With the emergence of IoT or Internet of Things solutions, many industries have been greatly benefited from IoT technologies that enhance productivity and operations’ reliability. The IoT solutions provide a setting that includes sensors, instruments, machines, and many other connected devices to operate without human intervention. That being said, we will slowly break down the IoT solution architecture to understand more about the step-by-step processes of IoT implementation.
What is IoT Solution Architecture?
IoT, short for Internet of Things, in which the word "things" may refer to a car, building, machine, or even a person. An IoT solution is a system of interconnected sensors, computing devices, and machines that are connected through a network to form one complete operation. Therefore, an IoT solution architecture is a design of the step-by-step data flow from collecting raw data to obtaining predictions or results. There is no universal standard for an IoT solution architecture, but typically this technology requires four major components, consisting of:
- Sensors/Actuators
- Gateways and Network
- Cloud or Data Server
-
Applications Layer
Step 1: Raw Data Collection
As the root for every IoT system, connected devices are responsible for providing the purpose of the IoT system, which is collecting the data. Therefore, this technology requires sensors to collect all the raw data needed for predictions. Sensors collect data from a process or an environmental condition, such as quality control, temperature, humidity, speed of an assembly line, and much more. Additionally, the IoT solution architecture allows bidirectional data flow in the form of instructions or commands that inform an actuator to take any action needed to control or maintain a process. In some cases, a sensor might detect a condition that requires an immediate response so that an actuator can perform remediation action in real-time. After all of the raw data has been compiled by the sensors, they will later be converted from analog to digital data then enable data analysis through the internet gateway.
IoT Solutions for Raw Data Collection:
- Temperature sensor
- GPS/Proximity
- Motion/Speed Sensor
- Electric Actuator
-
Hydraulic Motor
Step 2: Internet Gateway and Data Acquisition Systems (DAS)
Data Acquisition System (DAS) plays an essential role in converting raw analog to programmable digital data. Internet gateway provides a network connection between the sensors and gateway to perform DAS. This network connection can be through wireless or wired connections like LAN, USB, or GPIO. In this layer of IoT solution architecture, the gateway and DAS also help control, filter, and select data to minimize the volume of information sent to the cloud, affecting the power and overall performance.
Finding the right balance between power consumption and performance is vital for optimal overall performance. Therefore, power budgeting plays an important role. A power budget is an act of taking into account every detail of the possible power required to operate the whole IoT solution architecture. The operator must consider the performance ratio or the percentage that describes the relationship between the actual and calculated energy output. Therefore, by comparing both theoretical and practical results, a power budget can be more precisely calculated. One may wonder why this is so crucial; here are some important points of a power budget in IoT solution architecture:
- Power availability: power budget makes sure that it is still sufficient and has enough power to ensure operations in the future.
- Heat Generations: meeting the right balance between power and performance will prevent overheating. Overheating may be detrimental to the computer components or cause performance degradations.
- Cost: more power means larger components, which means a higher cost is needed to operate the computers.
IoT Solutions for Internet Gateway and Data Acquisition System (DAS):
1. SoC vs. Socket design
After calculating the power budget needed for the whole IoT solution architecture, the next step is to know which processor to use. Here we have the SoC (System-on-Chip) and Socket design. The SoC or System-on-Chip is an integrated circuit that combines all computer components onto a single substrate system. For example, along with a CPU, it also includes advanced peripherals like GPU and memory storage. As a result, this processor design is commonly used for power saving and space-constraint deployments.
On the other hand, socket design is a single connector on the motherboard that provides a mechanical connection and electrical interface with the CPU. Although socket chip design allows multiple complex processes, because it runs at high performances, it also means it has higher Thermal Design Power (TDP) or more power. Therefore, the socketed chip design processor requires additional cooling to avoid high thermal temperatures that might cause failure and thermal throttling. There is no definitive solution on which type of processor design to choose; each IoT solution is precisely selected according to the processing power and requirements.
Learn More About SoC and Socket Design
2. Performance acceleration – CPU, GPU, and m.2 accelerators for real-time processing
Performance accelerators are microprocessors capable of offloading tasks from the CPU and enhancing performance to obtain real-time decision making. A CPU alone might not be enough to process the massive influx of data coming from a growing number of IoT devices. Therefore, performance accelerators utilize parallel computing, where a system can process various tasks simultaneously at once. Some of the performance accelerators that an IoT solution architecture can leverage include multi-core CPUs, GPUs, VPUs, NVME M.2 Storage, and many more. With the help of performance accelerators, edge computers can handle all of the data from multiple IoT devices and perform complex analytics right where the data is generated.
Learn More About Performance Accelerators
Step 3: Edge Processing
In this layer of IoT solution architecture, all of the analog data that has been digitized and accumulated in the earlier stage will come down to this process, called pre-processing or edge processing. In this stage, machine learning can be very helpful to provide feedback to the system and manage the whole ongoing process without waiting for instruction from the cloud. As a result, machine learning helps reduce the data volume sent to the cloud or data center by processing some of the data right at the edge.
Workload Consolidation in IoT
Rugged edge computing solutions are required as the medium for all the data pre-processing to take place. In addition, rugged edge computing solutions offer scalable, advanced processing capabilities with multi-core processors, enormous data storage, and a variety of I/O options. Therefore, the IoT solution architecture processes can be performed with a reduced hardware footprint by leveraging robust edge computing solutions to connect all sensors, devices, and IoT infrastructure.
Learn More About Workload Consolidation
IoT Solutions for Edge Computing Solutions
Having said that, as the number of IoT devices continues to increase, so do the requirements for a more rugged and reliable edge computing solution. At Premio, we offer a wide range of IoT edge computing solutions, from board-level SBCs, small form factor IoT gateways, mini rugged computers, in-vehicle IoT systems, and even the high-performance AIoT (artificial intelligence of things) systems. In addition, each one of our products is specifically built to withstand the harshest industrial purposes without compromising performance. Here are some of the edge computers for IoT solution architecture:
Learn More About Our Edge Computing IoT Solutions
Step 4: Further Analysis in the Cloud or Data Center
At the fourth step of IoT solution architecture, the cloud or data center acts as the brain extension of the whole IoT structure processes. Datacenter or cloud-based system is purposely designed to store, process, and analyze volumes of data from multiple sensors/sites for deeper analysis. In this stage, the data center combines all data collected to obtain a more comprehensive picture of the overall IoT architecture and actionable predictions. Finally, the predictions can be transferred back to the sensors/actuators or to the end-user applications directly.
Learn More About Different Types of Machine Learning
Solutions:
Cloud-Based System
A cloud-based system is a physical facility that is used to store massive amounts of data and becomes the place to manage many applications. Rapid technology advancement allows data to be connected across multiple data centers, edge computing solutions, and cloud-based systems. Therefore, cloud-based systems must communicate across these multiple different sites by leveraging routers, switches, firewalls, storage systems, servers, and application controllers.
With rapidly growing IoT devices, cloud-based systems may encounter bottlenecks to cope with the huge increase in data volumes, thus requiring edge computers to relieve the stress placed on them. Therefore, with the presence of a powerful industrial computing solution, most of the data can be processed at the edge, and only some of the data will be sent to the cloud for further analytics. As a result, Edge computing is used to process time-sensitive data, while cloud computing is used to process and store data that is not time-driven.
Learn More About Data Center Accelerators
Step 5: Human Machine Interface (HMI) for condition and data management
Here comes the very last step of IoT solution architecture. As mentioned earlier, the final predictions from the cloud or data center will be transferred back to the sensors/actuators or directly to the end-user. Therefore, it is crucial to consider the IoT platform when it comes to direct contact with the end-user. HMIs or Human Machine Interfaces are the graphical user interface (GUI) that provides interaction between humans and machines. HMIs allow operators to manage the ongoing process and display data visualizations. As a result, HMI in the Internet of Things is vital to enable remote interaction and real-time information status from the machine systems.
IoT Solutions for HMI (Human Machine Interface)
Premio’s VIO and SIO series provide the solution for HMI in IoT implementations. Each of our rugged display monitors is specifically built to withstand harsh industrial deployments with a high level of durability and reliability. In addition, the VIO series supports modular solutions with different configurations and is tightly sealed with an IP65 rating, while the SIO series offers up to IP69K washdown IoT solutions built from ultra-durable and anti-corrosion stainless steel. Here are more details about our HMI for IoT solutions: 
Learn More About Our HMI IoT Solutions
What Is the Difference Between IoT and IIoT Architectures?
IIoT, or Industrial Internet of Things, is the use of the Internet of Things (IoT) in industrial settings and applications. IIoT has a strong focus on machine-to-machine (M2M) communication, massive data, and machine learning. In contrast, IoT focuses more on commercially used devices and applications. However, both IIoT and IoT have the same solution architecture in which they require sensors, devices, connectivity options, and data analytics. What makes them different is the built quality of the devices used. IIoT devices require a rugged and robust design quality to withstand the harshest industrial deployments.
With all that being said, IoT (Internet of Things) may include more commercially used devices, like smartwatches, smart gadgets, smart refrigerators, and many more while IIoT interconnects devices used in industrial settings, such as production lines, medical devices, intelligent transportation, and other mission-critical industrial deployments. Therefore, IIoT solution architectures use devices that must be rugged and able to withstand challenging industrial environments. Although there is definitely a difference between IoT and IIoT devices, they still do have the same IoT solution architecture.
Why Premio?
Premio is a global IoT solution provider that has been manufacturing and designing industrial IoT rugged computing solutions for over 30 years in the United States. Our solutions are carefully designed to operate reliable and optimally in the most challenging industrial deployments. In addition, Premio offers help to tailor and provide a wide range of options for customers to match different industrial requirements. As a result, Premio offers our customers the most high-end and robust IIoT solutions. If you need assistance finding the best solutions for your IoT architecture, don’t hesitate to contact us. One of our industrial computing professionals will assist you in finding the best solutions based on your specific needs.
