Describe the advantages and disadvantages of an automated system used for a given scenario

Automated and Emerging Technologies – IGCSE Computer Science (0478)

Learning Objective

Students will be able to describe the advantages and disadvantages of an automated system used in a given scenario and to apply the core concepts of the Cambridge Computer‑Systems and Algorithms syllabus to that scenario.

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1 – 6 Computer Systems (AO1)

1. Number Systems

• Binary (base‑2) – each digit is a bit (0 or 1). Example: 1011₂ = 11₁₀.
• Hexadecimal (base‑16) – digits 0‑9 and A‑F. Example: 1A₁₆ = 26₁₀.
• Two’s‑complement (signed integers) – invert all bits and add 1.
• Conversion steps (denary ↔ binary ↔ hexadecimal):
  1. Divide the decimal number by 2, record remainders → binary (reverse order).
  2. Group binary digits in sets of four (from the right) → each group ↔ a hex digit.
  3. To go from hex to decimal, multiply each digit by 16ⁿ and sum.
• Range limits (important for AO1):
  • 8‑bit unsigned: 0 – 255; 8‑bit two’s‑complement: –128 – 127.
  • 16‑bit unsigned: 0 – 65 535; 16‑bit two’s‑complement: –32 768 – 32 767.

2. Data Representation

• Text
  • ASCII – 7‑bit (0‑127). Example: T = 01010100₂.
  • Unicode – variable length (UTF‑8, UTF‑16). Includes code‑points beyond the Basic Multilingual Plane (e.g., 𝄞 U+1D11E).
• Images
  • Pixel – smallest addressable element.
  • Colour depth vs. bit‑depth: 24‑bit true colour = 8 bits per colour channel (R,G,B).
  • Resolution example: 800 × 600 pixels at 24‑bit → size = 800 × 600 × 24 bits = 1 152 000 bits ≈ 144 kB.
• Sound
  • Sampling rate (e.g., 44.1 kHz) and bit‑depth (e.g., 16 bits).
  • File‑size formula: size = duration × sampling rate × bit‑depth × channels.
• Compression
  • Lossless – Run‑Length Encoding (RLE), Huffman coding.
  • Lossy – JPEG for images, MP3 for audio.

3. Data Storage & Compression

• Units: bit, byte (8 bits), kilobyte (KB = 10³ bytes), kibibyte (KiB = 2¹⁰ bytes), etc.
• File‑size calculation example (audio):

  duration = 3 min = 180 s
  sampling = 44 200 Hz
  bit‑depth = 16 bits
  channels = 2 (stereo)
  size = 180 × 44 200 × 16 × 2 bits = 254 016 000 bits ≈ 30.5 MB

• Difference between lossless (exact reconstruction) and lossy (some data discarded for higher compression).

4. Data Transmission

• Analogue vs. digital signals.
• Packet structure – header (source/destination, length, checksum), payload (data), trailer (e.g., CRC).
• Error‑detection methods
  • Parity bit (single‑bit), parity byte (8 bits) – detects odd numbers of bit errors.
  • Checksum – sum of data bytes modulo 256; simple but less robust.
  • CRC (Cyclic Redundancy Check) – polynomial division, detects burst errors.
  • ARQ (Automatic Repeat reQuest) – receiver requests retransmission when error detected.
• Simple encryption example – Caesar cipher (shift = 3) for illustration only.
• Common interfaces: USB, Ethernet, Wi‑Fi.

5. Hardware Architecture

• Von Neumann architecture – shared memory for instructions and data; CPU fetches, decodes, executes.
• CPU components – ALU, Control Unit, registers, cache (L1, L2).
• Fetch‑Decode‑Execute cycle – illustrated with a diagram (placeholder).
• Multi‑core processors – parallel execution of threads.
• Memory hierarchy – registers → cache → RAM → secondary storage (HDD/SSD) → cloud.
• Purpose of an instruction set – defines operations the CPU can perform (e.g., arithmetic, logic, branch).

6. Network Hardware & Internet Basics

• Network Interface Card (NIC) – provides MAC address (48‑bit, e.g., 00:1A:2B:3C:4D:5E).
• IP addressing
  • IPv4 – 32‑bit dotted decimal (e.g., 192.168.1.10).
  • IPv6 – 128‑bit hexadecimal (e.g., 2001:0db8::1).
  • Static vs. dynamic (DHCP) allocation.
• Router – forwards packets between networks, uses IP routing tables.
• Internet services
  • URL – Uniform Resource Locator (protocol, host, path).
  • HTTP vs. HTTPS – HTTPS adds TLS encryption for confidentiality and integrity.
  • Request/Response cycle – client sends request, server returns status code and payload.
  • Cookies – small pieces of data stored on the client for session management.
  • DNS – translates domain names to IP addresses.

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Scenario – Automated Ticket‑Machine (T·M) at a Railway Station

A self‑service ticket‑machine allows passengers to purchase tickets without staff assistance. It accepts cash, debit/credit cards and mobile payments, prints a paper ticket and updates the central reservation system instantly.

How the Automated System Works

1. User selects destination and ticket type on a touch‑screen.
2. System calculates the fare using the stored fare table:
   Fare = BaseRate + Distance × RatePerKm
3. Payment is processed via card reader, NFC or cash acceptor.
4. Ticket is printed; a confirmation message is sent to the central database.

Advantages of the Ticket‑Machine

• Speed & Efficiency – typical transaction < 2 minutes.
• 24/7 Availability – service outside normal staffing hours.
• Cost Savings – reduced need for full‑time ticket clerks.
• Accuracy – automated fare calculation eliminates human arithmetic errors.
• Data Collection – real‑time sales data support demand forecasting.
• Secure Electronic Payments – encrypted card/NFC transactions.

Disadvantages of the Ticket‑Machine

• High Initial Capital Outlay – purchase, installation and integration costs.
• Technical Failures – hardware or software faults can halt service.
• Limited Assistance – passengers with disabilities, language barriers or low digital literacy may need help.
• Security Risks – vandalism, card‑skimming, cash theft, and potential malware on the system.
• Maintenance Requirements – regular servicing, software updates and cash handling.
• Privacy Concerns – storage of payment data must comply with GDPR/PCI‑DSS.

Extended Evaluation (Emerging Upgrades)

• Biometric authentication – faster identity verification, but raises privacy and data‑protection issues.
• Cloud‑based reservation API – real‑time seat allocation across the network; dependent on reliable internet connectivity.
• Blockchain ticket ledger – immutable record of each ticket sale; illustrates transaction hashing and consensus, but adds processing overhead.

Summary Comparison – Advantages vs. Disadvantages

Aspect	Advantage	Disadvantage
Cost	Long‑term labour savings	High upfront purchase & installation expense
Availability	Operates 24 hours a day	May be offline during technical faults
User Experience	Fast, self‑service transactions	Intimidating for users with disabilities or low digital literacy
Data Management	Real‑time sales data for forecasting	Requires secure handling, backups and privacy compliance
Security	Encrypted electronic payments	Risk of vandalism, card‑skimming, cash theft, malware

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7 – 10 Algorithms, Programming & Logic (AO2)

7. Problem Analysis & Decomposition (Ticket‑Machine)

1. Collect input – destination, ticket type, payment method.
2. Validate input (e.g., check that destination exists).
3. Calculate fare.
4. Process payment (including up to three retries).
5. Generate and print ticket.
6. Update central database and log the transaction.
7. Handle errors – display appropriate messages, log for maintenance.

8. Flowchart (Transaction Sequence)

9. Pseudocode for Fare Calculation & Payment

BEGIN
    // 1. Input
    READ destination, ticketType, paymentMethod
    IF NOT VALID(destination) THEN
        DISPLAY "Invalid destination"
        STOP
    ENDIF

    // 2. Fare calculation
    distance   ← LOOKUP(DistanceTable, destination)      // km
    baseRate   ← GET_BASE_RATE(ticketType)               // $ 
    ratePerKm  ← GET_RATE_PER_KM(ticketType)             // $/km
    fare       ← baseRate + distance * ratePerKm

    DISPLAY "Your fare is: $", fare

    // 3. Payment – allow up to 3 attempts
    attempts ← 0
    REPEAT
        attempts ← attempts + 1
        paymentOK ← ProcessPayment(paymentMethod, fare)
        IF paymentOK THEN EXIT REPEAT
        DISPLAY "Payment failed – try again (", 3-attempts, " attempts left)"
    UNTIL attempts = 3

    IF NOT paymentOK THEN
        DISPLAY "Transaction cancelled"
        STOP
    ENDIF

    // 4. Ticket generation
    CALL PrintTicket(destination, ticketType, fare)

    // 5. Database update
    ticketID ← GENERATE_ID()
    CALL UpdateDatabase(ticketID, destination, ticketType, fare, CURRENT_TIME)

    DISPLAY "Thank you – have a safe journey!"
END

10. Programming Concepts Illustrated

• Variables & Data Types – distance (integer), fare (float), ticketType (string).
• Selection – IF…ELSE for input validation and payment success.
• Iteration – REPEAT…UNTIL loop for payment retries.
• Procedures/Functions – ProcessPayment(), PrintTicket(), UpdateDatabase().
• Arrays – fare tables stored as parallel arrays destinations[] and distances[].
• File I/O / Database

  INSERT INTO Tickets (TicketID, Destination, Type, Fare, SaleTime)
  VALUES (ticketID, destination, ticketType, fare, NOW());

• SQL Query Example (sales report)

  SELECT Destination,
         COUNT(*)        AS TicketsSold,
         SUM(Fare)       AS Revenue
  FROM   Tickets
  WHERE  SaleDate = '2025-12-01'
  GROUP BY Destination;
  

• Boolean Logic – Card‑Read Error Detector

  Let A = “Card inserted”, B = “Signal received”, C = “Checksum OK”.
  Error flag E is true when a card is inserted but either no signal or a bad checksum occurs:

  E = A AND ( NOT B OR NOT C )

  A	B	C	E
  1	0	1	1
  1	1	0	1
  1	1	1	0
  0	–	–	0

• Testing & Validation
  • Trace table – record variable values after each step for a sample transaction.
  • Boundary testing – distance = 0 km, maximum distance, maximum fare.
  • Debugging – use print statements or break‑points to locate logic errors.

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Cyber‑Security – Threats, Social Engineering & Counter‑measures

• Threats specific to the ticket‑machine
  • Physical tampering / vandalism.
  • Card‑skimming devices.
  • Malware injection via USB or network.
  • Social engineering – e.g., an attacker posing as a technician to gain access.
• Mapping threats to mitigations (AO3)

  Threat	Mitigation
  Vandalism	Sturdy, tamper‑proof enclosure; CCTV monitoring.
  Card‑skimming	EMI shielding, regular inspection, encrypted PIN entry.
  Malware	Whitelisted firmware, disabled USB host ports, regular security patches.
  Social engineering	Strict access control, staff training, two‑factor authentication for maintenance mode.

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Emerging Technologies – Example Extensions

• Biometric authentication – fingerprint or facial recognition to speed up payment; must store biometric templates securely (hashing, encryption).
• Cloud‑based reservation API – RESTful service returning seat availability; requires HTTPS, API keys and rate‑limiting.
• Blockchain ticket ledger
  • Each ticket sale creates a transaction: {TicketID, Destination, Fare, Timestamp}.
  • Transaction hash = SHA‑256 of the transaction data + previous block hash.
  • Immutable chain provides auditability but adds processing time and storage.

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Suggested Activities & Quick‑Check Questions

Computer‑Systems Mini‑Tasks

1. Convert 101101₂ to decimal and hexadecimal.
2. Encode the word “TRAIN” in ASCII binary.
3. Calculate the file size of a 5‑second, 44.1 kHz, 16‑bit stereo audio clip.
4. Draw a packet diagram showing header, payload, CRC trailer.
5. Label the stages of the fetch‑decode‑execute cycle on a CPU diagram.
6. Identify three network‑hardware components (NIC, router, switch) and state their primary function.
7. List three social‑engineering attacks and suggest a specific counter‑measure for each.

Algorithms & Programming Mini‑Tasks

1. Complete a trace table for the pseudocode fare‑calculation using a 15 km journey, base rate $2.00 and $0.10 /km.
2. Write a flowchart for the “Payment Validation” step, including a retry loop (max 3 attempts).
3. Create an SQL INSERT statement to record a new ticket sale.
4. Construct a truth table for the Boolean error detector and verify the expression E = A AND (¬B OR ¬C).
5. Debug a short code fragment that incorrectly adds a surcharge twice (provide corrected version).

Extension – Critical Evaluation

Discuss how adding biometric authentication or a blockchain ledger would change the list of advantages and disadvantages. Evaluate the trade‑off between increased security/traceability and potential privacy or performance impacts.

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Key Take‑aways

• Understanding number systems, data representation and storage is essential for interpreting how an automated system processes information.
• Network hardware and internet protocols enable the ticket‑machine to communicate with central systems securely (HTTPS, ARQ, checksums).
• Algorithmic thinking (decomposition, flowcharts, pseudocode) allows students to model the complete transaction cycle.
• Evaluating advantages and disadvantages requires linking technical features to real‑world impacts such as cost, accessibility, security and privacy.
• Emerging technologies (biometrics, cloud APIs, blockchain) illustrate how automation evolves and introduce new considerations for the IGCSE syllabus.