Unit_II_ppt.pdf.pdf

EC8602 AD-HOC & WIRELESS
SENSOR NETWORKS
AD-HOC &WSN Unit II
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UNIT II
SENSOR NETWORKS – INTRODUCTION &
ARCHITECTURES
Challenges for Wireless Sensor Networks, Enabling Technologies
for Wireless Sensor Networks, WSN application examples, Single-
Node Architecture – Hardware Components, Energy Consumption
of Sensor Nodes, Network Architecture – Sensor Network
Scenarios, Transceiver Design Considerations, Optimization Goals
and Figures of Merit.
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Wireless Sensor Network
• In normal communication network,the participants are devices
close to a human user, interacting with humans.
• Instead of focusing interaction on humans, focus on
interacting with environment
• Network is embedded in environment
• Nodes in the network are equipped with sensing and actuation
to measure/influence environment
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• Wireless Sensor Network (WSN) consist of individual nodes
that are able to interact with their environment by sensing or
controlling physical parameters
• These nodes have to collaborate to fulfill their tasks as single
node is incapable of doing the task
• They use wireless communication to enable this collaboration
• Since they include actuators, it is also known as Wireless
Sensor and Actuator Networks(WSAN)
• Energy efficient is mor important as the nodes rely on board
batteries
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• In wireless sensor networks (WSNs), all the data collected by
the sensor nodes are forwarded to a sink node.
• Therefore, the placement of the sink node has a great impact
on the energy consumption and lifetime of WSNs.
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Challenges for Wireless Sensor Networks
For handling wide range of applications, WSN should have the
following characteristics. Realizing these characteristics with
new mechanisms is the major challenge of the vision of wireless
sensor networks.
1.Characteristic Requirements
(i)Type of Service (ii)Quality of Service
(iii)Fault tolerance (iv)Life time
(v) Scalability (vi)Wide range of densities
(vii)Programmability (viii)Maintainability
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2.Required Mechanism
(i)Multi hop Wireless Communication
(ii)Energy efficient operation
(iii)Auto Configuration
(iv)Collaboration and in network Configuration
(v)Data centric
(vi)Locality
(vii)Exploit trade offs
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1.Characteristic Requirements
(i)Type of Service
• The conventional communication network simply moves the
bits from one place to another.
• The WSN is expected to provide a meaningful information or
actions about a given task
• The concept like scoping of interactions to specific
geographic regions or to time intervals are needed in WSN
• New methods and interfaces are needed
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(ii)Quality of Service
• For multimedia-type applications ,traditional quality of service is
required
• When applications are tolerant to latency, bounded delay or
minimum bandwidth are irrelevant
• Some cases need only delivery of packets
• May be other cases needed high reliability requirements
• When actuators are to be controlled in a real-time fashion by the
sensor network,delay is important
• When the amount and quality of information is needed,the packet
delivery ratio is important
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(iii)Fault tolerance
• When the wireless communication between two nodes are
damaged or permanently interrupted,then the WSN should be
able to tolerate such faults.
• To tolerate node failure, redundant deployment is necessary
(iv)Scalability
• When WSN includes a large number of nodes, the employed
architectures and protocols must be able scale to these
numbers.
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(v)Lifetime
• Nodes will have only limited supply of energy (using batteries).
• Replacing these energy sources in the field is usually not
practicable
• So WSN must operate at least for a given mission time or as
long as possible.
• Since the lifetime of a WSN is very important figure of merit,
an energy-efficient WSN is necessary.
• A power source like solar cells might also be available on a
sensor node.
• Under such conditions, the lifetime of the network should
ideally be infinite.
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• The lifetime of a network also has direct trade-offs against
quality of service: investing more energy can increase quality
but decrease lifetime.
• The precise definition of lifetime depends on the application
• Case(i)Network life time is the time until the first node fails
• Case(ii)Life time is the time until the network is disconnected
in two or more partitions,
• Case(iii)Life time is the time until 50% (or some other fixed
ratio) of nodes have failed
• Case(iv) Life time is the time until a single sensor node is
present
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(vi)Wide range of densities
• In a WSN, the density of the network is the number of nodes
per unit .
• Different applications will have very different node densities.
• Density can vary over time and space because nodes fail or
move
• The density also does not have to homogeneous in the entire
network (because of imperfect deployment, for example)
• The network should adapt to such variations.
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(vii)Programmability
• When there is in need of new task, these nodes should be
programmable
• The nodes should be flexible while changing their task
• A fixed way of information processing is insufficient.
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(viii)Maintainability
• WSN has to adapt to changes, self-monitoring and adapt
operation
• WSN has to monitor its own health and status to change
operational parameters or to choose different trade-offs (e.g. to
provide lower quality when energy resource become scarce).
• WSN has to maintain itself and able to interact with external
maintenance mechanisms to ensure its extended operation at a
required quality
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2.Required Mechanism
(i)Multi hop Wireless Communication
• While wireless communication will be a core technique, a
direct communication between a sender and a receiver is faced
with limitations.
• Long distance communication needs high transmission power.
• The use of intermediate nodes as relays can reduce the total
required power. Hence,for WSN, multihop communication
will be a necessary ingredient.
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(ii)Energy-efficient operation
• To support long lifetimes, energy-efficient data transport between
nodes are needed and it is measured in J/Bit.
• Also, nonhomogeneous energy consumption – the forming of
“hotspots” – is an issue.
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(iii)Auto-configuration
• A WSN will have to configure most of its operational parameters
autonomously, independent of external configuration
• Nodes should be able to determine their geographical positions
only using other nodes of the network – so called “self-location”.
• The network should be able to tolerate failing nodes (because of a
depleted battery, for example) or to integrate new nodes (because
of incremental deployment after failure, for example).
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(iv)Collaboration and in-network processing
• In some applications, several sensors have to collaborate to
detect an event for providing enough information.
• Every node transmit all data to an external network and
process it “at the edge” of the network.
• An example is to determine the highest or the average
temperature within an area
• To solve such tasks efficiently, readings from individual
sensors can be aggregated as they propagate through the
network, reducing the amount of data to be transmitted and
hence improving the energy efficiency.
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• Data centric Traditional communication networks are
typically centered around the transfer of data between two
specific devices, each equipped with (at least) one network
address – the operation of such networks is thus address-
centric.
• In a WSN, where nodes are typically deployed redundantly to
protect against node failures or to compensate for the low
quality of a single node’s actual sensing equipment, the
identity of the particular node supplying data becomes
irrelevant.
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• In WSN , the answers and values themselves are important ,
not which node has provided them. Hence, switching from an
address-centric paradigm to a data-centric paradigm in
designing architecture and communication protocols is
promising.
• An example for such a data-centric interaction would be to
request the average temperature in a given location area, as
opposed to requiring temperature readings from individual
nodes.
• Such a data-centric paradigm can also be used to set
conditions for alerts or events (“raise an alarm if temperature
exceeds a threshold
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(vi)Locality
• Rather a design guideline than a proper mechanism, the
principle of locality will have to be embraced extensively to
ensure, in particular, scalability.
• Nodes, which are very limited in resources like memory,
should attempt to limit the state that they accumulate during
protocol processing to only information about their direct
neighbors.
• The hope is that this will allow the network to scale to large
numbers of nodes without having to rely on powerful
processing at each single node.
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(vii)Exploit trade-offs
• Various inherent trade-offs are there between mutually
contradictory goals, both during system/protocol design and at
runtime.
• Higher energy expenditure allows higher result accuracy
• Longer lifetime of the entire network trades off against
lifetime of individual nodes.
• Depending on application, deployment, and node failures at
runtime, the density of the network can change considerably –
the protocols will have to handle very different situations,
possibly present at different places of a single network.
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Enabling technologies for wireless sensor
networks
Miniaturization of hardware
• Smaller feature sizes in chips have driven down the power
consumption of the basic components of a sensor node
• Microcontrollers ,memory chips and the radio modems,
responsible for wireless communication have become much
more energy efficient.
• Reduced chip size will reduce the cost and improved energy
efficiency is necessary to make redundant deployment of
nodes affordable
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Enabling technologies for wireless sensor
networks
Sensing Equipment
• The three basic parts of a sensor node have to accompanied by
power supply.
• This requires,high capacity batteries that last for long times,
negligible self-discharge rate, and provide small amounts of
current.
• Sensor node also has a device for energy scavenging,
recharging the battery with energy gathered from the
environment – solar cells or vibration-based power generation
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Enabling Technologies for Wireless Sensor
Networks
• Software
• How the principal division of tasks and functionalities in a single
node – the architecture of the operating system or runtime
environment.
• This environment has to support simple retasking,cross-layer
information exchange, and modularity to allow for simple
maintenance.
• Single node architecture has to be extended to a network
architecture
1.How the division of tasks between nodes takes place
2.How to interface the structure for application programmers.
3.How to design appropriate communication protocols.
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Application of WSN
• Disaster relief applications
• Environment control
• Bio diversity Mapping
• Intelligent buildings
• Facility management
• Precision agriculture
• Medicine and health care
• Logistics
• Telematics
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Disaster relief applications
• Important application is wildfire detection
• Sensor nodes are equipped with thermometers and can
determine their own location
• These sensors are deployed over a wildfire, for example, a
forest, from an airplane.
• They collectively produce a “temperature map” of the area or
determine the perimeter of areas with high temperature that
can be accessed from the outside by firefighters equipped with
Personal Digital Assistants (PDAs).
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Disaster relief applications
• Control of accidents in chemical factories
• In military applications, where sensors should detect enemy
troops
• Sensors should be cheap enough to be considered disposable
since a large number is necessary
• Lifetime requirements are not particularly high.
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Environment control
• WSNs can be used to control the environment, for example,
with respect to chemical pollutants – a possible application is
garbage dump sites.
• For the the construction of offshore wind farms ,WSN is used
in the surveillance of the marine ground floor to understand
the erosion processes
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Biodiversity mapping
• WSNs is used to gain an understanding of the number of plant
and animal species that live in a given habitat (biodiversity
mapping).
• WSNs are the long-term, unattended, wirefree operation of
sensors close to the objects that have to be observed
• Sensors can be made small enough which they negligibly
disturb the observed animals and plants.
• Life time of sensor is highly required.
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Intelligent buildings
• Buildings waste vast amounts of energy by inefficient
Humidity, Ventilation and Air Conditioning (HVAC) usage.
• WSN is used to monitor the temperature, airflow, humidity,
and other physical parameters in a building
• This increase the comfort level of inhabitants and reduce the
energy consumption
• Improved energy efficiency as well as improved convenience
are some goals of “intelligent buildings”
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• WSN can be used to monitor mechanical stress levels of
buildings in seismically active zones.
• It is used to measure the bending load of girders to know
whether it is still safe to enter a given building after an
earthquake or whether the building is on the brink of collapse
• Similar systems can be applied to bridges.
• Sensors are used in detecting people enclosed in a collapsed
building and communicating such information to a rescue
team.
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• Collaborative mapping of physical parameters.
• Depending on the particular application, sensors can be
retrofitted into existing buildings (for HVAC type
applications) or have to be incorporated into the building
already under construction.
• If power supply is not available, lifetime requirements can be
very high
• Cost is relatively modest
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Facility management
• Keyless entry applications,WSN is used to check which
person(wear badge) is allowed to enter which areas of a larger
company site
• WSN is used to detect the intruders, for example of vehicles
that pass a street outside of normal business hours.
• WSN could track a vehicle’s position and alert security
personnel
• WSN could be used in a chemical plant to scan for leaking
chemicals.
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Facility management
Challenging requirements for these applications are
• Large number of sensors
• Should be able to collaborate (e.g. in the tracking example)
• They should be able to operate a long time on batteries.
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Machine surveillance and preventive
maintenance
• Sensor nodes are used to detect the vibration patterns that
indicate the need for maintenance.
• Examples for such machinery could be robotics or the axles
of trains for eg tire pressure monitoring
• The main advantage of WSNs here is the cable free operation,
avoiding a maintenance problem in itself and allowing a
cheap, often retrofitted installation of such sensors.
• Sensors should have long battery power since exchanging
batteries is usually impractical and costly.
• Size of nodes is often not a crucial issue, nor is the price
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Precision agriculture
• Applying WSN to agriculture allows precise irrigation and
fertilizing by
• placing humidity/soil composition sensors into the fields.
• one sensor per 100 m × 100 m area.
• Similarly, pest control can profit from a high-resolution
surveillance of farm land.
• Sensor is attached to each pig or cow, which controls the
health status of the animal (by checking body temperature,
step counting, or similar means) and raises alarms if given
thresholds are exceeded.
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Medicine and health care
• Sensors are directly attached to elderly patients for
surveillance
• No cable is an advantage
• Automatic drug administration (embedding sensors into drug
packaging, raising alarms when applied to the wrong patient,
is conceivable).
• Also, patient and doctor tracking systems within hospitals can
be literally life
saving.
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Logistics
• When a suitcase is moved around on conveyor belts in an
airport and passes certain checkpoints, passive RFID tag is
used to trace the luggage at any stage of transfer and it is
much simpler and cheaper than the active communication.
• A simple RFID tag cannot support more advanced
applications.
• It is very difficult to imagine how a passive system can be
used to locate an item in a warehouse
• It can also not easily store information about the history of its
attached object
• Hence sensors are used to track the parcels during
transportation or in warehouses.
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Telematics
• Sensors are embedded in the streets or roadsides and gather
information about traffic conditions
• This “intelligent roadside” could also interact with the cars to
exchange danger warnings about road conditions or traffic
jams ahead.
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Single-Node Architecture
The five main components of sensor node are
• Controller
• Communication
devices
• Sensors and
Actuators
• Memory
• Power supply
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Single-Node Architecture
• Controller -A controller to process all the relevant data, capable
of executing arbitrary code.
• Memory -Different types of memory are used to store programs
and data.
• Sensors and actuators The actual interface to the physical
world: devices that can observe or control physical parameters of
the environment.
• Communication - Device for sending and receiving information
over a wireless channel.
• Power supply - Batteries are necessary to provide energy.
Recharging the battery may obtain from solar cell
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Controller
The controller is the core and Central Processing Unit (CPU)
of a wireless sensor node.
• It collects data from the sensors
• Processes the data and decides when and where to send it
• Receives the data from other sensor nodes and decides on the
actuator’s behavior.
• It has to execute various programs, ranging from time-critical
signal processing and communication protocols to application
programs
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Controller
Main options: Microcontroller –General purpose processor
• Optimized for embedded applications
• Low power consumption
• Flexibility in connecting with other devices (like sensors)
• Instruction set amenable to time-critical signal processing
• In built memory
• They are freely programmable and hence very flexible.
• Reduce their power consumption by going into sleep states
where only parts of the controller are active
• Does not have Memory Management unit
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Controller
DSPs–optimized for signal processing tasks,
• Their architecture and their instruction set are used for
processing large amounts of vectorial data.
• In broadband wireless communication, DSPs are an
appropriate and successfully used platform.
• But in wireless sensor networks, the signal processing tasks
related to the actual sensing of data is also not overly
complicated.
• Hence, these advantages of a DSP are typically not required in
a WSN node and they are usually not used.
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Controller
FPGAs–may be good for testing
• An FPGA can be reprogrammed
• More time and energy
• It is not practical to reprogram an FPGA at the same frequency
as a microcontroller could change between different programs.
• An ASIC is a specialized processor, custom designed
• Flexibility is less
• Better energy efficiency and performance.
• ASICs provide the functionality in hardware, resulting in
potentially more costly hardware development.
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Controller
Microcontroller is the preferred solution as it is simple and
have bigger flexibility
Some examples for microcontrollers
Intel Strong ARM
• Fairly high-end processor as it is mostly geared toward handheld
devices like PDAs.
• The SA-1100 model has a 32-bit Reduced Instruction Set
Computer (RISC) core, running at up to 206 MHz.
Atmel AT mega
• 8-bit microcontroller, also intended for usage in embedded
applications
• Equipped with relevant external interfaces for common
peripherals.
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Controller
Texas Instruments MSP 430
• Intended for embedded applications.
• It runs a 16-bit RISC core at considerably lower clock
frequencies (up to 4 MHz)
• Wide range of interconnection possibilities and an instruction
set amenable to easy handling of peripherals of different kinds.
• It features a varying amount of on-chip RAM (sizes are 2–10
kB),
• several 12-bit analog/digital converters, and a real-time clock.
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Memory
• Random Access Memory (RAM)-Used to store intermediate
sensor readings, and packets from other nodes
• RAM is fast and loses its content if power supply is interrupted.
• Read-Only Memory (ROM)- Used to store Program codes
• Electrically Erasable Programmable Read-Only Memory
(EEPROM) or flash memory allows the data to be erased.
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Memory
• Flash memory can also serve as intermediate storage of data in
case RAM is insufficient or when the power supply of RAM
should be shut down for some time.
• Flash memory take long read and write delays
• Energy required is high.
• Correctly dimensioning memory sizes, especially RAM, can be
crucial with respect to manufacturing costs and power
consumption.
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Communication device
Choice of transmission medium
• The communication device is used to exchange data between
individual nodes.
• In some cases, wired communication can be used and is
frequently applied in many sensor Network like settings (using
field buses like Profibus, LON, CAN, or others).
• In wireless communication ,the first choice to make is that of
the transmission medium – the usual choices include radio
frequencies, optical communication,
• and ultrasound
• Other media like magnetic inductance are only used in very
specific cases.
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Communication device
Choice of transmission medium
• Radio Frequency (RF)-based communication is best for WSN
applications:
• It provides relatively long range
• High data rates
• Acceptable error rates at reasonable energy expenditure
• Does not require line of sight between sender and receiver.
• For a practical wireless, RF-based system, the carrier
frequency has to be carefully chosen.
• WSN typically use communication frequencies between about
433 MHz and 2.4 GHz.
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Transceivers
• For actual communication, both a transmitter and a receiver
are required in a sensor node.
• The essential task is to convert a bit stream coming from a
microcontroller (or a sequence of bytes or frames) and convert
them to and from radio waves.
• A device that combines these two tasks in a single entity are
called transceivers.
• Usually, half-duplex operation is realized since transmitting
and receiving at the same time on a wireless medium is
impractical in most cases
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Transceiver tasks and characteristics
• Some important characteristics of transceiver are
Service to upper layer
• A receiver has to offer certain services to the upper layer such
as Medium Access Control (MAC) layer.
• This service is packet oriented or byte interface or bit
interface
• MAC layer should initiate frame transmissions and to hand
over the packet from the main memory of the sensor node into
the transceiver
• In the other direction, incoming packets must be streamed into
buffers accessible by the MAC protocol.
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Power consumption and energy efficiency
• Energy efficiency is the minimum energy required to transmit
and receive a single bit.
• Transceivers should be switchable between different states for
example, active and sleeping.
• The idle power consumption in each of these states and during
switching between them is very important
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Carrier frequency and multiple channels
• Transceivers are available at different carrier frequencies for
application requirements and regulatory restrictions.
• It helps to alleviate some congestion problems in dense
networks.
• Such channels or “subbands” are relevant, for example, for
certain MAC protocols
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State change times and energy
• A transceiver can operate in different modes: sending or
receiving, use different channels, or be in different power-safe
states.
• In any case, the time and the energy required to change
between two such states are important figures of merit.
• The turnaround time between sending and receiving, for
example, is important for various medium access protocols
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Data rates
• The gross Data rate are determined by the Carrier frequency
and used bandwidth together with modulation and coding
• WSN data rates are lesser than broadband wireless
communication Typical values are a few tens of kilobits per
second
• Different data rates can be achieved by using different
modulations or changing the symbol rate.
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Modulations
• The transceivers typically support one or several of on/off-
keying, ASK, FSK, or similar modulations.
Coding
• Some transceivers allow various coding schemes to be
selected.
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Transmission power control
• Some transceivers can directly provide control over the
transmission power to be used
• Some require some external circuitry for that purpose.
• The actual transmission power can be chosen from a discrete
number of power levels are available .
• Maximum output power is usually determined by regulations.
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Noise figure
• The noise figure NF of an element is defined as the ratio of
the Signal-to-Noise Ratio (SNRi) ratio at the input of the
element to the SNR ratio (SNRo) at the element’s output:
NF = SNRi/ SNRo
• It describes the degradation of SNR due to the element’s
operation and is typically given in dB:
• NF dB = SNRi dB − SNRo dB
• Gain -The gain is the ratio of the output signal power to the
input signal power(dB).
• Amplifiers with high gain achieve good energy efficiency
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Power efficiency
• The efficiency of the radio front end is given as the ratio of the
radiated to the overall power consumed by the front end
• For a power amplifier, the efficiency describes the ratio of the
output signal’s power to the power consumed by the overall
power amplifier.
• Receiver sensitivity The receiver sensitivity (given in dBm)
specifies the minimum signal power at the receiver needed to
achieve a prescribed Eb/N0 or a prescribed bit/packet error
rate.
• Better sensitivity levels extend the possible range of a system.
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Range
• The range is considered in absence of interference and it
depends on the maximum transmission power, on the antenna
characteristics, on the attenuation caused by the environment,
• It depends on the used carrier frequency, on the
modulation/coding scheme and the bit error rate that is
accepted at the receiver.
• It also depends on the quality of the receiver and its
sensitivity.
• Products with ranges between a few meters and several
hundreds of meters are available.
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Blocking performance
• The blocking performance of a receiver is its achieved bit
error rate in the presence of an interferer.
• More precisely, at what power level can an interferer (at a
fixed distance) send at a given offset from the carrier
frequency such that target BER can still be met?
• An interferer at higher frequency offsets can be tolerated at
large power levels.
• Evidently, blocking performance can be improved by
interposing a filter between antenna and transceiver.
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Blocking performance
• An important special case is an adjacent channel interferer that
transmits on neighboring frequencies.
• The adjacent channel suppression describes a transceiver’s
capability to filter out signals from adjacent frequency bands
(and thus to reduce adjacent channel interference) has a direct
impact on the observed Signal to Interference and Noise Ratio
(SINR).
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Out of band emission
• The inverse to adjacent channel suppression is the out of band
emission of a transmitter.
• The transmitter should produce as little as possible of
transmission power outside of its prescribed bandwidth,
centered around the carrier frequency.
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Carrier sense and RSSI
• In many medium access control protocols, sensing whether the
wireless channel, the carrier, is busy (another node is
transmitting) is a critical information.
• The receiver has to be able to provide that information. The
precise semantics of this carrier sense signal depends on the
implementation.
For example, the IEEE 802.15.4 standard distinguishes the
following modes:
• The received energy is above threshold; however, the
underlying signal does not need to comply with the
modulation and spectral characteristics.
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Carrier sense and RSSI
• A carrier has been detected, that is, some signal which
complies with the modulation.
• Carrier detected and energy is present.
• Also, the signal strength at which an incoming data packet has
been received can provide useful information (e.g. a rough
estimate about the distance from the transmitter assuming the
transmission power is known)
• A receiver has to provide this information in the Received
Signal Strength Indicator (RSSI).
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Frequency stability
• The frequency stability denotes the degree of variation from
nominal center frequencies
• The frequency varies when environmental conditions of
oscillators like temperature or pressure change.
• When one node is placed in sunlight whereas its neighbor is
currently in the shade, poor frequency stability can break
down communication links
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• Voltage range
• Transceivers should operate reliably over a range of supply
voltages.
• Otherwise, voltage stabilization circuitry is required.
• Transceivers appropriate for WSNs are available from many
manufacturers
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• Simple transceivers often lack a unique identifier
• Each Ethernet device has a MAC-level address that uniquely identifies this
individual device.
• For simple transceivers, the additional cost of providing such an identifier is
relatively high with respect to the device’s total costs
• The availability of such device identifiers is very useful in many communication
protocols
• Improving these commercial designs
• To provide better performance at lower energy consumption
• Reduced cost
• Low transistor transconductance or limitations of integrated passive RF
components.
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Transceiver structure
• The structure of transceiver is divided into the Radio
Frequency (RF) front end and the baseband part
• The radio frequency front end performs analog signal
processing in the actual radio frequency band
• The baseband processor performs all signal processing in the
digital domain and communicates with a sensor node’s
processor or other digital circuitry.
• Between these two parts, a frequency conversion takes place,
either directly or via one or several Intermediate Frequencies
(IFs).
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• The RF front end performs analog signal processing in the
2.4 GHz Industrial, Scientific, and Medical (ISM) band
• It is the first stage of the interface between the electromagnetic
waves and the digital signal processing of the further
transceiver stages
• The boundary between the analog and the digital domain is
constituted by Digital/Analog Converters (DACs) and
Analog/Digital Converters (ADCs).
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• The Power Amplifier (PA) accepts upconverted signals from
the IF or baseband part and amplifies them for transmission
over the antenna.
• The Low Noise Amplifier (LNA) amplifies incoming signals
up to levels suitable for further processing without
significantly reducing the SNR
• The range of powers of the incoming signals may varies up to
100 dB
• The LNA is active all the time and can consume a significant
fraction of the transceiver’s energy
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• Local oscillators or voltage-controlled oscillators and mixers
are used for frequency conversion from the RF spectrum to
intermediate frequencies.
• The incoming signal at RF frequencies fRF is mixed with a
local oscillator fixed frequency (frequency fLO).
• The resulting intermediate-frequency signal has frequency
fLO − fRF.
• Filters are also present.
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Transceiver operational states
Many transceivers have four operational states
Transmit
• In the transmit state, the transmit part of the transceiver is
active and the antenna radiates energy.
Receive
• In the receive state the receive part is active.
Idle
• A transceiver that is ready to receive but is not currently
receiving anything is said to be in an idle state.
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Idle
• In this idle state, many parts of the receive circuitry are active,
and others can be switched off.
• For example, in the synchronization circuitry, acquisition
elements are active and the tracking elements are switched off
• A major source of power dissipation is leakage.
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Sleep
In the sleep state, significant parts of the transceiver are
switched off.
• IEEE 802.11 transceivers.
• These sleep states differ in the amount of circuitry switched
off and in the associated recovery times and startup energy
• For example, in a complete power down of the transceiver, the
startup costs include a complete initialization
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• The sensor node’s protocol stack and operating software must
decide the switching of transceiver states
• The operation of state changes also dissipate power
• For example, a transceiver waking up from the sleep mode to the
transmit mode requires some startup time and startup energy
• For example, to ramp up phase-locked loops or voltage-
controlled oscillators.
• During this startup time, no transmission or reception of data is
possible
• Power management is needed for scheduling the node states
(equivalently: switching on and off node/transceiver
components) so as to minimize average power consumption
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Examples of radio transceivers
RFM TR1000 family
• The TR1000 family of radio transceivers is available for the 916 MHz
and 868 MHz frequency range.
• It works in a 400 kHz wide band centered at, for example, 916.50 MHz.
• Short-range radio communication with up to 115.2 kbps.
• The modulation is either on-off-keying (at a maximum rate of 30 kbps) or
ASK
• The maximum radiated power is given 1.5 dBm,≈ 1.4 mW,
• The transceiver offers RSSI (Received Signal Strength Information).
• Low-power consumption in both send and receive modes and especially
in sleep mode.
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Infineon TDA 525x family
• The Infineon TDA 525x family provides flexible, single-chip,
energy-efficient transceivers.
• The TDA 5250 is a 868–870 MHz transceiver providing both
ASK and FSK modulation
• It has a highly efficient power amplifier, RSSI information, a
tunable crystal oscillator, an onboard data filter, and an intelligent
power-down feature.
• Self-polling mechanism, which can very quickly determine data
rate.
• Excellent blocking performance that makes it quite resistant to
interference.
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Chipcon CC1000
• The CC1000 operates in a wider frequency range, between 300
and 1000 MHz,
• Programmable in steps of 250 Hz.
• It uses FSK as modulation, provides RSSI, and has
programmable output power.
• An interesting feature is the possibility to compensate for crystal
temperature drift.
• It should also be possible to use it in frequency hopping
protocols.
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CC2420 family
• More complicated device.
• It implements the physical layer as prescribed by the IEEE
802.15.4 standard with the required support for this standard’s
MAC protocol.
• First commercially available single-chip transceiver for
IEEE 802.15.4.
• The transceiver operates in the 2.4 GHz band and features the
required DSSS modem, resulting in a data rate of 250 kbps.
• Low-power consumption
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IEEE 802.15.4/Ember EM2420 RF transceiver
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National Semiconductor LMX3162
• The radio hardware of the μAMPS-1 node consists of a digital
baseband processor implemented on an FPGA
• RF front end, National Semiconductor LMX3162 transceiver is used.
• The LMX3162 operates in the 2.4 GHz band
• It offers six different radiated power levels from 0 dBm up to 20 dBm.
• To transmit data,the baseband processor controls the VCO and also
provides timing information to a TDMA-based MAC protocol
• For data transmission, FSK with a data rate of 1 Mbps is used.
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Conexant RDSSS9M
• It consists of the RF part working in the ISM band between 902
and 928 MHz
• A microcontroller is responsible for processing DSSS signals
• The data rate is 100 kbps.
• The RF front end offers radiated power levels of 1 mW, 10 mW
and 100 mW.
• A number of 40 sub-bands are available, which can be freely
selected.
• The microcontroller implements portions of a MAC protocol also.
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Sensors
• Sensors can be roughly categorized into three categories
1.Passive and omnidirectional sensors
2.Passive and narrow-beam sensors
3.Active sensors
Passive or omnidirectional sensors
• These sensors can measure a physical quantity at the point of
the sensor node without actually manipulating the environment
by active probing – in this sense, they are passive.
• Self-powered in the sense that they obtain the energy from the
environment
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Sensors
• Energy is only needed to amplify their analog signal.
• There is no notion of “direction” involved in these
measurements.
• Typical examples for such sensors include thermometer, light
sensors,vibration, microphones, humidity, mechanical stress or
tension in materials, chemical sensors sensitive for given
substances, smoke detectors, air pressure, and so on.
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Sensors
Passive and narrow-beam sensors
• These sensors are passive
• Have a well-defined notion of direction of measurement.
• Camera, which can “take measurements” in a given direction,
but has to be rotated if need be.
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Active and Passive Sensors
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Power supply of sensor nodes
• The power supply is a crucial system component.
Two aspects
• First-storing energy and providing power
• Second,- “scavenging” the power from some node-external
power source over time.
• Storing power is conventionally done using batteries.
• A normal AA battery stores about 2.2–2.5 Ah at 1.5 V.
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Storing energy: Batteries
Traditional batteries
• The power source of a sensor node is a battery,
• Primary batteries –Non rechargeable
• Secondary batteries –Rechargeable if an energy scavenging
device is present on the node
• Batteries are electro-chemical stores for energy – the
chemicals being the main determining factor of battery
technology.
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Battery examples
• Energy per volume (Joule per cubic centimeter)
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Battery Requirements
• Capacity
• Self-discharge
• Capacity under load
• Efficient recharging
• Relaxation
• Voltage stability (to avoid DC-DC conversion)
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Capacity
• They should have high capacity at a small weight, small
volume, and low price.
• The main metric is energy per volume, J/cm3.
• Microscale batteries-deposited directly onto a chip is currently
ongoing.
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Self-discharge
• Their self-discharge should be low and they might also have to
last for a long time
• Using certain technologies, batteries are operational only for a
few months, irrespective of whether power is drawn from
them or not.
• Zinc-air batteries, for example, have only a very short lifetime
(on the order of weeks),which offsets their attractively high
energy density.
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Capacity under load
• They should withstand various usage patterns as a sensor node
can consume quite different levels of power over time and
actually draw high current in certain operation modes.
• Larger the battery, the more power can be delivered
instantaneously.
• In addition, the rated battery capacity specified by a
manufacturer is only valid as long as maximum discharge
currents are not exceeded, lest capacity drops or even
premature battery failure occurs
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Efficient recharging
• Recharging should be efficient even at low and intermittently
available recharge power
• Consequently, the battery should also not exhibit any
“memory effect”.
• Some of the energy-scavenging techniques are only able to
produce current in the μA region (but possibly sustained) at
only a few volts at best.
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Relaxation
• Their relaxation effect – the seeming self-recharging of an
empty or almost empty battery when no current is drawn from
it, based on chemical diffusion processes within the cell –
should be clearly understood.
• Battery lifetime and usable capacity is considerably
extended if this effect is leveraged.
• As but one example, it is possible to use multiple batteries
in parallel and “schedule” the discharge from one battery to
another, depending on relaxation properties and power
requirements of the operations to be supported
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Energy scavenging
Photovoltaics
• The well-known solar cells can be used to power sensor nodes.
• The available power depends on whether nodes are used
outdoors or indoors, and on time of day.
• Different technologies are best suited for either outdoor or indoor
usage.
• The resulting power is somewhere between 10 mW/cm2 indoors
and 15 mW/cm2 outdoors.
• Single cells achieve a fairly stable output voltage of about 0.6 V
• The drawn current does not exceed a critical threshold, which
depends, among other factors, on the light intensity.
Hence, solar cells are usually used to recharge secondary batteries.
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Energy scavenging
Temperature gradients
• Differences in temperature can be directly converted to electrical
energy.
• Theoretically, even small difference of, for example, 5 K can
produce considerable power
• But practical devices fall very short of theoretical upper limits
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Energy scavenging
Vibrations
• One form of mechanical energy is vibrations
• It occurs when walls or windows in buildings are resonating
with cars or trucks passing in the streets, machinery often has
low frequency vibrations, ventilations also cause it, and so on.
• The available energy depends on both amplitude and frequency
of the vibration and ranges from about 0.1 mW/cm3 up to 10,
000 mW/cm3
• Converting vibrations to electrical energy can be undertaken by
various means, based on electromagnetic, electrostatic, or
piezoelectric principles.
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Energy scavenging
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Energy scavenging
• A MEMS device for converting vibrations to electrical energy,
based on a variable capacitor
• Practical devices of 1 cm3 can produce about 200 mW/cm3
from 120 Hz vibration sources, actually sufficient to power
simple wireless transmitters
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Energy scavenging
Pressure variations
• Variation of pressure can also be used as a power source.
• Such piezoelectric generators are in fact used already.
• One well-known example is the inclusion of a piezoelectric
generator in the heel of a shoe, to generate power as a human
walks
• This device can produce, on average, 330 mW/cm2.
• It is, however, not clear how such technologies can be applied to
WSNs.
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Energy scavenging
Flow of air/liquid
• Another often-used power source is the flow of air or liquid in
wind mills or turbines.
• The challenge here is again the miniaturization, but some of the
work on milli meter scale
• MEMS gas turbines might be reusable
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Comparison of energy sources
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Energy consumption of sensor nodes
• Operation states with different power consumption
• Microcontroller energy consumption
• Memory
• Radio transceivers
• Relationship between computation and communication
• Power consumption of sensor and actuators
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• Different models usually support different numbers of such
sleep states with different characteristics
• For a controller -States are “active”, “idle”, and “sleep”
• A radio modem could turn transmitter, receiver, or both on or
off
• Sensors and memory could also be turned on or off.
• The usual terminology is to speak of a “deeper” sleep state if
less power is consumed.
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• At time t1, the decision whether or not a component (say, the
microcontroller) is to be put into sleep mode should be taken
to reduce power consumption from Pactive to Psleep.
• If it remains active and the next event occurs at time tevent,
then a total energy of Eactive = Pactive(tevent − t1)
has be spent uselessly idling.
• Putting the component into sleep mode, on the other hand,
requires a time τdown until sleep mode has been reached
• As a simplification, assume that the average power
consumption during this phase is (Pactive + Psleep)/2.
• Then, Psleep is consumed until tevent.
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• During sleep mode:
Esleep= τdown(Pactive + Psleep)/2 + (tevent − t1 − τdown)Psleep
energy is required
• During active mode Eactive = Pactive(tevent − t1) is required .
• The energy saving is thus Esaved =Eactive- Esleep
Esaved = (tevent − t1)Pactive −
(τdown(Pactive + Psleep)/2 +(tevent − t1 − τdown)Psleep).
• Once the event to be processed occurs, however, an additional
overhead of
Eoverhead = τup(Pactive + Psleep)/2
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Energy savings and overheads for sleep modes
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Energy savings and overheads for sleep modes
• Clearly, switching to a sleep mode is only beneficial if
Eoverhead < Esaved or, equivalently, if the time to the next
event is sufficiently large
• Careful scheduling of such transitions should be considered
by medium access control protocol in wireless sensor
networks.
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• Operation states with different power consumption
• Microcontroller energy consumption
• Memory
• Radio transceivers
• Relationship between computation and communication
• Power consumption of sensor and actuators
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Microcontroller energy consumption
The basic power consumption in discrete operation states
Intel StrongARM has three modes
Normal mode:
• All parts of the processor are fully powered.
• Power consumption is up to 400 mW.
Idle mode:
• Clocks to the CPU are stopped
• Clocks that pertain to peripherals are active.
• Any interrupt will cause return to normal mode.
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Sleep mode:
• The real-time clock remains active.
• Wakeup occurs after a timer interrupt and takes up to 160 ms.
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Texas Instruments MSP 430
Fully operational mode:
• Power consumption is 1.2 mW
Deepest sleep mode- LPM4:
• Power consumption is 0.3 mW, but the controller is only
woken up by external interrupts
LPM3 mode:
• Clock will be running, which can be used for scheduled wake
ups, and consumes only about 6 mW
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Atmel ATmega
• The Atmel ATmega has six different modes of power
consumption
• Similar to the MSP 430.
• Power consumption
▫ Idle mode - 6 mW
▫ Active mode- 15 mW
▫ Power down mode- 75 mW
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Dynamic voltage scaling
• Power adaptation can be done by adapting the speed with
which a controller operates.
• Choose the best possible speed to compute a task .
• There are two solutions
1. Switch the controller in full operation mode, compute
the task at highest speed, and go back to a sleep mode as
quickly as possible.
2.Compute the task only at the speed that is required to
finish it before the deadline.
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• The supply voltage can be reduced at lower clock rates while
still guaranteeing correct operation.
• The controller running at lower speed, that is, lower clock
rates, consumes less power than at full speed.
• This technique is called Dynamic Voltage Scaling (DVS)
• In CMOS chips: As the actual power consumption P depends
quadratically on the supply voltage VDD and frequency
• P ∝ f ・ V^2
• Reducing the voltage is a very efficient way to reduce power
consumption.
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Dynamic voltage scaling also reduces energy consumption.
• For example let us consider the Transmeta Crusoe processor
• Processor is scaled from 700 MHz at 1.65 V down to 200
MHz at 1.1 V
• This reduces the power consumption by a factor of 700・
1.65^2/200・1.1^2 = 7.875
• The speed is only reduced by a factor of 700/200 = 3.5.
• Hence, the energy required per instruction is reduced by
3.5/7.875 ≈0.44=44 %.
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When applying dynamic voltage scaling, care has to be taken
• Operate the controller within the specifications.
• Minimum and Maximum clock rates should be maintained
• Minimum and Maximum threshold must be obeyed.
• When there is nothing to process, sleep mode is still the only
option.
• Also, using arbitrary voltages requires a quite efficient DC-
DC converter
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Memory energy Consumption
• WSN uses On-chip memory of a microcontroller and FLASH
memory
• Off-chip RAM is rarely used.
• In fact, the power needed to drive on-chip memory is usually
included in the power consumption numbers given for the
controllers.
• The construction and usage of FLASH memory can heavily
influence node lifetime.
• The relevant metrics are the read and write times and energy
consumption
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Memory energy Consumption
• Writing is somewhat more complicated in Flash memory,as it
depends on the granularity with which data can be accessed
• Considerable differences in erase and write energy
consumption exist, up to ratios of 900:1 between different
types of memory.
• To give a concrete example, consider the energy consumption
necessary for reading and writing to the Flash memory used on
the Mica nodes
• Reading data takes 1.111 nAh, writing requires 83.333 nAh.
• Hence, writing to FLASH memory can be a time- and energy-
consuming task that is best avoided
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Radio transceivers
A radio transceiver has essentially two tasks:
• Transmitting and Receiving datas between a pair of nodes.
Radio transceivers can operate in different modes
• Turned on and Turned off.
For low total energy consumption,
• The transceivers only be activated when necessary
• But this gives additional complexity such as recovery time and
power overhead
• The energy consumption per bit for both sending and receiving
are required for understanding the energy consumption
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Modeling energy consumption during
transmission
• The energy consumption during transmission is due to two parts
• Part 1- Due to RF signal generation- depends on modulation and
target distance and hence on the transmission power Ptx-the
power radiated by the antenna.
• Part-2- Due to Electronic components necessary for frequency
synthesis, frequency conversion and filters
• Ptx is a function of system like energy per bit over noise Eb/N0,
the bandwidth efficiency ηBW, the distance d and the path loss
coefficient γ .
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transmission
• The transmitted power is generated by the amplifier of a
transmitter.
• Pamp depends on its architecture
• A more realistic model assumes that a certain constant power
level is always required irrespective of radiated power, plus a
proportional offset:
Pamp = αamp + βampPtx
where αamp and βamp are constants depending on process
technology and amplifier architecture
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transmission
• As an example, for the μAMPS-1 nodes, αamp = 174mW and
βamp = 5.0.
• Accordingly, the efficiency of the power amplifier ηPA for
Ptx = 1 mW
radiated power is given by
ηPA = Ptx/Pamp
= 1mW/(174mW + 5.0 ・ 1mW)
≈ 0.55%.
• This model implies that the amplifier’s efficiency Ptx/Pamp is
best at maximum output power.
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transmission
• In addition to the amplifier, power consumption is due to
baseband processors.
• This power is referred to as PtxElec.
• In addition, the start up power is also there when the
transceiver is turned on before transmission
• For eg Consider a packet of n-bits long (including all headers)
having nominal bit rate R and the coding rate Rcode
• The total consumed power during transmission is
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transmission
• The power equation does not depend on modulation scheme
and antenna efficiency
• It is assumed
▫ Perfect antenna
▫ Measurements based on IEEE 802.11 hardware have shown
that there is less than 10 % dependence on the modulation
▫ Coding overhead only depends on the coding rate
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Some examples of transceiver energy
consumption
• The power equation does not depend on modulation scheme
and antenna efficiency
• It is assumed
▫ Perfect antenna
▫ Measurements based on IEEE 802.11 hardware have shown
that there is less than 10 % dependence on the modulation
▫ Coding overhead only depends on the coding rate
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Modeling energy consumption during Reception
• The receiver can be either turned off or turned on.
• While being turned on, it can either actively receive a packet
or can be idle, observing the channel and ready to receive.
• Evidently, the power consumption while it is turned off is
negligible.
• Even the difference between idling and actually receiving is
very small and can, for most purposes, be assumed to be zero.
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The total energy Ercvd required to receive a packet
• It has a startup component TstartPstart
• It also has a component that is proportional to the packet time
n/RRcode
• PrxElec – Power required to drive the LNA in the RF front
end.
• The last component is the decoding overhead for every bit –
depends on the concrete FEC(Forward Error Correction) in
use
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• The decoding energy is relatively complicated to model
• It depends on a number of hardware and system parameters
➢Dedicated hardware ( Viterbi decoder for convolutional codes)
➢Software on a microcontroller-depends on supply voltage,
decoding time per bit (constraint length K of the used code, and
other parameters.
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Dynamic scaling of radio power consumption
• Scaling down supply voltage or frequency to obtain lower power
consumption will give higher latency
• DVS principles is only applicable to some of the electronic parts
of a transceiver
• The amplifier cannot be scaled down as its radiated
• The amplifier high power consumption depends on the
communication distance
• There is trade off between frequency/voltage versus performance.
• For radio communication,possible parameters include the choice
of modulation and/or code, giving raise to Dynamic Modulation
Scaling (DMS), Dynamic Code Scaling (DCS) and Dynamic
Modulation-Code Scaling (DMCS) optimization techniques to
reduce the power consumption and maximize throughput
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Relationship between computation and
communication
What is the relation in energy consumption between sending data and
computing?
• Typically, computing a single instruction on a microcontroller requires
about 1 nJ.
• Also, 1 nJ about suffices to take a single sample in a radio transceiver
• Bluetooth transceivers could be expected to require roughly 100 nJ to
transmit a single bit
• For other hardware, the ratio of the energy consumption to send one bit
compared to computing a single instruction is between 1500 to 2700 for
Rockwell WINS nodes, between 220 to 2900 for MEDUSA II nodes, and
about 1400 for WINS NG 2.0 nodes
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Relationship between computation and
communication
• For the RFM TR1000 radio transceiver, 1 mJ to transmit a
single bit and 0.5 mJ to receive one
• Their processor takes about 8 nJ per instruction.
• This results in a (actually quite good) ratio of about 190 for
communication to computation costs.
• In a slightly different perspective, communicating 1 kB of data
over 100 m consumes roughly the same amount of energy as
computing three million instructions
• Communication is a considerably more expensive undertaking
• than computation.
• Still, energy required for computation cannot be simply
ignored
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Power consumption of sensor and actuators
• Passive sensors like light or temperature sensors – the power
consumption is very small and can be ignored in comparison to
other devices on a wireless node
• Power consumption varies from 0.6 to 1 mA for a temperature
sensor
• Active sensors like sonar has considerable power consumption
• Power consumption of sensor/controller interfaces, namely, AD
converters, can be considered
• In addition, the sampling rate evidently is quite important.
• The more frequent sampling of data require more energy for the
sensors
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Power consumption of sensor and actuators
Some examples of sensor characteristics
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Sensor network scenarios
Types of sources and sinks
• A source is any entity in the network that can provide
information, that is, typically a sensor node
• It could also be an actuator node that provides feedback about
an operation.
• A sink is the entity where information is required.
• There are essentially three options for a sink:
▫ It could belong to the sensor network
▫ Another sensor/actuator node
▫ It could be an entity outside this network.
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Three Types of Sink
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Three Types of Sink
• For the second case, the sink could be an actual device - a
handheld or PDA used to interact with the sensor network
• For the third case -It could also be gateway to another larger
network such as the Internet where the actual request for
the information comes from some node
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Single-hop versus multihop networks
Direct communication between source and sink is not always
possible in WSNs.Why?
➢Power limitation of radio communication follows the limited
distance
➢Cannot cover a lot of ground (e.g. in environmental or
agriculture applications)
➢ Difficult to operate in radio environments with strong
attenuation (e.g. in buildings).
To overcome such limited distances, the data packets take multi
hops from the source to the sink.
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Single-hop versus multihop networks
• Multihop is a solution for
obstacles and large distance
problem
• Source send packets to the
intermediate nodes
• Intermediate send the
packets to the destination
• Store and forward multihop
network
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Multihopping
• Multihopping also improve the energy efficiency of
communication.
• The attenuation of radio signals is at least quadratic in most
environments
• It consumes less energy to use relays instead of direct
communication
• When targeting for a constant SNR at all receivers , the radiated
energy required for direct communication over a distance d is c
*d^α (c some constant, α ≥ 2 the path loss coefficient);
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Multihopping
• Using a relay at distance d/2 reduces this energy to
2c*(d/2)^α.
• But this calculation considers only the radiated energy, not the
actually consumed energy
• Energy is actually wasted if intermediate relays are used for
short distances d.
• Only for large d does the radiated energy dominate the fixed
energy costs consumed in transmitter and receiver electronics
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Three types of mobility
Wireless communication is able to support mobile participants.
In wireless sensor networks, mobility are in three main forms:
Node mobility Sink mobility Event mobility
Node mobility
• The wireless sensor nodes themselves can be mobile.
• The mobility is highly application dependent.
In examples like environmental control-No node mobility
Sensor nodes attached to cattle- Node mobility is there
• The network has to reorganize itself frequently enough to be
able to function correctly.
• Trade-offs between the speed of node movement and the
energy required to maintain a desired level of functionality in
the network
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Sink mobility
• The information sinks can be mobile.
• The mobility of an information sink is not part of the sensor
network
• For example, a human user requested information via a PDA
while walking in an intelligent building.
• In a simple case, such a requester can interact with the WSN at
one point and complete its interactions before moving on.
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Sink mobility
• In many cases, consecutive interactions can be treated as
separate, unrelated requests.
• Whether the requester is allowed interactions with any node or
only with specific nodes is a design choice for the appropriate
protocol layers.
• The network with the assistance of the mobile requester
should make the requested data reaches the requester despite
its movements
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Sink mobility
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Event mobility
• In applications like event detection and in particular in
tracking applications, the cause of the events or the objects to
be tracked can be mobile.
• The observed event is covered by a sufficient number of
sensors at all time.
• Hence, sensors will wake up around the object, engaged in
higher activity to observe the present object, and then go back
to sleep.
• As the event source moves through the network, it is
accompanied by an area of activity within the network – this
has been called the frisbee model
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• The task is to detect a moving elephant and to observe it as it
moves around.
• Nodes that do not actively detect anything are intended to
switch to lower sleep states
• Nodes become active only when elephant is near by and they
will convey information from the zone of activity to some
remote sink
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Event mobility
Dashed line -Elephant’s trajectory
Shaded ellipse- the activity area following the elephant
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Optimization goals and figures of merit
• Quality of service
• Energy efficiency
• Scalability
• Robustness
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• For all applications, different forms of networking solutions
can be found.
The challenging questions in networks are
• How to optimize a network?
• How to compare these solutions?
• How to decide which approach better supports a given
application?
• How to turn relatively imprecise optimization goals into
measurable figures of merit?
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Quality of Service
• WSNs differ from other conventional communication
networks mainly in the type of service they offer.
• These networks essentially only move bits from one place to
another.
• For multimedia applications, QoS can be regarded as a low-
level, networking-device-observable attribute – bandwidth,
delay, jitter, packet loss rate – or as a high-level, user-
observable, so-called subjective attribute like the perceived
quality of a voice communication or a video transmission.
• Delay should be considered in WSN
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Quality of Service
• High-level QoS attributes in WSN are highly depend on the
application.
• Some generic possibilities are:
▫ Event detection/reporting probability
▫ Event classification error
▫ Event detection delay Missing reports
▫ Approximation accuracy
▫ Tracking accuracy
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Quality of Service
Event detection/reporting probability
• The probability that an occurred event is not detected or not
reported to an information sink should be small
• For example, not reporting a fire alarm to a surveillance
station would be a severe shortcoming.
• Clearly, this probability can depend on the overhead spent in
setting up structures in the network that support the reporting
of such an event (e.g. routing tables)
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Quality of Service
Event classification error
• If events are not only to be detected but also to be classified,
the error in classification must be small.
Event detection delay
• The delay between detecting an event and reporting it to the
interested sinks should be small
Missing reports
• In applications that require periodic reporting, the probability
of undelivered reports should be small.
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Quality of Service
Approximation accuracy
• For function approximation applications (e.g. approximating
the temperature as a function of location for a given area),
• The average/maximum absolute or relative error with respect
to the actual function should be considered
• Similarly, for edge detection applications,
• The accuracy of edge descriptions should be considered
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Quality of Service
Tracking accuracy
• Tracking applications must not miss an object to be tracked,
the reported position should be as close to the real position as
possible, and the error should be small.
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Energy efficiency
• Energy is a precious resource in WSN and hence it is an
optimization goal.
• If amount of energy increases, most of the QoS metrics also
increases
• Except approximation and tracking accuracy as they depend
on the density of the network
• The term “energy efficiency” is an umbrella term for many
different aspects of a system
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Energy efficiency
Energy per correctly received bit
• Finding average energy consumed to transport one bit of
information (payload) from the source to the destination
including intermediate nodes is a useful metric for periodic
monitoring applications.
Energy per reported (unique) event
• what is the average energy spent to report one event?
• Since the same event is sometimes reported from various
sources, normalize this metric to only the unique events
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Energy efficiency
Delay/energy trade-offs
• Some applications have a notion of “urgent” events
• The energy investment is increased for a speedy reporting of
such events.
• There will be trade-off between delay and energy overhead
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Energy efficiency
Network lifetime
• Network time is the time for which the network is operational
or the time during which it is able to fulfill its tasks
• Possible definitions are:
• Time to first node death- Time taken by the first node in the
network to run out of energy or fail and stop operating.
• Network half-life – Time taken by 50% of the nodes to run
out of energy and stopped operating?
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Energy efficiency
• Time to partition – Time taken by the network to be
disconnected and partitioned into two or more
• This can be as early as the death of the first node or occur very
late if the network topology is robust.
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Energy efficiency
• Time to loss of coverage
• Usually, with redundant network deployment ,every point in
the region is observed by multiple sensor nodes
• A possible figure of merit is thus the time when for the first
time any spot in the deployment region is no longer covered
by any node’s observations.
• If k redundant observations are needed for tracking
applications- the loss of coverage is the first time any spot in
the deployment region is having less than k nodes
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Energy efficiency
• Time to failure of first event notification
• A network partition has the inability to deliver an event.
• If the responsible sensor is dead or a partition between source
and sink has occurred, then the event is not noticed and
reported
• It should be noted that simulating network lifetimes can be a
difficult statistical problem.
• The longer these times are, the better does a network perform.
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Scalability
• The ability to maintain performance characteristics
irrespective of the size of the network is referred to as
scalability.
• With WSN potentially consisting of thousands of nodes,
scalability is an evidently indispensable requirement.
• If the sensor nodes has limited resource such as memory,then
it is difficult to maintain the addresses or routing table entries
• Then the scalability is ill served
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Scalability
• The need for extreme scalability has direct consequences for
the protocol design.
• Architectures and protocols should implement appropriate
scalability support.
• Applications with a few dozen nodes might admit more
efficient solutions than applications with thousands of nodes
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Robustness
• Wireless sensor networks should also exhibit an appropriate
robustness.
• They should not fail just because a limited number of nodes
run out of energy, or because their environment changes and
severs existing radio links between two nodes
• If possible, these failures have to be compensated by finding
other routes.
• Evaluation of robustness is difficult in practice and depends
mostly on failure models for both nodes and communication
links.
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174

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