Cambridge Syllabus Notes

13. New and Emerging Technologies – Overview

13.1 Objective

Describe a range of new and emerging technologies, explain how they create value in different sectors, and evaluate their advantages, disadvantages and wider impacts on individuals, organisations, medicine and the environment.

13.2 Key Concepts from the Syllabus

Hardware and Software: Sensors, actuators, micro‑controllers, processors together with the programmes that control them.
Networks & the Internet: Wired, wireless (Wi‑Fi, Bluetooth, LoRaWAN, 5G) and cloud/edge infrastructures that enable data exchange.
System Life‑Cycle: Requirements → design → development → testing → deployment → operation → maintenance → disposal.
New / Emerging Technologies: Commercially viable within the last 5‑10 years and transforming how data are captured, processed or delivered.

13.3 Types of New / Emerging Technologies

Technology	Core Principle	Typical Applications
Internet of Things (IoT)	Physical objects equipped with sensors/actuators, connectivity and cloud/edge analytics.	Smart homes, industrial monitoring, precision agriculture, health wearables, smart cities.
Artificial Intelligence (AI) & Machine Learning (ML)	Algorithms that learn patterns from data and make predictions or decisions.	Chatbots, fraud detection, autonomous vehicles, predictive maintenance.
Edge AI & TinyML	Machine‑learning inference performed on ultra‑low‑power micro‑controllers at the network edge.	Real‑time defect detection on production lines, on‑device voice assistants, energy‑aware sensor analytics.
Augmented Reality (AR) & Virtual Reality (VR)	Computer‑generated visual/audio overlays (AR) or fully immersive simulated environments (VR).	Training simulators, design visualisation, retail “try‑on”, remote assistance.
Robotics & Autonomous Transport	Programmable machines that sense, plan and act without continuous human control.	Industrial robots – assembly, welding, palletising Service robots – cleaning, delivery, hospitality Autonomous transport – drones, self‑driving cars, AGVs in warehouses
Computer‑Assisted Translation (CAT)	Software that combines linguistic databases, statistical or neural models to aid human translators.	Real‑time multilingual chat, localisation of software, content localisation for global markets.
Vision‑Enhancement & Wearable Computing	Devices that augment human perception or provide on‑body computing power.	Smart glasses, heads‑up displays, health‑monitoring wearables, biometric rings.
Blockchain & Distributed Ledger Technology	Decentralised, tamper‑evident record‑keeping using cryptographic hashing and consensus.	Supply‑chain provenance, secure IoT data sharing, smart contracts.
3‑D Printing (Additive Manufacturing)	Layer‑by‑layer deposition of material guided by digital models.	Prosthetic limbs, spare parts on‑demand, rapid prototyping, aerospace components.
Holographic Imaging & Storage	Recording and reconstruction of light fields (imaging) or interference patterns (storage) to create three‑dimensional images or ultra‑high‑density data.	Medical imaging (e.g., holographic CT), cultural‑heritage visualisation, next‑generation archival storage (terabytes per cubic centimetre).
Molecular Data Storage	Encoding binary information in synthetic DNA or other molecular structures.	Ultra‑long‑term archival storage, high‑density data centres, space‑limited data vaults.

13.4 Advantages and Disadvantages (Pros & Cons)

Technology	Advantages	Disadvantages / Risks
IoT	Real‑time monitoring and control Resource optimisation (energy, water, material) Predictive maintenance reduces downtime Enables “product‑as‑a‑service” business models	Security and privacy vulnerabilities Interoperability problems between proprietary protocols Massive data volumes → storage & processing costs Dependence on network availability
AI & ML	Automation of repetitive tasks Data‑driven decision making Personalisation at scale	Bias in training data → unfair outcomes Opacity (“black‑box”) reduces explainability Potential job displacement
Edge AI & TinyML	Low latency – decisions made locally Reduced bandwidth and cloud‑processing costs Enhanced privacy (data stays on device)	Limited model size due to tiny memory footprints Complexity of on‑device optimisation
AR / VR	Immersive training reduces risk and cost Enhanced visualisation for design and planning	Motion sickness or eye strain for some users High hardware cost and content‑creation effort
Robotics & Autonomous Transport	Higher productivity & 24/7 operation Reduced workplace injuries Precision and repeatability	Complex safety certification and regulation Significant upfront capital investment Potential loss of manual skills
CAT	Speeds up translation workflow Improves consistency across large documents Reduces cost of multilingual communication	Quality depends on underlying linguistic data Risk of over‑reliance leading to errors in nuance Data‑privacy concerns for proprietary content
Blockchain	Immutable audit trail – enhances trust Decentralised control reduces single‑point failures	Energy‑intensive consensus mechanisms (e.g., PoW) Scalability limits for high‑throughput applications
3‑D Printing	On‑demand manufacturing reduces inventory Complex geometries impossible with subtractive methods Enables custom medical implants and prosthetics	Material limitations and post‑processing requirements Intellectual‑property concerns for digital models
Holographic Imaging & Storage	True 3‑D visualisation without glasses (imaging) Potential for ultra‑high‑density archival storage	Specialised equipment and high production cost Limited commercial maturity compared with conventional media
Molecular Data Storage	Exceeds 1 PB per gram – ultra‑high density Longevity of thousands of years under proper conditions	Slow read/write speeds versus electronic media Expensive synthesis and decoding infrastructure

13.5 Impacts on Different Stakeholders

Stakeholder	Positive Impacts	Negative / Environmental Impacts
Individuals	Convenient services (smart‑home control, wearables) Personalised health monitoring and early‑warning alerts Enhanced learning through AR/VR experiences	Privacy intrusion and data‑ownership concerns Digital addiction and over‑reliance on automation E‑waste from short‑life wearables and IoT gadgets
Organisations	Operational efficiency and cost reduction New revenue streams (IoT‑as‑a‑service, data‑as‑a‑service) Data‑driven strategy and real‑time decision making	Cyber‑security costs and regulatory compliance Need for up‑skilling staff in data analytics and AI Dependence on external cloud providers and network reliability
Medicine & Healthcare	Remote patient monitoring via wearables and IoT sensors AI‑assisted diagnostics (radiology, pathology) AR‑guided surgery and training 3‑D‑printed prosthetics, implants and anatomical models	Strict regulatory hurdles for medical devices Data‑ownership and consent issues for patient data E‑waste from disposable diagnostic sensors
Environment	Resource optimisation – smart irrigation, energy‑saving HVAC Reduced material waste through additive manufacturing Smart‑grid IoT enables higher penetration of renewable energy	Energy consumption of data centres and 5G/6G networks E‑waste from short‑life electronic components Mining of rare‑earth elements for sensors and batteries Carbon footprint of blockchain consensus mechanisms

13.6 Detailed IoT Example – Smart Home Energy Management

Components (linked to the system life‑cycle)

Hardware: Smart thermostat (temperature sensor + heating actuator), smart plugs, ambient light sensors, occupancy PIR detectors.
Software: Cloud‑based analytics engine, mobile dashboard app, OTA firmware updater.
Network: Wi‑Fi for intra‑home communication; broadband Internet to the cloud; optional Zigbee/Matter mesh for low‑power devices.
Lifecycle stages: Requirements (energy‑saving target), design (sensor placement, UI mock‑ups), development (firmware & cloud services), testing (simulation & field trial), deployment (installation), operation (continuous monitoring), maintenance (remote firmware patches), disposal (recycling of end‑of‑life devices).

How it works (algorithmic flow)

Sensors transmit temperature, occupancy and light‑level data to the cloud every 30 seconds.
Analytics engine evaluates the rule‑based logic:
$$\text{If Occupancy}=0 \land \text{Temp}>22^{\circ}\text{C} \rightarrow \text{Decrease heating by }2^{\circ}\text{C}$$
Command is sent to the thermostat actuator; smart plugs switch off non‑essential appliances.
User receives a summary on the mobile dashboard and can override any automatic action.

Outcome: Up to 30 % reduction in heating bills, lower carbon emissions and improved occupant comfort.

13.7 Integration of IoT with Other Emerging Technologies

IoT + AI (Edge AI): On‑device inference predicts equipment failure locally, sending only alerts to the cloud.
IoT + Blockchain: Tamper‑proof logging of sensor data (e.g., temperature logs for pharmaceutical cold‑chain) via smart contracts.
IoT + 5G/6G: Massive device density (up to 1 million devices / km²) enables city‑wide smart‑lighting and traffic‑management with sub‑millisecond latency.
IoT + AR: Maintenance technicians view real‑time sensor readings overlaid on equipment through smart glasses.
IoT + Digital Twins: Live virtual replica of a factory floor synchronised with sensor streams for optimisation and simulation.

13.8 Challenges (Balanced View)

Challenge	Impact on Advantages	Mitigation Strategies
Security & Privacy	Reduces trust in data‑driven services.	End‑to‑end encryption, regular firmware patches, zero‑trust architecture, hardware‑based security modules.
Interoperability	Limits seamless integration across vendors.	Adopt open standards (MQTT, CoAP, LwM2M, Matter), use middleware platforms, participate in industry consortia.
Data Management	High storage/processing costs can offset efficiency gains.	Edge processing, data compression, tiered storage (hot vs. cold), data‑as‑a‑service pricing models.
Environmental Sustainability	Device turnover contributes to e‑waste.	Design for recyclability, modular hardware, product‑as‑a‑service models, biodegradable electronics.
Regulatory & Ethical Issues	Can delay deployment and increase compliance costs.	Early engagement with regulators, transparent AI/IoT governance frameworks, ethical impact assessments.

13.9 Future Trends Across All Emerging Technologies

Edge AI & TinyML: Inference on micro‑controllers (< 1 mW) for ultra‑low‑latency, privacy‑preserving analytics.
5G & Beyond (6G research): Ultra‑reliable low‑latency communication (URLLC) for autonomous vehicles, remote surgery and massive IoT.
Digital Twins: Real‑time virtual replicas of physical assets for simulation, optimisation and predictive maintenance.
Standardised Data Models (oneM2M, FIWARE, Matter): Facilitate cross‑vendor IoT ecosystems and simplify integration.
Quantum‑Resistant Cryptography: Protect blockchain and IoT data against future quantum attacks.
Biodegradable Electronics: Reduce e‑waste from disposable sensors and wearables.
Holographic Storage Commercialisation: Emerging research aims for petabyte‑scale archival solutions.
Molecular Data Storage Scaling: Advances in DNA synthesis and nanopore reading aim to lower cost and increase speed.

Suggested diagram: Layered IoT architecture – Sensors → Connectivity (Wi‑Fi/5G/LoRa) → Edge Processing → Cloud Platform → User Interfaces (mobile, web, AR). Highlight integration points with AI, Blockchain, Digital Twins and Edge AI.

Describe Internet of Things (IoT) applications