âThe spokes in this model are the truck drivers who use edge technology like onboard computers, electronic logging devices, and more in the cab while theyâre on the road,â Orban says. âThese devices are connected to the cloud via 4G LTE networks, and are performing many calculations on the edge. These calculations include tracking a driverâs hours of service, reporting on safety events, and scanning electronic documents such as proof of delivery.â
The company has been using some iteration of edge computing for years, Orban says, starting when the tracking of commercial vehicles became regulated. Trimbleâs early electronic devices in the cabs of trucks relayed back simple information about where trucks were, as well as fuel levels, âproviding communication to the back office during a time when we didnât all have cell phones in our pockets,â he says.
The key business driver for this was the need for trucking companies to know where their assets were, and the ability to communicate with drivers and devices that might be outside of cellular coverage. âThe devices had to function on the edge with drivers, because satellite communication might have been their only connectivity option,â Orban says.
From a safety standpoint, all of Trimbleâs mobility devices interface between the engine control module (ECM) of a commercial truck and the companyâs own and third-party safety tools to provide functions such as hard braking alerts, following distance warnings, and roll stability control notifications.
âIn the cab, the driver can receive immediate feedback about their driving behavior and can modify it effectively in real time, or the device can do it for them,â Orban says. âFor example, if they see that they are going around a curve too fast and the roll stability control triggers, that device can actually trigger the brakes and slow the truck down and bring it into a state where that roll stability control is not firing anymore.â
Trimble also offers edge applications for calculating a driverâs fatigue, based on their hours of service.
Another big area where Trimble is investing in its data analytics and edge computing capabilities is video. âMany commercial vehicles today have cameras installed in the truck, either pointed outwards or inwards on the dash, on the side mirrors, a reverse camera in the back, or all of these,â Orban says. âThe amount of information that can be gleaned from these visual data sources is immense.â
Trimbleâs Video Intelligence tool is triggered when a safety event occurs, such as a hard braking event where a driver slams on the brakes to avoid a collision. These videos can be used to mitigate risk for a driver, to prevent liability during an accident, or for driver training purposes.
âWeâve gone so far beyond the standard dash cam to a place where now we have truly integrated video systems that can help a driver with their work by looking down the side of the truck to give them lane departure warnings, help them properly position at a loading dock, and more,â Orban says.
Trimbleâs data science team is deeply involved in its edge analytics offerings, Orban says. âOur team invests a great deal of time working directly with customers to understand their specific needs for their business,â he says.
Typically, as Trimble customers begin to apply safety systems, they see an incremental improvement over time.
âOften, they will experience a 10% to 15% reduction in their driversâ preventable accidents, as they apply different kinds of critical event warnings, such as hard brakes, roll stability control, and more,â Orban says. âThose sorts of edge solutions are literally changing the behavior of the driver whoâs taking these actions.â Traffic control
The City of Las Vegas is using edge computing, including an IoT deployment, for traffic control and to automate communications with autonomous vehicles.
âWe are looking for ways to improve operational efficiency while providing benefits to the community,â says Michael Lee Sherwood, chief innovation officer in the cityâs IT department. âEdge computing enables IoT systems to process critical data and provide real-time analytics and data.â
The traffic system, known as Blackjack, uses a platform supplied by Cisco that monitors traffic flow and provides real-time traffic statistics and capabilities for communicating with autonomous vehicles.
Las Vegas began deploying edge computing technology in 2018 while working on smart traffic solutions. A key driver for analyzing data at the network edge came from working with autonomous vehicle companies that needed near real-time data, Sherwood says.
âEdge computing allowed for data to be analyzed and provided to the recipient in a manner which provided the best in speed,â Sherwood says. Visualizing data in a real-time format âallows for decision-makers to make more informed decisions.â
The addition of predictive analytics and artificial intelligence (AI) is helping with decisions that are improving traffic flows, âand in the near future will have dramatic impacts on reducing traffic congestion and improving transit times and outcomes,â Sherwood says.
To help bolster its data analytics operations overall and at the edge, the city government is developing a data analytics group as an offshoot of the IT department. The Office of Data and Analytics will drive how data is governed and used within the organization, Sherwood says. âWe see lots of opportunities with many new technologies coming onto the market,â he says. âOur main goal has focused on building our team and working on the governance and the curation of data sources.â
The benefits of the early stages of the deployment include enabling traffic intersections to adjust signaling timing based on actual traffic flows. This âis a positive indication of how these new technologies can really help create solutions which benefit everyone in the community,â Sherwood says.
With AI now playing a role in some of the cityâs newest smart systems, the need for edge computing and analytics at the edge will only grow, Sherwood says.
Most of the challenges of edge computing involve deciding on what processing to do at the edge, and how the data is stored and for what time period, Sherwood says. âWe are still working through this process, and the more systems and pilot projects we undertake, the more we learn about the art of the possible and reality,â he says.
Earth observation
Satellogic, a company that provides commercial and government customers with high-frequency, high-resolution geospatial imagery and analytics, is taking the concept of edge computing to the extreme.
The company, which manufactures its own satellites, is working with several partners, including big data analytics software provider Palantir Technologies, to move its data analytics to the edge of its network â on board its satellites.
Satellogic is building and operating a constellation of satellites that collect multispectral and hyperspectral images as well as full-motion video, says Gerardo Richarte, CTO and co-founder of the company.
âWhen designing and building our first satellites â over 10 years ago â we knew that we needed to make decisions at the edge,â Richarte says. âOur first satellites flew with hardware and software onboard to take advantage of edge computing, and being vertically integrated meant we could be highly agile in developing and testing new technologies in orbit.â
Initially the satellite-based computing work was internal and experimental, Richarte says. âAs our customer base expanded, we started working with customers to stream their image-processing algorithms in orbit,â he says.
Edge computing unlocks three major enhancements to Satellogic customersâ experience, Richarte says. âFirst, edge computing will allow us to provide customers with real-time alerts,â he says. âThe closer we are to the source of information, the sooner we can generate and dispatch the alerts each of our customers requires.â
Second, the company can take actions at the edge, including retasking. âWhen a particular object of interest is flagged by an algorithm, we can instantly retask a satellite to lock onto, track that object, or enable a different product like a full-motion video capture,â Richarte says.
An algorithm might trigger a satellite to instantly turn on a particular payload to capture data that would have otherwise been missed. Full-motion video (FMV), for example, âis an excellent application for edge AI, as it can prove critical for certain kinds of decision-making,â Richarte says. âBut [itâs] far too data-intensive to run continuously.â Edge AI algorithms programmed according to precise customer needs can define the parameters for leveraging Satellogicâs FMV along with other data and cost-intensive payloads, he says.
Finally, edge computing can be leveraged to prioritize data transport. âRemote connections from orbit to ground have limited bandwidth, and data download may take longer than required by certain applications,â Richarte says. âBy running satellite data through algorithms at the edge, we can orchestrate data transport according to each individual customerâs priorities and goals.â
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