IoT Edge Computing: Key Points to Consider Before Integration 

With the rise of the Internet of Things (IoT), businesses are collecting more real-time data than ever. Traditionally, companies have relied on the cloud to store and analyze the data collected from IoT solutions. But, as data volumes increase, this approach comes with many challenges, such as latency, security concerns, and high operational costs. To get around these problems, IoT and edge computing bring data processing closer to end users, making it faster and more efficient.

In this article, we’ll explore what IoT edge computing is, its benefits, applications across industries, and steps for its effective integration into current workflows.

What is IoT edge computing?

IoT edge computing is the combination of two technologies: IoT and cloud computing. Let’s recall what they mean first. 

The Internet of Things, or IoT, is an ecosystem of connected physical devices that collect, exchange, and process data over the Internet without human intervention. These devices include sensors, smart home devices, connected cars, wearables, and more. Edge computing is a distributed computing model that brings data processing and storage near the source of data generation, such as sensors, IoT devices, etc. It is widely used in apps like IoT, autonomous vehicles, and smart cities, where real-time processing and low latency are crucial.

Thus, IoT Edge computing is about processing data closer to the source, or “edge,” where it is generated, either on IoT devices or nearby edge services. Instead of sending data to a cloud or a data center, IoT edge computing handles data locally and transmits only relevant data over the network to the cloud. For example, a smart security camera can analyze recordings locally and send alerts to the cloud only for suspicious activity. This reduces bandwidth and improves response time. 

To better understand “how does IoT edge computing work?” let’s consider its architecture. 

The architecture of IoT edge computing

IoT edge computing architecture is divided into three main layers: IoT, edge, and cloud. Each has its own role in data processing:

IoT layer

The IoT layer includes smart IoT devices, such as cars, robots, and industrial machinery. These devices collect the data (like temperature or humidity) from the environment or control various operations using sensors, controllers, and gateways. 

Edge layer

The edge layer serves as a bridge between IoT devices and the cloud. Its primary function is to receive, process, and transmit data while enabling real-time services like intelligent computing and analytics. This layer is divided into three sub-layers based on their processing capabilities:

• Far-edge layer (edge controller layer): located closest to IoT edge devices, this layer consists of IoT devices with built-in processing capabilities, such as smart sensors and autonomous systems. It handles initial data filtering, preprocessing, and real-time control.
• Mid-edge layer (edge gateway layer): includes edge gateways that connect to both wired networks and wireless networks (5G). These gateways receive data from edge controllers, perform more advanced analytics, and ensure secure data transmission to higher layers.
• Near-edge layer (edge server layer): includes robust edge services and offers more computational power and storage than the previous layers. It handles complex data processing, AI model execution, and integration with cloud services to reduce cloud dependency.

Cloud layer

The cloud layer receives data from the edge layer via a public network. It provides large-scale storage, advanced data analytics, and centralized management. This layer processes aggregated data from the edge, applies AI and ML models, and offers insights for decision-making. Also, it ensures long-term data storage, system updates, and overall network security.

These layers work together to process data efficiently – fast decisions happen at the edge, while deep analysis and storage occur in the cloud. 

Key benefits of edge computing in IoT

IoT and edge computing work together to make data processing faster and more efficient. Instead of sending all data from IoT devices to a central server far away, edge computing processes and stores data right where it is created. As a result, the usage of IoT and edge computing brings companies the following benefits: 

• Low latency: processing data closer to where it is generated (at the edge) reduces the time it takes to transmit data to a central server. This results in faster response times and minimizes delays.
• Bandwidth optimization: it filters and processes data locally, only sending relevant information to the central server. This reduces network traffic and lowers bandwidth costs.
• Reliability: edge computing works without relying on a central data center. Even if the network goes down, IoT devices keep processing data, ensuring uninterrupted operation.
• Security: by keeping more data local, edge computing reduces the need to transmit sensitive information over the network, reducing the risk of data breaches.
• Reduced operational costs: with less data being sent to central servers, businesses save on bandwidth and reduce latency, leading to low operational costs.

Combining IoT and edge computing boosts the performance, reliability, and security of IoT systems. After that, you may wonder, “is it better to process data at the edge or in the cloud?” To answer this question, you need to compare two approaches.

IoT edge computing vs. cloud computing

The key difference between IoT edge computing and cloud computing is where and how data is processed. Each approach comes with its own advantages and limitations. Let’s see how they compare.

Data processing location

As we said above, edge computing processes data locally. This reduces the need for cloud transmission and allows faster access to data. This is crucial for apps that require real-time decision-making, such as autonomous vehicles and monitoring systems.

In cloud computing, data is processed in centralized data centers that are often far from where the data is collected. This works well for handling large amounts of data and applications that don’t need immediate responses.

Latency 

Cloud computing has higher latency due to the data transmission to and from remote servers. For this reason, centralized cloud computing is less suitable for real-time applications. But for cases where immediate responses are not needed, it can still deliver sufficient performance.

Edge computing minimizes latency since data is processed on-site without cloud dependency. This is especially beneficial for real-time applications where immediate response is crucial. For example, for industrial automation, smart cities, and healthcare monitoring.

Security

Edge computing keeps sensitive data close and doesn’t have to travel over unsafe networks. This reduces the risk of data breaches and cyberattacks. This makes it a secure option for healthcare or government apps when security is a priority. Despite reduced attack risks, devices and connections remain vulnerable. So you still need robust security measures at multiple points.

When data is stored in one place (the cloud), it is exposed to potential risks, like cyberattacks, network vulnerabilities, and provider dependence. In the past, cloud computing was seen as a less secure option itself, but over time, it has improved significantly. Today, cloud providers use advanced security measures, strong protections, and conduct regular updates – all these often surpass edge computing. Thus, the best choice between both in terms of security will depend on your application needs.

Bandwidth usage

Edge computing helps reduce bandwidth usage by filtering the data before sending it to the cloud. This lowers network load, decreases data transfer delays, and cuts costs for processing large amounts of data. As a result, IoT systems operate faster and more efficiently, especially in real-time applications. This is also useful when the Internet connectivity is limited or when reducing network costs is a priority.

Cloud computing requires more bandwidth since it sends large amounts of raw data to remote servers for processing and storage. This increases network load, causes transmission delays, and adds extra costs. In some cases, it can even slow down system performance, especially for real-time data processing.

Scalability

In edge computing, scalability is limited by the physical capabilities of edge devices. If you want to expand, you will need to add more physical devices or edge nodes, which can be challenging to manage across multiple locations.

Cloud computing is highly scalable because providers offer almost unlimited resources on demand. Businesses can easily adjust capacity as needed without investing in additional hardware. This flexibility is beneficial for industries with fluctuating workloads, like ecommerce during sales events or data analytics with varying processing needs.

Cost

Edge computing requires a high initial investment in hardware, like sensors, gateways, IoT edge devices and local computing units. However, it can save money over time by reducing bandwidth usage and improving efficiency. For organizations that need real-time data processing, edge computing can be a beneficial solution, even though it requires a high initial cost.

Cloud computing is more cost-effective due to the centralization of resources in one place. It follows a pay-as-you-go model, so businesses only pay for what they use. This eliminates high upfront infrastructure costs, making it a flexible option. But costs can add up over time due to large data volumes, extra storage, and frequent data transfers.

IoT edge computing vs. cloud computing: a comparison table

In the table below, we’ve put together the key differences between IoT edge computing and cloud computing to help you with selection.  

Many modern businesses use a hybrid approach, combining edge computing and cloud computing to balance the need for real-time processing with the benefits of centralized data analysis and storage. In this way, the hybrid approach allows businesses to create a resilient and intelligent IoT infrastructure.

IoT and edge computing use cases

Combining IoT with edge computing has revolutionized various industries by bringing data processing closer to the source. Here are some top use cases of IoT edge computing:

Manufacturing & predictive maintenance

Industrial IoT apps benefit greatly from edge computing. Edge devices can track real-time equipment conditions, like vibration, temperature, etc. It helps to predict potential failures before they happen. As a result, factories can schedule maintenance in advance, reducing downtime and preventing costly breakdowns.

Also, edge computing in manufacturing helps maintain product quality and improve production efficiency. By analyzing sensor data from production lines in real-time, edge systems can quickly identify defects and make adjustments. For example, if energy consumption is too high, the system can instantly correct it, saving resources and improving overall efficiency.

Transportation & autonomous vehicles 

Autonomous vehicles regularly collect and analyze data about traffic, street signs, and stoplights. If the car needed to stop or turn fast to avoid an accident, sending data to the cloud would be too slow. To solve this problem, edge computing processes data (from sensors, cameras, or GPS) directly in the vehicle, enabling quick decisions and actions in real-time.

Moreover, edge computing supports Vehicle-to-Vehicle (V2V) communication, which provides faster data exchange between vehicles on the road. This allows autonomous vehicles to share information about road conditions, traffic, and accidents, improving overall driving safety.

Healthcare & remote patient monitoring 

Edge computing is vital in healthcare, especially with wearable devices. They can track real-time health data, like heart rate, blood pressure, and glucose levels. By processing data locally, they quickly detect issues and alert doctors when a problem is detected. This is crucial for patients with chronic conditions and those in remote areas.

Also, edge computing helps doctors analyze medical data (imaging, vitals) during remote consultations. Medical devices generate large files that usually take time to process on central servers. With edge computing, this data is analyzed directly on the device or a nearby edge server. As a result, doctors can review images almost instantly, make faster diagnoses, ultimately improving patient outcomes.

Smart cities 

Edge computing and IoT help make cities smarter by improving traffic flow, energy use, and waste management. For traffic management, it processes data from traffic lights, cameras, and sensors locally, reducing reliance on central systems. It enables real-time analysis, especially during peak hours. It helps optimize traffic lights, reduce congestion, and improve public transportation.

As a real-life example, SoftTeco developed C2 Smart Light, an IoT solution for smart lighting management. The system allows for remote control of outdoor lighting devices, brightness adjustment, real-time monitoring, and automated lighting based on time of day and natural light levels. This reduces energy consumption by 20% and lowers CO2 emissions.

For the system, we built a new backend for scalability, redesigned the interface, and introduced new features. This allowed us to greatly contribute to smart city growth, enhancing safety and improving infrastructure efficiency. 

Retail & supply chain 

IoT edge computing helps retailers maintain optimal inventory levels by processing data in real-time. For example, edge devices can automatically reorder items or change prices based on real-time demand. By processing data at the edge, retailers can make quicker decisions. This helps retailers monitor stock levels, predict demand, and reduce waste.

Edge computing optimizes the supply chain by tracking goods in real-time. IoT sensors monitor goods and environmental factors, like temperature and humidity, ensuring goods are delivered under the right conditions. If something goes wrong, like delay, edge devices quickly alert retailers so they can take action. As a result, retailers can identify problems, adjust delivery plans, and manage products better.

How to implement edge computing in IoT solutions

Implementing edge computing for IoT solutions requires a well-thought-out strategy to ensure optimal performance. Here are the main steps to achieve this:

Step 1. Define the use case

Before implementing edge computing, you’ll need to determine the goals of IoT deployment. For example, autonomous systems require low latency, while manufacturing equipment requires predictive maintenance. It’s also important to consider the type and volume of data generated by IoT devices. 

The next step is determining the technical requirements, such as security, latency, bandwidth, data processing, and storage. This data will help determine what computing resources are needed and how much capacity should be deployed for your IoT edge computing solution.

Step 2. Select the right hardware (edge devices)

You’ll need to choose hardware that meets the requirements of your IoT app. When selecting edge devices, consider such factors as sufficient processing power, memory, storage capacity, and connectivity options (Wi-Fi, Bluetooth, 5G). Choose from the following edge devices:

• IoT gateways: connect sensors to the cloud and handle basic data processing. Examples: Cisco IoT Gateway, AWS Greengrass.
• Embedded systems: they are small yet powerful devices with dedicated processors and memory for local data processing. Examples: NVIDIA Jetson, Raspberry Pi 
• Edge servers: they handle complex analytics, AI processing, and large-scale industrial workloads with minimal latency. Examples: HPE Edgeline, Dell EMC Edge Servers.

Edge devices require an operating system or platform to process data without relying on the cloud. So it’s important to choose software compatible with your hardware that meets the requirements of your IoT solution. 

Step 3. Implement data processing and AI at the edge

Not all data needs to go to the cloud. So, you’ll need to implement data filtering and aggregation to process only relevant data. This will reduce the amount of data sent to the cloud. You should use local AI models for anomaly detection, predictive analytics, and automated decision-making. A great tool for edge AI is TensorFlow Lite, which helps run AI efficiently on IoT devices. 

This approach requires skilled AI specialists to handle pre-trained models, optimize them for edge deployment, and integrate AI into IoT systems. Nevertheless, with edge AI, IoT devices become smarter and faster, making real-time decisions with less reliance on the cloud.

Step 4. Ensure security and compliance

To ensure robust connectivity, you’ll need to choose the right protocol built for the IoT environment – MQTT (Message Queuing Telemetry Transport) or CoAP (Constrained Application Protocol). CoAP is suitable for apps with limited bandwidth and power, while MQTT is for those that require continuous messaging and reliable communication in low-bandwidth environments. They ensure compatibility across different IoT devices and networks.

You must also set up local storage so your system can handle temporary connection issues. And for extra reliability, you can consider network redundancy, like switching between cellular and Wi-Fi when needed. As data transfers between devices and the cloud, you’ll need to implement strong security measures at the edge, including:

• Device authentication and authorization
• Data encryption (in transit and at rest)
• Secure boot and firmware update

As edge devices are often in remote and diverse environments, it’s essential to regularly update them with firmware patches, security updates, etc. This reduces the risk of attacks from malicious actors. You should use firewalls and intrusion detection systems (IDS) to protect edge notes. Firewalls block unauthorized access, while IDS monitors network activity and system logs for suspicious behavior, warning a security team of potential threats.

Step 5. Build a scalable architecture

A scalable IoT edge computing system must be designed with flexibility, security, and efficiency in mind. For this, you’ll need to design a layered architecture that usually includes: 

• IoT devices collecting data
• Edge nodes processing data locally
• A cloud backend for advanced analytics and storage

Also, you’ll need to adopt containerization (Docker) and orchestration (Kubernetes) for flexible deployments across various edge nodes. This ensures easy scalability and efficient resource management, which is vital for future growth.

Step 6. Monitor and maintain edge devices

Once edge computing deployment is done, next you’ll need to set up tools for monitoring and maintaining edge devices. Since these devices work autonomously in different locations, tracking their performance and health is vital. Below are some tips on how you can effectively manage them:

• Use real-time monitoring tools to check the health performance metrics and device health 
• Set up automated diagnostics to identify issues early
• Use over-the-air (OTA) updates to keep software and firmware up to date
• Use centralized dashboards to manage edge devices at scale

By actively monitoring and maintaining edge devices, you can ensure your IoT solution stays reliable, secure, and efficient, minimizing downtime and disruptions. 

Conclusion

To answer the question “what is edge computing in simple terms?” – it is a technology that handles data processing near its source, reducing the need to send it to the cloud for analysis. It makes IoT devices run faster, more efficiently and more reliably, reducing the load on the network and cloud servers. Beyond this, it unlocks new opportunities for automation, predictive analytics, and seamless connectivity across industries. As IoT grows, edge computing is becoming a vital solution for future success. 

At SoftTeco, we offer a range of IoT services to help businesses harness the full potential of IoT. From consulting and system integration to software development, we ensure seamless and efficient IoT solutions tailored to your business needs. We can also help you with IoT edge computing integration to enhance your data processing capabilities and operational efficiency.