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Global Positioning System (GPS), the principal component of Global Navigation Satellite System (GNSS), is a technological marvel that traces its history back to 1951. In that year, Raytheon scientist Dr. Ivan Getting designed a 3D position locating system based on the time difference in the arrival of radio signals from different transmitters.
Soon after, scientists confirmed that Doppler shift could be used to either calculate the position of objects in Earth orbit or, if the satellite’s position was known, the Doppler shift could be used to determine the location of an earthbound receiver. Just two years after the launch of Sputnik in 1957, the first of five low-altitude satellites for global navigation were launched.
An American program called “621B” developed many of the characteristics of today’s GPS and, together with the original five satellites and a further fleet carrying highly accurate clocks, the program formed the basis of the 1973 NAVSTAR-GPS. In 1983, the U.S. allowed GPS to be used for civilian purposes and an expanded constellation of 24 operational satellites was deployed between 1989 and 1994. The modern GPS was declared fully operational the following year. Finally, in 2000, the precision of the civilian system was upgraded to match that of military applications.
Today, billions of people benefit from GNSS daily, whether navigating at sea, surveying, mapping, farming, guiding heavy machinery or performing thousands of other tasks. GNSS is also now providing a foundation for many IoT applications in the logistics and transportation sectors.
GNSS for asset tracking
If you want to know the location of a high-value cargo with a precision of tens of meters, nothing can better GNSS. This is a key reason why Nordic included the capability in its nRF9160 SiP, a cellular IoT solution. A cellular IoT asset tracker, such as the Aovx G Series tracker, switches the GPS on, looks for the satellites and, once it has found several, waits for the satellite assistance data that will determine its position.
- Read more: Cellular IoT asset tracking applications gain momentum
- Read more: Cellular IoT solutions growth are among tech trends for 2023
One downside is that a cold-start GPS modem ‘time-to-first-fix’ (TTFF) on a group of satellites can take several minutes and use significant battery capacity. To overcome this, the nRF9160 works seamlessly with Nordic’s nRF Cloud Location Services’ Assisted- and Predicted-GPS (A-GPS and P-GPS) services.
These services use satellite assistance data stored in a ground-based GPS database which is relayed to the IoT device via the LTE-M network - saving significant power. When required, the IoT device can then find the satellites in seconds, conserving energy. The P-GPS technique builds on A-GPS by providing over two weeks of assistance data to the IoT device. The result is even greater power savings.
Cellular location services
But while GNSS is impressive it is not foolproof. The system relies on ‘line-of-sight’ between satellites and receiver and that’s often restricted. For example, ‘urban canyons’—formed by rows of tall buildings on each side of a city street—can obstruct the signal. Moreover, there’s little chance of GNSS signals penetrating buildings. Further, GNSS can take a heavy toll on cells; that’s an important consideration when designing an asset tracker for months or years of battery life.
One option to overcome the limitations of GNSS—and another location service built into nRF Cloud Location Services and used in conjunction with the nRF9160—is to use the known location of cellular base stations to narrow down the position of the asset tracker. The single-cell location method relies on identifying in which cell the tracked device is situated and then referencing the cell identification against a database of known base station locations. It offers accuracy down to kilometre level while only modestly impacting battery life.
Multi-cell location builds on the single-cell technique by referencing the position of several nearby base stations instead of just one to offer accuracy down to a few hundred meters while still keeping power consumption low.
The Wi-Fi option
A second locationing alternative for when GNSS isn’t available—and which can also be used to trade-off location precision against battery life—is Wi-Fi Service Set Identification (SSID). Every Wi-Fi network is identified with a SSID – essentially a technical reference for Wi-Fi network’s name. If you can find out the network’s SSID you can then cross reference it against one of several databases that will detail its location.
Nordic’s nRF7002 Companion IC is a 2.4 and 5 GHz Wi-Fi 6 device providing low-power and secure Wi-Fi for the IoT. It is the ideal device to be used in conjunction with the nRF9160 SiP and nRF Cloud Location Services for GNSS, single or multi-cell, or Wi-Fi locationing.
When used for Wi-Fi locationing, the nRF7002 interrogates any nearby Wi-Fi access point (AP) for its SSID; the nRF9160 then sends the SSID to nRF Cloud using NB-IoT or LTE-M connectivity. nRF Cloud then checks one or more Wi-Fi SSID databases and returns the SSIDs location, plus the degree of uncertainty for that location, to the nRF9160, or elsewhere as directed. Wi-Fi SSID locationing is more accurate than cell-based location features and less power-hungry than GNSS.
Where am I?
In the more than 70 years since Dr. Getting came up with his 3D position locating system, GNSS has come to dominate as the most reliable and accurate method of pinpointing the position of an object or person on the Earth’s surface. If you need to know where something is with high precision, there are few alternatives.
But when a precision of a few hundred meters is acceptable, and battery life is critical, or when the GNSS signal is obstructed, Wi-Fi SSID locationing is a great alternative. And if you just need to know where your asset is to within a kilometer or so and want to really get the most from the batteries, cell-based locationing is the answer. With Nordic’s nRF9160 SiP, nRF7002 Companion IC and nRF Cloud Location Services, you can seamlessly switch between all three methods to optimally trade-off precision against battery life.
