As connected devices become more advanced, the volume of data they generate has exploded, with vast amounts moving from IoT-enabled hardware to the cloud every second. While the cloud has driven enormous gains in data accessibility and processing power, there are scenarios where cloud will not be available 100% of the time, especially at the edge.
What happens when smart devices in an operation can’t reach the cloud quickly enough to share updates, insights, or warnings? High latency can mean delayed data—or, in the worst cases, missed data entirely—along with information that could enhance efficiency or even help prevent critical issues.
For applications that require real-time responses or operate in remote environments, edge computing offers a powerful alternative that brings data processing closer to the source.
IoT’s growth increasingly relies on this shift from the cloud to the edge. Here’s why.
Expanding flexibility beyond cloud reliance
Today, many IoT devices are designed to send all data to the cloud for storage and analysis. This model works well with constant connectivity but introduces challenges when network speeds fluctuate or low latency is required.
Imagine a smart device—say, a phone in a remote corner of a massive warehouse—where even on Wi-Fi, the signal drops. Texts don’t send, and photos don’t upload until you’re back in range. Now, apply this scenario to critical devices like a manufacturing robot or a smart tractor.
When these devices lose connectivity, they can’t share or store real-time data, leading to operational delays and data loss.
Edge computing enhances IoT systems' ability to function reliably in diverse environments, allowing devices to process and store data locally when cloud connectivity is unavailable. This approach ensures no data is lost, and gaps in historical data are minimized, supporting consistent operations and AI/ML model development.
Long-term storage will still reside in the cloud, where its scalability and accessibility provide immense value. However, with a system that uses local storage as a backup, devices can cache data during outages and ensure the full data picture is preserved. Once connectivity is restored, this local data can sync to the cloud, enabling organizations to take full advantage of cloud analysis while maintaining resilience in the face of network disruptions.
Understanding IoT edge computing
Edge computing is a way to collect, process, and initiate actions with data when IoT devices are working on the edge.
With Viam, this looks like storing information in temporary caches within a device with the ability to upload data into the cloud once the device regains service. That way, the device is not reliant on IoT alone or susceptible to losing data records when it goes just out of reach.
Edge devices come in many forms—sensors, cameras, applications, phones, robots, and more. They often make autonomous decisions based on local data, ensuring reliable operation even when disconnected.
The role of edge computing in IoT growth
As IoT scales across industries, edge computing is becoming essential for fast, reliable, and secure operations. Here are some of the key ways edge computing essential is driving IoT growth:
Instant, autonomous decision-making
Edge computing enables IoT devices to make decisions on their own, without waiting for instructions from the cloud. For instance, a factory sensor on the edge can detect equipment malfunctions and alert operators immediately.
This local processing capability empowers IoT systems to act in real-time, minimizing delays and improving overall efficiency.
Reduced latency for real-time applications
Latency is a significant hurdle for IoT applications that require instant responses, such as robotics in manufacturing or safety notifications in marine applications. By processing data at the edge, these devices can respond to conditions faster than if they had to communicate with a distant cloud server.
This reduced latency improves responsiveness, enabling applications that demand real-time accuracy.
Enhanced data privacy and security
For industries like manufacturing or international shipping, data security is critical. Edge computing adds a layer of security by processing sensitive data locally, rather than constantly transmitting it to the cloud. By storing information closer to its origins, organizations can reduce the risk of interception and keep sensitive data within controlled environments, even while using connected devices.
Efficient use of bandwidth
As IoT networks grow, bandwidth consumption becomes a costly factor. Edge computing conserves bandwidth by filtering data at the source, only sending necessary information to the cloud.
For example, a video camera on the edge might only upload footage to the cloud when unusual activity is detected, reducing the volume of data transmitted and saving on bandwidth.
Reliable operations in remote locations
Many IoT applications function in areas with inconsistent connectivity, such as on marine vessels, agriculture fields, or remote factories. Edge computing allows devices in these settings to continue operating, even when they’re offline. Data can be cached locally and uploaded to the cloud once a stable connection is re-established, ensuring no valuable information is lost.
How edge devices operate in IoT systems
Edge devices offer the best of both worlds: they process and record data locally in close proximity to where it’s gathered, and at the end of the day, upload it to the cloud when connectivity allows. Here’s how they do it:
1. Cache and process data locally
Picture a robot on a manufacturing line working on the edge that has its own set of autonomous sensors. Using edge computing, it can detect and remove defective items in real-time, like pulling a broken cookie from a batch of thousands. As it makes these quick decisions, the robot caches data locally—logging each action and tracking insights, such as how many cookies were rejected.
The local processing allows it to identify and act on tiny data pieces rapidly, like rising temperatures in a food manufacturing facility or unexpected movement, and automatically shut off or alter vulnerable components.
2. Upload when connected
When the robot’s connectivity returns, it securely uploads data onto applicable cloud servers. Viam’s edge solutions ensure that data sent to the cloud is encrypted and protected through robust role-based access protocols, minimizing the risk of interference during transmission.
3. Integrate seamlessly
Integrating data from various edge devices can feel like a puzzle, especially across different hardware and software. Viam’s platform connects with existing systems through one clear interface, so you can monitor and manage all real-time and historical data in one place.
Data transmission strategies for edge devices
Edge devices typically work in IoT by implementing a hardware solution that pairs with cloud services for safe, reliable transmission of inbound and outbound data onto all edge devices, no matter their make, model, or hardware. However, those processes take on different types of key tasks.
Inbound data
IoT edge computing solutions offer a massive benefit to users:
Inbound data can enter all working parts of a fleet. Think about that robot on a manufacturing line that may be one of many scattered in different parts of a facility, or even across numerous territories outside of the facility itself. Inbound data capabilities ensure that applicable updates can reach all types of devices and download automatically, without human interference.
You can also send out applicable code tweaks or rewrites with inbound functionality without that manual step of sending an employee out to physically update equipment.
Outbound data
Outbound data on IoT edge devices works in the opposite direction. A variety of robots in a manufacturing facility, no matter their make, model, or vendor, can communicate via edge computing software, cache their locally collected data points, and sync information to their encrypted cloud home when they have the service to do so.
Real-world applications of edge computing in IoT
IoT edge computing is vital to managing far-reaching fleet performance when numerous smart devices collect and transmit information on the edge of their processing limits.
Specific examples of the benefits of IoT edge computing are in industries like:
- Marine transportation: Ships are constantly on the move in remote waters and require second-by-second data on fuel usage, safety metrics, and location to transport cargo safely. IoT edge computing solutions ensure that data acquired when a ship is spotty or goes completely offline is saved locally and synced to the cloud later.
- Factories: A key task of IoT devices across factories and manufacturing facilities is monitoring the status of various pieces of machinery to detect that all systems are functioning properly without unexpected delays. If a piece of equipment is on the edge and not updating in real time, it can put an entire manufacturing operation at risk.
- Food and beverage: Similarly to factories, food and beverage manufacturing facilities work with equipment lines that require daily monitoring and maintenance. If a portion of the line is computing on the edge, key parts of your production operation like ovens, refrigerators, and conveyor belts may not report their status properly in real time, which can result in unexpected downtime or compliance errors.
How Viam enables edge computing
Viam is designed to empower edge computing by providing the tools and infrastructure needed to capture, process, and manage data locally, even in the most demanding IoT environments.
By integrating Viam’s platform, organizations can unlock the full potential of edge computing to optimize operations and reduce reliance on constant cloud connectivity.
Here's how Viam supports edge computing:
Edge data capture and local processing
With Viam, devices can capture and process data locally, reducing latency and enabling real-time decision-making.
For instance, Viam’s platform allows IoT devices to analyze and act on data immediately, such as identifying anomalies or triggering automated actions without waiting for cloud instructions. This ensures that critical tasks are performed without delays, even in environments with limited connectivity.
Secure data storage and transmission
With Viam, security is built into every aspect of edge computing. Viam's platform uses encrypted communication for all data transfers, ensuring that sensitive information remains protected both on the device and in transit to the cloud.
Additionally, access to devices and cloud services is managed with real-user credentials, providing robust access control to safeguard critical operations. By combining edge computing with Viam's security features, organizations can confidently protect their data while enabling seamless connectivity.
Seamless cloud integration
Viam bridges the gap between edge and cloud computing. Devices can cache data locally and upload it to the cloud once a stable connection is available.
This flexibility ensures no data is lost and allows for a centralized analysis of both real-time and historical data. Viam's platform handles this seamlessly, providing a unified interface to manage all devices and data.
AI and machine learning at the edge
Viam supports AI and machine learning deployment directly on edge devices. By enabling these capabilities at the edge, devices can perform advanced tasks such as image recognition, predictive maintenance, or quality control autonomously, reducing dependence on cloud resources while delivering actionable insights in real time.
Viam partnered with the Whale and Vessel Safety Task Force (WAVS) to create an open-source program using AI to detect marine life and reduce vessel strikes. Viam’s platform integrates data from diverse equipment, organizes it for AI/ML training, and deploys models back to vessels for tasks like triggering alerts or slowing boats.
The collaboration addresses challenges like low connectivity and fragmented systems with an effective edge-to-cloud solution.
Scalable and modular architecture
Viam’s platform is designed to adapt to diverse use cases and industries. Its modular architecture allows for seamless integration with existing hardware and software, ensuring that businesses can implement edge computing solutions without overhauling their current systems.
Canyon Runner’s web interface, which thanks to Viam allowed them to integrate data from diverse sources into a single view, including marine electronics, sensors, radar, GPS, AIS, National Oceanic and Atmospheric Administration (NOAA) climate and ocean data, and much more.
For example, Viam partnered with Canyon Runner, a charter fishing company, to modernize their fleet with IoT-enabled systems. By integrating Viam’s platform, Canyon Runner connected legacy equipment like sensors, cameras, and other onboard devices into a unified system. This allowed them to monitor operations in real-time, track fishing activity, and optimize fleet performance—all without replacing their existing infrastructure.
Integrate edge computing into your IoT system
As IoT expands into more sectors and applications, integrating edge-based processing alongside cloud computing will be critical to driving future growth.
With Viam, edge computing solutions are designed to integrate seamlessly with existing IoT setups. Viam’s platform provides secure, encrypted data transmission, real-time decision-making capabilities, and compatibility with diverse IoT devices. By supporting both edge and cloud operations, Viam empowers businesses to harness the full potential of IoT while keeping operations efficient, responsive, and safe.
Ready to take your IoT data to the next level? Explore our edge data capture solution to see how Viam can help you collect, process, and act on data efficiently at the edge. For more tips and strategies, visit our blog on how to better optimize your IoT data.
