Unit -4
Topics to be
covered
Sensor Technology, Participatory Sensing, Industrial
IOT and Automotive IOT, Actuator, Sensor data Communication Protocols, Radio
Frequency Identification Technology, Wireless Sensor Network Technology.
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Sensor
Technology
The Internet of Things (IoT) couldn’t exist without smart sensors, and
the growing use of smart technology is already transforming how manufacturers
implement the IoT. Smart sensors are also bringing more connectivity and
analytics to the supply chain. There are some things to know about how and why
this is happening.
First, smart sensors are the indispensable enablers of the IoT and
the industrial IoT. Smart sensors, including radio frequency identification
(RFID) tags, serve three broad purposes. They identify items, locate them and
determine their environmental conditions, all of which have major implications
for the supply chain and manufacturing. Smart sensors are particularly useful
in plants or warehouses because they can keep track of temperature and
humidity, log data for historical records and quality management, or be used as
triggers for alarms or process management.
Second, smart sensors impact the supply chain by being embedded in
products, which can help improve the manufacturing process or the products
themselves. “ensors can live inside products to
create “smart products” and new revenue sources from the enhanced features.
They can also permeate the manufacturing process to monitor, control, and
improve operations, or be added to logistics to streamline how products are
delivered. There are a number of specific purposes of sensors, such as
measuring temperature, humidity, vibrations, motion, light, pressure and
altitude. Companies will need to develop new applications to take advantage of
all the big data that the sensors are generating.
Third, the lower costs and more advanced capabilities of RFID tags
are starting to enable wider and more effective use. The cost of RFID, which
has come down dramatically, is in more than just the tag itself. To determine
the true cost per use you have to include the software applications and
deployment costs. The combination of lowered costs for tags and improved
capabilities means that their value proposition has changed, and represents an
opportunity for enterprises to rethink RFID.
A sensor is a device that detects and responds to some type of input
from the physical environment. The specific input could be light, heat, motion,
moisture, pressure, or any one of a great number of other environmental
phenomena. The output is generally a signal that is converted to human-readable
display at the sensor location or transmitted electronically over a network for
reading or further processing.
Here are a few examples
of the many different types of sensors:
In a mercury-based glass
thermometer, the input is temperature. The liquid
contained expands and contracts in response, causing the level to be higher or
lower on the marked gauge, which is human- readable.
An oxygen sensor in a car's emission control system detects the gasoline/oxygen
ratio, usually through a chemical reaction that generates a voltage. A computer
in the engine reads the voltage and, if the mixture is not optimal, readjusts
the balance.
Motion sensors in various systems including home security lights, automatic doors
and bathroom fixtures typically send out some type of energy, such as microwaves,
ultrasonic
waves or light beams and detect when the flow of energy is
interrupted by something entering its path.
A photo sensor detects
the presence of visible light, infrared
transmission (IR), and/or ultraviolet (UV) energy.
Participatory Sensing - Participatory sensing is the process whereby individuals and
communities use ever- more-capable mobile phones and cloud services to collect
and analyze systematic data for use in discovery. The convergence of technology
and analytical innovation with a citizenry that is increasingly comfortable
using mobile phones and online social networking sets the stage for this
technology to dramatically impact many aspects of our daily lives.
1.
Applications and Usage Models
One application of participatory sensing is as a tool for health and
wellness. For example, individuals can self-monitor to observe and adjust their
medication, physical activity, nutrition, and interactions. Potential contexts
include chronic-disease management and health behavior change. Communities and
health professionals can also use participatory approaches to better understand
the development and effective treatment of disease.
The same systems can be used as tools for sustainability. For
example, individuals and communities can explore their transportation and
consumption habits, and corporations can promote more sustainable practices
among employees.
In addition, participatory sensing offers a powerful
“make a case” technique to support advocacy and civic engagement. It can
provide a framework in which citizens can bring to light a civic bottleneck,
hazard, personal-safety concern, cultural asset, or other data relevant to
urban and natural-resources planning and services, all using data that are
systematic and can be validated.
These different applications imply several different usage models.
These models range from public contribution, in which individuals collect data
in response to inquiries defined by others, to personal use and reflection, in
which individuals log information about themselves and use the results for
personal analysis and behavior change. Yet across these varied applications and
usage models, a common workflow is emerging, as Figure 4.1 illustrates.
Fig
4.1 Participatory Sensing Model Workflow
2.
Essential Components
Ubiquitous Data Capture
While empirical data can be collected in a variety of ways, mobile
phones are a special and, perhaps, unprecedented tool for the job. These
devices have become mobile computing, sensing, and communication platforms,
complete with image, audio, video, motion, proximity, and location data capture
and broadband communication, and they are capable of being programmed for
manual, automatic, and con-text-aware data capture.
Because of the sheer ubiquity of mobile phones and associated
communication infrastructure, it is possible to include people of all
backgrounds nearly everywhere in the world. Because these devices travel with
us, they can help us make sustainable observations on an intimately personal
level. Collectively, they provide unmatched coverage in space and time.
Leveraged Data Processing and Management
In some cases, the data collected with a mobile device are enough to
reveal an interesting pattern on their own. However, when processed through a
series of external and cross-user data sources, models, and algorithms, simple
data can be used to infer complex phenomena about individuals and groups.
Mapping and other interactive capabilities of today's Web enhance the
presentation and interpretation of these patterns for participants. Many
applications will call for the comparison of current measures to past trends,
so robust and long term storage and management of this data is a central
requirement.
The Personal Data
Vault
A common feature uniting these applications is the highly individualized,
and therefore personal, nature of the data. By building mechanisms for
protecting personal data directly into the emerging participatory sensing
architecture’ we can create a healthy marketplace of content and services in
which the individual has visibility and negotiating power with respect to the
use and disposition of his or her personal data streams. By specifying standard mechanisms instead
of standard policy, we enable support of diverse policies that are tailored to particular applications and users - this
is the narrow waist of this
participatory- sensing architecture. Without such architecture, critical
applications will be encouraged to create bundled, vertically integrated,
non-interoperable, and nontransferable vehicles for personal data streams,
thereby making those streams opaque to their creators. By creating such a
user-transparent architecture that places individuals and communities at the
locus of control over information flow, we will
simultaneously support participant rights and create a healthier market
for competitive services.
To support this function, we propose the personal data vault. It
decouples the capture and archiving of personal data streams from the sharing
of that information. Instead of individuals sharing their personal data streams
directly with services, we propose the use of secure containers to which only
the individual has complete access. The personal data vault would then
facilitate the selective sharing of subsets of this information with various
services over time. Selective sharing may take the form of exporting filtered
information from specific times of day or places in space, or may import
service computations to the data vault and export resulting computational
outputs. Essential to this scheme are tools to audit information flows and
support meaningful usage. Finally, legal consideration is essential to protect
and preserve the individual's control over his or her own data streams.
Industrial IOT - The IIoT is part of a larger concept known
as the Internet of Things (IoT). The IoT is a network of intelligent computers,
devices, and objects that collect and share huge amounts of data. The collected
data is sent to a central Cloud-based service where it is aggregated with other
data and then shared with end users in a helpful way. The IoT will increase
automation in homes, schools, stores, and in many industries.
The application of the IoT to the manufacturing industry is called
the IIoT (or Industrial Internet or Industry 4.0).
The IIoT will revolutionize manufacturing by enabling
the acquisition and accessibility of far greater amounts of data, at far
greater speeds, and far more efficiently than before. A number of innovative
companies have started to implement the IIoT by
leveraging intelligent, connected devices in their factories.
Benefits of IIOT
The IIoT can greatly improve connectivity,
efficiency, scalability, time savings, and cost savings for industrial
organizations. Companies are already benefitting from the IIoT
through cost savings due to predictive maintenance, improved safety, and other
operational efficiencies. IIoT networks of
intelligent devices allow industrial organizations to break open data silos and
connect all of their people, data, and processes from the factory floor to the
executive offices. Business leaders can use IIoT data
to get a full and accurate view of how their enterprise is doing, which will
help them make better decisions.
IIOT Protocols
One of the issues encountered in the transition to the IIoT is the fact that different edge-of-network devices
have historically used different protocols for sending and receiving data.
While there are a number of different communication protocols currently in use,
such as OPC-UA, the Message Queueing Telemetry Transport (MQTT) transfer
protocol is quickly emerging as the standard for IIoT,
due to its lightweight overhead, publish/subscribe model, and bidirectional capabilities.
Challenges of IIOT
Interoperability and security are probably the two biggest
challenges surrounding the implementation of IIoT. As
technology writer Margaret Rouse observes, “A major concern surrounding the
Industrial IoT is interoperability between devices and machines that use
different protocols and have different architectures.” Ignition is an excellent
solution for this since it is cross-platform and built on open-source,
IT-standard technologies.
Companies need to know that their data is secure. The proliferation
of sensors and other smart, connected devices has resulted in a parallel
explosion in security vulnerabilities. This is another factor in the rise of MQTT
since it is a very secure IIoT protocol.
Future of IIOT
The IIoT is widely considered to be one of
the primary trends affecting industrial businesses today and in the future.
Industries are pushing to modernize systems and equipment to meet new regulations,
to keep up with increasing market speed and volatility, and to deal with
disruptive technologies. Businesses that have embraced the IIoT
have seen significant improvements to safety, efficiency, and profitability,
and it is expected that this trend will continue as IIoT
technologies are more widely adopted.
The Ignition IIoT solution greatly
improves connectivity, efficiency, scalability, time savings, and cost savings for industrial organizations. It
can unite the people and systems on the plant floor with those at the
enterprise level. It can also allow enterprises to get the most value from
their system without being constrained by technological and economic
limitations. For these reasons and more, Ignition offers the ideal platform for
bringing the power of the IIoT into your enterprise.
Automotive IOT-With the number of networked sensors increasing across production,
supply chains and products, manufacturers are beginning to tap into a new
generation of systems that enables real-time, automatic interactions among
machines, systems, assets and things. The pervasiveness of connected devices is
finding applicability across multiple segments of manufacturing and “upply chain throughout the value chain. Following are the
functions provided by Automotive IOT:
·
Ability to view the status of the Assets at anytime,
Anywhere & Faster service response from
dealer.
· By hooking
equipment into the IoT, original equipment manufacturers (OEMs) or dealers
could use that stream of data to adjust preventative maintenance schedules
based on actual wear and be able to better optimize uptime
· Understand,
monitor, predict and control process variability
· Enhance equipment
and process diagnostics capabilities
· IoT helps more
hands-off way to track goods and the progress of work. RFID tags and readers
can play a role in this by allowing materials, locations, or tooling to
essentially talk with each other.
· Faster Response
time and less operations cost for machine configuration requests that could be services
remotely
· Ability to view
the entire population of connected products together marketing data and product trends
& increased trouble shooting ability for
Manufacturer’s tech support
· Real-time remote
monitoring of performance
· Multi site
monitoring improving the operational efficiency and reducing the site downtime
· Availability of
real time data for the production environment and alerts generated to the local
administrators mobile phone reducing the clean room downtime
·
Full manufacturing & SCM traceability
· Predictive
Maintenance and quality
Information value loop
The suite of technologies that enables the Internet of Things
promises to turn most any object into a source of information about that
object. This creates both a new way to differentiate products and services and
a new source of value that can be managed in its own right. Realizing the IoT’s
full potential motivates a framework that captures the series and sequence of
activities by which organizations create value from information: the
Information Value Loop.
Fig 4.2 Information value loop
For information to complete
the loop and create value, it passes through the loop’s stages, each enabled by specific technologies. An act is
monitored by a sensor that creates information, that information
passes through a network so that it
can be communicated, and standards—be they technical, legal,
regulatory, or social—allows that information to be aggregated across time and space. Augmented intelligence is a generic term meant to capture all
manner of analytical support, collectively used to analyze information. The loop is completed via augmented behavior technologies that either enable automated
autonomous action or shape human decisions in a manner leading to improved action.
Getting information around the Value Loop allows an organization to
create value; how much value is created is a function of the value drivers, which capture the
characteristics of the information that makes its way around the value loop.
The drivers of information value can be captured and sorted into the three
categories: magnitude, risk, and time.
Actuator-An actuator is a
component of a machine that is responsible for moving or controlling a
mechanism or system, for example by actuating (opening or closing) a valve; in simple terms, it
is a mover. An actuator requires a control signal and a source of energy. The
control signal is relatively low energy and may be electric voltage or current,
pneumatic or hydraulic pressure, or even human power. The supplied main energy
source may be electric
current, hydraulic
fluid pressure, or pneumatic (gas
pressure). When the control signal is received, the actuator responds by
converting the energy into mechanical motion.
An actuator is the mechanism by which a control system acts upon an
environment. The control system can be simple (a fixed mechanical or electronic
system), software-based (e.g. a printer driver, robot control system), a human,
or any other input.
In typical IoT systems, a sensor may collect information
and route to a control center where a decision is made and a corresponding
command is sent back to an actuator in response to that sensed input.
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Fig 4.3 Sensor to
Actuator Flow
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There are many different types of sensors. Flow sensors, temperature
sensors, voltage sensors, humidity sensors, and the list goes on. In addition,
there are multiple ways to measure the same thing. For instance, airflow might
be measured by using a small propeller like the one you would see on a weather
station. Alternatively, as in a vehicle measuring the air through the engine,
airflow is measured by heating a small element and measuring the rate at which
the element is cooling.
Communications Protocols
Sensor data Communication
Protocols-Wireless sensors networks are networks of
tiny, battery powered sensor nodes with limited on –board processing storage
and radio capabilities. Nodes sense and send their reports towards a processing
centre which is called “sink”. The design of protocols and applications for
such network has to be energy aware in order to prolong the lifetime of the
network, because the replacement of the embedded batteries is a very difficult
process once these nodes have been deployed. the regular nodes sense the field,
generate the data, and send them to associated nodes. Then the after performing
some processes transmit them to the BS in a multi-hop approach. Eventually the
user receives the data from the BS through the
Internet.
Wireless sensor networks (WSNs) are composed of a huge number of
sensor nodes. There are many applications for WSNs and depending on the
application, different types of sensors are used, such as sensors measuring
moisture, temperature, pressure and movement. WSNs have themselves
characteristics that make them different from other types of networks. One for
example is that the applicability of the networks is related to energy supply
of the nodes, so energy conservation is one of the most important challenges in
these networks. Different types of protocols for WSN are following:
Direct transmission
protocols-Using a direct communication protocol,
each sensor sends its data directly to the base station. If the base station is far away from the nodes, direct
communication will require
a
large amount of transmit power from each node. This will quickly
drain the battery of the nodes and reduce the system lifetime. How- ever, the
only receptions in this protocol occur at the base station, so if either the
base station is close to the nodes, or the energy required receiving data is
large, this may be an acceptable method of communication.
Minimum transfer energy
protocols- In these protocols, nodes act as routers
for other nodes’ data in addition to sensing the environment. These protocols
differ in the way the routes are chosen. Some of these protocols only consider
the energy of the transmitter and neglect the energy dissipation of the
receivers in determining the routes. Depending on the real time costs of the
transmit amplifier and the radio electronics, the total energy expended in the
system might actually be greater using MTE routing than direct transmission to
the base station.
Clustering protocol -A final conventional protocol for wireless networks is clustering,
where nodes are organized into clusters that communicate with a local base
station, and these local base stations transmit the data to the global base
station, where it is accessed by the end-user. This greatly reduces the distance
nodes need to transmit their data, as typically the local base station is close
to all the nodes in the cluster. Thus, clustering appears to be an
energy-efficient communication protocol. However, the local base station is
assumed to be a high-energy node; if the base station is an energy-constrained
node, it would die quickly, as it is being heavily utilized. Thus, conventional
clustering would perform poorly for our model of micro sensor networks.
Genetic Algorithm- a genetic algorithm (GA) is used to create energy efficient clusters
for data dissemination in wireless sensor networks. A GA is used at the base
station, which provides energy
efficient solutions to the optimizer. This provides the base station with the
ability to determine the best cluster formation that will give minimum energy
consumption during run time. The base station analyses the current network
condition and applies the GA after every iteration. The optimizer at the base
station selects the best solution based on the acquired knowledge through the GA fitness function. The proposed fitness
function is based on parameters such as energy consumption, number of clusters,
cluster size, direct distance to sink, and cluster distance. Upon completion of
iteration, the optimizer improves its decisions by receiving feedback, which is
then used to adjust the weights of the parameters of the fitness function for
the next iteration
Hierarchical Cluster-Based
Routing (HCR) Protocol- In HCR nodes self organize
into clusters and each cluster is managed by a set of associates called
head-set. Using round-robin technique, each associate acts
as a cluster head (CH).[3] The sensor nodes transmit data to their cluster
heads, which transmit the aggregated data to the base station. Moreover, the
energy-efficient clusters are retained for a longer period of time; the
energy-efficient clusters are identified using heuristics-based approach.
Radio Frequency
Identification Technology-Radio-frequency
identification (RFID) uses electromagnetic
fields to
automatically identify and track tags attached to objects. The tags contain
electronically stored information. Passive tags collect energy from a nearby
RFID reader's interrogating radio waves.
Active tags have a local power source (such as a battery) and may operate
hundreds of meters from the RFID reader. Unlike a barcode, the tag need not
be within the line of sight of the reader, so it may be embedded in the tracked
object. RFID is one method for Automatic
Identification and Data Capture (AIDC).
RFID tags are used in many industries, for example, an RFID tag
attached to an automobile during production can be used to track its progress
through the assembly line; RFID-tagged pharmaceuticals can be tracked through
warehouses; and implanting
RFID microchips in livestock and pets allows for positive
identification of animals.
Wireless Sensor Network
Technology-Wireless sensor network (WSN) refers to
a group of spatially dispersed and dedicated sensors for monitoring and
recording the physical conditions of the environment and organizing the
collected data at a central location. WSNs measure environmental conditions
like temperature, sound, pollution levels, humidity, wind, and so on.
These are similar to wireless ad
hoc networks in the sense that they rely on wireless connectivity
and spontaneous formation of networks so that sensor data can be transported
wirelessly. Sometimes they are called dust networks, referring
to minute sensors
as small as
dust. WSNs are
spatially distributed autonomous sensors to monitor
physical or environmental conditions, such
as temperature, sound, pressure, etc. and to
cooperatively pass their data through the network to a main locations. The more
modern networks are bi-directional, also enabling control of sensor activity.
The development of wireless sensor networks was motivated by military
applications such as battlefield surveillance; today such networks are used in
many industrial and consumer applications, such as industrial process
monitoring and control, machine health monitoring, and so on.
A wireless sensor network (WSN) is a wireless network consisting of
spatially distributed autonomous devices using sensors to monitor physical or
environmental conditions. A WSN system incorporates a gateway that provides
wireless connectivity back to the wired world and distributed nodes (see Figure
1). The wireless protocol you select depends on your application requirements.
Some of the available standards include 2.4 GHz radios based on either IEEE
802.15.4 or IEEE 802.11 (Wi-Fi) standards or proprietary radios, which are
usually 900 MHz.
Fig 4.4 WSN Components, Gateway,
and Distributed Nodes
Potential Applications-Engineers
have created WSN applications for areas including health care, utilities, and
remote monitoring. In health care, wireless devices make less invasive patient
monitoring and health care possible. For utilities such as the electricity
grid, streetlights, and water municipals, wireless sensors offer a lower-cost
method for collecting system health data to reduce energy usage and better
manage resources. Remote monitoring covers a wide range of applications where
wireless systems can complement wired systems by reducing wiring costs and
allowing new types of measurement applications. Remote monitoring applications include:
· Environmental
monitoring of air, water, and soil
· Structural
monitoring for buildings and bridges
·
Industrial machine
monitoring
· Process monitoring
· Asset tracking
WSN System Architecture
Fig 4.5. WSN System Architecture
Wireless technology offers several advantages for those who can
build wired and wireless systems and take advantage of the best technology for
the application. To do this, you need flexible software architecture like the
NI Lab VIEW graphical system design platform. Lab VIEW offers the flexibility
needed to connect a wide range of wired and wireless devices.
WSN Network Topologies-WSN
nodes are typically organized in one of three types of network topologies. In a
star topology, each node connects directly to a gateway. In a cluster tree
network, each node connects to a node higher in the tree and then to the
gateway, and data is routed from the lowest node on the tree to the gateway.
Finally, to offer increased reliability, mesh networks feature nodes that can
connect to multiple nodes in the system and pass data through the most reliable
path available. This mesh link is often referred to as a router.
Fig. 4.6. Common WSN
Network Topologies
Components of a WSN Node-A
WSN node contain several technical components. These include the radio,
battery, microcontroller, analog circuit, and sensor interface. When using WSN
radio technology, you must make important trade-offs. In battery-powered
systems, higher radio data rates and more frequent radio use consume more
power. Often three years of battery life is a requirement, so many of the WSN
systems today are based on ZigBee due to its low-power consumption.
The second technology consideration for WSN systems is the battery.
In addition to long life requirements, you must consider the size and weight of
batteries as well as international standards for shipping batteries and battery
availability. The low cost and wide availability of carbon zinc and alkaline
batteries make them a common choice. To extend battery life, a WSN node
periodically wakes up and transmits data by powering on the radio and then
powering it back off to conserve energy. WSN radio technology must efficiently
transmit a signal and allow the system to go back to sleep with minimal power
use. This means the processor involved must also be able to wake power up, and return to sleep mode
efficiently. Microprocessor trends for WSNs include reducing power consumption
while maintaining or increasing processor speed. Much like your radio choice,
the power consumption and processing speed trade-off is a key concern when
selecting a processor for WSNs. This makes the x86 architecture a difficult
option for battery-powered devices.
Fig 4.7 WSN Sensor Node
Components
