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The architecture of IoT

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Understanding IoT architecture

IoT architecture is the conceptual model that structures the IoT ecosystem. It ensures the efficient flow of data among devices, enabling them to communicate, process information, and take intelligent action. In industries ranging from manufacturing to healthcare, IoT architecture serves as the vital infrastructure that underpins smart solutions. Its significance in enhancing operational efficiency, reducing costs, and driving innovation cannot be overstated.

Core layers of IoT architecture

Key components & technologies

  • Sensors & actuators

    From temperature sensors in smart thermostats to actuators in automated manufacturing lines, these are the physical components that interact with the physical world.

  • Connectivity solutions

    Essential for data transmission, technologies including Wi-Fi, Bluetooth, LPWAN, and cellular connectivity ensure that IoT devices stay connected.

  • Data processing & analytics

    Sophisticated data processing and analytics are at the heart of IoT, enabling the transformation of raw data into meaningful insights.

  • Cloud & edge computing

    These technologies offer scalable and flexible data storage and processing capabilities, critical for managing the vast amounts of data generated by IoT devices.

  • Security & privacy considerations

    With the increasing prevalence of IoT, ensuring robust security measures and maintaining privacy is paramount.

IoT architecture models

Centralised IoT architecture

Centralised architecture relies on a single central server that manages communication and data processing for all IoT devices. This model is akin to a hub-and-spoke system, where the hub controls all operations.


Centralised systems are easier to manage and secure, as all data processing and decision-making happen in one location.


They can become a bottleneck and a single point of failure. If the central server goes down, the entire system can be compromised.

Use cases

Ideal for environments where rapid decision-making isn’t critical, such as in basic home automation systems or centralised monitoring systems in controlled industrial settings.

Decentralised IoT architecture

In a decentralised model, decision-making and data processing are distributed across various nodes, instead of being centralised in one single location. Each node in the network can make decisions independently.


This approach reduces the risk of a single point of failure and can improve system resilience. It’s more scalable as adding new devices doesn’t burden a central server.


More complex to manage and secure due to the distributed nature of processing and decision-making.

Use cases

Suitable for complex industrial environments where decisions need to be made quickly and locally, such as in manufacturing automation.

Distributed IoT architecture

Distributed architecture takes decentralisation a step further by spreading out data processing and decision-making across a wider network. Here, devices communicate directly with each other, often using peer-to-peer communication.


This model offers high resilience and scalability. It’s highly effective in managing large, complex networks without a central control point.


The complexity of managing and securing a distributed network is significantly higher, and ensuring smooth interoperability between devices can be challenging.

Use cases

Ideal for large-scale IoT applications such as smart cities, where various systems including traffic, utilities, and emergency services need to operate both independently and harmoniously.

Challenges & future trends in IoT architecture

Security vulnerabilities

One of the most significant challenges facing IoT is ensuring robust security. As IoT networks expand, they become more vulnerable to cyber threats.

Specific challenges

These include securing data transmission, preventing unauthorised access, and safeguarding sensitive data.


A common concern is the hacking of smart devices, which can lead to privacy breaches and unauthorised control over systems within your business or home.

Scalability issues

As IoT networks grow in size and complexity, maintaining performance and efficiency becomes challenging.

Specific challenges

These include managing large volumes of data, ensuring consistent connectivity, and maintaining system performance.


Scaling IoT solutions in smart cities, where thousands of devices must communicate and function seamlessly, poses significant logistical and technical challenges.


Interoperability refers to the ability of various IoT devices and systems to work together seamlessly.

Specific challenges

These include standardising communication protocols and ensuring that different devices from various manufacturers can communicate effectively.


In a smart office, devices such as smart thermostats, lights, and security systems often need to interact with one another, but differences in standards and protocols can hinder seamless integration.

Real-world applications & case studies of IoT architecture

These systems typically use a centralised architecture, where data from individual meters is sent to a central server for processing and analysis.

Smart meters in homes across the UK measure energy usage in real-time, transmitting data to utility providers for efficient energy management and billing.

This allows for better energy consumption tracking, personalised tariff plans, and encourages energy-saving practices among consumers.

Utilises a distributed IoT architecture, where traffic sensors and cameras across the city collect and process data locally.

IoT sensors and AI algorithms analyse traffic flow, adjusting traffic signals in real-time to reduce congestion and improve road safety.

This system enhances traffic efficiency, reduces commute times, and lowers pollution levels.

Air quality monitoring in urban areas

Uses a decentralised model with multiple sensors across urban areas, transmitting data to local processing units.

Sensors monitor pollutants and environmental conditions, providing real-time data to authorities and the public.

Enables better pollution control measures and informs public health initiatives.

IoT architecture of predictive maintenance in manufacturing

Employing a decentralised architecture, where sensors on machinery collect data that is processed at local nodes.

IoT sensors monitor equipment conditions, predicting maintenance needs before breakdowns occur, reducing downtime and maintenance costs.

This proactive approach optimises maintenance schedules and extends the lifespan of machinery.

Utilises a combination of centralised and distributed architectures for different stages of the supply chain.

IoT sensors track goods from production to delivery, providing real-time visibility of inventory levels, location during transit, and condition of goods.

This leads to more efficient inventory management, improved delivery times, and enhanced customer satisfaction.

Remote patient monitoring

Typically uses a centralised model, where patient data collected by wearable devices is sent to a central healthcare server.

Wearable connected devices such as heart rate monitors and blood pressure cuffs collect health data, which is then analysed by healthcare professionals to assist with delivering personalised care.

This allows for continuous monitoring of patients, particularly those with chronic conditions, improving the quality of healthcare services.

A combination of centralised and decentralised architectures, depending on the application (for example, patient data management vs automated medication dispensers).

IoT applications range from tracking patient flow to managing inventory of medical supplies, improving operational efficiency and patient care.

Increases hospital efficiency, reduces errors, and enhances patient experiences.

Wildlife tracking for conservation

Utilises a distributed IoT system where tracking devices on animals traverse data to various nodes.

IoT-enabled smart tracking devices provide valuable data on animal movements and behaviours, aiding in conservation efforts.

Helps in understanding wildlife patterns, protecting endangered species, and preserving biodiversity.

Future trends in IoT architecture

AI & machine learning:

Artificial intelligence and machine learning are becoming integral to IoT, offering advanced data analysis and predictive capabilities.


This trend allows for more intelligent decision-making, predictive maintenance, and enhanced automation.


In predictive maintenance for manufacturing, AI can analyse data from IoT sensors to predict equipment failures before they occur, significantly reducing downtime.

Edge computing:

Edge computing involves processing data closer to where it’s generated rather than relying solely in the cloud.


This reduces latency and bandwidth use, making IoT systems faster and more efficient.


In autonomous vehicles, edge computing allows for real-time processing of sensor data, crucial for immediate decision-making.

5G connectivity:

The rollout of 5G networks is set to revolutionise IoT with faster, more reliable, and lower latency communications.


This will enable new IoT applications that require real-time data transfer, such as telemedicine and advanced robotics.


In telemedicine, 5G can enable real-time remote monitoring and consultation, ensuring timely medical interventions.