New Trends: Cloud, Mobile, IoT, AI and Big Data
Domain 10 - New trends: cloud, mobile, IoT, AI and big data
Tests service models, architecture and realistic benefits and limits of emerging technologies.
Core rules
- Cloud service models: IaaS (virtual machines/storage), PaaS (runtime/platform), SaaS (ready application). Deployment: public, private, hybrid, community.
- Mobile computing: portability, connectivity, limited battery/input; mobile apps vs responsive web.
- IoT: sensors/actuators → connectivity → data processing → analytics/action; issues include power, bandwidth, security and privacy.
- AI: systems performing tasks requiring intelligence; machine learning learns patterns from data. AI ≠ ML.
- Big data: volume, velocity, variety, veracity and value; it needs distributed storage and analytics.
Worked example
A farm uses soil-moisture sensors, a gateway, cloud storage and a model that opens irrigation valves. Classify the cloud model and explain the pipeline. The farm buys an application that predicts watering, so SaaS is the simplest service model; if it builds custom code on a managed platform, PaaS; if it manages virtual servers, IaaS. Reasoning: sensors send readings via gateway to cloud; the model analyses trends and sends a control signal to actuators. Edge processing can reduce bandwidth by filtering data locally. Security must cover devices, network and cloud.
Traps
Do not say cloud means data are intangible or automatically safe. AI is not always machine learning. Big data is not merely 'a lot of data' if velocity and variety are absent. IoT includes actuators, not only sensors.