Accurate and collaborative real-time geospatial information is needed in all operational environments and complex situations across the globe. This is necessary for organizations to make timely and relevant decisions with the best available information. Our current and future customers need much of this location-rich data in real-time that will improve with the power of a 5G network.
This intersection of geospatial intelligence and 5G becomes a critical link to empower our developers to exploit these advancements for real-time analytics that require ultra low latency. 5G’s lower latencies, without the obvious limitations of a tethered ethernet cord, enable near-instant decision-making and collaboration to help critical applications. One clear example is self-driving vehicles where the data flow is increased to make better autonomous decisions keeping the driver and neighboring vehicles safe.
Deploying our machine learning algorithms to the edge is a paradigm-shifting technology that will further empower Slingshot to truly serve its purpose: shifting intelligence to the edge where it is needed, at speeds, latency, and costs that make it affordable for every enterprise and worker. We believe learning-enabled edges will survive natural selection in an increasingly competitive AI ecosystem.
Slingshot has developed Slingshot Edge for custom, real-time machine learning with advanced analytics, deployed to the tactical edge for defense applications. One requirement is to develop a commercial go-to-market strategy for parallel enterprise verticals such as power utilities, pipeline industries, construction, and insurance to ensure ROI on taxpayer investment. Many of these customers require single-digit millisecond latency in order to deliver real-time intelligence.
This has been nearly impossible until a fully operational 5G network exists, because these applications currently have to reach out to the cloud infrastructure to get to that last-mile analysis. To do that from a fielded device, you have to go from the device to the cell tower to the metro aggregation tower to the regional aggregation tower to the internet to the cloud infrastructure and then back. Our many users see that as a performance disadvantage that would leave them with a suboptimal response rate. Having Slingshot’s situational intelligence capabilities embedded at the 5G towers would solve this.
Leveraging our defense contract for Slingshot Edge and our relationship with AWS, Slingshot will be a first mover to implement 5G commercially. Slingshot Edge and 5G will make a perfect pair to deliver real-time aspects of our Platform. By utilizing upcoming AWS services, our developers will be able to seamlessly innovate by building and deploying new Slingshot Edge applications as close to the end-user as possible.
AWS Wavelength is being tested in Los Angeles in partnership with Verizon’s 5G. AWS has embedded their compute and storage services at the tower metro aggregation site to eliminate the delays of traversing the public Internet. Slingshot’s current cloud-based Platform already eliminates the need for developers to negotiate for space and equipment in a datacenter; and once AWS Wavelength is operational, developers will no longer need to negotiate with multiple telecommunications providers as well.
As the Slingshot Platform is natively designed for running in the cloud, it will translate easily to AWS’ Wavelength service. Utilizing AWS for 5G deployments allows us to stay with our familiar CI/CD pipeline for deploying applications just as we do currently. This also allows a seamless connection back to our cloud-based Platform empowering mutual cognition gain.
Enabling Use Case: Accurate Network Location Planning
5G will require a denser telecom network — more towers placed selectively and strategically. Not just accurate geodata but advanced spatial analytics from tools like Slingshot’s is crucial to planning the placement of such infrastructure. Spatial planning is critical to make a cost-effective 5G network including high-resolution, geospatial data integrated with a range of other information types served via a functionally-rich planning tool. Physical features not currently considered in network planning - including street furniture, vegetation, and weather conditions - have a significant role to play here.
Current utility workflows are still dependent on humans to pilot helicopters and drones, who then transfer the recorded data from memory cards to the internet. Many see this as a roadblock for greater autonomy, scalability, speed, and efficiency. 5G will enable an autonomous drone to inspect damage in a remote service territory after a storm with immediate change detection.
With intelligence onboard or within the telecom tower, the customer can quickly determine what critical infrastructure the storm has damaged. This insight would help utility managers make quick decisions about which crews and skill sets are needed and where to carry out the most urgent repairs. Once onsite, our collaborative teaming could be used in real-time across globally distributed groups.
With public and private 5G networks, Oil and Gas companies will now be able to autonomously deploy drones in a wider variety of settings, including locations that are actively being explored. In this realm, drones should be thought of as flying sensor platforms. The data they gather is fed into the network and then into Slingshot’s Platform where it is digested and becomes part of the knowledge that determines the most promising sites to explore.
If engaged in surveillance, drones provide insight into and pictures of areas that were previously too costly to explore or physically difficult to access. Together, drones and our 5G-enabled edge computing will help cut the cost of exploration and improve monitoring that was once human-centric. In the current business climate, this is important where commodity prices are likely to remain so low for some time to come.
Construction sites are notoriously difficult to streamline processes and increase productivity at, as traditional methods result in siloed and disjointed workflows. This compounds with under-digitization and decades of flat productivity output. Connected cameras and beyond-line-of-site autonomous drones at a job site can provide continuous monitoring, alerting workers of unauthorized access; perform change detection by analyzing activities such as truck arrivals and departures; or determine contractor presence and equipment utilization in real time. 5G-enabled Slingshot will be able to leverage inferencing at the edge on live feeds from streaming video cameras mounted on job sites and autonomous deployable drones to provide actionable insights to reduce bottlenecks, track progress, and be instantly informed regarding security matters.
Typical insurance workflows involve sending humans to assess damage, or trusting the client to provide accurate information regarding the claim. Slingshot’s situational analytics - applied to data captured by 5G-equipped drones, satellite imagery, weather data, or customer supplied data - provides immediate support to these insurance customers, CAT response teams, and claims adjusters in the field. This allows them to triage claims as well as optimize team deployment during or directly following an event at a greater speed than ever before.
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