Dicker Data has added two new companies to its vendor roster to expand its internet of things (IoT) portfolio.

The two user and device tracking providers, Digital Matter and kontakt.io, produce GPS outdoor tracking and Bluetooth indoor tracking applications and devices.

The Perth-headquartered Digital Matter was founded in Johannesburg South Africa in 2000 by Ken Everett. It produces GPS and IoT devices for the agriculture, asset tracking, fleet management, supply chain management, oil and gas industries.

The solutions are focused on long-range, outdoor tracking applications using 2G, 4G LTE-M, NB-IoT, LoRaWAN and Sigfox connectivity.

Dicker Data said its partners could now co-sell Digital Matter solutions with cellular network access from the distributor via its existing Telstra and NNNCo LoRaWAN distribution agreements.

Stuart German, Digital Matter’s business development director said Dicker Data’s technical expertise aligned with the vendor’s “commitment to innovation”.

“Asset tracking is one of the highest growth application segments for the Internet of Things, making this an exciting time to introduce our range of future-proofed LTE-M / NB-IoT hardware and white-label software as a recurring revenue business model.”

Poland-based Kontakt.io was founded in 2013 and makes short-range indoor tracking devices using Bluetooth Low Energy (BLE) connectivity. The vendor has integrations with Dicker vendors Cisco Meraki and Mist.

“The combination of Dicker Data’s IoT solution focus and Kontakt.io’s global leadership in IoT innovation creates the conditions for a perfect storm, one that can change the landscape of the Australian connected enterprise market,” Kontakt.io exec Rom Eizeberg said.

“Together, we support the mission of simplifying IoT, removing obstacles to adoption and creating a path to value for a wide market spectrum of partners and end-users.”

Dicker Data’s COO Vlad Mitnovetski said the two companies’ offerings “complement our IoT portfolio, boosting Dicker Data’s ability to deliver end-to-end IoT solutions”.

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While GPS is a widely-used technology for geolocation, one that is especially useful for navigation while driving, it isn’t necessarily as accurate as it could be. Mapping applications like Apple Maps occasionally show the wrong location for the user for a variety of different reasons.

These issues can include interference in the GPS signal caused by trees and mountains, going underground or indoors, signals reflecting off buildings in a city, solar storms, and even rare cases of radio interference or jamming.

These problems aren’t just limited to GPS, as other Global Navigational Satellite Systems (GNSS) such as Glonass, Galileo, Beidou, and others can suffer from the same issues.

In the patent application published on Thursday by the US Patent and Trademark Office, Apple has come up with “Machine learning-assisted satellite-based positioning.” In short, it is a way to analyze GPS data by comparing it against data acquired by a machine-learning model.

The idea is that the device receives its estimated position based on a GNSS signal, then acquiring a set of parameters associated with the estimated position. A reference position is then provided, close to where the estimated position of the device is, to help with correction.

A machine learning model is then generated based on the estimated device position, the reference position, and a set of parameters. This machine learning model is then used to estimate the device’s location for future GPS readings, until a period of time has elapsed or they have moved to an area where the parameters and the model are inaccurate.

In effect, the device generates the model using the two sets of positioning data to determine how far out from its real position its received GPS coordinates are. For example, in a city with tall buildings, the model could be informed the signal could be reflected, and take that into account along with previous position readings and the general direction of transit to work out a more accurate position based on misguided data.

Apple has included extra claims to take into account the use of a second device, including providing the model to others for use and storage. A Kalman filter, which can be used to estimate data based on a collection of noisy measurements, is also suggested for use, as well as accounting for “an amount of uncertainty” for measurements and subsequent positions, and for alerting the user that the position is revised and either takes into account or disregards the GPS data.

The filing lists its inventors as Benjamin A. Werner, Brent M. Ledvina, Dennis P. Hilgenberg, and Aarti Sathyanarayana.

Apple files numerous patent applications every week, and though the filings indicate areas of interest for Apple’s research and development teams, it doesn’t guarantee the possibility of Apple adding it to a future product or service.

Apple has been keen to increase its work on machine learning in recent years, including hiring senior Google AI scientist and noted AI expert Ian Goodfellow in 2019 and acquiring firms like Drive.ai and Laserlike. The majority of its public-facing ML work is with Siri, and that too has seen some location-aware improvements.

In August 2018, Apple detailed its use of geographic language models to improve Siri’s knowledge of local terminology and locations, helping reduce point of interest-based searched by 18.7 percent.

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Electric scooters are invading America, with scooters from popular brands including Lime, Bird, Spin, Lyft, and Uber becoming commonplace in major city centers. These dockless scooters, which are controlled by mobile app, accounted for 45.8% of all micromobility trips in 2018; this number is significant, considering 84 million micromobility trips were taken in 2018 alone, the National Association of City Transportation Officials reported. 

The success of scooters are built on the promises of reducing traffic congestion, providing a more efficient and cost-effective means of travel, and decreasing harmful emissions, according to the INRIX’s Micromobility Potential in the US, UK and Germany report. However, scooters are also responsible for at least 1,500 injuries and eight fatalities of US riders since late 2017. 

The injuries and deaths have caused many US cities to take pause, with Atlanta recently banning scooter usage during nighttime hours, and San Diego considering a temporary ban. As a city with one of the highest scooter-to-citizen ratios in the US, Austin has seen a spike in scooter-related accidents. Nearly half of the electric scooter injuries in Austin were classified as severe, and 190 riders in a three-month period were reportedly injured while riding a scooter, the Centers for Disease Control and Prevention found.

Scooter accidents can occur in a multitude of ways: By falling off the scooter, being hit by another vehicle, or scootering into another scooter, vehicle, pedestrian, or object. 

At the request of Austin’s health and transportation departments, the CDC launched its study of e-scooter accidents in April 2019. Of the 190 injured riders, nearly half (48%) sustained head injuries, and 70% sustained injuries to their upper limbs. 

“I was with a few friends and decided the quickest way to get home was to take a scooter. I was crossing a street and was hit by a car,” one Austin scooter driver said. “There were a few seconds left in the light, but the car had not seen me and proceeded to move.” 

Austin has since passed a rider safety ordinance, which forbids scooter riders from using portable electronic devices while in motion, requires riders under the age of 18 to wear a helmet, and bans wrong-way riders and having two people riding on one scooter. Many other state and local governments have followed suit. 

Edmund Selby, founder of ScooterTalk.org, emphasized the dangers of scooters in Atlanta. “We’ve had four e-scooter fatalities,” Selby said. “It’s a visibility problem with them. So if you’re a car driving down the road and it’s not a well-lit street, and there’s an e-scooter there, the only light is about three inches off the ground, and half the time they don’t even work.” 

Selby suggested scooter companies place a small light on the back of the scooter that would shine onto the rider’s back, illuminating the person. 

While new ordinances and better lighting are steps in the right direction, technology might be the answer to safer scooter rides, according to Alan Messer, CTO of connected car platform Mojio. 

How IoT and machine learning may help

Messer’s company has integrated connected technology into automobiles, collecting more than seven billion miles of driving data from more than 500 million trips. Using what he learned from his experiences with connected cars, Messer said some of the same technology could easily be transferred over to scooters, hopefully making them safer. 

“Having a few things on [the scooter] like GPS and an accelerometer could tell a lot about a scooter, in the same way that we can tell a lot about a car,” Messer said. “You can tell not only where the scooter’s going, but we can tell things like supply and demand.”

“There could even be ways of telling if people are scooting the wrong way down a street, or going on areas of a city that are not safe,” he added. Additionally, by attaching an accelerometer to a scooter, users, or companies could see when a rider accelerates too quickly or brakes to sharply. 

If scooter providers equipped all scooters with cellular, GPS, and accelerometer technology, they could use machine learning to interpret the habits of their riders and either notify the rider of dangerous habits, or alter their own machines to produce safer conditions, Messer said. 

“It’s in everybody’s best interest to have this kind of machine learning happening,” Messer said. “These technologies are fairly well known and being deployed now in the automobile space. Transferability to the scooter market is very much there, and because it all can happen in the cloud, it doesn’t require making the scooter any more expensive.”

Messer predicted scooter companies will eventually integrate machine learning and IoT sensors on their scooters. Riders will eventually be able to look at the app after a trip and see where they went, how fast they drove, if they made any dangerous moves, and tips for a safer trip. 

This technology is already in the works Messer’s company, Mojio, but the connected devices haven’t yet been deployed. 

Scooters are here to stay, and despite injuries, they are overall a good thing, Messer said. “Being able to keep people off the road and using more efficient transport is always good,” he noted. “The question is, where does that go from here? [Future] scooters may not look like the scooters that we see today, but it’s going to be with us—particularly in large cities—for a long time. Micromobility is here to stay in some form.”

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