Managing M2M (Machine-to-Machine)Data
-Data Generation
Unit-2 - Data Management Introduction and IoT Components
Managing M2M data: Data generation, Data acquisition, Data validation, Data storage, Data processing, Data
remanence, Data analysis, Business Process in IOT, M2M and IoT Analytics, Basics of Sensors and Actuators,
Introduction to Arduino and its applications, Sensor Interfacing Using Tinker CAD,
N.Interfacing
Saravana Sensor with
Kumar
Raspberry Pi 4.
AP/CSE
CINTEL
Managing M2M (Machine-to-Machine) Data:
Data Generation
• In Machine-to-Machine (M2M) communication systems, data
generation is the first and crucial step in the data lifecycle.
What is Data Generation in M2M?
• Data Generation refers to the process of collecting raw data from
various devices, sensors, or machines involved in M2M networks.
• These devices constantly monitor their environment or operations
and generate data based on events, measurements, or triggers.
Key Sources of Data Generation
in M2M:
Source Examples Type of Data
Sensors Temperature, humidity, Environmental and status data
pressure, motion sensors
Meters Smart electricity/water/gas Usage and consumption data
meters
Industrial Devices CNC machines, assembly Operational and diagnostic data
robots, PLCs
Vehicles GPS trackers, speed sensors Location and movement data
Wearables Health bands, smartwatches Physiological and activity data
Characteristics of M2M Data
Generation:
• Real-time or near real-time
• High volume from many devices
• Often small-sized data packets
• Can be structured (e.g., sensor logs)
Techniques Used in Data
Generation:
• Periodic Sampling - Devices generate data at fixed intervals.
• Event-Driven - Data generated only when a specific condition is met (e.g.,
temperature exceeds threshold).
• Request-Based - Data is produced when requested by a remote system or
application.
• Streaming - Continuous data flow (e.g., video feeds, telematics).
Managing M2M (Machine-to-Machine)Data
-Data Acquisition
What is Data Acquisition?
• Data acquisition refers to the process of collecting, retrieving, and
transmitting data generated by M2M devices (like sensors, meters, and
machines) to a central system for processing, analysis, or storage.
It involves:
• Capturing data from various physical sources
• Converting signals to digital form (if needed)
• Transmitting it securely and accurately
Types of Data Acquisition:
1. Analog Data Acquisition
Example: Voltage from a temperature sensor Requires Analog-to-Digital Converters
(ADC)
2. Digital Data Acquisition
Example: On/off status from a door sensor Fasier to collect and transmit
3. Hybrid
Many systems use both analog and digital data sources
Communication Technologies
Used:
Technology Use Case
Zigbee Smart homes, short-range loT
Wi-Fi Smart buildings, factor floors
Ethernet High-speed industrial M2M
setups
Bluetooth Personal area networks, wearables
DATA ANALYSIS
What is IoT analysis
• IoT analysis is the process of evaluating data generated and
gathered by IoT devices using a particular set of data
analytics tools and techniques.
DESCRIPTIVE ANALYSIS
• Descriptive analysis helps us
understand what happened in the
past. It looks at historical data and
summarizes it in a way that makes
sense. For example, a company might
use descriptive analysis to see how
much they sold last year or to find out
which product was most popular.
PREDICTIVE ANALYSIS
• By forecasting future trends based on historical data, Predictive
analysis predictive analysis enables organizations to prepare
for upcoming opportunities and challenges. For example, a store
might use predictive analysis to figure out what products will be
popular in the upcoming season. It helps businesses prepare for
future events and make plans.
PRESCRIPTIVE ANALYSIS
• Prescriptive Analysis is an advanced method that takes Predictive
Analysis insights and gives suggestions on the best actions to
take. For example, if predictive analysis shows that a certain
product will be popular, prescriptive analysis might suggest how
much stock to buy or what marketing strategies to use.
It’s about giving businesses clear advice on how to act.
DIAGNOSTIC ANALYSIS
• Diagnostic analysis works hand in hand with Descriptive Analysis.
As descriptive Analysis finds out what happened in the past,
diagnostic Analysis, on the other hand, Finds out why did that
happen or what measures were taken at that time, or how
frequently it has happened. It helps businesses figure out the
reasons behind certain outcomes.
SMART TRAFFIC SYSTEMS
• Singapore uses IoT as part of its traffic management system to
enhance mobility.
• The system’s analytics takes in live stream information on all car
movements and pedestrian activities to then adjust the traffic lights’
cycle time at specific intersections where congestion tends to occur
most
SMART PARKING SYSTEM
• Smart parking systems use sensors in parking spaces to check if a spot is vacant or occupied in real-
time.
• Subsequently, these sensors send the information wirelessly to a cloud server. Once the data reaches the
server, it processes the data and creates a real-time map that shows available parking spots.
TOOLS FOR DATA ANALYSIS
• Several tools are available to facilitate effective data
analysis.
• Microsoft Excel, which is great for simple data manipulation and
visualizations;
• R, a free language for statistical analysis;
• Python, a versatile programming language with libraries for data
science;
• Tableau Public, for creating interactive data visualizations;
• Power BI, a service for creating business intelligence dashboards
and reports.
BUSINESS PROCESS IN IoT
BUSINESS PROCESS IN IoT
• A business process is a set of structured activities or tasks that
produce a specific service or product for customers or stakeholders.
• IoT (Internet of Things) allows businesses to collect real-time data,
automate workflows, optimize operations, and enhance customer
experiences through smart, connected devices.
• Goal: Improve efficiency, reduce costs, and drive innovation using IoT-
enabled systems.
Benefits of IoT in Business Processes
• Increased Efficiency: Automation reduces delays and errors.
• Cost Savings: Predictive maintenance lowers downtime and repair costs.
• Faster Decision-Making: Real-time data speeds up responses.
• Improved Safety and Compliance: Real-time monitoring reduces risks.
• Innovation Enablement: Enables new services and business models.
