

The normalized throughput of the nodes with user prioritiesvarying from 0 to 7 are shown in Fig. 7- Fig. 9. At first,we evaluate the normalized throughput of nodes with no inter-ference. In other words, there is no other coexisting WBANsin the communication range of the target WBAN N1. Therelationship between normalized throughput and the variedpayloads is shown in Fig. 7. There are 8 sensor nodes exceptthe hub in the network. It is obvious that the normalizedthroughput of UPk node increases as the payload increases andthe UP7 node increases faster than any other nodes. The reason

Fig. 7. Normalized throughput versus payloads.

Fig. 8. Normalized throughput versus number of nodes.

Fig. 9. Normalized throughput versus velocity.

is that the nodes with highest user priority traffic can transmitdata in the EAP period and RAP period. Moreover, UP7 nodeenjoys a smallest contention window value and has a higheraccess probability as well as lower collision probability thanany other nodes in the CSMA/CA period. We can see that thenormalized throughput of UP5 and UP4 nodes, UP3 and UP2

nodes, UP1 and UP0 nodes almost have the same tendencywhile the payload increases. This is because some of themshare the same minimum contention window value and havesmall difference in access probability.

Fig. 8 describes the relationship between normalizedthroughout and the number of nodes with a payloadof 100 bytes and there is no interfering nodes. As the numberof nodes in the network increases to the maximum WBANsize, i.e. 64 sensor nodes in a WBAN, the normalized through-put suffers a sharply decrease. Especially when the number


Fig. 10. Normalized throughput versus velocity and payloads.

Fig. 11. Normalized throughput versus and velocity and number of nodes.

of nodes is more than 40 in the network, the normalizedthroughput of UPk nodes (except UP7 nodes) is almost zero.All this is owing to the high collision probability of increasingnumber of nodes. Although in such a condition, the highestpriority UP7 node can still transmit data in the particular EAPperiod, contending to access the channel with few UP7 trafficdata. The QoS of nodes with the higher user priority can beguaranteed. The number of nodes really has a big influenceon the normalized throughput. It is suggested that the numberof nodes in one WBAN is better no more than 24 from Fig. 8.

We investigate how a random mobile WBAN Nm affects theperformance of target WBAN N1 on normalized throughputin Fig. 9. There are 8 nodes with payload of 100 bytes intarget WBAN N1. The communication range of N1 is 1.5 m.The movement of Nm does not affect the performance of N1

when the inter-WBAN distance is large than 3 m. The velocityof Nm regards zero to N1. When the inter-WBAN distanceis equal to or less than 3 m, the Nm becomes interferencesource. We focus on the slow motion of patients or oldpeople in health monitoring. Nm keeps in slow linear motiontowards to N1 during the interference duration. Within thespecific time, the velocity of Nm generates varying interfer-ence dynamics. The normalized throughput of UPk nodes

in N1 decrease with the increasing velocity of Nm. Thenormalized throughput variation of high UP node is larger thanlower UP node. The normalized throughput of UP7 decreasesalmost 32% when Nm moves to N1 at the speed 1.2m/s.The higher user priority nodes have higher access probabilityand suffer higher collision probability with the increasinginterference.

The impacts of velocity combined the effects of payloadsand number of nodes acting together on normalized throughputof UPk nodes are shown in Fig. 10 and Fig. 11, respectively.The comparison of normalized throughput of different userpriority traffic varieties is obvious. The normalized throughputof UPk nodes increases with the payloads increasing, anddecreases with the increasing velocity. UP7 traffic alwayshas highest throughput while UP0 has the lowest throughput.The normalized throughput of several typical user priorityare shown in Fig. 10 (b) and Fig. 11 (b). The normalizedthroughput is affected greater by the number of nodes in thenetwork than the velocity parameter in Fig. 11. The variationof velocity means the varying number of interference nodesin the network, which seriously affects higher UPs. Once thenumber of nodes in the WBAN is larger than 40, the packetscan hardly be transmitted successfully.


Fig. 12. Average access delay versus payloads.

Fig. 13. Average access delay versus number of nodes.

B. Average Access Delay

The variations of average packet access delay against thepayloads for different UPs with no interference are shownin Fig. 12. We choose the typical UPk(k = 3, . . . , 7) nodesto illustrate the results. The results of all the UPk nodes canbe found in Fig. 15 and Fig. 16. The delay increases as thelength of payloads increases. In this case, only 8 nodes areconsidered in a WBAN. Every node produces one kind ofuser priority data. The higher UP of the node is, the loweraccess delay is. In the maximum payload 250 bytes, the delayof UP7 node is merely ninth of the UP0 node, and is only62% of the UP6 node. The reason is that nodes with higherUPs have higher packet transmission probability compared tonodes with lower UPs.

The delay of nodes increases with the increasing number ofnodes in the network, as shown in Fig. 13. All the nodes arein a fixed payload (100 bytes). Nodes have to back off moretimes in a successful data transmission with the increasingnumber of nodes in the network. The access probabilities ofall UPk nodes become smaller with the increasing number ofnodes, leading to the increasing delay. As the number of nodesincreases from 8 to 32, the access delay of UP7 have doubled,and the delay of UP6 increases 70%, while the growth ofUP5 and UP4 is 60% and 38%, respectively. If there are toomany nodes in a WBAN, all nodes suffer a severe contentionprogress and very high average access delay.

The average access delay of the target WBAN N1

increases with the increasing velocity of neighbor interferenceWBAN Nm. The higher velocity of Nm within the specific

Fig. 14. Average access delay versus velocity.

time makes more nodes in Nm become interference nodesto N1. Fig. 14 shows impacts of interference nodes on theaverage access delay of UPk nodes. The UP7 nodes with nointerference cost less than 70% time of Nm in walking motion.The average access delay of UPk nodes increases with theincreasing interference nodes due to the changing velocity.The reason is that the nodes have to suffer a relative longback-off procedure.

The combined action of velocity and different length ofpayloads on average access delay are shown in Fig. 15. Allthe nodes are in normal states that there is no dramatic shifton the monitoring data. The delay increases as the length ofpayloads increases and higher UP nodes have lower delay. TheUP7 always has the lowest delay while UP0 always has thehighest delay. The reason is that nodes with higher UPs havehigher packet transmission probability compared to nodes withlower UPs. From Fig. 15 (b), we can see that the delay is muchinfluenced by the payload when it is larger than 100 bytes. Thevarying velocity of neighbor WBAN makes little effect on theaverage access delay of UPk nodes in the target WBAN inspite of the changing length of payloads.

The average access delay of the IEEE 802.15.6 basedCSMA/CA protocol for different UP nodes by consideringthe interference of coexisting mobile neighbour WBAN andthe different number of nodes in intra-WBAN are shown inFig. 16 (a) and (b). The network performance on delay isseverely deteriorated when the number of nodes increases,since the packet collision happens not only among nodes withdifferent UPs but also among nodes with same UPs. The higherUPs are more sensitive to the collision due to the high accessprobability. Though the UP7 nodes are more volatile than anyother UP nodes when the number of nodes and the velocityof neighbor WBAN changes, the average access delay of UP7

nodes is the lowest in the same condition. The delay of UP3

is almost two to three times as the delay of UP7 nodes indifferent number of nodes at the same velocity. To guaranteethe monitoring data be transmitted in time, the number ofnodes in one WBAN had better not exceed 30.

Human movements lead to the increasing number of inter-fering WBANs and interfering nodes, which increases thecollision probability of nodes accessing the channel. Theimpacts of dynamic mobility with different number of coexist-ing WBANs on the normalized throughput and average access


Fig. 15. Average access delay versus velocity and payloads.

Fig. 16. Average access delay versus velocity and number of nodes.

Fig. 17. Normalized throughput versus number of coexisting WBANs.

delay are reflected in Fig. 17 and Fig. 18. The performance ofUP7 node in target WBAN is evaluated with different WBANpriorities. The BP3, BP2 and BP1 make the target WBANin highest, moderate and low health critical index among thecoexisting WBANs, respectively. The normalized throughputdecreases 70% while the average access delay is doubledcompared with single WBAN. The higher WBAN priorityguarantees the node in higher normalized throughput and

Fig. 18. Average access delay versus number of coexisting WBANs.

lower average access delay with the increasing interference.The evaluation results in Fig. 17 and Fig. 18 facilitate thenetwork configuration and optimization in WBAN coexistencescenario.

VII. CONCLUSION

In this paper, we have analyzed the dynamic interfer-ence generated by varying number of interfering nodes from


neighbour WBANs owing to human mobility in multi-WBANcoexistence scenario. We have developed a three dimensionalMarkov chain model to investigate the interference dynamicson network performance of IEEE 802.15.6-based CSMA/CAprotocol. Moreover, a health critical index considering the datatraffic differentiate, data severity and WBANs priority has beendesigned to guarantee the emergency data taking precedenceover general traffic to access the channel with lower delay andhigh reliability. The normalized throughput and average accessdelay under different interference conditions have been derivedin tractable expressions. The appropriate number of nodes in aWBAN and distance to the nearby WBANs could be acquiredaccording to the analytical results, which is useful to networkconfiguration and interference mitigation. In the future, we willconsider the dynamic channel status in different movementmanners and analyze the impacts on network performance. Theinterference mitigation schemes are also scheduled in WBANscoexistence.

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Xiaoming Yuan (S’17) received the B.E. degreein electronics and information engineering fromHenan Polytechnic University, China, in 2012, andthe Ph.D. degree in communication and informationsystem from Xidian University, China, in 2018.From 2016 to 2017, she was a Visiting Scholar withthe Broadband Communications Research Group,Department of Electrical and Computer Engineer-ing, University of Waterloo, Waterloo, ON, Canada.Her research interests include cloud/edge computing,medium access control and performance analysis for

wireless body area networks, and the Internet of Things.

Changle Li (M’09–SM’16) received the Ph.D.degree in communication and information systemfrom Xidian University, China, in 2005. He con-ducted his postdoctoral research in Canada and theNational Institute of information and Communica-tions Technology, Japan, respectively. He had beena Visiting Scholar with the University of Tech-nology Sydney. He is currently a Professor withthe State Key Laboratory of Integrated ServicesNetworks, Xidian University. His research interestsinclude intelligent transportation systems, vehicular

networks, mobile ad hoc networks, and wireless sensor networks.

Qiang Ye (S’16–M’17) received the B.S. degreein network engineering and the M.S. degree incommunication and information systems from theNanjing University of Posts and Telecommunica-tions, China, in 2009 and 2012, respectively, and thePh.D. degree in electrical and computer engineeringfrom the University of Waterloo, Waterloo, ON,Canada, in 2016. He has been a Post-Doctoral Fel-low with the Department of Electrical and ComputerEngineering, University of Waterloo, since 2016. Hiscurrent research interests include software-defined

networking and network function virtualization, network slicing for 5G net-works, virtual network function chain embedding and end-to-end performanceanalysis, medium access control and performance optimization for mobile adhoc networks, and the Internet of Things.

Kuan Zhang (S’13–M’17) received the B.Sc.degree in communication engineering and theM.Sc. degree in computer applied technology fromNortheastern University, China, in 2009 and 2011,respectively, and the Ph.D. degree in electricaland computer engineering from the University ofWaterloo, Canada, in 2016. He was a Post-Doctoral Fellow with the Broadband Communi-cations Research Group, Department of Electricaland Computer Engineering, University of Waterloo,Canada, from 2016 to 2017. He has been an Assis-

tant Professor with the Department of Electrical and Computer Engineering,University of Nebraska-Lincoln, Lincoln, NE, USA, since 2017. His researchinterests include security and privacy for mobile social networks, e-healthcaresystems, cloud/edge computing, and cyber physical systems.

Nan Cheng (S’12–M’16) received the B.E. andM.S. degrees from the Department of Electron-ics and Information Engineering, Tongji University,Shanghai, China, in 2009 and 2012, respectively, andthe Ph.D. degree from the Department of Electricaland Computer Engineering, University of Waterloo,in 2015. He is currently a Post-Doctoral Fellow withthe Department of Electrical and Computer Engi-neering, University of Toronto, and the Departmentof Electrical and Computer Engineering, Universityof Waterloo, under the supervision of Prof. B. Liang

and Prof. S. Shen. His current research focuses on big data in vehicularnetworks and self-driving system. His research interests also include perfor-mance analysis, MAC, opportunistic communication, and application of AIfor vehicular networks.

Ning Zhang (M’15) received the Ph.D. degree fromthe University of Waterloo, Canada, in 2015. He wasa Post-Doctoral Research Fellow with the Universityof Waterloo and the University of Toronto, Canada.He is currently an Assistant Professor with TexasA&M University–Corpus Christi, TX, USA. Hiscurrent research interests include next generationmobile networks, physical layer security, machinelearning, and mobile edge computing. He was arecipient of the Best Paper Awards at IEEE GLOBE-COM 2014 and IEEE WCSP 2015. He served as

the Workshop Chair for the first IEEE Workshop on Cooperative Edge andthe IEEE Workshop on Mobile Edge Networks and Systems for ImmersiveComputing and IoT. He serves/served as an Associate Editor of the IEEEACCESS, IET Communication, and the Journal of Advanced Transportation,an Area Editor of the Encyclopedia of Wireless Networks (Springer) andCambridge Scholars, and a Guest Editor of Wireless Communication andMobile Computing, the International Journal of Distributed Sensor Networks,and Mobile Information System.


Xuemin (Sherman) Shen (M’97–SM’02–F’09)received the B.Sc. degree in electrical engineeringfrom Dalian Maritime University, China, in 1982,and the M.Sc. and Ph.D. degrees in electricalengineering from Rutgers University, NJ, USA,in 1987 and 1990, respectively. He is currentlya University Professor and the Associate Chairfor Graduate Studies, Department of Electricaland Computer Engineering, University of Waterloo,Canada. His research focuses on wireless resourcemanagement, wireless network security, social net-

works, smart grid, and vehicular ad hoc and sensor networks. He is a registeredprofessional engineer of Ontario, Canada, an Engineering Institute of CanadaFellow, a Canadian Academy of Engineering Fellow, and a Royal Societyof Canada Fellow. He received the IEEE ComSoc Education Award, theJoseph LoCicero Award for Exemplary Service to Publications, the ExcellentGraduate Supervision Award in 2006, and the Premiers Research Excellence

Award in 2003 from the Province of Ontario, Canada. He was the electedIEEE ComSoc Vice President of Publications and was a member of the IEEEComSoc Board of Governors. He was the Chair of the Distinguished LecturersSelection Committee. He is a Distinguished Lecturer of IEEE VehicularTechnology Society and Communications Society. He served as the TechnicalProgram Committee Chair/Co-Chair for the IEEE Globecom16, Infocom14,the IEEE VTC10 Fall, and Globecom07, the Symposia Chair for the IEEEICC10, the Tutorial Chair for the IEEE VTC11 Spring and the IEEE ICC08,the General Co-Chair for the ACM Mobihoc15, Chinacom07, and QShine06,and the Chair for the IEEE Communications Society Technical Committeeon Wireless Communications and P2P Communications and Networking.He also serves/served as the Editor-in-Chief for the IEEE INTERNET OF

THINGS JOURNAL, the IEEE Network Magazine, Peer-to-Peer Networkingand Application, and IET Communications; a Founding Area Editor for theIEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS; and an AssociateEditor for the IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY and theIEEE WIRELESS COMMUNICATIONS.

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