VAPR: Void Aware Pressure Routing for Underwater Sensor

Networks

Youngtae Noh, Student Member, IEEE, Uichin Lee, Member, IEEE, Paul Wang, Member, IEEE, Brian Sung Chul Choi, Member, IEEE, and Mario Gerla, Fellow, IEEE

IEEE TRANSACTIONS ON MOBILE COMPUTING 2012

Outline

• Introduction

• Overview

• VAPR

• Simulations

• Conclusions

Introduction

• Underwater acoustic sensor networks have many applications– Environmental monitoring

– Intrusion detection

Introduction

• A large number of mobile sensor nodes are deployed in the region of interest for exploration

• Acoustic transmissions consume far more energy than terrestrial radio communications

Introduction

• Each sensor reports data to any one of the sonobuoys with acoustic multi-hop routing

• Simple greedy pressure routing often fails in sparse underwater networks due to the presence of 3D voids

• Each node is equipped with a variety of sensors (ex : pressure gauges) and a low bandwidth acoustic modem

Goal

• Design an efficient routing protocol for underwater data collection – Addresses several challenges unique to underwater

communications

Overview

• Enhanced beaconing– Sonobuoys broadcast the beacon to sensor nodes

– The direction is set as up when a beacon is received from a shallower depth node

• Opportunistic directional data forwarding

Overview

• Enhanced beaconing– Sonobuoys propagate their reachability information to sensor

nodes

– The direction is set as up when a beacon is received from a shallower depth node

• Opportunistic directional data forwarding– Sensor nodes forwarding the data

VAPR

• Assume– Sonobuoys on the surface are equipped with GPS, clocks are

synchronized

– Use the same sequence number for periodic beaconing

– DF_dir(node) ← up

– Hop_count(node) ← 0

VAPR

• Local max node : a node whose depth level is shallower than neighboring node, but deeper than the sonobuoys

• Trapped node : a node in which greedy forwarding eventually leads to a local max node

• Trap area : the area in which trapped nodes reside

• Regular node : the rest of the nodes

VAPR

• Enhanced beaconing1. broadcast beacon message

→Sender’s depth, DF_dir, SN, Hop_cnt

2. check SN(↑), Hop_cnt(↓)

3. set DF_dir

y

w

x

c

b

az

Sobobuoy’s depthDF_dir : UPSN : 104Hop_cnt : 0

Sonobuoy

a’s depthDF_dir : UPSN : 103Hop_cnt : 1

b’s depthDF_dir : UPSN : 102Hop_cnt : 2

x’s depthDF_dir : DNSN : 101Hop_cnt : 3

y’s depthDF_dir : DNSN : 100Hop_cnt : 4

Local Maximum

Monitoring Center

n

m

l

k

VAPR

• Enhanced beaconing– Multiple direction from different sonobuoys are received

• Node updates its status based on the higher SN

• SN is the same – Smaller Hop_cnt Monitoring

Center

n

m

l

ky

w

x

c

b

az

Sobobuoy’s depthDF_dir : UPSN : 104Hop_cnt : 0

Sonobuoy

a’s depthDF_dir : UPSN : 103Hop_cnt : 1 x’s depth

DF_dir : DNSN : 101Hop_cnt : 3

Local Maximum z’s depth

DF_dir : DNSN : 99Hop_cnt : 5

MC’s depthDF_dir : UPSN : 104Hop_cnt : 0

n’s depthDF_dir : UPSN : 103Hop_cnt : 1

l’s depthDF_dir : UPSN : 101Hop_cnt : 3

VAPR

• Opportunistic directional data forwarding– UP-UP, DN-DN, DN-UP, UP-DN

– Based on DF_dir, NDF_dir()

– Choosing the nodes whose DF_dir = NDF_dir of the current node

DN-UP

DN-DN

UP-DN

UP-UP

UP-UP

UP-UP

Sonobuoy

VAPR

• Higher priority node (based on the distance) transmits a packet – Suppress forwarding to prevent redundant packet transmissions and

collisions

• Finding an optimal set is computationally hard

VAPR

• Greedy clustering approach• Each node knows 2-hop connectivity and neighboring nodes’ pairwise

distances

• Data are periodically reported to the surface

S

F

A

BC

D

E

F

Can hear each other →no hidden terminals

Group

Simulations

Simulations

• Fraction of nodes reachable to sonobuoys

Simulations

• PDR(1 sonobuoy scenario)

Simulations

• Energy consumption per message(1 sonobuoy scenario)

Simulations

• Average latency(1 sonobuoy scenario)

Simulations

• PDR(64 sonobuoy scenario)

Simulations

• Energy consumption per message(64 sonobuoy scenario)

Simulations

• Greedy forwarding success rate

Simulations

• Average PDR(beacon intervals)

Simulations

• Energy per node per message(beacon intervals)

Conclusions

• This paper proposed a Void Aware Pressure Routing (VAPR) protocol in sparse underwater networks has been the efficient handling of 3D voids

• The simulations showed that VAPR outperforms existing schemes

Thanks you