IoT (Internet of Things) is steadily penetrating everyday life, without humans even noticing it. IoT devices are already widely used across many industries, including electricity, gas, steam & air conditioning supply, water & waste management, retail & wholesale, and transportation & storage. The number of IoT-powered devices deployed by enterprises from these industries has surpassed 100 million, according to Statista.
Analysts expect the number to increase across all industry verticals to 8 billion by 2030. This implies that businesses are going to invest in IoT device architecture more in the coming years. Hence, they need to develop expertise in the field to make informed decision-making and avoid budget wastage. In this article, you will explore best practices for the Internet of Things architecture, its key components, and useful tips when designing a system.
Although there is no single successful IoT architecture that suits various projects, you can always use this four-layer architecture as a template or a best practice when designing your custom one:
- The Perception Layer, in which the focus is on sensors and data they generate, as well as actuators.
- The Network Layer takes the data a step further and determines how it’s being transferred across the app, how the devices are connected, and how the data is sent to back-end services.
- The Application Layer is the user-side of the process, which implies an app that allows you to control the connected device and manage the overall smart ecosystem.
- The Business or Business Intelligence (BI) Layer is about analyzing and processing all the data from the application layer to provide valuable insights for improved decision-making.
However, the IoT architecture diagram often excludes the BI layer, narrowing it down to a three-layer model:
Perception – Network – Application
While such a model doesn’t compromise primary IoT capabilities, it significantly limits the application of the collected data.
Another alternative is a five-layer IoT diagram, in which Transport replaces the Network layer, and Processing and Business layers are also added. It looks as follows:
Perception – Transport – Processing – Application – Business
The process begins at the Perception layer, which remains the same: data collection. The Transport stage specifies which networks transfer the data between sensors in the Perception layer and the Processing layer.
The Processing layer (which can also be referred to as the Middleware layer) may be located on the edge of the cloud for low-latency communications. Here, the data generated by sensors are stored, analyzed, and pre-processed. Then comes the Application layer, in which the user controls smart devices, and the Business layer, in which the data is analyzed to provide valuable insights.
Let’s examine an IoT system architecture in more detail, focusing on an efficient four-layer model.
This layer revolves around the devices primarily. Some of them generate data that is further processed — they can also be referred to as “things” or objects equipped with sensors to gather data that further will be transferred over the network. Sensors aren’t often closely connected or directly installed on the equipment. Often, they are located in close proximity to monitor the nearby environment — for example, temperature, pressure, acceleration, and more.
The generated data can’t be transmitted to the next stage immediately. It’s converted from analog to digital form, and it can also be filtered or normalized. This allows the facilitation of data processing at further stages. However, the latter is often neglected, as the devices are known for their limited resources. That’s why the raw data is usually transmitted to the internet gateway stage.
There are also actuators — or devices that allow things to act — for instance, to turn the light on/off, to adjust the temperature, to increase/decrease rotation speed, to unlock/lock the door, and more.
At this layer, you need to set up quality, fast, and cost-efficient communication between your devices on the Perception layer, networks, and cloud-based systems that store your data — and then deploy it for further usage. Often, the connection is established with a TCP/IP or UPD/IP stack. But you can also opt for gateways to convert signals to different protocols.
At this stage, you need to decide on the connectivity and data transmission options. They depend on several factors, including the system design, types of devices used, the distance a signal should pass, and possible obstructions. Here’s a breakdown of the connectivity options for IoT devices depending on the type of technology and the distance the signal has to travel.
- Short-range wireless technologies like Bluetooth, Zigbee, and Wi-Fi. If you need high-speed connectivity over a short distance, Wi-Fi will satisfy your needs. Those who focus on security can opt for NFC (Near Field Communication) to establish a power-efficient connection between two devices four inches away from each other.
- Long-range wireless technologies likeLoRaWAN, NB-IoT, and 5G. Today, many use LPWAN (Low-power wide-area network), which provides low power consumption, an extended battery life, and long-range wireless connectivity. However, the industry is likely to be revolutionized with the adoption and advancements of 5G, which will provide a long-range connection at extremely high speed.
- Wired technologies like Ethernet to establish a secure connection over a short distance with a high transmitting speed.
When thinking through data transmission and routing, you have several messaging protocols to choose from to ensure stable and secure data sharing between your devices and cloud solutions:
- MQTT or Message Queue Telemetry Transport for data collection from low-powered devices.
- CoAP or Constrained Application Protocol for devices with limited memory and power resources.
- AMQP or Advanced Message Queuing Protocol to support data exchange between servers.
- DDS or Data Distribution Service to establish a real-time connection between IoT-powered devices.
At this layer, people can deal with the generated data: they can analyze it to generate meaningful insights and facilitate decision-making, as well as manage and control the connected devices.
At this stage, the process may begin with edge computing, which allows you to speed up data processing in order to quickly determine critical signals. Moreover, edge computing may imply pre-processing to send more accurate information to the cloud.
Then comes the cloud or data center, where all your data is stored unless it’s being further processed. It can be done in several ways:
- Big data analytics: Data analysts extract filtered and preprocessed data from a data lake to a big data warehouse to determine trends and patterns. These provide actionable insights about the system performance, equipment state, potential dangers or improvements, etc.
- Artificial Intelligence (AI) and Machine Learning (ML) algorithms: They are often applied to analyze historical data and update the control application. For example, they can be used to determine the exact time to dim the light in the bedroom, according to your recent sleep schedule.
At the application layer, it’s essential to integrate IoT software with middleware. This is often addressed through APIs (Application Programming Interfaces) or built-in SDKs (Software Development Kits) that allow for data visualization and analytics.
You can often facilitate the process with IoT systems like:
- AWS IoT to connect smart devices to the AWS platform, as well as to establish secure data transfer and data processing. It offers SDKs for AWS IoT devices, a device advisor, a device gateway, message broker authentication and authorization features, and more.
- Microsoft Azure IoT to build IoT-powered digital solutions with intelligent edge-to-cloud technologies, without compromising security and compliance. You can also integrate AI algorithms for enhanced analysis.
Discover also: AWS Consulting Practice Done Right
At this layer, data-driven decisions and solutions are extracted from the application layer, allowing businesses to facilitate infrastructure management by relying on extensive, data-rich insights.
Often, the business layer should be integrated with core enterprise systems and workflows so responsible managers can quickly get insights about the IoT platform infrastructure without switching to another system. What is more, this often allows you to automate some repetitive tasks like manual data entry or collection, which promotes digital transformation across your organization and fosters enhanced efficiency.
When implementing systems integrations, don’t forget about security, privacy, and compliance. To avoid data leakage, which often results in excessive costs and reputational losses, ensure data integrity and confidentiality. Besides technical features, you need to set up authentication and access control mechanisms to prevent random employees from accessing valuable data.
Read also: Big Data: Security Issues and Challenges
Here are four useful tips that will help you to design an efficient architecture of Internet of Things (IoT) systems:
- Assess and prioritize IoT use cases: focus on those that will deliver the most value like enhanced efficiency or important device features. This will help you to tailor the IoT infrastructure to your needs.
- Balance scalability, performance, and cost: IoT devices generate huge amounts of data, and it only increases over time. Think through the potential number of users, flows of data, and scope in advance to correlate this with the desired performance and budget.
- Ensure interoperability and future-proofing: Your infrastructure should be flexible enough for you to easily adjust it instead of starting from scratch in case of sudden changes.
- Address security and privacy concerns: Generating tons of data means you’ll get many intangible assets that need to be overseen and protected. Otherwise, you might incur reputational and financial losses because of non-compliance or data leakage.
Here are some use cases of successful IoT network architecture implementation:
- Transportation can be used for enhanced fleet management, giving the manager and the client comprehensive visibility of the state of the transported goods. The devices can transmit data on the temperature, humidity, and pressure levels in the cabin, as well as the truck’s speed, location, fuel level, etc.
- Smart buildings provide comprehensive monitoring of what is happening inside the facility, including equipment and resource usage. The system can set up automatic lighting, authorization, and more.
- Predictive maintenance allows manufacturers, transportation companies, and retailers to improve or even automate condition monitoring to timely carry out predictive maintenance.
Uncover more benefits: From Reactive to Proactive: How IoT Transforms Condition Monitoring
- Healthcare can be used for enhanced and more personalized treatment by analyzing personal medical conditions based on the data collected from wearables. It even enables remote predictive treatment, allowing the system to inform the patient about a potentially critical condition.
With IoT becoming a top-playing technology that fosters digital transformation and speeds up automation across various industries, businesses are going to increase their investments in IoT infrastructures. Although there are some dominant IoT infrastructure models like three- or five-layer ones, every project requires a custom approach. Nonetheless, they are often based on the four-layer approach, where the following components are included: Perception – Network – Application – Business.
You can design these components from scratch or facilitate the process by using IoT systems like AWS IoT or Microsoft Azure IoT. Although they provide a convenient environment with extensive functionality to build an IoT infrastructure, it’s still best to seek expert assistance to ensure you build with scalability, privacy, compliance, and acceptable costs in mind. Let’s talk and pick the right solution, ensuring the best practices for your industry.