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.