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Design Challenges of Multi-User WBAN MAC

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Chapter 1 Introduction of Multiuser WBANs

1.2 Design Challenges of Multi-User WBAN MAC

As mentioned, inter WBAN interference comes from the mobility of WBAN users. WBAN densities can vary when WBAN users are alone or in a crowded place. Mutual interferences between WBANs might seriously impact the channel efficiency in high density WBANs. This is the reason that IEEE 802.15 TG6, the standard task group of WBAN, requires that the WBAN protocol support at least the sensor density: 60 sensors in a 6 m space [13]. Furthermore, densities could change very 3 3 rapidly in certain scenarios, before and after entering a crowded elevator for example. Therefore, improvements on maintaining high channel efficiency in crowded WBANs and conducting short response time to the density change of WBANs are major design targets of WBAN MAC. However, these two performance indexes are tradeoffs according to traditional coloring theory, an important theory for resource scheduling, and will thus be discussed in chapter 3).

Mutual interference also leads to complex priority issues in WBAN QoS designs. In each WBAN, WSNs may have difference priorities according to signals they collect. For example, the priority of ECG sensors might require higher priority than that of temperature sensors, since ECG sensor directly respond to life-threatening instances. Also, priorities of sensors may dynamically increase when abnormal signals are detected. This can ensure immediate triggering of related emergency procedure. In addition, priority definitions may various depending on the status of WBAN users. For example, the priority of the temperature of a user in fever may require higher priority than that of a healthy user. As a result, complex priority requirement is another major design challenge for multiuser WBAN MAC.

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In this study, the impact of inter-WBAN interference will be first evaluated in chapter 2.

Analytical throughput and energy consumption models are provided to point out the key of performance degradation. Chapter 3 further considers the rapid topology change of WBAN and discusses the tradeoff between fast-response-time and channel-efficiency of WBAN resource scheduling. A possible relaxed coloring method is also proposed to skip this tradeoff limitation.

Based on the MAC design from chapter 3, solutions for complex priority control of WBAN QoS is further suggested in chapter 4. Finally, chapter 5 summarizes concluding remarks in this WBAN research.

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Chapter 2

Analysis of multiuser interference

The reason of power consumption may comes from many reasons including overhearing, idle listening, collision, control message, which are identified in [14]. However, for WBAN, collision is the major source of power waste due to its imbalanced network structure. A WBAN consists of a central processing node (CPN) and several wireless sensor nodes (WSNs), which is illustrated in Fig. 2-1(a). For the general WBAN scenario, vital signal collection, the major traffic loading comes from the uplink vital signals (Fig. 2-1(b)), which implies that WSN is the major transmitter and CPN is the major transmitter. Thus, for WSN, collision is the major source of power consumption. For CPN, overhearing and idle listening are the major sources.

The energy spent on control messages can be ignored by assuming that data volume is much larger than the control message. However, the importance of low power CPN is lower than WSN for the reason that CPN, which will most likely be embedded in smart phone or equipments with plug-in power, usually has larger batter than WSN. As a result, how to solve the collision problem of WSN becomes the major issue of low power WBAN.

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CPN

WSN

WBAN WBAN

WBAN

(a)

CPN

WSN ECG

(b)

Fig. 2-1 Wireless body area networks (WBAN) (a) Topology of WBAN (b) Traffic load of WBAN

Similar analytic model for the inter piconet interference of Bluetooth on 2.4GHz unlicensed-band has been studied by [15, 16]. However, for WBAN, WMTS bands [17], which located at 608-614 MHz, 1395-1400 MHz, and 1427-1432 MHz are suggested. These 5 or 6 MHz narrow bands do not allow the frequency hopping of Bluetooth that hops across 80MHz.

Thus, [15, 16] cannot be directly used for WBAN analysis.

In this chapter, a time slotted based analytical model is developed to observe the impact of inter WBAN interference of WSN in narrow band. Moreover, this model considers the physical transmission rate and the application data rate, which is expected to provide more realistic mode for WBAN analysis.

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2.1 WBAN Performance Model of Beacon and Slotted CSMA/CA Modes

To evaluate the performance impact from the inter WBAN collision, two classic medium access methods, Beacon and carrier sense multiple access and collision avoidance (CSMA/CA), are chosen.

The tradeoffs between adopting the deterministic (Beacon) and non-deterministic (CSMA/CA) scheduling are expected to be observed through the modeling of the throughput and the power consumption of these two modes. Beacon and CSMA/CA modes have different approaches to handle the WBAN collisions. For Beacon mode, the transmission time of different nodes in the same network are interleaved to avoid the intra network collision (IANC) by using the Beacon message.

However, without knowing the transmission schedule of neighbor WBANs, inter WBAN collision (IRNC) cannot be avoided. Different from Beacon mode, CSMA/CA can avoid IANC and IRNC by using detection-backoff-transmission procedure. If a node senses a busy channel, it performs the backoff-detection procedure until an idle channel is available and thus collisions could be avoided.

However, the waste of transmission opportunity due to the random backoff might be the potential problem that limits the throughput.

The power model of WBAN considers a fully connected network with G simple-star WBANs and N WSNs per WBAN. As for the WBAN traffic model, 8kbps ECG traffic is continuously transmitted from each WSN to CPN. The power model of WSN can be formulated by two steps. First, we calculate the energy consumption per packet for Beacon and slotted CSMA/CA modes. Next, by calculating the transmission time per packet, the average power consumption in time can be obtained.

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We first define an access period L(slots) to fairly compare the performance of these two modes.

In both modes, each node attempts to select one slot to transmit a packet within the access period L. For the Beacon mode, one access period equals to one Beacon period. By using the Beacon message, CPN can assign one transmission slot for each WSN within the Beacon period. As for slotted CSMA/CA, one access period equals to one contention period. Each WSN will contend for one slot within the contention period of L slots.

Slotted CSMA-CA might need more than one attempts and backoff mechanisms to avoid collision.

Each node repeatedly detects channel until the idle channel exists. If the channel is busy, the node

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Different from slotted CSMA/CA, the Beacon mode resolves the intra network collision (IANC) by allocating separated time slots for different WSNs. In this case, the cost in the Beacon mode,

_ cos

BCN t

J , is the listening cost. We assume the Beacon uniformly assigns the slots for each WSN within Beacon period. Collision happens only when two or more nodes from different networks are assigned to the same slot. PIRNC in the Beacon mode is the same as that in CSMA/CA and equals to

(G 1)N L

. The energy consumption per packet under the Beacon mode JBCN can be expressed as:

 

have the same fixed size. Thus the throughput can be defined as the packets successfully transmitted per slot time. For instance, in Beacon mode, ATTEMPsBCN implies the number of transmission attempts per packet. Thus, the throughput of WSN in the Beacon mode can be expressed as:

12 is a minimum throughput requirement, the maximum number of G implying the number of WBAN users is limited. We found that L has two possible solutions. The throughput in (2-4) is supposed to be higher for small L, which means WSN attempts to transmit more often. However, the collision also increases with the increase of the transmission rate. This can be proved from PIRNC in (2-2) with small L . Collision decreases the number of successful transmission and makes the throughput lower than expected. From (2-3), larger L has smaller energy consumption. This implies that for

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In the second step, the average power consumption can also be computed by dividing the energy per packet by the transmission time per packet. We assume that the energy only consumed when WSN in TX and RX periods in both modes. During the OFF period, WSN goes to sleep and consumes no power. The power consumption in TX and RX modes is denoted as WON. Thus, the average power of WSN in Beacon mode, WBCN, is expressed as:

_ cos

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Where Tpbps (bits/s) is the throughput. BPKT(bits) and BBCN_ cost are the size of data and Beacon packets respectively. RPHY(bits/s) is the minimum transmission rate of physical layer that allows a packet to be transmitted per slot. Following similar steps, WCS can be expressed as:

_ cos

2.2 Analysis and Hybrid Mode

2.2.A Power consumption of WBAN

We found that both Beacon and slotted CSMA/CA modes have their own advantage. In Fig. 2-2, N is fixed to evaluate the power consumption of WSN with various number of WBAN groups.

CSMA/CA mode has lower power consumption than Beacon mode (Fig. 2-2) because CSMA/CA adopts the channel detection to avoid collision. The cost of channel detection is lower than the packet collision because the detection will not listen to the channel for whole time of packet receiving.

However, with given throughput requirement, CSMA/CA supports less number of WBAN groups treated as overall WBAN user capacity than Beacon mode (Fig. 2-3). The body signals monitoring may fail or stop for the insufficient throughput of WSN when the number of user is higher than the limit. Although CSMA/CA has the lowest power consumption, it might fail to meet high user

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capacity criterion. To provide both high user capacity and low power WBAN, a new access method should be developed.

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2.2.B Hybrid access mode

The proposed Hybrid mode adopting both Beacon and channel detection achieves high user capacity and low power. In the Hybrid mode, Beacon is used to solve the IANC and channel detection is used to handle the IRNC. The Beacon assigns non-overlapped sub contention periods for all WSNs within the Beacon period, which is illustrated in Fig. 2-4. This solves the collision from the intra network. For solving IRNC, WSN can perform the channel detection within its own sub contention period to avoid the collision. From Fig. 2-2 and 2-3, Hybrid mode has at least 63.4%

power consumption of Beacon mode in multi-WBAN scenarios and double user capacity of CSMA/CA mode. Although Hybrid mode costs more power than CSMA/CA because it adopts both Beacon and channel detection, Hybrid mode is still a better access method than CSMA/CA when the required user capacity is high.

Time Beacon i Sub contention period Beacon i+1

Fig. 2-4 Hybrid access mode

2.3 Summary

In this chapter, we have provided an analytical model to analyze the power consumption and capacity limits of different medium access control schemes in mobile WBAN. Results show that neither the Beacon-based nor detection-based access scheme can effectively support both the low power consumption and high capacity (in terms of WSN nodes) simultaneously. A hybrid mode

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proposed in this chapter can provide higher capacity while the power consumption is still well managed. This implies a mixed method may be a better solution for WBAN MAC.

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Chapter 3

Distributed multiuser resource scheduling

However, unlike a sensor network focused on static or low mobility scenarios [3], a WBAN has a higher moving speed and more frequent topology changes due to user movement [2]. The moving topology of multiple WBANs is similar to that of mobile ad hoc networks (MANETs) [4], but with group-based rather than node-based movement. The “high mobility” and “group-based movement”

make the WBAN neither equivalent to a sensor network nor to a MANET. A WBAN thus has a high chance of encountering other WBANs, which creates new issues of inter-WBAN scheduling (IWS).

Corresponding discussions [16, 18-21] have just been opened and comprehensive studies are still required.

Distributed interference-avoidance scheduling of wireless networks can be modeled by the notion of distributed graph coloring, which is commonly adopted in sensor networks or MANET [9, 22, 23].

Network topology is modeled as a graph G( , )V E . The vertices V of G represent the wireless nodes; the edges E of G represent radio resource conflicts between mutually-interfering node pairs; the color set C in a coloring of G represents the set of distinct resource units (can be time slots, frequency bands, or code sequences). A complete vertex k-coloring of a graph G is a mapping V G( )C, where Ck, such that adjacent vertices receive distinct colors. Thus, interference between wireless nodes can be avoided by mapping different colors (resource units) to adjacent vertices (mutually-interfering nodes). A standard message-passing model is adopted in this study. To negotiate the color mapping between vertices, they can have a two-way message exchange

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with its adjacent vertices. We assume the system is fully synchronous. In other words, all vertices start their coloring algorithms at the same time and execute each step of the algorithm simultaneously.

Other terminologies in graph theory refer to [24]. The chromatic number ( )G is defined as the minimum k used to completely color graph G; N v( ) is defined as the set of adjacent vertices of vertex v ; the maximum degree ( )G is defined as the maximum d v( ) and v V G ( ), where vertex degree d v( ) N v( ).

Two basic requirements of IWS are: (i) fast convergence and (ii) high channel utilization. In the case of a WBAN user walking on a sidewalk, network topology changes frequently when the user keeps encountering other WBAN users. Therefore, a quick IWS that rapidly detects and responds to every topology change is expected, which could adopt the quick 1 ( )G coloring for MANET [25].

Also, IEEE 802.15 TG6, the standard task group of WBAN, requires that the WBAN protocol should support at least the sensor density: 60 sensors in a 6 m space [13]. Such dense WBANs create a 3 3 high probability of mutual interference. It can significantly decrease the number of coexisting WBAN users due to poor channel utilization [19]. Thus, high channel-utilization IWS is also required, which could adopt an optimal spatial-reuse coloring for dense sensor networks [22]. Optimal spatial-reuse implies the maximal number of sensors that access the wireless channel at the same time.

However, references [26-28] show that quick (low time-complexity) coloring and optimal spatial-reuse coloring are trade-offs which cannot be simultaneously achieved with conventional complete coloring. Optimal spatial-reuse coloring uses a minimum number of colors, the chromatic number ( )G , to color a graph. The fewer colors used, the more there is color-reuse among vertices.

It implies more wireless nodes can simultaneously transmit packets using the same resource unit, that is, the system has higher spatial-reuse and channel-utilization. However, completely color a graph by

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using ( )G colors is known to be NP-complete. The fastest ( )G -coloring so far still needs time-complexity O(2nnO(1)) [26]. Nevertheless, study [27] indicates that time-complexity can be significantly reduced to O(log )n if colors are increased to 1 ( )G (sacrificing spatial reuse) and distributed coloring techniques are adopted. 1 ( )G ( )G is known as Brooks’ theorem, which is a loose upper bound for ( )G . A recent work [29] adopts a similar 1 ( )G coloring method. It guarantees not only O(log )n time-complexity but also O(log )n bit complexity, which is the amount of information exchanged between vertices during coloring. Low-bit-complexity of a coloring algorithm makes it applicable to low-computing-power applications, such as sensor networks. Although [28] further decreases the time complexity from O(log )n to2 O( log )n by applying vertex priority, 1 ( )G colors are still required. As a result, neither ( )G [26] nor 1 ( )G -coloring [27, 28] may be directly applied to IWS due to their high time-complexity and low spatial-reuse, respectively.

This chapter proposes Random Incomplete Coloring (RIC) to realize quick and high spatial-reuse IWS. RIC consists of 1) a proposed random-value coloring method and 2) an incomplete-coloring approach. The random-value coloring method is a technique which realizes prioritized vertex coloring [28] (so-called oriented coloring) and will be proven to have a low time-complexity3

(2ln ) 2

W n

O e , which is even lower than [28] . Besides, for high spatial-reuse coloring, conventional

complete coloring using ( )G colors is known as the solution for optimal spatial-reuse.

Surprisingly, for designs of wireless resource scheduling, this study found that higher spatial-reuse (on average) than that of ( )G coloring is possible if partial vertices are allowed to be uncolored

2 Soft-O: O g n( ( )) is short for O g n( ( )logkg n( )).

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and only k( )G colors are used. In wireless networks, we observe that uncolored vertices imply wireless nodes with no transmission, which do not interfere each other. Hence, following the nature of wireless communication, there is no conflict between adjacent uncolored vertices (or with the special color: no color). Based on this fact, we can color a subgraph of graph G. This subgraph is constructed using vertices, excluding uncolored ones. Clearly, it is possible to use less colors for the subgraph. For convience, we name this kind of coloring “incomplete” coloring. The proposed Random Incomplete Coloring (RIC) will be implemented as an inter WBAN scheduling protocol with a TDMA framing structure, a common structure used in sensor or body area networks [2], to test its convergence speed and spatial reuse in various mobile WBAN scenarios.

The rest of this chapter is organized as follows: section 3.1 introduces the details of related works and the problem formulation. Section 3.2 reveals the proposed RIC algorithm. The corresponding analytical models of RIC are provided in section 3.3. Section 3.4 presents the simulation settings and results. Section 3.5 concludes this chapter.

3.1 Related Works and the WBAN System Model

3.1.A Oriented and Non-oriented Coloring

Oriented vertex coloring [28] is a distributed coloring technique that utilizes predefined edge orientation (vertex priority) to improve the coloring speed of (non-oriented) random vertex coloring [27] from O(log )n to O( log )n . Here we refer to [27] as non-oriented vertex coloring to distinguish [28] from [27]. The major difference between oriented and non-oriented coloring is the

3 W x( ) is the Lambert W function [18]. W x( ) is solved by inverting the equation,

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manner of solving color conflicts, as shown in Fig. 3-2 Step 3. For non-oriented coloring, when color conflict cucv happens to vertices pair ( , )u v , both vertices give up that color and re-choose new colors for the color contention in the next coloring round. In contrast, oriented coloring tries to force the vertex that has a higher priority (having an outward edge orientation) to win the color. Although an oriented conflict-path may exist (u v w, cu cv cw) and only the vertex u at the start of the path can win the color (no vertex has a higher priority than u has), oriented coloring generates at least one winner for each path in each round (an exception, deadlock circle, will be mentioned later). This is the reason why oriented coloring speeds up coloring.

Given G( , )V E ; ,u v V G ( ); C ru( ) is the set of

Fig. 3-2 Oriented and non-oriented colorings (Pri-arts)

( ) exp[ ( )]

zW z W z , for any complex number z.

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However, to realize the oriented coloring for IWS, two particular issues need to be considered: (i) fairness and (ii) oriented conflict-circle (deadlock circle). First, oriented coloring assumes the priorities of vertices are pre-defined, which is not practical for the dynamic topology of a WBAN.

How to dynamically and fairly decide the priority of a WBAN should be further addressed. The second issue is the oriented conflict-circle (deadlock circle) problem. An oriented conflict-circle is defined as a circle graph with one-way orientation and vertices in the circle contending for the same color (e.g. u  v w u c, u cv cw). There is no vertex in that circle that can be colored because there is always another adjacent vertex with a higher priority. Thus, the coloring cannot be completed unless vertices try to contend using different colors in subsequent rounds. To solve these two problems, a method that implements oriented coloring, random value coloring, is proposed.

Moreover, we will show that this method can further decrease the time complexity from O( log )n [28] to O e W(2ln ) 2n

.

Aside from the above implementation issues, conventional complete colorings [26-28], including oriented coloring [28], have an optimum spatial reuse bounded by ( )G . However, for designs of wireless resource scheduling, spatial reuse can be further improved if the coloring rule is relaxed.

The relaxed coloring approach, referred to as incomplete coloring, will be introduced in the next section.

3.1.B CPN-based IWS

A CPN-based IWS will be adopted in this study due to the imbalanced CPN/WSN architecture of a simple star (Fig. 3-1). In a single WBAN, the CPN plays the role of the master and the WSNs are

A CPN-based IWS will be adopted in this study due to the imbalanced CPN/WSN architecture of a simple star (Fig. 3-1). In a single WBAN, the CPN plays the role of the master and the WSNs are

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