Cloud vs. Edge Computing for Telematics

For years, commercial fleets have depended on operational data to make decisions, improve safety and compliance, and plan for the future. As trucks have gotten smarter and telematics richer, more and more data has streamed into the back office to be processed, stored, and accessed in the cloud. 

But cloud computing has some very specific limitations that work against the nature of trucking — specifically a latency that hampers real-time data and response times. Today’s fleets looking to the future are learning more about the benefits of edge computing, and how it can work hand-in-hand with cloud computing to deliver more responsive and affordable data processing solutions.

The Benefits and Limitations of Cloud Computing

Cloud computing is widely known and understood across many of today’s commercial and consumer industries, as many businesses have moved away from maintaining their own on-site hardware and software management in exchange for secure, virtual data processing and storage. For commercial fleets, cloud computing has many useful features. For example, it offers virtually unlimited scalability, allowing fleets to scale up or down based on demand, which is particularly beneficial for fluctuating workloads.

Additionally, cloud computing centralizes management, making it easier to deploy, monitor, and manage applications and resources. While this centralized approach simplifies administration, it can also introduce dependencies and single points of failure that can impact the entire fleet. 

Cloud computing also depends on constant connectivity, as data is collected and transmitted to the back office for processing. This can cause issues when a truck loses connectivity or goes offline, introducing latency in the data transfer.

The Benefits and Limitations of Edge Computing

An emerging technology gaining momentum is edge computing, which shifts the data processing capabilities to the deployed device itself  — in the case of telematics, to the truck. The ability for a device to perform complex computation on a real-time basis and trigger an immediate response can be incredibly helpful in trucking, as many on-road decisions need to be made quickly to avoid serious accidents or malfunctions. Access to real-time truck data can make the back office more responsive, allowing it to advise on a new route, connect maintenance with malfunction alerts, and identify and address potentially dangerous driver actions.

The limitations to edge computing typically include lower processing power and smaller storage capabilities. Additionally, edge devices must be specially designed and equipped with this new technology, and staff must be properly trained to use and respond to it accordingly. 

Combining Cloud with Edge for the Best of Both Worlds

Fleets looking to utilize the storage and processing power of the cloud with the real time responsiveness and reliability of edge computing are finding a hybrid approach to be a beneficial tool. By implementing an architecture that combines cloud and edge, fleets can introduce more automation into drivers’ and back office tasks, while improving reliability and security of their data sets.

For example, since edge computing processes at the truck level, a vehicle that goes out of network range does not lose its data collection, and can continue processing until a network is rejoined. This is especially important in situations where seconds count, like notifications for pedestrian motion detected, trailer refrigeration levels low, or low tire pressure. 

At the same time, cloud connectivity ensures that the ever-increasing amount of data sent from smart trucks on a massive scale can be properly processed into actionable information. This data can be used for tasks like driver training, fuel efficiency guidelines, route planning, and much more. Both cloud and edge computing also offer encryption and security features that support data processing and storage regulations, as well as fleet security and data privacy.

Finally, both cloud and edge computing are typically cost-effective solutions for fleets, since clients are usually charged only for the services they use, removing the costs associated with traditional, custom-made networks that involve onsite hardware that must be acquired, managed, and maintained. Virtual computing allows for easier expansion and responsiveness to each fleet’s business current status, and helps future-proof operations.

Real World Example: Cloud and Edge Working Together

The idea of combining cloud and edge computing for seamless coverage and responsiveness makes sense on paper, but how might it look in the real world? As a practical example, you could consider a driver with an overnight delivery of perishable goods, who experiences a critical fault in the vehicle during the night. The driver is promptly notified on the edge to pull over the vehicle and that the truck is no longer driveable. Simultaneously, backend systems use the cloud to automatically identify the driver’s location and route another driver/vehicle in the appropriate vicinity to go and pick the load up from the driver. 

Combining the range of the edge with the comprehensiveness of the cloud allows a seamless handshake and constant connectivity to keep drivers safe and jobs on track.

Platform Science’s Approach to Cloud + Edge Computing

Working with fleets across the globe and with numerous application developers, we have seen firsthand the convergence of cloud with edge computing, and the benefits a hybrid approach delivers to fleet operations. Our telematics tools operate both at the edge and with the cloud, ensuring that data collection is never interrupted, even when a truck is offline. Our third-party add-on app ecosystem allows customization that operates on the edge as well, for special focus on fuel monitoring, tire pressure monitoring, navigation, safety cameras and more.

‍Explore the Platform Science platform and how it combines cloud and edge technology, or set up a consultation by contacting us today.