Data processing methods used by IoT devices to ensure efficiency and reliability

1. Data Acquisition and processing
Learning Objectives
• Define data acquisition and signal conditioning;
• Illustrate the impact of IoT on multiplexing and sampling theory;
• Explain the electrical, temperature and strain measurements;
• Define Machine-to-Machine (M2M) communication (a major component of
the IoT portfolio of technologies);
• Discuss the security issue and challenges of collaborative data acquisition in
IoT.

2. Introduction
In large-scale IoT applications, thousands of sensors are possible
Examples of large scale IoT applications:
• IoT-based irrigation scheme on a large farm
• An IoT-based monitoring system of a smart power grid
• An IoT-based data collection solution in a large city
• An IoT-based machine-state monitoring system in a large industry
• A fire-monitoring IoT solution covering a large forest or any other place

3. Introduction
• The data from these sensors may be used to make a single decision
• However, sensor data is often noisy
• A temperature sensor can return 250C and then 260C in the next second, even if
temperature has not changed
• Noise can be caused by the sensor quality or the environment
• Due to noise, sensor data should be filtered to reduce noise effects. This is even
more critical with actuation
• It is also necessary to ensure that the sensors are working within their
specifications and are not damaged by the environmental conditions.
• How do you combine these noisy values to make a single decision?

4. Data Acquisition and Signal Conditioning
• Data acquisition refers to the process of capturing, measuring and digitizing signals
from various sources, such as sensors, and converting them into a format that can
be processed and analyzed by a computer.
• Data acquisition systems typically include hardware and software components,
such as analog-to-digital converters, signal conditioning circuits, and data
acquisition software.
• Signal conditioning refers to the process of preparing a signal for further
processing or analysis.
• Signal conditioning is an important step in data acquisition, as it can improve the
accuracy and reliability of the data and make it more suitable for further processing
and analysis.

5. Data Acquisition and Signal Conditioning
Wither App
Sales
App
Coop
App
Heat Controllers
SMS Tea Reports
Withering Tables
and Sensors
Edge
Application
Does the Tea Withering App really want the wither raw
data? Not always, because:
• Data is noisy (pre-processing is necessary)
• Wither app may only need to decide about the wither data
(e.g., is it too hot or not?)
• What kind of data? Is it continuous or discrete-time?
Signal processing is necessary to prepare data into a format
that is useful, addresses the noise, and to filter the
measurements to reduce uncertainties
• Where is processing done?
• If bandwidth is a problem, process closer to the
edge. Otherwise, processing in the cloud is an
option.

6. Signal Conditioning
• This typically involves amplifying, filtering, or otherwise modifying the signal
to improve its quality or make it more suitable for the intended application.
• Signal conditioning can include various techniques such as:
•Amplification: Increasing the amplitude of a signal to make it stronger or
more easily measurable.
•Filtering: Removing unwanted noise, or unwanted frequency components,
from a signal to improve its quality.
•Offset and Gain adjustment: Adjusting the level of the signal, so it is within
the range of the measuring device or the data acquisition system.
•Linearization: adjusting the non-linearity of the sensor to make the output
linear with respect to the input.
•Isolation: Protecting the measuring device from the electrical noise and
interference of the environment.

7. Sensor Data Filtering
• Sensor data is subject to uncertainty and noise
• Sensors take periodic readings
• Readings are not always consistent but have uncertainty or contain errors
• Making decisions based on such readings can lead to overall wrong decisions
• Some methods are used to determine the actual value of the stimulus:
• These methods rely on estimation using incoming data to enhance their
accuracy
• They have many applications in computer science e.g.
• In robotics, they help the robot to correct its position in a coordinate system

8. Kalman Filtering
• Kalman filtering is a mathematical technique used to estimate the state of a
system from noisy and incomplete measurements.
• An iterative mathematical process that uses a set of equations and
consecutive data inputs to estimate the true value (position, velocity, etc).
• The basic idea behind Kalman filtering is to use a prediction-correction
algorithm to estimate the state of the system at each time step, taking into
account the previous state, the current measurement, and the system
dynamics.
• It can be used for prediction, smoothing, and filtering.
• It is widely used in control systems and navigation systems.
• Used in navigation, esp. aircraft
• Robotic motion planning, etc.

9. Kalman Filtering
• Data being tracked can be multi-
dimensional
• For example, while tracking a plane or
other object, you may track velocity,
acceleration, and position in x, y, and z
directions.
• The Kalman filter helps to narrow down all
these parameters to their actual values
• Obviously, the process is more complex in
multi-dimensional tracking
• This case uses two or more different
parameters to reach a decision
0 10 20 30 40 50 60 70 80 90 100
68
69
70
71
72
73
74
75
Measurement Time
Measurement
Value
Filtered Value
Measured Value
Based on normally distributed values
Kalman filtering applied in:
• Radar tracking of planes, Satellite tracking,
Missile tracking, Drone tracking, Robot
movement, etc.

10. Kalman Filter
We require as inputs:
• Measured values (current value
and previous value)
• Estimate values (current estimate
and previous estimate)
• We use these to:
• Enhance accuracy of the
estimate, whether measured
values are accurate or not
We could use the average of
measured values but that would
require to have many values
available initially
• May not be as accurate
Error in
Estimate
Calculate the
Kalman Gain (KG)
Calculate Current
Estimate 𝐸𝑆𝑇𝑡
Calculate New Error
in Estimate 𝐸𝑒𝑠𝑡𝑡
Error in
Measurement
Previous
Estimate
Measured
Value
Data Input
Original
Estimate
Original Error
in Estimate
Update
Estimate

11. Multiplexing and Sampling
Sampling
• IoT devices often have sensors that generate
continuous streams of data, such as
temperature or humidity readings.
• This data must be sampled at regular intervals to
be transmitted over a network and analyzed.
• The high volume of data generated by IoT
devices can put a strain on sampling systems, as
they must be able to handle large amounts of
data in real-time.
• IoT devices generate a large amount of data, which can have an impact on multiplexing and
sampling theory.
• Multiplexing is the process of combining multiple signals into one,
• Sampling is the process of measuring a signal at discrete points in time.
Multiplexing
• In IoT, multiplexing can be used to
combine data from multiple devices
into a single stream for transmission
over a network.
• This can increase the efficiency of
data transmission, as multiple devices
can share a single communication
channel.

12. Common types of measurement
• Electrical measurements: Typically involve measuring various electrical parameters such
as voltage, current, and power. These measurements can be used to monitor the
performance of electrical systems, detect problems, and optimize energy usage.
• Temperature measurements are used to monitor and control the temperature of various
systems and environments. This can include monitoring the temperature of buildings,
equipment, and industrial processes, as well as monitoring the temperature of food,
pharmaceuticals, and other perishable goods.
• Strain measurements involve measuring the deformation or change in shape of a material
caused by an applied load. This can be used to monitor the health of structures, such as
bridges and buildings, as well as to monitor the performance of mechanical systems, such
as engines and turbines.
• Others:
• Humidity
• Pressure
• Light
• Acceleration etc

13. Machine to Machine (M2M) communication
• Machine-to-machine (M2M) communication refers to
the automatic, wireless or wired communication
between devices or machines.
• These devices can include sensors, actuators, or other
types of equipment that are connected to a network
and can communicate with each other.
• No human intervention when operating
• This allows for a variety of applications in which
machines can leverage an increasingly prevalent IoT
interconnectivity to work together in a more cohesive
and effective manner.
• M2M communication allows devices to communicate
with each other and with centralized systems,
enabling them to share data and control functionality

14. M2M communication

15. M2M communication: Example