Centre for Wireless
Communications
Wireless Sensor Networks
Presenter: Carlos Pomalaza-Ráez
carlos@ee.oulu.fi
International Workshop on Wireless Ad Hoc Networks
May 31 – June 3, 2004
University of Oulu, Finland
http://www.ee.oulu.fi/~carlos/IWWAN_04_WSN_Tutorial.ppt
Outline
 Introduction
 Examples of sensor networks and sensor nodes
 WIRO – A sensor node developed at CWC
 Typical features of WSN
 Design considerations
 Sensor Network Protocol Stack
 Energy consumption model – Physical layer
 MAC power saving mechanisms
 Data aggregation and Data centrality
 Transport and Applications layers
Outline
 Networking Issues
 MAC
 Routing
 Transport layer
 Summary
 Energy Efficiency Issues
 Node energy model for multihop WSN
 Energy efficient error control mechanisms
 Cooperative communications
 Distributed source coding
Introduction
What is a sensor?
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 grouping of low-cost, low-power, multifunctional, and
small-sized 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 micro-sensors and
wireless interfaces
Circulatory Net
Environmental
Monitoring
Sensing
Networking
Computation
Structural
Some Applications of WSN
Battlefield
Detection, classification and tracking
Examples: AWAIRS
(UCLA & Rockwell Science Center)
Habitat Monitoring Micro-climate and wildlife monitoring
Examples:
 ZebraNet (Princeton)
 Seabird monitoring in Maine’s Great
Duck Island (Berkeley & Intel)
Some Applications of WSN
 Structural, seismic
Bridges, highways, buildings
Examples: Coronado Bridge San Diego
(UCSD), Factory Building (UCLA)
 Smart roads
Traffic monitoring, accident detection,
recovery assistance
Examples: ATON project (UCSD)
highway
camera
 Contaminants detection
Examples: Multipurpose Sensor Program
(Boise State University)
microphone
WSN Communications 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 576 kbps
speeds. The radio interface on this board is configured for 230.4 kbps. Data encoding and
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 a 2-axis accelerometer, a 2-axis magnetometer, as well as 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 /tsleep
Avg Power
Avg Battery Current
Op Time/550mAh
100%
325.3mW
104mA
5.3h
10%
44.2mW
14.1mA
39h
1%
16.1mW
5.2mA
107h
0.1%
13.3mW
4.3mA
129h
0%
13.0mW
4.2mA
132h
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-to-point
 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 with the 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.
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 registers 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
Application
medical (ISM) bands has often been proposed
 Carrier frequency generation and Signal detection – Depend on Transport
the transceiver and hardware design constraints which aim for
Network
simplicity, low power consumption, and low cost per unit
Data Link
 Modulation
Physical
 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
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
EL (m, d )  ET (m, d )  ER (m)
ET (m, d )  ETC (m)  ETA (m, d )
ETC = energy used by the transmitter circuitry
ETA = energy required by the transmitter amplifier to achieve an acceptable
signal to noise ratio at the receiver
Physical Layer
Assuming a linear relationship for the energy spent per bit by the transmitter
and receiver circuitry

ET (m, d )  m eTC  eTA d 

E R (m)  meRC
eTC, eTA, and eRC are hardware dependent parameters
An explicit expression for eTA can be derived as,
eTA
S
 4 
  ( NFRx )( N 0 )( BW )

 N r
  

(Gant )( am p )( Rbit )

Physical Layer

 4 
( NFRx )( N 0 )( BW )

  

(Gant )( am p)( Rbit )
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 the data stream, data frame detection, medium
access and error control. Ensures reliable point-topoint and point-to-multipoint connections in a
communication network
Application
Transport
Network
Data Link
Medium Access Control (MAC)
Physical
Let multiple radios share the same communication media
Functions:
Code
 Local Topology Discovery and Management
 Media Partition By Allocation or Contention
 Provide Logical Channels to Upper Layers
MAC protocols for sensor networks must have built-in power
conservation mechanisms, and strategies for the proper
management of node mobility or failure
Time
Wireless MAC Protocols
Wireless MAC protocols can be classified into two categories, distributed
and centralized, according to 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 it 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
Error Control
Error control is an important issue in any radio link. In general terms
there are two 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 energy efficient will depend on the channel conditions and the
network requirements such as throughput and delay.
Network Layer
Basic issues to take into account when designing the
network layer for WSNs are:
Application
Transport
Network
 Power efficiency
 Data centric – The nature of the data (interest requests Data Link
and advertisement of sensed data) determines the
Physical
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
Routing
Phenomenon
being sensed
Data aggregation
takes place here
Sink
Multihop routing is 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 at a node. This situation
can happen when more than one node senses 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 that 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 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 upon receiving the packet, 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 an 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
(†) W. Heinzelman, J. Kulik, and H. Balakrishnan, “Adaptive Protocols for Information Dissemination in Wireless
Sensor Networks,” Proc. 5th ACM/IEEE Mobicom Conference (MobiCom '99), Seattle, WA, August, 1999.
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
 The border node floods the region with
modified packet
 Sensor nodes in the region use cost
information to schedule which border
nodes to use
(†) D. Petrovic, R. C. Shah, K. Ramchandran, and J. Rabaey, “Data Funneling: Routing with Aggregation and Compression
for Wireless Sensor Networks,” SNPA 2003, pp. 1-7.
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 join 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 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 (PSFQ)(†) – 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 protocol are:
 Ensure that all data segments are delivered to the intended destinations
with minimum special 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
(†) C-Y Wan, A. T. Campbell, and L. Krishnamurthy, “PSFQ: A Reliable Transport Protocol For Wireless Sensor Networks,”
First ACM International Workshop on Wireless Sensor Networks and Applications (WSNA 2002), Atlanta, September
28, 2002, pp. 1-11.
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 is 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
(†) Y. Sankarasubramaniam, O. B. Akan, and I. F. Akyildiz, “ESRT: Event-to-Sink Reliable Transport in Wireless Sensor
Networks” Proceedings of ACM MobiHoc`03, Annapolis, Maryland, USA, June 2003, pp. 177-188.
Application Layer
There has not been as much development for this layer as for the
Application
other layers. Several general potential areas have been suggested as
Transport
listed below but little work of substance has been reported in any
particular area
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 sent 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(†). Proposed DSC methods
make use of the Slepian-Wolf coding theorem that states that if the joint
distribution quantifying the sensor correlation structure is known then there
is no theoretical loss in performance using DSC under certain conditions.
(†) S. Pradhan and 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 decoding of X and Y is done jointly. It puts
more burden on the decoding side
Some Words About 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 do not have many of the features 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
Networking Issues
 Unlike conventional wireless networks, the protocols
designed for the efficient networking of nodes in a WSN
have to allow for a closer collaboration or awareness among
the layers of the protocol stack, in particular the first three
layers
 For example, the MAC protocols must try to have the radio
transceivers in a sleeping mode as much as possible in order
to save energy, however if the MAC protocol is not jointly
designed with the routing algorithms (network layer) the
overall performance of the network could be severely
degraded, e.g. excessive packet delay
 Conversely, WSN routing algorithms designed with the
concepts of data centric and data aggregation create special
requirements on the underlying MAC protocols that should be
met for the routing mechanisms to work as intended
 These observations can be extended to the design of other
layers as well since WSNs call for new networking paradigms
Application
Transport
Network
Data Link
Physical
Example of a MAC Protocol for WSN
Sensor-MAC (S-MAC)(†) – Is an energy-aware protocol that
illustrates design considerations that MAC protocols for WSNs
Data Link
should address. Assumptions made in the design of S-MAC are:
 Most communications will be between neighboring sensor nodes rather
than between a node and a base station
 There are many nodes that are deployed in a casual, e.g. not precise,
manner and as such the nodes must be able to self-configure
 The sensor nodes are dedicated to a particular application and thus pernode fairness (channel access) is not as important as the application level
performance
 Since the network is dedicated to a particular application the application
data processing can be distributed through the network. This implies that
data will be processed as whole messages at a time in store-and-forward
fashion allowing for the application of data aggregation techniques which
can reduce the traffic
 The application can tolerate latency and has long idle periods
(†) W. Ye, J. Heidemann and D. Estrin, “An Energy-Efficient MAC Protocol for Wireless Sensor Networks,” In Proceedings of
the 21st International Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 2002),
New York, NY, USA, June, 2002, pp. 1-10.
Sensor-MAC (S-MAC)
The main features of S-MAC are:
 Periodic listen and sleep
 Collision and Overhearing avoidance
 Message passing
The basic scheme for each node is:
 Each node goes into periodic sleep mode during which it switches the radio
off and sets a timer to awake later
 When the timer expires it wakes up and listens to see if any other node wants
to talk to it
 The duration of the sleep and awake cycles are application dependent and they
are set the same for all nodes
 Requires a periodic synchronization among nodes to take care of any type of
clock drift
Sensor-MAC (S-MAC)
 The listen and awake periods are much longer than typical clock drift rates
 The duration of the sleep and awake cycles are application dependent and they
are set the same for all nodes
 Unlike conventional TDMA schemes S-MAC tolerates a much looser
synchronization among neighboring nodes
 Requires a periodic synchronization among nodes to take care of any type of
clock drift
 Nodes are free to choose their own listen/sleep schedules but to reduce control
overhead the protocol prefers that neighboring nodes are synchronized
 Because of the multihop scenario not all neighbors can be synchronized, e.g.
Nodes A and B are neighbors but they are synchronized to their “other”
neighbors, C and D respectively. Nodes broadcast their schedules from time to
time to ensure that neighboring nodes can talk to each other even if they have
different schedules. If multiple neighbors want to talk to a node, they need to
contend for the medium.
Sensor-MAC (S-MAC)
Choosing and Maintaining Schedules
 Each node maintains a schedule table that stores schedules of all its
known neighbors
 To establish the initial schedule the following steps are followed:
 A node first listens for a certain amount of time
 If it does not hear a schedule from another node, it randomly
chooses a schedule and broadcasts its schedule immediately
 This node is called a Synchronizer
 If a node receives a schedule from a neighbor before choosing its
own schedule, it just follows this neighbor’s schedule, i.e. becomes
a Follower and it waits for a random delay and broadcasts its
schedule
 If a node receives a neighbor’s schedule after it selects its own
schedule, it adopts both schedules and broadcasts its own schedule
before going to sleep
 It is expected that very rarely a node adopts multiple schedules since
every node tries to follow existing schedules before choosing an
independent one
Sensor-MAC (S-MAC)
Maintaining Synchronization
 Timer synchronization among neighbors is needed to prevent clock drift.
The updating period can be relatively long (tens of seconds)
 Done by periodically sending a SYNC packet that only includes the
address of the sender and the time of its next sleeping period
 Time of next sleep is relative to the moment that the sender finishes
transmitting the SYNC packet
 A node will go to sleep when the timer fires
 Receivers will adjust their timer counters immediately after they receive
the SYNC packet
 A node periodically broadcasts a SYNC packet to its neighbors even if it
has no followers
Sensor-MAC (S-MAC)
Maintaining Synchronization (cont.)
 Listen interval is divided into two parts: one for receiving SYNC
packets and the other for receiving RTS (Request To Send)
Sensor-MAC (S-MAC)
Collision and Overhearing Avoidance
 Similar to IEEE 802.11, i.e. use RTS/CTS mechanism to address the hidden
terminal problem
 Perform carrier sense before initiating a transmission
 If a node fails to get the medium, it goes to sleep and wakes up when the
receiver is free and listening again
 Broadcast packets are sent without RTS/CTS
 Unicast packets follow the sequence of RTS/CTS/DATA/ACK between the
sender and receiver
 Duration field in each transmitted packet indicates how long the remaining
transmission will be, so if a node receives a packet destined for another node, it
knows how long it has to keep silent
 The node records this value in network allocation vector (NAV) and sets a timer
for it
 When a node has data to send, it first looks at NAV. If this value is not zero, then
the medium is busy (virtual carrier sense)
 The medium is determined as free if both virtual and physical carrier sense
indicate the medium is free
 All immediate neighbors of both the sender and receiver should sleep after they
hear the RTS or CTS packet until the current transmission is over
Sensor-MAC (S-MAC)
Message Passing
 A message is a collection of meaningful, interrelated units of data
 Transmitting a long message as a packet is disadvantageous as the retransmission cost is high if the packet is corrupted
 Fragmentation into small packets will lead to high control overhead as
each packet should contend using RTS/CTS
 S-MAC fragments message into small packets and transmits them as a
burst
 Only one RTS and one CTS packets are used
 Every time a data fragment is transmitted the sender waits for an ACK
from the receiver, if it does not arrive the fragment is retransmitted and
the reservation is extended for the duration of the fragment
 Advantages:
 Reduces latency of the message
 Reduces control overhead
 Disadvantage:
 Node-to-node fairness is reduced, as nodes with small packets to
send will have to wait until the message burst is transmitted
Sensor-MAC (S-MAC)
Implementation
 Testbed
 Rene motes, developed at UCB
 Atmel AT90LS8535 microcontroller with TinyOS
 Uses the TR 1000 from RFM which provides a transmission rate of 19.2
Kbps (OOK). Three working modes: receiving (4.5mA), transmitting
(12mA, peak), and sleeping (5μA)
 Two type of packets. Fixed size data packets with a 6-byte header, a 30byte payload, and a 2-byte CRC. Control packets (RTS, CTS, ACK) with
a 6-byte header and a 2-byte CRC
 MAC protocols implemented
 Simplified IEEE 802.11 DCF
 Message passing with overhearing avoidance (no sleep and listen
periods). The radio goes to sleep when its neighbors are in transmission
 The complete S-MAC. Listen period is 300 ms and sleep time can take
different values, e.g. 300 ms, 500 ms, 1 s, etc.
The duration of the carrier sensing is random within the contention window.
The microcontroller does not go to sleep.
Sensor-MAC (S-MAC)
Topology
 Two-hop network with two sources and two sinks
 Sources periodically generate a sensing message which is divided into
fragments
 Traffic load is changed by varying the inter-arrival period of the messages
Sensor-MAC (S-MAC)
Sensor-MAC (S-MAC)
Sensor-MAC (S-MAC)
Sensor-MAC (S-MAC)
Conclusion
 The S-MAC protocol has good energy conserving properties when
compared with the IEEE 802.11 standard
Comments
 Need of a mathematical analysis
 Need to study the effect of different topologies
 Fragmenting long packets into smaller ones is not energy efficient. The argument
about more chances of the packet being corrupted is not correct unless other
options such as the use of error control coding have also been explored
 Several features behind the S-MAC protocol are still “captured” in the traditional
way to do business at the Link Layer level, e.g. use of RTS/CTS/ACK, etc.
 The protocol does not address the fact that in most sensor net applications
neighboring nodes are activated almost at the same time by the event to be
sensed and as such they will attempt to communicate at approximately the same
time. There is also a high degree of correlation between the data they want to
communicate
Deep Sleep is Healthy not just for WSN
sol 101-102 (May 10, 2004)
“... Opportunity awoke on sol 102 from its first “deep sleep.” This set of
activities was initiated to conserve the energy that ...”
http://marsrovers.jpl.nasa.gov
Routing
Problem – How to efficiently route:
 Data from the sensors to the sink and,
 Queries and control packets from the sink to the sensor nodes
Routing
In addition to the concepts of data aggregation, data
centrality, flooding, and gossiping that were described
earlier it is important to identify the nature of the WSN
traffic, which will depend on the application.
Assuming a uniform density of nodes, the number of
transmissions can be used as a metric for energy
consumption.
Since receiving a packet consumes almost as much energy
as transmitting a packet it is then important that the MAC
protocol limits the number of listening neighbors in order
to conserve energy.
Routing
If N is the number of nodes, Q the number of queries, and E the number of
events, and some type of flooding mechanism is being used then:
 If the number of events is much higher than the number of queries it
is better to use some type of query flooding since the number of
transmissions is proportional to N*Q which is much less than N*E
 If the number of events is low compared with the number of queries
it is better to use some type of event flooding since now N*E is
much less than N*Q
 In both cases it is assumed that the “return path” (for the events or
the queries) is built during the flooding process
 Other underlying routing mechanisms are recommended if the
number of events and queries are of the same order
Directed Diffusion(†)
A mechanism developed for the case where it is expected that the number of
events is higher than the number of queries
 Is data-centric in nature
 The sink propagates its queries or “interests” in the form of attributevalue pairs
 The interests are injected by the sink and disseminated throughout the
network. During this process, “gradients” are set at each sensor that
receives an interest pointing towards the sensor from which the interest
was received
 This process can create, at each node, multiple gradients towards the
sink. To avoid excessive traffic along multiple paths a “reinforcement”
mechanism is used at each node after receiving data, e.g. reinforce:
 Neighbor from whom new events are received
 Neighbor who consistently performing better than others
 Neighbor from whom most events received
 There is also a mechanism of “negative reinforcement” to degrade the
importance of a particular path
(†) C. Intanagonwiwat, R. Govindan, and D. Estrin, “Directed Diffusion: A Scalable and Robust Communication Paradigm
for Sensor Networks,” Proc. ACM Mobicom, Boston MA, August 2000, pp. 1-12.
Directed Diffusion
Gradient
represents
both
direction
towards
data
matching
Uses
application-aware
communication
primitives
This
Consumer
Nodes
The
process
choice
diffuse
of sets
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path
the
up
initiates
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is made
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at
network
data
every
with
node
tovia
certain
draw
afor
Every
route
has a probability
ofcost
being
chosen
Probability
 1/energy
Collect
energy
metrics
along
the way
and status
of demand
with
desired
expressed
terms
of
namedupdate
data rate
sequence
events
matching
every
ofin
attributes
local
packet
the
interactions
interest
Four-legged
animal
Source
Sink
Directed Diffusion
Reinforcement
negative to
reinforcement
used
Has built and
in tolerance
nodes moving
to converge
to range
efficient
out of
ordistribution
dying
Source
Sink
Directed Diffusion
 Pros
 Energy – Much less traffic than flooding. For a network of size N
the total cost of transmissions and receptions is O(n N ) whereas for
flooding the order is O(nN )
 Latency – Transmits data along the best path
 Scalability – Local interactions only
 Robust – Retransmissions of interests
 Cons
 The set up phase of the gradients is expensive
 It does not propose the type of MAC layer needed to support an
efficient implementation of this protocol. The simulation analysis
uses a modified 802.11 MAC protocol
Sensor Protocol for Information via
Negotiation (SPIN)(†)
A mechanism developed for the case where the number of queries is higher
than the number of events.
 Use information descriptors or meta-data for negotiation prior to
transmission of the data
 Each node has its own energy resource manager which is used to adjust
its transmission activity
 The family of SPIN protocols are:
 SPIN-PP – For point-to-point communication
 SPIN-EC – Similar to SPIN-PP but with energy conservation
heuristics added to it
 SPIN-BC – Designed for broadcast networks. Nodes set random
timers after receiving ADV and before sending REQ to wait for
someone else to send the REQ
 SPIN-RL – Similar to SPIN-BC but with added reliability. Each node
keeps track of whether it receives requested data within the time limit,
if not, data is re-requested
(†) J. Kulik, W. Rabiner Heinzelman, and H. Balakrishnan, “Negotiation-Based Protocols for Disseminating Information in
Wireless Sensor Networks,” ACM/IEEE Int. Conf. on Mobile Computing and Networking, Seattle, WA, Aug. 1999.
SPIN-BC
It
Sensor
sends
broadcasts
meta-data
data
toitself
neighbors
A
Neighbor
node
senses
sends
something
a REQ
listing
“interesting”
of the
data
The
Neighbors
process
aggregate
repeats
data
across
andall
broadcast
the
network
it would likemeta-data
to acquire
(advertise)
ADV
REQ
DATA
SPIN-BC
Advertise meta-data
Advertise
Send data
Send data
I am tired
Send
I need to
sleep …
data
Advertise
meta-data
Advertise
Send
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Request
Nodes dodata
need
to
Requestnot
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participate in the
process
Request data
SPIN
 Pros




Energy – More efficient than flooding
Latency – Converges quickly
Scalability – Local interactions only
Robust – Immune to node failures –
 Cons
 Nodes always participating
 It does not propose the type of MAC layer needed to support an
efficient implementation of this protocol. The simulation analysis
uses a modified 802.11 MAC protocol
Summary
 In recent years a very large number of routing algorithm for WSNs
have been proposed and analyzed
 For most of the proposed techniques the analysis has been mainly
carried out using simulation experiments
 Recent routing algorithms such as the “Data Funneling”(†) scheme
described earlier are more in line with the WSN paradigm
 Most if not all of the proposed routing algorithms are not supported by
a proper MAC protocol
 A proper MAC protocol should also be more in tune with the important
features of the WSN paradigm, e.g. asymmetric flow, no need to have
to use individual node addresses or links, have the radio in sleep mode
as much as possible, etc.
 Another Data Link Layer aspect that needs more research is the impact
of error control coding on the consumption of energy
(†) D. Petrovic, R. C. Shah, K. Ramchandran, and J. Rabaey, “Data Funneling: Routing with Aggregation and Compression
for Wireless Sensor Networks,” SNPA 2003, pp. 1-7.
Spatiotemporal MAC
Rationale
To be able to save energy it is
necessary to have a schedule for
the radios to be awake or asleep.
This means that there is a
mechanism to distribute this
schedule across the network, e.g.
a long range broadcast from the
sink node.
There is no reason why the MAC
schedule has to be the same for
all nodes.
A spatiotemporal schedule would
help to avoid contention for the
channel, allowing time for data
aggregation, and finally
“forcing” the sensed data to come
to the sink
Sink
Spatiotemporal MAC
Pump Slowly, Fetch Quickly (PSFQ)
A transport protocol for WSNs that attempts to pace the data from a source
node at a relatively low speed to allow intermediate nodes to fetch missing
data segments from their neighbors, e.g. hop-by-hop recovery instead of
traditional transport layer end-to-end recovery mechanisms
PSFQ
Three basic operations: pump, fetch, and report
Pump
 Node broadcasts a packet to its neighbors every Tmin until all the data
fragments have been sent out
 Neighbors who receive the packet check against their local cache
discarding any duplicates
 If it is just a new message the packet is buffered and the Time-To-Live
(TTL) field in the header is decreased by 1
 If TTL is not zero and there is no gap in the sequence number the packet
then is scheduled for transmission within a random time Ttx, where
Tmin  Ttx  Tmax
 The random delay before forwarding the message allows a downstream
node to recover missing segments before the next segment arrives from an
upstream node
 It also allows reducing the number of redundant broadcasts of the same
packet by neighbors
PFSQ
Fetch
A node goes into fetch mode when a sequence number gap is detected
 In fetch mode a node aggressively sends out NACK messages to its
immediate neighbors to request missing segments
 Since it is very likely that consecutive packets are lost because of fading
conditions, a “window” is used to specify the range of missing packets
 A node that receives a NACK message checks the loss window field
against its cache. If found the packet is scheduled for transmission at a
random time in (0, Tr)
 Neighbors cancel a retransmission when a reply for the same segment is
overheard
 NACK messages are not propagated to avoid message implosion
 There is also a “proactive fetch” mode to take care of situations such as
when the last segment of a message is lost. In this case the node sends a
NACK for the remaining segments when they have not been received after
a time period Tpro
PFSQ
Report
 Used to provide feedback data of delivery status to source nodes
 To minimize the number of messages, the protocol is designed so that a
report message travels back from a target node to the source nodes
intermediate nodes can also piggyback their report messages in an
aggregated manner
Simulation and experimental evaluation
 When compared to a previously proposed similar protocol (Scalable
Reliable Multicast) the simulation results show that the PFSQ protocol
has a better performance in terms of error tolerance, communications
overhead, and delivery latency
 The experimental results were obtained by using the TinyOS platform
on RENE motes. The performance results were much poorer than the
simulation results. The discrepancy is attributed to the simulation
experiment being unable to accurately model the wireless channel and
the computational demands on the sensor node processor
Event-to-Sink Reliable Transport (ESRT)
 In a typical sensor network application the sink node is only interested in
the collective information of the sensor nodes within the region of an event
and not in any individual sensor data
 Traditional end-to-end reliability requirements do not then apply here
 What is needed is a measure of the accuracy of the information received at
the sink, i.e. and event-to-sink reliability
ESRT
 The basic assumption is that the sink does all the reliability evaluation using
parameters that are application dependent
 One such parameter is the decision time interval τ
 At the end of the decision interval the sink derives a reliability indicator ri
based on the reports received from the sensor nodes
 ri is the number of packets received in the decision interval
 If R is the number of packets required for reliable event detection then
ri > R is needed for reliable event detection
 There is no need to identify individual sensor nodes but instead there is the
need to have an event ID
 The reporting rate, f, of a sensor node is the number of packets sent out per unit
time by that node
 The ESRT protocol aims to dynamically adjust the reporting rate to achieve the
required detection reliability R at the sink
ESRT
r versus f based on simulation results
n = number of source nodes
for f > fmax the reliability
drops because of network
congestion
ESRT – Protocol Overview
 The algorithms mainly run on the sink
 Sensor nodes:
 Listen to sink broadcasts and update their reporting rates accordingly
 Have a simple congestion detection mechanism and report to the sink
 The sink:
 Computes a normalized reliability measure ηi = ri /R
 Updates f based on ηi and if f > fmax or < fmax in order to achieve the
desired reliability
 Performs congestion decisions based on feedback reports from the
source nodes
 Congestion detection:
 Uses local buffer level monitoring in sensor nodes
 When a routing buffer overflows the node informs the sink by setting
the congestion notification bit in the header packets traveling
downstream
ESRT – Network States
Optimal Operating Region
(Congestion, High reliability)
(No congestion, High reliability)
(Congestion, Low reliability)
(No congestion, Low reliability)
ESRT – Frequency Update
State
f update
fi
(NC, LR)
f i 1 
(NC, HR)
f
f i 1  i
2
(C, HR)
f i 1 
(C, LR)
OOR
Multiplicative increase f to achieve required
reliability as soon as possible
i

1  Decrease f conservatively, reduce energy
1  
consumption and not lose reliability
 i 
fi
i
fi 1  fi
(i k )
fi 1  fi
Action
Aggressively decrease f to relieve
congestion as soon as possible
Exponential decrease. k is the number of
successive decision intervals spent in state
(C, LR)
Unchanged
ESRT – Summary and Conclusions
 Uses a new paradigm for transport layer reliability
 Sensor networks are more interested in event to sink
reliability than on individual end-to-end reliability
 The congestion control mechanism results in energy savings
 Analytical performance evaluation and simulation results
show that the system converges to the state OOR regardless
of the initial state
 This self configuration property of the protocol is very
valuable for random and dynamic topologies
 Issues still to be addressed are:
 Extension to handle concurrent multiple events
 Development of a bi-directional reliable protocol that
includes the sink-to-sensor transport
Energy Efficiency Issues
Node Energy Model(†)
A typical node has a sensor system, A/D conversion circuitry, DSP and a
radio transceiver. The sensor system is very application dependent. As
discussed earlier the communication components are the ones who
consume most of the energy on a typical wireless sensor node. A simple
model for a wireless link is shown below
(†) H. Karvonen, Z. Shelby, and C.A. Pomalaza-Ráez, “Coding for Energy Efficient Wireless Embedded Networks,” to be
presented at the International Workshop on Wireless Ad Hoc Networks, May 31 - June 3, 2004, Oulu, Finland
Energy Model
The energy consumed when sending a packet of m bits over a one
hop wireless link can be expressed as,
EL (m, d )  ET (m, d )  PT Tst  Eencode ER (m)  PRTst  Edecode
where,
ET
= energy used by the transmitter circuitry and power amplifier
ER
= energy used by the receiver circuitry
PT
= power consumption of the transmitter circuitry
PR
= power consumption of the receiver circuitry
Tst
= startup time of the transceiver
Eencode = energy used to encode
Edecode = energy used to decode
Energy Model
Assuming a linear relationship for the energy spent per bit at the transmitter
and receiver circuitry ET and ER can be written as,

ET (m, d )  m eTC  eTA d 

E R (m)  meRC
eTC, eTA, and eRC are hardware dependent parameters and α is the path
loss exponent whose value varies from 2 (for free space) to 4 (for
multipath channel models).
The effect of the transceiver startup time, Tst, will greatly depend on the
type of MAC protocol used. To minimize power consumption it is
desirable to have the transceiver in a sleep mode as much as possible
however constantly turning on and off the transceiver also consumes
energy to bring it to readiness for transmission or reception.
Energy Model
An explicit expression for eTA can be derived as(†),
eTA
S
 4 
  ( NFRx )( N 0 )( BW )

 N r
  

(Gant )( am p )( Rbit )

Where,
(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
Gant = antenna gain
ηamp = transmitter power efficiency
Rbit = raw bit rate in bits per second
(†) P. Chen, B. O’Dea, E. Callaway, “Energy Efficient System Design with Optimum Transmission Range for Wireless Ad Hoc
Networks,” IEEE International Conference on Comm. (ICC 2002), Vol. 2, pp. 945-952, 28 April -2 May 2002, pp. 945-952.
Energy Model
The expression for eTA can be used for those cases where a particular
hardware configuration is being considered. The dependence of eTA on
(S/N)r can be made more explicit if the previous equation is written as:

eTA    S N r
where
 4 
( NFRx )( N0 )( BW )

  

(Gant )(amp )( Rbit )
This expression shows explicitly the relationship between eTA and (S/N)r.
The probability of bit error p depends on Eb/N0 which in turns depends
on (S/N)r.
Eb/N0 is independent of the data rate. In order to relate Eb/N0 to (S/N)r,
the data rate and the system bandwidth must be taken into account, i.e.,
Energy Model
S N r  Eb
N 0 R BT    b R BT 
where
Eb = energy required per bit of information
R = system data rate
BT = system bandwidth
γb = signal-to-noise ratio per bit, i.e., (Eb/N0)
Typical Bandwidths for Various Digital Modulation Methods
Modulation Method
Typical Bandwidth
(Null-To-Null)
QPSK, DQPSK
1.0 x Bit Rate
MSK
1.5 x Bit Rate
BPSK, DBPSK, OFSK
2.0 x Bit Rate
Energy Model
Power Scenarios
Two possible power scenarios are:
 Variable transmission power. In this case the radio dynamically adjust its
transmission power so that (S/N)r is fixed to guarantee a certain level of
Eb/N0 at the receiver. The transmission energy per bit is given by,
S
eTA d      d 
 N r
Since (S/N)r is fixed at the receiver this also means that the probability p
of bit error is fixed at the same value for each link.
Node Energy Model
 Fixed transmission power. In this case the radio uses a fixed power for all
transmissions. This case is considered because several commercial radio
interfaces have a very limited capability for dynamic power adjustments.
In this case eTA d  is fixed to a certain value (ETA) at the transmitter and the
(S/N)r at the receiver will then be,
ETA
S

 

 N r  d
Since for most practical deployments d is different for each link, then
(S/N)r will also be different for each link. This translates to a different
probability of bit error for each wireless hop.
Energy Consumption - Multihop Networks
Consider the following linear sensor array
To highlight the energy consumption due only to the actual
communication process; the energy spent in encoding,
decoding, as well as on the transceiver startup is not
considered in the analysis that follows.
Energy Consumption - Multihop Networks
The initial assumption is that there is one data packet being relayed
from the node farthest from the sink node towards the sink. The
total energy consumed by the linear array to relay a packet of m
bits from node n to the sink is,
Elinear

  e
 e RC
 e RC

 eTA (d i ) 


 m eTC  eTA (d1 ) 

n
TC
i2


 eTA (d i ) 


or

 m eRC 

 e
n
TC
i 1


It then can be shown that Elinear is minimum when all the distances
di’s are made equal to D/n, i.e. all the distances are equal.
Energy Consumption - Multihop Networks
It can also be shown that the optimal number of hops is,
nopt
 D   D 

 or 

d
d
 char   char 
where
1
d char
 eTC  eRC  

 
 eTA (  1) 
dchar depends only on the path loss exponent α and on the transceiver
hardware dependent parameters. Replacing the value of dchar in the
expression for Elinear
opt
Elinear
 nopt (eTC  eRC )

 m
 eRC 
 1


Energy Consumption - Multihop Networks
A more realistic assumption for the linear sensor array is that there is a
uniform probability along the array for the occurrence of events(†).
In this case, on the average, each sensor will detect the same number of
events and the information collected needs to be relayed towards the sink.
Without loss of generality one can then assume that each node senses one
event. This means that sensor i will have to relay (n-i) packets from the
upstream sensors plus the transmission of its own packet. The average
energy per bit consumption by the linear array is then
 e
n
Elinearbit  neRC 
TC


 eRC  eTA (d i ) n  1  i 
i 1
 neRC
(e  eRC )n(n  1)
 TC
 eTA
2
n

( n  1  i )( d i )
i 1
(†)Z. Shelby, C.A. Pomalaza-Ráez, and J. Haapola, “Energy Optimization in Multihop Wireless Embedded and Sensor
Networks,” to be presented at the 15th IEEE International Symposium on Personal, Indoor, and Mobile Radio
Communications, September 5-8, 2004, Barcelona, Spain.
Energy Consumption - Multihop Networks
n
Minimizing Elinearbit with constraint D   d i is equivalent to
i 1
minimizing the following expression,
 n  1  i (d ) 
n
L  eTA

i
i 1
 n



di  D 


 i 1


where λ is a LaGrange’s multiplier. Taking the partial derivatives of L
with respect to di and equating to 0 gives,
L
 eTA (n  1  i)( d i )  1    0
d i




d i  
 eTA (n  1  i) 
1
 1
Energy Consumption - Multihop Networks
n
The value of λ can be obtained using the condition  d i  D
i 1
Thus for α=2 the values for di are,
di 
D
 n

 1 i n  1  i 


 i 1


For n=10 the next figure shows an equally spaced sensor array and a linear
array where the distances are computed using the equation above (α=2)
Energy Consumption - Multihop Networks
The sensors farther away consume most of their energy by transmitting over
longer distances whereas sensors closer to the sink consume a large portion
of their energy by relaying packets from the upstream sensors towards the
sink. The total energy per bit spent by a linear array with equally spaced
sensors is
equidistant
Elinear
 bit 


n(n  1)
2
eTC  eRC  eTA D n   neRC
2
The total energy per bit spent by a linear array with optimum separation
and α=2 is,
optimum
linear  bit
E
n(n  1)
eTC  eRC   eTA

2
D2
n
 1 i 
i 1
 neRC
Energy Consumption - Multihop Networks
For eTC= eTR= 50 nJ/bit, eTA= 100 pJ/bit/m2, and α = 2, the total energy
consumption per bit for D= 1000 m, as a function of the number of sensors
is shown below.
Equally spaced
Optimum spaced
0.12
Energy (m J)
0.10
0.08
0.06
0.04
0.02
0.00
0
5
10
15
20
Sensor Array Size (n )
25
30
Energy Consumption - Multihop Networks
The energy per bit consumed at node i for the linear arrays discussed can be
computed using the following equation. It is assumed that each node relays packets
from the upstream nodes towards the sink node via the closest downstream neighbor.
For simplicity’s sake only one transmission is used, e.g. no ARQ type mechanism

Elinear (i)  [( n  1  i)(eTC  eTA d i )  (n  i)eRC ]
Energy consumption at each node (n=20, D=1000 m)
Equally Spaced
Optimum Spaced
8.0
Total Energy=72.5 u J
Energy (u J)
6.0
4.0
2.0
Total Energy = 47.8 u J
0.0
0
5
10
15
Distance (hops) from the sink
20
Error Control – Multihop WSN
For link i assume that the probability of bit error is pi. Assume a packet length
of m bits. For the analysis below assume that a Forward Error Correction
(FEC) mechanism is being used. Then call plink(i) the probability of receiving a
packet with uncorrectable errors.
Conventional use of FEC is that a packet is accepted and delivered to the next
stage which in this case is to forward it to the next node downstream. The
probability of the packet arriving to the sink node with no errors is then:
n
Pc   1  p link (i ) 
i 1
Error Control – Multihop WSN
Assume the case where all the di’s are the same, i.e. di = D/n. Since
variable transmission power mode is also being assumed the probability
of bit error for each link is fixed and Pc is,
Pc  (1  plink ) n
The value of plink will depend on the received signal to noise ratio as well
as on the modulation method used. For a noncoherent (envelope or
square-law) detector with binary orthogonal FSK signals in a Rayleigh
slow fading channel the probability of bit error is
p FSK
1

2b
Where  b is the average signal-to-noise ratio.
Error Control – Multihop WSN
Consider a linear code (m, k, d) is being used. For FSK-modulation with
non-coherent detection and assuming ideal interleaving the probability of
a code word being in error is bounded by
 2wi  1


w
i

i 2 
M
PM 

2   
d min
b
where wi is the weight of the ith code word and M=2k. A simpler bound is:
PM  (M  1)[4 pFSK (1  pFSK )]dmin
For the multihop scenario being discussed here plink = PM and the
probability of packet error can be written as:
Pe  1  Pc  1  (1  plink ) n  1  (1  PM ) n
 1  {1  (2 k  1)[4 p FSK (1  pFSK )]d min }n
Error Control – Multihop WSN
The probability of successful transmission of a single code word is,
Psuccess  (1  Pe )
Radio parameters used to obtain the results shown in the next slides
Parameter
Value
NFRx
10dB
N0
-173.8 dBm/Hz or 4.17 * 10-21 J
Rbit
115.2 Kbits

0.3 m
Gant
-10dB or 0.1
amp
0.2

3
BW
For FSK-modulation, it is assumed to be the same as Rbit
eRC
50nJ/bit
eTC
50nJ/bit
Error Control – Multihop WSN
The expected energy consumption per information bit is defined as:
i  bit
Elinear

Elinear
k Psuccess
Parameters for the studied codes are shown in the table below, t is the
error correction capability.
Code
m
k
dmin
Code rate
t
Hamming
7
4
3
0.57
1
Golay
23
12
7
0.52
3
Shortened
Hamming
6
3
3
0.5
1
Extended
Golay
24
12
8
0.5
3
Error Control – Multihop WSN
Characteristic distance, dchar, as a function of bit error probability
for non-coherent FSK-modulation
Error Control – Multihop WSN
D = 1000 m
Error Control – Multihop WSN
D = 1000 m
Error Control – Multihop WSN
D = 1000 m
Error Control – Multihop WSN
D = 1000 m
Error Control – Multihop WSN
D = 1000 m
Error Control – Multihop WSN
D = 1000 m
Cooperative Communications in
Wireless Sensor Networks
 Diversity techniques have been proposed and developed to minimize the
errors in reception when the channel attenuation is large
 In frequency diversity the same information is transmitted on L carriers,
where the separation between successive carriers equals or exceeds the
coherence bandwidth of the channel
 In time diversity the same information is transmitted in L different time
slots, where the separation between successive time slots equals or
exceeds the coherence time of the channel
 In space, or multi-antenna, diversity the antennas are sufficiently far
apart that the multipath components of the signal have different
propagation delays so they fade independently
 Unlike conventional space diversity techniques that rely on antenna
arrays at the transmitter or receiver terminal the cooperative
communications discussed here assume that each node has its own
information to transmit and has one antenna. The terminals cooperate,
i.e. share their antennas and other resources, to create a “virtual array”
Cooperative Communications
In the example below:
 T1 and T2 transmit to terminals T3 and T4
 Transmitted signals can be received and processed by any number of terminals
 T1 and T2 can listen to each other’s transmission and jointly communicate their
information
 For sensor networks the objective is to attain an improved performance, e.g.
decreased BER and/or decreased energy consumption
 In cooperative mode each user transmits its own bits as well as some information
bits from its partners
T1
T3
T2
T4
Coded Cooperation(†)
Works by sending different portions of each user’s code word via independent
fading paths. Each terminal tries to transmit incremental redundancy for its
partner, if that is not possible the terminal transmits incremental redundancy
for itself. It is important that this mechanism works through code design and
not through actual communication between the cooperating terminals.
Diversity provided
by the coded bits
T1
T3
Percent of cooperatio n
T2
N2
N1  N 2
(†) M. Janani, A. Hedayat, T. E. Hunter, and A. Nosratinia, “Coded Cooperation in Wireless Communications: Space-Time
Transmission and Iterative Decoding,” IEEE Trans. Signal Processing, vol. 52, pp. 362-371, February 2004.
Coded Cooperation
 In this proposed method K bits of source bits (including CRC) are encoded with an
FEC code with an overall code rate R = K / N
 The users divide their transmissions into two time segments. In the first segment
each user transmits N1 bits (rate R1 = K / N1)
 Each user also receives and decodes the partner’s transmission. If this reception is
successful the user computes and transmits N2 additional parity bits for the
partner’s data, N1 + N2 = N. These additional parity bits are such that they can be
combined with the first frame code word to produce a more powerful R code word
 If the user does not successfully decode the partner, N2 additional parity bits for the
user’s own data are transmitted.
N1 bits
User 1
User 1 bits
N2 bits
User 2 bits
Rx User 2 bits
N1 bits
User 2
Rx User 1 bits
Idle or sleep
User 2 bits
Idle or sleep
User 1 bits
User 2 bits
Rx User 1 bits
Idle or sleep
N2 bits
User 1 bits
time
U1 time slot
U2 time slot
U1 time slot
Efficient Source Coding and Modulation(†)
 This example is a simple attempt to combine source coding (application layer) with
modulation (physical layer)
 An On/Off (OOK) modulation and a simple Minimum Energy (ME) coding
mechanism is used
 It is based on the assumption that the transceiver consumes energy only when
transmitting a high bit
Typical OOK Modulation
(†) Y. Prakash, S.K.S Gupta, “Energy Efficient Source Coding and Modulation for Wireless Applications,” IEEE Wireless
Communications and Networking Conference, 2003. WCNC 2003. Volume: 1, 16-20 March 2003, pp. 212-217.
Efficient Source Coding and Modulation
k source bits
n coded bits
 k information bits from the source are mapped into n coded bits
 The encoder generates code words that have a reduced number of 1’s
 The code words are OOK modulated and then transmitted
Minimum Energy code for k=2 and n=3
Source bits
Codeword
00
000
01
001
10
010
11
100
Efficient Source Coding and Modulation
 The proposed ME coding method can be seen as a form of Pulse Position
Modulation (PPM)
 To maintain the same rate of information transmission, the price is an
increase in the bandwidth
 For ME coding,
n  2k  1
which translates into the following increase in bandwidth (BW)
2k  1
BW ( ME (n, k ), OOK ) 
BW (OOK )
k
 Another drawback of this simple approach to saving energy through a
combination of source and channel coding is that the proposed code is
not balanced. Balanced codes are recommended for OOK modulation
 In balanced codes each code word contains the same number of ones
and zeros to aid the receiver in distinguishing a period when no
transmission takes place from a period when a long sequence of 0’s is
sent. Also the receiver needs enough symbol transitions to maintain bit
synchronization
Distributed Source Coding(†)
 In many applications of WSNs an event will cause several sensors to
generate highly correlated measurements
 It is of interest to explore how this correlation can be exploited to
compress the information being sent to the controller or sink node
(†) J. Chou, D. Petrovic, K. Ramchandran, “A Distributed and Adaptive Signal Processing Approach to Reducing Energy
Consumption in Sensor Networks,” IEEE INFOCOM 2003
Distributed Source Coding
 In the example below assume that the measurements X and Y are correlated
 Y is available at the decoder on the sink node but not at the node where X
is encoded
 How can X be encoded in a compressed manner knowing that Y is
available at the decoder?
X  Y  N
N is a random variable that
represents the correlation
between X and Y. If N is
Gaussian, one can get the
same performance by
knowing only the statistics
of N at the encoder as when
Y is known at the encoder
Distributed Source Coding
 Consider an 8-level scalar quantizer, and the correlated variables X and Y
 Partition the set of 8 quantization levels into 2 cosets (even and odd levels)
 If Y is available at the decoder only the index of the coset containing X, the
red coset, is needed to be transmitted (1 bit)
 Knowing Y the decoder finds the closest quantization level in the red coset,
and estimates that X takes on the value r4
Coding of Correlated Information
 All the recently proposed distributed source coding mechanisms can trace
back their origins to the fundamental paper by Slepian and Wolf (†)
 X and Y are correlated sources characterized by a bivariate distribution p(x,y)
 The sequences ..., X-1, X0, X1, ... and ..., Y-1, Y0, Y1, ... obtained by
independent drawings from (X, Y ) can be compressed at rates R1 and R2 if
and only if
R1  H ( X / Y )
R2  H (Y / X )
R1  R2  H ( X , Y )
(†) D. Slepian and J. Wolf, “Noiseless Coding of Correlated Information Sources,” IEEE Trans. Information Theory, vol.
19, pp. 471-480, July 1973.
Routing and Source Coding
In traditional routing the upstream data is (at best) attached to the local
data and forwarded downstream
(, S n1 )
X n1
Combined
representation of
several sensors’ data
(, Sn1, Sn )
Xn
An improved technique is to re-encode
jointly the data in the queues before
sending it downstream
Final Words
 Wireless Sensor Networks provide a fundamentally new set
of research and application challenges
 WSNs are a rich source of problems in communication
protocols, sensor tasking and control, sensor fusion,
distributed data bases, probabilistic reasoning, and
algorithmic design
 Some of the WSN topics not covered in this tutorial are:
 Deployment strategies
 Node localization
 Network capacity
 Fault tolerance
 Security
Web site
http://www.ee.oulu.fi/~carlos/IWWAN_04_WSN_Tutorial.ppt
carlos@ee.oulu.fi