Number Systems

Binary Place Values (8-bit)

Binary to Denary

Add the place values where there is a 1. Example: 10110011 = 128 + 32 + 16 + 2 + 1 = 179

Denary to Binary

Subtract the largest place value that fits, put a 1 in that column, repeat. Example: 200 = 128+64+8 = 11001000

Binary to Hexadecimal

Split binary into groups of 4 bits (nibbles), convert each group. Example: 1010 1111 = A F = AF

Hexadecimal to Binary

Convert each hex digit to 4 binary digits. Example: 3C = 0011 1100 = 00111100

Binary Arithmetic

Binary Addition Rules

0+0=0, 0+1=1, 1+0=1, 1+1=10 (0 carry 1), 1+1+1=11 (1 carry 1)

Overflow

Overflow occurs when the result of a calculation is too large to be stored in the available number of bits. For example, adding two 8-bit numbers and getting a 9-bit result.

Two's Complement (Signed Integers)

Two's complement is a method for representing negative numbers in binary. The most significant bit (MSB) represents a negative value.

For 8-bit two's complement, the range is -128 to +127.

To convert a positive number to its negative equivalent:

1. Write the positive number in binary
2. Flip all bits (0 becomes 1, 1 becomes 0) - this is the one's complement
3. Add 1 to the result

Example: -5 in 8-bit two's complement:

 5 = 00000101
Flip: 11111010
 +1 : 11111011  = -5

Floating Point Basics

Floating point representation stores real numbers using a mantissa (the significant digits) and an exponent (the power/position of the binary point).

• Increasing the mantissa bits increases precision (more significant figures)
• Increasing the exponent bits increases range (larger/smaller numbers)

Character Sets

Why Was Unicode Needed?

• ASCII only supports 128 characters - not enough for non-Latin scripts
• The internet is global - web pages need to display Chinese, Arabic, Cyrillic, etc.
• Different countries had different extended character sets, causing compatibility issues
• Unicode provides a single universal standard for all languages

Bitmap Images

A bitmap image is made up of a grid of tiny dots called pixels. Each pixel is stored as a binary value representing its colour.

Key Terms

Calculating Bitmap File Size

File size (bits) = width (pixels) x height (pixels) x colour depth (bits)

Example: An image is 800 x 600 pixels with 24-bit colour depth.

File size = 800 x 600 x 24 = 11,520,000 bits
         = 11,520,000 / 8 = 1,440,000 bytes
         = 1,440,000 / 1024 = 1,406.25 KB
         = 1,406.25 / 1024 = 1.37 MB (approx)

Colour Depth Examples

How Sound Is Digitised

Sound is an analogue waveform. To store it digitally, an analogue-to-digital converter (ADC) takes regular samples of the sound wave's amplitude.

Calculating Sound File Size

File size (bits) = sample rate x bit depth x duration (seconds) x number of channels

Example: 30 seconds of stereo audio, 44,100 Hz sample rate, 16-bit depth:

File size = 44,100 x 16 x 30 x 2 = 42,336,000 bits
         = 42,336,000 / 8 = 5,292,000 bytes
         = 5,292,000 / 1,024 = 5,168 KB
         = 5,168 / 1,024 = 5.05 MB

Why Compress Files?

• Reduce file size for faster transmission over networks
• Reduce storage space required
• Faster download/upload times
• More files can fit on storage media

Two Types of Compression

Run-Length Encoding (RLE)

Run-Length Encoding is a simple lossless compression technique. It replaces consecutive repeated values with a count and the value.

Example: AAAAABBBCCDDDDDD becomes 5A3B2C6D

RLE works well for images with large areas of the same colour but poorly for photographs with many colour variations.

Von Neumann Architecture

The Von Neumann architecture is the design used by most modern computers. Its key principle is that both program instructions and data are stored together in main memory and share the same bus.

CPU Components

Registers

Buses

The Three Stages

FETCH

1. The address in the PC is copied to the MAR
2. The address is sent along the address bus to main memory
3. The instruction stored at that address is transferred along the data bus to the MDR
4. The instruction in the MDR is copied to the CIR
5. The PC is incremented by 1 (to point to the next instruction)

DECODE

1. The Control Unit decodes the instruction in the CIR
2. It determines the type of instruction (arithmetic, data movement, branch, etc.) and what operands are needed

EXECUTE

1. The instruction is carried out. This might involve:
   • The ALU performing a calculation
   • Data being read from or written to memory
   • The PC being changed (for a branch/jump instruction)

This cycle repeats continuously while the computer is running.

RAM vs ROM

Cache Memory

Cache is a small amount of very fast, expensive memory located on or near the CPU. It stores frequently and recently used instructions and data so the CPU can access them faster than from RAM.

• L1 cache: smallest, fastest, located on the CPU core itself
• L2 cache: larger, slightly slower, may be shared between cores
• L3 cache: largest, slowest of the caches, shared between all cores

Virtual Memory

Virtual memory is a section of the hard drive that is used as if it were extra RAM when physical RAM is full. The OS swaps data between RAM and virtual memory as needed.

• Advantage: Allows programs to run even when RAM is insufficient
• Disadvantage: Much slower than RAM (HDD/SSD access speeds); excessive swapping causes disk thrashing, which dramatically slows the computer

Categories of Software

System Software

System software manages the computer's hardware and provides a platform for application software to run. Includes the operating system and utility programs.

Application Software

General purpose advantages: cheaper, available immediately, well tested, online support and tutorials

Bespoke advantages: tailored to exact needs, no unnecessary features, can provide competitive advantage

Bespoke disadvantages: expensive, takes time to develop, may contain bugs initially

Utility Software

Utility software performs specific maintenance or housekeeping tasks:

Functions of an Operating System

An operating system (OS) is system software that manages computer hardware and provides services for application software. Key functions:

• Processor management - scheduling processes, managing multitasking, allocating CPU time
• Memory management - allocating RAM to programs, managing virtual memory, preventing programs accessing each other's memory space
• File management - organising files in directories/folders, managing read/write permissions, handling file naming
• I/O management - managing communication with peripheral devices through device drivers, handling print queues
• User interface - providing the GUI/CLI for users to interact with the system
• Security - user authentication (usernames/passwords), access control, encryption
• Error handling - detecting and responding to hardware/software errors

A device driver is a small piece of system software that acts as a translator between the operating system and a hardware device.

• Each hardware device needs its own specific driver
• The driver converts the OS's general I/O commands into device-specific instructions
• Without the correct driver, the OS cannot communicate with the device
• Drivers must be updated to fix bugs, improve performance, or add features
• Different operating systems require different drivers for the same device

An embedded system is a computer system designed to perform a dedicated function within a larger device. It has a specific, fixed purpose and is usually programmed once.

Characteristics

• Dedicated to one specific task
• Built into the device it controls
• Usually runs a simple, specialised OS (or no OS)
• Low power consumption
• Often operates in real-time
• Difficult/impossible for end user to reprogram

Examples

Washing machine controller, car engine management system, traffic lights, smart thermostat, microwave oven, digital watch, fitness tracker, anti-lock braking system (ABS).

Serial vs Parallel

Direction of Transmission

Key Terminology

Entity Relationships

Resolving M:M relationships: A many-to-many relationship cannot be directly implemented in a relational database. It must be broken down into two one-to-many relationships using a junction table (link entity) containing the foreign keys from both tables.

Why Normalise?

Normalisation is the process of organising a database to reduce data redundancy and eliminate anomalies. Without normalisation, you get:

• Insertion anomaly - cannot add data without other unrelated data (e.g. cannot add a new course without a student enrolled)
• Deletion anomaly - deleting a record unintentionally removes other useful data
• Update anomaly - changing data in one place but not another leads to inconsistency

The Normal Forms

UNF (Unnormalised Form)

Data as it comes - may contain repeating groups and redundant data. A flat table with no structure.

1NF (First Normal Form)

Rules:

• No repeating groups - each cell contains a single atomic value
• Each record is unique (has a primary key)
• All entries in a column are of the same data type

2NF (Second Normal Form)

Rules (must already be in 1NF plus):

• No partial dependencies - every non-key field must depend on the WHOLE primary key, not just part of it
• Only an issue when you have a composite key. If the primary key is a single field, a table in 1NF is automatically in 2NF.

3NF (Third Normal Form)

Rules (must already be in 2NF plus):

• No transitive dependencies - no non-key field should depend on another non-key field
• Every non-key field must depend on the primary key, the whole key, and nothing but the key

Worked Example

UNF - Student Orders table:

Problem: Items field contains repeating groups.

1NF - Remove repeating groups:

Composite key: (OrderID, ItemName). Student depends only on OrderID (partial dependency).

2NF - Remove partial dependencies:

Orders table: OrderID, Student

OrderItems table: OrderID*, ItemName*

Data Manipulation Language (DML)

SELECT - Retrieving Data

SELECT FirstName, Surname, Mark
FROM Students
WHERE Mark >= 50
ORDER BY Mark DESC;

Use * to select all fields: SELECT * FROM Students;

INSERT - Adding Records

INSERT INTO Students (StudentID, FirstName, Surname, Mark)
VALUES (101, 'Jackie', 'Hughes', 95);

UPDATE - Modifying Records

UPDATE Students
SET Mark = 85
WHERE StudentID = 101;

DELETE - Removing Records

DELETE FROM Students
WHERE Mark < 30;

WHERE Clause Operators

Wildcards

% matches any number of characters. _ matches exactly one character.

• LIKE 'B%' - starts with B
• LIKE '%son' - ends with son
• LIKE '%an%' - contains an
• LIKE '_a%' - second character is a

Aggregate Functions

JOIN (Combining Tables)

An INNER JOIN combines rows from two tables where there is a matching value in both:

SELECT Students.FirstName, Courses.CourseName
FROM Students
INNER JOIN Enrolments ON Students.StudentID = Enrolments.StudentID
INNER JOIN Courses ON Enrolments.CourseID = Courses.CourseID
WHERE Courses.CourseName = 'Digital Technology';

Types of Network

Network Topologies

Client-Server vs Peer-to-Peer

What Is a Protocol?

A protocol is a set of rules that govern how data is transmitted and received across a network. Protocols ensure devices from different manufacturers can communicate.

Common Protocols

TCP/IP Layers Model

Addressing

DNS (Domain Name System)

DNS translates human-readable domain names (e.g. www.google.com) into IP addresses (e.g. 142.250.187.46) that computers use to locate servers.

1. User types a URL into the browser
2. Browser checks its local cache for the IP address
3. If not found, a request is sent to the DNS server
4. DNS server looks up the domain name and returns the corresponding IP address
5. Browser uses the IP address to connect to the web server

Packet Switching

Data is broken into small packets before being sent across a network. Each packet contains:

• Header: source IP address, destination IP address, packet sequence number, total number of packets
• Payload: the actual data being transmitted
• Trailer: error checking data (e.g. checksum)

Packets may take different routes across the network and arrive out of order. The receiving device uses the sequence numbers to reassemble them in the correct order. Missing or corrupt packets are re-requested.

Important distinction: These methods detect errors but do not correct them. If an error is detected, the data must be retransmitted.

Service Models

Advantages of Cloud Computing

• Access files from anywhere with an internet connection
• No need to buy/maintain expensive hardware
• Scalable - can increase/decrease resources as needed
• Automatic backups and updates managed by provider
• Enables easy collaboration on shared documents

Disadvantages of Cloud Computing

• Requires a reliable internet connection
• Data stored on third-party servers - security and privacy concerns
• Ongoing subscription costs can add up
• Less control over data - dependent on the provider
• Potential data sovereignty issues (data stored in another country)

Deployment Models

Service Models Compared

Virtualisation

Virtualisation creates virtual versions of physical resources (servers, storage, networks), allowing multiple virtual machines to run on a single physical server.

Hypervisors

A hypervisor is software that creates and manages virtual machines:

Virtual Machines vs Containers

Scalability and Elasticity

Cloud Security Considerations

• Data sovereignty - data may be stored in a different country, subject to that country's laws
• Shared responsibility model - the provider secures the infrastructure; the customer secures their data and applications
• Encryption - data should be encrypted both at rest and in transit
• Multi-tenancy risks - multiple customers share the same physical infrastructure
• Compliance - organisations must ensure cloud providers meet regulatory requirements (e.g. GDPR)
• Vendor lock-in - difficulty migrating data/applications between providers

Expert Systems

An expert system is a computer program that mimics the decision-making of a human expert in a specific domain. It has three components:

Examples: medical diagnosis, fault diagnosis in machinery, financial advice, mineral prospecting.

Turing Test

The Turing Test, proposed by Alan Turing in 1950, tests whether a machine can exhibit intelligent behaviour indistinguishable from a human. A human interrogator communicates with both a machine and a human via text. If the interrogator cannot reliably tell which is the machine, the machine is said to have passed the test.

Machine Learning

Machine learning is a branch of AI where systems learn from data to improve performance without being explicitly programmed. The system identifies patterns in training data and uses them to make predictions or decisions on new data.

• Supervised learning: trained on labelled data (input-output pairs)
• Unsupervised learning: finds patterns in unlabelled data (clustering, associations)

Neural Networks

Artificial neural networks are computing systems inspired by the structure of the human brain. They consist of layers of interconnected nodes (artificial neurons). Data passes through input, hidden, and output layers. Used for image recognition, speech recognition, and natural language processing.

Natural Language Processing (NLP)

NLP enables computers to understand, interpret, and generate human language. Applications include: chatbots, voice assistants (Siri, Alexa), translation services, sentiment analysis.

Encryption

Encryption converts plaintext data into unreadable ciphertext using an algorithm and a key. Only someone with the correct key can decrypt it.

Other Security Measures

Malware Types in Detail

Social Engineering Attacks

Social engineering exploits human psychology rather than technical vulnerabilities to gain access to systems or data.

Advanced Attack Types

DDoS Attack (Distributed Denial of Service)

A DDoS attack uses a botnet (network of compromised computers) to flood a target server with traffic, overwhelming it and making it unavailable. The attack is "distributed" because it comes from thousands of sources simultaneously, making it hard to block.

• Volumetric attacks: flood bandwidth (e.g. UDP flood)
• Protocol attacks: exploit weaknesses in network protocols (e.g. SYN flood)
• Application layer attacks: target specific services (e.g. HTTP flood)

SQL Injection

SQL injection exploits web forms that pass user input directly into SQL queries without sanitisation:

-- Vulnerable query:
SELECT * FROM users WHERE username = ' + input + ';

-- Attacker enters:  ' OR 1=1 --
-- Resulting query:
SELECT * FROM users WHERE username = '' OR 1=1 --'
-- This returns ALL rows because 1=1 is always true

-- Prevention: use parameterised queries
SELECT * FROM users WHERE username = ?;

Cross-Site Scripting (XSS)

XSS occurs when an attacker injects malicious JavaScript into a web page viewed by other users. The script runs in the victim's browser with the same permissions as the legitimate site.

• Stored XSS: malicious script is permanently stored on the server (e.g. in a forum post)
• Reflected XSS: script is embedded in a URL and reflected back by the server

Prevention: input validation, output encoding, Content Security Policy (CSP) headers.

Man-in-the-Middle (MitM) Attack

The attacker secretly intercepts and potentially alters communication between two parties who believe they are communicating directly. Common on unsecured Wi-Fi networks.

Prevention: HTTPS/TLS encryption, certificate pinning, VPN usage, avoiding public Wi-Fi for sensitive transactions.

Zero-Day Exploit

A zero-day exploit targets a software vulnerability that is unknown to the vendor - meaning there are "zero days" of available patches. These are extremely dangerous because no fix exists when the attack occurs.

Mitigation: defence in depth, intrusion detection systems, regular software updates, threat intelligence sharing.

Brute Force Attacks

Systematically trying every possible password combination. Variants include:

• Dictionary attack: tries common words and known passwords from leaked databases
• Credential stuffing: uses username/password pairs stolen from one site on other sites
• Rainbow table attack: uses pre-computed hash values to reverse-engineer passwords

Prevention: account lockout, CAPTCHA, rate limiting, strong password policies, salted hashing, multi-factor authentication.

Firewalls

A firewall monitors and controls network traffic based on predefined security rules. There are several types:

Intrusion Detection and Prevention Systems

VPNs (Virtual Private Networks)

A VPN creates an encrypted tunnel between a device and a remote server, protecting data from interception on untrusted networks.

How VPNs Work

1. The VPN client on the user's device encrypts all outgoing data
2. Encrypted data travels through a tunnel to the VPN server
3. The VPN server decrypts the data and forwards it to the destination
4. Responses return through the same encrypted tunnel

Tunnelling Protocols

Penetration Testing

Penetration testing (pen testing) is the authorised simulation of a cyber attack to identify vulnerabilities before real attackers exploit them. Stages include: reconnaissance, scanning, exploitation, reporting.

Hacking Classifications

Security Policies

A security policy is a formal document that defines an organisation's rules and procedures for protecting its information systems. It should cover:

• Acceptable use of IT resources
• Password requirements (length, complexity, change frequency)
• Data classification and handling procedures
• Incident response procedures
• Backup and disaster recovery plans
• Employee training requirements

Access Control Models

Biometric Authentication

Biometric authentication uses unique physical or behavioural characteristics to verify identity:

• Physiological: fingerprint, facial recognition, iris/retina scan, hand geometry
• Behavioural: voice recognition, typing rhythm, gait analysis

Data Protection Act 2018 / UK GDPR

Controls how personal data is collected, stored, used, and shared by organisations.

The 7 Principles

1. Lawfulness, fairness and transparency - data processed lawfully, fairly, and transparently
2. Purpose limitation - collected for specified, explicit purposes only
3. Data minimisation - only collect data that is necessary
4. Accuracy - data must be accurate and kept up to date
5. Storage limitation - not kept longer than necessary
6. Integrity and confidentiality - processed securely, protected against unauthorised access
7. Accountability - the data controller must demonstrate compliance

Individual Rights

• Right to be informed about data collection
• Right of access (Subject Access Request)
• Right to rectification (correction of inaccurate data)
• Right to erasure ("right to be forgotten")
• Right to restrict processing
• Right to data portability
• Right to object to processing

Computer Misuse Act 1990

Three offences:

1. Unauthorised access to computer material - e.g. guessing someone's password to access their account. Up to 2 years in prison.
2. Unauthorised access with intent to commit further offences - e.g. hacking into a bank to commit fraud. Up to 5 years in prison.
3. Unauthorised modification of computer material - e.g. spreading a virus, deleting files, modifying data. Up to 10 years in prison.

Copyright, Designs and Patents Act 1988

Protects the intellectual property of creators. It is illegal to copy, distribute, or modify software, music, images, or other creative works without permission. This covers:

• Software piracy (copying/distributing software illegally)
• Downloading music/films without paying
• Copying code from the internet without permission

Freedom of Information Act 2000

Gives the public the right to request information held by public authorities (e.g. government, councils, NHS, schools). The authority must respond within 20 working days. Some exemptions apply (national security, personal data, commercial interests).

Key Issues in Digital Technology

Digital Divide

The digital divide is the gap between those who have access to digital technology and the internet, and those who do not. Factors include age, income, location (rural vs urban), education, and disability. This creates inequality in education, employment, and access to services.

Algorithmic Bias

Algorithmic bias occurs when AI/machine learning systems produce discriminatory results because the training data reflects existing prejudices (e.g. facial recognition systems being less accurate for certain ethnicities, recruitment algorithms favouring certain demographics).

Privacy and Surveillance

Issues include: mass data collection by companies and governments, CCTV monitoring, tracking through smartphones/apps, cookies tracking browsing habits, social media companies selling user data to advertisers.

Automation and Job Loss

Automation and AI are replacing jobs in manufacturing, retail (self-checkout), transport (autonomous vehicles), customer service (chatbots). However, technology also creates new job roles (data scientists, cybersecurity analysts).

Environmental Impact

• E-waste from discarded electronics contains toxic materials
• Data centres consume massive amounts of energy
• Mining rare earth metals for components causes environmental damage
• Positive: technology enables remote working (reducing travel), smart energy management, climate modelling

Surveillance and Privacy

The tension between security and privacy is a central ethical debate in digital technology:

• State surveillance: government monitoring of communications (e.g. GCHQ, NSA programmes revealed by Edward Snowden) - justified as national security but criticised as mass intrusion
• Corporate surveillance: tech companies tracking user behaviour for targeted advertising; data brokers selling personal profiles
• Workplace monitoring: employee email monitoring, keystroke logging, screen recording - legal if disclosed, but raises trust issues
• Facial recognition: used in policing and security but concerns about accuracy, racial bias, and chilling effect on protest/free expression

Autonomous Systems Ethics

Autonomous systems (self-driving cars, drones, robotic surgery) raise unique ethical questions:

• The trolley problem: how should a self-driving car choose between two harmful outcomes? Who programs these decisions?
• Accountability: if an autonomous vehicle causes an accident, who is responsible - the manufacturer, the programmer, or the owner?
• Autonomous weapons: should machines be allowed to make life-or-death decisions without human oversight?
• Job displacement: autonomous lorries, delivery drones, and warehouse robots replacing human workers

Social Media Impact

• Echo chambers: algorithms show users content that reinforces their existing views, reducing exposure to diverse perspectives
• Misinformation: fake news spreads faster than corrections; deepfakes make it harder to distinguish real from fabricated content
• Cyberbullying: anonymity and reach of social media amplify harassment
• Mental health: studies link excessive social media use to anxiety, depression, and body image issues, particularly in young people
• Positive uses: connecting communities, raising awareness of social issues, enabling democratic movements

Digital Wellbeing

Digital wellbeing refers to maintaining a healthy relationship with technology:

• Screen time management and digital detoxes
• Notification management to reduce distraction
• Awareness of addictive design patterns (infinite scrolling, autoplay, gamification)
• Healthy ergonomics - posture, eye strain (20-20-20 rule), repetitive strain injury

Environmental Impact of Technology

Mitigation: Right to repair legislation, circular economy principles, energy-efficient hardware, carbon-neutral data centres, responsible recycling programmes.

Accessibility and Inclusive Design

Inclusive design ensures technology is usable by people with the widest range of abilities and situations:

• Screen readers: software that reads page content aloud for visually impaired users - requires proper semantic HTML and alt text
• Colour contrast: sufficient contrast between text and background for low-vision users (WCAG 2.1 guidelines)
• Keyboard navigation: all functionality accessible without a mouse
• Captions and transcripts: for audio/video content (benefits deaf users and non-native speakers)
• WCAG (Web Content Accessibility Guidelines): international standard - four principles: Perceivable, Operable, Understandable, Robust

Net Neutrality

Net neutrality is the principle that Internet Service Providers (ISPs) must treat all internet traffic equally, without blocking, throttling, or prioritising specific content or services.

RFID (Radio Frequency Identification)

RFID uses radio waves to automatically identify and track tags attached to objects. Components: RFID tag (chip + antenna), RFID reader, database.

• Passive tags: powered by the reader's signal, short range, cheap (e.g. contactless bank cards, inventory tags)
• Active tags: have their own battery, longer range, more expensive (e.g. vehicle tracking, livestock)

Uses: access control, inventory management, contactless payment, passport chips, library books, luggage tracking.

Internet of Things (IoT)

The IoT is a network of physical devices embedded with sensors, software, and connectivity that enables them to collect and exchange data over the internet.

Examples: smart thermostats, fitness trackers, smart fridges, connected security cameras, industrial sensors, smart meters.

Data Mining

Data mining is the process of analysing large datasets to discover patterns, correlations, and trends. Used by businesses for customer profiling, market basket analysis (identifying products frequently bought together), fraud detection, and targeted advertising.

Big Data

Big data refers to datasets that are too large and complex for traditional data processing tools. Characterised by the three Vs:

• Volume: enormous amounts of data (terabytes/petabytes)
• Velocity: data generated and processed at high speed (real-time)
• Variety: many different types and formats (text, images, video, sensor data)

Applications: healthcare (predicting outbreaks), retail (personalised recommendations), social media analysis, traffic management, weather forecasting.

Mobile App Development Approaches

Cross-platform frameworks: React Native, Flutter, Xamarin - allow developers to write once and deploy to both iOS and Android.

Mobile Operating Systems

IoT Architecture

The Internet of Things (IoT) follows a layered architecture:

IoT Protocols

Smart Applications

Smart Home

• Smart thermostats (e.g. Nest) learn usage patterns and optimise heating
• Smart lighting - controlled remotely, automated schedules, energy saving
• Smart security - cameras, doorbells, locks with remote monitoring and alerts
• Voice assistants (Alexa, Google Home) as central control hubs

Smart City

• Intelligent traffic management - adaptive traffic lights based on real-time flow
• Smart waste management - sensors in bins alert when collection is needed
• Environmental monitoring - air quality, noise levels, flooding sensors
• Smart street lighting - dims when no pedestrians/vehicles detected
• Connected public transport - real-time tracking, passenger counting

IoT Security Challenges

• Large attack surface: every connected device is a potential entry point for attackers
• Weak default credentials: many devices ship with default passwords that users never change
• Limited processing power: constrained devices may not support strong encryption or regular updates
• Firmware updates: many IoT devices lack automatic update mechanisms, leaving known vulnerabilities unpatched
• Data privacy: continuous data collection by sensors raises surveillance concerns
• Botnet recruitment: compromised IoT devices can be recruited into botnets for DDoS attacks (e.g. Mirai botnet)
• Interoperability: lack of security standards across different manufacturers and protocols

Wearable Technology

Wearable devices are IoT devices worn on the body that collect and transmit data:

What is a Trace Table?

A trace table is used to track the values of variables as a program executes, line by line. This technique is essential for dry running an algorithm - manually working through code without a computer to verify it produces the correct output.

Technique: Read each line of code in order, update the relevant variable values in the table, and move to the next line. For loops, repeat the body until the condition fails. Record the value of every variable after each statement that changes it.

Exercise 1: Simple Loop - Accumulating a Sum

Trace the following pseudocode:

SET sum TO 0
FOR i FROM 1 TO 5
    SET sum TO sum + i
END FOR
OUTPUT sum

Copy this empty trace table and fill in the values:

Exercise 2: While Loop - Fibonacci Sequence

Trace the following pseudocode that generates Fibonacci numbers below 20:

SET a TO 0
SET b TO 1
WHILE b < 20
    OUTPUT b
    SET temp TO b
    SET b TO a + b
    SET a TO temp
END WHILE

Exercise 3: Nested Loop Trace

Trace the following pseudocode with nested loops:

SET count TO 0
FOR i FROM 1 TO 3
    FOR j FROM 1 TO 2
        SET count TO count + 1
        OUTPUT i, j
    END FOR
END FOR
OUTPUT count

Approaching Algorithm Design

When asked to design an algorithm, follow these steps:

1. Understand the problem - What are the inputs? What output is expected?
2. Identify the steps - Break the problem into smaller sub-tasks
3. Choose a structure - Decide on sequence, selection, or iteration
4. Write pseudocode - Use clear, structured language
5. Dry run / test - Trace through with sample data to verify correctness

OUTPUT "How many numbers?"
INPUT n
OUTPUT "Enter number 1:"
INPUT largest

FOR i FROM 2 TO n
    OUTPUT "Enter number ", i, ":"
    INPUT num
    IF num > largest THEN
        SET largest TO num
    END IF
END FOR

OUTPUT "The largest number is: ", largest

OUTPUT "Enter a word:"
INPUT word
SET isPalindrome TO TRUE
SET length TO LENGTH(word)

FOR i FROM 0 TO (length DIV 2) - 1
    IF word[i] <> word[length - 1 - i] THEN
        SET isPalindrome TO FALSE
    END IF
END FOR

IF isPalindrome = TRUE THEN
    OUTPUT word, " is a palindrome"
ELSE
    OUTPUT word, " is not a palindrome"
END IF

CONSTANT CORRECT_USER = "admin"
CONSTANT CORRECT_PASS = "secret123"
SET attempts TO 0
SET loggedIn TO FALSE

WHILE attempts < 3 AND loggedIn = FALSE
    OUTPUT "Enter username:"
    INPUT username
    OUTPUT "Enter password:"
    INPUT password
    SET attempts TO attempts + 1

    IF username = CORRECT_USER AND password = CORRECT_PASS THEN
        SET loggedIn TO TRUE
        OUTPUT "Login successful. Welcome!"
    ELSE
        OUTPUT "Incorrect. Attempts remaining: ", 3 - attempts
    END IF
END WHILE

IF loggedIn = FALSE THEN
    OUTPUT "Account locked. Contact administrator."
END IF

OUTPUT "How many numbers?"
INPUT n

IF n <= 0 THEN
    OUTPUT "Error: must enter at least 1 number"
ELSE
    SET total TO 0
    FOR i FROM 1 TO n
        OUTPUT "Enter number ", i, ":"
        INPUT num
        SET total TO total + num
    END FOR
    SET average TO total / n
    OUTPUT "The average is: ", average
END IF

OUTPUT "Enter a sentence:"
INPUT sentence
SET vowelCount TO 0
SET vowels TO "aeiou"

FOR i FROM 0 TO LENGTH(sentence) - 1
    SET ch TO LOWER(sentence[i])
    IF ch IN vowels THEN
        SET vowelCount TO vowelCount + 1
    END IF
END FOR

OUTPUT "Number of vowels: ", vowelCount

What is Algorithmic Complexity?

Algorithmic complexity measures how the time or space requirements of an algorithm grow as the input size increases. It answers the question: "If I double the input, how much longer will it take?"

We use Big-O notation to classify algorithms by their worst-case performance. Big-O describes the upper bound of growth - the maximum number of operations as input size n increases.

Time Complexity Classes

Visualising Growth

Consider how these complexities compare for an input of n = 1000:

How to Identify Complexity from Code

Rule 1: No loops = O(1)

SET result TO array[5]   // O(1) - direct access, no looping

Rule 2: Single loop over n items = O(n)

FOR i FROM 0 TO n - 1   // O(n) - one pass through all items
    OUTPUT array[i]
END FOR

Rule 3: Nested loops over n = O(n²)

FOR i FROM 0 TO n - 1       // Outer: n times
    FOR j FROM 0 TO n - 1   // Inner: n times each
        OUTPUT i, j          // Total: n x n = O(n²)
    END FOR
END FOR

Rule 4: Halving the problem each step = O(log n)

WHILE n > 1            // O(log n) - divides by 2 each iteration
    SET n TO n DIV 2
END WHILE

Rule 5: Recursive calls that double = O(2ⁿ)

FUNCTION fib(n)           // O(2ⁿ) - two recursive calls per level
    IF n <= 1 THEN RETURN n
    RETURN fib(n-1) + fib(n-2)
END FUNCTION

Why Does Complexity Matter?

Choosing the right algorithm is critical for large datasets. Consider searching a sorted list of 1 million items:

• Linear search O(n): up to 1,000,000 comparisons
• Binary search O(log n): at most 20 comparisons

For small datasets the difference is negligible, but at scale, an inefficient algorithm can take hours where an efficient one takes milliseconds. This is why understanding complexity helps you select the most appropriate algorithm for a given problem.

Complexity Exercises

SET found TO FALSE
FOR i FROM 0 TO n - 1
    IF array[i] = target THEN
        SET found TO TRUE
    END IF
END FOR

FOR i FROM 0 TO n - 1
    FOR j FROM i + 1 TO n - 1
        IF array[j] < array[i] THEN
            // swap array[i] and array[j]
            SET temp TO array[i]
            SET array[i] TO array[j]
            SET array[j] TO temp
        END IF
    END FOR
END FOR

SET low TO 0
SET high TO n - 1
WHILE low <= high
    SET mid TO (low + high) DIV 2
    IF array[mid] = target THEN
        OUTPUT "Found at index ", mid
        SET low TO high + 1   // exit loop
    ELSE IF array[mid] < target THEN
        SET low TO mid + 1
    ELSE
        SET high TO mid - 1
    END IF
END WHILE

Stacks (LIFO - Last In, First Out)

A stack is a linear data structure where elements are added and removed from the same end (the "top"). Think of a stack of plates - you always take from the top.

Key Operations

Pseudocode Implementation (Array-based)

// Initialise stack
SET stack TO ARRAY[0..MAX_SIZE]
SET top TO -1   // -1 means empty

FUNCTION push(item)
    IF top = MAX_SIZE - 1 THEN
        OUTPUT "Stack overflow"
    ELSE
        SET top TO top + 1
        SET stack[top] TO item
    END IF
END FUNCTION

FUNCTION pop()
    IF top = -1 THEN
        OUTPUT "Stack underflow"
    ELSE
        SET item TO stack[top]
        SET top TO top - 1
        RETURN item
    END IF
END FUNCTION

Applications of Stacks

• Undo functionality - each action is pushed; undo pops the most recent
• Browser back button - visited pages are pushed onto a stack
• Expression evaluation - converting infix to postfix notation
• Function call stack - the runtime stack tracks active function calls and local variables
• Bracket matching - compilers use stacks to check balanced parentheses

Queues (FIFO - First In, First Out)

A queue is a linear data structure where items are added at the rear and removed from the front. Think of a queue of people waiting - the first person in line is served first.

Key Operations

Pseudocode Implementation (Circular Array)

// Initialise circular queue
SET queue TO ARRAY[0..MAX_SIZE]
SET front TO 0
SET rear TO -1
SET size TO 0

FUNCTION enqueue(item)
    IF size = MAX_SIZE THEN
        OUTPUT "Queue full"
    ELSE
        SET rear TO (rear + 1) MOD MAX_SIZE
        SET queue[rear] TO item
        SET size TO size + 1
    END IF
END FUNCTION

FUNCTION dequeue()
    IF size = 0 THEN
        OUTPUT "Queue empty"
    ELSE
        SET item TO queue[front]
        SET front TO (front + 1) MOD MAX_SIZE
        SET size TO size - 1
        RETURN item
    END IF
END FUNCTION

Priority Queues

A priority queue is a variant where each item has a priority value. Items with higher priority are dequeued before items with lower priority, regardless of insertion order. Used in:

• Operating systems - process scheduling (high-priority tasks run first)
• Network routers - urgent packets processed before normal traffic
• A* pathfinding - nodes with lowest cost explored first
• Hospital triage - most critical patients seen first

Applications of Queues

• Print queue - documents printed in the order they were submitted
• CPU task scheduling - processes handled in order of arrival
• Breadth-first search - graph traversal algorithm uses a queue
• Message buffers - handling requests in order on a server

Linked Lists

A linked list is a dynamic data structure made up of nodes. Each node contains two parts: the data and a pointer (reference) to the next node. The last node points to NULL.

Structure of a Node

// Each node contains:
RECORD Node
    data : DATA_TYPE
    next : POINTER TO Node   // NULL if last node
END RECORD

// The list is accessed via a head pointer
SET head TO NULL   // empty list

Advantages over Arrays

Types of Linked List

• Singly linked - each node points to the next only (can only traverse forward)
• Doubly linked - each node points to both next AND previous (can traverse both directions)
• Circular linked - the last node points back to the first instead of NULL

Binary Trees

A binary tree is a hierarchical data structure where each node has at most two children: a left child and a right child. The topmost node is called the root.

A binary search tree (BST) is a binary tree with an ordering property: for every node, all values in the left subtree are smaller, and all values in the right subtree are larger.

Key Terminology

Tree Traversals

There are three standard ways to visit every node in a binary tree. For a tree with root value 8, left subtree rooted at 3, right subtree rooted at 10:

Traversal Pseudocode

FUNCTION inOrder(node)
    IF node <> NULL THEN
        inOrder(node.left)
        OUTPUT node.data      // visit BETWEEN children
        inOrder(node.right)
    END IF
END FUNCTION

FUNCTION preOrder(node)
    IF node <> NULL THEN
        OUTPUT node.data      // visit BEFORE children
        preOrder(node.left)
        preOrder(node.right)
    END IF
END FUNCTION

FUNCTION postOrder(node)
    IF node <> NULL THEN
        postOrder(node.left)
        postOrder(node.right)
        OUTPUT node.data      // visit AFTER children
    END IF
END FUNCTION

Choosing the Right Data Structure