English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We address the problem of localizing non-collaborative WiFi devices in alarge region. Our main motive is to localize humans by localizing their WiFidevices, e.g. during search-and-rescue operations after a natural disaster. Weuse an active sensing approach that relies on Unmanned Aerial Vehicles (UAVs)to collect signal-strength measurements at informative locations. The problemis challenging since the measurement is received at arbitrary times and theyare received only when the UAV is in close proximity to the device. For thesereasons, it is extremely important to make prudent decision with very fewmeasurements. We use the Bayesian optimization approach based on Gaussianprocess (GP) regression. This approach works well for our application since GPsgive reliable predictions with very few measurements while Bayesianoptimization makes a judicious trade-off between exploration and exploitation.In field experiments conducted over a region of 1000 $\times$ 1000 $m^2$, weshow that our approach reduces the search area to less than 100 meters aroundthe WiFi device within 5 minutes only. Overall, our approach localizes thedevice in less than 15 minutes with an error of less than 20 meters.

UAVs using Bayesian Optimization to Locate WiFi Devices