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!
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