EFFICIENT MAC PROTOCOL FOR WIRELESS

BODY AREA SENSOR NETWORKS

SEMINAR REPORT

Submitted by

LADVINE D ALMEIDA

in partial fulfillment for the award of the degree of

Bachelor of Technology

in

ELECTRONICS AND BIOMEDICAL ENGINEERING of

COCHIN UNIVERSITY OF SCIENCE AND TECHNOLOGY

DEPARTMENT OF BIOMEDICAL ENGINEERING

MODEL ENGINEERING COLLEGE

COCHIN 682 021

2012

GOVERNMENT MODEL ENGINEERING COLLEGE

THRIKKAKARA

KOCHI

DEPARTMENT OF BIOMEDICAL ENGINEERING

Cochin University of Science and Technology

BONAFIDE CERTIFICATE

This is to certify that the Seminar entitled

……………………………………………………………………………………

Submitted by

……………………………………………………………………………………

is a bonafide work done by him/her under our supervision.

Dr. Jessy John Mrs. Suja Markose Mrs. Sincy P S

HEAD OF THE DEPARTMENT CO-ORDINATOR GUIDE

i

ACKNOWLEDGEMENT

At this moment of accomplishment, I am presenting my work with great pleasure. I

would like to express my sincere gratitude to all those who helped me in the successful

completion of my seminar.

First of all, I would like to thank our Principal Prof. Dr. V P Devasia, who provided

us with all facilities and support for the development of my work.

I would like to thank Dr. Jessy John, Head of Department of Electronics and

Biomedical Engineering for helping me in the successful accomplishment of this seminar. I

would also like to thank my guide Mrs. Sincy P S, Assistant professor who gave me constant

guidance and support throughout this journey of turbulence.

I thank my seminar coordinator, Mrs. Suja Markose, Faculty staff who gave timely

and valuable suggestions. I also thank Ms. Honey Hycinth Thomas, Ms. Sruti S, Mrs.

Sajitha S, Ms. Nisha Krishnan Assistant Professors of Electronics and Biomedical

Engineering for their support and encouragement of seminar.

Above all, I thank God almighty for constantly motivating me with his love, and

giving me courage at each stride to step forward with confidence and self –belief.

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ABSTRACT

This Paper presents a novel energy- efficient MAC protocol for wireless body area

sensor Networks (WBASN) focused towards pervasive healthcare applications. Network

adopts master-slave architecture where body worn sensors send readings to central node. To

reduce energy consumption, all the sensor nodes are in standby or sleep mode until the

centrally assigned time slot. A standard Listen-before-transmit algorithm is used to avoid

network collisions. Using single-hop communication and centrally controlled sleep/wakeup

times leads to significant energy reductions for this application compared to more “flexible”

network MAC protocols such as 802.11 or Zigbee. As duty cycle is reduced, the overall

power consumption approaches the standby power. System implemented as a part of

Sensium™ System-on-chip WBASN IC showing easiness in implementation.

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TABLE OF CONTENTS

SL NO. TOPIC PAGE NO.

LIST OF FIGURES

LIST OF TABLES

v

vi

LIST OF SYMBOLS AND ABBREVATIONS vii

1. INTRODUCTION 1

2.

3.

MOTIVATION

METHODOLOGY

2

3.1 ATTRIBUTES OF WBASN 4

3.2 DESIGN GOALS FOR EFFICIENT MAC

3.2.1 SCALABILITY 5

3.2.2 DELAY PREDICTABILITY 5

3.2.3 ADAPTABILITY 6

3.2.4 ENERGY EFFICIENCY 7

3.2.5 RELIABILITY 7

iv

3.3 MAC PROTOCOL DESIGN

3.3.1 NETWORK ARCHITECTURE 8

3.3.2 BASIC OPERATION

A. LINK ESTABLISHMENT 10

B. WAKEUP SERVICE 11

C. ALARM 12

3.3.3 WAKEUP FALL BACK TIME 12

3.3.4 CROSS LAYER FUNCTIONALITY 14

3.4 MAC PROTOCOL IMPLEMENTATION

3.4.1 IMPLEMENTATION ON SENSIUM PLATFORM 15

3.4.2 MAC COMPLEXITY 17

3.4.3 SYSTEM POWER AND DUTY CYCLE ANALYSIS 18

3.4.4 MEASURED RESULTS 22

3.4.5 COMPARING WITH EXISTING SYSTEM 24

4. APPLICATIONS 25

5. CONCLUSION 26

6. FUTURE SCOPE

27

REFERENCES 28

v

LIST OF FIGURES

FIGURE 3.1 PROPOSED MAC TOPOLOGY 9

FIGURE 3.2 LINK ESTABLISHMENT 11

FIGURE 3.3 WAKE UP SERVICE 11

FIGURE 3.4 ALARM PROCESSING 12

FIGURE 3.5 SENSIUM SYSTEM ON CHIP 17

FIGURE 3.6 DUTY CYCLE VS SLEEP TIME 21

FIGURE 3.7 DUTY CYCLE VS SYMBOL RATE 21

FIGURE 3.8 TRANSMIT POWER PIE CHART 22

FIGURE 3.9 APPLICATION DEMONSTRATION BOARD 23

FIGURE 3.10 COMPARISION BAR CHART 24

vi

LIST OF TABLES

TABLE 3.1 APPLICATION REQUIREMENTS 19

TABLE 3.2 POWER CONSUMPTION FOR SENSIUM 23

TABLE 3.2 POWER MANAGEMENT UNITS 23

vii

LIST OF SYMBOLS AND ABBREVIATIONS

µm - Micrometre

WBAN - Wireless Body Area Networks

WSN - Wireless Sensor Networks

MAC - Medium Access Control

CCA - Clear Channel Assessment

ISM - Industrial Scientific and Medical Band

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1. INTRODUCTION

The wireless communications revolution which is leading the convergence of all

media and data services appears to be gaining wide acceptance. The healthcare sector is

becoming increasingly interested in using this new technology to more effectively administer

healthcare delivery. In particular, wireless vital signs monitoring is an area of modern

healthcare that is growing very fast. This is due to its potential for slowing down the

unsustainable growth of healthcare spending due to an increasing number of people living for

years or even decades with chronic conditions that require on-going clinical management [1],

[2].

Evolution of wireless, medical and computer networking technology has merged into

an emerging horizon of science and technology called Wireless Body Area Networks

(WBANs). Miniaturization and connectivity are notable parameters of this field.

Vital signs monitoring using wireless sensor network technologies have previously

been described, but these systems are typically bulky and power hungry and rely on MAC

protocols such as Bluetooth and 802.11 which are inefficient for such WBASN applications

[3], [5].More general Wireless Sensor Network (WSN) MAC protocols, which have been the

focus of fairly intensive research, are also not well suited to these specific biomedical

WBASN applications either. Zigbee/IEEE 802.15.4[5] which is designed for similar

networks does not have sufficient ‗network device‘ flexibility in non-beacon mode. It also

lacks the cross-layer optimization features which the proposed protocol brings to this

particular area.

This paper describes a novel MAC Protocol designed specifically for wireless body

area sensor networks focused on pervasive healthcare applications. Like other wireless sensor

network MAC protocols, a primary design goal was low power consumption. This is

achieved through a focus on collision avoidance (a primary source of energy wastage, and

the use of centrally controlled time slotting for sensor nodes. The complete hardware MAC

also incorporates cross-layer optimization, performing some ISO/OSI upper layer functions

(from session layer down to PHY) at the hardware MAC layer to reduce the power overhead

of software implementations. As a result of the network topology adopted in the MAC

protocol, many of the traditional problems that plague wireless sensor networks have been

either eliminated or significantly reduced.

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2. MOTIVATION

MAC Protocol design is a very broad research area, and a lot of recent work has

focused on the area of wireless sensor networks [1], [2], [3], [5]. Coming along with the

urgent development of wireless technology, wireless devices have invaded the medical area

with a wide range of capability. Not only improving the quality of life of patients and doctor-

patient efficiency, wireless technology enables clinicians to monitor patients remotely and

give them timely health information, reminders, and support – potentially extending the

reach of health care by making it available anywhere, anytime.

As widely reported major causes of energy wastage in wireless sensor networks are

collisions, idle listening, overhearing, traffic fluctuations and protocol overhead. In the more

specific area of wireless body area networks, the first three sources of wastage can be

eliminated by using master-slave architecture with time division multiple access with clear

channel assessment (TDMA/CCA) network access scheme. The main goal of the proposed

MAC Protocol is to reduce power consumption from sources like idle listening, overhearing

and collision.

In the more specific area of wireless body area networks, the first three sources of

wastage can be eliminated by using master-slave architecture with time division multiple

access with clear channel assessment (TDMA/CCA) network access scheme. MAC protocol

proposed by Lamprinos [2] imposes a limitation on the duty cycles of the slaves on the

network that some would have low duty cycle because they are serviced first while others

would have a higher duty cycle since they are serviced later in the Rx slot.

The limitation of energy supply on-board the sensor nodes, has motivated a lot of the

research on sensor networks. Such a research can be classified into two general categories

addressing the main causes of energy consumption; signal processing and radio

communication. The first category of work is dedicated to extending the life of the network

through the selective engagement of a subset of the sensors in monitoring the environment.

Unselected sensor can switch to a low-power sleep mode.

The goal of this work is to maintain enough live sensors to have organization. Sensor

organization often involves signal processing techniques. The second category includes

research on energy efficient radio communication. The network and link layers have received

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the most notable attention with the bulk of the work focusing on energy-awareness and

minimization through clever route setup. Energy-efficient link layer protocols tackle the

energy wastage due to collisions among the radio transmission of nodes, keeping the receiver

unnecessarily active and the excessive state changes of the radio circuit.

We are hereby proposing a new MAC protocol which overcomes all the limitations

discussed earlier. Specifically, idle listening and over-hearing are not an issue in this protocol

as traffic is managed centrally. In the following sections, the proposed MAC Protocol is

presented in more detail, from conception to design, implementation together with measured

results.

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3. METHODOLOGY

3.1 Attributes of Wireless Body Area Sensor Networks

The main goal of the proposed MAC Protocol is to reduce power consumption from

sources like idle listening, overhearing and collision. The closest existing MAC Protocol to

the one presented is IEEE 802.15.4 [6]; however it had 3 differences which were not well

suited to this specific application.

1) Data reliability isn‘t handled in the MAC layer.

2) Multiple communication modes increase the complexity of implementation. Hence, this

new scheme is easily implemented in hardware.

3) Time-slotting is limited (16 slots in a super frame) and must all be equally spaced

Before describing the MAC Protocol, assumptions about wireless body area networks are

outlined.

In specifying this MAC Protocol, the following attributes can be inferred about the

wireless body area sensor network.

1) All wireless sensor nodes are attached to the body.

2) The data being monitored is of low frequency, thus the network does not need to

respond immediately to changes.

3) Sensors monitor a range of vital signs which are typically at a low data rate kB e.g.,

temperature, pressure or heart-rate reading. However some higher data rate applications must

also be catered for, such as streaming of electrocardiogram (ECG) signals.

4) The nodes are miniature; battery powered and need to run ideally for days from very low

capacity batteries such as flexible printed battery technologies or miniature coin cells.

5) Sensor nodes are resource constrained, i.e., they have low processing power and limited

memory.

6) Data from the wireless sensor nodes is forwarded to a central master node for processing;

this central node is significantly less resource and power constrained relative to the wireless

sensor nodes.

These listed attributes are the main influences leading to the specific MAC Protocol

implementation described in this paper. These attributes also differentiate the particular

application from more generic wireless sensor network protocols, and other protocols which

have been deployed in biomedical applications such as Bluetooth and Zigbee.

5

3.2 Design Goals for Efficient MAC Protocols

In this subsection we briefly discuss the main design goals for the MAC protocols of

sensor networks. As will become clear, some of these goals may be conflicting and may

force a trade-off.

3.2.1 Scalability

It is envisioned that most applications of unattended sensor networks will involve

large number of nodes. Therefore, scalability of the employed protocols is crucial. The

resources, i.e. time and bandwidth, sharing method and the arbitration strategy have to allow

for fair access to the medium and to prevent excessive collisions. In addition, the potential

for large set of communicating nodes would impose a restriction on the use of some MAC

schemes such as CDMA. In almost all sensor networks, nodes relay other sensors‘ data and

can even perform data aggregation. Pursuing a pure CDMA scheme would require a sensor

to store many code sequences, which may be impractical for tiny sensor devices with very

limited computational resources.

It is worth noting that the scalability of the link layer protocols is influenced by the

network architecture and routing methodology. Hierarchical network structures can allow the

employment of multiple resource sharing strategies and shape the network flow into patterns

that can be exploited at the link layer.

For example, grouping sensors into disjoint clusters allows designating non-

overlapping frequency bands to clusters, similar to FDMA, and applying a TDMA or CSMA

schemes for intra-cluster communication among sensors. In addition, the methodology for

route setup can rule out some MAC schemes. For example, flooding-style data dissemination

makes time-based medium arbitration strategy impractical.

3.2.2 Delay-predictability

A number of applications of sensor networks such as target tracking require delay-

bounded delivery of data. Ensuing timeliness of data reception is typically handled at

multiple layers in the communication stack.

For example, special consideration at the network layer would alleviate the burden of

long queuing time that affect the overall end-to-end delay. However, the link-layer would

play a major role through careful packet scheduling and predictable strategy for medium

arbitration.

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The employed MAC scheme determines the schedule for packet transmission not

only for the individual node but for the entire network. At the node level, a suitable packet

classification and priority mechanism is the base for a service differentiation that allows

delay centric handling of out-going packets. On the network level, a well-defined and easily

enforced strategy is needed to prevent inter-node competition for medium access from

causing contention that makes the time for packet transmission and reception non-

deterministic.

For example, collision based medium arbitration mechanisms such as CSMA would

not be appropriate for large and densely populated sensor networks since it is not known how

many times a node will back off until it successfully transmit. On the other, reservation based

approaches such as TDMA would be a good match despite their scalability problems.

3.2.3 Adaptability

In most applications of sensor networks traffic density varies significantly over time

and from part of the network to another. Such observation is valid for both event-triggered

and query-based models of network operation.

For example in an ECG monitoring setup only periodic status updates are sent in

normal conditions while many sensor reports are generated in case of detecting an

arrhythmia.

In a typical query-based operation, sensors transmit only in response to requests and

little traffic is generated otherwise. In addition, generated data can be subject to aggregation

on route to the sink. Data aggregation can take the form of averaging the reported data,

picking the maximum value, removal of redundant report, etc.

In some cases the traffic pattern will not change if the aggregating nodes are fixed at

the time of route setup and the transformation of the data is many-to-one, e.g. averaging

multiple readings. However, data aggregation is mostly performed when applicable and thus

can cause variability in the traffic flow. For example, elimination of redundant sensor

readings filters out repetitions and prevents resource wastage.

Needless to say that dropping the packets of unneeded data depends on the type of

sensors and the detected events. The MAC scheme should adapt to such high fluctuation in

traffic and should allow medium access rescheduling to efficiently handle burst high-priority

traffic.

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3.2.4 Energy-efficiency

Energy is a scarce resource for sensor networks. As explained earlier, medium access

is a major consumer of sensor energy, especially for long-range transmission and when the

radio receiver is kept on all the time. The output power of the radio transmitter is directly

proportional to distance squared and can significantly magnify in a noisy environment.

Energy-aware routing typically pursues multi-hop paths in order to optimize the

transmission energy.

On the other hand, energy-conscious medium access control (MAC) can save

transmission and reception energy by limiting the potential for collisions, minimizing the use

of control messages, utilizing most of the available frequency band to shorten the

transmission time, turning the radio into low power sleep mode when it is idle and finally,

avoiding the excessive transitions among active and sleep states.

3.2.5 Reliability

Reliable delivery of data is a classical design goal for all network infrastructures.

Guaranteed packet delivery is ensured by the careful selection of error free links, avoidance

of overloaded nodes, and the detection and the recovery from packet drops.

There is usually a trade-off between the control traffic overhead and the level of

reliability.

For example, acknowledging each packet minimizes the recovery time and limits its

scope, at the price of a high control traffic that can lower the effective link bandwidth, boost

end-to-end delay and increase energy consumption.

In wireless networks, packet drops are mainly caused by buffer overflow and signal

interference. Avoiding buffer overflow is the responsibility of both the routing and the MAC

protocols. Balancing load among available routes would reduce the potential for reaching the

maximum capacity of the in-bound traffic buffer in relay nodes.

Meanwhile, the employed medium arbitration scheme determines the buffer

management strategy and has to ensure a service rate for the outbound flow that is high

enough to stop the number of backlogged packets from exceeding the maximum buffer size.

Packet drops due to signal interference can be minimized through the use of sufficiently high

transmission power and the prevention of contention for medium access among nodes.

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3.3 MAC Protocol Design

In the seven-layer OSI model of computer networking, media access control (MAC)

communication protocol is a sub layer of the data link layer, which itself is layer 2. The

MAC sub layer provides addressing and channel access control mechanisms that make it

possible for several terminals or network nodes to communicate within a multiple access

network that incorporates a shared medium, e.g. Ethernet. The hardware that implements the

MAC is referred to as a medium access controller.

The MAC sub layer acts as an interface between the logical link control (LLC) sub

layer and the network's physical layer. The MAC layer emulates a full-duplex logical

communication channel in a multi-point network. This channel may provide unicast,

multicast or broadcast communication service. Much efficiency in data communication can

be achieved through proper design of MAC layer as it comes as highest layer in OSI Model

of network Architecture. The various characteristics of network architecture include types of

interconnection between nodes, operation modes, cross-layer functionality etc.

3.3.1 Network Architecture

Network architecture is the design of a communications network. It is a framework

for the specification of a network's physical components and their functional organization

and configuration, its operational principles and procedures, as well as data formats used in

its operation.

The Open Systems Interconnection model (OSI model) is a product of the Open

Systems Interconnection effort at the International Organization for Network

Standardization. It is a way of sub-dividing a communications system into smaller parts

called layers. A layer is a collection of similar functions that provide services to the layer

above it and receives services from the layer below it. On each layer, an instance provides

services to the instances at the layer above and requests service from the layer below. The

different layers that comes under OSI Model includes Physical Layer, Data Link Layer,

Network Layer, Transport layer, Session layer, Presentation layer and Application layer.

In our Work, as a result of the attributes in the previous section, a point to multi-point

(star) network architecture is proposed.

In this architecture, the central node acts as the master while the other nodes are

slaves. The slave nodes are the actual WBASN nodes which acquire sensor data and transmit

9

to the central node for processing. Each individual master-slaves network is referred to as a

cluster. For ease of management, the maximum number of slaves connected to a master in

one cluster is 8 (many more can be connected, but the time-slotting would have to be

managed outside the protocol).

Also in this architecture, the network access is clear channel assessment [3] and

collision avoidance with time division multiplexing (CCA/TDMA). This network access

scheme significantly reduces the likelihood of collision and idle listening, leading to

significant power savings. In addition time-slot allocation is dynamically controlled by the

master, so a slave time slot could be changed every time it communicates with the master.

This enables the system to better cope with fluctuating traffic. The penalty is increased

complexity of the central node. However, this is not a major problem because the central

node is expected to have significantly more power and processing resources.

The key idea used in this network architecture is to move much of the network and

protocol complexity away from the power constrained wireless sensor nodes and into the

much more capable central node.

This network topology is shown in Fig. 3.1. To accommodate for intercommunication

between clusters, access to an IP network may be used. This way complex network structures

can still be built which extend wide areas.

Fig 3.1. Proposed MAC Protocol Network topology

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3.3.2 Basic Operation

The proposed MAC protocol operations are based on three main communication

processes. The first is when a wireless sensor node wants to join a cluster. This is called the

Link establishment process. The second is when a slave and master wake-up after an

assigned sleep period. This is called the wakeup service process. The last process is an

exception process which occurs when a slave urgently wants to send information to the

cluster master. This is called an Alarm process. In all three processes, communication can

only initiated by the master. In addition only one slave can join the network at a time as the

network is non- ad hoc.

A. Link Establishment

When a master node is first enabled, it continuously tries to establish a link with

unattached slave nodes. It does this by first scanning for a vacant RF channel. When it finds

one, it remains on that channel and starts sending out a beacon containing a unique address

and configuration for a slave and then listening for a fixed time for a response. The sum of

the master‘s beacon transmit and listen time is termed Tseek.

Alternatively, when a slave node is enabled, it also scans the available RF channels to

find the master beacon. If a channel is vacant for Tseek, it hops to the next one. If it is

occupied, it listens for fixed period 2 x Tseek for preamble from the master beacon and if it

doesn‘t receive it, it moves on again to the next channel.

Once the beacon is received, it responds with an acknowledgement to the master. The

master node then assigns a sleep time and ends the transaction. At the end of the link

establishment process, the slave has a unique address, configuration information and sleep

time [Fig. 3.2]. Subsequent additions to the network have to be specifically initiated (e.g., by

software) on the master.

After Link establishment, the RF channel of communication is fixed and can only be

changed by higher layer intervention.

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Fig 3.2. Stages in the 3 processes - Link establishment.

B. Wakeup Service

After link establishment, both master and slave sleep timers start to count up to the

sleep time. Hence, they both wake-up at about the same time, the difference in wake-up times

determined by the offsets between both timers and the length of the sleep time.

On wake-up, the master interrogates the slave which alternatively listens. It (master)

may simply request for its (slave‘s) sensor data, or request status information. Whatever the

communication, a new sleep time is assigned to the slave, setting the next wakeup time-slot

[Fig. 3.3].

To mitigate long-term time-slot drift between the master and slaves in a cluster, there

is an optional synchronization phase during every communication when the slave can

synchronize its timer to that of the master. The master‘s timers never change.

This dynamic time-slotting does not in any way preclude the use of the protocol in a

fixed time-slotting application. It just offers this added functionality which may be used if

required.

Fig 3.3 Wakeup servicing.

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C. Alarm

If the slave detects an ―alarm condition‖ while performing some local processing, it

may communicate with the master without waiting for its next wake-up. This alarm condition

may be due to an out of bounds measurement (of say body temperature or blood glucose

level) or a ―sensor memory overflow‖ alert.

When this mode is enabled, the master continuously sends out a request to all the

slave addresses on the network sequentially. The slave listens for its address and

communicates the alarm condition when it receives it. This only happens when the master is

not busy servicing a scheduled wakeup, and would be terminated when a slave wake-up

needs to be serviced. [Fig. 3.4].

Fig 3.4 Alarm Processing.

3.3.3 Wakeup Fallback Time

The energy waste caused by idle listening is reduced by sleep schedules. In addition

to its implementation simplicity, time synchronization overhead may be prevented with sleep

schedule announcements.

In the preamble sampling technique, a preamble precedes each data packet for

alerting the receiving node. All nodes in a network sample the medium with a common

period, but their relative schedule offsets are independent. If a node finds the medium busy

after it wakes up and samples the medium, it continues to listen until it receives a data packet

or the medium becomes idle again. The size of the preamble is initially set to be equal to the

sampling period.

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However, the receiver may not be ready at the end of the preamble, due to reasons

like interference, which causes the possibility of over emitting type energy waste. Moreover,

over emitting is increased with the length of the preamble and the data packet, since no

handshake is done with the intended receiver.

To reduce the power consumption incurred by the predetermined fixed-length

preamble, this MAC implementation offers a method to dynamically determine the length of

the preamble. That method uses the knowledge of the sleep schedules of the transmitter

node‘s direct neighbours. The nodes learn and refresh their neighbour‘s sleep schedule

during every data exchange as part of the acknowledgement message. In that way, every

node keeps a table of sleep schedules of its neighbours.

Based on neighbours‘ sleep schedule table, MAC layer schedules transmissions so

that the destination node‘s sampling time corresponds to the middle of the sender‘s preamble.

To decrease the possibility of collisions caused by that specific start time of wake-up

preamble, a random wake-up preamble is advised.

Another parameter affecting the choice of the wake-up preamble length is the

potential clock drift between the source and the destination. A lower bound for the preamble

length is calculated as the minimum of destinations sampling period, Tw, and the potential

clock drift with the destination which is a multiple of the time since the last ACK packet

arrival. Considering this lower bound, a preamble length, Tp, is chosen randomly.

The central management of time slotting can be a complex task for the master

especially when complicated by the occurrence of sporadic alarm conditions. To ensure that

every sensor slave node maintains a guaranteed time slot [3] even if another slave flags an

alarm condition, the novel concept of wakeup fallback time (WFT) is proposed.

If a slave wakes up and fails to communicate with the master (either because it is

busy servicing an alarm, or the channel is temporarily occupied by an interferer), it goes back

to sleep with a sleep time set by the WFT. During this time it continues to buffer the sensor

data. After the WFT, it wakes up and searches for the master again.

Similarly, if the master is unable to communicate with the slave at the wakeup time, it

also defaults to the WFT. Hence, both master and slave wakeup at the common WFT and

communicate, restoring the schedule.

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The WFT is a programmable parameter and is a fraction of the shortest sleep time on

the network to mitigate continuous time-slot collisions. Also it is global to the network and

originally set by the master during the link establishment process. This scheme ensures that

time slot overlaps are seamlessly managed and do not degrade the network in the long run.

Also it allows a slave with a long sleep time more opportunities to communicate its data to

the master without having to wait for the whole sleep-time again.

3.3.4 Cross Layer Functionality

When a data packet transmission fails, the MAC automatically retries a

programmable number of times before dropping the packet. In addition large packets can be

automatically broken in to smaller frames and transmitted one at a time. The protocol also

provides for the receiver to reassemble the fragmented data packets as they are received. One

additional function provided is the control of the frequency and rate of sensor data

acquisition depending on the application. These functions are usually handled by higher

layers in the ISO/OSI protocol stacks.

The cross layer approach transports feedback dynamically via the layer boundaries to

enable the compensation for e.g. overload, latency or other mismatch of requirements and

resources by any control input to another layer but that layer directly affected by the detected

deficiency [1],[2]. In the original OSI networking model, strict boundaries between layers are

enforced, where data are kept strictly within a given layer. Cross-layer optimization removes

such strict boundaries to allow communication between layers by permitting one layer to

access the data of another layer to exchange information and enable interaction. For example,

having knowledge of the current physical state will help a channel allocation scheme or

automatic repeat request (ARQ) strategy at the MAC layer in optimizing trade-offs and

achieving throughput maximization.

Cross-layer optimization shall contribute to an improvement of quality of services under

various operational conditions. Such adaptive quality of service management is currently

subject of various patent applications. The cross-layer control mechanism provides a

feedback on concurrent quality information for the adaptive setting of control parameters.

The quality aspect is not the only approach to tailor the cross-layer optimization strategy. The

control adjusted to availability of limited resources is the first mandatory step to achieve at

least a minimum level of quality.

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Communication systems that need to operate over media with non-stationary background

noise may benefit from having a close coordination between the MAC layer (which is

responsible for scheduling transmissions) and the PHY layer (which manages actual

transmission and reception of data over the media).

In some communications channels (for example, in power lines), noise may be non-

stationary and might vary synchronously with the 50 or 60 Hz AC current cycle. In scenarios

like this, overall system performance can be improved if the MAC can get information from

the PHY regarding when and how the noise level is changing, so that the MAC can schedule

transmission during the periods of time in which noise levels are lower.

In this protocol, hardware implementation directly at the MAC layer is preferred as

significant power savings over software implementations is achieved. This is because the

processor would normally need to run continuously (significantly increasing standby power)

to perform these functions like determining when to take the next sensor reading, how many

should be taken and when to switch to another sensor.

Also the delay involved in communicating through the protocol stack layers is eliminated

[5].

3.4 Mac Protocol Implementation

3.4.1 Implementation on Sensium™ Platform

Wireless sensor nodes gather, store and locally process vital signs data, before transmission

to a central base-station node. Although prototype modules for such WBSN applications are

becoming available, these devices tend to be multi-chip solutions manufactured from off-the-

shelf components, and suffer from excessive power consumption and relatively large form

factors [1], [2].

Improvements to the patient‘s quality of care can be achieved through miniaturization

and a reduction in power consumption. These objectives dictate the development of a custom

system-on-chip (SoC). Although ultra-low-power wireless transceiver ASICs have

previously been reported [3-5], this paper describes the integration of a system solution with

a full-custom hardware MAC, digital microprocessor core and I/O peripherals, on-chip

memory, micro power ADC, wireless transceiver and custom sensor interfaces. This SoC

platform device is capable of achieving ubiquitous medical monitoring when interfaced to

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appropriate body-worn sensors, and represents the state of the art in terms of functionality

and ultra-low-power consumption. The encapsulated wireless sensor node is in the form of a

thin and flexible patch, comprising sensors, SoC, battery and antenna as shown in Figure 3.5.

The patch is attached to the patient for a period of typically four to seven days, after which it

is thrown away and a new patch attached, if necessary.

The battery is manufactured from environmentally friendly materials such that it can be

safely disposed of or recycled. It provides typically 3mAh/cm2 at 1.4V, dropping to 0.9V at

end of battery life. The limited energy capacity means that the average current drain must be

of the order of micro amps to achieve the target operating lifetime. In addition, the battery

peak currents must be limited to be no higher than a few milliamps to avoid battery collapse.

These energy constraints require a novel low-power design methodology to be applied at

all levels—network protocol, system architecture, circuit topology and implementation—in

order to guarantee reliable and robust operation within the battery‘s maximum peak current

discharge capacity.

The MAC Protocol was implemented as a key part of a custom system-on-chip (SoC)

ASIC for biomedical WBASN applications.

This mixed-signal SoC, known as Sensium™, integrates a half-duplex transceiver,

programmable sensor interface circuitry and a digital block containing the hardware MAC

plus a low power 8051 microcontroller integrated with 32 kB of code and 32 kB of data

memory.

The data memory is directly accessible via a DMA controller by both the Sensor

Interface ADC (to write sensor readings) and by the hardware MAC (to read/write sensor

readings for direct transmission/reception). Having direct access to system memory allows

the slave devices to operate entirely without processor intervention. The processor can

therefore be switched to a low clock frequency and used to service irregular events like link

errors. On the master, processor intervention is also minimal, and so it is freed up to handle

higher layer functions or transferring acquired data to a PC for further processing. Which

blocks are active in a given mode is controlled by the power management unit.

The Sensium™ system block diagram is shown below in Fig. 3.5. The physical layer for

the radio operates in the 870/900 MHz SRD/ISM bands, employing FSK modulation with

50 KHz deviation to give an over air bit rate of 50 kbps. The sensor interface block features

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sensor driving and interface circuitry for a range of biomedical sensors, and includes a 10-

bit, 50–500 Hz sampling rate DSM-ADC.

For error control, a hamming code is implemented in the MAC hardware together with

CRC frame checking. This provides 2 levels of error correction and detection.

Fig.3.5 Sensium™ System on chip block diagram.

The Sensium SoC is implemented in a 0.13 um CMOS technology and occupies an area

of 16 mm2. Full functionality for centre-processed samples has been verified down to

0.85V; initial yield across corner lots is greater than 95% at a test time of <3s on a Teradyne

J750.

In WBSN applications this SoC is able to provide typically one to two orders of

magnitude lower power consumption than competing solutions, and thus offers the

possibility for truly unobtrusive and disposable vital-sign monitoring.

3.4.2 Mac Complexity

When MAC protocol is implemented in hardware, the processor needs to execute

many instructions in sequential order, the complexity of instructions determined by different

protocols in use. A more generalized MAC protocol will have long sequence of instructions.

A processor that executes every instruction one after the other (i.e. a non-pipelined scalar

architecture) may use processor resources inefficiently, potentially leading to poor

performance. The performance can be improved by executing different sub-steps of

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sequential instructions simultaneously (this is pipelining), or even executing multiple

instructions entirely simultaneously as in superscalar architectures. Further improvement can

be achieved by executing instructions in an order different from the order they appear in the

program; this is called out-of-order execution.

As often implemented, these three techniques all come at a cost: increased hardware

complexity. Before executing any operations in parallel, the processor must verify that the

instructions do not have interdependencies, for example, a first instruction's result is used as

a second instruction's input. Clearly, they cannot execute at the same time, and the second

instruction can't be executed before the first. Modern out-of-order processors have increased

the hardware resources which do the scheduling of instructions and determining of

interdependencies.

In our work, the entire Hardware MAC protocol, including the error control and

framing block, was under 12 K-gates for the slave and ~25K-gates for the master. The gate

count of the hardware implementations points to the simplicity of the protocol.

Since no hardware implementations of 802.15.4 were found, we should compare the

software implementations of both protocols. The proposed protocol can be implemented in

around 16 kB of code (including application code) while 15.4 would require at least 32 kB.

The power consumption for this implementation is around 500 W while it is 15.4 is

10 mW. This is because of the difference in clock frequency required to run both protocols.

3.4.3 System Power and Duty Cycle Analysis

The average power consumption is dependent on the duty cycle of operation. So even

though a sensor node has a very long sleep time, but also has a long active time, the duty

cycle would be high and hence average power. This can be computed for spot measurement

applications like for temperature and glucose and also for continuous monitoring applications

like ECG. Table 3.1 compares common applications.

A detailed analysis of the relationships between the parameters that affect duty cycle and

average power computation follows. From the above analysis, it has been shown that the

duty cycle in continuous monitoring applications like ECG is affected mainly by the

communication symbol rate.

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TABLE 3.1 Typical Application Requirements

Table 3.1 illustrates this using typical numbers for 3 important applications. For spot

measurement applications, we can reduce duty cycle by increasing the sleep time because

more payload data means that the overhead time becomes less.

This is however not the case for continuous monitoring applications like ECG as the

amount of sensor data must increase with sleep time. For applications like this, the sleep time

is usually limited by the system memory resources available for storing the sensor data.

The payload size was kept fixed; while the sleep time was changed. It can be concluded

that the power is dependent on the sleep time as well as the number of retransmissions. Also,

the power consumption approaches the standby power as sleep time increases.

PAVE = average power

PA = active power

PSB = standby power (sensors acquiring data)

TS = allocated sleep time (time between wakeups)

NR = number of retransmissions

TFO = Frame overhead time (RF Setup, preamble, sync, CRC)

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FADC = ADC sampling frequency

NS = Number of samples taken in a sleep period

Es = Effective sampling rate = NS/TS

Nbps = Number of bits per sample

FSYM = TX/ (RX bits/sec)

RECC = Error Overhead Ratio ~ 1.5

DC = Duty Cycle = TA/TSB (1+NR) (1)

The general equation for average power is

PAVE = PA*DC – PSB * (1-DC) (2)

Expading the DC equation further, we have,

DC = (TC+ 3/2 * N2/T2 * (Nbps*TA)/FSYM ) * (1+NR)

TS

= 2*TFO*FSYM + 3*NS*Nbps * (1+NR) (3)

2* TS * FSYM

For spot measurement applications, TFO is significant because of the small data payload

and hence cannot be ignored. However for continuous monitoring applications like ECG,

where the payload bits are >> frame overhead bits, NS = FADC * TS and so TFO becomes

insignificant. Equation (3) then becomes,

DC = 3 FADC * Nbps (1+NR) (4)

2 FSYM

The transmit time for the data payload is 40 ms (100 samples), giving a typical duty

cycle (for 1 sec sleep time) of 4%. The majority of target applications however have much

21

longer sleep times, so the duty cycle would be much smaller and hence lead to greater power

savings.

A more realistic plot is shown below in Fig. 3.6. The plot shows that as sleep time

increases, the duty cycle decreases, quickly converging even for 9 retries. In the case of an

ECG streaming application, the duty cycle is fixed by the Transmit/Receive symbol rate. Fig.

3.7. is a plot of duty cycle versus symbol rate for this implementation. In all applications, the

duty cycle would determine the time slot allocations to the slave devices in a cluster network

and ultimately limits how many can be supported.

Hence, network scalability is mainly application dependent. Also an application like

blood glucose monitoring (0.0014% duty cycle) could have up 255 slave nodes which is the

maximum number that can be supported by the master node.

Fig. 3.6 Duty Cycle versus Sleep time

Fig. 3.7 Duty Cycle versus symbol rate

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3.4.4 Measured Results

The fabricated chip was mounted on a demo board with other interfaces for SPI,

UART and USB [4], [5] as well as a bread boarding area for connecting the application

sensors. The constructed demo board is shown below in Fig. 3.9.

Table 3.2 below gives the component and system standby and active power with a 1

V supply. These are actual measured current consumptions from the fabricated PCB

including the sensor currents for body temperature sensing and ECG streaming applications.

As shown in Table 3.2, there are 3 power states; active, sleep/standby and deep sleep. In

active mode, all the blocks are turned on. For sleep mode, the 16 MHz clock is turned off,

but the sensor interface remains on.

The sensor interface and 16 MHz clock are turned off in deep sleep which is the

lowest power mode. In all the modes, the MAC timers remain active as they control when to

enter or exit the different power modes.

These are run off a separate 32 kHz XTAL. So the power consumption of the digital

block is significantly reduced. On wake-up, the clocks are turned on again. Table 3.3

illustrates these power modes and the states of all the blocks. The relative power

contributions are illustrated in Fig. 3.8 below.

Fig 3.8. Transmit power (~10 dBm) pie chart.

23

Fig 3.9. Application demonstration board photo

TABLE 3.2 Power Consumption for SENSIUM™ SOC

TABLE 3.2 Power Management Unit Modes

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One of the observations from measurements is that on average, the number of retries

is very low, but ultimately depends on the agility of the radio when there is relative

movement between the communicating nodes.

In addition, the overall measured packet error rate is 0.04%. This is however

detectable using CRC and so the data can be retransmitted at a later communication. All

performance measurements used a dipole antenna with both master and slave nodes

stationary.

Also the separation distance was 5 meters and RF transmit power was ~10 dBm.

3.4.5 Comparing With Existing Systems

The observations about traffic in sensor networks impact the design of the MAC

protocol as well as the network and transport layers. Instead of optimizing for high

throughput, low latency, and fairness, MAC protocols for sensor networks must first and

foremost be energy efficient. Consequently, they should be optimized for the case that there

is little or no network traffic. Classical MAC protocols for WLANs like 802.11waste a lot of

energy by so-called idle listening, that is, listening to receive messages that are never sent.

The power consumption of this work compared with other systems is bar charted in

Fig. 3.10. The comparison is based on extensive simulation driven by traffic that varies over

time and location; sensor nodes are inactive unless they observe some physical event, or send

status updates to the sink node providing the connection to the wired world.

One of the key differences that comes out of this is that the RF power requirement is

significantly low for this work. This makes it possible for much smaller batteries like

flexible-thin or zinc-air which cannot be used for any of the other standards. It is concluded

that power is the penalty these protocols pay for their generality.

Fig 3.10. Bar chart comparing battery power requirements for different wireless standards

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4. APPLICATIONS

The low-power protocol proposed here provides a saving in transmission power,

which can then be used to carry out some supplemental signal processing at the sensors in a

WBAN. This allows near real-time monitoring of patient condition and can reduce the work

load on the medical staff by providing only the information of importance, while keeping

power usage to a minimum. Low power usage means that small devices equipped with small

and light batteries can still have acceptable lifetime. Given the low power usage of the

proposed protocol, battery power is now available to implement signal processing

algorithms, whose power usage is in the order of a few milliwatts. This means that for the

same power as using Zigbee or similar protocols, we can both transmit and process data.

Furthermore, if the results of the signal processing in central node are exploited

fully, the amount of data that needs to be transmitted can be greatly reduced, thus lowering

power usage still further. Therefore, the power saved by transmitting using the proposed

protocol can be used to provide clinically meaningful information and greatly reduce the

overall power usage of the device.

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5. CONCLUSION

This paper presents a new energy-efficient MAC Protocol targeted at wireless body

area sensor networks focused on pervasive healthcare applications. The protocol exploits the

attributes of this type network to implement a very low power architecture which is still

capable of fast reaction to sporadic Alarm events. Reducing the power requirements for the

communication part of the system allows allocation of more energy to more accurate DSP for

different applications. The proposed scheme also results in very reliable data transfer, which

is crucial in medical applications.

The novel concept of ‗wakeup fallback‘ time is also presented as a means of reducing

the complexity of time-slot management in the presence of link failures resulting from Alarm

events or other interference. The MAC has been implemented as part of a larger SoC

(Sensium™), and measured results have validated the effective operation of the new MAC

protocol.

The New Protocol was also compared with three other popular protocols, and results

showed improvements in communication power consumption and duty cycle.

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6. FUTURE SCOPE

Future Scope of work includes incorporating more efficient forward error correction

codes to improve reliability of data transfer, as well as research into the best strategies for

sensor to central node communication. The central node can be made more intelligent by

incorporating more capable signal processors to analyse data.

If the need for the more flexible network should arise, various solutions for network

forming and management should be investigated.

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REFERENCES

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[2] I. E. Lamprinos, A. Prentza, E. Sakka, and D. Koutsouris, ―Energy efficient MAC

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3799–3802.

[3] N. Chevrollier and N. Golmie, ―On the use of wireless network technologies in healthcare

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[4] E. Jovanov, A. Milenkovi, C. Otto, P. D. Groen, B. Johnson, S.Warren, and G. Taibi, ―A

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