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SINTEF ICT

ITEM, NTNU, Aug 29, 2014

– their practical use and networking protocols research 

Arne Lie, 

SINTEF ICT, 

Dept. of Communication Systems

(2)

SINTEF ICT

ITEM NTNU, Aug 29, 2014

1.

Motivation

 

by

 

history

2.

SINTEF

 

ICT

 

projects

3.

Protocol development framework

2

(3)

SINTEF ICT

ITEM NTNU, Aug 29, 2014

1.

Motivation

 

by

 

history

2.

SINTEF

 

ICT

 

projects

3.

Protocol development framework

3

(4)

SINTEF ICT

History

Underwater

 

communication

 

has

 

been

 

used

 

by

 

marine

 

animals

 

for

 

millions

 

of

 

years

 

– Man: first noted over 2,000 years ago by Aristotle

1490,

 

Leonardo

 

Da

 

Vinci:

– "If you cause your ship to stop and place the head of 

a long tube in the water and place the outer 

extremity to your ear, you will hear ships at a great 

distance from you"

4

(5)

SINTEF ICT

History

 

— Applications:

 

depth

 

of

 

sea

• One of the first applications that scientists explored was to 

determine the depth of the sea by listening for echos.

• Need to know speed of acoustics in water

– 1826 

• Fresh water (Lake Geneva, "Genfersjøen", 8 deg C): 1435 m/s

• Fresh water modern calculator

• In 1838, Charles Bonnycastle performed the first known echo 

sounding experiments

5

In 1826 on Lake Geneva, 

Switzerland, Jean‐Daniel 

Colladon, a physicist, and 

Charles‐Francois Sturm, a 

mathematician, made the first 

recorded attempt to determine 

the speed of sound in water.

(6)

SINTEF ICT

History

 

— World

 

War

 

I

Detection

 

of

 

submerged

 

submarines

 

and

 

mines

– underwater acoustics became closely associated with military applications 

During

 

WWI,

 

submarines

 

detected

 

by

 

listening for

 

their

 

engines

 

or

 

propellers

– Two hydrophones used for bearing detection

Later,

 

submarines

 

became

 

much

 

quieter

– passive listening difficult

6

(7)

SINTEF ICT

Wave

 

refraction

Hugo

 

Lichte,

 

a

 

German

 

scientist,

 

developed

 

a

 

theory

 

in

 

1919

 

on

 

the

 

bending,

 

or

 

refraction

,

 

of

 

sound

 

waves

 

in

 

sea

 

water.

Sound

 

waves

 

would

 

be

 

refracted

 

when

 

they

 

encountered

 

slight

 

changes

 

in

 

temperature,

 

salinity,

 

and

 

pressure,

 

i.e.,

 

speed

 

of

 

sound

 

changes

Need

 

to

 

know

 

how

 

sound

 

speed

 

changed

 

with

 

water

 

depth

to

 

predict

 

echo

 

ranging

 

performance

.

 

7

(8)

SINTEF ICT

Sound

 

speed

 

c,

 

SSP,

 

and

 

the

 

"SOFAR"

• SOFAR: SOund Fixing And Ranging

– Long‐range sound channel that allows low‐frequency sound to travel great distances.

8

(m/s)

(m)

(9)

SINTEF ICT

• Modified Sonar equations along each ray

• Channel impulse response: important for communication

9

Ray

tracing

 

tools

 

can

 

calculate

 

channel

 

response

(10)

SINTEF ICT

Wireless

 

acoustic

 

communication

WHAT:

 

Underwater

 

sensors

 

&

 

online

 

data

 

harvesting

– Environmental monitoring, 

– underwater exploration, 

– robot control, 

– object detection, etc.

WHY

 

not

 

use

 

radio

 

electromagnetics?

– Salt water attenuates radio waves very fast

– Fresh water may use it, e.g., sensor networks in rivers

WHY

 

not

 

use

 

wired networks?

– Cables are heavy, deployment is expensive

– Infrastructure may not be available

HOW:

 

data

 

modulation

 

of

 

audio

 

frequencies

 

(e.g.,

 

FSK,

 

BPSK,

 

QPSK)

10

(11)

SINTEF ICT

Characteristics

 

&

 

challenges

 

of

 

the

 

acoustic

 

channel

Long

 

range

– Several km possible

Significant

 

propagation

 

delay

– 1.5 km  1500m/1500m/s = 1 second in water

– 1.5 km  1500m/3e8m/s = 5 microseconds RF 

in air

• 5 orders of magnitude larger!

Small

 

bandwidth

larger

 

at

 

shorter

 

distance!

   

============>

A

 

few

 

kbps

 

data

 

rate

 

possible

Time

Variable

 

link

 

quality

Refraction

 

and

 

reflections

 

gives

 

time

varying

 

multipath

 

channel

Adaptive

 

channel

 

equalization

 

is

 

necessary

Range

 

can

 

vary

 

by

 

the

 

minute

11

(12)

SINTEF ICT

Other

 

constraints

Battery

 

powered

– Output Tx power need to be high, 

• e.g. 300W can produce about 190 dB rel. 1 uPa

• deployed system should stay long in the water

Half

duplex

 

channel

12

(13)

SINTEF ICT

ITEM NTNU, Aug 29, 2014

1.

Motivation

 

by

 

history

2.

SINTEF

 

ICT

 

projects

3.

Protocol development framework

13

(14)

SINTEF ICT

Part

 

II

SINTEF

 

ICT

 

underwater

 

acoustic projects 2007– :

NNN

UAN

OSS

CLAM

SensIs

(15)

SINTEF ICT

Del

 

II

SINTEF

 

ICT

 

underwater

 

acoustic projects 2007– :

NNN

UAN

OSS

CLAM

SensIs

cooperation with Kongsberg

 

Maritime

»

transducers,

 

PA,

 

underwater

 

housing,

 

DSP

(16)

SINTEF ICT

Del

 

II

SINTEF

 

ICT

 

underwater

 

acoustic projects 2007– :

NNN

UAN

OSS

CLAM

SensIs

(17)

SINTEF ICT 17

SEP: SINTEF ICT internally financed project 2006‐2007

• Underwater acoustic propagation measurements

• Publication IEEE Oceans 2008, Quebec 

NNN‐UWSN – Underwater sensor network 2007 ‐ 2010

• Kongsberg Maritime, Statoil, Western Geco, Institute 

of Marine Research 

• Communication solutions & protocols

• Network tests in Horten

• Publication IEEE Oceans 2009, Bremen 

NNN‐UWSN – Underwater sensor network 2007 ‐ 2010

• Kongsberg Maritime, Statoil, Western Geco, Institute 

of Marine Research 

• Communication solutions & protocols

• Network tests in Horten

• Publication IEEE Oceans 2009, Bremen 

NNN

 

(NFR

 

project

 

2007–2010)

Nordområdenes Nye

 

Nervesystem

(18)

SINTEF ICT

Del

 

II

SINTEF

 

ICT

 

underwater

 

acoustic projects 2007– :

NNN

UAN

OSS

CLAM

SensIs

(19)

SINTEF ICT

UAN

 

partners

 

(EU

 

project

 

2009–2011)

CINTAL, Portugal (co‐located with University of Algarve)

– Centro de Investigação Tecnologica do Algarve, non‐for‐profit research organization

– Project coordinator

FOI, Sweden (Swedish Defense Research Agency)

– UAN: Turbo equalization techniques

Kongsberg Maritime, Norway

– Underwater acoustic modems

ISME, Italy (University of Genova, Pisa, ++)

– Integrated Systems for the Marine Environment, Inter‐university Research Centre

– AUV (Folaga), MOOS middleware, Security

SELEX, Italy (Sistemi Integrati, Genova & La Spezia)

– Defense and electronics company

– Command and control

• SINTEF

– PHY, MAC, Network routing, IP interfacing

19

(20)

SINTEF ICT

UAN

 

goals

Demonstrate

 

at

 

sea

 

an

 

underwater

 

sensor

 

network

 

that

Is

 

capable

 

of

 

collecting

 

sensor

 

data

Include

 

AUV

 

that

 

can

 

support

 

• relaying for communication beyond shadow zones

• Intruder detection

Supports

 

IP

 

connectivity

 

end

to

end

Target

 

application:

 

surveillance

 

of

 

industrial

 

infrastructure

20

(21)

SINTEF ICT

Pianosa

 

arrival…

ITEM NTNU, Aug 29, 2014 21

• Trial at sea, Pianosa, Italy, 2010

(22)

SINTEF ICT

(23)

SINTEF ICT

(24)

SINTEF ICT

Del

 

II

SINTEF

 

IKT

 

prosjekter

 

2007– :

NNN

UAN

OSS

CLAM

SensIs

(25)

SINTEF ICT 25

OSS:

 

Ocean

 

Space

 

Surveillance,

 

2009

 ‐

2013

• SINTEF consortium strategic research project

• SINTEF ICT, Materials and Chemistry, Fisheries and 

Aquaculture

• Final test April 2013

• Publications IEEE Oceans 2013, Bergen

• SINTEF consortium strategic research project

• SINTEF ICT, Materials and Chemistry, Fisheries and 

Aquaculture

• Final test April 2013

• Publications IEEE Oceans 2013, Bergen

25

• Based upon 

– Ocean Models 

– Underwater Wireless Sensor Networks

• A forecasting method similar to meteorology

– Optimized combination of models and 

measurements

• Based upon 

– Ocean Models 

– Underwater Wireless Sensor Networks

• A forecasting method similar to meteorology

– Optimized combination of models and 

measurements

Reliable monitoring and 

prediction of 

• seaborne pollution

• harmful algae

• deposition of matter

• …

Reliable monitoring and 

prediction of 

• seaborne pollution

• harmful algae

• deposition of matter

(26)

SINTEF ICT

ITEM NTNU, Aug 29, 2014 26

(27)

SINTEF ICT

ITEM NTNU, Aug 29, 2014 27

(28)

SINTEF ICT

ITEM NTNU, Aug 29, 2014 28

OSS:

 

Sensor

 

data

 

overføring

:

Seaguard/ADCP

 

via

 

akustisk

 

link

 

+

 

radio

 

link

 

+

 

Internet

SINTEF

 

Server

Overvåking av radio‐

link, bøye posisjon 

og batteri‐tilstand

• 1 sensor sample per 10 min lagret lokalt

(29)

SINTEF ICT

Del

 

II

SINTEF

 

IKT

 

prosjekter

 

2007– :

NNN

UAN

OSS

CLAM

SensIs

(30)

SINTEF ICT

CLAM

 

partners

 

(EU

 

project

 

2010–2013)

C

o

l

l

a

borative

 

e

m

bedded

 

networks

 

for

 

submarine

 

surveillance

University

 

of

 

Twente (coordinator),

 

NL

Kongsberg

 

Maritime

SINTEF

 

ICT

University

 

of

 

Rome

 

“La

 

Sapienza”,

 

IT

University

 

of

 

Padova,

 

IT

Consorzio

 

Interuniversitario

 

Nazionale

 

per

 

l’Informatica

 

(CINI),

 

IT

Microflown Technologies,

 

NL

(31)

SINTEF ICT

CLAM

 

project

 

and

 

sea

 

trial

 

in

 

2013

• http://www.euronews.com/2013/06/10/echoes‐from‐the‐deep/

31

(32)

SINTEF ICT

CLAM

 

sea trial

 

May,

 

2013

(33)

SINTEF ICT

Del

 

II

SINTEF

 

IKT

 

prosjekter

 

2007– :

NNN

UAN

OSS

CLAM

SensIs

(34)

SINTEF ICT

ITEM NTNU, Aug 29, 2014

Sanntids Undervanns Trådløst Sensornettverk for å Overvåke Isdrift i Nordområdene

 Realtime undewater wireless sensor networks for 

surveillance of ice in the high North/Arctic seas

• Innovasjonsprosjekt i næringslivet – PETROMAKS, ramme 20 Mkr

• Partnere:

 Nortek, Kongsberg Maritime, SINTEF, Statoil, NTNU Akustikk

• Arbeidspakke 4:

 Nettverksprotokoller, integrasjon

 bruke Kongsberg Maritime modems

TF1=200 bps

TF2=400 bps

TF3=1600 bps

34

(35)

SINTEF ICT

ITEM NTNU, Aug 29, 2014

1.

Motivation

 

by

 

history

2.

SINTEF

 

ICT

 

projects

3.

Protocol development framework

35

(36)

SINTEF ICT

ITEM NTNU, Aug 29, 2014 36

SensIs

 

protocol architecture 2013–2015

app_   (1) cm d_   (2) app2_   (3) app_   (3) cm d_   (2) app2_   (1) app_   (3) cm d_   (1) app2_   (1) cm d_   (2) app_   (1) app2_   (3)

(37)

SINTEF ICT

ITEM NTNU, Aug 29, 2014 37

Gumstix

 

embedded

 

platform

 

for

 

network

 

protocol

 

stack

(38)

SINTEF ICT

ITEM NTNU, Aug 29, 2014 38

Developing

 

phases

 

using

 

"DESERT"

1. Simulation

 Compressed time (discrete event time simulation)

 Dummy data

 Modem/Water channel is simulated

2. Hybrid simulation/emulation

 Real‐time clock

 Real application data

 Modem/Water channel is simulated

3. Emulation, lab‐setup

 Real‐time clock

 Real application data

 Real modem

no transducer

4. Emulation, at sea

 Real‐time clock

 Real application data

 Real modem

with transducer

)))  ((( DESERT is a framework of libraries developed for 

• ns‐2 network simulator

• based on ns‐miracle modular extension

(39)

SINTEF ICT

ITEM NTNU, Aug 29, 2014

• Network protocols & control / monitoring of network

(40)

SINTEF ICT

ITEM NTNU, Aug 29, 2014 40

(41)

SINTEF ICT

ITEM NTNU, Aug 29, 2014 41

(42)

SINTEF ICT

ITEM NTNU, Aug 29, 2014

• Forwarding data is put into 

sendDown with a delay:

• In no‐ARQ mode, this delay 

is independent on  propagation delay  ksi = 2.0 • In ARQ‐mode, it is a  function of propagation  delay, #of  retransmissions,…  ksi > 3.0 42

ARQ

 

support

 

(automatic

 

repeat

 

reQuest)

delay 1

(43)

SINTEF ICT

ITEM NTNU, Aug 29, 2014

• No UC communication 

between seabed closest 

neighbour nodes

• 34 and 45 had UC 

comm.

• The rest of communication 

was between master node 1 

and seabed nodes at direct 

links

43

SensIs

 

sea

 

trial

 

June

 

5,

 

2014

(44)

SINTEF ICT

ITEM NTNU, Aug 29, 2014

• Transmission from Mac‐2 Reception at Mac‐1

44

Nortek

 

Sensor

 

#1

 

info

 

transmission

 

and

 

reception

10:000 11:00 12:00 13:00 14:00 15:00 16:00 50 100 150 200 250 300 HH:MM ui d_

Logs\MAC2\140605: DATA Tx UC/BC/not_cnfrmed/busy/timeout 331/648/26/22/4 pkts

10:00 11:00 12:00 13:00 14:00 15:00 16:00 68.7 68.8 68.9 69 69.1 69.2 69.3 HH:MM pt yp e_

Tx ptype: 69=DATA, 65=ICRP STATUS, 63=ICRP ACK, 60=MAC ACK, 1006/139 pkts Tx/Retrans. Data UC Data BC MAC ACK !confirmed busy timeout Data BC Data UC !confirmed busy timeout retrans 11:300 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 50 100 150 200 250 time (HH:MM) PD R ( % ) / L os tPk ts

Logs\MAC1\140605: #of complete sens msg Rx from Port 1: 138 LostPkts PDR

(45)

SINTEF ICT

ITEM NTNU, Aug 29, 2014

• Transmission from Mac‐4 Reception at Mac‐1

45

Simulated

 

Sensor

 

#2

 

info

 

transmission

 

and

 

reception

11:300 12:00 12:30 13:00 13:30 14:00 14:30 15:00 10 20 30 40 50 60 70 80 90 100 time (HH:MM) P D R (% ) / L os tP kt s

Logs\MAC1\140605: #of complete sens msg Rx from Port 3: 187 LostPkts PDR

Stat. reset event

12:000 12:30 13:00 13:30 14:00 14:30 50 100 150 200 250 300 HH:MM ui d_

Logs\MAC4\140605: DATA Tx UC/BC/not_cnfrmed/busy/timeout 432/107/35/34/1 pkts

12:00 12:30 13:00 13:30 14:00 14:30 62 64 66 68 70 HH:MM pt yp e_

Tx ptype: 69=DATA, 65=ICRP STATUS, 63=ICRP ACK, 60=MAC ACK, 573/165 pkts Tx/Retrans. Data UC Data BC MAC ACK !confirmed busy timeout Data BC Data UC ICRP STATUS !confirmed busy timeout retrans

(46)

SINTEF ICT

ITEM NTNU, Aug 29, 2014 46

Broadcast

 

Paths

 

through

 

the

 

network

10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 1.0.0.3 ==>1.0.0.1 1.0.0.2 ==>1.0.0.1 1.0.0.5 ==>1.0.0.1 1.0.0.4 ==>1.0.0.1 1.0.0.5 ==>1.0.0.4 ==>1.0.0.1 1.0.0.4 ==>1.0.0.3 ==>1.0.0.1 1.0.0.6 ==>1.0.0.1 1.0.0.4 ==>1.0.0.5 ==>1.0.0.1 1.0.0.5 ==>1.0.0.4 ==>1.0.0.3 ==>1.0.0.1 1.0.0.2 ==>1.0.0.3 ==>1.0.0.1 1.0.0.2 ==>1.0.0.3 ==>1.0.0.4 ==>1.0.0.1 1.0.0.2 ==>1.0.0.4 ==>1.0.0.1 1.0.0.2 ==>1.0.0.4 ==>1.0.0.5 ==>1.0.0.1

(47)

SINTEF ICT

ITEM NTNU, Aug 29, 2014 47

Established

 

Unicast Paths

 

through

 

the

 

network

11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 1.0.0.3 ==>1.0.0.1 1.0.0.2 ==>1.0.0.1 1.0.0.5 ==>1.0.0.1 1.0.0.4 ==>1.0.0.1 1.0.0.5 ==>1.0.0.4 ==>1.0.0.1 1.0.0.4 ==>1.0.0.3 ==>1.0.0.1 1.0.0.6 ==>1.0.0.1 1.0.0.4 ==>1.0.0.5 ==>1.0.0.1

(48)

SINTEF ICT

ITEM NTNU, Aug 29, 2014 48

ARQ

 

for

 

UC

 

robustness

 

improvements,

 

performance

10:000 11:00 12:00 13:00 14:00 15:00 16:00 5 10 15 20 25

Logs\MAC2\140605: Tx retrans. stats. wo/ Busy events

N o_ of_ T x, t im ed iff ( s) #of Tx events Timediff 1st-last (s) TxPow 12:000 12:30 13:00 13:30 14:00 14:30 5 10 15 20 25

Logs\MAC4\140605: Tx retrans. stats. wo/ Busy events

N o_ of _T x, ti m ed iff ( s) #of Tx events Timediff 1st-last (s) TxPow

(49)

SINTEF ICT

ITEM NTNU, Aug 29, 2014 49

Reception

 

quality

 

(UC=orange, BC=red, not_me=blue, pkt w/ error=black)

10:00 11:00 12:00 13:00 14:00 15:00 16:00 -10

0 10

Logs\MAC1\140605: SNIR (dB) for 442/107/99/627 pkts

10:000 11:00 12:00 13:00 14:00 15:00 16:00 1 2 Logs\MAC1\140605: CRC for 442/107/99/627 pkts 10:000 11:00 12:00 13:00 14:00 15:00 16:00 2 4

6 Logs\MAC1\140605: FEC for 442/107/99/627 pkts

10:000 11:00 12:00 13:00 14:00 15:00 16:00 2 4 Logs\MAC1\140605: TF for 442/107/99/627 pkts 10:00 11:00 12:00 13:00 14:00 15:00 16:00 -10 0 10

Logs\MAC2\140605: SNIR (dB) for 223/1/230/644 pkts

10:000 11:00 12:00 13:00 14:00 15:00 16:00 1 2 Logs\MAC2\140605: CRC for 223/1/230/644 pkts 10:000 11:00 12:00 13:00 14:00 15:00 16:00 2 4 6

Logs\MAC2\140605: FEC for 223/1/230/644 pkts

10:000 11:00 12:00 13:00 14:00 15:00 16:00 1 2 3 Logs\MAC2\140605: TF for 223/1/230/644 pkts 10:00 11:00 12:00 13:00 14:00 -10 0 10

Logs\MAC3\140605: SNIR (dB) for 80/44/790/1019 pkts

10:000 11:00 12:00 13:00 14:00 1 2 Logs\MAC3\140605: CRC for 80/44/790/1019 pkts 10:000 11:00 12:00 13:00 14:00 2 4 6

Logs\MAC3\140605: FEC for 80/44/790/1019 pkts

10:000 11:00 12:00 13:00 14:00 1

2 3

(50)

SINTEF ICT

ITEM NTNU, Aug 29, 2014

• Node 2‐9: Sensor nodes

• Node 10‐13: relay nodes

• Node 1: Sink (master) node

• Single computer:

 Simulate 

(compressed event driven time)

 Hybrid simulation/emulation

(real‐time sources, simulate water 

channel)

50

(51)

SINTEF ICT

ITEM NTNU, Aug 29, 2014 51

Node_dist 900m

 

and

 

TxPow 137dB

 

and

 

145dB

15 20 25 30 35 40 45 0 20 40 60 80 100

Node-dist=900 (m). TxPow=137 (dB). TxVariance=0 (dB). Traffic: CBR

Time between sensor data (s)

PD R ( % ) 15 20 25 30 35 40 45 0 20 40 60 80 100

Node-dist=900 (m). TxPow=137 (dB). TxVariance=0 (dB). Traffic: CBR

Time between sensor data (s)

IC R P S T A T U S / D at a pay load pk ts ( % ) del=3s, dt=5s del=1s, dt=1s del=3s, dt=5s del=1s, dt=1s 15 20 25 30 35 40 45 0 20 40 60 80 100

Node-dist=900 (m). TxPow=145 (dB). TxVariance=0 (dB). Traffic: CBR

Time between sensor data (s)

P DR ( % ) 15 20 25 30 35 40 45 0 20 40 60 80 100

Node-dist=900 (m). TxPow=145 (dB). TxVariance=0 (dB). Traffic: CBR

Time between sensor data (s)

IC R P S T A T U S / D at a pay load pk ts ( % ) del=3s, dt=5s del=3s, dt=5s

(52)

SINTEF ICT

ITEM NTNU, Aug 29, 2014 52

Node_dist 900m

 

and

 

1500m.

 

Links

 

variable performance

15 20 25 30 35 40 45 0 20 40 60 80 100

Node-dist=1500 (m). TxPow=143 (dB). TxVariance=8 (dB). Traffic: CBR

Time between sensor data (s)

P DR ( % ) 15 20 25 30 35 40 45 0 20 40 60 80 100

Node-dist=1500 (m). TxPow=143 (dB). TxVariance=8 (dB). Traffic: CBR

Time between sensor data (s)

IC R P S T A T U S / D at a p ay load pk ts ( % ) del=3s, dt=5s del=3s, dt=5s 15 20 25 30 35 40 45 0 20 40 60 80 100

Node-dist=900 (m). TxPow=137 (dB). TxVariance=6 (dB). Traffic: CBR

Time between sensor data (s)

P DR ( % ) 15 20 25 30 35 40 45 0 20 40 60 80

100 Node-dist=900 (m). TxPow=137 (dB). TxVariance=6 (dB). Traffic: CBR

Time between sensor data (s)

IC R P S T A T U S / D ata p ay lo ad p kts ( % ) del=3s, dt=5s del=3s, dt=5s

(53)

SINTEF ICT

ITEM NTNU, Aug 29, 2014

• 1 x ns csma_icrp_intf12_cmd.tcl 6 7 9 11 13 15 17 19 21 23 25 27 29 31 >  ns_log.txt 2>&1 • 8 x  ./nmea_sim_new.sh /dev/pts/X 6 53

(54)

SINTEF ICT

Conclusions

 

Underwater

 

acoustic

 

networking

Acoustic

 

channels

 

can

 

be

 

used

 

to

 

create

 

underwater

 

sensor

 

networks

carrying

 

data

 

at

 

a

 

few

 

kbps

at

 

ranges

 

~1

2

 

km

 

per

 

hop

multi

hop

 

networks

 

possible

Main

 

challenges

link

 

reliability

• latency and connectivity may vary

• "fading characteristics" not known

Packet

 

delivery

 

ratio

 

(PDR)

 

not

 

100%

• Vertical links: 90–100%

• Non‐vertical links: 50–80%

"Alarm

 

modes"

 

might

 

overload

 

the

 

network

 

capacity

Energy

 

constraints

(55)

SINTEF ICT

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