Home Articles TechKnow Buzz – Offload GPS data on cloud, get better battery life!

TechKnow Buzz – Offload GPS data on cloud, get better battery life!

Location technology is ubiquitous. Social media, navigation maps, games or snapping, all require location information. In addition, getting indoor location is also proving to be a tech puzzle. Cloudbased solution and radio map could address these issues.

Offload GPS data on cloud, get better battery life!

Nowadays, we are so tightly integrated with smartphones that it is frustrating to run out of battery in the middle of the day. A major battery guzzler is the GPS receiver equipped with the smartphone. There have been several technical mumbo jumbos about optimising battery power, but none of them is effective.

Microsoft researchers have observed that in many location-sensing scenarios, the location information can be post-processed when the data is uploaded to a server. They designed a cloud-offloaded GPS (CO-GPS) solution that allows location-sensing devices to aggressively duty-cycle its GPS receiver and log just enough raw GPS signal for postprocessing. Through experiments, researchers demonstrated that leveraging publicly available information such as GNSS satellite ephemeris and an earth elevation database, a cloud service can derive good quality GPS locations from a few milliseconds of raw data. They designed a portable sensing device platform, CLEO, to evaluate the accuracy and efficiency of the solution. Compared to more than 30 seconds of heavy signal processing on standalone GPS receivers, they could achieve three orders of magnitude lower energy consumption per location tagging. In other words, with a pair of AA batteries (2Ah), CLEO can theoretically sustain continuous GPS sensing (at 1s/sample granularity) for 1.5 years.

Battery-consuming factors
There are two reasons behind the high energy consumption of GPS receivers. First, the time and satellite trajectory information (called ephemeris) are sent from the satellites at a data rate as low as 50bps. A standalone GPS receiver has to be turned on for up to 30 seconds to receive the full data packets from the satellites for computing its location.

Second, the amount of signal processing required to acquire and track satellites is substantial due to weak signal strengths and Doppler frequency shifts. As a result, a GPS chip cannot easily be duty-cycled for energy saving. In addition, it requires a powerful CPU for postprocessing and least-square calculation.

CO-GPS solution
Researchers addressed the problem by splitting the GPS data into a device part and a cloud part. Due to the split between local and cloud processing, the device only needed to run for a few milliseconds at a time to collect enough GPS baseband signals and tag them with a rough time stamp. A cloud service can then process the signals offline, leveraging its much greater available processing power, online ephemeris and geographical information to disambiguate the signals and to determine the location of the receiver. They called this approach CO-GPS.

The CO-GPS uses a combination of NGA and NGS data. First of all, they use NGA Precise as much as possible for historical dates. When NGA Precise is not available, they use NGS Rapids to the most recent date. After that, they use NGS Ultra-Rapids for real-time and near real-time location queries. Putting everything together, the CO-GPS backend web service performs the steps, as shown in the adjacent figure.

Locating indoors using radio map!

The two most important questions, to be answered by a digital map maker, are ‘where I am and what’s around me’. Using GNSS technology, these map makers answer such questions but they remain silent indoors because GNSS does not work indoors.

To address this inability of the GNSS, researchers have been using convergence of positioning technologies like WiFi, Bluetooth, Near field communication (NFC), and sensorbased location technologies. But so far their efforts could not yield the desired result.

For example, the WiFi Positioning System (WPS) collects both GPS and WiFi signals and many companies including Google and Apple utilise this technology to provide locationbased services (LBS). The WPS is helpful, but up to certain extent, because the technology needs GPS signals to tag the location of WiFi fingerprints collected from mobile devices.

To address this issue, researchers from the Department of Computer Science, Korea Advanced Institute of Science and Technology (KAIST), developed a new method to build a WiFi radio map that does not require GPS signals. The WiFi radio map shows the RSSs of WiFi access points (APs) at different locations in a given environment. Therefore, each WiFi fingerprint on the radio map is connected to location information. The KAIST research team collected fingerprints from users’ smartphones every 30 minutes through the modules embedded in mobile platforms, utilities, or applications and analysed the characteristics of the collected fingerprints. As a result, they discovered that mobile devices such as cell phones are not necessarily on the move all the time, meaning that they have locations where they stay for a certain period of time on a regular basis. If users have a full-time job, then their phones, at least, have a fixed location of home and office.

By using smartphone users’ home and office addresses as location references, researchers segregated fingerprints collected from the phones into two groups: home and office. They then converted each home and office address into geographic coordinates (with the help of Google’s geocoding) to obtain the location of the collected fingerprints. The WiFi radio map includes both the fingerprints and coordinates whereby the location of the phones can be identified or tracked.

For evaluation, the research team selected four areas in Korea (a mix of commercial and residential locations), collected 7,000 WiFi fingerprints at 400 access points in each area, and created a WiFi radio map. The tests, conducted in each area, showed that location accuracy becomes hinged on the volume of data collected, and once the data collection rate rises above 50%, the average error distance is within less than 10 metre.

According to lead researcher Professor Dong-Soo Han, “Although there seem to be many issues like privacy protection that have to be cleared up before commercialising this technology, there is no doubt that we will face a greater demand for indoor positioning system in the near future. People will eventually have just as much desire to know their locations in indoor environments as in outdoor environments.”

Once the address-based radio map is fully developed for commercial use, home- and office-level location identification will be possible, thereby opening the door for further applications such as emergency rescue or indoor location-based services that pinpoint the location of lost cell phones, missing persons, and kidnapped children, or that find stores and restaurants offering promotional sales.