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Nestwave is proud to announce the publication of two papers in the 2020 Journal of the IEEE / Institute of Navigation (ION) Position, Location and Navigation Symposium (PLANS). Both technical papers describe novel advances in geolocation algorithms providing substantially increased performance and robustness for our geolocation solutions.

The first paper, an improved method for multilateration, reformulates the classic time difference of arrival problem into a globally valid, time-of-transmission problem, further taking into account clock bias and the effects of motion (Doppler). The second paper describes techniques to substantially improve location accuracy in the presence of severe multipath using unique, near-causal filters and reduced complexity Maximum Likelihood (ML) estimation.

Congratulations to the R&D team at Nestwave! And stay tuned, there’s more to come.

Links to the papers (fee required):

https://www.ion.org/publications/abstract.cfm?articleID=17376

https://www.ion.org/publications/abstract.cfm?articleID=17342

Updated: Mar 4

In the rapidly growing world of IoT, tracking the location of ‘things’ including equipment, assets, parcels and pets is a valuable capability. In fact, one might even consider geolocation to be one of the killer apps for IoT. But there is a use of geolocation that may be equally important yet not quite as obvious to the casual observer – the automated locating of stationary objects.


This seems counter intuitive at first – if something is not moving, why do I need to know and track its location?


Let’s explain using an example. Perhaps your city is rolling out a smart parking meter program – converting coin-based systems to convenient digital solutions. These meters often accept multiple forms of payment, allow customers to pay or top off a meter with a smartphone app, and help cities analyze parking trends by incorporating sensors for vehicle detection. Some even embed pollution sensors enabling a large-scale web of environmental monitoring.


Knowing the location of each parking meter is important. Location enables users to quickly find their cars using an app, allows cities to pinpoint short term and long-term capacity needs across city geography and is critical for service crews to quickly find meters when repairs or upgrades are needed.


Under the hood, this means that each datapoint sent by the meter needs to have an associated location - a geotag. Deeper still, this means that each communication module ID needs to be paired with the meter’s location during installation.


How is this done? A technician may take a location reading on a smartphone or GPS device and record it in a spreadsheet or enter it on an electronic form. Perhaps they simply record the communication module ID and the corresponding meter pole ID on a sheet of paper. Later that module ID is correlated with a physical location based on a pre-existing database, spreadsheet or paper document.



Sounds like this could quickly get complicated, right? It gets worse.


In each scenario, there is a person involved in the process and one or more manual operations. As we know, people make mistakes. Numbers are transposed. Tracking sheets are lost. Excel spreadsheets are overwritten. Over time, this problem grows. What happens when the smart module is replaced? Will the repair crew be as well trained or meticulous as the installation crew? Will the updates propagate all the way through the system?


The solution is to employ a simple, automated means to accurately assess geolocation during install and at any time in the future, directly on the device. Having geolocation “ground truth” simplifies deployment and virtually eliminates the risk of faulty object geotags. Money is saved by automating manual processes. Equally, since reliability and accuracy of the services are improved, the ultimate value of the solution is increased.


We used smart parking meters as an example, but this can apply to many other IoT use cases: streetlights, traffic lights and vacancy sensors in parking garages. It works equally well for fixed enterprise and industrial assets including buildings, HVAC units, tanks, pipelines, storage containers, industrial machinery and surveillance cameras.


To extract the most value from this automation, it’s critical to use the right geolocation technology. An embedded GNSS chip or module designed for a smartphone is unsuited for IoT as the performance requirements and capability constraints are substantially different.


For one, IoT solutions are often battery powered with some devices needing to last 5-10 years or longer on a simple coin cell. Ultra-low power operation is the key here. Solutions must be extremely small, very inexpensive and connectivity is often intentionally intermittent to further save power.


Back to our example – single space smart parking meters incorporate rechargeable batteries. Despite being opportunistically recharged via small integrated solar cells, power consumption remains a critical problem. A Sacramento, CA audit of smart parking meters revealed that 17% of meters suffered low battery problems which led, in part, to consumer frustration with the new technology.


Thankfully, a new breed of geolocation solutions is designed specifically with these constraints in mind. Here at Nestwave, we have GNSS solutions that can reduce acquisition time by a factor of 10. By reducing the amount of time receiving circuitry is powered, we can gain a substantial reduction in power consumption per location fix. Additionally, our architecture can offload the most processing intensive operations to the cloud, further saving power. Beyond GNSS, Nestwave has solutions enabling geolocation via 4G/5G signal mutlilateration (via time difference of arrival or TDOA) and WiFi signal sniffing. Hybrid approaches like these enable extremely low-cost and low-power solutions as the existing wireless communication hardware is re-used for geolocation purposes.


These new technologies are key enablers to the emergence of purpose-built geotagging solutions needed for billions of IoT devices in the coming years. And if we do a good job, we might just end the use of clipboards and spreadsheets to track stationary objects.

Here at Nestwave, we are excited about the future of geolocation and our next generation GNSS and hybrid GNSS + 4G + WiFi solutions. We’re working to dramatically move the needle for IoT devices in the areas of power consumption, accuracy and indoor coverage.


Many of us are familiar with the reliability problems of geolocation when using our smartphone. When walking or driving in an urban area, it remains common that our phones think we’re a half a block or more up the street, sometimes even locating us in the middle of a building. Once we step indoors or enter an underground parking structure, we often lose location entirely. Turns out, the problems (and implications) are critically magnified when we factor in the additional constraints and requirements associated with new IoT applications.


Let’s take the example of dockless scooters, a micro-mobility solution familiar to many of us residing in urban centers. Paris alone has 20,000 scooters in operation. Scooters from Lime, Bird and others are equipped with geolocation solutions based on GNSS satellites with some using additional signal sources such as WiFi to build an ensemble map of the most likely scooter position. Problem solved, right? Not so fast.




The most visible use of scooter location is the pin map showing customers the nearest available scooters using a service provider’s app. This allows each of us to quickly find and rent a scooter, in real time. However, scooters operate and are often left in a variety of environments, and often in places particularly challenging to obtain an accurate geolocation fix – urban areas with tall buildings, parking garages, bridge underpasses and even in building lobbies. The effect is that customers waste time looking for scooters while companies lose revenue opportunities and damage to their reputation. Examples of this frustration abound on the internet, including here and here.


Rather less obvious are two location-based issues that are even more critical to the long term success of micromobility.


First, is the location problem involved in the collection, charging and release of scooters. Scooter companies such as Bird and Lime pay freelancers to find, recharge and relocate scooters on a nightly basis.



The backbone of the solution is the location reported by the scooter used by freelancers to find each scooter. Due to poor GNSS performance, this location is often incorrect, leading workers to spend time walking and driving to find the missing scooters. The problem is so pervasive, there are entire forum posts and blogs (here, here, here) dedicated to the problem.

There is an environmental impact as well. With 40% of a scooter’s CO2 footprint coming from the pickup/return service, the effect of inaccurate location is substantial. The 20,000 scooters in Paris result in over 4.7 million km being driven each year to pick up, recharge and return scooters to service. Improving the location accuracy on just 5% of these trips would result in a CO2 reduction of almost 30,000 kg per year.


Perhaps the most acute problems that location accuracy has on the dockless scooter business relate to safety and policy. City governments around the world face intense pressure to manage and regulate micromobility to address the growing problems and risks around use, including safety, liability and property rights. Injuries from scooter use – both drivers and pedestrians – is a growing problem.



Cities are attempting to control scooter use by creating speed restricted and exclusion zones, an approach called geofencing. But here again, poor location accuracy is causing significant problems. For one, the accuracy is not good enough to distinguish between areas that are nearby to each other – a street vs a sidewalk, or a park vs a bike path. This leads to unpredictable, automatic changes in scooter behavior (e.g. speed limiting) and has resulted in customers simply ending the ride and walking away in frustration.


Conversely, location errors of just 5-10m can allow a scooter to enter a restricted zone at speed, frustrating and endangering groups of people in locations where scooters just don’t make sense (densely populated public plazas, pedestrian-only walkways).

According to a CNN article “organizations who have requested restrictions described the GPS technique as ineffective”


Speed is determined on a scooter based on location samples. Depending on the battery level, the update rate may be very much reduced, dramatically affecting speed accuracy and thus improperly triggering changes to the scooter behavior (e.g. speed limiting). There are even examples cited where this can cause a safety issue – while crossing a street or going around a car.

These are just a few examples of how critical geolocation performance is to emerging IoT applications. For micromobility, location accuracy, indoor/underground capability and low power performance plays a key, central role in the usability, safety, policy and economics of solutions.

At Nestwave, we will continue to work on new and better ways to bring improved location accuracy (3-5x), reduced power dissipation (1/10th) and indoor capabilities to the next generation of IoT devices.


Perhaps we’ll be seeing you on an electric scooter soon!

© Nestwave SAS 2020