Improving Spectral and Temporal Efficiency of ... - Semantic Scholar

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Improving Spectral and Temporal Efficiency of Collocated IEEE 802.15.4 LR-WPANs † Tae

Hyun Kim, Student Member, IEEE, ‡ Jae Yeol Ha, Non-member , and § Sunghyun Choi, Senior Member, IEEE

✦

Abstract—The number of frequency channels specified for IEEE 802.15.4 low-rate wireless personal area networks (LR-WPANs) does not suffice to operate a variety of collocated WPAN applications that the standard is targeting. To overcome this limit, we introduce Virtual Channel, a novel concept to increase the number of available channels by efficiently managing given spectral and temporal resources. A virtual channel is created by scheduling a superframe and selecting a logical channel. This extends the notion of a channel from spectral domain to spectral and temporal domain. Specifically, we propose a superframe scheduler using throughput estimation (SUTE) of the IEEE 802.15.4 carrier sense multiple access with collision avoidance (CSMA/CA). In addition, nearest vacancy search (NEVS) is proposed, both of which are for temporal efficiency of the collocation. For both spectral and temporal efficiency, virtual channel selector (VCS) is proposed. The simulation results show that a remarkable improvement on the collocation efficiency of IEEE 802.15.4 can be achieved by our proposals. Moreover, this study also reveals the fundamental drawback of the current standard in terms of the collocation efficiency that the beacon interval and superframe duration are adjustable only by exponent parameters. Index Terms—Algorithm/protocol design and analysis, wireless sensor networks, standards, mobile communication systems, scheduling.

1

I NTRODUCTION

IEEE 802.15.4 for low-rate wireless personal area networks (LR-WPANs) [1] has been standardized for lowrate, low-cost, low-power, and short-range wireless networking. It is expected that the standard would be widely adopted for a wide range of applications including industrial automation, home control, cable replacement, wireless sensor networks, etc. ZigBee Alliance [2], the industrial association to standardize the protocol stacks and application profiles for IEEE 802.15.4-based WPANs, anticipates that there will be a variety of applications over IEEE 802.15.4 [3]. Consequently, one can easily imagine that many applications of this standard, as enumerated in Table 1, would simultaneously operate † T. H. Kim is with the Coordinated Science Laboratory in the University of Illinois at Urbana-Champaign, IL 61801. ‡ J. Y. Ha is with MZEN Co. Ltd., Seoul, Korea.§ S. Choi is with the Multimedia Wireless Networking Laboratory in the School of Electrical Engineering and INMC, Seoul National University, Seoul, 151-744 Korea. (E-mail: † [email protected], ‡ [email protected] and § [email protected]) This research was supported in part by US Army Research Office grant W911NF-05-1-0246, the MKE (Ministry of Knowledge Economy), Korea, under the ITRC support program supervised by the IITA (IITA-2008-C10900801-0013) and Seoul R&BD Program (10544).

in the same area in the near future. This is an important feature of 802.15.4 that has not been thoroughly addressed yet in the literature. Nevertheless, the available logical channels1 are limited for a practical deployment of IEEE 802.15.4 networks. An 802.15.4 WPAN operates in a single logical channel, and the current IEEE 802.15.4 specifies 27 logical channels across 868 MHz, 915 MHz, and 2.4 GHz frequency bands [1]. If we focus on the globally-available 2.4 GHz ISM band, due to the commercial success of other wireless technologies such as IEEE 802.11 wireless local area networks (WLAN) and IEEE 802.15.1 Bluetooth, only 4 logical channels out of 16 are left to IEEE 802.15.4 [4], [5]. We expand the notion of a spectral channel in IEEE 802.15.4 to the extent of spectral and temporal domain so that a large number of 802.15.4 applications can collocate despite the lack of the available logical channels. One can observe that carrier sense medium access with collision avoidance (CSMA/CA) algorithm in IEEE 802.15.4 is robust to the temporal collocation. Using CSMA/CA, the collocated WPANs are able to operate simultaneously without additional coordination. As the number of devices in the same logical channel increases, however, the WPANs without any coordination suffer from performance degradation. This is even worsened by the periodic beacon transmissions of the 802.15.4 CSMA/CA. Thus the challenge is how we let WPANs collaborate to share the spectral and temporal resources while keeping the performance loss to the acceptable level. In this paper, we introduce a notion of virtual channel into IEEE 802.15.4 to improve spectral and temporal efficiency for collocated WPANs. A virtual channel is a channel specified not only by frequency band, but also by time duration and offset. To create a virtual channel, a superframe of each WPAN should be thus appropriately scheduled while considering other superframes and beacon periods of the collocated WPANs. This can bring dramatic improvement of the WPAN collocation since WPANs are mostly inactive to save battery energy. 1. The term logical channel represents disjoint physical frequency channels in IEEE 802.15.4.

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We decompose the process to create virtual channels into two iterative steps. First, given a logical channel, a time schedule described by duration and offset is determined. Second, the quality of such schedules in different logical channels is compared to find the most efficient virtual channel. If necessary, other logical channels are input to the first step. Specifically, we propose superframe scheduler using throughput estimation (SUTE) and nearest vacancy search (NEVS) to find such time durations in a given logical channel and virtual channel selector (VCS) to choose one of the available logical channels to fit the superframe into. Since SUTE requires estimation of given resources, we develop saturation throughput estimation algorithm for the superframe-structured CSMA/CA of IEEE 802.15.4 and its performance loss when more than one superframe from different WPANs are overlapped with each others. Our proposals support the backward compatibility and require the least management overhead to a network while achieving collocation efficiency enhancement, which is evaluated by both large-scale and scenario-specific simulations. Contribution: We have made the following contributions through this work: 1) To our best knowledge, we are the first to raise the collocation problem of IEEE 802.15.4 in addition to the coexistence of IEEE 802.15.4 and 802.11. 2) We introduce the virtual channel as a solution to increase the number of available channels spectrally and temporally. 3) We define the superframe scheduling problem for temporal collocation efficiency and design schedulers. 4) For the superframe scheduler design, we develop a saturation throughput estimation algorithm for the CSMA/CA with superframe structure which is possibly overlapped with other superframes. 5) We significantly enhance the spectral resource management in IEEE 802.15.4 by proposing a new channel selector, VCS. 6) Through this study, the drawback of the standard that hampers the WPAN collocation is identified, which corresponds to the restriction on an exponential beacon interval and superframe duration. Organization: In Section 2 IEEE 802.15.4 is briefly summarized, and the overview of virtual channel management is given. Before introducing superframe schedulers, an estimation algorithm for collocated WPANs’ saturation throughput is designed in Section 3. In Section 4, the superframe schedulers for a given logical channel are designed by using the estimation algorithm and then VCS is developed for multiple channel management in Section 5. The proposed algorithms within the virtual channel management are evaluated via simulations in Section 6. Section 7 summarizes the previous work related to the collocation and channel management issues. Then, we finally conclude the paper with some future research issues in Section 8.

TABLE 1 IEEE 802.15.4 Target Applications [2] Category

Application

Delay

BO

Vital Monitoring

Heart-rate monitor Body heat monitor Personal equipment control Remote controls PC-peripherals Control of blinds/shades/ rollers/windows Dimmer/switches Electricity/gas/water AMR

1-5 sec 1 min 50 ms 100 ms 50 ms 1 sec

6-8 12 2 3 2 6

200 ms No bound 1 sec 1 sec 1 sec 1-5 sec 1-5 min

4 14 6 6 6 6-8 12-14

100 ms 1 sec

3 6

Consumer Electronics

Automatic Meter Reader Alarm/Security System

Environmental Monitoring Industrial Automation

2

Smoke detector Burglary and social alarms Access control Water leakage alarms Temperature/carbondioxide/ humidity/vibration, HVAC Facility control Monitoring critical equipment

OVERVIEW

This section summarizes the IEEE 802.15.4 standard in terms of spectral and temporal resource management, which involves frequency channel selection and medium access, and gives the overview of the virtual channel management. 2.1 IEEE 802.15.4 IEEE 802.15.4 has two different operating modes with respect to the use of periodic beacons, i.e., nonbeaconenabled and beacon-enabled modes, respectively. The nonbeacon-enabled mode consumes more energy than the other due to the absence of the periodic sleep coordination. Meanwhile, beacon-enabled networks are coordinated by the periodic beacons that tell child devices about the sleep schedule. We assume that WPAN applications operate in the beacon-enabled mode to conserve limited battery energy. If such networks are single hop configured, they are composed of PAN coordinators and child devices. A PAN coordinator provides a periodic broadcast message to child devices, and child devices communicate with their coordinator only. For the beacon-enabled mode, medium access strictly follows the superframe structure depicted in Fig. 1, and its parameters are specified by a periodic beacon packet from the coordinator. All medium access in this structure should be performed in a time slot, which is referred to as backoff period (BP) in IEEE 802.15.4. A superframe is composed of a beacon and contention access period (CAP), which is followed by an inactive period. The length of the superframe is determined by a superframe order (SO) while the beacon interval (BI) is by a beacon order (BO). Note that the periodic beacons are immediately transmitted without backoff and carrier sense unlike other packets that follow the CSMA/CA algorithm. Let us define a traffic load as the amount of bits requested to be delivered at the MAC layer from an

3

s cap

aBaseSuperframeDuration×2SO ×2SO

BI = aBaseSuperframeDuration×2BO

Fig. 1. Superframe structure.

upper layer. With the 802.15.4 MAC, every WPAN has fixed BO and SO that secure the resources to afford a traffic load of which amount is typically predictable. This is particularly true for the applications with periodic sensing such as automatic meter reader (AMR), monitoring equipment, heart-rate, body heat, etc.2 One observation is that the secured resources for a WPAN is usually more than what it actually needs so as to make the network stable. This is in part because the time resources that the WPAN acquires is allocated by the two exponents, SO and BO, which are only capable of specifying exponentially large resources. Thus the margin between the secured resources and traffic load can be exploited to enhance the temporal efficiency of networks while still maintaining the network stability. IEEE 802.15.4 supports spectral efficiency by dividing frequency band into multiple sub-bands, which are called logical channels. Each WPAN can occupy one of these channels to form a network without any interference from other WPANs. However, the occupied logical channel by one WPAN application does not tend to be heavily utilized. For example, AMR might require very low duty cycle of 0.1% so that 99.9% of the time resources is wasted. To completely use up these under-utilized resources, the exclusive occupancy of a channel in the current standard should be avoided. This motivates our virtual channel management.

2.2 Virtual Channel Management We define a virtual channel as a channel created by using temporal resources as well as the spectral ones. To create such a virtual channel we exploit the fact that most of the logical channels are under-utilized by low duty cycle applications, and even the secured resources for a WPAN in a form of superframe are typically more than what the WPAN actually needs. What PAN coordinators should perform for the virtual channel management is thus to select “a proper time offset” for its first beacon transmission and “a proper logical channel.” Once the periodic beacon is transmitted in a certain logical channel, all child devices that attempt to associate with the WPAN synchronize with the superframes of the PAN coordinator, which is specified in 2. For other applications, estimating traffic load is not in the scope of this paper.

the beacon.3 We assume that adjusting the time to call MLME-START.request enables us to control the time for starting the first beacon transmission.4 Then the natural question is “could it be the best to select time offset for a superframe not allowing it overlapped with others?” Our answer is no. It is not sufficient to maximize the temporal efficiency because the restriction on the overlap does not admit the advantage of the CSMA/CA algorithm; if a slight performance loss is tolerable for a WPAN, then it improves overall efficiency by allowing partial overlaps of its superframe with others. This is particularly true for the 802.15.4 superframes as their durations are determined by the exponent parameters, SO and BO, having a difficulty to fit multiple superframes into a channel without any overlaps. Next, what is the proper logical channel in terms of the WPAN collocation? Suppose the extreme case where half of WPANs have BO1 and SO1 and the others have BO2 and SO2 . If two logical channels are available and BO1 = BO2 and SO1 = SO2 , it is intuitively the best to allocate the WPANs with the same parameters into the same logical channel. Motivated by this simple observation, we design a sophisticated channel selector to maximize the collocation capability by appropriately selecting a logical channel. The beauty of the proposed virtual channel management is that it not only increases the number of available channels significantly by efficiently managing the spectral and temporal resources, but also is easily implementable as a firmware add-on at the PAN coordinators in a completely distributed manner.

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S ATURATION T HROUGHPUT E STIMATION

To allow partial overlaps for collocation efficiency, the performance loss by those overlaps should be estimated. For this purpose, we analyze the saturation throughput of collocated WPANs and propose a universal throughput estimation algorithm at the end of this section. 3.1 Superframe Structure Analysis We derive the saturation throughput of a WPAN with given BO and SO, which has a superframe structure. The derivation starts with Smod , i.e., the modified saturation throughput reflecting acknowledgement (ACK) time out duration in [7]. All variables related to a time duration is in a BP unit. Notation definitions are collected in Table 2. The previous model in [7] does not capture the losses by the superframe structure. The losses come in part from the fact that no transmission can be initiated in the 3. It is assumed that a WPAN has a star-topology. If a WPAN forms a clustered-tree structure for multi-hop communications, then each coordinator needs to schedule its superframe as the PAN coordinator does in our work. 4. With IEEE 802.15.4-2006 devices, the beacon transmission time is scheduled using the parameter StartT ime when calling MLME-START.request [6].

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TABLE 2 Notation Definitions δ(t) ψ u(t) BOj BOmax D Db Lbcn Lc Lcap Ld Lu Nj Ps Ptr Sbo,so Sj Smod Straf,j SOj Tc Ts

delta function relative time offset unit step function beacon order of the j-th WPAN maxj BOj base superframe length [1] (=960 symbols) base superframe length (=48 BPs) beacon transmission length length of collision-related transmission before a beacon from other WPAN actual contention period for data exchanges length of collision-related transmission after a beacon from other WPAN the length of used duration within the last Ts of the superframe number of contending devices in the j-th WPAN probability of successful transmission, given that a device transmits a packet [8] probability for a device to transmit a packet [8] saturation throughput when BO = bo and SO = so saturation throughput of the j-th WPAN saturation throughput modified from S in [7] traffic load of the j-th WPAN superframe order of the j-th WPAN time duration of a collided transmission [8] time duration of a successful transmission [8] Lu=5 DATA (collision) DATA

one BP

CCA

ACK

Lu=3 DATA (collision) DATA

ACK

End of CAP

Duration of interests for Lu

Fig. 2. Wasted BPs in a superframe by deferring transmission to protect beacon transmissions. last (Ts −1) slots at the end of each superframe (Fig. 1), so as to protect the periodic beacons, which are transmitted without using the CSMA/CA. If it were allowed, the transmission that starts at any slot in the duration would have always collided with the beacon transmitted at the beginning of the consecutive superframe. Thus the devices that have finished the backoff procedure within that duration should defer their transmissions to the next superframe. In addition, a part of the throughput derived is not achievable due to the inactive period, if any, between two consecutive superframes. By considering these, the saturation throughput with the superframe structure is computed by Sbo,so =

ReceivedBitsDuringLcap Lcap , · Lcap BeaconInterval

where Lcap is a part of contention access period that is indeed used for the CSMA/CA operation. The long-term average of the first term is equivalent to the throughput Smod in [7], and Lcap is given by Lcap = Db · 2SO − Lbcn − Ts + Lu ,

where it is assumed that the length of a data packet is fixed, thereby Ts being a constant. The random variable Lu is the length of the utilized duration out of the last Ts slots in the superframe. This is determined by the transmission trials that may be initiated before the Ts BPs at the end of the superframe. In [7], [8] we model the CSMA/CA as a p-persistent CSMA with p := τ ; every node in a network has the same probability τ to transmit a packet. To analyze throughput of a WPAN, two probabilities Ptr and Ps are derived from τ , which are mainly used throughout the analysis in this paper. Ptr is the probability that at least one device transmits, and is given by Ptr = 1 − (1 − τ )N where N is the number of devices in a WPAN. Ps is the success probability of a packet transmission given that a transmission begins, which is derived as Ps = P1tr N τ (1 − τ )N −1 . The value for τ can be found by a numerical method, which gives Ptr and Ps as well. Using both, Ps Ptr is a probability that there is a successful transmission, and a transmission failure occurs with probability (1 − Ps )Ptr . To derive the distribution of Lu using Ptr and Ps , one additional assumption is made that there is no more than one transmission trial during the last (2Ts − 1) BPs.5 By this assumption, the transmission trials in the last (2Ts − 1) BPs in the superframe can be modeled as a truncated geometric distribution with a success probability Ptr . With Ps , the distribution of Lu can be found as follows. Since there is no more than one transmission, there are three possibilities, i.e., transmission success, failure and no transmission. If there is no transmission, Lu = 1 as one BP is still used as an idle slot (in our analysis the use of a certain BP is determined by whether it contributes to the throughput in the previous analysis or not). Thus we have P[Lu = 1, NO TX] = (1 − Ptr )Ts ,

(1)

where “NO TX” refers to the event of no transmission. Similarly we denote the other events with “TX SUCC” and “TX FAIL.” Ts BPs of no transmission trial happen in the duration of our interests depicted in Fig. 2. If there is a successful transmission, Lu is determined, depending on the number of idle BPs before that transmission as shown in Fig. 2. This gives P[Lu = k, TX SUCC] = Ps Ptr (1 − Ptr )k−1 ,

(2)

where (k − 1) idle BPs are preceded by the successful transmission and k = 2, · · · , Ts . In case of the transmission failure, the last Ts BPs cannot be used up since the transmission terminates in Tc BPs, which is less than Ts . This gives restricted range of k, which varies from 1 to Tc . By counting the number 5. Without this assumption, one another transmission could start before the end of the duration if a transmission starts within the first (Ts − Tc ) BPs of the last (2Ts − 1) BPs in a superframe and finally fails.

5

P0={ 1 }

P1={ 1, 2 }

P2={ 2 }

Lc(N1)=1 DATA

Ld(N1)

Lbcn B C N

ȥ2 B C N

Lbcn

Lu(N2)

Lu(N1)

ACK

Lc(N1)=Ts-1 DATA

WPAN #2 with N2

ACK

BCN

WPAN #1 with N1

d1

DATA

Duration of interests for Lc

d2

d3

Lc(N1) Beacon collision with a data packet transmitted by WPAN #1

Fig. 3. Overlap of two superframes. of idle BPs, this event gives P[Lu = k, TX FAIL]  Ts −Tc (1 − Ps )Ptr (1 − Ptr )i−1 , i=1 = (1 − Ps )Ptr (1 − Ptr )Ts −Tc +k−1 ,

(3) k=1 k = 2, · · · , Tc ,

where the probability when k = 1 is derived based on the assumption that there is no more than one transmission within the duration of our interests; the failed transmissions that start at the first (Ts − Tc ) BPs from the last (2Ts − 1) BPs of the superframe are regarded as what utilizes only one BP, which is the (Ts − 1)-th BP from the end of the superframe. Eqs. (1), (2) and (3) together give the complete distribution of Lu , thus allowing us to compute the mean throughput of the IEEE 802.15.4 CSMA/CA with the superframe structure: Sbo,so = Smod ·

Db · 2so − Lbcn − Ts + E[Lu ] , Db · 2bo

(4)

which can be interpreted as the saturation throughput that a network can achieve using given time resources. Note that the distribution of Lu is determined by two probabilities, Ptr and Ps , which are characterized by the number of devices in a network. For the rest of the paper, we use a notation E[Lu (N )], which is the mean Lu when N devices are competing for network resources. By the same convention, Smod (N ) means the modified throughput in [7] when N competing nodes are in a network. 3.2 Analysis of Two Superframe Overlap One may notice from the previous subsection that the saturation throughput can be obtained by computing the time duration used for the CSMA/CA operation. Similarly the saturation throughput of overlapped superframes is estimated in this subsection. Let us consider one simple case where two superframes with the same beacon interval are overlapped with each other as shown in Fig. 3. If two superframes of WPANs #1 and #2 are overlapped, the beacon transmitted by WPAN #2 during the CAP of WPAN #1 interferes with the data packet transmissions of WPAN #1. As a result, the time resources around the beacon cannot be consumed to contribute to

Fig. 4. Wasted BPs by the collision between a beacon and data packet.

the throughput of the both networks. This time duration corresponds to the collided packet duration in Fig. 3, which is composed of Lc (N1 ), Lbcn , and Ld (N1 ). The random variable Lc is the duration from the beginning of a transmission involved in a collision with a beacon to the beginning of a beacon. Focused on the (Ts − 1) BPs before the beacon, the same approach taken to find the distribution of Lu can be applied for Lc as well (see Fig. 4). In case of Lc , two events, “NO TX” and “TX FAIL” do not contribute to Lc since they are not related to the throughput decrease by the collisions between a beacon and data packet. If the slots are all idle (NO TX), the modeling for Smod still holds as there is no additional collision.6 If a transmission fails (TX FAIL), which means that the transmitted packet collides with another packet, it does not matter whether or not it additionally collides with a beacon. All the cases above yield Lc = 0, which is obtained by P[Lc = 0] = (1 − Ptr )Ts −1 +

T s −1

(1 − Ps )Ptr (1 − Ptr )Ts −i−1 .

(5)

i=1

When there is a successful transmission, P[Lc = k] = Ps Ptr (1 − Ptr )Ts −k−1 ,

(6)

where k = 1, · · · , Ts − 1. Eqs. (5) and (6) together characterize the distribution of Lc . Now consider the wasted BPs after the collided beacon transmission. The transmissions related to Lc may go beyond the beacon duration as shown in Fig. 4. The length of the wasted BPs thus depends on Lc . Denoting the number of the wasted BPs as Ld , the relationship is easily obtained as ⎧ ⎪ if Lc = 0, ⎨0 Ld = Ts − Lbcn − Lc if Lc = 1, · · · , Ts − Lbcn , ⎪ ⎩ 0 if Lc = Ts − Lbcn + 1, · · · , Ts − 1. Using the means of Lu , Lc and Ld , the saturation throughput of the overlapped superframes depicted in 6. Note that IEEE 802.15.4 CSMA/CA assesses a channel state sporadically, which is different from 802.11 CSMA/CA; if a device has no packet to send, it does not assess a channel. By this fact, the model for Smod is still valid for idle BPs even with a beacon transmission from other WPAN.

6

Wasted duration

Duration for CSMA-CA

3.3 Universal Throughput Estimation Algorithm

Ts Lu(N1) WPAN #1

BCN

WPAN #3

WPAN #2 Lc(N2)

Ld(N2)

Fig. 5. Special case that the analysis on two overlapped superframes does not cover.

Fig. 3 is approximately given by Snet ≈

1 D2BO



d1 Smod (N1 )+d2 Smod (N1 +N2 )+d3 Smod (N2 ) ,

where

We design a universal algorithm that estimates both a net throughput and those of individual WPANs’ in a logical channel, regardless of the patterns of superframe overlaps. It is described in Algorithm 1. This requires set Pi , which comprises the indices of WPANs of which superframes occupy i-th time portion. For example, Pi for the overlap of the simple two superframes are enumerated at the top of Fig. 3. Each time portion is bounded by the moments when a superframe starts or ends. Thus the number of competing devices varies at the boundaries of the identified time portions. We denote the preceding and following time portions of Pi by Pi−1 and Pi+1 , respectively. In addition, saturation throughput of the j-th WPAN is denoted by Sj . Note that patterns of superframes repeat in a periodic manner.

d1 = ψ2 − Lbcn − E[Lc (N1 )],

Algorithm 1 Universal saturation throughput estimation

d2 ≈ D2 − ψ2 − Lbcn − E[Ld (N1 )] − E[Lu (N1 )], and  SO2  d3 ≈ D2 + ψ2 − E[Lu (N2 )] − D2SO1 − E[Lu (N1 )] ,

1: Sj ← 0, ∀j ∈ ∪i Pi 2: for each Pi = ∅ do 3: Lac ← time duration for Pi 4: /* Head: between Pi−1 and Pi */ 5: if ∃ ending superframes then  6: Lac ← Lac + (Ts − E[Lu ( j∈P \P Nj )]) i i−1 7: end if 8: if ∃ beginning superframes then  9: Lac ← Lac − (Lbcn + E[Ld ( j∈P ∩P Nj )]) i−1 i 10: end if 11: /* Tail: between Pi and Pi+1 */ 12: if ∃ ending superframes then  13: Lac ← Lac − (Ts − E[Lu ( j∈P \P Nj )]) i i+1 14: end if 15: if ∃ beginning superframes then  16: Lac ← Lac − E[Lc ( j∈P ∩P Nj )] i i+1 17: end if 18: N ← j∈P Nj

SO1

where d2 and d3 are the approximates since Lu is previously derived for a single WPAN. Specifically, the case is ignored that there may be more than one transmission by the WPAN #2 even in the last Ts BPs of the WPAN #1. This approximation simplifies the analysis, and enables the development of the universal algorithm in the next subsection. One special case, however, needs to be analyzed before generalizing the analysis to arbitrary overlap patterns. It is the case where one superframe finishes and another superframe starts at the same time while one another superframe is ongoing, which is depicted in Fig. 5. It requires a joint analysis on Lu and Lc , which is exhaustive and hardly gives an insight to design an efficient algorithm to estimate the throughput of arbitrary patterns of superframe overlaps. Our approach is that the accuracy of the estimation is compromised with the computational efficiency. In the case like Fig. 5, the time durations for Lu and Lc are both subtracted from the overlapped superframe. As will be seen in the next subsection, this leads to a very efficient and generalized algorithm that computes the saturation throughput of collocated WPANs. In Section 6.4, the accuracy of this algorithm will be evaluated by comparing its output with ns-2 simulation results. In addition, the beacon collision probability with a successful data packet can be found under the same assumptions for the derivation of Lc as follows: pbc = 1 − P[Lc = 0],

(7)

which will be used to quantify the degree of beacon collisions. This definition does not include the beacon collision with other colliding data packets, which brings another complexity for run-time calculations, but it is sufficient for the purpose of superframe scheduling in Section 4.1.3.

i

N

Lac 19: Sj ← Sj + Nj D·2BO Smod (N ), ∀j ∈ Pi max 20: end for  21: Snet ← S j j 22: return Snet , and Sj

The key of this algorithm is to find the time durations that are used by the CSMA/CA analytic model in [8]. Given time duration Pi , Algorithm 1 first considers the head of the time duration, which corresponds to Lines 4∼10. If some of superframes end at the head of this duration, the remaining superframe(s) should gain additional time resources, (Ts − Lu ) BPs, which are not accessible by the finishing superframe(s). If additional superframes begin, the ongoing superframe(s) should lose some time resources from Pi , which corresponds to (Lbcn + Ld ), by the beacon and the possible collision of a data packet with the beacon. Note that two conditions above can hold simultaneously and then both adjustments are applied. In Lines 11∼17, the tail of the duration is considered. If some superframes end at this boundary, (Ts − Lu ) BPs are not used as analyzed in the previous subsection, thus being subtracted from Pi . If some superframes begin at the boundary, the beacon transmission of the following

7

superframe prevents from using Lc BPs out of Pi before the beacon. If there are some superframes that begin and some ongoing superframes that end at the same time, which is the case depicted in Fig. 5, the approximation is applied. By this approximation, Algorithm 1 can avoid the complexity rooted in the dependence between Lu and Lc and keep a very efficient form.

4

S UPERFRAME S CHEDULERS

In this section, we propose superframe scheduler using throughput estimation (SUTE) and much simpler scheduler, nearest vacancy search (NEVS). These superframe schedulers find “the proper time offset,” given one logical channel. By doing so, it helps VCS select the most temporally and spectrally efficient logical channel when there are multiple available logical channels. 4.1 Scheduler Using Throughput Estimation (SUTE) Two main reasons of the performance loss originated by the superframe overlaps are the beacon collisions and the increase of contending devices. With the throughput estimation algorithm developed in the previous section, we devise SUTE to minimize beacon collisions while making efforts to meet the needs of all WPANs. We define functions to describe the patterns of the superframes using the relative time offsets ψj ≥ 0, which are measured by referring to the beacon transmission time of the WPAN with the largest BO. The channel occupancy function is defined as ∞  u(t − ψj − m · D · 2BOj ) (8) A(ψj , t) := m=−∞

  − u t − ψj − D · (2SOj + m · 2BOj ) ,

where 0 ≤ ψj < D · 2BOj , n is the index for the incoming WPAN, and j = 1, 2, · · · , n. While A(ψ, t) describes a periodic superframe of one WPAN in a certain logical channel, all the WPANs in the same logical channel except for the incoming one are simultaneously portrayed by the aggregate channel n−1 occupancy function Q(t) := j=1 A(ψj , t). The basic idea for scheduling is to attach the superframe of the incoming WPAN to any of the existing superframes. Due to the periodicity of a superframe, this does not guarantee the avoidance of any overlap, but does help have high efficiency by reducing the gaps between superframes. By excluding time offsets that lead to fatal beacon collisions or critical performance degradation, the incoming WPAN can successfully find its schedule. 4.1.1 Beacon Collision Avoidance To determine the time offsets at which the incoming superframe would be attached, Q(t) is differentiated by t, finding the discontinuous points.   αi · δ(t − ti ) + βi · δ(t − ti ), Q (t) = i∈Ipos

i∈Ineg

 infinity where Ipos = i | ti is the time of positive  occurrence and limd→0 Q (ti − d) = 0 and Ineg for negative infinity, respectively. The terms αj and βj are the coefficients associated with the derivative Q (t). Thus the superframe may start at ti , i ∈ Ineg while attached to the end of existing superframes or start at (ti −2SOn ), i ∈ Ipos , being attached to the front of other superframes. Formally two sets for these are defined as

Ψneg := ti rmod 2BOn | i ∈ Ineg and

Ψpos := (ti − D · 2SOn ) rmod 2BOn | i ∈ Ipos , where ‘rmod’ is the modulo operator for a real number. The time offsets in Ψneg and Ψpos can be thought of as those for the incoming WPAN to avoid its beacon collision. Reversely, the time offsets that cause the collisions of the existing WPANs’ beacons, which are given by set   Ψbc−f ix := ψj |j = 1, 2, · · · , n − 1 , should be avoided as well. Even after excluding Ψbc−f ix from (Ψneg ∪ Ψpos ) it is still possible for some WPANs to experience periodic beacon collisions. Should the time offsets that may cause any single beacon collision be all removed from the consideration? Doing so should be too conservative to gain temporal efficiency. IEEE 802.15.4 specifies that child devices are orphaned after aM axLostBeacons 7 times of the consecutive beacon losses. In our virtual channel management, the key to improve the temporal efficiency is to allow multiple superframes slightly overlapped if they can still meet their own needs. Instead of removing all time offsets that incur a beacon collision, only those leading to the successive beacon collisions are eliminated from the candidate set. Let us define G(ψ, t) := Q(t) + A(ψ, t). Those time offsets are found by  Ψbc−con = ψ | G ψ, k · D · 2BOm + ψm > 1, m = 1, · · · , n, and k = i, i + 1, · · · , i + Nlimit − 1, ∀i



where Nlimit is the number of consecutive beacon collisions allowed by the scheduler and should be strictly less than aM axLostBeacons. If Nlimit = 1, SUTE does not allow any beacon collision. The tradeoff between the network stability by successful beacon receptions and the collocation efficiency by superframe overlaps is well captured by this parameter. Note that N limit is not a global parameter, so each WPAN coordinator may have a different Nlimit specifically for its running applications. Finally, the time offsets those lead to the critical beacon collision are subtracted from the candidate set



Ψ = Ψneg ∪ Ψpos \ Ψbc−f ix ∪ Ψbc−con , 7. The constant aM axLostBeacons is defined as one of MAC sublayer constants and set to 4 in the IEEE 802.15.4-2003.

8

which is the set of time offsets that meet the minimum requirement for network stability. 4.1.2 Performance Loss by Overlap To address the issue of the performance loss due to the overlapped superframes, the saturation throughput is considered. Assume that each existing WPAN delivers to incoming WPAN the information on the traffic load, which is denoted by Straf,j , in broadcast management packet like a beacon. The incoming WPAN can estimate the degraded throughput of each operating WPAN by using Algorithm 1 whenever it checks if a time offset in set Ψ provides the sufficient saturation throughput that meets Straf ,j . 4.1.3 SUTE as Beacon Collision Minimization SUTE is finally formulated as a minimization problem seeking for the time offsets that minimize the expected number of beacon collisions and satisfies the resource needs. rj n     Ψ∗ = ψ | arg min pbc Kj (kD2BOj + ψj ) ψ∈Ψ

s. t.

j=1 k=0

Kj (t) =

n 

Ni A(ψi , t),

i=j,i=1

Straf,j ≤ Sj (ψn ), rj = 2BOmax −BOj − 1,

ψn = ψ, and j = 1, · · · , n ,

where Sj is a function of ψn since Pi is subject to ψn . Kj (t) is the number of competing devices against the jth WPAN’s beacon transmission at time t. Thus the summation of pbc is the expected number of beacon collisions with successful data transmissions during D · 2BOmax . Given Straf,j ≤ Sj , the minimization of the expectation is to select ψ that makes the networks most stable while their temporal efficiency is improved by allowing more collocated WPANs. By the condition, Straf,j ≤ Sj , SUTE conducts selfadmission control based on the throughput estimates of itself and others. Thanks to the universal algorithm, SUTE is able to estimate its own performance as well as other WPANs’ when it chooses the time offset that causes its superframes overlapped. It is possible that Ψ∗ has more than one element. The choice out of the multiple candidates should be made while taking the next incoming WPAN into account, yet the information cannot be known a priori. In SUTE, the earliest time offset, minψ∈Ψ∗ ψ, is chosen for shorter delay to transmit the first beacon. 4.2 Nearest Vacancy Search (NEVS) While SUTE is not computationally simple, NEVS is very straightforward and simple superframe scheduler. The main idea is that, after building G(ψ, t), NEVS searches the closest vacancy, which corresponds to the

SF

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Fig. 6. An incoming WPAN (upper) has an active period of 18.75% and the channel that it enters (lower) has an inactive portion of 25%. Dashed arrows represent beacon intervals, and SF means a superframe. inactive period, that fits the incoming WPAN’s superframe and does not make incoming WPAN’s superframe overlapped with others. If there is no such vacancy, NEVS denies to create a virtual channel and notifies the self-admission failure to VCS. Compared to SUTE, NEVS does not make efforts to find a virtual channel that satisfies the needs of all collocated WPANs with an allowance of the overlaps. Instead, it searches vacancy that is the most helpful to the robustness of all WPANs’ operations. Note that when the traffic load is the same as the saturation throughput that a WPAN gains by its BOj , SOj and Nj without superframe overlaps, NEVS and SUTE perform the same.

5

V IRTUAL C HANNEL S ELECTOR (VCS)

While the superframe schedulers are developed to find a proper time offset where a virtual channel is created, VCS is developed to select the most beneficial channel out of all logical channels by considering even the potential WPANs which would come in the future. Suppose the ideal case where unlimited logical channels are available and we want to minimize the number of logical channels in use. One good solution is to put WPANs with the same beacon interval into the same logical channel. The underlying philosophy of this solution is that the fixed beacon interval of WPANs in the same logical channel makes the collocation of WPANs much more efficient. Specifically, there is a case where an incoming WPAN with a different beacon interval cannot avoid an overlap even if its proportion of the active period is less than that of the inactive portion in the logical channel (Fig.6(a)). However, this does not happen if all the WPANs including the incoming one have the same beacon interval; we can always find an inactive period that the incoming WPAN can fit its superframe into if its proportion of the active period is less than that of the inactive portion in the logical channel (Fig. 6(b)). By VCS, WPANs are grouped into a few set to achieve the advantage of the ideal case with the limited number of available channels. Furthermore, BO of a certain WPAN may be adjusted along with SO when they are sufficiently large. This approach roughly takes advantage of the fixed beacon interval even in a practical scenario where a variety of different BOs are used by collocated WPANs.

9

5.1 WPAN Grouping The WPAN grouping is done by classifying logical channels according to the distribution of the BOs of the WPANs in each logical channel and by selecting a logical channel with the WPANs which have similar BO to the incoming WPAN. For the logical channel classification, VCS needs the predetermined set Φ, which is also used for BO adjustment later. The set Φ contains BOs frequently used by collocated WPANs. By using the elements in the set Φ as the boundary values for grouping, BOs from 0 to 14 can be categorized into |Φ|+1 groups. In VCS, each logical channel with at least one operating WPAN is classified into one of 2 types by investigating the mostly-used BO, BOmost , in the channel: (1) a public channel (PC), and (2) a dedicated channel for a certain BO denoted by φk , k = 1, · · · , |Φ| (DC-φk ), where φk indicates the i-th element of Φ in the ascending order as follows. ⎧ if BOmost < φ1 , ⎪ ⎪PC ⎪ ⎪ ⎨DC-φ1 if φ1 ≤ BOmost < φ2 , (9) .. ⎪ ⎪ . ⎪ ⎪ ⎩ DC-φ|Φ| if φ|Φ| ≤ BOmost . In addition, logical channels without any operating WPAN are classified as empty channels (ECs). There might be multiple BOs, which are used the most, in one logical channel. In such a case, if the candidate BOs are smaller than φ1 , the channel is marked as PC. This is because WPANs with small BO have less possibility to leave room for other WPANs to operate together. Logical channels that are not given to those WPANs may be utilized by other WPANs that have large BOs. If there is only one BO that is mostly used, VCS checks whether all of the candidate BOs can be classified into the same DC-φk , i.e., whether they are within the range from φk to (φk+1 − 1) or not. If they can be, the logical channel under classification is identified as DC-φk . If not, the channel is set to PC. Reflecting the anticipated applications shown in Table 1, we assume that the predetermined set Φ of BOs for the channel classification includes two elements: Φ = {6, 12}. 5.2 BO and SO Adjustment After the WPAN grouping, we may have a couple of DCs with different BOmost ’s. If all WPANs in the same DC have the same BO, the resources are efficiently shared by them, thanks to the regularity. BO and SO are adjusted to gain such benefits. Interestingly, changing BO and SO while keeping the same duty cycle yields almost the same throughput and energy consumption as shown in Fig. 7, which is obtained by ns-2 simulations [9]. The throughput is normalized by the physical transmission rate, 2.5 · 105 b/s. It is observed that MAC packet delay, which consists

of propagation and transmission delays, channel access time and MAC queuing time, increases as BO increases. If a certain BO is intentionally reduced while having the same duty cycle, the major three performance metrics remain almost the same. Inspired by this fact, incoming WPAN’s BO is reduced to have BO which is closer than before to one in set Φ. At the same time, SO is also adjusted to maintain the same duty cycle as before. Note that it is acceptable to further reduce BO to under 5 if Straf,j < Sj after the adjustment. In our VCS implementation, however, this is prohibited so that the margin could be exploited by SUTE-like scheduler which allows the superframe overlap. 5.3 Logical Channel Selection After the WPAN grouping and BO adjustment, VCS attempts to select the logical channel and the time offset for the superframe. VCS first checks if its hosting WPAN (incoming one) has BO less than φ1 . If this is the case, the incoming WPAN is forced to take one of PCs. The rationale behind this is that WPANs with small BOs hardly collocate with others and thus it is not desirable for them to take an EC to make it DC. Only when superframe scheduling on PCs all fails, the incoming WPAN is allowed to access ECs if there is any. The WPAN cannot start its operation if there exists no EC at all in this case. Provided that the incoming WPAN has BO larger than φ1 , it seeks a DC which includes the WPAN’s BO. Then VCS of the WPAN runs a superframe scheduler to obtain the time offset for the found DC. In both cases where the superframe scheduler cannot find the offset, returning admission failure, and where there is no DC that supports the WPAN’s BO, VCS is allowed to access an EC if there is any. VCS considers PCs only when it can find neither a suitable DC nor an empty EC.

6

P ERFORMANCE E VALUATION

In this section we first study the accuracy of our saturation throughput estimation algorithm for SUTE and evaluate the performance of two schedulers and VCS by a series of simulations. For large scale simulations, the simulator that does not have the CSMA/CA and other MAC functions has been developed because general event-driven network simulators are not scalable to the extent of those with hundreds of WPANs. However, in addition, we also use an event-driven simulator with the specific network scenario that is concise and sufficient to show the characteristics of the proposals. Upon doing this, the 802.15.4 module in ns-2 simulator [9] has been modified and extended. 6.1 Throughput Estimation In Section 3 we have derived a formula for the saturation throughput of WPAN with given BO and SO in which

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Fig. 7. BO distribution and performance with 10 devices and 50% of duty cycle with different BOs by ns-2 simulations. 0.34

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Fig. 8. Normalized throughput comparisons between the estimates by Algorithm 1 and those by ns-2 simulation.

periodic inactive periods are included for energy saving. The curves in Fig. 8(a) are by Eq. (4), and the points are marked by ns-2 simulation with the fixed length of a data packet, 7 BPs, i.e., T s = 12. The saturation throughput is normalized by the physical transmission rate 2.5 · 105 b/s [1]. They match with each other, supporting our assumption on the number of transmissions in the last Ts BPs of a superframe in Sections 3.1 and 3.2. As discussed in Section 3 the shorter the length of superframe is, the more overheads for each transmission exist because of the transmission deferment during the Ts in Fig. 2. In Fig. 8(a) it is observed that the throughput begins decreasing with the same duty cycle if the beacon order is less than 5. With BO larger than or equal to 5, the throughput loss is negligible if the same duty cycle is maintained. This fact is used by the BO and SO adjustment for VCS in Section 5.2. Fig. 8(b) shows that the estimation results well match with the simulation under different BO and SO values and fixed N = 10, having different duty cycles. Fig. 8(c) shows the throughput when two superframes with the same beacon order overlap with each other in which the numbers of devices are set to 10 and 5 for the first and second superframes, respectively (N1 = 10 and N2 = 5 in Fig. 3). The throughput linearly decreases when the overlap portion increases. In addition to the analytic and simulation results, the curves by the analytic results without the consideration on Lc and Ld are also depicted in the figure, denoted by ’Appx.’

By comparing those altogether, it is observed that our analysis successfully captures the beacon collision, which is not ignorable especially for the WPANs with small BO. 6.2 Large Scale Simulation Setup In this section, we evaluate the proposed schemes by the house simulator dedicated to the virtual channel management. To obtain meaningful results, a realistic model for the beacon order (BO) distribution of incoming WPANs is required. Based on the target applications shown in Table 1, the distribution as shown in Fig. 7(a) is considered throughout the simulations. On the other hand, SOs are selected by the given BO: When BO is less than 4, SO is set to 0. If BO is larger than 3 and less than 6, we set SO to 1. In other cases, uniformly chosen number between 0 and (BO − 2) is set to SO. This setup reflects the fact that typical 802.15.4 applications have less than 2−3 · 100% duty cycle. We choose Nlimit = 21 aM axLostBeacons for VCS as well. To measure the number of achievable virtual channels, we first have to determine how many WPANs will try to start operation until the end of each simulation run. As the number of such trials increases, the possibility to encounter a WPAN whose BO is large and SO is small enough to “squeeze” more virtual channels out of inactive period becomes high, even though the trials of many WPANs keep failing. The sum of the selfadmission control failure counts is monitored to prevent such meaningless trials. We name this parameter as

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Fig. 9. The performance with respect to traffic loads normalized by saturation throughput.

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self-admission control limit and set it to 10, that is, a simulation runs until 10 self-admission control fails. Most of the simulations are conducted with given traffic load, which is 75% of the saturation throughput when there is no overlap. It is assumed that each WPAN exactly knows its load as explained in Section 2.1. The number of child devices is uniformly selected from 3 to 20. To emulate the collocation situation with IEEE 802.11b/g WLANs, only four logical channels are given. All simulation results are averaged from 100 different runs. PAN coordinators are synchronized by either an additional algorithm such as inter-WPAN synchronization (IWS) [10] for 802.15.4-2003 compatible devices or an enhanced feature in IEEE 802.15.4-2006 [6]. A simple VCS substitute is considered for the comparisons as well. The substitute chooses the least crowded channel by counting the number of WPANs in each logical channel, and then run the superframe scheduler on that channel. This is repeated until the scheduler returns a valid time offset or there remains no logical channel. In the figures it is denoted by “sub.” 6.3 Large Scale Simulation Results Fig. 9 shows the performance of SUTE and NEVS with or without VCS, according to traffic loads, which are normalized by the corresponding saturation throughput. In Fig. 9(a) the number of virtual channels, i.e., the number of WPANs those can collocate, remarkably increases with the aid of the superframe schedulers and channel

selector irrespective of their combinations (4 WPANs can collocate without virtual channel management). This shows that the notion of the virtual channel management itself is indeed a solution to the collocation problem. Observing each combination, approximately 10 virtual channels are more created by VCS than the substitute channel selector. Because NEVS does not allow overlaps of any superframes, it shows the constant number of achievable channels even when the load reduces to the half. On the other hand, SUTE successfully creates more virtual channels as the traffic load decreases. In Fig. 9(a), though NEVS prohibits the superframe overlap, we observe that the number of virtual channels increases as the load decreases from 1 to 0.9 when it is with VCS, thanks to the BO/SO adjustment feature of VCS. Even if the amount of loads largely reduces, having much more margin to accommodate more overlaps by other superframes, it is shown in Fig. 9(b) that the actual overlap is less than 7% out of all active time durations. As the beacon interval and superframe duration are restricted to be exponential by the standard, more overlap yields larger performance loss than what the WPANs are tolerable up to. As discussed before, this restriction fundamentally makes it difficult for WPANs to share the temporal resources. This small overlap also affects the number of beacons exposed to collision as shown in Fig. 9(c). We count the number of beacons exposed to collision and normalize it by the total beacons in Fig. 9(c). It decreases as the

12

overlap reduces, and less than 10% of the beacons are exposed to the collision with data packets from other WPANs. Remind that the consecutive beacon collision is prevented by SUTE (Section 4.1.1). With the fixed normalized traffic load 0.75, we observe the performance of the proposals with respect to the number of given logical channels in Fig. 10. Fig. 10(a) shows that the benefits by VCS increase as more logical channels are available. Eventually, if all logical channels at 2.4 GHz band become available, the schedulers with VCS can create more than 200 virtual channels, which means the collocation of more than 200 WPAN applications. The gain by using SUTE rather than NEVS looks marginal in Fig. 10(a).8 This is again because of the exponent-parameterized beacon interval and superframe duration in IEEE 802.15.4. Since the overlap portion is small (Fig. 9(b)) and the gain by SUTE over NEVS increases as the traffic load reduces (Fig. 9(a)), we conjecture that SUTE would achieve much higher gain if that constraint is relaxed. The study on this remains as future work. The huge gain by the virtual channel management is due to the improved spectral and temporal efficiency. Fig.10(b) shows the inactive portion, which is left over by the schedulers and VCS. It is normalized by total inactive durations. By using SUTE with VCS, the portion can be reduced down to 15%. Recall that it is typical that more than 75% of the time is wasted in many 802.15.4 applications. Fig. 10(c) depicts the number of WPANs whose BO and SO are adjusted by VCS for the collocation. From 15% to 20% of the WPANs experience the adjustment. Overall, it is noticeable in Fig. 10 that VCS plays a major role to improve the collocation efficiency. We observe how many virtual channels can be created while increasing the self-admission control limit in Fig. 11. Fig. 11(a) shows the virtual channels that can be further created by finding an incoming WPAN which fits into one of the logical channels. By allowing larger control limit, the remained inactive portion is filled with the superframes of the newly added WPANs as shown in Fig. 11(b). From this, we see the possibility of creating more virtual channels if incoming WPANs have adequate BO, SO and the number of child devices so as for them to fit into the logical channels. Fig. 11(c) depicts the overlap portion as self-admission control limit increases. VCS efficiently manages multiple logical channels, achieving lower overlap portion than its substitute and creates more virtual channels as depicted in Fig. 11(a). 6.4 Ns-2 Simulation Results We perform ns-2 simulations with the scenarios in which 17 WPANs are successfully collocated by SUTE with VCS 8. If the unit of y axis is compared to that in Fig. 9(a), it is noticeable that the gain is still significant in terms of the number of additional applications that can operate together.

and 11 WPANs are by NEVS with VCS, respectively. Since the different schedulers result in different decisions on the self-admission control, the resulting collocated WPANs are not necessarily the same. The scenario is randomly picked up among the 100 runs of the large scale simulations. In Figs. 12(a) and 12(b), the saturation throughput results by the analytic model, i.e., Eq. (4), ns-2 simulations and Algorithm 1 are compared with the traffic load. All values are normalized by the physical transmission rate. The results by Eq. (4) are without superframe overlaps, thus indicating the maximum loads that WPANs can handle. If the overlaps are allowed, the saturation throughput by either the simulations or Algorithm 1 can be thought of as the maximum loads that WPANs can accommodate with such superframe overlaps. Fig. 12(a) shows the saturation throughput of SUTE with VCS, obtained by the simulation and Algorithm 1, which are marked with crosses and Xs, respectively. WPANs can deal with the maximum load as much as the saturation throughput without overlaps. The overlaps are allowed as long as the resulting superframes can still give the saturation throughput more than the traffic load. Having crosses and Xs between the saturation throughput without overlap and traffic load, we can confirm that the superframe overlaps by SUTE does not prevent WPANs from processing all their traffic loads. Therefore the resources represented by time and frequency are successfully redistributed for collocation. Some WPANs show minor mismatch between the simulation and Algorithm 1 results. Since the overlap analysis on two WPANs has been verified in Fig. 8, the mismatch is rooted in the approximation applied at the end of Section 3.2, which is to develop Algorithm 1 to compute the throughput of multiple WPANs. Indeed the estimation accuracy of Algorithm 1 is compromised with the computation efficiency for a practical use. Fig. 12(b) shows the saturation throughput by NEVS with VCS. Since no overlap is allowed by NEVS, the throughput results by Eq. (4) are identical to those given by Algorithm 1. Due to the absence of overlapped superframes, the simulation results marked with crosses well match with the results by Eq. (4) and Algorithm 1.

7

R ELATED WORK

In part, the available logical channels are limited by other ISM band technologies, especially IEEE 802.11b and IEEE 802.11g [4], [5]. With the mobile stations using IEEE 802.11, the available channels cannot exceed 4 in 2.4 GHz ISM band. If one of the overlapped channels with 802.11’s is used, the packet error rate dramatically increases as studied in [11], [12]. By the analysis a conservative solution, which recommends to assign a non-overlapped channel to a WPAN, is suggested, e.g., [5], [13], [14]. As discussed, this cannot be a fundamental solution since non-overlapped channels are much fewer than the applications that we may have in the same

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area. The proposed channel assignment solutions in the above papers may be combined with our virtual channel management so that the temporal resources wasted as inactive period are utilized. Our virtual channel management can be thought of as the frequency channel assignment and time scheduling in cellular networks. For such networks, the graph coloring algorithm is widely used to assign conflictfree channels [15], [16], and some are based on genetic algorithms [17], [18]. A unified framework for orthogonal channel assignment is developed in [19], and it is applied to time division multiple access (TDMA) scheduling in a distributed manner in [20]. Generally speaking, the virtual channel management shares the features of cognitive radios [21], [22], which has far more general setting in channel assignment and time scheduling for coexistence of wireless networks. The virtual channel management, however, is distinguishable from this body of research as its unique constraint on the time scheduling (BO and SO parameters), which also has a strong correlation to the frequency channel assignment. Another branch of work related to the virtual channel management should be the study on multiple access with multichannel capability [23]–[26], which considers the channel assignment and the time scheduling together. It shares the common property with the virtual channel management that the existence of multiple avail-

able channels are exploited to avoid interference. However, the additional constraint for the routing decisions does not exist in our work. The problem becomes similar to those of the multichannel protocols if WPANs connect devices using multiple hops. Nevertheless, the temporal scheduling in the virtual channel management is still a unique feature of WPANs distinguished from ad hoc networks or mesh networks because of the exponentially long durations and periodicity with different periods. In multichannel protocols CSMA/CA is usually adopted for temporal scheduling.

8

C ONCLUSION

AND

F UTURE WORK

In this paper the collocation property of the IEEE 802.15.4 WPANs is identified and virtual channel management is proposed as a solution. The notion of virtual channel management is implemented by our superframe schedulers and VCS. Two superframe schedulers, namely SUTE and NEVS, are designed to improve the temporal efficiency of collocated WPANs, and VCS is developed to further enhance the efficiency in a spectral as well as temporal domain of given resources. As a byproduct, a universal algorithm is devised to estimate the saturation throughput of the collocated WPANs in a computationally efficient manner as well. Through the development of this set, we have confirmed that the virtual channel

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management can substantially improve the collocation efficiency. Under practical situation the improvement is from four to fifty WPANs to collocate. Through the study on the WPAN collocation, we also identify the important drawback of the IEEE 802.15.4 standard: the beacon interval and superframe duration, which are restricted to be exponentially increasing, fundamentally hampers the enhancement of the spectral and temporal collocation efficiency. The revision on the standard by this collocation consideration is thus necessary for the ubiquity of the WPAN applications. Subsequent work on the following issues would complement this paper: • Our work is under the constraints of IEEE 802.15.4 on the beacon interval and superframe duration. The achievable collocation capability with a relaxed constraint would be interesting, which should be far more powerful, but is not standard compatible. • Multiple WPANs could be hidden terminals to each other if their communication range is partially overlapped. The scheduler design that takes into account it is necessary for a real deployment. • Throughout this paper, time varying wireless channel is not discussed. Experimental work on this issue should follow. • Many applications exploit the multi-hop capability of IEEE 802.15.4 with upper-layer standards such as ZigBee [2] and IEEE 802.15.5 [27]. More study should be carried out to extend the virtual channel management for multi-hop networks.

R EFERENCES [1]

IEEE Std 802.15.4-2003, Part 15.4: wireless medium access control (MAC) and physical layer (PHY) specification for low rate wireless personal area networks (LR-WPANs), Std., Dec. 2003. [2] ZigBee Alliance: Instustrial association to enable reliable, cost effective, low-power, wirelessly networked monitoring and control products, http://www.zigbee.org/, Std. [3] P. Covell, ZigBee Applications Profiles, Introduction Material, ZigBee Alliance slides 084 995r00ZB MWG, July 2008. [4] A. Sikora and V. F. Groza, “Coexistence of IEEE 802.15.4 with other systems in the 2.4 GHz-ISM-band,” in IEEE Instr. Measur. Tech. Conf. (IMTC’05), vol. 3, May 2005, pp. 1786–1791. [5] S. Pollin et al., “Distributed cognitive coexistence of 802.15.4 with 802.11,” in Proc. IEEE Intern. Conf. Cog. Radio Oriented Wireless Netw. Comm. (CROWNCOM’06), Mykonos Island, Greece, Jun. 2006, pp. 1–5. [6] IEEE 802.15.4-2006 (Revision of IEEE Std. 802.15.4-2003), Part 15.4: wireless medium access control (MAC) and physical layer (PHY) specification for low rate wireless personal area networks (LR-WPANs), Std., Jun. 2006. [7] T. R. Park, “Latency reduction algorithms for wireless sensor networks,” Ph.D. Dissertation, Seoul National University, 2005. [8] T. R. Park et al., “Throughput and energy consumption analysis of IEEE 802.15.4 slotted CSMA/CA,” IEE Elec. Lett., vol. 41, no. 18, pp. 1017–1019, Sept. 2005. [9] “The network simulator - ns-2,” http://nsnam.isi.edu/nsnam/. [10] T. H. Kim et al., “Virtual channel management for densely deployed IEEE 802.15.4 LR-WPANs,” in Proc. IEEE Intern. Conf. Pervasive Compt. Comm. (PerCom’06), Mar. 2006. [11] I. Howitt and J. A. Gutierrez, “IEEE 802.15.4 low rate wireless personal area network coexistence issues,” in Proc. IEEE Wireless Comm. Netw. Conf. (WCNC’03), vol. 3, Mar. 2003, pp. 1481–1486. [12] S. Y. Shin et al., “Packet error rate analysis of zigbee under wlan and bluetooth interferences,” IEE Trans. Wireless Comm., vol. 6, no. 8, pp. 2825–2830, Aug. 2007.

[13] C. Won et al., “Adaptive radio channel allocation for supporting coexistence of 802.15.4 and 802.11b,” in Proc. IEEE Veh. Tech. Conf. (VTC’05-Fall), vol. 4, Sept. 2005, pp. 2522–2526. [14] R. C. Shah and L. Nachman, “Interference detection and mitigation in IEEE 802.15.4 networks,” in Proc. IEEE Intern. Conf. Info. Process Sensor Netw. (IPSN’08), Apr. 2008, pp. 553–554. [15] W. K. Hale, “Frequency assignment: Theory and applications,” Proceedings of IEEE, vol. 68, no. 12, pp. 1497– 1514, Dec. 1980. [16] K. I. Aardal et al., “Models and solution techniques for frequency assignment problems,” Springer Annals on Operational Research, vol. 153, no. 1, pp. 79–129, Sept. 2007. [17] M. Cuppini, “A genetic algorithm for channel assignment problems,” Euro. Trans. Telecomm. Related Tech., vol. 5, no. 2, pp. 285– 294, Mar. 1994. [18] C. Y. Ngo and V. O. K. Li, “Fixed channel assignment in cellular radio networks using a modified genetic algorithm,” IEEE Trans. Veh. Tech., vol. 47, no. 1, pp. 163–172, Feb. 1998. [19] S. Ramanathan, “A unified framework and algorithm for channel assignment in wireless networks,” Springer Wireless Netw., vol. 5, no. 2, pp. 81–94, Mar. 1999. [20] I. Rhee et al., “DRAND: distributed randomized TDMA scheduling for wireless ad-hoc networks,” in ACM Intern. Conf. Mobile Ad Hoc Netw. Comp. (MobiHoc’06), May 2006, pp. 190–201. [21] J. Mitola and J. Gerald Q. Maguire, “Cognitive radio: Making software radio more personal,” IEEE Personal Comm., vol. 6, no. 4, pp. 13–18, Aug. 1999. [22] S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE Jr. Sel. Ar. Comm., vol. 23, no. 2, pp. 201–220, Feb. 2005. [23] S.-L. Wut et al., “A new multi-channel MAC protocol with on-demand channel assignment for multi-hop mobile ad hoc networks,” in Proc. Intern. Symp. Parallel Arch. Algo. Netw. (ISPAN’00), Dallas, TX, Dec. 2000, pp. 232–237. [24] J. So and N. H. Vaidya, “Multi-channel MAC for ad hoc networks: Handling multi-channel hidden terminals using a single transceiver,” in ACM Intern. Conf. Mobile Ad Hoc Netw. Compt (MobiHoc’04), Roppongi, Japan, May 2004, pp. 222–233. [25] M. Kodialam and T. Nandagopal, “Characterizing the capacity region in multi-radio multi-channel wireless mesh networks,” in Proc. ACM Intern. Conf. Mobile Comp. Netw. (MobiCom’05), Cologne, Germany, Aug.–Sept. 2005, pp. 73–87. [26] K. N. Ramachandran et al., “Interference-aware channel assignment in multi-radio wireless mesh networks,” in IEEE Intern. Conf. Comp. Comm. Netw. (INFOCOM’06), Apr. 2006, pp. 1–12. [27] IEEE 802.15.5: IEEE 802.15 Task Group 5 (TG5) to Enable Mesh Networking, http://ieee802.org/15/pub/TG5.html, Std.

Tae Hyun Kim (S’06) received the BS degree from Yonsei University in 2004, and the MS degree in the department of electrical engineering and computer science from Seoul National University, Seoul, Korea in 2006, respectively. He is currently pursuing his PhD in the department of electrical and computer engineering in the University of Illinois at Urbana-Champaign. The focus of his current research is MAC and networking layer protocol design for wireless networks, especially with an emphasis on how advanced physical layer techniques such as MIMO interact to upperlayer protocols. In Oct. 2005, he was awarded the Grand Prix of 1st RFID/USN Research Paper Contest by the Minister of Information and Communication, Korea.

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Jae Yeol Ha received the B.S. and Ph.D. degrees in electrical engineering and computer from Seoul National University in 2003 and 2008, respectively. He is the team leader in research and development team of MZEN Co. His current research interests include biomechanics, 3D motion capture and analysis, 3D vision, 3D Human-Computer-Interface.

Sunghyun Choi (S’96-M’00-SM’05) is currently an associate professor at the School of Electrical Engineering, Seoul National University (SNU), Seoul, Korea. Before joining SNU in September 2002, he was with Philips Research USA, Briarcliff Manor, New York, USA as a Senior Member Research Staff and a project leader for three years. He received his B.S. (summa cum laude) and M.S. degrees in electrical engineering from Korea Advanced Institute of Science and Technology (KAIST) in 1992 and 1994, respectively, and received Ph.D. at the Department of Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor in September, 1999. His current research interests are in the area of wireless/mobile networks with emphasis on wireless LAN/MAN/PAN, next-generation mobile networks, mesh networks, cognitive radios, resource management, data link layer protocols, and cross-layer approaches. He authored/coauthored over 120 technical papers and book chapters in the areas of wireless/mobile networks and communications. He has coauthored (with B. G. Lee) a book “Broadband Wireless Access and Local Networks: Mobile WiMAX and WiFi,” Artech House, 2008. He holds over 30 patents, and has tens of patents pending. He has served as a General Co-Chair of COMSWARE 2008, and a Technical Program Committee Co-Chair of ACM Multimedia 2007, IEEE WoWMoM 2007 and IEEE/Create-Net COMSWARE 2007. He has also served on program and organization committees of numerous leading wireless and networking conferences including ACM MobiCom, IEEE INFOCOM, IEEE SECON, IEEE MASS, and IEEE WoWMoM. He is also serving on the editorial boards of IEEE Transactions on Mobile Computing, ACM SIGMOBILE Mobile Computing and Communications Review (MC2R), Computer Communications, and Journal of Communications and Networks (JCN). He has served as a guest editor for IEEE Journal on Selected Areas in Communications (JSAC), IEEE Wireless Communications, Pervasive and Mobile Computing (PMC), ACM Wireless Networks (WINET), Wireless Personal Communications (WPC), and Wireless Communications and Mobile Computing (WCMC). From 2000 to 2007, he was a voting member of IEEE 802.11 WLAN Working Group. He has received a number of awards including the Young Scientist Award awarded by the President of Korea (2008); IEEK/IEEE Joint Award for Young IT Engineer (2007); the Outstanding Research Award (2008) and the Best Teaching Award (2006) both from the College of Engineering, Seoul National University; the Best Paper Award from IEEE WoWMoM 2008; and Recognition of Service Award (2005, 2007) from ACM. He is a senior member of IEEE, and a member of ACM, KICS, IEEK, KIISE.