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Context

As part of an innovation topic around its Fair Gates product line, Scheidt & Bachmann needs to evaluate Bluetooth Low Energy (BLE) signal behavior with a precision and sampling frequency well beyond what generic, off-the-shelf beacon-scanning apps provide. The broader application context and business rationale behind this evaluation are confidential at this stage and are not part of this challenge; what is needed now is purely the technical groundwork.

The exploratory setup involves BLE beacons installed at defined physical locations. A smartphone app would need to scan, distinguish and precisely evaluate the resulting signals in near real time.

Generic, off-the-shelf beacon-scanning apps are not suitable for this evaluation: they typically limit BLE scanning to a sampling interval of about three seconds, which is far too coarse to determine a platform's true technical limits. Scheidt & Bachmann therefore needs a dedicated, purpose-built test application before any further product decision can proceed.

Problem Definition

The core issue is:

No suitable tool exists today: standard beacon/BLE scanning apps cap sampling at roughly 3-second intervals and do not expose the shortest reading interval a platform can actually sustain.
Feasibility is unproven: without a dedicated test application, Scheidt & Bachmann cannot determine how precisely and how quickly smartphone-side BLE signal detection can actually perform under real-world conditions.
Foreground vs. background constraint: mobile operating systems apply much stricter BLE scanning limits once an app is not in active foreground use. Neither platform vendor publishes an exact figure for this (Apple's own documentation, for example, only states that the scan interval 'increases' in the background without giving a number), so these limits must be measured empirically for this specific use case.
No structured way to correlate signal and motion data: raw RSSI values fluctuate due to multipath reflections and body shadowing; without synchronized motion-sensor data and systematic recording, patterns cannot be identified or reproduced.

The goal for this challenge is not to build the final product, but to deliver a robust test application that lets Scheidt & Bachmann's team explore beacon placement, signal behavior and platform limits under real conditions.