As technology advances, the opportunity for optimizing operations within the trucking industry increases.
Recent technological developments in trucking including the Federal Motor Carrier Safety Administration’s 2017 electronic logging device (ELD) mandate. Carriers of all sizes suddenly were expected to have an advanced system for monitoring drivers’ locations and hours of service. But the mandate also presented an opportunity to upgrade technology in an industry that most would consider antiquated at best.
In response, ELD vendors began emerging to meet the new marketplace. Most opted for a cloud-computing model, where mandated data is gathered remotely, transferred via an internet connection to an external server, and returned to the user after processing.
At the same time, another technology emerged within the ELD market: edge computing. Unlike cloud computing, it moves logic and data processing from a centralized server out to the furthest point (or edge) of the network. In an edge computing model, data is gathered, processed, and analyzed at the point nearest to its origin, with the results then transmitted to the cloud for viewing, reporting, and sharing. Edge computing technology works seamlessly even when a network connection isn’t available.
The edge computing model provides enhancements for live data computing. With data processing done at the source, utilizing vast amounts of energy-efficient computing power at the edge of the network’s ecosystem, the inherent latency of the network is significantly improved, and data-transmission requirements and power are reduced.
Edge Computing for Telematics
Telematics customers have the opportunity for significant improvement in performance and user experience with the new distributed edge computing architecture.
In an edge computing-designed system, all gathering and processing of telematics data occurs within the cab of the vehicle. This offline computing architecture is more reliable than the prior generation of cloud computing, which often breaks down when network service is unavailable or there are problems on the cloud server.
To understand the rationale for edge computing, consider a recent real-life example of a cloud-computing failure. In late 2019 and early 2020, customers of two of the largest fleet management telematics providers, Qualcomm Inc. and Omnitracs, reportedly experienced ELD outages affecting hundreds of thousands of vehicles and drivers. Incidents of that type don’t affect edge computing telematics systems, which continually gather and process live data in the cab regardless of the availability of a network connection or server in the cloud. As a result, fleet managers can be confident that data from the driver’s source will be accurate and readily available.
Edge computing provides the resources to support ELD compliance at all times, with complete visibility into fleet operations. Oftentimes, fleets lose that visibility when a driver passes through a remote location out of cell range. With an edge computing device, once the network and service become available, data is synchronized and transmitted to the cloud for sharing, viewing, and reporting. Fleet managers are able to communicate with drivers, review hours-of-service data, and optimize backhaul opportunities in real time.
Do you need an edge computing system for ELD and fleet management? The short answer is yes. Edge computing telematics provides real-time visibility, with all gathering and processing of data occurring within the cab. This offline computing architecture is more reliable than the prior generation of cloud computing, which often breaks down when network service is unavailable or there are problems on the cloud server.
Latency is the delay between requesting data and the start of the transmission. The latency of edge computing technology in the cab is more than 15 times less than that of a 4G LTE network. Many legacy fleet-management providers still have hundreds of thousands of units operating on 3G, which is twice as slow as 4G LTE.
With edge computing technology in the cab, data can be secured through biometrics (fingerprints or facial recognition). Additionally, security risks are greatly reduced, as an attacker would have to simultaneously attack multiple mobile edge computing systems to meaningfully impact a business.
With edge computing, transmission errors are greatly reduced. Properly designed edge computing ELD and fleet-management systems are fully redundant, with all data being positively validated before it’s entered into the system of record database.
By utilizing the supercomputing power of today’s most advanced smartphones, companies can obtain a lower total cost of ownership for their ELD and fleet-management technology. Gone are the days of buying expensive, unreliable, and proprietary hardware. Additionally, installations of edge computing systems can be done in a little as 10 seconds, meaning that the fleet owner isn’t taking trucks out of service for a day or more when installing hardware.
With edge computing, there’s no longer a need for expensive proprietary hardware that’s locked into a single application. Systems can be built on standard iOS or Android devices, to lower cost and improve the user experience.
According to a recent Gartner report, only 10% of enterprise systems currently utilize edge computing, but its use is predicted to grow to 75% by 2025. The next generation of fleet-management systems providers are already offering edge computing. By adopting technology that’s faster, more reliable, more secure and more affordable, fleets can focus on what truly matters — running their businesses and moving freight.
Ken Evans is founder and CEO of Konexial, a provider of technology for truckers.
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