Layers & Components of IoT Architecture in 2025

Though businesses are investing in IoT, buyers may not be clear about all the components that they need to invest in.

See below the overall IoT architecture, the difference between IoT ecosystem and IoT architecture, its ten different components, and a real-life example for contextualization:

What is IoT architecture?

IoT architecture comprises several IoT building blocks connected to ensure that sensor-generated data is collected, transferred, stored, and processed in order for the actuators to perform their designated tasks.

Layers of IoT architecture

The Internet of Things (IoT) is transforming networks with its ability to improve quality of life through a vast network of interconnected, internet-enabled devices. These devices are equipped with sensors, storage, and computational and communication capabilities, generating massive amounts of critical data that require secure handling.

Figure 1. Three layers of IoT architecture

Source: Anomaly detection using deep neural network for IoT architecture¹

A typical three-layer IoT architecture includes:

1. Perception Layer (Physical Layer)

This is the first and most basic layer. It includes sensors and devices that collect data from the real world. For example, a temperature sensor in a smart thermostat or a motion sensor in a security camera.

These devices use wireless technologies like Bluetooth, Wi-Fi, ZigBee, or 6LowPAN to share data. They may also include actuators, which are tools that act based on data (like turning on a light).

In short, this layer helps IoT systems see, hear, and feel what’s happening around them.

2. Network Layer (Transport Layer)

The network layer moves the data collected by sensors to where it needs to go. It connects all the devices and ensures secure and reliable communication.

This layer can use networks like 4G, 5G, Wi-Fi, ZigBee, or IPv6. It also includes internet gateways, access points, and routers. These help send the data from the sensor devices to the cloud or other systems.

Security is a big part of this layer. For example, it can use encryption methods like public and private keys to keep data safe.

3. Application Layer

This is the part users see and interact with. It shows useful data and allows users to control devices.

Examples include mobile apps, smart home dashboards, or health tracking platforms. With one tap, a user can turn on a smart light or check the air quality in their home.

This layer makes IoT easy to use and useful in everyday life.

Network Intrusion Detection System (NIDS) stages

For security, the Network Intrusion Detection System (NIDS) can be deployed at network entry points, such as edge routers, to guard against anomalies.² The proposed two-stage NIDS operates as follows:

• Stage 1: Captures data from within or outside the IoT network, performs feature extraction, and prepares data for further analysis.
• Stage 2: Processes the prepared data using a deep learning (DL)-based anomaly detection model to identify and protect against anomalous traffic.

This architecture ensures efficient data collection, secure transmission, and effective anomaly detection for IoT networks.

Different elements of IoT architecture

IoT architecture is a complex system composed of multiple layers and components that enable the seamless operation of IoT devices and applications. Below, we explore ten critical components of an IoT structure, emphasizing their roles in data collection, processing, and informed decision-making within the context of IoT technology.

1. IoT Sensors

IoT sensors play a key role in data acquisition systems by enabling the collection of raw data from the environment. These physical devices can be embedded within connected devices or remotely located to gather information about their surroundings. Practical applications include:

• Temperature detectors for smart homes or smart buildings
• Smoke detectors for improving security during natural disasters
• Cameras and CCTVs for monitoring systems in healthcare facilities or the manufacturing industry

2. Actuators

Actuators are devices that respond to data inputs to automate tasks without direct human intervention. They transform instructions into physical actions, ensuring seamless operations. Examples include:

• Smart lights turning on or off
• Door locks opening or closing
• Thermostats adjusting temperatures in smart homes

3. Internet Gateways

The network layer in an IoT system includes internet gateways, which facilitate cloud connectivity for transferring device data between edge devices and the cloud infrastructure. These gateways ensure efficient data transfer for real-time insights and decision-making.

4. Cloud Gateways

A subset of internet gateways, cloud gateways, compress and transfer data collected from edge devices to the cloud infrastructure for further processing. Cloud gateways enable cloud-based solutions like AWS IoT Core to manage internet of things ecosystems.

5. Control Applications

Control applications form part of the application layer in IoT systems. These applications automate tasks by sending instructions to actuators. For example, in smart buildings, sensors detect occupant density, triggering sprinklers for irrigation or adjusting ventilation systems to optimize operational efficiency.

6. User Applications

Mobile apps and other user interfaces allow individuals to interact with IoT systems. They enable users to control physical objects like house monitors and door locks, toggle features, or analyze data from remote patient monitoring systems.

7. Data Lake

A data storage layer in IoT architecture, a data lake retains vast volumes of raw data from IoT devices. This includes unstructured and semi-structured data points, such as images and videos from monitoring systems, which are stored for future data processing and analyze data trends.

8. Data Warehouse

The business layer of IoT architecture involves extracting filtered and structured data from the data lake to the data warehouse. This processed data is critical for business operations, enabling enterprises to derive actionable insights and enhance operational efficiency.

9. Data Analytics

Data analytics helps analyze data stored in the data warehouse to identify trends and patterns. By leveraging artificial intelligence, businesses gain real-time insights to make informed decisions. For example, detecting inefficiencies in systems like autonomous vehicles or optimizing resources in healthcare facilities.

10. Machine Learning

At the heart of IoT applications lies machine learning, which enables the creation of predictive models using historical data stored in the data warehouse. These models allow IoT systems to anticipate outcomes, optimize business operations, and introduce new features to enhance the overall IoT system.

Real-life example IoT architecture

Let’s consider a simple system managing lights in a city:

1. Sensors take relevant data, such as daylight or people’s movement.
2. The lamps are equipped with actuators to switch the light on and off.
3. If the sensor stops sensing sufficient levels of light, an on-board control application could decide to send a signal to the actuator to turn on the light.
4. Field gateways or cloud gateways process the data from sensors and send them to applications.
5. A city official could use the control application to
   • change the threshold for turning on the light and could push this update to devices.³
   • place lights in neighborhoods with limited foot traffic on power-saver mode to conserve energy.
   • Feed data from nearby sensors to lights to help them make better decisions. For example, a light with a malfunctioning sensor could be prevented from turning on in the morning.
6. A user could apply to turn lights on and off to celebrate an event via a user application. The application could also provide information on the locations of lights, when they would be turned on etc.
7. The data lake would store these raw data coming from the sensors.
8. An on-prem or cloud-based data warehouse could use this data to enrich a larger dataset that includes the inhabitants’ behavior on various days of the week, energy costs, and more.
9. Data analytics applications could be used to create visualizations for reporting or identifying patterns. These applications could also include machine learning modules that can detect patterns such as
   • Reduction in crime with increased lighting to suggest improvements.
   • The inhabitants not leaving home before 6 am and coming back not after 11 pm. The lights could there be programmed to be on power saver mode when the streets are empty. These smart adjustments would, reduce the need for human intervention and support a better user experience.

Main Challenges of IoT Architecture

The development and deployment of IoT architecture face several major challenges that must be addressed to ensure the efficiency, scalability, security, and interoperability of IoT systems.⁴ These challenges affect the ability of IoT devices to collect, process, and utilize data efficiently in various systems, from edge computing to cloud services. Below, the most significant challenges are explored:

1. Scalability

Scalability remains a major challenge in IoT systems, as the number of IoT devices and the volume of data they generate continue to grow exponentially. The ability to handle this growth in data processing and device integration is critical.

Key strategies to address scalability include:

• Data reduction and big data processing using machine learning algorithms to manage and analyze the large amounts of raw data collected.
• Adoption of edge computing and fog computing to decentralize data processing and reduce latency.
• Implementing protocols such as IPv6 and 6LoWPAN to support addressing a vast number of devices.
• Using virtualization technologies and peer-to-peer networks to ensure flexibility and extendibility of the IoT architecture.
  Scalability also depends on ensuring that all the devices within the internet of things can be effectively managed and configured, which requires advanced control systems and semantic descriptions for discovery and topic searches.

2. Security

Security is another key challenge within IoT architecture, as the large number of connected devices and distributed systems create vulnerabilities. Security concerns such as privacy, integrity, confidentiality, and availability must be addressed comprehensively.

Solutions to enhance security include:

• Ensuring availability through distributed architectures to eliminate single points of failure.
• Employing encryption algorithms like AES and RSA for confidentiality and tokenization to protect sensitive data.
• Utilizing digital signature mechanisms, including ECDSA, to ensure integrity and non-repudiation.
• Implementing incident detection and redundancy measures to improve the reliability of IoT systems.

Security in IoT devices must balance the limitations of edge devices, such as computation power and network bandwidth, with robust solutions to prevent breaches.

3. Interoperability

The heterogeneity of IoT devices and systems is a significant challenge for interoperability. Effective communication between devices requires standardization and compatibility across different protocols, technologies, and data formats.

Approaches to address interoperability include:

• Utilizing standards such as CoAP and MQTT to enable consistent communication.
• Leveraging semantic web ontologies (e.g., OWL and RDF) for meaningful data processing and abstraction.
• Implementing virtualization techniques like SDN and NFV to provide flexibility and abstraction layers for heterogeneous systems.
• Using internet gateways to convert and aggregate data from diverse technologies.
• Applying service-oriented architectures (SOA) to encapsulate and abstract resources, improving integration across IoT applications.

The ability to handle this heterogeneity ensures a seamless flow of data across all the devices in the internet of things and supports application layer functionality.

4. Efficiency

Efficiency is a key challenge as many IoT devices are limited by power, processing, and memory resources. Achieving efficiency involves addressing:

• Network efficiency using edge computing, local gateways, data reduction, and caching mechanisms to reduce unnecessary communications and data transmission. These approaches also enhance time efficiency by storing data closer to the edge devices.
• Energy efficiency through virtualization techniques, caching mechanisms, and sleep scheduling. Energy-efficient hardware and protocols further improve resource consumption.
• Resource efficiency in terms of CPU and memory utilization, throughput, and bandwidth optimization.

Quality of Service (QoS) metrics such as response time, computation time, transmission time, latency, packet loss, and jitter are crucial for measuring and improving efficiency. Additionally, reducing energy consumption and improving processing time can lower costs, though cost efficiency has been addressed less frequently in the literature.

Difference between IoT ecosystem and IoT architecture

IoT ecosystem is the encompassing term attributed to the five general components: 1) devices, 2) communication protocols, 3) the cloud, 4) monitoring systems, and 5) the end-user in the IoT system.

IoT architecture is the breakdown of the inner workings of these building blocks to make the ecosystem function.

Further reading

• Top 10 IoT Communication Protocols
• IoT Cloud: Accessible and Scalable
• Edge Computing: A Better Alternative Than Cloud for IoT

Finally, if you believe your business will benefit from an IoT solution, feel free to check out our data-driven hub of IoT solutions and tools.

External Links

• 1. Ahmad, Z., Shahid Khan, A., Nisar, K., Haider, I., Hassan, R., Haque, M. R., … & Rodrigues, J. J. (2021). Anomaly detection using deep neural network for IoT architecture. Applied Sciences, 11(15), 7050.
• 2. Ahmad, Z., Shahid Khan, A., Nisar, K., Haider, I., Hassan, R., Haque, M. R., … & Rodrigues, J. J. (2021). Anomaly detection using deep neural network for IoT architecture. Applied Sciences, 11(15), 7050.
• 3. Temperature-controlled transport operations by road and by air. World Health Organization. Accessed: January/10/2025.
• 4. Samizadeh Nikoui, T., Rahmani, A. M., Balador, A., & Haj Seyyed Javadi, H. (2021). Internet of Things architecture challenges: A systematic review. International Journal of Communication Systems, 34(4), e4678.