U3 Flashcards
M2M involves:
an autonomous device communicating directly to another autonomous device. In this context, autonomous refers to the ability of a node to start the communication with a neighboring node without human intervention.
In M2M, devices do ——— necessarily depend on an ———————–connection. In such cases, the devices may communicate over ——————— channels, e.g., a serial port or ————–protocol. A simple example of M2M is controlling electrical appliances, such as bulbs and fans, using ———————— from a smartphone.
not
Internet
non-IP-based
custom
RF or Bluetooth
Alternatively, IoT may incorporate some M2M?
nodes, but aggregates data at an edge router or gateway connected to the internet.
Other scenarios implement the internet networking capabilities on each IoT device to?
separately deliver its data to cloud services.
In fact, several technological advancements have driven the evolution from M2M to IoT, including?
the cloud technologies advancement,
the pervasiveness of wireless and mobile communication, the cost-effective new energy devices like lithium-ion,
and the arrival of advanced deep learning and AI tools.
In fact, several technological advancements have driven the evolution from M2M to IoT, including the cloud technologies advancement, the pervasiveness of wireless and mobile communication, the cost-effective new energy devices like lithium-ion, and the arrival of advanced deep learning and AI tools. In this manner, IoT is a broader term than M2M solutions as?
it encompasses much more technology and connectivity.
DevOps engineer:
These engineers will work with IT developers to facilitate better coordination among operations, development, and testing functions by automating and streamlining the integration and deployment processes.
In general, IoT ecosystems start with sensors deployed in a certain location to ?
convert physical phenomena, such as movement, temperature, and pressure, into digital signals.
Such signals represent the data to be transmitted to the Internet.
At this stage, the collected data traverse various ———–arriving at a ——————–. In fact, the powerful potential of IoT emerges from collecting millions of ———————–. To build such an ecosystem, we need experts from different ————————–disciplines.
channels
cloud service
sensory data
engineering
At the outset, physicists are required to?
develop new sensor technologies and long lifetime batteries.
Embedded system engineers are necessary to?
drive the sensors at the edge.
For data collection, we need:
=network engineers who are capable working in a personal area network (PAN)
= or wide area network (WAN)
=as well as a software-defined networking.
=Furthermore, data scientists are needed to develop novel data analysis and machine learning algorithms at the edge and at the cloud.
= Finally, DevOps engineers have the responsibility of deploying scalable cloud solutions in addition to fog solutions.
Obviously, IoT architecture may comprise several technologies and connectivity solutions. Hence, there exist a myriad of design choices for cloud storage:
IoT security systems,
networking,
and data analytics (Lea, 2018).
For example,
selecting the wrong PAN protocol may lead to poor communication and significantly low signal quality.
Selecting the wrong PAN protocol may lead to? interference effects in the local area network (LAN) and WAN and how costly the loss of data is. Furthermore, a decision must be made about which Internet protocols, such as message queuing telemetry transport (MQTT), constrained application protocol (CoAP), and advanced message queuing protocol (AMQP), to adopt. In the context of data processing, a system designer must decide whether or not to apply fog computing via processing data close to its source to solve latency problems and to reduce bandwidth and communication costs.
poor communication and significantly low signal quality.
System designers have also to consider?
interference effects in the local area network (LAN)
and WAN and how costly the loss of data is.
Furthermore, a decision must be made about which Internet protocols, such as?
message queuing telemetry transport (MQTT), constrained application protocol (CoAP), and advanced message queuing protocol (AMQP), to adopt.
In the context of data processing, a system designer must decide?
-whether or not to apply fog computing via processing data close to its source to solve latency problems
- and to reduce bandwidth and communication costs.
The figure below demonstrates the main components of IoT system architecture with several design options. Specifically, there exist five main components:
(1) sensing and power;
(2) data communication;
(3) Internet and routing protocols;
(4) cloud and fog computing;
and (5) IoT security.
The first component involves?
any device that is capable sensing the world.
In many cases, a single sensor can generate a massive amount of data,
such as auditory sensing for preventative maintenance of machinery.
Other applications may only acquire a single bit of data indicating, for example, vital health data from a patient.
In general, sensors have widely grown in scale up to ————————- sizes with significant cost ————. To drive the sensors at the edge,————————————- power systems are required. Collections of billions of small sensors still require a ———————- amount of energy to power. Therefore, novel energy supply methods, such as ————–, have been recently developed to enable sensors to function for many years.
moving the sensory data from the edge to cloud services and data centers located, for example, at Google, Amazon, Microsoft, and IBM. Such sensors are mostly battery powered, with minimal computing and storage resources.
sub-nanometer
reduction
low-size and low-cost
massive
harvesting
The second component deals with?
moving the sensory data from the edge to cloud services and data centers located,
for example, at Google, Amazon, Microsoft, and IBM. Such sensors are mostly battery powered, with minimal computing and storage resources.
Owing to these resources’ constraints, there exist several communication challenges :
-addressing and identification. According to the concept of IoT, billions of devices are to be connected to the Internet. Accordingly, each device has to be identified through a unique address. Hence, we need a large address space and a unique address for each IoT device.
low power communication. Wireless communication typically consumes a significant amount of energy. Therefore, we need solutions that facilitate data communication with low power consumption.
routing protocols with low memory requirement and efficient communication patterns.
high-speed and non-lossy communication.
mobility of smart IoT devices.
In addition to the IP-based communication (which is used by IoT devices to connect to the Internet), non-IP networks are?.
also used to locally connect the IoT devices
Non-IP communication channels, such as Bluetooth, RFID, and NFC, are popular but?
limited in their range (up to a few meters). Accordingly, these facilities are limited to small PANs widely used in IoT applications such as wearables connected to smartphones.
To design successful IoT systems, preliminary tools and models have to be?
incorporated, such as wireless radio dynamics like range and power analysis, signal-to-noise ratio, path loss, and interference.
The leading communication technologies used in the IoT world are?
IEEE 802.15.4,
low-power Wi-Fi,
6LoWPAN,
RFID, NFC,
Sigfox, LoRaWAN,
and other proprietary protocols for wireless networks.
The third component deals with?
bridging IoT data from sensors to the Internet through gateway routers
and supporting IP-based protocols.
Specifically, the primary role of the routers is securing, managing,
and steering the sensory data.
In fact, a single edge router can serve thousands of IoT devices.
IoT data needs :
efficient,
power-aware,
and low-latency protocols that can be easily steered and secured in and out of the cloud.
The fourth component deals with ?
processing the collected data by means of various aspects of cloud architectures such as SaaS, IaaS, and PaaS systems.
Nevertheless, not everything belongs in the ———-. There is a measurable cost in —————— data to a cloud vs. processing it at the ————(edge computing) or extending cloud services ————————into an edge router (fog computing).
cloud
moving all IoT
edge
downward
Thanks to the advanced analytics and rules engines, the collected sensory data are mostly turned into?
=a set of decisions and actionable consequences. In some scenarios, a simple rules engine,
=e.g., fuzzy logic controller, can be adopted to easily detect anomalous temperature extremes on an edge router monitoring several IoT devices.
=In other scenarios, a massive amount of structured and unstructured data may be streaming in real time to a cloud service while requiring both fast processing for predictive analytics and long-range forecasting using advanced machine learning models.
Since the IoT devices are mostly in the public, in very remote areas, moving vehicles, or even inside a person, several security threats?
emerge. Several security primitives, such as asymmetric encryption, have been discussed.
In general, sensors are:
devices that can monitor and measure the physical world.
As the primary source of IoT data, ——————represent the most important building block of all IoT applications. IoT sensors are mostly ————————————————————————————–. These IoT sensors come in a variety of forms and complexities, from simple ———————–to advanced ———–systems.
sensors
small, low cost, and constrained by battery capacity
thermocouples
video
With the increasing popularity of smartphones, many smart IoT solutions have recently been built using smartphones because of ?
their embedded sensors.
Examples of the sensors embedded in modern smartphones are:
accelerometers,
gyroscopes,
cameras,
microphones,
GPS,
light sensors,
and proximity sensors.
In general, accelerometers sense?
the motion and acceleration through measuring changes in velocity of the smartphone in three dimensions.
Similarly, gyroscopes precisely detect the orientation of the smartphones through measuring?
capacitive changes when a seismic mass moves in a particular direction.
Modern accelerometers and gyroscopes are?
-fabricated as micro-electromechanical systems (MEMS).
-MEMS sensors incorporate miniaturized mechanical structures that can spin, stretch, bend, move, or alter form which in turn affects an electrical signal.
The figure below shows the components and basic principle of accelerometers
. Accelerometers use a MEMS piezoelectric to?
produce a voltage in response to movement.
Specifically, a central mass is attached to a spring which responds to acceleration in a certain direction.
When the accelerometer experiences an acceleration, the mass deflects from its —————————-. This deflection can be measured by varying ———————-in a —————————-. Accelerometers are designed to respond to ——- dimensions (X, Y, Z) rather than ————–dimension.
neutral position
capacitance
MEMS circuit
multiple
one
Alternatively, gyroscopes operate slightly?
differently
A disk is driven to?
rotate a fraction of a full turn around its axis.
The tilt of the disk is measured to?
produce a signal related to the rate of rotation.
Both gyroscopes and accelerometers require?
power supplies and an op-amp for signal conditioning.
After conditioning, the output can be directly sampled by?
a digital controller.
These sensors can be manufactured in very small packages. For instance,
the Invensense MPU-6050 comprises a 6-axis gyro and accelerometer in a small 4 mm x 4 mm x 0.9 mm package with operating current of 3.8 mA, thus it is well-suited for low power sensing.
Additionally, magnetometers measure?
the strength and/or direction of magnetic fields.
Additionally, magnetometers measure the strength and/or direction of magnetic fields. This can be used as ?
a digital compass and in applications to detect the presence of metals.
Magnetometers are mostly harnessed to obtain?
directional information in three dimensions by being paired with accelerometers and gyroscopes.
An inertial measurement unit (IMU).
Magnetometers are mostly harnessed to obtain directional information in three dimensions by being paired with accelerometers and gyroscopes.
This device is called an inertial measurement unit (IMU)
IMUs are typically used to obtain location information in indoor environments.
To estimate the location information in outdoor environments?
-GPS sensors use a network of about 30 satellites orbiting the earth at an altitude of 20,000 km.
-The location is detected using the principle of trilateration in which the intersection point among three or more circles, whose centers are the satellites, is determined.
Aside from location detection, light sensors have recently been used to?
measure the intensity of ambient light.
Smart home applications employ such sensors to control ?
the lights in a room without human intervention. Furthermore, light sensors are used in many other IoT sensing activities, such as security systems, smart switches, and smart street lighting.
There exist two types of light sensors:
namely photoresistor and photodiodes.
Photoresistor varies in resistance depending on?
light intensity, while photodiodes convert light into an electrical current.
Similarly, proximity sensors employ infrared (IR) signals to measure?
the distance between the sensor and a certain object.
The main idea is to emit IR signals and wait for reflections from an object. Based on the difference in time, we can calculate the distance.
As an example,
proximity sensors can be used in applications in which we have to trigger some event when an object approaches the phone.
For long ranging and scanning, light detecting and ranging (LiDAR) sensors estimate the distance to an object by measuring?
a laser pulse reflection on the object.
The emitted laser power is typically constrained for safety reasons to prevent?.
eye damage
LiDAR sensors are heavily used in?
agriculture, automated and self-driving vehicles, robotics, surveillance, and environmental studies.
They are not only capable of estimating the range but are also capable of ?
analyzing anything that crosses its path.
For instance,
they can analyze gases, atmospheres, cloud formations and compositions, particulates, the speed of moving objects, and so on.
The figure below shows the utilization of LiDAR sensors in the automated sector to estimate the distance between moving vehicles.
In this realm,:
Google,
known for this project as Waymo (n.d.)
and Uber (2019) are developing self-driving vehicles.
Their vehicles feature a bulky box on top of the roof which spins continuously giving 360° visibility and precise, in-depth information about the exact distance to an object to an accuracy of ±2 cm
Some smartphones have a thermometer, barometer, and humidity sensor to measure ?
the temperature,
atmospheric pressure,
and humidity, respectively.
In IIoT, temperature sensors typically exist?
almost anywhere, e.g., smart thermostats, IoT cold storage logistics, refrigerators, and industrial machinery.
In general, there are three common temperature sensors in the IoT market:
thermocouples,
resistance temperature detectors (RTD), and thermistors.
Thermocouples produce very small signals, e.g., microvolts in amplitude?
because thermocouples do not receive an excitation signal to operate.
Thermocouples are well suited for?
- long distance measurements with long leads
- and are often used in industrial and high-temperature environments.
Thermocouples may suffer from aging? Hence,
i.e., high-heat environments can reduce the accuracy sensors over time.
IoT solutions must account for changes over the life of a sensor.
Thermocouples may suffer from aging
Alternatively, RTDs require an excitation current to operate?
They operate within a narrow range of temperatures but have much better accuracy than thermocouples.
RTDs’ application in industry is limited?
Because RTDs are rarely used above 600˚C
Finally, thermistors are resistors that ?
change based on temperature and are suitable where a high resolution is needed for a narrow temperature range.
Thermistors are heavily used in:
medical devices,
scientific equipment,
food handling equipment,
incubators,
and home appliances such are thermostats.
The following table summarizes the main
differences between the three types of temperature sensors.
Comparison between the three types of temperature sensors
Thermocouples -180 to 2,320
Resistance temperature detectors -200 to 500
Thermistors -90 to 130
Thermocouples Fast (microseconds)
Resistance temperature detectors Slow (seconds)
Thermistors Slow (seconds)
Thermocouples Low
Resistance temperature detectors Medium
Thermistors High
In general, wearable sensors are equipped with?
medical sensors that are capable measuring different parameters, e.g., the heart rate, pulse, blood pressure, body temperature, respiration rate, and blood glucose levels.
Such wearables comprise —————————————————————————. In fact, smart watches and fitness trackers recently became popular as companies such as Apple, Samsung, and Sony (2016) are————————– innovative features, such as connectivity with smartphones, sensors such as an ——————————–.
smart watches, wristbands, monitoring patches, and smart textiles
incorporating
accelerometer, and heart rate monitors
In the same category, smart patches are pasted on ?
the skin to continuously monitor vital health parameters.
All the electronic components are?
embedded in these rubbery structures, and they are stretchable, disposable, and relatively cheap.
The figure below demonstrates a microneedle containing a smart insulin patch that is designed to sense elevated blood glucose levels.
It responds to abnormal glucose levels by?
releasing insulin. In fact, such smart patches offer people with diabetes a less-painful and more-reliable way to manage their condition.
RFID is?
an identification technology which consists of two main components: a tag and a reader.
The tag is?
a small chip with an antenna to periodically,
or upon interrogation,
transmit short,
digital,
radio frequency (RF) messages.
Such messages typically contain a unique identification code as well as some data stored in the tag’s memory.
To read the information encoded on a tag,?
the RFID reader emits a signal to the tag using an antenna.
The tag responds with the information written in its ———————– and the reader will then ———————-the read results to an—————— computer program.
memory
transmit
RFID
Furthermore, it measures the received signal strength (RSSI) of the received RF signal which is ?
an indicator of the range from tag to reader.
There exist two types of RFID tags:
active and passive.
Active tags have ?
a power source and passive tags do not have any power source.
Passive ?
tags draw power from the electromagnetic waves emitted by the reader and are thus cheap and have a long lifetime.
RFID sensors are employed in ?
many IoT applications, such as inventory management, supply chain management, asset tracking, access control, identity authentication, and object tracking.
For instance,
an RFID tag is attached to the front of vehicles. When the vehicle reaches a barricade on which there is a reader, it reads the tag data and decides whether it is an authorized vehicle. If the vehicle is found to be authorized, the barricade opens automatically.
In such an application, RFID cards are?
issued to the people who can then be identified by an RFID reader and given access accordingly.
Modern smartphones include:
powerful microphones and camera sensors for capturing audio and visual information, respectively.
Readings from both sensors can be?
=analyzed to detect various types of contextual information,
=such as the surrounding environment and users’ activities.
= Furthermore, sound and vibration measurements are common in IIoT and predictive maintenance applications.
For instance,
an industrial machine that rotates or mixes a load of material in chemical manufacturing needs precise leveling. A microphone can typically be used to monitor the health and safety of such equipment.
Video cameras and vision systems are ?
smart sensors that have substantial processing power in the form of high-end processors, digital signal processors, FPGAs, and custom ASICs.
In modern vision systems, one of two types of sensing elements is used:
charge-coupled devices (CCD) or complementary metal-oxide (CMOS) devices.
In charge-coupled devices (CCD) the charge is moved from?
the sensor to the edge of the chip to be sampled sequentially via an analog-to-digital converter.
CCDs create:
high-resolution
and low-noise images.
Nevertheless, they consume considerable amount of power.
In the latter type, individual pixels contain transistors to?
sample the charge and allow each pixel to be read individually.
CMOS sensors are more common in the IoT market?
since they consume little power. However, they are mostly susceptible to noise.
The figure below demonstrates the various components of a typical CMOS camera and the various processing steps.
In general, CMOS sensors are integrated into ?
a silicon die that appears as a two-dimensional array of transistors arranged in rows and columns over a silicon substrate.
A sensor starts with a ————-that observes a scene. Afterward, an image ————————————— is used to expose the captured image to a series of steps to ————————————-the image several times into a usable digital image.
lens
signal processor (ISP)
filter, normalize, and convert
The amount of data passing through?
a camera sensor at a conservative 60 frames per second and 1080p resolution is massive.
Hence, it is not recommended to stream this massive data to the cloud for processing.
As previously mentioned, most IoT applications require ?
the incorporation of several sensors to obtain higher-level inferences, thus making precise decisions.
sensor fusion.
The process of combining readings from different sensors is referred to as sensor fusion.
For instance,
a single temperature sensor has no clue of what causes a rapid temperature change since it has no contextual awareness.
Nevertheless, by combining these temperature readings with location detection and light intensity sensors, the IoT system could infer that a large number of people are congregating in a certain area while the sun is shining. In such a scenario, the IoT system may?
decide to increase air circulation in a smart building.
In general, there exist two major modes of sensor fusion:
centralized and de-centralized.
In the centralized mode,
the raw data is streamed and aggregated to a central service, e.g., cloud-based service, where fusion is performed.
In the de-centralized mode,
the data is correlated at the sensor or close, e.g., edge or fog nodes.
Autonomous vehicles stand as a prominent example of
de-centralized sensor fusion.
In fact, sensor fusion widely helps in?
constructing a hypothesis about the state of the environment the vehicle is in.
Above, an autonomous vehicle is depicted where ? Such inferred data leaves drivers safe since the autonomous vehicle becomes fully prepared for all road scenarios.
readings from five different sensors are fused together to track stationary and moving objects which is an important feature of autonomous driving technology.
