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B Performance Evaluation of CPN-based IWS

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Chapter 3 Distributed multiuser resource scheduling

3.4. B Performance Evaluation of CPN-based IWS

Consequently, RIC is applied in a CPN-based IWS to observe how its high coloring-speed and high spatial-reuse improve IWS performance in mobile and dense WBAN scenarios.

Simulation Settings of Coloring-based IWS

A proposed CPN-based IWS adopts a time division multiple access (TDMA) with two distinct communication channels: inter and intra-WBAN channels. It helps to realize and compare different kinds of coloring-based IWSs. A CPN uses both channels for inter-WBAN resource contention and intra WBAN data (vital signals) collection respectively; a WSN only uses an intra-WBAN channel for data (vital signals) transmission. Following the steps of a CPN-based IWS in section 3.1, a CPN first uses the inter-WBAN channel to contend time slots through a coloring algorithm. The CPN then sends a beacon through an intra-WBAN channel to allocate obtained time slots to its WSN. Finally,

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the WSN transmits a data packet to its associated CPN through the intra WBAN-channel at the time slot that was pre-scheduled by the CPN.

The framing structure is illustrated in Fig. 3-9. A coloring cycle is performed for each superframe.

The intra-WBAN channel consists of one beacon slot and p data slots. Each CPN can reserve at most one slot after each coloring cycle. The number of data slots to be scheduled in each coloring cycle is set as k(equals to the number of colors used in the coloring). The duration of each data slot is 1ms. On the other hand, the inter WBAN channel consists of r slot-groups for r coloring rounds in a cycle. Each coloring round has q slots and is subdivided into (q k ) coloring slots and k winner notification slots, where k is the number of colors used for coloring. Coloring slots are used for exchanging coloring messages between CPNs (e.g. Fig. 3-4 step 2). The CPN chooses a coloring slot in each coloring round to transmit its coloring message. The coloring slot is randomly chosen to reduce potential collisions between coloring messages. Once a CPN wins the slot (color), it broadcasts the winner message at the associated winner notification slot (e.g. Fig. 3-4 step 4).

Because the coloring message exchanged across the inter-WBAN channel contains only color and random value information, the duration of coloring and winner notification slots are set as 25μs, which is 1

40 of the data slot at the intra-WBAN channel. In this study, we skip the steps of superframe synchronization between CPNs, which is beyond the scope of this study. Related work on superframe synchronization can be found in [32].

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Fig. 3-9 Superframe for coloring-based IWS

The settings of p(number of data slots in a superframe) and q (number of coloring slots in a coloring round) are two important control variables that dominate two kinds of data collision: (1) out-of-date scheduling and (2) ill-scheduling. Out-of-date scheduling happens when the frequency of the IWS cannot catch up to the frequency of topology changes due to user mobility. The data transmissions of WSNs scheduled by the out-of-date scheduling might collide with other transmissions from un-negotiated WBANs. Because IWS is performed for each superframe, such collisions are dominated by the duration of the superframe p. The second kind of collision is caused by ill-scheduling, which results from the collision of coloring messages (in short, coloring collision) while CPNs broadcast their coloring messages in the same coloring slot. Without correctly receiving coloring messages from adjacent CPNs, a CPN could make a mistake on treating itself as the slot (color) winner. As a result, data transmissions of WSNs belonging to different WBANs could be scheduled to the same data slot and then collide with each other. Since coloring slots are randomly chosen by each CPN, the larger the number of coloring slots, the lower the collision rate. The number

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of coloring slots is decided by the value q. To closely study both kinds of collisions, two types of settings are used: fixed-p and fixed-q. For the fixed-p model, p is set as the number of colors k

(coloring numbers). Thus, each coloring round at the inter-WBAN channel has

1 40

ms25 k k

qs r r , where r is the number of coloring rounds. As for the fixed-q model, we

choose ( , ) (1,5)k r  to set the baseline size of q as 8 slots, which introduces the most serious coloring-message collision9 of RV INC . Thus, 8

5 r40 r

p  . Because each k coloring provides k slots scheduling, it is more convenient to make p an integral multiple of qINC only . As a result,

RV INC

q is modified as q40kr and the coloring result repeats every INC only slots.

The generation of mobile WBAN topology is similar to the 2-D random graph. The initial positions of n WBANs are randomly located in a 10 10 m  2 square. n can be 12, 25, 50, 100 to simulate low, middle, high, and extremely high WBAN densities. The mutually-interfering-range of WBANs is set as 2 m. Also, to simulate WBAN mobility, the location change of WBANs follows the Gauss-Markov mobility model [33]. We use [33] to simulate the smooth movement path of a human, while avoiding the sudden stops and sharp turns that happen in the random walk mobility model [34].

The Gauss-Markov mobility model has a tuning factor  to control the randomness of WBAN movement.  is set as 0.3 in this study (0and 1 correspond to Brownian motion and linear motion respectively).

System throughput is the performance index used to evaluate IWS. Without loss of generality, system throughput is defined as effective transmissions per slot (Tps ), which counts data

9 Fig. 6, RIC can be finished within 5 rounds (r5). While r5,k1 is the setting that yields a minimum q from the equation q40kr, which introduces the most serious coloring-message

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transmissions of all WSNs that are actually received by CPNs in the system. Tps is similar to the vertex per color (Vpc) but further considers performance degradation caused by data collision.

System Throughput of IWS

Coloring speed is found to be the key factor that affects the performance of IWS. Fig. 3-10 compares the system throughput of IWS using the RIC and INC-only algorithms. INC-only can perform sub-( )G coloring but has a much higher time-complexity than RIC (see Fig. 3-6). First, in Fig. 3-10, ill-scheduling is temporarily ignored to make the observation of out-of-date scheduling much more clear. Because the collisions of coloring messages are ignored, the performances of RIC and INC-only in the fixed-p model are identical. Also, rRIC5 leads to pRICk, which makes the performance of RIC in fixed-p and fixed-q models equivalent. Fig. 3-10 shows that the fixed-p model overcomes the mobility much better than the fixed-q model. In the fixed-q model,

INC only 5

p r k

k

 is prolonged by the high time-complexity (high number of r) of INC-only and

thus fails to respond in a timely fashion to topology changes. On the other hand, in the fixed-p model, p k is independent of coloring rounds r. Throughput is only slightly degraded while mobility is increased from 3m/s to 9m/s. Furthermore, in the fixed-p model, throughput is improved while the coloring number k is decreased. This improved throughput ought to lead to more out-of-date scheduling. Fortunately, the frequency of IWS is inversely proportional to the duration of the superframe (k1). Decreasing k increases IWS frequency to compensate for the collisions caused by throughput improvement.

collision.

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3m/s 12 WBANs Fixed-p (RIC / INC-only) Fixed-q (RIC) r=5 3m/s 50 WBANs Fixed-p (RIC / INC-only) Fixed-q (RIC) r=5 9m/s 12 WBANs Fixed-p (RIC / INC-only) Fixed-q (RIC) r=5 9m/s 50 WBANs Fixed-p (RIC / INC-only) Fixed-q (RIC) r=5 3m/s 12 WBANs Fixed-q (INC-only) r=r(k) 3m/s 50 WBANs Fixed-q (INC-only) r=r(k) 9m/s 12 WBANs Fixed-q (INC-only) r=r(k) 9m/s 50 WBANs Fixed-q (INC-only) r=r(k)

Note:

Fig. 3-10 Transmission per Slot (Tps) of IWS (ignoring ill-scheduling)

Now we focus on the fixed-p model. Ill-scheduling (coloring collision) is found to be the major factor that seriously degrades the performance of IWS. Fig. 3-11 illustrates the system throughput of IWS in the fixed-p model after considering both out-of-date and ill-scheduling collisions. It shows that RIC has a much higher Tps than INC-only. The reason comes from the substantially fewer coloring rounds of RIC than that of INC-only. rINC only might be larger than thousands of slots due to the inefficient re-coloring of INC-only, which makes qINC only much lower than qRIC (q40kr)

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and introduces serious collisions of coloring messages. For INC-only, 40 k ( )

qr k can be increased

by increasing k and decreasing ( )r k (coloring rounds ( )r k decrease while color choices k increase, as shown in Fig. 3-6). The coloring collision of INC-only is relieved when k is larger than 4 and 5 for the scenarios of 12 and 25 WBANs, respectively. However, increasing k also decreases Tps (referring to the decreasing Vpc in Fig. 3-7). For RIC, the tradeoff between coloring collision and Tps reduction yields optimum throughputs when k is 2 and 5 (instead of 1) for the 50 and

Fig. 3-11 System throughput of IWS with the Fixed-p Model

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3.5 Summary

In this work, random incomplete coloring (RIC) is proposed to realize a fast and high spatial-reuse inter-WBAN scheduling (IWS). Unlike conventional complete coloring schemes, RIC is not limited by the tradeoff between coloring speed and spatial reuse. RIC can always provide fast convergence with time complexity O e W(2ln ) 2n in any spatial reuse requirement. Furthermore, RIC can support an increase of up to 90% of spatial reuse over the conventional complete coloring using chromatic

( )G

 -colors, which is known to be the optimal coloring of complete coloring. In the simulation, RIC is applied in a CPN-based IWS protocol with TDMA framing structure. Simulation results show that RIC does overcome inter-WBAN collisions and thus provides high system throughput for mobile wireless body area networks.

This study focuses on the scenario of random-user position, which is modeled as a 2-D random graph. In the future, we would like to analyze the performance of RIC in other special scenarios. For example, users in a waiting line, a movie theater, or a coffe bar. These scenarios can be modeled as a line, a grid, and a clustered graph, respectively.

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Chapter 4

Distributed Multiuser QoS Designs

Quality of Service (QoS) for medical applications is an emerging issue for wireless body area networks (WBAN) [1, 35]. To reliably transmit data streams of medical applications (e.g. vital signals or diagnosis audio / video), WBAN QoS is asked to meet more harsh requirements than those of other wireless networks in terms of transmission latency, packet error rate (PER), and energy consumption, as mentioned in [36-42]. Furthermore, WBAN QoS is featured by considering different critical levels of vital signals. For instance, electrocardiograms (ECG) are deemed to have more important information than body temperature to indicate the health status of a person, hence ECG signals are supposed to have higher priority than that of body temperature. Many centralized scheduling technologies of medium access control (MAC) layer have been proposed to support QoS for a single WBAN (single user) [36-42]. In these works, a central processing node (CPN) of a WBAN centrally schedules radio resources of wireless sensor nodes (WSNs) illustrated in Fig. 4-1.

These centralized controls can effectively meet various QoS requirements of vital signals. They also save energy consumptions of WSNs due to their light control loading of WSN in the CPN-centralized controls [42]. Nevertheless, some WBAN scenarios involve co-existence of multiple users, e.g. a hospital waiting room or a crowded subway station. Co-channel and co-location interference happens when WBANs move close to each other. It causes packet collisions and energy waste, which hence impact WBAN QoS. Besides, multiuser scenarios might need extra definitions of critical levels of medical data. The critical levels of vital signals might vary according to not only signal properties (like the ECGs v.s. Body temperature example in a single WBAN QoS) but also

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user status. For instance, vital signals of an injured person might need higher priority than that of a healthy one. Thus, inter-WBAN priority scheme would be necessary. As a result, a new challenge of multiuser QoS that considers above inter-WBAN issues is introduced. To the best of our knowledge, there is no existing works addressing on solutions of multiuser WBAN QoS so far. Comprehensive studies are still required.

Vital Signals CPN

WSN

WBAN WBAN

WBAN

Fig. 4-1 Wireless Body Area Network

QoS designs for overlapped wireless local area networks (WLANs) or Bluetooth piconets might be the closest problems to multiuser QoS. Jiang and Howitt [43] analyze load-balancing between co-channel and co-location (overlapped) WLANs. Access points (APs) properly share bandwidth according to an optimized load-balancing through backhaul (wire-line) communications. On the other hand, for overlapped piconets, the inter-piconet interference is overcome by interconnecting discrete piconets into a scatternet [44-49]. A scatternet (cross piconet) scheduling is thus applied to provide collision free transmissions among overlapped piconets. However, these approaches might not be suitable multiuser QoS solutions for several reasons. First, these approaches are originally designed for non-medical transmissions, which have less strict QoS requirements and lack priority schemes for medical data. Furthermore, the WLAN approach focuses on static or low mobility scenarios. Its backhaul optimization is only suitable for fixed wireless nodes, which is not possible to

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be applied for mobile WBANs. On the other hand, the scatternet approach introduces extra control/traffic loading and energy consumption of slave nodes (similar to WSNs in WBAN) when they serve as scatternet bridges. However, for many WBAN applications, WSNs are expected to be very low power and have a very long battery-life, e.g. an implanted pacemaker is requested to perform years heart-pacing without battery changes. Thus, neither the QoS solutions for overlapped WLANs nor piconets can be directly applied to multiuser QoS.

In this study, proposed Random Contention-based Resource Allocation (RACOON), which is extended from our previous work [50], provides a multiuser QoS scheme for mobile WBANs.

RACOON is featured by:

 Simple inter-WBAN resource allocation, which simplifies the control overhead of inter-WBAN QoS control. Resource allocation between WBANs is decided through random-value comparisons between WBANs. In RACOON, only one broadcasting packet that carries random values will be required to complete every inter-WBAN resource contention.

 Iterative inter/intra-WBAN QoS control, which supports a dynamic QoS adjustment in mobile WBAN scenarios. The adjustment will consider both critical-level differences among (i) adjacent WBANs and (ii) Vital signals.

 Hierarchy CPN/WSN resource allocation, which utilizes the asymmetric CPN/WSN structure and thus decrease control loadings and energy consumptions of WSNs. In the hierarchy CPN/WSN resource allocation, CPN is in charge of both inter/intra resource scheduling.

WSN only wakes up while it is polled by its associated CPN.

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 Probing base inter-WBAN interference detection, which detects potential inter-WBAN interferences before actual collisions happen in both downlink (CPN to WSN) and uplink (WSN to CPN) transmissions. The interference detection avoids packet collisions and energy waste caused by WBAN mobility and hence extends battery life of WSNs.

The rest of this chapter is organized as follows: section 4.1 introduces requirements of WBAN QoS. Section 4.2 reveals the proposed RACOON multiuser QoS protocol. Section 4.3 presents the experimental results and section 4.4 concludes this chapter.

4.1 WBAN Quality of Services (QoS) 4.1.A Requirements of WBAN QoS

WBAN QoS controls that simultaneously support both intra and inter WBAN QoS are studied in this work. A WBAN consists of a single central processing node (CPN) and several wireless sensor nodes (WSNs). These WSNs collect various medical data (including vital signals from human body and diagnosis audio/video) and forward them to the CPN, which is depicted in Fig. 4-1. Intra WBAN QoS controls should make sure these medical data are timely transmitted by following their delay-bound and delay-variation requirements [51]. However, when total bandwidth requirements of a WBAN overflow its capacity, transmissions should be scheduled in an order from the highest-priority data to that has the lowest priority, which guarantees the QoS level of high priority data. Such priority settings are usually designed by medical experts according to their clinical experiences. For example, a heart failure could introduce much instant life risk than an abnormal body-temperature. Hence, ECG signals directly reflecting heart activity should have higher priority than that of temperature records. This kind of priority is called as intrinsic data priority.

Furthermore, if abnormal vital signals are detected, the priorities of these signals should be

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dynamically increased to be higher than those of normal signals. Such priority is called as emergent data priority. Therefore, intra WBAN QoS controls should meet various latency requirements of difference medical data and follow proper intrinsic and emergent data priorities simultaneously.

On the other hand, for inter-WBAN QoS designs, proper user priority should be further provided.

In scenarios of multiple overlapped WBANs, WBANs need to share radio resource with each other.

Once the overall capacity is not sufficient to support all transmission bandwidth of WBANs, radio resources should be allocated to WBANs that has higher user priority. Such priority should also be defined by medical experts. Usually, priority settings follow an order from the highest to the lowest life-critical WBAN users. High user-priority WBANs should be allowed to transmit all necessary medical data; low user-priority WBANs should transmit only partial medical data to maintain normal health monitoring. As a result, WBAN QoS controls should simultaneously satisfies (i) intrinsic data priority (ii) emergent data priority and (iii) user priority for both intra and inter WBAN QoS.

Aside from transmission qualities above, a WBAN QoS control should try to lower energy consumption of WSNs as well [1, 51, 52]. In a WBAN, a CPN will most likely be embedded in personal devices such as cellular phones or PDAs with larger and rechargeable batteries. In contrast, WSNs are expected to be light weight (small battery) and even un-rechargeable for certain implantable applications. Thus, WSNs are expected to keep their energy consumptions as low as possible.

4.1.B Performance Metrics

To qualify a QoS control for WBAN, following performance metrics will be evaluated.

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 Transmission Latency: transmission latency affects smoothness of real-time display of vital signals. A transmission latency of a medical packet is calculated from the time of a packet is generated in a WSN to the time of the packet is successively received by a CPN. To ensure a vital signal is timely displayed, every packet of the signal should be received before its delay bound expires.

 Joule per bit of WSN: energy consumption of a WSN affects its battery life. To evaluate energy consumptions of WSNs with various traffic loading, an energy measurement is normalized by its transmission bandwidth with the unit, Joule per bit. An energy measurement of a WSN will count all its packet transmissions (successful/unsuccessful packet transmissions from WSN to CPN) and receptions (successful/unsuccessful polling message receptions from CPN to WSN).

 User capacity: user capacity affects the density of coexistence WBAN users, which is important for dense WBAN scenarios. User capacity is defined as the maximum number of coexistence WBANs that satisfy desired WBAN QoS requirements.

4.1.C Related Works

Significant contributions toward high quality WBAN QoS designs have been made in recent years [36-41, 53, 54]. These works adopt different framing, scheduling, and novel hardware techniques to optimize emergency transmission, packet latency, and power consumption of a single user WBAN.

Huasong [38] creates a framing-structure-turning procedure to simultaneously improve throughput, queuing delay, and energy consumption of IEEE 802.15.4, a candidate protocol for WBAN. Yoon [36]

further modifies the framing structure of 802.15.4 to remarkably reduce the packet delay of

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emergency alarm. He further introduces a preemptive scheduling to guarantee the transmission priorities of various medical data. There are also scheduling techniques utilizing TDMA-overhead-reduction [53], adaptive duty cycle [54], prioritized retransmission [39], delayed retransmission [37], fuzzy-logic controls [41], and wake-up radio [54] to enhance WBAN QoS. Su and Zhang further combine scheduling with realistic battery charging/discharging effect to significantly prolong battery life of WBAN sensors. More complete introductions and comparisons of existing WBAN QoS solutions are summarized by Ullah [52]. Different from above single WBAN solutions, proposed RACOON protocol puts more focus on multi-user WBAN QoS solution, which will be introduced in following sections.

4.2 Random Contention-based Resource Allocation (RACOON)

The proposed Random Contention-based Resource Allocation (RACOON) is a bandwidth control

The proposed Random Contention-based Resource Allocation (RACOON) is a bandwidth control

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