✨ Add proximity for finding people to scan

[matt-goldman]

Cc: @adamcogan

Hi

Pain

Original concept for SSW Rewards was a "Pokemon Go" style experience. This never got implemented due to complications around XR and positioning (BLE etc). Achievable, but requires more work than time allowed.

Suggested Solution

@michalpobuta just released a proximity library for .NET MAUI. This could be a way to implement similar functionality. Instead of doing full AR and positioning/wayfinding, you could do proximity - warmer and colder would work, as would notifications when within a certain distance of someone. You could mark people as "want to meet" and be notified when they are nearby, etc.

Tasks

• Investigate this library
• Do a tech spike
• Identify any useful or relevant functionality
• If valid, define a full feature implementation

Acceptance Criteria

• Meets Definition of Done - https://github.com/SSWConsulting/SSW.Rewards.Mobile/blob/main/_docs/Definition-of-Done.md
• Library evaluated
• Tech spike results shared

Thanks!

[michalpobuta]

Hi @matt-goldman! Thanks so much for your interest in the library 😊 I didn't include the research details in the docs, but if you're planning to use this in production, here's what you really need to know.

I did extensive research on how two devices can find each other over short distances when GPS fails. UWB is the ideal tech, and this library supports both Android and iOS. But here's the catch: hardware availability is limiting.

The Hardware Reality:

• iOS: iPhone 11+ have it (except SE/Air models)
• Android: Only 5-7% of devices actually have UWB
• Both devices need it to work together
• Even most new Androids still don't have it

My Current Solution:
Fall back to BLE in most cases, which currently just gives us "warmer/colder" proximity. But there's tons of room to improve this! Here's what I'm working on:

RSSI Filtering & Smoothing:

• arxiv.org/pdf/2001.02396 — Shows Kalman filters reduce distance errors by ~27%
• MDPI Study — Compares Kalman filters vs. moving averages
• Path-Loss Model — Explains environmental variation (1.6–3.5)
• AltBeacon Docs — Default filtering approach with accuracy/lag tradeoffs
• ARMA Filter Code — Good reference for C# port

TX Power Calibration:

• Google's COVID Tracing Approach — Per-model TX power measurement
• Attenuation Formula — How to compute it with public correction tables
• Contact Tracing Study — Even with calibration, absolute distance from RSSI stays probabilistic
• Beacon Ranging Primer — Why iBeacon uses zones instead of meters
• TX Power Explained — Practical guide
• Real Phone Measurements — Shows huge variance between phone models

BLE Channels:

• arxiv.org/pdf/2006.09099 — 15 dB differences between channels explain RSSI jumps

Sensor Fusion:

• arxiv.org/pdf/2009.04991 — Combining RSSI with accelerometer data

Future Possibilities:

• Android 16 Ranging API — Unified API for next-gen ranging
• MAUI Bindings — Channel Sounding without custom code
• Channel Sounding Tests — Real-world examples
• Support Matrix — Which phones actually support it

If you have ideas or want to help improve this, PRs are absolutely welcome! We're basically solving the same problem :D