Underwater Acoustic Sensor Networks: Medium Access Control,

Routing and Reliable Transfer

Peng XieDissertation Proposal

Committee: Jun-Hong Cui, Reda A Ammar, Sanguthevar Rajasekaran, Bing Wang

Computer Science & Engineering

University of Connecticut

Outline

• Introduction– Motivation & challenges

• Three fundamental networking problems– Medium access control– Multi-hop routing– Reliable data transfer

• Conclusions and future work

Why Underwater?

• The Earth is a water planet– About 2/3 of the Earth covered by oceans

• Largely unexplored, huge amount resources to discover

• Many potential applications– Long-term aquatic monitoring

• Oceanography, seismic predictions, pollution detection, oil/gas field monitoring …

– Short-term aquatic exploration• Underwater natural resource discovery, anti-submarine

mission, loss treasure discovery …

Application Requirements

• Desired properties– Unmanned underwater exploration

– Localized and precise data acquisition for better knowledge

– Tetherless underwater networking for motion agility/flexibility

– Scalable to 100’s, 1000’s of nodes for bigger spatial coverage

Underwater Sensor Networks (UWSNs)

The Ideal Technique:

State-of-the-Art of UWSNs

• Pioneering projects:– Seaweb, AOSN, SNUSE, NIMS

• Current status:– Static sensor networks– Medium/long communication range– Small scale design and deployment

• Demands for mobile UWSNs (M-UWSNs)– Submarine detection, estuary monitoring, etc.

Application Scenario ISubmarine Detection

Buoys

Radio

Acoustic

Data Report

Sonar Transmitter

Application Scenario IIEstuary Monitoring

Fresh

Salty

Fresh Water Current

Salty Water Current

BuoyancyControl

BuoyancyControl

Underwater Communication Characteristics

• Narrow available bandwidth– Radio is unsuitable for underwater sensor networks– Must use acoustic channels

• High attenuation– Data rate x Range = 40 Kbps x Km

• Very slow acoustic signal propagation– 1.5x103 m / sec vs. 3x108 m / sec– Causes large propagation delay

Research Challenges

• UnderWater Acoustic (UW-A) channel: – Narrow available band: hundreds of kHZ at most– Huge propagation latency– High channel error rate

• Random topology and sensor node mobility (1--2m/s due to water current)

– Existing protocols in terrestrial sensor networks assume stationary sensor node

• Mobility & UW-A channel limitations open the door to very challenging networking issues

Objective & Contributions

• The final objective: – Build efficient, reliable, and scalable M-UWSNs

• This dissertation work address three fundamental networking issues:– Medium access control (resolving collision efficiently)– Multi-hop routing (routing data to sink efficiently)– Reliable data transfer (improving network reliability)

• This is the first Ph.D. proposal in the domain of underwater sensor networks at UCONN

Related PublicationsMedium Access Control• Peng Xie and Jun-Hong Cui, Exploring Random Access and Handshaking

Techniques in Large-Scale Underwater Wireless Acoustic Sensor Networks , Proceedings of IEEE/MTS OCEANS'06, Boston, Massachusetts, USA, September 18-21, 2006

• Peng Xie and Jun-Hong Cui, An Energy-Efficient MAC Protocol for Underwater Sensor Networks, to-be-submitted

Multi-hop Routing• Peng Xie and Jun-Hong Cui, SDRT: A Reliable Data Transport Protocol for

Underwater Sensor Networks , UCONN CSE Technical Report: UbiNet-TR06-03, February 2006

• Zheng Guo, Peng Xie, Jun-Hong Cui, and Bing Wang, On Applying Network Coding to Underwater Sensor Networks , Proceedings of ACM WUWNet'06 in conjunction with ACM MobiCom'06, Los Angeles, California, USA, September 25, 2006

Reliable Data Transfer• Peng Xie, Jun-Hong Cui, and Li Lao, VBF: Vector-Based Forwarding Protocol for

Underwater Sensor Networks , In Proceedings of IFIP Networking'06, Coimbra, Portugal, May 15 - 19, 2006

• Peng Xie, Jun-Hong Cui, and Li Lao, VBF: Vector-Based Forwarding Protocol for Underwater Sensor Networks , UCONN CSE Technical Report: UbiNet-TR05-03 , February 2005

Outline

• Introduction– Motivation & challenges

• Three fundamental networking problems– Medium access control– Multi-hop routing– Reliable data transfer

• Conclusions and future work

Medium Access Control

• General objectives: – Resolve collisions efficiently and effectively

• Evaluation metrics:– Channel utilization– Energy efficiency– Fairness– Delay– …(More depending on applications)

Challenges in M-UWSNs

• UW-A channel characteristics:– Long propagation delay

• Signal cannot reach dest. instantaneously

– Narrow communication bandwidth• Low data rate• Bandwidth must be shared by all nodes

• Passive sensor node mobility– Dynamic neighborhood makes coordination

very difficult if not impossible

Examine MAC Techniques

• Contention-free approach– TDMA, FDMA, CDMA

• Contention-based approach– Random access: ALOHA, slotted ALOHA– Collision avoidance with handshaking (RTS/CTS):

MACA, MACA-W

• We conducted a systematic study of random access and handshaking [Xie06:Oceans]

– Random access: sparse networks & low data traffic– RTS/CTS: dense networks & high data traffic

Existing MAC Protocols for Underwater Sensor Networks

• [Rodoplu05:Oceans]:– Network with ultra-low data traffic– Energy efficiency– Random access

• [Molins06:Oceans]:– Sparse networks– Channel utilization– RTS/CTS-based

Our Solution

• We propose R-MAC– A reservation-based MAC protocol

• Targeted networks– Traffic unevenly distributed & sporadic – Energy-efficiency is the highest priority – Channel utilization is not a critical concern

Basic Idea of R-MAC

• Each node works in cycles – Each node wakes/sleeps periodically

• A node sends data to another node– Sender reserves a time slot in receiver– Receiver informs all neighbors of reserved time slot – Sender sends data in reserved time slot

• How to make reservation? – Measuring propagation delays – Scheduling transmissions

The R-MAC Protocol

• Three phases– Latency detection

• Measure latencies between neighbors

– Period announcement• Collect period start times of neighbors

– Periodic operation• Reserve slot in intended node and send data

Phase I: Latency Detection

• Latency between A and B is: L= (T1-T2)/2

Node A

Node B

T1

T2L L

Phase II: Period Announcement

• Each node randomly selects period start time• Node B calculates difference of period start time

of node A with its own start time

LB-LA+LAB

LA

LAB

LB

LB-LA+LAB

A

B

Phase III: Periodic Operation (1)

• Each node powers on (listen window) and off (sleep window) periodically

• Data transmission is completed through REV/ACK-REV/DATA/ACK-DATA

• ACK-REV is treated with the highest priority – The first part of the listen window is reserved for

ACK-REV exclusively, called R-window– REV, DATA, ACK-DATA are scheduled to avoid the

R-windows of all nodes in the neighborhood

Phase III: Periodic Operation (2)

• The sender:– deliver REV to the target node in its listen window– specify the offset and duration of the reserved time

slot for data transmission in REV

• The receiver:– deliver ACK-REV to the sender in its R-window– reserve a timeslot for data transmission – deliver ACK-DATA after receiving data packets

• Other nodes: – Back off if receiving the ACK-REVs or sensing

collision in their R-windows

Sender in R-MAC

• Sender A schedules the transmission of REV to receiver B• Sender A specifies offset and duration of reserved time slot

Reserved time slot

STA

B

C

REV

REV

Receiver in R-MAC

• Receiver B schedules to send ACK-REVs to all neighbors• Sender A schedules the reserved time slot and Node C

keeps silence in this time period

time slot

A

B

C

Silence

ACK-REV

ACK-REV

Performance Evaluation

• Simulation settings:– Power consumption (UWM1000)

• Tx:2 Watts, Rx:0.75 Watts, idle:8 mW– Data rate

• 10kbps– Transmission range

• 90 m

• Performance metrics:– Goodput:

• Number of packets successfully received by receiver– Overhead:

• Energy consumption per data packet

Topology for Fairness

Node 3

Node 1

Node 4

Node 2

Node 0

80 m

20 m

60 m

20 m

Fairness

• All the nodes have almost equal goodputs

0 0.1 0.2 0.3 0.4 0.50

100

200

300

400

500

600

700

data rate (pkts/sec)

Go

od

pu

t

Node 1Node 2Node 3Node 4

Topology for Energy Efficiency

Node 3

Node 1

Node 4

Node 2

Node 0

30 m

20 m

40 m

20 m

Energy Efficiency

• R-MAC is more energy efficient than T-MAC

0 0.05 0.1 0.15 0.2 0.25

0.2

0.25

0.3

0.35

0.4

0.45

0.5

data rate (pkts/sec)

ove

rhe

ad

(Jo

ule

/pkt) R-MAC

T-MAC

Summary

• R-MAC – is energy-efficient– can achieve fairness– guarantees data packets collision-free (formal

proof)

Future Work

• Improve robustness of R-MAC against noisy channels

• Design efficient MAC solutions for mobile networks

Outline

• Introduction– Motivation & challenges

• Three fundamental networking problems– Medium access control– Multi-hop routing– Reliable data transfer

• Conclusions and future work

Challenges in M-UWSNs

• Hardest network environments for routing– Dynamic network topology– Large network scale– 3-dimensional space– High error probability– Energy constraint– Routing “voids”

Existing Routing Protocols for Terrestrial Sensor Networks

• Protocols for terrestrial sensor networks:– Directed Diffusion (DD)– GRADient Broadcast (GRAB)– Two-Tier Data Dissemination (TTDD)

• They are unsuitable for M-UWSNs– Dynamic network topology – 3-dimensional deployment

Our Solution

• We propose Vector-Based Forwarding (VBF) – A scalable, efficient and robust geo-routing approach

• The basic idea of VBF– Forwarding path represented by a vector– Node receiving packets

• Calculate its relative position• Forward packets if close to the vector

– Qualified nodes are in “routing pipe”• Controlled by pipe radius: W

VBF – An Illustration

VBF Enhancement

• Observations in dense networks– Too many nodes involved in data forwarding

• Solution: self-adaptation– Each node weighs the gain to forward a packet – Forwards packets adaptively

• Benefits of self-adaptation– Reduce energy consumption– Reduce packet collision– Can find optimal path (formal proof)

Self-Adaptation Algorithm

Pd

D Ad

F

Source(s1)

Sink(s0)

WW

R

A

W

p dd R B

Performance Evaluation

• Simulation settings:– 100×100×100 m3 cube– Transmission range: 20m– Source and sink are fixed– Other nodes are mobile

• Performance metrics:– Success rate (measure robustness)– Communication time (measure energy cost)

Impact of Density and Mobility

• VBF handles node mobility efficiently and effectively, and node density affects success rate and energy consumption

significantly

012345

500700

9001100

130015000

0.2

0.4

0.6

0.8

1

Number of nodesSpeed of nodes

Succ

ess

rate

(%)

01

23

45

500700

9001100

130015000

200

400

600

Number of nodesSpeed of nodes

com

mun

icat

ion

time

(sec

ond)

Impact of Pipe Radius

• When the pipe radius is large enough, VBF has the same success rate as naive flooding but with much less energy consumption

0 10 20 30 40 500

0.2

0.4

0.6

0.8

1

Radius (meter)

Succ

ess

rate

(%)

VBF

Naive Flooding

0 10 20 30 40 500

500

1000

1500

2000

Radius (meter)

Com

mun

icat

ion

time

(sec

ond)

VBFNaive Flooding

Robustness

• VBF is robust against packet losses and node failures

0 0.1 0.2 0.3 0.4 0.50

0.2

0.4

0.6

0.8

1

Error probability

Su

cce

ss r

ate

(%)

Robustness-packetlossRobustness-nodefailure

Summary

• VBF is– Energy efficient– Scalable– Robust (formal analysis)

Future Work

• Improve VBF– Adapt to non-uniformly distributed networks– Propose solutions to avoid routing “voids”

Outline

• Introduction– Motivation & challenges

• Three fundamental networking problems– Medium access control– Multi-hop routing– Reliable data transfer

• Conclusions and future work

Challenges in M-UWSNs

• Hardest network environments for RT– Highly error-prone communication channel– Long end-end propagation delay– Half-duplex acoustic channel– Dynamic network topology– Energy constraint

Examining Common Wisdoms• End-to-end approach

– not work well due to large RTT & high error probability

• Half-duplex channels limit complex ARQ– can only use Stop & Wait protocols – enhanced version to improve channel utilization

• S & W protocols with many feedbacks– have low energy efficiency

• Pure FEC approach– usually not energy efficient

Our Solution

• We propose segmented data reliable transport (SDRT) – A hybrid approach of FEC and ARQ

• The basic idea of SDRT– Data are first grouped into blocks at source– Each block encoded in simple & efficient codes– Source keeps pumping encoded data into

network till receiving a positive feedback in half-duplex channels

– Block-by-block and hop-by-hop

Advantages

• Reduce # of feedbacks

• Reduce # of packets transmitted

• Improve channel utilization

• Enhance energy efficiency

• Simplify protocol management

Performance Evaluation

• Simulation settings– Packet size 40 B– 1000 data packets and 600 check packets (per block)– Simple Variant Tornado (SVT) codes: Λ=(0,0,0,1)

and ρ= (0,0,0,0,1/8,0,7/8)

• Performance metrics:– Goodput

• The ratio of # of orig. data packet to the total time

– Inefficiency• The ratio of # of total packets sent to # of orig. data packets

Goodput

• SDRT improves the goodput significantly

0.1 0.2 0.3 0.4 0.50.01

0.1

1

10

Error probability

Go

od

pu

t (k

bp

s)

SDRTNaive ARQAccumulative-ARQ

Inefficiency

• SDRT reduces the number of packets sent

0.1 0.2 0.3 0.4 0.50

5

10

15

20

Error probability

Ine

ffic

ien

cy

Carousel-r3SDRT-r3Naive ARQ-r3Accumulative-ARQ-r3

SDRT Enhancement

• Observation: – Distance between sender and receiver: 30m RTT

(single hop) is 40ms time for trans. more than 60 packets (if packet size is 40bytes, data rate=500kbps)

– Too much overhead before receiving ACK

• Window size control– estimate # of packets for data reconstruction– send packets within window faster– send packets outside window slower– Thus save energy

• Critical to estimate the window size!

Simple Variant of Tornado Code

• Two-layer encoding scheme• Left degree is at least 3• Smaller maximum degree

dcb

dcba dca

a

b

c

d

dba

Model Validation (using SVT codes)

• Our model approximates the simulation results very well

0 0.1 0.2 0.3 0.4 0.5 0.61

1.5

2

2.5

3

3.5

4

4.5

Error probability

Ine

ffic

ien

cy

SimulationModel

Summary

• SDRT:– Improves channel utilization & energy efficiency– Relieves sender & receiver of manage burden – Well addresses dynamic network topology

Future Work

• Examine network coding for robustness

• Investigate congestion control

Outline

• Introduction– Motivation & challenges

• Three fundamental networking problems– Medium access control– Multi-hop Routing– Reliable data transfer

• Conclusions and future work

Medium Access Control• Current Status:

– Modeled and compared random access and handshaking (RTS/CTS) techniques

– Proposed an energy efficient protocol (R-MAC) for static networks

– Developed a simulation package for physical acoustic link and MAC in ns-2

• Future Work– Improve robustness of R-MAC in noisy channels – Design MAC solutions for mobile networks

Multi-hop Routing

• Current Status:

– Proposed a robust and energy-efficient routing protocol (VBF)

– Developed a self-adaptation algorithm to enable VBF to be adaptive to network density

– Implemented VBF in ns-2

• Future Work:– Enable VBF to handle non-uniform networks– Propose solutions to avoid routing voids

Reliable Data Transfer

• Current Status:– Proposed an efficient reliable protocol (SDRT)– Developed a model to estimate # of packets

needed

• Future Work:– Implement SDRT in ns-2– Examine network coding for robustness– Investigate congestion control and avoidance

Simulation Toolkit

• Current Status– Implemented acoustic physical link– Implemented R-MAC and VBF – Implemented MAC broadcast

• Future Work– Develop a complete package for all layers– Validate acoustic model with measurements

• Goal: release UWSN simulation package to the research community

Questions?

Thank You!