Control aspects in Wireless sensor networks

April 11, 2023]Rushin $hah1

Control Aspects in WSN UNIT : 5


Control aspects in WSN

1. Admission Control

2. Connection control

3. Power Control

4. Baud rate Control

5. Congestion Control

6. QoS Control

Adm Conn ???he is Power full

Baud Rate of Conges

beside the QoS


Congestion Control in ATM Networks

ATM is a key Technology for integrating broad-band

multimedia services in heterogeneous networks.

ABR guarantees zero Loss.

UBR source neither specifies nor receives a Bandwidth,

Delay or Loss guarantees.

ATM Service

Category

Guaranteed Service

Best effort Services

Unspecified Bit Rate

(UBR)

Absolute Bit Rate (ABR)


Ambiguity of Traffic Control & Congestion Congestion: It is define as condition of an ATM network where

the network does not meet a stated performance objective.

Traffic control: It contain combination of Connection Admission

Control (CAC) to avoid congestion.

The most simplest form of congestion control scheme is the

binary feed back with FIFO queuing.

- Here when the buffer occupancy exceeds a predefined

threshold value, the switch begins to issue a binary notifications

to the sources and it continues to do so until the buffer

occupancy falls below the threshold.

Second scheme is Rate Feedback Control


Congestion Control in Neural Network

Number of methods are available to reduce congestion in

communication network.

One of them is NN( Neural Network) Based approach.

NN based scheme possess function approximation and

learning capabilities which can be used directly in many

applications.

The NN based methods can be broadly categorized based on

the learning scheme they employ both offline and online.

The offline learning schemes are used to train the NN,

once trained NN, weights are not updated during run time.


Congestion Control in Neural Network

The online learning NN Scheme:

Requires More real computation,

Relaxes the offline training phase,

Avoids the weight initialization problems and

Performs learning and adaptation simultaneously.


When Congestion Occurs ?

Σ Input rate > available link capacity

Congestion occurs when the input rate is more than

available link Capacity


When Congestion Occurs ?

Most congestion control scheme consist of adjusting the

input rates to match the available link capacity.

Depending upon the duration of congestion, different

scheme can be applied.

If congestion time is less then the connection time then

end to end feedback control scheme can be applicable.


Network Model


Network Model

x(k +1) = f (x(k)) +T*u(k) + d(k)

x(k) – buffer length

T – Sampling time

f(.) – non linear traffic accumulation

d(k) – disturbance at time instant k

Ini – traffic arrival rate at destination buffer

Hd – buffer size

Q – bottle neck queue level

Sr – Service Capacity


Predictive Congestion Control for WSN

Predictive congestion control scheme for wireless sensor

network takes into account energy efficiency and

fairness.

This scheme will be implemented through feed back

obtained from one hop.

Existing congestion control scheme, for example Transport

Control Protocol when applied to wireless network, result

in:

Large number of Packet drops,

Unfair scenario and

Low throughput

with significant amount of wasted Energy due to

Retransmission.

Packet na tapkkaBadsurat scene wada

Ochha throw thya

April 11, 202312


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The network congestion occurs when either:

- The incoming traffic exceeds the capacity of the outgoing

link or

- Link bandwidth drops because of channel fading caused

by path loss.

April 11, 202313


]Rushin $hah

To predict onset of congestion, the proposed scheme uses

both queue utilization and transmission power under

the current channel state at each node.

When node is become congested, the traffic will

accumulate the nodes because there will be an excess

amount of incoming traffic over the outgoing one. Hence

queue utilization has been selected as an indicator of the

onset of congestion.

On the other hand in wireless networks during fading, the

available bandwidth is reduced and ongoing rate will be

lowered. The channel fading is estimated by the feedback

information provided by DPC protocol for the next packet transmission.

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Working:

The DPC algorithm predicts the channel state for the

subsequent time interval and calculates the required

power, if this power exceeds the maximum threshold

then the channel is considered to be unsuitable for

transmission and proposed congestion control scheme

can initiate back-off process by reducing incoming

traffic.

There are many algorithms which can be applied for

congestion control like predictive congestion control and

Rate adaption.

Predictive congestion control minimized queue overflow

by regulating the incoming flow.

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The incoming traffic can be calculated by three

factors:

Predicted Outgoing flow

Wireless Link State

Queue utilization

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Predicted Outgoing flow: The outgoing flow is

periodically measured and an adaptive scheme is used to

accurately predict the outgoing flow in the next period,

moreover the next hop node can reduce the outgoing flow

assessment by applying a control over its incoming flow.

Wireless Link State: The predicted outgoing flow rate is

further reduced when the DPC protocol predicts a severe

channel fading which will disrupt communication on the

link.

Queue utilization: The algorithm restricts the incoming

flow based on the current queue utilization and predicted

outgoing flow, thus reducing buffer overflows.


Energy Harvesting in WSN

Energy harvesting also known as:

Power Harvesting or Energy Scavenging.

It is the process by which energy is derived from external

sources (e.g., solar power, thermal energy, wind energy and

kinetic energy), captured and stored for small wireless

autonomous devices, like those used in wearable

electronics and wireless sensor networks.

Energy harvesters provide a very small amount of power

for low-energy electronics.


Energy Harvesting in WSN The energy source for energy harvesters is present as ambient

background and is free.

For example, temperature gradients exist from the operation of

a combustion engine and in urban areas, there is a large

amount of electromagnetic energy in the environment because

of radio and television broadcasting.

Energy harvesting devices converting ambient energy into

electrical energy have attracted much interest in both the

military and commercial sectors.

Some systems convert motion, such as that of ocean waves, into

electricity to be used by oceanographic monitoring sensors for

autonomous operation.


Energy Harvesting in WSN Future applications may include high power output devices

(or arrays of such devices) deployed at remote locations to

serve as reliable power stations for large systems.

Another application is in wearable electronics, where energy

harvesting devices can power or recharge cell phones,

mobile computers, radio communication Equipment, etc.

All of these devices:

must be sufficiently robust to endure long-term exposure to hostile

environments and

have a broad range of dynamic sensitivity to exploit the entire

spectrum of wave motions.


Accumulation of Energy

Energy can also be harvested to power small autonomous

sensors such as those developed using MEMS

technology .

These systems are often very small and require little power,

but their applications are limited by the reliance on battery

power

Scavenging energy from ambient vibrations, wind, heat or

light could enable smart sensors to be functional

indefinitely.


Storage of Power

In general, energy can be stored in a capacitor, super

capacitor, or battery.

Capacitors are used when the application needs to provide

huge energy spikes.

Batteries leak less energy and are therefore used when the

device needs to provide a steady flow of energy.


Use of the Power

Current interest in low power energy harvesting is for

independent sensor networks.

In these applications an energy harvesting scheme puts

power stored into a capacitor then boosted/regulated to a

second storage capacitor or battery for the use in the

microprocessor.

The power harvesting is usually used in a sensor

application as the data stored or is transmitted possibly

through a wireless method.


Devices There are many small-scale energy sources that generally cannot

be scaled up to industrial size:

Piezoelectric crystals or fibers generate a small voltage

whenever they are mechanically deformed. Vibration from

engines can stimulate piezoelectric materials, as can the heel

of a shoe

Some wristwatches are already powered by kinetic energy

(called automatic watches), in this case movement of the arm.

The arm movement causes the magnet in the electromagnetic

generator to move. The motion provides a rate of change of

flux, which results in some induced emf on the coils. The

concept is simply related to Faraday's Law.


Devices Photovoltaic is a method of generating electrical power by

converting solar radiation (both indoors and outdoors) into direct

current electricity using semiconductors that exhibit the photovoltaic

effect. Photovoltaic power generation employs solar panels

composed of a number of cells containing a photovoltaic material.

Thermoelectric generators (TEGs) consist of the junction of two

dissimilar materials and the presence of a thermal gradient. Large

voltage outputs are possible by connecting many junctions

electrically in series and thermally in parallel. Typical performance is

100-200 μV/K per junction. These can be utilized to capture mW of

energy from industrial equipment, structures, and even the human

body. They are typically coupled with heat sinks to improve

temperature gradient


Devices

Micro wind turbine are used to harvest wind energy

readily available in the environment in the form of kinetic

energy to power the low power electronic devices such as

wireless sensor nodes. When air flows across the blades of

the turbine, a net pressure difference is developed between

the wind speeds above and below the blades. This will

result in a lift force generated which in turn rotate the

blades. This is known as the aerodynamic effect.

Special antennae can collect energy from stray radio

waves or theoretically even light (EM radiation).


Ambient-Radiation Sources

A possible source of energy comes from radio transmitters.

Historically, either a large collection area or close

proximity to the radiating wireless energy source is needed

to get useful power levels from this source.

The nantenna is one proposed development which would

overcome this limitation by making use of the abundant

natural radiation One idea is to deliberately broadcast RF energy to power

remote devices:


Ambient-Radiation Sources One idea is to deliberately broadcast RF energy to power

remote devices: Biomechanical harvesting Photovoltaic harvesting Piezoelectric energy harvesting Tree metabolic energy harvesting Blood sugar energy harvesting


Ambient-Radiation Sources Biomechanical harvesting

Biomechanical energy harvesters are also being created. One current model is the biomechanical energy harvester

of Max Donelan which straps around the knee. Devices as this allow the generation of 2.5 watts of power

per knee. This is enough to power some 5 cell phones


Ambient-Radiation Sources Photovoltaic harvesting Photovoltaic (PV) energy harvesting wireless technology

offers significant advantages over wired or solely battery-powered sensor solutions: virtually inexhaustible sources of power with little or no adverse environmental effects. Indoor PV harvesting solutions have to date been powered by specially tuned amorphous silicon (aSi)a technology most used in Solar Calculators. In recent years new PV technologies have come to the forefront in Energy Harvesting such as Dye Sensitized Solar Cells (DSSC). The dyes absorbs light much like chlorophyll does in plants. Electrons released on impact escape to the layer of TiO2 and from there diffuse, through the electrolyte, as the dye can be tuned to the visible spectrum much higher power can be produced. At 200 lux a DSSC can provide over 15 µW per cm².

http://en.wikipedia.org/wiki/Dye-sensitized_solar_cell

http://en.wikipedia.org/wiki/Chlorophyll

http://en.wikipedia.org/wiki/Lux


Ambient-Radiation Sources Piezoelectric energy harvesting he piezoelectric effect converts mechanical strain into

electric current or voltage. This strain can come from many different sources. Human motion, low-frequency seismic vibrations, and acoustic noise are everyday examples. Except in rare instances the piezoelectric effect operates in AC requiring time-varying inputs at mechanical resonance to be efficient.

http://en.wikipedia.org/wiki/Piezoelectric_effect

http://en.wikipedia.org/wiki/Strain_(materials_science)


Ambient-Radiation Sources Blood sugar energy harvesting [edit] Another way of energy harvesting is through the oxidation

of blood sugars. These energy harvesters are called Biofuel cells. They could be used to power implanted electronic devices (e.g., pacemakers, implanted biosensors for diabetics, implanted active RFID devices, etc.). At present, the Minteer Group of Saint Louis University has created enzymes that could be used to generate power from blood sugars. However, the enzymes would still need to be replaced after a few years.[47] In 2012 a pacemaker was powered by implantable biofuel cells at Clarkson University under the leadership of Dr. Evgeny Katz.[48]

http://en.wikipedia.org/w/index.php?title=Energy_harvesting&action=edit&section=15

http://en.wikipedia.org/wiki/Microbial_Fuel_Cells

http://en.wikipedia.org/wiki/Microbial_Fuel_Cells

http://en.wikipedia.org/wiki/Energy_harvesting



Ambient-Radiation Sources Tree-based energy harvesting [edit] Tree metabolic energy harvesting is a type of bio-energy

harvesting. Voltree has developed a method for harvesting energy from trees. These energy harvesters are being used to power remote sensors and mesh networks as the basis for a long term deployment system to monitor forest fires and weather in the forest. Their website says that the useful life of such a device should be limited only by the lifetime of the tree to which it is attached. They recently deployed a small test network in a US National Park forest.[49]

http://en.wikipedia.org/w/index.php?title=Energy_harvesting&action=edit&section=16


Control aspects in Wireless sensor networks

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