Unit Ii
Unit Ii
Unit Ii
SENSOR NETWORKS –
INTRODUCTION & ARCHITECTURES
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
Sensor And Sensor Node
• A sensor is a electronic device that measures a physical quantity
and converts it into a signal which can be read by an observer or
by an instrument.
• Sensor Node : Basic unit in Sensor Network
Super Node
Motes
A general work process of WSN
Goal of the sensor node
• The goal from the sensor node is to collect the data at regular
intervals, then transform the data into an electrical signal and
finally send the signal to the sink or the base node.
• Energy supply?
– Limited from point of deployment?
– Some form of recharging, energy scavenging from environment?
– E.g., solar cells
Eruption
Assignment
• Explain any one application ofWSN (Agriculture,
Medical, Military, Under water, Animal Habitat,
IOT, IIOT etc..) in Detail
– What isWSN?
– Type of Sensor Used
– Application in Detail
– Working
– References
15
Design Challenges
• Heterogeneity
– The devices deployed maybe of various types and need to
collaborate with each other.
• Distributed Processing
– The algorithms need to be centralized as the processing is carried
out on different nodes.
• Low Bandwidth Communication
– The data should be transferred efficiently between sensors
• Large Scale Coordination
– The sensors need to coordinate with each other to produce required
results.
• Utilization of Sensors
– The sensors should be utilized in a ways that produce the maximum
performance and use less energy.
• Real Time Computation
– The computation should be done quickly as new data is always
being generated.
Challenges for WSNs
• Type of service of WSN
– Not simply moving bits like another network
– Rather: provide answers(not just numbers)
– Issues like geographic scoping are natural requirements, absent from other
networks
• Quality of service
– Traditional QoS metrics do not apply
– Still, service of WSN must be “good”: Right answers at the right time
• Fault tolerance
– Be robust against node failure (running out of energy, physical destruction,
…)
• Lifetime
– The network should fulfill its task as long as possible –definition depends
on application
– Lifetime of individual nodes relatively unimportant
– But often treated equivalently
• Scalability
– Support large number of nodes
• Programmability
– Re-programming of nodes in the field might be necessary, improve
flexibility
• Maintainability
– WSN has to adapt to changes, self-monitoring, adapt operation
– Incorporate possible additional resources, e.g., newly deployed
nodes
Operational Challenges of Wireless Sensor Networks
• Energy Efficiency
• Limited storage and computation
• Low bandwidth and high error rates
• Errors are common
– Wireless communication
– Noisy measurements
– Node failure are expected
• Scalability to a large number of sensor nodes
• Survivability in harsh environments
• Experiments are time- and space-intensive
Required mechanisms to meet requirements
• Multi-hop wireless communication
• Energy-efficient operation
– Both for communication and computation, sensing, actuating
• Auto-configuration
– Manual configuration just not an option
• Collaboration & in-network processing
– Nodes in the network collaborate towards a joint goal
– Pre-processing data in network (as opposed to at the edge) can
greatly improve efficiency
• Data centric networking
– Focusing network design on data, not on node identifies(id-centric
networking)
– To improve efficiency
• Locality
– Do things locally (on node or among nearby neighbors) as far as
possible
• Exploit tradeoffs
– E.g., between invested energy and accuracy
Enabling technologies for WSN
• Cost reduction
– For wireless communication, simple microcontroller, sensing,
batteries
• Miniaturization
– Some applications demand small size
– “Smart dust” as the most extreme vision
• Energy scavenging
– Recharge batteries from ambient energy (light, vibration, …)
Single-node Architecture
Goals
• Survey the main components of the composition of a node for a
wireless sensor network
– Controller, radio modem, sensors, batteries
Microcontroller
• use general-purpose processors
• These processors are highly overpowered, and their energy consumption
is excessive.
key characteristics why these microcontrollers
• particularly suited to embedded systems
• flexibility in connecting with other devices (like sensors),
• instruction set amenable to time-critical signal processing,
• typically low power consumption;
• have memory built in.
• freely programmable and very flexible.
Coding
• Some transceivers allow various coding schemes to be selected.
Gain
• The gain is the ratio of the output signal power to the input signal
power and is typically given in dB.
• Amplifiers with high gain are desirable to achieve good energy
efficiency.
Power efficiency
• The efficiency of the radio front end is given as the ratio of the radiated
power 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.
Range
• The range is considered in
– absence of interference;
– the maximum transmission power,
– the antenna characteristics,
– the attenuation caused by the environment,
– the used carrier frequency, the modulation/coding scheme,
– the bit error rate
– the quality of the receiver, essentially captured by its sensitivity.
Blocking performance
• The blocking performance of a receiver is its achieved bit error rate in
the presence of an interferer.
• An interferer at higher frequency offsets can be tolerated at large power
levels.
• blocking performance can be improved by interposing a filter between
antenna and transceiver.
• 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).
Out of band emission
• The inverse to adjacent channel suppression is the out of band emission
of a transmitter.
• To limit disturbance of other systems, or of the WSN itself in a
multichannel setup, the transmitter should produce as little as possible of
transmission power outside of its prescribed bandwidth, centered around
the carrier frequency.
• 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).
Frequency stability
• The frequency stability denotes the degree of variation from nominal
center frequencies when environmental conditions of oscillators like
temperature or pressure change.
• In extreme cases, poor frequency stability can break down
communication links, for example, when one node is placed in sunlight
whereas its neighbor is currently in the shade.
Voltage range
• Transceivers should operate reliably over a range of supply voltages.
Otherwise, inefficient voltage stabilization circuitry is required.
Transceiver structure
A fairly common structure of transceivers is 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, whereas
• 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 Frequency's (IFs).
• The boundary between the analog and the digital domain is constituted
by Digital/Analog Converters (DACs) and Analog/Digital Converters
(ADCs).
• ChipconCC1000
– Range 300 to 1000 MHz, programmable in 250 Hz steps
– FSK modulation
– Provides RSSI
• ChipconCC 2400
– Implements 802.15.4
– 2.4 GHz, DSSS modem
– 250 kbps
– low power consumption than above transceivers
• Advantages
– Pretty resilient to multi-path propagation
– Very good ranging capabilities
– Good wall penetration
Sensors and actuators
• Sensors
– Sensors can be roughly categorized into three categories
• Passive, 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.
– Moreover, some of these sensors actually are self-powered in the
sense that they obtain the energy they need from the environment –
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.
• Passive, narrow-beam sensors
– These sensors are passive as well, but have a well-defined notion of
direction of measurement.
– A typical example is a camera, which can “take measurements” in a
given direction, but has to be rotated if need be.
• Active sensors
– This last group of sensors actively probes the environment, for
example, a sonar or radar sensor or some types of seismic sensors,
which generate shock waves by small explosions.
• Obvious trade-offs include accuracy, dependability, energy
consumption, cost, size, and so on – all this would make a detailed
discussion of individual sensors quite ineffective.
• Overall, most of the theoretical work on WSNs considers passive,
omnidirectional sensors.
• Narrow-beam-type sensors like cameras are used in some practical
testbeds, but there is no real systematic investigation on how to control
and schedule the movement of such sensors.
• each sensor node has a certain area of coverage for which it can
reliably and accurately report the particular quantity that it is observing.
Actuators
• In principle, all that a sensor node can do is to open or close a switch or
a relay or to set a value in some way.
• Whether this controls a motor, a light bulb, or some other physical
object is not really of concern to the way communication protocols are
designed.
• In a real network, however, care has to be taken to properly account for
the idiosyncrasies of different actuators.
• Also, it is good design practice in most embedded system applications
to pair any actuator with a controlling sensor – following the principle
to “never trust an actuator”
Power supply of sensor nodes
• Goal: provide as much energy as possible at smallest cost/ volume/
weight/ recharge time/longevity
– In WSN, recharging may or may not be an option
• Options
– Primary batteries –not rechargeable
– Secondary batteries –rechargeable, only makes sense in combination
with some form of energy harvesting
• Requirements include
– Low self-discharge
– Long shelf live
– Capacity under load
– Efficient recharging at low current
– Good relaxation properties (seeming self-recharging)
– Voltage stability (to avoid DC-DC conversion)
• Storing power is conventionally done using batteries.
• As a rough orientation, a normal AA battery stores about 2.2–2.5 Ah at
1.5 V.
• Battery design is a science and industry in itself, and energy scavenging
has attracted a lot of attention in research.
Self-discharge
• Their self-discharge should be low; 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.
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.
• Current battery technology would basically not recharge at such values.
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.
• 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
Energy scavenging
• Some of the unconventional energy stores– fuel cells, micro heat
engines, radioactivity – convert energy from some stored, secondary
form into electricity in a less direct and easy to use way than a normal
battery would do.
• The entire energy supply is stored on the node itself – once the fuel
supply is exhausted, the node fails.
• To ensure truly long-lasting nodes and wireless sensor networks, such a
limited energy store is unacceptable.
• Rather, energy from a node’s environment must be tapped into and
made available to the node – energy scavenging should take place.
Several approaches exist
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 and whether for outdoor usage.
• Different technologies are best suited for either outdoor or indoor
usage.
• The resulting power is somewhere between 10 μW/cm2 indoors and 15
mW/cm2 outdoors.
• Single cells achieve a fairly stable output voltage of about 0.6 V (and
have therefore to be used in series) as long as 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.
Temperature gradients
• Differences in temperature can be directly converted to electrical
energy.
• Theoretically, even small difference of, for example, 5K can produce
considerable power, but practical devices fall very short of theoretical
upper limits (given by the Carnot efficiency).
• Seebeck effect-based thermoelectric generators are commonly
considered; one example is a generator, which will be commercially
available soon, that achieves about 80 μW/cm2 at about 1V from a 5
Kelvin temperature difference
Vibrations
• One almost pervasive form of mechanical energy is vibrations:
• 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 μW/cm3 up to 10, 000 μW/cm3 for
some extreme cases (typical upper limits are lower).
• Converting vibrations to electrical energy can be undertaken by various
means, based on electromagnetic, electrostatic, or piezoelectric
principles.
Pressure variations
• Somewhat similar to vibrations, a 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 about.
• This device can produce, on average, 330 μW/cm2. It is, however, not
clear how such technologies can be applied to WSNs.
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 millimeter scale MEMS gas turbines might be reusable.
• However, this has so far not produced any notable results.
Comparison of energy sources
• As these examples show, energy scavenging usually has to be
combined with secondary batteries as the actual power sources are not
able to provide power consistently, uninterruptedly, at a required level;
rather, they tend to fluctuate over time.
• This requires additional circuitry for recharging of batteries, possibly
converting to higher power levels, and a battery technology that can be
recharged at low currents
Energy consumption of sensor nodes
Operation states with different power consumption
• Energy supply for a sensor node is at a premium:
– batteries have small capacity, and
– recharging by energy scavenging is complicated and volatile.
• Hence, the energy consumption of a sensor node must be tightly
controlled.
• The main consumers of energy are
– the controller,
– the radio front ends,
– to some degree the memory, and,
– depending on the type,the sensors.
• A “back of the envelope” estimation
• Number of instructions (the energy consumed by a microcontroller per
instruction)
– Energy per instruction: 1 nJ
– Small battery (“smart dust”): 1 J = 1 Ws
– Corresponds: 109instructions!
Lifetime
• Or: Require a single day operational lifetime = 24*60*60 =86400 s
• 1 Ws / 86400s =11.5 μW as max. sustained power consumption!
• Not feasible!
• One important contribution to reduce power consumption of these
components comes from chip-level and lower technologies:
• Designing low-power chips is the best starting point for an energy-
efficient sensor node.
• But this is only one half of the picture, as any advantages gained by
such designs can easily be squandered when the components are
improperly operated.
• Introducing and using multiple states of operation with reduced energy
consumption in return for reduced functionality is the core technique
for energy-efficient wireless sensor node
• Advanced Configuration and Power Interface (ACPI) introduces one
state representing the fully operational machine and four sleep states of
graded functionality/power consumption/wakeup time (time necessary
to return to fully operational state)
• Different models usually support different numbers of such sleep states with
different characteristics;
• For a controller, typical 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. Multiple modes possible, “deeper” sleep modes
– Strongly depends on hardware
– TI MSP 430, e.g.: four different sleep modes
– Atmel ATMega: six different modes
Microcontroller
• TI MSP 430 (@ 1 MHz, 3V):
– Fully operation 1.2 mW
– Deepest sleep mode 0.3 μW –only woken up by external interrupts
(not even timer is running any more)
• Atmel ATMega
– Operational mode: 15 mWactive, 6 mWidle
– Sleep mode: 75 μW
• 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.
Atmel ATmega
• The Atmel ATmega 128L has six different modes of power
consumption, which are in principle similar to the MSP 430.
• Its power consumption varies between 6 mW and 15 mW in idle and
active modes and is about 75 μW in power-down modes.
Dynamic voltage scaling
• A more sophisticated possibility than discrete operational states is to
use a continuous notion of functionality/power adaptation by adapting
the speed with which a controller operates.
• The idea is to choose the best possible speed with which to compute a
task that has to be completed by a given deadline.
• One obvious solution is to switch the controller in full operation mode,
compute the task at highest speed, and go back to a sleep mode as
quickly as possible.
• The alternative approach is to compute the task only at the speed that is
required to finish it before the deadline.
• The rationale is the fact that a controller running at lower speed, that is,
lower clock rates, consumes less power than at full speed.
• This is due to the fact that the supply voltage can be reduced at lower
clock rates while still guaranteeing correct operation. This technique is
called Dynamic Voltage Scaling (DVS)
Memory
• From an energy perspective, the most relevant kinds of memory are on-
chip memory of a microcontroller and FLASH memory – off-chip
RAM is rarely if ever used.
• In fact, the power needed to drive on-chip memory is usually included
in the power consumption numbers given for the controllers.
• Hence, the most relevant part is FLASH memory – in fact, the
construction and usage of FLASH memory can heavily influence node
lifetime.
• The relevant metrics are the read and write times and energy
consumption.
• All this information is readily available from manufacturers’ data sheets
and do vary depending on several factors.
• Read times and read energy consumption tend to be quite similar
between different types of FLASH memory
• Writing is somewhat more complicated, as it depends on the granularity
with which data can be accessed
• 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.
Radio transceivers
• A radio transceiver has essentially two tasks: transmitting and receiving
data between a pair of nodes.
• Similar to microcontrollers, radio transceivers can operate in different
modes, the simplest ones are being turned on or turned off.
• To accommodate the necessary low total energy consumption, the
transceivers should be turned off most of the time and only be activated
when necessary – they work at a low duty cycle.
• But this incurs additional complexity, time and power overhead that has
to be taken into account.
• To understand the energy consumption behavior of radio transceivers
and their impact on the protocol design, models for the energy
consumption per bit for both sending and receiving are required.
Relationship between computation and communication
• Looking at the energy consumption numbers for both microcontrollers and
radio transceivers, an evident question to ask is which is the best way to invest
the precious energy resources of a sensor node: Is it better to send data or to
compute? What is the relation in energy consumption between sending data and
computing?
• This relationship heavily depend on the particular hardware in use.
• Typically, computing a single instruction on a microcontroller requires about
1nJ. Also, 1nJ about suffices to take a single sample in a radio transceiver;
• Bluetooth transceivers could be expected to require roughly 100nJ to transmit a
single bit (disregarding issues like startup cost and packet lengths).
• 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
• For the RFM TR1000 radio transceiver, 1μJ to transmit a single bit
and 0.5 μJ to receive one; their processor takes about 8nJ per
instruction.
• This results in a (actually quite good) ratio of about 190 for
communication to computation costs.
• In a slightly different perspective, communicating 1kB of data over
100m consumes roughly the same amount of energy as computing three
million instructions
• Disregarding the details, it is clear that communication is a considerably
more expensive undertaking than computation.
• Still, energy required for computation cannot be simply ignored;
depending on the computational task, it is usually still smaller than the
energy for communication, but still noticeable.
• The core idea is to invest into computation within the network whenever
possible to safe on communication costs, leading to the notion of in-
network processing and aggregation.
Power consumption of sensor and actuators
• Providing any guidelines about the power consumption of the actual
sensors and actuators is next to impossible because of the wide diversity
of these devices.
• for example, passive light or temperature sensors – the power
consumption can perhaps be ignored in comparison to other devices on
a wireless node (a power consumption of 0.6 to 1 mA for a temperature
sensor).
• For others, in particular, active devices like sonar, power consumption
can be quite considerable and must even be considered in the
dimensioning of power sources on the sensor node, not to overstress
batteries.
• To derive any meaningful numbers, requires a look at the intended
application scenarios and the intended sensors to be used..
• In addition, the sampling rate evidently is quite important. Not only
does more frequent sampling require more energy for the sensors as
such but also the data has to processed and, possibly, communicated
somewhere.
Network Architecture
Goals
• Having looked at the individual nodes in the previous topics, we
look at general principles and architectures how to put these
nodes together to form a meaningful network
• We will look at design approaches to both the more
conventional ad hoc networks and the non-standard WSNs
Basic scenarios: Ad hoc networks
• (Mobile) ad hoc scenarios
• Nodes talking to each other
• Nodes talking to “some” node in another network (Web server on the
Internet, e.g.)
– Typically requires some connection to the fixed network
• Applications: Traditional data (http, ftp, collaborative apps, …) &
multimedia (voice, video) !humans in the loop
Basic scenarios: sensor networks
• Sensor network scenarios
• Sources: Any entity that provides data/measurements
• Sinks: Nodes where information is required
– Belongs to the sensor network as such
– Is an external entity, e.g., a PDA, but directly connected to the WSN
• Main difference: comes and goes, often moves around, …
– Is part of an external network (e.g., internet), somehow connected
to the WSN
•Applications: Usually,
machine to machine,
often limited amounts
of data, different notions
of importance
Network Architecture - Sensor Network Scenarios
Single-hop versus multihop networks
• One common problem: limited range of wireless communication
– Essentially due to limited transmission power, path loss, obstacles
• Option: multi-hop networks
– Send packets to an intermediate node
– Intermediate node forwards packet to its destination
– Store-and-forward multi-hop network
• Basic technique applies to both WSN and MANET
– Note: Store & forward multi-hopping NOT the only possible
solution
– E.g., collaborative networking,
network coding
– Do not operate on a per-packet
basis
• To overcome such limited distances, an obvious way out is to use relay
stations, with the data packets taking multi hops from the source to the
sink.
• This concept of multihop networks is particularly attractive for WSNs
as the sensor nodes themselves can act as such relay nodes, foregoing
the need for additional equipment.
• Depending on the particular application, the likelihood of having an
intermediate sensor node at the right place can actually be quite high –
• While multihopping is an evident and working solution to overcome
problems with large distances or obstacles, it has also been claimed to
improve the energy efficiency of communication.
• The intuition behind this claim is that, as attenuation of radio signals is
at least quadratic in most environments (and usually larger), it consumes
less energy to use relays instead of direct communication:
• When targeting for a constant SNR at all receivers (assuming for
simplicity negligible error rates at this SNR), the radiated energy
required for direct communication over a distance d is cdα (c some
constant, α ≥ 2 the path loss coefficient); 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 – in particular, the energy consumed in the
intermediate relay node.
• It is an easy exercise to show that 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 – the concrete
distance where direct and multihop communication are in balance
depends on a lot of device-specific and environment-specific
parameters.
• Nonetheless, this relationship is often not considered.
• only multihop networks operating in a store and forward fashion
• In such a network, a node has to correctly receive a packet before it can
forward it somewhere.
• Alternative, innovative approaches attempt to exploit even erroneous
reception of packets.
• for example, when multiple nodes send the same packet and each
individual transmission could not be received, but collectively, a node
can reconstruct the full packet.
Energy efficiency of multi-hopping?
• Obvious idea: Multi-hopping is more energy-efficient than direct
communication
– Because of path loss α>2, energy for distance d is reduced from
cdαto 2c(d/2)α
– c some constant
• However: This is usually wrong, or at least very over-simplified
– Need to take constant offsets for powering transmitter, receiver into
account
Multiple sinks and sources
• In the most challenging case, multiple sources should send information
to multiple sinks, where either all or some of the information has to
reach all or some of the sinks.
Multiple sources and/or multiple sinks. Note how in the scenario in the lower half,
both sinks and active sources are used to forward data to the sinks at the left and
right end of the network
Three types of mobility
• one of the main virtues of wireless communication is its ability to
support mobile participants.
• In wireless sensor networks, mobility can appear in three main forms:
Node mobility
• The wireless sensor nodes themselves can be mobile.
• The meaning of such mobility is highly application dependent.
• In examples like environmental control, node mobility should not
happen; in livestock surveillance (sensor nodes attached to cattle, for
example), it is the common rule.
• In the face of node mobility, the network has to reorganize itself
frequently enough to be able to function correctly.
• It is clear that there are trade-offs between the frequency and speed of
node movement on the one hand and the energy required to maintain a
desired level of functionality in the network on the other hand.
Different sources of mobility
• Node mobility
– A node participating as source/sink (or destination) or a relay node
might move around
– Deliberately, self-propelled or by external force; targeted or at
random
– Happens in both WSN and MANET
• Sink mobility
– In WSN, a sink that is not part of the WSN might move
– Mobile requester
• Event mobility
– In WSN, event that is to be observed moves around (or extends,
shrinks)
– Different WSN nodes become “responsible”for surveillance of such
an event
WSN sink mobility
Area of sensor nodes detecting an event – an elephant– that moves through the
network along with the event source (dashed line indicate the elephant’s trajectory;
shaded ellipse the activity area following or even preceding the elephant)
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.
• Possibly, additional requirements about the offered Quality of Service
(QoS) are made, especially in the context of multimedia applications.
• Such 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.
• While the first kind of attributes is applicable to a certain degree to
WSNs as well (bandwidth, for example, is quite unimportant), the
second one clearly is not, but is really the more important one to
consider!
• Hence, high-level QoS attributes corresponding to the subjective QoS
attributes in conventional networks are required.
• But just like in traditional networks, high-level QoS attributes in WSN
highly depend on the application.
Event detection/reporting probability
• What is the probability that an event that actually occurred is not
detected or, more precisely, not reported to an information sink that is
interested in such an event? For example, not reporting a fire alarm to a
surveillance station would be a severe shortcoming.
• Clearly, this probability can depend on/be traded off against the
overhead spent in setting up structures in the network that support the
reporting of such an event (e.g. routing tables) or against the run-time
overhead (e.g. sampling frequencies).