Transcript Slide 1

Centre for Wireless
Communications
Wireless Sensor Networks
Introduction
Instructor: Carlos Pomalaza-Ráez
Fall 2004
University of Oulu, Finland
What is a Sensor?
Definition: A device that produces a measurable response to a
change in a physical or chemical condition, e.g. temperature,
ground composition.
Sensor Networks
A large number of low-cost, low-power, multifunctional,
and small sensor nodes
They benefit from advances in 3 technologies
• digital circuitry
• wireless communication
• silicon micro-machining
Wireless Sensor Networks (WSN)
New technologies
have reduced the
cost, size, and
power of microsensors and
wireless interfaces
Sensing
Circulatory Net
Environmental
Monitoring
Networking
Computation
Structural
Applications
Battlefield
Detection, classification and
tracking
Habitat Monitoring
Micro-climate and wildlife monitoring
 Examples:
– ZebraNet (Princeton)
– Seabird monitoring in Maine’s Great Duck
Island
(Berkeley & Intel)
Applications
 Structural, seismic
 Bridges, highways, buildings
• Examples: Coronado Bridge San Diego
(UCSD), Factor Building (UCLA)
 Smart roads
 Traffic monitoring, accident detection,
recovery assistance
• Examples: ATON project (UCSD)
highway
camera
 Contaminants detection
microphone
Communication Architecture
Sensing node
Sensor nodes can be
data originators and
data routers
Internet
Sink
Manager Node
Sensor nodes
Sensor field
Examples of Sensor Nodes
Sensor Node Evolution
Mote Type
Date
WeC
Rene
Rene2
Dot
Mica
Sep-99
Oct-00
Jun-01
Aug-01
Feb-02
Microcontroller (4MHz)
Type
Prog. mem. (KB)
RAM (KB)
AT90LS8535
ATMega163
ATMega103/128
8
16
128
0.5
1
4
Communication
Radio
Rate (Kbps)
Modulation Type
RFM TR1000
10
10/40
OOK
OOK/ASK
WIRO Platform
WIRO (WIreless Research Object ) is a modular embedded system developed
by the Centre for Wireless Communications, Oulu, Finland. The system consists
of a set of boards 35 mm x 35 mm in size. They are:
 CPU board - Controls all other WIRO boards and is needed in all WIRO stacks. It has an
AVR Mega128 microcontroller running at 7.37 MHz and a 4 Mb serial flash memory. The
CPU has a 128 kB flash memory for programs, 4 kB of SRAM, and 8 ADCs
 RF board – It has an RFM model TR3100 radio transceiver chip capable of up to 576kbit/s
speeds. The radio interface on this board is configured for 230.4 kbps. Data encoding &
decoding can use the onboard CPLD (Complex Programmable Logic Device) or the
microcontroller on the CPU board. The transceiver uses ASK modulation
 Power supply board – It has electronics to charge a battery pack from the USB bus and to
provide the other boards with 5V, +3.3V and +1.8V voltages

 Sensor board – It has 2-axis accelerometer, 2-axis magnetometer, pressure, temperature
and humidity sensors
 Prototype board and Test-Pad board
CPU Board
2 Euro coin & RF Board
WIRO Box
WIRO – Power Consumption
CPU Board
CPU Active
CPU Sleep
CPLD
3 mA/3.3 V
9.9 mW
0.01 mA/3.3 V
0.033 mW
AT mega128
15 mA/3.3 V
49.5 mW
0.04 mA/3.3 V
0.13 mW
Flash-memory
4 mA/3.3 V
13.2 mW
0.002 mA/3.3 V
0.007 mW
RF Board
Tx
Rx
Sleep
CPLD
3mA/3.3V
9.9mW
3mA/3.3V
9.9mW
0.01mA/3.3V
0.033mW
RF-Transceiver
10mA/3.3V
33mW
5.8mA/3.3V
19mW
0.7μA/3.3V
0.0023mW
Other Circuitry
0.5mA/5V
2.5mW
0.5mA/5V
2.5mW
0.5mA/5V
2.5mW
RF Board Total
Power Consumption
Power (mW)
50
40
45.5
30
20
10
0
Tx
31.5
Rx
Sleep
2.5
WIRO – Power Consumption
Sensor Board
Active
Sleep
Magnetometer
20mA/5V
100mW
0
0
Accelerometer
0.6mA/3.3V
2mW
0.6mA/3.3V
2mW
Humidity Sensor
0.55mA/3.3V
2mW
0.3μA/5V
0.0015mW
Pressure Sensor
6mA/5V
30mW
0
0
CPLD
3mA/3.3V
9.9mW
0.01mA/3.3V
0.033mW
Amplifier
0.5mA/5V
2.5mW
0.5mA/5V
2.5mW
150
Sensor Board Total
Power Consumption
Power (mW)
150
100
50
0
Active
Sleep
4.5
WIRO – Power Consumption
Power Supply Board
Connected to the USB-bus
Not Connected to the USB-bus
CPLD
1mA/3.3V
3.3mW
0.01mA/3.3V
0.033mW
EEPROM
1mA/5V
5mW
0.005mA/5V
0.025mW
USB
25mA/5V(from USB)
125mW
0.2mA/5V
1mW
Estimated Operation Time on Battery Power
ton /tidle
Avg Power
Avg Battery Current
Op Time/550mAh
Op Time/100mA
100%
325.3mW
104mA
5.3h
0.3h
10%
44.2mW
14.1mA
39h
2.3h
1%
16.1mW
5.2mA
107h
6.2h
0.1%
13.3mW
4.3mA
129h
7.5h
0%
13.0mW
4.2mA
132h
7.7h
Typical Features of WSN
 A very large number of nodes, often in the order of thousands
 Asymmetric flow of information, from the observers or sensor
nodes to a command node
 Communications are triggered by queries or events
 At each node there is a limited amount of energy which in
many applications is impossible to replace or recharge
 Almost static topology
 Low cost, size, and weight per node
 Prone to failures
 More use of broadcast communications instead of point-topoint
 Nodes do not have a global ID such as an IP number
 The security, both physical and at the communication level, is
more limited than conventional wireless networks
Design Considerations
 Fault tolerance – The failure of nodes should not severely degrade
the overall performance of the network
 Scalability – The mechanism employed should be able to adapt to a
wide range of network sizes (number of nodes)
 Cost – The cost of a single node should be kept very low
 Power consumption – Should be kept to a minimum to extend the
useful life of network.
 Hardware and software constraints – Sensors, location finding
system, antenna, power amplifier, modulation, coding, CPU, RAM,
operating system
 Topology maintenance – In particular to cope to expected high rate
of node failure
 Deployment – Pre-deployment mechanisms and plans for node
replacement and/or maintenance
 Environment – At home, in space, in the wild, on the roads, etc.
 Transmission media – ISM bands, infrared, etc.
Node Energy Consumption Projections
10,000
Average Power (mW)
• Deployed (5W)
1,000
100
• (50 mW)
10
1
 (1mW)
.1
2000
2002
2004
Node Hardware
In-node
processing
Wireless communication
with neighboring nodes
Event
detection
Acoustic, seismic,
magnetic, etc. sensors
interface
CPU
radio
battery
Limited battery supply
Electro-magnetic
interface
Energy Limitations
 Each sensor node has limited energy supply
 Nodes may not be rechargeable
 Energy consumption in
 Sensing
 Data processing
 Communication (most energy intensive)
Power (mW)
20
15
10
5
0
Sensing
CPU
TX
RX
IDLE
SLEEP
Power consumption of node subsystems
Power Consumption (mW)
TR 1000 Parameters
16.00
14.88
12.00
12.50
12.36
8.00
4.00
0.02
0.00
Transmit
Receive
Idle
Parameter
Off
Value
Transition time from sleep to receive mode
500 μs
Transition time from sleep to transmit mode
16 μs
Transition time from transmit to receive mode
500 μs
Transition time from receive to transmit mode
12 μs
Bit rate
115.2 kbps
Medusa II Sensor Node (UCLA)
Power Analysis
MCU Mode Sensor
Mode
Active
On
Radio Mode
Modulation Data-Rate
Power (mW)
Tx(Power:0.74 mW)
OOK
2.4 kb/s
24.58
Tx(Power:0.01 mW)
OOK
2.4 kb/s
19.24
Tx(Power:0.74 mW)
OOK
19.2 kb/s
25.37
Tx(Power:0.01 mW)
OOK
19.2 kb/s
20.05
Tx(Power:0.74 mW)
ASK
2.4 kb/s
26.55
Tx(Power:0.01 mW)
ASK
2.4 kb/s
21.26
Tx(Power:0.74 mW)
ASK
19.2 kb/s
27.46
Tx(Power:0.01 mW)
ASK
19.2 kb/s
22.06
Active
On
Rx
Any
Any
22.20
Active
On
Idle
Any
Any
22.06
Active
On
Off
Any
Any
9.72
Idle
On
Off
Any
Any
5.92
Sleep
Off
Off
Any
Any
0.02
Sensor Network Protocol Stack
Data Link
Physical
Task Management
Network
Mobility Management
Transport
Power Management
Application
Power Management – How the
sensor uses its power, e.g. turns
off its circuitry after receiving a
message.
Mobility Management – Detects
and register the movements of the
sensor nodes
Task Management – Balances and
schedules the sensing tasks given
to a specific region
Physical Layer
 Frequency selection – The use of the industrial, scientific, and
medical (ISM) bands has been often proposed
 Carrier frequency generation and Signal detection – Depend on
the transceiver and hardware design constraints which aim for Application
simplicity, low power consumption, and low cost per unit
Transport
 Modulation
Network
 Binary and M-ary modulation schemes can transmit multiple bits
per symbol at the expense of complex circuitry
 Binary modulation schemes are simpler to implement and thus
deemed to be more energy-efficient for WSN applications
 Low transmission power and simple transceiver circuitry make
Ultra Wideband (UWB) an attractive candidate




Baseband transmission, i.e. no intermediate or carrier frequencies
Generally uses pulse position modulation
Resilient to multipath
Low transmission power and simple transceiver circuitry
Data Link
Physical
Physical Layer
Energy consumption minimization is of paramount importance when
designing the physical layer for WSN in addition to the usual effects such
as scattering, shadowing, reflection, diffraction, multipath, and fading.
Radio Model – Energy Consumption
E L (m , d )  ET (m , d )  E R (m )
E T ( m , d )  E TC ( m )  E TA ( m , d )
ETC = energy used by the transmitter circuitry
ETA = energy required by the transmitter amplifier to achieve an acceptable
signal to noise ratio or at the receiver
Physical Layer
Assuming a linear relationship for the energy spent per bit by the transmitter
and receiver circuitry

E T ( m , d )  m e TC  e TA d


E R ( m )  me RC
eTC, eTA, and eRC are hardware dependent parameters
An explicit expression for can be derived as,
e TA 
 S 
 4 
  ( NF Rx )( N 0 )( BW ) 

 N r
  
( G ant )( am p )( R bit )

Physical Layer
 4 
( NF Rx )( N 0 )( BW ) 

  
 
( G ant )( am p )( R bit )

eTA
 S 


 N r
(S/N)r = minimum required signal to noise ratio at the receiver’s
demodulator for an acceptable Eb/N0
NFrx = receiver noise figure
N0 = thermal noise floor in a 1 Hertz bandwidth (Watts/Hz)
BW = channel noise bandwidth
λ
= wavelength in meters
α
= path loss exponent whose value varies from 2 (for free space) to
4 (for multipath channel models)
Gant = antenna gain
ηamp = transmitter power efficiency
Rbit = raw bit rate in bits per second
Data Link Layer
The data link layer is responsible for the multiplexing
of data stream, data frame detection, medium access
and error control. Ensures reliable point-to-point and
point-to-multipoint connections in a communication
network
Application
Medium Access Control (MAC) – Let multiple radios
share the same communication media
Code
Functions:
 Local Topology Discovery and Management
 Media Partition By Allocation or Contention
 Provide Logical Channels to Upper Layers
Physical
Transport
Network
Data Link
Time
MAC protocol for sensor network must have built-in power
conservation mechanisms, and strategies for the proper
management of node mobility or failure
Wireless MAC Protocols
Wireless MAC protocols can be classified into two categories, distributed
and centralized, according of the type of network architecture for which they
have been designed. Protocols can be further classified, based on the mode of
operation, into random access protocols, guaranteed access protocols, and
hybrid access protocols
Wireless MAC protocols
Distributed
MAC protocols
Random
access
Centralized
MAC protocols
Random
access
Guaranteed
access
Hybrid
access
Since it is desirable to turn off the radio as much as possible in order to
conserve energy some type of TDMA mechanism is often suggested for
WSN applications. Constant listening times and adaptive rate control
schemes have also been proposed.
Power Saving Mechanisms
 The amount of time and power needed to wake-up (start-up) a radio is not
negligible and thus just turning off the radio whenever is not being used is not
necessarily efficient
 The energy characteristics of the start-up time should also be taken into account
when designing the size of the data link packets. The values shown in the figure
below clearly indicate that when the start-up energy consumption is taken into
account the energy per bit requirements can be significantly higher for the
transmission of short packets than for longer ones
TR 1000 (115kbps)
60
Ebit ( pJ )
50
40
30
20
10
0
10
100
1000
Packet Size (bits)
10000
Power Saving Mechanisms
 Based on using an ultra low power radio to wake-up the neighbors.
 This second radio uses much less power via either a low duty cycle or
hardware design
 Usually this second radio can only transmit a busy tone
 This broadcast tone should not disrupt any on-going data transmission, e.g.
use a different channel
Sleeping nodes
Communicating nodes
Error Control
Error control is an important issue in any radio link. There are two
important modes of error control:
 Forward Error Correction (FEC) – There is a direct tradeoff between the
overhead added to the code and the number of errors that can be corrected. The
number of bits in the code word impacts the complexity of the receiver and
transmitter. If the associated processing power is greater than the coding gain,
then the whole process in energy inefficiency.
 Automatic Repeat Request (ARQ) – Based on the retransmission of packets
that have been detected to be in error. Packets carry a checksum which is used
by the receiver to detect errors. Requires a feedback channel.
With FEC one pays an a priori battery power consumption overhead and packet
delay by computing the FEC code and transmitting the extra code bits. In return
one gets a reduced probability of packet loss. With ARQ one gambles that the
packet will get through and if it does not one has to pay battery energy and delay
due to the retransmission process. Whether FEC or ARQ or a hybrid error
control system is most energy efficient will depend of the channel conditions and
the network requirements such as throughput, delay.
Network Layer
Basic issues to take into account when designing the network
layer for a WSN are:
Application
 Power efficiency
 Data centric – The nature of the data (interest requests
and advertisement of sensed data) determines the traffic
flow
 Data aggregation is useful to manage the potential
implosion of traffic because of the data centric routing
 Rather than conventional node addresses an ideal sensor
network uses attribute-based addressing, e.g. “region
where humidity is below 5%”
 Locationing systems, i.e. ability for the nodes to establish
position information
 Internetworking with external networks via gateway or
proxy nodes
Network
Transport
Data Link
Physical
Routing
Phenomenon
being sensed
Data aggregation
takes place here
Sink
Multihop routing common due to limited transmission range
Some routing issues in WSNs





Low node mobility
Power aware
Irregular topology
MAC aware
Limited buffer space
Data Aggregation
It is a technique used to solve the problem of implosion in WSNs. This problem
arises when packets carrying the same information arrive a node. This situation can
happen when more than one node sense the same phenomenon. This is different
than the problem of “duplicate packets” in conventional ad hoc networks. Here it
is the high level interpretation of the data in the packets is what determines if the
packets are the “same.” Even for the case when the packets are deemed to be
different they could still be aggregated into a single packet before the relaying
process continues. In this regard data aggregation can be considered as data fusion.
Phenomenon being sensed
Data coming from multiple sensor
nodes are aggregated, if they have
about the same attributes of the
phenomenon being sensed, when they
reach a common routing or relaying
node on their way to the sink. In this
view the routing mechanism in a
sensor network can be considered as a
form of reverse multicast tree.
Data Centrality
In data-centric routing, an “interest ” dissemination is performed in order to
assign the sensing tasks to the sensor nodes. This dissemination can take
different forms such as:
 The sink or controlling nodes broadcast the nature of the interest, e.g. “four
legged animals of at least 50 Kg in weight”
Four-legged animal of
at least 50 Kg
Sink
Flow of the request
Data Centrality
 Sensor nodes broadcast an advertisement of available sensed data and wait for a
request from the interested sinks
Tiger, tiger, burning bright,
In the forest of the night,
What immortal hand or eye
Could frame thy fearful symmetry?
Flow of the advertisement
Sink
Flooding & Gossiping
Flooding is a well known technique used to disseminate information across a
network. It is a simple, easy to implement reactive mechanism that could be used
for routing in WSNs but it has severe drawbacks such as,
 Implosion – When duplicated messages are sent to the same node
 Overlap – When two or more nodes share the same observing region, they may
sense the same stimuli at the same time. As a result, neighbor nodes receive
duplicated messages
 Resource blindness – Does not take into account the available energy resources.
Control of the energy consumption is of paramount importance in WSNs, a
promiscuous routing technique such as flooding wastes energy unnecessarily
Gossiping is a variation of flooding attempting to correct some of its drawbacks.
Nodes do not indiscriminately broadcast but instead send a packet to a randomly
selected neighbor who once it receives the packet it repeats the process. It is not as
simple to implement as the flooding mechanism and it takes longer for the
propagation of messages across the network.
Proposed Routing Techniques
SPIN – Sensor Protocols for Information via Negotiation – Attempts to correct the
major deficiencies of classical flooding in particular the indiscriminate flow of
packets with the related energy waste. The sensor nodes minimize the amount of
traffic and transmissions by first sending and advertisement of the nature of the
sensed data in a concise manner followed by the transmission of the actual data to
only those nodes that are interested in receiving it.
 SPIN messages
 ADV- advertise data
 REQ- request specific data
 DATA- requested data
ADV
A
B
REQ
A
 Resource management
 Nodes decide their capability of
participation in data transmissions
B
DATA
A
B
Proposed Routing Techniques
Data Funneling – Attempts to minimize the amount of communication from the
sensors to the information consumer node (sink). It facilitates data aggregation and
tries to concentrate, e.g. funnel, the packet flow into a single stream from the group
of sensors to the sink. It also attempts to reduce (compress) the data by taking
advantage that the destination is not that interested in a particular order of how the
data packets arrive.
Setup phase:
 Controller divides the sensing area into
regions
 Controller performs a directional flood
towards each region
 When the packet reaches the region the
first receiving node becomes a border
node and modifies the packet (add
fields) for route cost estimations within
the region
 Border node flood the region with
modified packet
 Sensor nodes in the region use cost
information to schedule which border
nodes to use
Proposed Routing Techniques
Data Funneling
Data Communication Phase:
 When a sensor has data it uses the
schedule to choose the border node
that is to be used
 It then waits for time inversely
proportional to the number of hops
from the border
 Along the way to the border node,
the data packets joined together
until they reach the border node
 The border node collects all
packets and then sends one packet
with all the data back to the
controller
Transport Layer
TCP variants developed for the traditional wireless networks are
not suitable for WSNs where the notion of end-to-end reliability
has to be reinterpreted due to the “sensor” nature of the network
which comes with features such as:
Application
Transport
Network
Data Link
 Multiple senders, the sensors, and one destination, the sink,
Physical
which creates a reverse multicast type of data flow
For the same event there is high level of the redundancy or correlation in
the data collected by the sensors and thus there is no need for end-to-end
reliability between individual sensors and the sink but instead between the
event and the sink
On the other hand there is need of end-to-end reliability between the sink
and individual nodes for situations such as re-tasking or reprogramming
The protocols developed should be energy aware and simple enough to be
implemented in the low-end type of hardware and software of many WSN
applications
Proposed Transport Layer Techniques
Pump Slowly, Fetch Quickly (PFSQ) – Designed to distribute data from a
source node by pacing the injection of packets into the network at relatively
low speed (pump slowly) which allows nodes that experience data loss to
aggressively recover missing data from their neighbors (fetch quickly).
Goals of this protocols are:
Ensure that all data segments are delivered to the intended destinations
with minimum especial requirements on the nature of the lower layers
Minimize number of transmissions to recover lost information
Operate correctly even in situations where the quality of the wireless links
is very poor
Provide loose delay bounds for data delivery to all intended receivers
PFSQ) has been designed to guarantee sensor-to-sensor delivery and to
provide end-to-end reliability for control management distribution from the
control node (sink) to the sensors. It does not address congestion control
Proposed Transport Layer Techniques
Event-to-Sink Reliable Transport (ESRT) – Designed to achieve reliable
event detection (at the sink node) with a protocol that is energy aware and
has congestion control mechanisms. Salient features are:
Self-configuration – even in the case of a dynamic topology
Energy awareness – sensor nodes are notified to decrease their frequency of
reporting if the reliability level at the sink node are above the minimum
Congestion control – takes advantage of the high level of correlation between the
data flows corresponding to the same event
Collective identification – sink only interested in the collective information from a
group of sensors not in their individual reports
Biased implementation – most of the complexity of the protocol falls on the sink
node minimizing the requirements on the sensor nodes
Application Layer
There has not been a lot of development on this layer for WSNs.
Some potential applications have been suggested as listed below
but little work of substance has been reported on any particular
area.
Application
Transport
Network
 Sensor Management Protocol (SMP) – Carries out tasks such as: Data Link
 Turning sensors on and off
Physical
 Exchanging data related to the location finding algorithms
 Authentication, key distribution, and other security tasks
 Sensor movement management
 Interest Dissemination – Interest is send to a sensor or a group
of sensors. The interest is expressed in terms of an attribute or a
triggering event.
 Advertisement of Sensed Data – Sensor nodes advertise sensed
data in a concise and descriptive way and users reply with
requests of data they are interested in receiving
Distributed Source Coding (DSC)
Aims to take advantage of the high level of correlation of
the data collected by spatially close sensor nodes in
response to an event.
Application Layer
The goal is to remove this redundancy in a distributed manner. There is the
need to be able to make reliable decisions from the contribution of a large
number of individual unreliable components with a considerable amount of
system redundancy. Any method that can strip this redundancy in a
distributed manner, e.g. minimizing inter-node communications, will make
efficient use of the bandwidth and also save energy.
One way to remove the redundancy is by joint processing based on
exchange of information between the sensors. What is then the price for
minimizing this exchange (to save energy)? Proposed DSC methods make
use of the Slepian-Wolf coding theorem that states if the joint distribution
quantifying the senor correlation structure is known then there is no
theoretical loss in performance under certain conditions.
S. Pradhan, K. Ramchandran, “Distributed Source Coding Using Syndromes (DISCUS): Design and
Construction,” IEEE Trans. Information Theory, vol. 49, no. 3, March 2003, pp. 626-643
Distributed Source Coding (DSC)
X
Y
X
Y
Encoder 1
Xˆ , Yˆ
Joint Decoder
Encoder 2
The encoders collaborate and a rate of H(X,Y) is sufficient
Encoder 1
Xˆ , Yˆ
Joint Decoder
Encoder 2
The encoders do not collaborate. The Slepian-Wolf
theorem says that a rate H(X,Y) is also sufficient
provided the decoding of X and Y are done jointly. It
puts more burden on the decoding side
Distributed Source Coding (DSC)
A
B
C
D
Suppose node A wants to collect the readings
from the other sensor nodes. Let’s assume that
the readings are all a 3-binary values, and that
the reading of each “child” node is correlated
with the one from its “parent” node in a manner
that the Hamming distance between their
readings is no more than 1 bit.
Application of the Slepian-Wolf theorem results
in nodes C and D communicating their 3-bit
readings to their parent, node B, using 2-bit
syndromes. Node B relay these messages to
node A, along with its own 2-bit syndrome with
respect to node A. Node A performs a successive
decoding process by first decoding its correlated
node B (using its own reading) and then
decoding the readings of C and D relative to the
decoded reading of B.
Cross-Layer Design
Motivations:
 Avoid Conflicting Behavior – For example a routing protocol that favors
smaller hops to save transmission energy consumption does require a
proper MAC protocol to coordinate the transmissions along the data flow
that minimizes contention and keeps the transceivers off as much as
possible
 Remove Unnecessary Layers – Some applications do not require all
layers
 New Paradigm – WSNs does not have many of the feature of the
conventional networks for which the OSI protocol layer stack model has
proven to be successful. Therefore it is quite possible that a different mix
of layers might prove to be more efficient for many WSN applications