Chapter-1

Basics of Networking
INTRODUCTION:
IOT: It is a system of interconnecting devices connected to the internet to
transfer and receive the data from one another.

EXAMPLE: smart home appliances, smart watches, smart cars, smart

cities, etc.

CLASSIFICATION OF IOT:

1.GENERAL DEVICES- these are the main components of the data for
information exchange. They are connected by wired or wireless connection.

Ex: smoke detectors, etc.

2. SENSING DEVICES-It is used to measure humidity, light intensity,

temperature, and other parameters.

Ex: sensors and actuators

EXAMPLES OF IOT DEVICES:

- Home appliances: refrigerator, washing machines

- Smart phones and computers(laptops)
- Wearable electronics: smart watches
- Automobiles – cars and trunks
- Energy system: wind, solar, thermal
- Retail payment systems: cards and machines
- Printers, cameras, industrial machines, healthcare systems.
 EMERGENCE OF IOT
 Definition: “The Internet of Things (IOT) is the network of physical
objects that contain embedded technology to communicate and
sense or interact with their internal states or the external
environment.”
 It is used for connection of network
 Advent of network connected devices
 Heterogeneous traffic
 Increase in number of connected devices
 Evolution of smart phones
 Consumer with more than one unit
 IOT – anytime, anywhere, anything it can be used
 EVOLUTION OF IOT

ATM:

 ATMs or automated teller machines are cash distribution machines,

which are linked to a user’s bank account.
 ATMs dispense cash upon verification of the identity of a user and
their account through a specially coded card.
 The central concept behind ATMs was the availability of financial
transactions even when banks were closed beyond their regular
work hours. These ATMs were ubiquitous money dispensers.
 The first ATM became operational and connected online for the first
time in 1974.

Web:

 World Wide Web is a global information sharing and communication

platform.
 The Web became operational for the first time in 1991.
 It has been massively responsible for the many revolutions in the
field of computing and communication.

Smart Meters:

 The earliest smart meter was a power meter, which became

operational in early 2000.
 These power meters were capable of communicating remotely with
the power grid.
 They enabled remote monitoring of subscribers’ power usage and
eased the process of billing and power allocation from grids.
Digital Locks:

 Digital locks can be considered as one of the earlier attempts at

connected home-automation systems.
 Present-day digital locks are so robust that smart phones can be used
to control them.
 Operations such as locking and unlocking doors, changing key codes,
including new members in the access lists, can be easily performed,
and that too remotely using smart phones.

Connected Healthcare:

 Health care devices connect to hospitals, doctors, and relatives to

alert them of medical emergencies and take preventive measures.
 The devices may be simple wearable appliances, monitoring just the
heart rate and pulse of the wearer, as well as regular medical devices
and monitors in hospitals.
 The connected nature of these systems makes the availability of
medical records and test results much faster, cheaper, and
convenient for both patients as well as hospital authorities.

Connected Vehicles:

 Connected vehicles may communicate to the Internet or with other

vehicles, or even with sensors and actuators contained within it.
 These vehicles self-diagnose themselves and alert owners about
system failures.

Smart Cities:

 This is a city-wide implementation of smart sensing, monitoring, and

actuation systems.
 The city-wide infrastructure communicating amongst themselves
enables unified and synchronized operations and information
dissemination.
 Some of the facilities which may benefit are parking, transportation,
and others.
Smart Dust:

 These are microscopic computers. Smaller than a grain of sand, they

can be used in numerous beneficial ways, where regular computers
cannot operate.
 For example, smart dust can be sprayed to measure chemicals in the
soil or even to diagnose problems in the human body.

Smart Factories:

 These factories can monitor plant processes, assembly lines,

distribution lines, and manage factory floors all on their own.
 The reduction in mishaps due to human errors in judgment or
unoptimized processes is drastically reduced.

UAVs:

 UAVs or unmanned aerial vehicles have emerged as robust public

domain solutions.
 Tasked with applications ranging from agriculture, surveys,
surveillance, deliveries, stock maintenance, asset management, and
other tasks.

The interdependencies of IOT with other domains

and networking paradigms
1.M2M: (machine to machine)

 The machine-to-machine paradigm signifies a system of connected

machines and devices, which can talk amongst themselves without
human intervention.
 The communication between the machines can be for updates on
machine status (stocks, health, power status, and others)
 Collaborative task completion, overall knowledge of the systems and
the environment, and others.

2.CPS: (cyber physical system)

 The cyber physical system paradigm insinuates a closed control

loop—from sensing, processing, and finally to actuation—using a
feedback mechanism.
 CPS helps in maintaining the state of an environment through the
feedback control loop, which ensures that until the desired state is
attained, the system keeps on actuating and sensing.
 Humans have a simple supervisory role in CPS-based systems; most
of the ground-level operations are automated.

IOE: (internet of environment)

 The IOE paradigm is mainly concerned with minimizing and even

reversing the ill-effects of the Internet-based technologies on the
environment.
 The major focus areas of this paradigm include smart and sustainable
farming, sustainable and energy-efficient habitats, enhancing the
energy efficiency of systems and processes, and others.
 We can safely assume that any aspect of IOT that concerns and
affects the environment, falls under the purview of IOE .

Industry 4.0:

 Industry 4.0 is commonly referred to as the fourth industrial revolution

pertaining to digitization in the manufacturing industry.
 The previous revolutions chronologically dealt with mechanization,
 mass production, and the industrial revolution, respectively.
 This paradigm strongly puts forward the concept of smart factories,
where machines talk to one another without much human involvement
based on a framework of CPS and IOT.
 The digitization and connectedness in Industry 4.0 translate to better
resource and workforce management, optimization of production time
and resources, and better upkeep and lifetimes of industrial systems.

IOP: (internet of people)

 IOP is a new technological movement on the Internet which aims to

decentralize online social interactions, payments, transactions, and
other tasks while maintaining confidentiality and privacy of its user’s
data.
 A famous site for IOP states that as the introduction of the bitcoin has
severely limited the power of banks and governments, the acceptance
of IOP will limit the power of corporations, governments, and their spy
agencies .

Enabling IOT and the Complex Interdependence of

Technologies
1. Services

2. Local Connectivity

3. Global Connectivity

4. Processing
IOT Networking Components
The components in the establishment of IOT network
into 6 types:
IOT Node:

 These are the networking devices within an IOT LAN.

 Each of these devices is typically made up of a sensor, a processor,
and a radio, which communicates with the network infrastructure
(either within the LAN or outside it).
 The nodes may be connected to other nodes inside a LAN directly or
by means of a common gateway for that LAN.
 Connections outside the LAN are through gateways and proxies.

IOT Router:

 An IOT router is a piece of networking equipment that is primarily

tasked with the routing of packets between various entities in the IOT
network.
 It keeps the traffic flowing correctly within the network.
 A router can be repurposed as a gateway by enhancing its
functionalities.

IOT LAN:

 The local area network (LAN) enables local connectivity within the
purview of a single gateway.
 They consist of short-range connectivity technologies.
 IOT LANs may or may not be connected to the Internet. Generally,
they are localized within a building or an organization.

IOT WAN:

 The wide area network (WAN) connects various network segments

such as LANs.
 They are typically organizationally and geographically wide, with their
operational range lying between a few kilometers to hundreds of
kilometers.
  IOT WANs connect to the Internet and enable Internet access to the
segments they are connecting.

IOT Gateway:

 An IOT gateway is simply a router connecting the IOT LAN to a WAN

or the Internet.
 Gateways can implement several LANs and WANs.
 Their primary task is to forward packets between LANs and WANs,
and the IP layer using only layer 3.

IOT Proxy:

 Proxies actively lie on the application layer and performs application

layer functions between IOT nodes and other entities.
 Typically, application layer proxies are a means of providing security to
the network entities under it
 It helps to extend the addressing range of its network.

COMMUNICATION NODES

 DATA COMMUNICATION: it is the exchange of data between two

nodes via some form of link (transmission medium).
 DATA FLOW: data going to flow from one node to another and there
are three different flows – simplex, half duplex and full duplex.
 SIMPLEX DATA FLOW: it is a one-way method of data transmission
where information is sent from a sender to a receiver, but the receiver
cannot send information back.
Ex: keyboard is connected to CPU. Keyboard is going to give data to
the CPU, whereas CPU is not going to give any data to the CPU.
 HALF DUPLEX: communication is in both directions but not at the
same time. If one device is sending, the other can only receive and
vice-versa.
Ex: walkie-talkie
 FULL DUPLEX: both sending and receiving happening at the same
time or simultaneously.
Ex: telephone lines.
 Chapter2
IOT Sensing and Actuation
Sensors:
 Sensors are devices that can measure, or quantify, or
respond to the ambient changes in their environment or within
the intended zone of their deployment.
 They generate responses to external stimuli or physical
phenomenon through characterization of the input functions
(which are these external stimuli) and their conversion into
typically electrical signals.
 For example, heat is converted to electrical signals in a
temperature sensor, or atmospheric pressure is converted to
electrical signals in a barometer.

Here, a temperature sensor keeps on checking an environment for

changes. In the event of a fire, the temperature of the environment
goes up. The temperature sensor notices this change in the
temperature of the room and promptly communicates this
information to a remote monitor via the processor.
SENSORS CAN BE CLASSIFIED BASED ON:
1. POWER REQUIREMENTS
2. SENSOR OUTPUT
3. PROPERTY TO BE MEASUREMENTS
1. POWER REQUIREMENTS:
 The way sensors operate decides the power requirements that
must be provided for an IOT implementation.
 Some sensors need to be provided with separate power sources
for them to function, whereas some sensors do not require any
power sources.
 Depending on the requirements of power, sensors can be of two
 types:
 ACTIVE:
 Active sensors do not require an external circuitry or mechanism to
provide it with power.
 It directly responds to the external stimuli from its ambient
environment and converts it into an output signal.
 For example, a photodiode converts light into electrical impulses.
 PASSIVE:
 Passive sensors require an external mechanism to power them up.
 The sensed properties are modulated with the sensor’s inherent
characteristics to generate patterns in the output of the sensor.
 For example, a thermistor’s resistance can be detected by applying
voltage difference across it or passing a current through it.
2. SENSOR OUTPUT:
 The output of a sensor helps in deciding the additional components to
be integrated with an IOT node or system.
 Almost all modern-day processors are digital; digital sensors can be
directly integrated to the processors. However, the integration of
analog sensors to these digital processors or IOT nodes requires
additional interfacing mechanisms such as analog to digital converters
(ADC), voltage level converters, and others.
 Sensors are broadly divided into two types, depending on the type of
output generated from these sensors, as follows:
 ANALOG:
 Analog sensors generate an output signal or voltage, which is
proportional (linearly or non-linearly) to the quantity being measured
and is continuous in time and amplitude.
 Physical quantities such as temperature, speed, pressure,
displacement, strain, and others are all continuous and categorized as
analog quantities.
 For example, a thermometer or a thermocouple can be used for
measuring the temperature of a liquid (e.g., in household water
heaters).
 DIGITAL:
 These sensors generate the output of discrete time digital
representation (time, or amplitude, or both) of a quantity being
measured, in the form of output signals or voltages.
 Binary output signals in the form of a logic 1 or a logic 0 for ON or OFF,
respectively are associated with digital sensors.
3. MEASURED PEROPERTY:
 The property of the environment being measured by the sensors
can be crucial in deciding the number of sensors in an IOT
implementation.
 Some properties to be measured do not show high spatial variations
and can be quantified only based on temporal variations in the
measured property, such as ambient temperature, atmospheric
pressure, and others.
 Whereas some properties to be measured show high spatial as well
as temporal variations such as sound, image, and others.
 Depending on the properties to be measured, sensors can be of two
types.
 SCALAR:
 Scalar sensors produce an output proportional to the magnitude of the
quantity being measured.
 The output is in the form of a signal or voltage.
 Scalar physical quantities are those where only the magnitude of the
signal is sufficient for describing or characterizing the phenomenon
and information generation.
 Examples of such measurable physical quantities include color,
pressure, temperature, strain, and others.
 A thermometer or thermocouple is an example of a scalar sensor that
has the ability to detect changes in ambient or object temperatures.
 VECTOR:
 Vector sensors are affected by the magnitude as well as the direction
and/or orientation of the property they are measuring.
 Physical quantities such as velocity and images that require additional
information beside their magnitude for completely categorizing a
physical phenomenon are categorized as vector quantities.
  Measuring such quantities are undertaken using vector sensors.
 For example, an electronic gyroscope.

The functional blocks of a typical sensor node in IOT:

Some common commercially available sensors used

for IOT-based sensing applications:
a) Camera sensor
b) Color sensor
c) Compass and barometer
d) Current sensor
e) Digital temp. and humidity sensor
f) Flame sensor
g) Gas sensor
h) Infrared sensor
i) Rainfall sensor
j) Ultrasonic distance measurement sensor
 SENSOR CHARACTERISTICS
 Sensors can be characterized by their ability to sense the
phenomenon based on the following three fundamental properties.

Sensor Resolution:

 The smallest change in the measurable quantity that a sensor can

detect is referred to as the resolution of a sensor.
 For digital sensors, the smallest change in the digital output that the
sensor is capable of quantifying is its sensor resolution.
 The more the resolution of a sensor, the more accurate is the
precision. A sensor’s accuracy does not depend upon its resolution.
 For example, a temperature sensor A can detect up to 0.5 ◦ C changes
in temperature; whereas another sensor B can detect up to 0.25◦ C
changes in temperature. Therefore, the resolution of sensor B is higher
than the resolution of sensor A.

Sensor Accuracy: The accuracy of a sensor is the ability of that sensor to

measure the environment of a system as close to its true measure as
possible.

 For example, a weight sensor detects the weight of a 100 kg mass as

99.98 kg. We can say that this sensor is 99.98% accurate, with an error
rate of ±0.02%.

Sensor Precision:

 The principle of repeatability governs the precision of a sensor. Only if,

upon multiple repetitions, the sensor is found to have the same error
rate, can it be deemed as highly precise.
 For example, consider if the same weight sensor described earlier
reports measurements of 98.28 kg, 100.34 kg, and 101.11 kg upon
three repeat measurements for a mass of actual weight of 100 kg.
Here, the sensor precision is not deemed high because of significant
variations in the temporal measurements for the same object under the
same conditions.
 SENSORIAL DEVIATIONS
 Sensorial deviations are considered as errors in the sensors.
 Some critical applications of IOT, such as healthcare, industrial
process monitoring, and others, do require sensors with high-quality
measurement capabilities.
 If a sensor’s output signal going beyond its designed maximum and
minimum capacity for measurement, the sensor output is truncated to
its maximum or minimum value, which is also the sensor’s limits.
 The measurement range between a sensor’s characterized minimum
and maximum values is also referred to as the full scale range of that
sensor.
 Sensitivity Error: - The sensitivity of a sensor may differ from the
value specified for that sensor which leads to sensitivity error. This
deviation is mostly attributed to sensor fabrication errors and its
calibration.
 Offset Error or Bias: - The output of a sensor differs from the actual
value to be measured by a constant Hence the sensor is said to have
an offset error or bias.
 Example, while measuring an actual temperature of 0◦C, a
temperature sensor outputs 1.1◦C every time. In this case, the sensor
is said to have an offset error or bias of 1.1◦C.
 Non-Linear Behavior: If a sensor’s transfer function (TF) deviates
from a straight line transfer function, it is referred to as its non-linearity.
 Drift: the output signal of a sensor changes slowly and independently
of the measured property, this behavior of the sensor’s output is
termed as drift.
 Hysteresis Error: if a sensor’s output varies/deviates due to
deviations in the sensor’s previous input values, it is referred to as
hysteresis error. The present output of the sensor depends on the past
input values provided to the sensor. Hysteresis can be observed in
analog sensors, magnetic sensors, and during heating of metal strips.
 Quantization Error: The error can be defined as the difference
between the actual analog signal and its closest digital approximation
during the sampling stage of the analog to digital conversion.
  Aliasing errors: The dynamic errors caused due to mishandling of
sampling frequencies leads to aliasing errors.
 The environment itself plays a crucial role in inducing sensorial
deviations. For example, as most sensors are semiconductor based,
they are influenced by the temperature of their environment.

SENSING DEVICES
1) scalar sensing
2) multimedia sensing
3) hybrid sensing
4) virtual sensing

1. SCALAR SENSING:

 Scalar sensing encompasses the sensing of features that can be

quantified simply by measuring changes in the amplitude of the
measured values with respect to time.
 Example ambient temperature, current, atmospheric pressure, rainfall,
light, humidity, flux.
 The sensors used for measuring these scalar quantities are referred to
as scalar sensors, and the act is known as scalar sensing.

2. MULTIMEDIA SENSING:

 Multimedia sensing encompasses the sensing of features that have a

spatial variance property associated with the property of temporal
variance. They are used for capturing the changes in amplitude of a
 quantifiable property concerning space (spatial) as well as time
(temporal).
 Example images, direction, flow, speed, acceleration, sound, force,
mass, energy, and momentum have both directions as well as a
magnitude

3.HYBRID SENSING:
 The act of using scalar as well as multimedia sensing at the same time
is referred to as hybrid sensing. In sensors it is necessary to measure
vector as well as scalar properties of an environment at the same time.
 For example, in an agricultural field, it is required to measure the soil
conditions at regular intervals of time to determine plant health.
4.VIRTUAL SENSING:

 The data from A’s field is digitized using an IOT infrastructure and this
system advises him regarding the appropriate watering, fertilizer, and
pesticide regimen for his crops, this advisory can also be used by B for
maintaining his crops.
 In short, A ’s sensors are being used for actual measurement of
parameters whereas virtual data (which does not have actual
physical sensors but uses extrapolation-based measurements) is
being used for advising B. This is the virtual sensing paradigm.

SENSING CONSIDERATIONS
1) Sensing range

2) Accuracy and Precision

3) Energy

4) Device Size

1) SENSING RANGE:

 The sensing range of a sensor node defines the detection fidelity of

that node.
 Typical approaches to optimize the sensing range in deployments
include fixed k-coverage and dynamic k-coverage.
 A lifelong fixed k-coverage tends to usher in redundancy as it requires
a large number of sensor nodes, the sensing range of some of which
may also overlap.
  Dynamic k-coverage incorporates mobile sensor nodes post detection
of an event, which, however, is a costly solution and may not be
deployable in all operational areas and terrains.
 The sensing range of a sensor may also be used to signify the upper
and lower bounds of a sensor’s measurement range.
 For example, a proximity sensor has a typical sensing range of a
couple of meters, a camera has a sensing range varying between tens
of meters to hundreds of meters. As the complexity of the sensor and
its sensing range goes up, its cost significantly increases.

2) Accuracy and Precision:

 The accuracy and precision of measurements provided by a sensor

are critical in deciding the operations of specific functional processes.
 Typically, off-the-shelf consumer sensors are low on requirements and
often very cheap. However, their performance is limited to regular
application domains.
 For example, a standard temperature sensor can be easily integrated
with conventional components for hobby projects and day-to-day
applications, but it is not suitable for industrial processes.
 Regular temperature sensors have a very low-temperature sensing
range, as well as relatively low accuracy and precision.
 The use of these sensors in industrial applications, where a precision
of up to 3–4 decimal places is required, cannot be facilitated by these
sensors.
 Industrial sensors are typically very sophisticated, and as a result,
very costly. However, these industrial sensors have very high accuracy
and precision score, even under harsh operating conditions.

3) Energy:

 The energy consumed by a sensing solution is crucial to determine the

lifetime of that solution and the estimated cost of its deployment.
 If the sensor or the sensor node is so energy inefficient that it requires
replenishment of its energy sources quite frequently, the effort in
maintaining the solution and its cost goes up; whereas its deployment
 feasibility goes down.
 If the energy requirements of the sensor nodes are too high, such a
deployment will not last long, and the solution will be highly infeasible
as charging or changing of the energy sources of these sensor nodes
is not an option.

4) Device Size:

 Modern-day IOT applications have a wide penetration in all domains of

life. Most of the applications of IOT require sensing solutions which are
so small that they do not hinder any of the regular activities that were
possible before the sensor node deployment was carried out.
 Larger the size of a sensor node, larger is the obstruction caused by it,
higher is the cost and energy requirements, and lesser is its demand
for the bulk of the IOT applications.
 Consider a simple human activity detector. If the detection unit is too
large to be carried or too bulky to cause hindrance to regular normal
movements, the demand for this solution would be low. It is because of
this that the onset of wearables took off so strongly.
 The wearable sensors are highly energy-efficient, small in size, and
almost part of the wearer’s regular wardrobe.

ACTUATORS
An actuator can be considered as a machine or system’s component that
can affect the movement or control of the system. Control systems affect
changes to the environment or property they are controlling through
actuators.
ACTUATOR TYPES – it is divided into 7 classes

1) Hydraulic actuator

2) pneumatic actuator

3) electrical actuator

4) thermal/magnetic actuator

5) mechanical actuator

6) soft actuator

7) shape memory polymers actuator

1) HYDRAULIC ACTUATOR:
 A hydraulic actuator works on the principle of compression and
decompression of fluids. These actuators facilitate mechanical tasks
such as lifting loads through the use of hydraulic power derived from
fluids in cylinders or fluid motors.
 The mechanical motion applied to a hydraulic actuator is converted to
either linear, rotary, or oscillatory motion.
 The almost incompressible property of liquids is used in hydraulic
actuators for exerting significant force. These hydraulic actuators are
also considered as stiff systems.
 The actuator’s limited acceleration restricts its usage.

2)Pneumatic actuators:
 A pneumatic actuator works on the principle of compression and
decompression of gases. These actuators use a vacuum or
compressed air at high pressure and convert it into either linear or
rotary motion.
 Pneumatic rack and pinion actuators are commonly used for valve
controls of water pipes.
 Pneumatic actuators are considered as compliant systems.
 The actuators using pneumatic energy for their operation are typically
 characterized by the quick response to starting and stopping signals.
 Small pressure changes can be used for generating large forces
through these actuators.
 Pneumatic brakes are an example of this type of actuator which is so
responsive that they can convert small pressure changes applied by
drives to generate the massive force required to stop or slow down a
moving vehicle.
 Pneumatic actuators are responsible for converting pressure into
force. The power source in the pneumatic actuator does not need to be
stored in reserve for its operation.

3) Electric actuators:
 Electric motors are used to power an electric actuator by generating
mechanical torque. This generated torque is translated into the motion
of a motor’s shaft or for switching (as in relays).
 For example, actuating equipments such as solenoid valves control
the flow of water in pipes in response to electrical signals. This class of
actuators is considered one of the cheapest, cleanest and speedy
actuator types available.
 Some of the commonly used electrical actuators are – brushless DC
motors, stepper motor, geared stepper motor, DC motor, etc.

4)Thermal or magnetic actuators:

 The use of thermal or magnetic energy is used for powering this class
of actuators. These actuators have a very high power density and are
typically compact, lightweight, and economical.
 One classic example of thermal actuators is shape memory materials
(SMMs) such as shape memory alloys (SMAs). These actuators do not
require electricity for actuation. They are not affected by vibration and
can work with liquid or gases.
 Magnetic shape memory alloys (MSMAs) are a type of magnetic
actuators.

5)Mechanical actuators:
  Here, the rotary motion of the actuator is converted into linear motion
to execute some movement.
 The use of gears, rails, pulleys, chains, and other devices are
necessary for these actuators to operate.
 These actuators can be easily used in conjunction with pneumatic,
hydraulic, or electrical actuators. They can also work in a standalone
mode.
 The best example of a mechanical actuator is a rack and pinion
mechanism.
 Some of the commonly available mechanical actuators are –
hydroelectric generator, DPDT switch, push button switch.

6)Soft actuators:
 Soft actuators (e.g., polymer-based) consists of elastomeric polymers
that are used as embedded fixtures in flexible materials such as cloth,
paper, fiber, particles, and others.
 The conversion of molecular level microscopic changes into tangible
macroscopic deformations is the primary working principle of this class
of actuators.
 These actuators have a high stake in modern-day robotics. They are
designed to handle fragile objects such as agricultural fruit harvesting,
or performing precise operations like manipulating the internal organs
during robot-assisted surgeries.

7)Shape memory polymers:

 Shape memory polymers (SMP) are considered as smart materials
that respond to some external stimulus by changing their shape, and
then revert to their original shape once the affecting stimulus is
removed.
 Features such as high strain recovery, biocompatibility, low density,
and biodegradability characterize these materials.
 SMP-based actuators function similar to our muscles. Modern-day
SMPs have been designed to respond to a wide range of stimuli such
as pH changes, heat differentials, light intensity, and frequency
 changes, magnetic changes, and others.
 Photopolymer/light-activated polymers (LAP) are a particular type of
SMP, which require light as a stimulus to operate.
 LAP-based actuators are characterized by their rapid response times.
Using only the variation of light frequency or its intensity, LAPs can be
controlled remotely without any physical contact. The development of
LAPs whose shape can be changed by the application of a specific
frequency of light have been reported. The polymer retains its shape
after removal of the activating light. In order to change the polymer
back to its original shape, a light stimulus of a different frequency has
to be applied to the polymer.

ACTUATOR CHARACTERISTICS
1) Weight: The physical weight of actuators limits its application scope. For
example, the use of heavier actuators is generally preferred for industrial
applications and applications requiring no mobility of the IOT deployment. In
contrast, lightweight actuators typically find common usage in portable
systems in vehicles, drones, and home IOT applications

2) Power Rating: This helps in deciding the nature of the application with
which an actuator can be associated. The power rating defines the minimum
and maximum operating power an actuator can safely withstand without
damage to itself.

3) Torque to Weight Ratio: The ratio of torque to the weight of the moving
part of an instrument/device is referred to as its torque/weight ratio. This
indicates the sensitivity of the actuator. Higher is the weight of the moving
part, lower will be its torque to weight ratio for a given power

4) Stiffness and Compliance: The resistance of a material against

deformation is known as its stiffness, whereas compliance of a material is
the opposite of stiffness. Stiffness can be directly related to the modulus of
elasticity of that material.Stiff systems are considered more accurate than
compliant systems as they have a faster response to the change in load
applied to it. For example, hydraulic systems are considered as stiff and
non-compliant, whereas pneumatic systems are considered as compliant.
 Chapter6
IOT Processing Topologies and Types
DATA FORMAT: In an IOT system, the massive volume of data generated
by this huge number of users is further enhanced by the multiple devices
utilized by most users.

In addition to these data-generating sources, non-human data generation

sources such as sensor nodes and automated monitoring systems further
add to the data load on the Internet. This huge data volume is composed of a
variety of data such as e-mails, text documents (Word docs, PDFs, and
others), social media posts, videos, audio files, and images.

There are 2 types:

1) STRUCTURED DATA:
 These are typically text data that have a pre-defined structure.
 Structured data are associated with relational database management
systems (RDBMS).
 These are primarily created by using length-limited data fields such as
phone numbers, social security numbers, and other such information.
Even if the data is human or machine generated, these data are easily
searchable by querying algorithms as well as human generated
queries.
 Common usage of this type of data is associated with flight or train
reservation systems, banking systems, inventory controls, and other
similar systems.
 Established languages such as Structured Query Language (SQL) are
used for accessing these data in RDBMS. However, in the context of
IOT, structured data holds a minor share of the total generated data
over the Internet.
2) UNSTRUCTURED DATA:
 In simple words, all the data on the Internet, which is not structured, is
categorized as unstructured. These data types have no pre-defined
structure and can vary according to applications and data-generating
 sources.
 Some of the common examples of human-generated unstructured
data include text, e-mails, videos, images, phone recordings, chats,
and others.
 Some common examples of machine-generated unstructured data
include sensor data from traffic, buildings, industries, satellite imagery,
surveillance videos, and others.
 This data type does not have fixed formats associated with it, which
makes it very difficult for querying algorithms to perform a look-up.
 Querying languages such as NoSQL are generally used for this data
type.
IMPORTANCE OF PROCESSING IN IOT
Data processing is more crucial with the rapid advancements in IOT.
Intelligent and resourceful processing techniques are required to
process vast amounts and types of data flowing through the internet. It
is also important to decide when to process and what to process. We
first divide the data to be processed into 3 types based on the urgency
of processing:
1) very time-critical data
2) Time critical data
3) Normal data
1) VERY TIME-CRITICAL DATA:
 These data have a very low processing latency, typically in the range
of a few milliseconds. Data from sources such as flight control
systems, healthcare, etc need immediate decision support. Processing
requirements are exceptionally high. Processing the data in place or
nearer to the source is crucial.
2) TIME CRUCIAL DATA:
 Data from sources that can tolerate normal processing latency are
deemed as time-crucial data. These data, generally associated with
the sources such as vehicles, traffic, smart home systems,
surveillance systems, and others, can tolerate a latency of a few
seconds. These data are to be processed in remote locations
processors such as clouds.
3) NORMAL DATA: these data, can tolerate a processing latency of a
few minutes to a few hours and are typically associated with less
data-sensitive domains such as agriculture, environmental monitoring
and others. Typically, have no particular time requirements for
processing urgently.