Guide for investors and decision makers

Understanding IoT Edge

The essentials of a fast-growing market

IoT Edge combines sensors, embedded AI, and edge computing to create autonomous smart devices. This page gives you the keys to understand this market and its opportunities.

The IoT Edge Market in Numbers

A fast-growing market, driven by AI and Industry 4.0

$261B
Edge Computing Market 2025
Source: IDC
$380B
Projection 2028
CAGR 13.8%
75%
Data processed at the edge by 2025
Source: Gartner (vs 10% in 2018)
$143B
Edge AI Market 2034
CAGR ~21%

Why Now?

🤖

AI + Robotics

Convergence between artificial intelligence and autonomous physical systems (robots, cobots, vehicles).

💻

Mature Embedded AI

TensorFlow Lite, PyTorch Mobile make AI accessible on low-cost devices.

💰

Affordable Hardware

Raspberry Pi, industrial edge devices are becoming accessible for all budgets.

🛡️

Data Sovereignty

GDPR, industry requirements mandate local processing and data control.

⏱️

Real-time Required

Industry 4.0, autonomous vehicles, robotics require minimal latency.

📶

5G Deployment

5G accelerates edge use cases with low latency and high bandwidth.

What is IoT Edge?

Smart devices that process data locally

Traditional IoT vs IoT Edge

Traditional IoT sends all raw data to the cloud for processing. IoT Edge processes data directly on the device, close to the source. Only relevant information is sent to the cloud.

Offline Mode

An IoT Edge device can operate without internet connection: it stores data locally and automatically synchronizes when the network returns. This is critical for industry, mobility, and anywhere connectivity is unstable.

Application Examples

💳

Kiosks & Terminals

Retail payment terminals, information kiosks, vending machines, autonomous checkouts.

🏭

Industry

AI-powered predictive maintenance, vibration monitoring, machine vision for quality control.

🚗

Mobility

Connected delivery fleets, field maintenance vehicles, optimized route management.

🏢

Smart Building

Intelligent energy management, dynamic displays, occupancy analytics, access control.

IoT Edge Glossary

Essential terms explained simply

Device / Connected Object

Physical device equipped with sensors and capable of communicating via internet or local network. Examples: payment terminal, industrial sensor, smart thermostat.

Edge Computing

Processing data directly on the device or nearby, instead of sending everything to the cloud. Enables instant responses and reduces network dependency.

Offline Mode

A device's ability to function normally without internet connection. Data is stored locally then automatically synchronized when the network returns.

OTA (Over-The-Air)

Remote software update without physical intervention on the device. Allows bug fixes or feature additions on thousands of devices simultaneously.

Fleet

Collection of IoT devices deployed and managed centrally. A fleet can range from a few devices to several thousand spread geographically.

Cloud Backend

Remote servers that centralize data, authentication, business APIs and shared services. Complements edge for heavy processing and long-term storage.

Sensor

Component that measures a physical quantity: temperature, motion, light, vibration, pressure, etc. Transforms the physical world into digital data.

Actuator

Component that acts on the physical environment: motor, electronic lock, relay, valve, display. Allows the system to produce concrete effects.

API

Interface allowing software to communicate with each other. APIs enable integrating IoT data with other systems (CRM, ERP, business applications).

Embedded AI

Artificial intelligence executed locally on the device: image recognition, anomaly detection, predictions. Works without cloud connection.

Why IoT Edge Projects Fail

Obstacles companies face

🧩

Technical Complexity

Companies want to innovate on their business use case, but spend most of their time on IoT infrastructure.

🧱

Scaling Wall

The POC works in the lab, but field deployment requires a complete architecture overhaul.

⚙️

Operational Complexity

Managing an IoT Edge fleet manually: time-consuming deployments, reactive monitoring, risky updates.

🎓

Rare Skills

IoT Edge requires combined expertise: embedded + cloud + security + DevOps. These profiles are hard to recruit.

The Arkipelis Opportunity

Two complementary products to industrialize IoT Edge

🚀

Fleet - The Platform

Turnkey IoT Edge infrastructure

Fleet handles technical complexity: accelerated development, frictionless scaling, enterprise-grade security, native offline resilience.

Serverless architecture (Azure/AWS)
Same code from POC to production
Native offline mode
👨‍💻

Dock - The Expertise

Support and skills transfer

Architecture consulting, technical team coaching, custom R&D, training. We accelerate projects and make teams autonomous.

IoT Edge architecture consulting
Team coaching
Custom R&D and modules

Business Model

Transparency and scalability

Fleet - Recurring Revenue

Fleet license per device
Client pays their cloud consumption directly
No hidden margin on infrastructure

Dock - Billed Engagements

One-time or recurring consulting
Project missions (time & materials or fixed price)
Skills transfer included

Positioning: Enabler, Not Integrator

Arkipelis provides infrastructure and expertise so companies can develop and deploy their own IoT Edge solutions. We don't sell turnkey solutions: we provide the means to create.

Data Sovereignty

Fleet deploys on the client's cloud tenant (Azure or AWS). Data remains under client control, in the region of their choice. GDPR compliant and suitable for regulated industries.

Let's Discuss Your Project

Investor, decision maker, or project owner: let's talk about IoT Edge

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