IoT: The Very Basics, ELI5

Imagine every device in your home has sensors and all that is connected to the internet. For example to your home wifi. That’s the idea of the Internet of Things, IoT. Advantages? Many.

What IoT Actually Is

IoT is basically about making “dumb” devices smart by connecting them to the internet. Your washing machine, your doorbell, your car, your coffee maker - they all become data collectors and remote-controlled devices.

The basic setup is always the same:

Sensors collect data (temperature, motion, sound, etc.)
Connectivity sends data to the cloud (WiFi, cellular, Bluetooth, etc.)
Processing analyzes the data (usually in the cloud)
Action is taken based on that analysis (notifications, automation, etc.)

Connectivity: The Foundation

This is where it gets interesting from a technical perspective. You’ve got several options for connecting your IoT devices:

WiFi

Most common for home applications. Easy to set up, good data rates, but power-hungry. Your smart thermostat probably uses this.

Cellular (4G/5G)

Great for devices that need to work anywhere - think fleet tracking or remote monitoring. More expensive but very reliable. Industrial applications love this.

Satellite Broadband

This is what Starlink offers, your device connects directly to a satellite. As of 2024, it’s not yet used significantly in IoT systems but it’s a growing trend, for simple reasons. You have global and wide-area coverage and you have very high resilienve and reliability (they don’t have issues like local power outages or natural disasters).

LPWAN (Low Power Wide Area Network)

This includes technologies like LoRaWAN, Sigfox, and NB-IoT. Perfect for devices that need to send small amounts of data over long distances while using very little power. Think smart city applications - parking sensors, environmental monitoring.

Bluetooth/Zigbee

Short-range, low power. Good for creating mesh networks in homes or for devices that communicate with a hub.

5G - The Game Changer

5G is where things get really interesting for IoT. It brings three key improvements:

Ultra-low latency (1ms vs 50ms for 4G) - critical for autonomous vehicles, industrial robots, remote surgery
Massive device density - up to 1 million devices per square kilometer
Network slicing - dedicated virtual networks for different use cases with guaranteed performance

This enables entirely new IoT applications that weren’t possible before. Think real-time factory automation, smart city infrastructure that responds instantly, or augmented reality applications that require zero lag.

Current IoT Communication Usage

Here’s roughly how IoT connectivity breaks down today:

WiFi: ~40% - Dominates consumer IoT (smart homes, retail)
Cellular (4G/LTE): ~25% - Industrial applications, fleet management
LPWAN: ~20% - Smart cities, agriculture, utilities
Bluetooth/Zigbee: ~10% - Home automation, wearables
5G: ~5% - Still growing rapidly, mainly in pilot projects

Current trends:

5G adoption is accelerating fast, especially in manufacturing and autonomous vehicles
LPWAN is growing for utility and smart city deployments
WiFi 6 is gaining ground for high-density IoT deployments
Hybrid approaches (multiple connectivity options) are becoming more common

The choice depends on your use case: battery life, data requirements, range, and cost.

The Cloud Part: Where the Magic Happens

Here’s where tech giants make their money. AWS IoT Core, Azure IoT Hub, Google Cloud IoT - they all provide the infrastructure to:

Collect millions of data points from devices
Store massive amounts of time-series data
Process data in real-time or batch
Manage device fleets (updates, configuration, monitoring)
Secure everything with certificates and encryption

AWS IoT Core, for instance, can handle millions of connected devices publishing data to topics. It integrates with other AWS services like Lambda for processing, DynamoDB for storage, and QuickSight for analytics.

Real Case Studies

Let me give you three examples from different industries that show why IoT actually matters:

Case Study 1: Smart Agriculture - Precision Farming

Problem: A large farming operation was struggling with water usage and crop yields. They were essentially guessing when to irrigate and fertilize.

IoT Solution:

Soil moisture sensors throughout fields
Weather stations collecting microclimate data
Drone-mounted cameras for crop monitoring
All connected via cellular networks to a central platform

Result: 30% reduction in water usage, 20% increase in yield. The system automatically triggers irrigation only when soil moisture drops below optimal levels and adjusts fertilizer application based on soil conditions.

Business Impact: Saved $2.3 million annually in water costs and increased revenue by $4.1 million through better yields.

Case Study 2: Predictive Maintenance in Manufacturing

Problem: A manufacturing company had expensive unplanned downtime when critical machinery failed unexpectedly.

IoT Solution:

Vibration sensors on motors and pumps
Temperature sensors on bearings
Pressure sensors in hydraulic systems
Edge computing devices for real-time analysis
Connected via industrial Ethernet and cellular backup

Result: 40% reduction in unplanned downtime. The system predicts failures 2-4 weeks before they happen, allowing for planned maintenance during scheduled downtime.

Business Impact: Avoided $8.5 million in lost production and reduced maintenance costs by 25%.

Case Study 3: Smart City Traffic Management

Problem: A mid-sized city had horrible traffic congestion and outdated traffic light systems that couldn’t adapt to real conditions.

IoT Solution:

Traffic flow sensors at major intersections
Air quality monitors
Smart traffic lights with adaptive timing
Connected parking meters
All connected via a combination of fiber and LoRaWAN

Result: 35% improvement in traffic flow during peak hours. Air quality improved due to reduced idling. Parking availability information reduced circling time by 20%.

Business Impact: Estimated $12 million annual economic benefit from reduced commute times and improved air quality.

Case Study 4: Volkswagen Industrial Cloud - Manufacturing Revolution

Problem: As Europe’s largest automaker producing 11 million cars annually, Volkswagen needed to transform their manufacturing and logistics processes across 120+ factories worldwide.

IoT Solution:

Connected all machines, equipment, and systems across 120+ factories
Built the Volkswagen Industrial Cloud on AWS
Used AWS IoT services to collect and process manufacturing data
Real-time monitoring of production lines and supply chain

Result: The Industrial Cloud enables real-time visibility into manufacturing operations, predictive maintenance, and optimized production scheduling across the entire global network.

Business Impact: Targeting 30% productivity increase, 30% reduction in factory costs, and €1 billion savings in supply chain costs. Beyond manufacturing, VW is expanding into ride-sharing services, connected vehicles, and virtual car buying experiences.

Case Study 5: BMW ConnectedDrive - Global Vehicle Connectivity

Problem: BMW’s ConnectedDrive services (navigation updates, entertainment, remote diagnostics, car sharing) needed a scalable global platform to handle rapidly increasing demand from new vehicle models.

Reason: it was working but had caveats, such as overly monolithic parts that prevent a high degree of scalibility as well as much of it being hosted on-prem that also impose limitations. So Capgemini worked closely with key BMW stakeholders to develop a migration strategy that minimizes risk and impact on end customers. The goal was: cloud-native, highly scalable - they used AWS, common DevOps practices such as containerization, autosclaing, and data streams.

IoT Solution:

Cloud-native vehicle connectivity platform on AWS
MQTT-based communication protocols for vehicle-to-backend connectivity
DevOps model with 24/7 operations team
High-level functions for security, data upload/download, vehicle wakeup, and message delivery
Containerized applications with autoscaling capabilities

Result: Higher availability and scalability for connected services, as well as improved cost efficiency. The platform now handles mass software updates for millions of vehicles simultaneously and supports autonomous driving features.

Business Impact: Improved service continuity, reduced response times to customer requests, and enhanced capacity for data-intensive products. The platform supports BMW’s strategy for gaining digital-savvy customers and shapes the future of mobility.

Note: the services we talk about here rely data connections between vehincles and backend services. This is a typical IoT scenario. Think of updates for navigation, online entertainment, software, functions activated overthe-air, remote diagnostics, vehicle wakup, and car sharing.

What Consulting Companies Actually Do

When a large enterprise wants to implement IoT, they usually don’t know where to start. That’s where consulting firms come in. Here’s what they typically help with:

Strategy & Use Case Identification

Most companies know they “should do IoT” but don’t know where it makes business sense. Consultants help identify high-ROI use cases and create implementation roadmaps.

Technology Architecture

Choosing the right connectivity, cloud platform, and edge computing setup. This includes decisions like: Should we use AWS or Azure? Do we need edge computing? What’s our data strategy?

Integration with Existing Systems

IoT doesn’t exist in isolation. It needs to integrate with ERP systems, CRM platforms, and existing databases. This is often the most complex part.

Security Implementation

IoT security is a nightmare. Consultants help implement device authentication, data encryption, and security monitoring.

Change Management

IoT projects often require new processes and training. People need to understand how to act on the new data they’re getting.

Proof of Concept Development

Most enterprises want to see results before committing. Consultants build small-scale PoCs to demonstrate value.

The Business Reality

Look, IoT is not just about cool gadgets. It’s about data-driven decision making at scale. The real value comes from:

Operational efficiency (like the farming example)
Predictive capabilities (like the manufacturing example)
New business models (subscription services, usage-based pricing)
Customer experience improvements (smart home automation, personalized services)

But here’s the thing - most IoT projects fail. Not because the technology doesn’t work, but because companies don’t think through the business case properly. They get excited about the tech and forget about the fundamental question: What business problem are we solving?

The Trade-offs

IoT isn’t all sunshine and rainbows. Here are the real challenges:

Security Nightmares

Every connected device is a potential attack vector. We’re talking about billions of devices with varying security standards. Many IoT devices have default passwords that never get changed.

Data Overload

IoT generates massive amounts of data. If you don’t have a clear plan for what to do with it, you’ll just have expensive storage bills and no insights.

Complexity

Managing thousands of devices across different locations with different connectivity requirements is genuinely complex. Firmware updates alone can be a logistical nightmare.

Privacy Concerns

Your smart doorbell knows when you’re home. Your car knows where you drive. Your health tracker knows your heart rate patterns. This data is incredibly valuable - and incredibly sensitive.

Looking Forward

IoT is not going away. 5G is the real catalyst here - it’s not just faster internet for your phone. For IoT, 5G enables applications that were literally impossible before:

Industrial automation with sub-millisecond response times
Autonomous vehicle swarms communicating in real-time
Remote surgery where a surgeon in Berlin operates on a patient in Munich
Smart city infrastructure that responds instantly to changing conditions

The numbers tell the story: we’re moving from millions of connected devices to billions. Edge computing will bring processing power closer to devices, reducing latency even further. AI will make the analytics more sophisticated, enabling predictive capabilities we can barely imagine today.

But the fundamentals remain the same: it’s about connecting the physical and digital worlds to make better decisions. Whether that’s optimizing a supply chain, predicting equipment failures, or just making your morning coffee automatically - IoT is about using data from the real world to improve how things work.

The question isn’t whether IoT will be important - it already is. The question is whether your organization can think through the business case clearly enough to make it work for you.