Data compression techniques in SCADA systems - Mini Project: Build & Apply

Data Compression Techniques in SCADA Systems

📖 Scenario: You work as a technician managing a SCADA (Supervisory Control and Data Acquisition) system. The system collects sensor data continuously, but the storage space is limited. To save space, you want to compress the data by keeping only readings above a certain threshold.

🎯 Goal: Build a simple program that stores sensor readings, sets a compression threshold, filters the readings to keep only those above the threshold, and then displays the compressed data.

📋 What You'll Learn

Create a dictionary called sensor_readings with exact sensor names and their readings

Create a variable called compression_threshold with the exact value 50

Use a dictionary comprehension to create a new dictionary compressed_data with readings above the threshold

Print the compressed_data dictionary

💡 Why This Matters

🌍 Real World

SCADA systems collect large amounts of sensor data. Compressing data by filtering out less important readings helps save storage and speeds up data transmission.

💼 Career

Understanding data compression techniques is important for SCADA technicians and engineers to optimize system performance and resource use.

1

Create initial sensor readings dictionary

Create a dictionary called sensor_readings with these exact entries: 'temp_sensor': 45, 'pressure_sensor': 55, 'flow_sensor': 60, 'level_sensor': 40

SCADA systems

# Create the sensor_readings dictionary with exact values
# Your code here

Hint

Use curly braces to create a dictionary with keys and values separated by colons.

2

Set compression threshold

Create a variable called compression_threshold and set it to the integer value 50

SCADA systems

sensor_readings = {'temp_sensor': 45, 'pressure_sensor': 55, 'flow_sensor': 60, 'level_sensor': 40}
# Set compression_threshold to 50
# Your code here

Hint

Assign the number 50 to the variable compression_threshold.

3

Filter readings above threshold

Use a dictionary comprehension to create a new dictionary called compressed_data that includes only the entries from sensor_readings where the reading is greater than compression_threshold

SCADA systems

sensor_readings = {'temp_sensor': 45, 'pressure_sensor': 55, 'flow_sensor': 60, 'level_sensor': 40}
compression_threshold = 50
# Create compressed_data with readings above compression_threshold
# Your code here

Hint

Use dictionary comprehension syntax: {key: value for key, value in dict.items() if condition}.

4

Display compressed data

Write a print statement to display the compressed_data dictionary

SCADA systems

sensor_readings = {'temp_sensor': 45, 'pressure_sensor': 55, 'flow_sensor': 60, 'level_sensor': 40}
compression_threshold = 50
compressed_data = {sensor: reading for sensor, reading in sensor_readings.items() if reading > compression_threshold}
# Print the compressed_data dictionary
# Your code here

Hint

Use print(compressed_data) to show the filtered dictionary.

Practice

1. What is the main purpose of data compression in SCADA systems?

easy

A. To reduce the size of data for easier storage and faster transfer

B. To increase the size of data for better security

C. To convert data into a different format for display

D. To delete unnecessary data permanently

Solution

1. Step 1: Understand data compression purpose

   Data compression reduces the size of data to save space and speed up transfer.

2. Step 2: Apply this to SCADA systems

   In SCADA, smaller data means faster communication and less storage needed.

3. Final Answer:

   To reduce the size of data for easier storage and faster transfer -> Option A

4. Quick Check:

   Compression = smaller data size [OK]

Hint: Compression makes data smaller to save space and time [OK]

Common Mistakes:

• Confusing compression with encryption
• Thinking compression deletes data
• Believing compression changes data meaning

2. Which of the following is the correct syntax to compress data using a function named compress in a SCADA script?

easy

A. compressed_data = compress(data)

B. compressed_data = compress data

C. compressed_data <- compress(data)

D. compressed_data = compress[data]

Solution

1. Step 1: Identify correct function call syntax

   Functions are called with parentheses enclosing arguments, like compress(data).

2. Step 2: Check each option

   compressed_data = compress(data) uses correct syntax with parentheses and assignment.

3. Final Answer:

   compressed_data = compress(data) -> Option A

4. Quick Check:

   Function call syntax = parentheses [OK]

Hint: Use parentheses to call functions with arguments [OK]

Common Mistakes:

• Omitting parentheses in function calls
• Using wrong assignment operators
• Using brackets instead of parentheses

3. Given the following SCADA script snippet:

data = "sensor_reading_12345"
compressed = compress(data)
decompressed = decompress(compressed)
print(decompressed)

What will be the output?

medium

A. compressed data bytes

B. Error: decompress function not found

C. sensor_reading_12345

D. sensor_reading

Solution

1. Step 1: Understand compression and decompression

   compress() shrinks data, decompress() restores it to original form.

2. Step 2: Follow the script flow

   Data is compressed then decompressed, so print shows original data.

3. Final Answer:

   sensor_reading_12345 -> Option C

4. Quick Check:

   Decompress(compress(data)) = original data [OK]

Hint: Decompress reverses compress, output original data [OK]

Common Mistakes:

• Thinking print shows compressed bytes
• Assuming decompress changes data
• Ignoring function order

4. A SCADA script uses compressed = compress(data) but later decompressed = decompress(data) is called instead of decompress(compressed). What is the likely problem?

medium

A. Data will be compressed twice

B. Compression will fail because decompress is called too early

C. No problem, decompress can use original data

D. Decompression will fail or give wrong data because wrong variable is used

Solution

1. Step 1: Identify variable usage error

   Decompress must use compressed data, not original data variable.

2. Step 2: Understand effect of wrong variable

   Using original data in decompress causes failure or incorrect output.

3. Final Answer:

   Decompression will fail or give wrong data because wrong variable is used -> Option D

4. Quick Check:

   Decompress(compressed) needed, not decompress(data) [OK]

Hint: Always decompress the compressed variable [OK]

Common Mistakes:

• Passing original data to decompress
• Assuming decompress auto-detects input
• Mixing variable names

5. You need to compress SCADA data but want to keep it quickly accessible for real-time monitoring. Which compression technique is best?

hard

A. No compression to avoid delay

B. Lossless compression for exact data recovery

C. Lossy compression to reduce size drastically

D. Encrypt data instead of compressing

Solution

1. Step 1: Understand real-time monitoring needs

   Real-time needs exact data quickly without loss.

2. Step 2: Choose compression type

   Lossless compression keeps data exact and fast to decompress.

3. Step 3: Evaluate other options

   Lossy loses data, no compression wastes space, encryption is different.

4. Final Answer:

   Lossless compression for exact data recovery -> Option B

5. Quick Check:

   Real-time + exact data = lossless compression [OK]

Hint: Use lossless compression for exact, fast data access [OK]

Common Mistakes:

• Choosing lossy compression for critical data
• Skipping compression to save time
• Confusing encryption with compression