How Internet of Things Data Collection Works: A Full Guide

There are over 15 billion IoT-connected devices worldwide right now. Can you imagine how much data these gadgets generate? For an organization that plans to adopt the IoT or has already done so, proper Internet of Things data collection is no small feat.

Luckily, this article will guide you in data collection and analysis in IoT. We will explain a robust IoT data-gathering system’s core principles, layers, and architecture and IoT data collection methods. Also, we will outline the potential challenges you may encounter along the way.

 As an experienced IoT vendor, WebbyLab will share its profound expertise in IoT-based data collection using our Smart Heat project as an example. Read along to get valuable insights on this topic backed up by our in-depth knowledge.

Importance of the Right IoT Data Collection for Businesses

Proper data collection from Internet of Things devices can deliver numerous benefits to businesses. Here are the main ones:

• Improved operational efficiency. An IoT device data collection system boosts productivity by automating sensor data gathering, eliminating the need for manual collection.
• Accurate real-time insights. IoT data collection enables real-time monitoring and prompt issue resolution for businesses.
• Better decision-making. Proper IoT data collection allows businesses to gain insights into customer behavior, market trends, and operational performance, assisting in decision-making, predictive maintenance, and strategic planning.
• Saved costs. IoT for data collection offers significant advantages by identifying process inefficiencies, enabling businesses to optimize operations, reduce costs, and improve profitability.
• Improved customer experience. If businesses deliver services to consumers, e.g., smart home technologies or industrial IoT systems, Internet of Things data collection can help them improve customer experience with their products by studying clients’ preferences and behavior.

On top of that, McKinsey states that IoT and data collection have significant economic-value potential for various industries. The company estimates that the Internet of Things can unlock from $5.5 trillion to $12.6 trillion by 2030, with B2B applications creating the majority (65%) of that value.

Various Types of IoT Data You Can Collect

As you’ve seen the undeniable benefits of Internet of Things data collection systems, you might now wonder what IoT collects and how this data can be utilized. Let’s look at the core types of data:

• Automation data. It comprises insights into the automated systems’ performance, status, and operation.
• Equipment data. It provides insights into the usage, wear and tear, and performance of sensors, machines, or vehicles.
• Environmental data. It includes information on the physical environment, such as temperature, humidity, movement, air quality, noise levels, etc.
• Submeter data. Submetering includes information on energy consumption, such as electricity, gas, or water.
• Location data. It comprises information on the location and movement of people, objects, or vehicles.

How Does IoT Data Collection Work?

The Internet of Things data collection system leverages several principles for its proper operation. They include the following ones:

• Scalability — robust IoT data collection systems must be scalable enough to gather and store large volumes of data
• Security — IoT-based data collection systems must provide top-notch security to prevent data breaches or unauthorized access
• Interoperability —  IoT data collection systems must be able to get data from different IoT devices.Notably, IoT devices can collect data from various sources, including sensors, meters, and user interactions.
  
• Flexibility — IoT data collection systems must accept different data formats and adapt to changing requirements

IoT Data Collection Stages: WebbyLab Experience

Our team at WebbyLab divides IoT data collection into two stages:

• IoT Data Collection at the Firmware Level

At this stage, we implement the firmware, which will receive the necessary data from the peripherals, i.e., sensors, controlled devices, built-in modules, etc.

In this case, we may collect and store IoT data in two ways:

• Data storage on a local or cloud server
• Data storage on the IoT devices’ internal memory

Both data storage options are viable for different cases:

• In the first case, the device must be able to send data to the server immediately after collection. That is, there must be a stable connection with the server to transmit data in real time.
• In the second case, this approach is suitable if the device doesn’t have a permanent connection to the server. There also may be edge cases when the connection isn’t established. Under these conditions, the data needs to be saved and transferred to the server later.
• IoT Data Collection on the Server Side

Our experts provide smooth Internet of Things data collection and server-side storage at this stage.

First, we ensure the server supports the API for receiving data from all connected devices. Once received, this data must be stored and available for further processing. It is also worth considering that the data amount will typically be large, and the platform’s server part must be ready to withstand this load.

IoT Data Collection Methods as Evidenced by WebbyLab’s Background

WebbyLab utilizes three methods for efficient IoT data collection. Here’s a description of each:

• Collecting Data from IoT Devices via MQTT

In such a scenario, we connect all IoT devices to a local or remote MQTT broker, ensuring real-time IOT device connectivity.

This IoT data collection method simplifies firmware implementation for devices. They only need to connect to the broker and update their sensor and telemetry values without storing them in memory. The server, in turn, is responsible for collecting and saving the data.

WebbyLab already has a service that tracks broker updates and saves historical data to the InfluxDB database. It enables real-time collection and storage of device information without putting a heavy load on the gadget.

InfluxDB is specially designed for such tasks, and it’s scalable and provides access to data for the next level of the application. It also has an API that enables the filtering and aggregation of data. For example, we can study the average temperature for each day over the last six months.

There are many combinations of filters and aggregation functions in InfluxDB. The only drawback and complication of such a process is the guarantee of data delivery and preservation. In this approach, there are several stages at which data can be lost, so you should work out both the firmware itself and the server part carefully. Additionally, the IoT device must always be online.

• Using IoT Device Firmware to Save Data

In this approach, IoT data is stored in firmware memory. Our experts set the current time for the firmware to record data changes accurately. How does it work?

 The IoT device works in stand-alone mode and collects a certain amount of data during operation but may not have a connection to the server due to implementation logic or other reasons. Yet, since we set the current time, the firmware stores all events in memory with accurate timestamps. Once connected, the device transfers all data to the server, which records it in the database. Since each event is tied to a timestamp, the data will be available to the user or administrator with reference to the moment it occurred.

For this approach to work correctly, you need to work out the rotation of the stored data on the device. It prevents the accumulation of data and ensures that the controller’s memory isn’t completely consumed.

• A Hybrid Model

With this approach of IoT data collection, the device sends data to the server in real time if it is online, such as via MQTT. If it goes offline or loses connection, it stores the data on the built-in memory and sends it to the server when the connection is restored.

This approach should be used where there is a clear need for it, as it will take a long time to implement and often require support for different APIs.

Each of the three approaches is best suited for specific situations:

• We recommend using the first approach when gathering data and controlling the device in real time is critical. MQTT is perfect for this since it enables instant data collection with minimal delays. Yet, this process requires a stable connection.
• The second approach is recommended when it’s essential to store all device data and there’s a risk associated with potential device accessibility to the server. For instance, this can be relevant for battery-powered devices where maintaining a constant connection to the server significantly impacts device longevity on a single charge.
• The third option covers most scenarios, but it’s essential to consider its necessity, as it will take the most time to implement in the IoT ecosystem.

Kostiantyn Oliynyk

Head of IoT at Webbylab

Helped dozens of startups create effective IOT products.

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IoT Data Collection Architecture

The Internet of Things data collection architecture consists of several layers. Here’s a brief description of each:

• Device Layer

This layer comprises the IoT devices like sensors, cameras, or smart home appliances. The devices collect data from the physical environment and transmit it to the next layer.

• Communication Layer

This layer transfers data from the IoT devices to the processing systems. It includes communication protocols and networks like Wi-Fi, Bluetooth, MQTT, Zigbee, etc.

• IT Edge Layer

This layer is located at the edge of the network, closer to the devices, and comprises the hardware, firmware, and operating systems of the IoT devices. It plays a crucial role in data processing for IoT, performing preliminary processing and analysis of the data collected from connected devices.

• Event Processing Layer

This layer processes and analyzes the data in real time, identifying patterns and anomalies and triggering appropriate actions or alerts. It also cleans the data, organizes datasets, and generates insights.

• Client Communication Layer

This layer communicates the analysis results to the end-users through dashboards, reports, or mobile applications. It’s essentially a bridge between the database and the user interface.

ll layers of the  IoT sensor data collection system are critical to create a complete end-to-end solution that gathers, processes, and delivers insights from IoT devices.

Challenges of IoT Data Collection

As we said at the beginning of our post, handling proper IoT data acquisition and collection might be challenging. But if you learn about the potential obstacles beforehand, you can overcome them in your project effortlessly.

• Historical data integrity. Your IoT project’s efficiency depends on historical data. It’s necessary to preserve such information continuously and maintain its integrity in real time. For example, if a particular metric updates every 5 seconds, its real-time storage is critical.
• Offline data. Ensuring data storage when your IoT device is offline is essential for retrospective analysis. It’s best to leverage tools that save data locally on the device before transmitting it to the cloud.An effective IoT data collector can facilitate this process by managing local storage until connectivity is restored.
  
• Various types of data visualization. Your IoT ecosystem usually comprises different sorts of data, like numeric, string, logical, and others. To visualize them properly, you need a library of dedicated UI components. These may include tables, bar charts, line graphs, or custom elements, given that each data type requires a distinct analysis approach.
• Large volume of data visualization. When dealing with large datasets, it’s crucial to load them gradually in small batches rather than all at once. It’ll help you avoid the user interface freezes. Implement lazy loading, pagination, and scaling differently for each visualized component and focus on the UX part along with the UI for a smooth user experience.
• Data rotation. The amount of data within your IoT system will grow day by day. That’s why it’s necessary to leverage tools for data rotation and, conversely, implement automatic scaling in backend applications. This approach allows you to use resources efficiently and adapt the software to the required volume.

Examples of IoT Data Collection Systems [Based on WebbyLab Experience]

Our project on the heat pump enhancement Smart Heat is an IoT data collection example that resulted in better preventive maintenance. With the help of our team, a heat pump manufacturer could collect telemetry remotely and access historical data from all connected sensors.

Viewing the heat pump status through a dashboard.

The manufacturer also reduced maintenance costs by connecting to a specific heat pump and receiving data from its sensors in real time. With our IoT solution, any occurring issues can be resolved remotely by making the necessary changes to the configuration of the equipment.

IoT data collection and storage is generally the primary focus of the WebbyLab team. As part of the Smart Heat project, our experts made it possible to store historical data from each sensor in the cloud by connecting the IoT devices to our 2Smart Cloud platform.

The manufacturer, in turn, can provide access to this information to their devices’ end users. All that’s necessary is to configure the mobile application interface by selecting one of the widgets that supports the IoT data collection and visualization, e.g., Number, and connect it to the required sensor.

Adding the Number widget to a 2Smart Cloud platform.

• Viewing and analyzing the IoT data of connected devices is also a critical part of the 2Smart dashboard, originally developed for manufacturers of EV charging stations MyBox. As part of this project, our developers enabled the following:
• In the Device Management section, users can find an interface of a specific connected device with the current state of all its sensors displayed. It’s similar to what our team implemented in the Smart Heat project but with possibilities of deployment following the white label model.

Viewing the interfaces of connected devices through the MyBox dashboard.

• In the System Monitoring section, users can access the Grafana web application, which provides a complete picture of the system’s state based on device data. The administrator can visualize this data for analysis through graphs, tables, and charts.

All in all, most of our IoT projects enable the Internet of Things data collection and analysis. When building our products, our team aims to facilitate decision-making and real-time insights gathering through robust IoT-based data collection tools.

WebbyLab Can Drive Your Internet of Things Data Collection

Data collection for IoT applications can facilitate businesses in decision-making, offering valuable and accurate real-time insights. Having overcome the associated challenges, companies can cut expenses, optimize their processes, improve customer experience, and gain a competitive edge.

If you need a partner to help with your IoT data collection, IoT device development, processing, and measurement, WebbyLab has an expert team for you. We can guide you through the entire process, from the device to the client communication layer, to meet your business needs. So why wait? Contact our team today to streamline your IoT data gathering and storage.