Lecture 1 - Introduction
TDDI07 Distributed Embedded Software and Networks
Mikael Asplund
Department of Computer and Information Science
Linköping University
Next generation networks
[Photo: Cybelter]
[Photo: Obra19]
[Photo: Patrick B]
Course information
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Website: http://www.ida.liu.se/~TDDI07
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Lecturers
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Mikael Asplund (mikael.asplund@liu.se)
Course assistant
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Lectures, labs, etc
Ekhiotz Vergara (ekhiotz.vergara@liu.se)
Course secretary
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Carita Lilja (carita.lilja@liu.se)
Course aims
This course will introduce fundamental concepts needed to
understand, design, and implement distributed embedded
systems that include elements of wireless communication
and have some dependability requirements.
Three themes
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Energy-efficient network protocols
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Dependability and security
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Clock synchronisation and positioning
Course literature
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See course web page!
Laboratory
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Create two applications using Motes
Emulate a distributed sensor network and transfer data from
several sources to a sink node
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Uses the TinyOS mote platform
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More details given in Lesson 1
You are required to read the material before each lab.
Especially read the first lab before the lesson.
Rules of conduct
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Do not cheat
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See web page
How to study
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Go to lectures
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Read the literature
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Prepare for labs
Seminar
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New for this year
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Optional
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Replaces one part of the exam 10p/40p
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Topic: Dependability and security
This lecture
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Introduction to Embedded Distributed Systems
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Wireless sensor networks:
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Applications
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Enabling technologies
Embedded systems
Distributed systems
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Di students: recall TDTS04
Multiple autonomous computers connected through a network with a
common goal
Challenges: Time and clocks, fault tolerance, security, etc
Infrastructure-based wireless networks
Typical wireless network: Based on infrastructure
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E.g. GSM, UMTS, Wifi...
Mobility is supported by switching from one base station
to another
er s Gateways
h
rt o rk
u
F w
t
ne
IP backbone
Server
Router
Ad hoc networks
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A network without infrastructure, using networking
abilities of the participants
Limited range → multi hop networks
Infrastructure-free networks
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Factory floor automation
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Disaster area networks
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Car 2 car communication
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Body-area networks
Mobility
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In infrastructure-based networks: handover
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In ad-hoc networks: dynamic routing
What about scale?
Scale constraints
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Single-sink single-hop WSN
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Rα T
8D
Single-sink multi-hop WSN
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N≤
N≤
Rα T
8Dh
Multi-sink multi-hop WSN
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N≤
S Rα T
8Dh
N −number of nodes
R−channel bit rate
α −overhead factor (≤1)
T −time between samples
D−sample size (bytes)
h−number of hops
S −number of sinks
Application examples
Remote monitoring
Photo: Samuli Lintula
Photo: Baytownbert
Photo: Petritap
Machine surveillance
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Cables cannot go everywhere
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E.g., tire pressure monitoring (required by law in US)
Disaster prevention and relief
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Drop sensor nodes from an
aircraft over a wildfire
Each node measures
temperature
Derive a “temperature map”
Photo: Lotus R
Precision agriculture
Bring out fertilizer/irrigation only where needed
Car2car communication
eCall
San-Francisco parking
Sports
Industry 4.0
Enabling technologies for WSAN
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Cost reduction
Miniaturization
 “Smart dust”
Energy scavenging
Vision: intelligent dust
1 mm3 computer
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
Thickfilm battery
Solar cell
Analog I/O
Microcontroller
Sensors
Optical transmitter
Optical receiver
RF front-end
Two main application categories
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Event detection
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Boolean condition
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Position of the event
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Bounded delay
Process estimation
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Comparatively more data
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Signal processing (possibly in the network)
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Time synchronization
Sensor node architecture
Communication
device
Sensors/
actuators
CPU
Memory
Power supply
Sensor node architecture
Communication
device
Sensors/
actuators
CPU
Memory
Power supply
Energy density
[Thackeray, Wolverton and Isaacs “Electrical energy storage for transportation—approaching the limits
of, and going beyond, lithium-ion batteries”, DOI: 10.1039/C2EE21892E, 2012]
Energy scavenging – overview
Sensor node architecture
Communication
device
Sensors/
actuators
CPU
Memory
Power supply
Examples
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Microcontrollers suitable for WSANs
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Texas Instruments MSP430
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16-bit RISC core, up to 4 MHz, versions with 2-10 kbytes RAM, several
DACs, RT clock, prices start at 0.49 US$
Atmel ATMega
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8-bit controller, larger memory than MSP430, slower
Multiple power consumption modes
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Typical modes
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Controller: Active, idle, sleep
Radio mode: Turn on/off transmitter/receiver, both
Multiple modes possible, “deeper” sleep modes
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Strongly depends on hardware
TI MSP 430, e.g.: four different sleep modes
Atmel ATMega: six different modes
Some energy consumption figures
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Microcontroller
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TI MSP 430 (@ 1 MHz, 3V):
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Fully operation 1.2 mW
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Deepest sleep mode 0.3 W – only woken up by external interrupts (not
even timer is running any more)
Atmel ATMega
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Operational mode: 15 mW active, 6 mW idle
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Sleep mode: 75 W
Switching between modes
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Simplest idea: Greedily switch to lower mode whenever possible
Problem: Time and power consumption required to reach higher
modes not negligible
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Introduces overhead
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Switching only pays off if Esaved > Eoverhead
Esaved
Example:
Eoverhead
Event-triggered
wake up from
Pactive
sleep mode
Scheduling problem
Psleep
with uncertainty
t1
down
tevent
up
time
Sensor node architecture
Communication
device
Sensors/
actuators
CPU
Memory
Power supply
Flash energy consumption
[Lee, Yang, Tseng, IEEE TVLSI 2008]
Sensor node architecture
Communication
device
Sensors/
actuators
CPU
Memory
Power supply
Transceiver states
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Transceivers can be put into different operational states, typically:
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Transmit
Receive
Idle
Sleep
Some transceiver numbers
Comparison: GSM Base Station
Heat 602W
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Overview
AC power
3802W
PS
84%
DC power
TRX
3200W
2400W
-48V
CE
800W
BTS
AC Power
supply
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Details
220V
RF power
480W
Central
equipm.
Combining
Heat 800W
Total Heat
3682W
-48V
-48V
300W
Common
500W
Fans
cooling
99%
3232W
PAs consume
dominant part of power
(12*140W)/2400W=70%
3200W
2400W
(just to put things
into perspective)
Usable PA efficiency
40W/140W=28%
Overall efficiency
(12*10W)/3802W=3.1%
(No active cooling)
12 transceivers
200W
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TOC RF
120W
ACE
TRXs
Rack
cabling
85%
3802W
Heat 360W
Heat 1920W
idle
140W
60W
Converter
85%
-48V/+27V
119W
Erlang
9W
efficiency 75%
DTX activity
47%
110W
Bias
Combiner
PA
40W
Diplexer
TOC
15W
10W
Energy cost
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Computation vs. Communication Tradeoff?
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Energy ratio of “sending one bit” vs. “computing one instruction”:
Anything between 220 and 2900 in the literature
To communicate (send & receive) one kilobyte
= computing three million instructions!
A brief look at channel modelling
Attenuation
© http://141.84.50.121/iggf/Multimedia/Klimatologie/physik_arbeit.htm
50
2011-02-17
Baspresentation LiU
Distortion effects
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Because of reflection, scattering, …, radio communication is not
limited to direct line of sight communication
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Effects depend strongly on frequency, thus different behavior at higher
frequencies
LOS pulses
Non-line-of-sight path
Line-ofsight path
multipath
pulses
signal at receiver
© Jochen Schiller, FU Berlin
51
2011-02-17
Baspresentation LiU
Wireless signal strength
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Brighter color = stronger signal
© Jochen Schiller, FU Berlin
52
2011-02-17
Baspresentation LiU
Allocation...
Noise and interference
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54
Noise – due to effects in receiver electronics, depends on
temperature
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Typical model: an additive Gaussian variable, mean 0, no
correlation in time
Interference from third parties
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Co-channel interference
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Adjacent-channel interference
2011-02-17
Baspresentation LiU
Wireless channel quality – summary
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Wireless channels are substantially worse than wired channels
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In throughput, bit error characteristics, energy consumption, …
Wireless channels are extremely diverse
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There is no such thing as THE typical wireless channel
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Various schemes for quality improvement exist
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See book chapter 3!
2011-02-17
Baspresentation LiU
Sensor node architecture
Communication
device
Sensors/
actuators
CPU
Memory
Power supply
Sensors
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Active / passive
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Narrow-beam / omnidirectional
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Coverage
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Noise and contradictory sensor readings!
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Sensor fusion
Programming WSANs
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Extremely simple devices
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Little system support (OS, virtual memory etc)
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Event-based systems
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NesC and TinyOS suitable! More on this in coming lesson.
www.liu.se