E2DTS: An energy efficiency distributed time synchronization algorithm

for underwater acoustic mobile sensor networks

Zhengbao Li, Zhongwen Guo, Feng Hong, Lu Hong

Department of Computer Science, College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China

Ad Hoc Networks 2011

2

Outline

Introduction Related Works Goals Algorithms Simulations Conclusions

3

Introduction

Time synchronization is an important issue in a variety of network.

Underwater acoustic mobile sensor networks (UAMSNs) acoustic channel the speed of sound in water cause acoustic wave to travel on

curved paths temperature, density and salinity

high propagation delay can reduce the throughput

4

Introduction

This multivariant speed adds troubles in predicting propagation delay.

Thus, time synchronization is also estimated difficultly.

5

Introduction

Time synchronization

skew offset

unsynchronized timeaccurate time

( )i i iT t a t b

7

Related Works

TSHL

A.A. Syed, J. Heidemann, Time synchronization for high latency acoustic networks, in: Proc. INFOCOM 2006, April 2006, pp. 1–12.

TSHL is designed for static underwater sensor networks, which assume long but constant propagation delay

8

Related Works

MU-Sync The clock skew and offset is estimated by applying linear

regression twice over a set of n reference beacons.

Nitthita Chirdchoo, Wee-Seng Soh, Kee Chaing Chua, MU-Sync: a time synchronization protocol for underwater mobile networks, in: Proceedings of WuWNet’08, September 15, 2008.

9

Related Works

Linear regression

( )f x ax b Least Squares

10

Related Works

MU-Sync The clock skew and offset is estimated by applying linear

regression twice over a set of n reference beacons.

It does not take into account the propagation delay different from round trip time, which causes the estimate error

11

Goals

In underwater acoustic mobile sensor networks(UAMSNs) Design an energy efficiency distributed time synchronization

algorithm (called “E2DTS”)

12

Assumptions

The speed of sound in underwater is 0.3 m/s Mobility of each node is considered in this paper

Underwater node: resulted in the mobility of ocean current Beacon node (AUV): automatically moving

13

Algorithms_overview

S

B

REF REF REF

Beacon node

Unsynchronized node

14

Algorithms_overview

skew

offset

15

Algorithms

Phase I: estimated clock skew a

Ts=at+b

iiB tT

bttaT id

iis )(

)()(1

1 a

bTTvT

a

bTvLL

isi

Bnodei

B

is

nodeii

s

iid v

Lt

idt

1 idt

16

Algorithms

Phase I: estimated clock skew a

a

Tvvt

is

nodesid

a

T

v

vt

is

s

nodeid

=>

bttaT id

iis )(

17

Algorithms

Phase II: estimated clock offset b

19

Simulations

Parameter Value

Simulator NS2

The maximum speed of sensor (Vmax) 3 m/s

Clock initial skew 40 ppm

Clock initial offset 10 ppm

Clock granularity 1 s

Receive jitter 15 s

Number of beacon messages 25

The speed of each sensor 0 to Vmax

The direction of each sensor [ -45, 45]

The time interval of sending beacon message 1 s

20

Simulations

22

Simulations

23

Simulations

24

Conclusions

The E2DTS reduces the synchronization errors in underwater acoustic mobile sensor networks.

In simulations, the E2DTS has the best performance in synchronization accuracy.