DAREPage 1 Distance Aware Relaying Energy-efficient: DARE to Monitor Patients in Multi-hop Body Area Sensor Networks Prepared by: Anum Tauqir.

DARE Page 1

Distance Aware Relaying Energy-efficient:DARE to Monitor Patients in Multi-hop

Body Area Sensor Networks

Prepared by:Anum Tauqir

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Outline Overview

Problem Statement

Motivation

Brief Overview of M-ATTEMPT and DARE

DARE

DARE Scenarios

Communication Flow

Differences and Similarities

Simulation Results

Conclusion

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Overview

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In medical field:

WBAN makes use of the tiny sensors for detecting and monitoring

different biological characteristics of a human body.

The sensors can either be:

in-vivo

wearable

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Problem Statement

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Major concerns for BANs:

minimizing energy consumption of the nodes

enhancing network lifetime

enhancing stability period of the network

maximizing throughput

minimizing delay

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Motivation

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Monitoring different organs of a human body for detecting

an ailment or any disorder.

The proposed protocol DARE aims:

to improve the deficiencies in a BAN protocol of M-ATTEMPT

namely,

minimum stability period

minimum network lifetime

high energy consumption

low throughput

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Brief Overview ofM-ATTEMPT and DARE

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M-ATTEMPTa heterogeneous protocol named as, Mobility-supporting Adaptive Threshold-based Thermal-aware Energy-efficient Multi-hop ProTocol

DAREa heterogeneous protocol named as, Distance Aware Relaying Energy-efficient Protocol to Monitor Patients in Multi-hop Body Area Sensor Networks

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DARE

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Ward dimensions - 40 x 20 ft2

Five scenarios

Eight beds

Seven sensors measuring parameters

LOS communication

Network Topology

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Classification of Body Sensors

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Energy Model

Parameter Value

ETXelec 16.7 nJ/bit

ERXelec 36.1 nJ/bit

Eamp (3.8) 1.97 nJ/bit

Eamp (5.9) 7.99 nJ/bit

w 4000 bits

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Equations

Etx(k,d) = ETXelec * k + Eamp(n) * k * dn

Erx(k) = ERXelec * k

Transmitter Energy

Receiver Energy

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Protocol’s Patient

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DARE Scenarios

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Scenario-1

The BSs on each patient carry information and transmit to their respective BR which, then aggregates and relay the received data to the sink located at the center of the

ward. The communication flow is from BSs to BR to Sink.

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Scenario-2

Four sinks have been used that are separately deployed in the middle of the walls of the ward. The BSs of each patient, on sensing the vital sign transmit data to their

respective BR. The BR checks for the nearest sink by calculating it’s distance with each sink. Whichever, sink is found nearest, the BR communicates with that particular sink. The communication flow is from BSs to BR to nearest Sink (Sink1 or Sink2 or Sink3 or

Sink4).

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Scenario-3

MS is incorporated on each bed which, can be a PDA type device. The deployment of MS helps the BR to consume little energy as, BR transmits data over shorter distance.

However, this scenario increases the delay in the network, as the data traverses through a long route towards the destination node, the Sink. Communication flow is

from BSs to BR to MS to Sink.

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Scenario-4

It follows the same communication flow as sceanrio-1, however, now the sink is made mobile which, moves along the center of ward.

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Scenario-5

Multiple sinks move around the walls of the ward altogether. In this scenario also, each BR measures it’s distance with each sink. Whosoever is found close, the BR starts communicating with that sink. The communication flow is from BSs to BR to the

nearest moving Sink (Sink1 or Sink2 or Sink3 or Sink4).

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Communication Flow

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Differences and Similarities

• DARE• M-ATTEMPT

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Parameter DARE M-ATTEMPTTypes of devices Body Sensors (BSs), Body Relay (BR),

Main Sensor (MS), SinkSensors, Sink

Deployment BSs, BRs and MS are fixedSink can either be static or mobile

Sensors and Sink bothare fixed

Topology per patient 7 BSs1 BR on chest

7 Sensors1 Sink on chest

Communication flow Depending upon scenario Sensors to Sinkor Sensors to other Sensorsto Sink

Energy parameters E0BSs = 0.3 JE0BR = 1 JEMS = infiniteESink = infinite

E0Sensors = 0.3 JESink = infinite

Network type Heterogeneous in terms of energy of BSs and BRs

Homogeneous interms of energy of Sensors

Communication type Multi-hop Single-hopMulti-hop

Types of data reporting Event-drivenTime-driven

Event-drivenTime-driven

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Simulation Results

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Alive Nodes (BSs and Sensors)

Number of remaining alive nodes (BSs) in the network

0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

10

20

30

40

50

Number of Rounds (r)

Num

ber

of A

live

(BS

s)

M-ATTEMPT AliveDARE Alive

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Alive Nodes (BSs, BRs and Sensors)

Number of remaining alive nodes (BSs + BRs) in the network

0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

10

20

30

40

50

60

Number of Rounds (r)

Num

ber

of A

live

node

s (B

Ss

+ B

Rs)

Scene1-AliveScene2-AliveScene3-AliveScene4-AliveScene5-AliveM-ATTEMPT-Alive

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Residual Energy

Residual energy (BSs) of the network

0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

0.05

0.1

0.15

0.2

0.25

Number of Rounds (r)

Res

idua

l Ene

rgy

of B

Ss

(J)

M-ATTEMPT-EnergyDARE-Energy

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Packets Sent to Sink

Number of packets sent to sink

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Throughput (%)

Packet delivery ratio

1 2 3 4 5 60

10

20

30

40

50

60

70

80

90

100

Number of Rounds (r)

Pac

ket D

eliv

ery

Rat

io (

%)

Scene1-ThrouhgputScene2-ThrouhgputScene3-ThrouhgputScene4-ThrouhgputScene5-ThrouhgputM-ATTEMPT-Throuhgput

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Conclusion

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DARE achieves:

increased network lifetime

increased stability period

From 23% (M-ATTEMPT) to 72% (DARE)

minimum energy consumption

increased throughput

Suitable for networks requiring:

no human intervention

huge data to transmit

However, M-ATTEMPT provides:

minimum propagation delay

Suitable for networks where:

critical data needs to be sent, urgently

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Comparison results between DARE and M-ATTEMPT

Parameter DARE M-ATTEMPT

Stability period high low

Network lifetime high low

Energy consumption minimum maximum

Throughput high low

Propagation delay high low

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