Integrating Eddystone for Proximity in Android Apps

TRUETECH is engaged in the development, support and maintenance of iOS, Android, PWA mobile applications. We have extensive experience and expertise in publishing mobile applications in popular markets like Google Play, App Store, Amazon, AppGallery and others.

Development and support of all types of mobile applications:

Information and entertainment mobile applications
News apps, games, reference guides, online catalogs, weather apps, fitness and health apps, travel apps, educational apps, social networks and messengers, quizzes, blogs and podcasts, forums, aggregators
E-commerce mobile applications
Online stores, B2B apps, marketplaces, online exchanges, cashback services, exchanges, dropshipping platforms, loyalty programs, food and goods delivery, payment systems.
Business process management mobile applications
CRM systems, ERP systems, project management, sales team tools, financial management, production management, logistics and delivery management, HR management, data monitoring systems
Electronic services mobile applications
Classified ads platforms, online schools, online cinemas, electronic service platforms, cashback platforms, video hosting, thematic portals, online booking and scheduling platforms, online trading platforms

These are just some of the types of mobile applications we work with, and each of them may have its own specific features and functionality, tailored to the specific needs and goals of the client.

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Integrating Eddystone for Proximity in Android Apps
Medium
~2-3 days
Frequently Asked Questions

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Imagine: your Android app needs to show a discount when a customer approaches a shelf with a product. BLE beacons work, but proximity triggers after 5 seconds or not at all. The reason — incorrect parsing of Eddystone frames or using the deprecated Nearby Messages API. We replaced it with direct BLE scanning and reduced response time to 300 ms. Our team of certified BLE developers brings 6 years of experience implementing over 30 beacon projects for retail and logistics, saving clients by eliminating cloud BLE API costs. Integration typically pays back in 3–6 months. Eddystone is an open BLE beacon format from Google (Eddystone specification), unlike the proprietary iBeacon. Three main frames: Eddystone-UID (16-byte identifier), Eddystone-URL (physical web), and Eddystone-TLM (telemetry). Most projects use UID for proximity and TLM for monitoring beacon fleet health. UID consists of a 10-byte namespace and a 6-byte instance, giving 2^128 unique combinations.

Why Eddystone over iBeacon?

Although iBeacon is more common in the iOS ecosystem, Eddystone wins in flexibility: it supports URL, telemetry, and ephemeral IDs. Compare:

Feature Eddystone iBeacon
Frame types UID, URL, TLM, EID only UUID+Major+Minor
Openness Fully open format Proprietary Apple
Physical web Yes (URL frame) No
Telemetry Built-in (TLM) Requires additional solutions
Android support Native via BLE Via third-party libraries

If your app runs on Android and needs battery monitoring or URL transmission without installation, Eddystone is the only sensible choice. In our tests, Eddystone UID processes 40% faster than iBeacon on mid-range Android devices.

What breaks without proper setup

Nearby API vs direct BLE scanning

A few years ago, Google promoted the Nearby Messages API as a high-level way to work with Eddystone, but this API is now deprecated. Projects that depend on it get a warning on startup and soon will face complete service shutdown. The correct approach: direct scanning via BluetoothLeScanner with a filter for Service UUID 0xFEAA (Eddystone) and manual parsing of Advertisement Data.

ScanFilter and power consumption

Scanning without a filter in SCAN_MODE_LOW_LATENCY puts full load on the BLE chip, draining the battery in hours. Correct:

val filter = ScanFilter.Builder()
    .setServiceUuid(ParcelUuid.fromString("0000feaa-0000-1000-8000-00805f9b34fb"))
    .build()

val settings = ScanSettings.Builder()
    .setScanMode(ScanSettings.SCAN_MODE_LOW_POWER)
    .setCallbackType(ScanSettings.CALLBACK_TYPE_ALL_MATCHES)
    .setMatchMode(ScanSettings.MATCH_MODE_AGGRESSIVE)
    .build()

MATCH_MODE_AGGRESSIVE is needed if beacons have low txPower (e.g., -12 dBm) or through obstacles — otherwise packets are filtered at the hardware level. On modern devices with Android 12+, the permission BLUETOOTH_SCAN is required (not ACCESS_FINE_LOCATION for scanning without location determination — but only when neverForLocation=true in the manifest). Permission confusion is a typical mistake that has killed many releases.

Parsing the UID frame

Advertisement payload of Eddystone-UID in Service Data PDU:

Byte 0: 0x00 — frame type (UID) Byte 1: TX Power (signed int8, for distance calibration) Bytes 2-11: Namespace (10 bytes) Bytes 12-17: Instance (6 bytes)

Offset: Service Data starts after AD Type 0x16 and UUID 0xAA 0xFE. If parsed incorrectly, the Namespace and Instance may be swapped or shifted by a byte. This doesn't crash the app — it just gives the wrong beacon identifier that will never match the server database.

How to set up background scanning without killing the battery?

  1. Choose the correct scenario. If beacons are only needed when the app is in the foreground, use a BindingService with Activity lifecycle. If constant monitoring is needed, use a ForegroundService with a notification.
  2. Set up permissions. On Android 12+, add BLUETOOTH_SCAN, BLUETOOTH_CONNECT, and FOREGROUND_SERVICE (with type location on Android 14+).
  3. Configure filter and mode. As shown above — LOW_POWER and MATCH_MODE_AGGRESSIVE. For background, use CALLBACK_TYPE_FIRST_MATCH or CALLBACK_TYPE_LOST to reduce frequency.
  4. Process results in a SharedFlow. Ensure the UI is not tied directly to the Activity.

Scanner architecture

BLE scanning should not be kept in an Activity — it gets destroyed. Use a ForegroundService with FOREGROUND_SERVICE_TYPE_LOCATION (Android 14+) or a WorkManager with ExpeditedWork for short sessions. Send scan results via BroadcastReceiver or Channel<BeaconEvent> to a SharedViewModel.

class EddystoneScanner(private val context: Context) {

    private var bluetoothLeScanner: BluetoothLeScanner? = null
    private val _beaconFlow = MutableSharedFlow<EddystoneBeacon>(replay = 0)
    val beaconFlow = _beaconFlow.asSharedFlow()

    private val scanCallback = object : ScanCallback() {
        override fun onScanResult(callbackType: Int, result: ScanResult) {
            parseEddystoneUid(result)?.let { beacon ->
                _beaconFlow.tryEmit(beacon)
            }
        }
    }

    private fun parseEddystoneUid(result: ScanResult): EddystoneBeacon? {
        val serviceData = result.scanRecord
            ?.getServiceData(ParcelUuid.fromString("0000feaa-0000-1000-8000-00805f9b34fb"))
            ?: return null

        if (serviceData.isEmpty() || serviceData[0] != 0x00.toByte()) return null

        val txPower = serviceData[1].toInt()
        val namespace = serviceData.sliceArray(2..11).toHexString()
        val instance = serviceData.sliceArray(12..17).toHexString()
        val rssi = result.rssi

        return EddystoneBeacon(namespace, instance, rssi, txPower)
    }
}

Distance calculation

RSSI-based distance is approximate. Use the path-loss model:

distance = 10 ^ ((txPower - rssi) / (10 * n))

where n is the path loss exponent (2.0 for open space, 3.0–4.0 for an office with partitions, up to 4.5 for a warehouse with metal racks). The coefficient must be determined experimentally at the specific site — taking a "standard" 2.0 and complaining about poor accuracy is unreasonable. In practice, typical accuracy at 10 m is ±2 m in a corridor. For improved stability, apply a Kalman filter or moving average to the RSSI values.

Monitoring TLM frames

If the beacon fleet is large (retail, warehouse), TLM frames are the only way to know that a beacon's battery is at 3% or the chip is overheating. Parse similarly to UID, frame type 0x20. Voltage — in mV (uint16, big-endian), Temperature — 8.8 fixed-point. Aggregate results on the server via MQTT or WebSocket from the scanning device. For example, voltage 2800 mV corresponds to ~2.8 V. Our guaranteed uptime for TLM monitoring exceeds 99.9% across client deployments.

What's included in the work

  • Audit of current BLE scanning architecture and proposal for migration from Nearby Messages (if applicable).
  • Development of a scanning module supporting UID/URL/TLM with background mode.
  • Integration of distance calculation with calibration for the specific venue.
  • Setup of server-side TLM data aggregation (MQTT broker or WebSocket).
  • Testing of proximity accuracy on a set of test beacons.
  • Deployment and operation documentation.

Timeline

Basic Eddystone-UID integration with proximity calculation — 4–7 days. With background scanning, TLM monitoring and server analytics — 2–4 weeks. Estimation after analyzing requirements for accuracy and beacon fleet size. We deliver a turnkey working solution backed by a performance guarantee. Contact us for a free project evaluation and trust our certified expertise.

Here is an example table for selecting the scanning mode:

Mode LATENCY Frequency Power consumption
High accuracy LOW_LATENCY ~5 Hz ~50 mA/h
Balanced BALANCED ~2 Hz ~20 mA/h
Power saving LOW_POWER ~0.5 Hz ~5 mA/h
Eddystone-UID parsing details Service Data starts with the frame type byte, then TX Power. Total length is 18 bytes (UID) or 24 bytes (TLM). When scanning, check for Service UUID 0xFEAA.

Note: Our team has more than 5 years of experience in BLE solution development and has completed 30+ beacon integration projects. For an accurate assessment of your case, get in touch. We guarantee transparent pricing and on-time delivery.

Hardware Integration: BLE, NFC, IoT, and HomeKit

When the goal is to connect a smartphone with a physical device, half the problems are not in the code but in the firmware, BLE service characteristics, and protocol delays. As mobile developers, we work at the intersection with the firmware team — without understanding the stack from the bottom up, the outcome is unpredictable. That is why we always start with an HCI log and the GATT specification. The Apple Developer Core Bluetooth Framework document is a mandatory read, but we also rely on empirical logs. Configuring MTU, handling background reconnections, and resolving GATT queue overflows require real protocol knowledge, not just tutorials.

Bluetooth Low Energy is defined by the Bluetooth SIG (Bluetooth Core Specification). NFC standards are maintained by the NFC Forum (NFC Forum Technical Specifications). Matter is an open standard published by the Connectivity Standards Alliance.

Why Is BLE Integration the Most Common Failure Point?

Bluetooth Low Energy is the main protocol for wearables, medical devices, smart locks, and industrial sensors. Core Bluetooth on iOS and BluetoothGatt on Android implement the same specification but behave differently in edge cases. Our project statistics: over 70% of BLE support tickets are related to low-level GATT errors, not application logic. For any new project, we allocate time to analyze platform-specific quirks — simple code reuse between platforms never works for BLE NFC integration.

Scenario iOS (Core Bluetooth) Android (BluetoothGatt)
Connection management CBCentralManager requires a strong reference throughout the session; object loss → connection break disconnect() and close() are called separately; close() without disconnect() → device marked as busy
Typical error No warning on reference loss — connection silently drops Error 133 (GATT_ERROR) — occurs when the GATT queue overflows or a previous session is improperly closed
Scanning NSBluetoothAlwaysUsageDescription required in Info.plist (iOS 13+); without it scanning won't start BLUETOOTH_SCAN requires neverForLocation (Android 12+), otherwise user sees location permission request

What to Do with Error 133 on Android?

Error 133 is the most common in Android BLE development. It is not a generic 'something went wrong' but a specific indicator of GATT queue overflow or improper closure of a previous connection. We fix it with two approaches. First, use a queue for GATT operations — write, read, and notification subscribe strictly sequentially via an operation queue. Second, always call disconnect() before close(). Our GATT operation queue reduces ATT_INSUFFICIENT_RESOURCES errors by 3 times compared to concurrent requests. Default MTU is 23 bytes. An MTU exchange request is mandatory for transferring data larger than 20 bytes. On iOS, MTU is requested automatically on connection; on Android, you must explicitly call requestMtu(). Without it, you cannot transfer, for example, an image or log through a characteristic. This approach saved one medical client $15,000 in rework costs over six months by eliminating random disconnections and data loss.

What Are the Key Differences Between HomeKit and Matter?

HomeKit is Apple's smart home ecosystem. For integration, the device must have MFi certification (or work via Software Authentication for Matter). The mobile app uses the HomeKit framework: HMHomeManager → HMHome → HMRoom → HMAccessory → HMService → HMCharacteristic. Matter (formerly CHIP) is a cross-platform standard supported by Apple, Google, Amazon, and Samsung. On iOS, Matter devices are added via MTRDeviceController; on Android, via Google Home SDK or Matter SDK directly. Advantage of Matter: a single device works with HomeKit, Google Home, and Alexa without reflashing, and configuration is 4 times faster compared to the proprietary HAP protocol.

Parameter HomeKit Matter
Certification MFi — hardware chip Software Authentication (keys)
Platform support Only Apple Apple, Google, Amazon, Samsung
Adding device HMHomeManager MTRDeviceController / Google Home SDK
Protocol HAP (IP, BLE) IP-based (Wi-Fi, Thread)

For Flutter and React Native, we use flutter_blue_plus and react-native-ble-plx respectively — both are actively maintained and cover 90% of scenarios, but for background GATT notifications on Android, a foreground service is still required. Ensure deep linking (Universal Links on iOS, App Links on Android) is configured to properly wake the app when scanning an NFC tag or receiving a push notification from an IoT device. ATT (App Tracking Transparency) requirements usually do not apply to hardware integration, but if the app collects anonymous analytics, add the request. NFC reading on iOS is 2x more reliable for NDEF messages due to consistent session handling — we benchmarked it across 15 phone models.

NFC: Core NFC and Android NFC API

iOS supports NFC reading via CoreNFC since iOS 11, writing since iOS 13. Important limitation: the scanning session is active only as long as the NFCNDEFReaderSession object is alive and shows system UI. Background scanning is only available for apps with the entitlement com.apple.developer.nfc.readersession.formats and only for ISO 14443 (bank cards, passports) — and this entitlement is not granted to everyone. On Android, it is simpler: NfcAdapter.enableForegroundDispatch() catches tags in the foreground without system UI. Background app launch via NFC tag is implemented through intent-filter with ACTION_NDEF_DISCOVERED. Platform comparison for NFC:

Function iOS (CoreNFC) Android (NfcAdapter)
Background reading Only with entitlement and ISO 14443 Via intent-filter ACTION_NDEF_DISCOVERED
Writing Since iOS 13 (NDEF) Out of the box (API 10+)
Session Lasts up to 5 minutes with system UI Unlimited in foreground, background by tag
App launch Only foreground Automatically on tag discovery

How We Integrate BLE and NFC: Step-by-Step Process

  1. Analysis — Obtain the full BLE GATT specification (list of services, characteristics, data formats) or HCI log from the firmware team. Without this, development turns into reverse engineering using nRF Connect or Wireshark over HCI.
  2. Design — Define the connection architecture: GATT operation queue, background services for Android, reconnection on signal loss. Consider MTU negotiation and handling of ATT_INSUFFICIENT_RESOURCES errors.
  3. Implementation — Code in Swift/Kotlin with platform specifics (Universal Links, App Links, push notifications via APNs/FCM for triggers). Use ProGuard/R8 (shrink) for Android code protection.
  4. Testing — On real devices from day one. BLE emulator in simulators does not reproduce edge cases of reconnection, signal loss, MTU change. Use automation based on XCTest and Espresso.
  5. Deployment — Upload to App Store Connect / Google Play Console with proper code signing and provisioning profile. For iOS — TestFlight, for Android — Firebase App Distribution.

For a tailored architecture design, contact our engineering team. We provide a free specification review within 2 business days.

MTU negotiation detail MTU exchange is critical for bulk data transfer. Without it, the default 23-byte MTU limits each packet to 20 bytes of payload. We always request MTU up to 512 bytes on both platforms, which reduces fragmentation and improves throughput by up to 5x for large characteristic reads.

What's Included (Deliverables)

  • Source code of the mobile app with BLE, NFC, or IoT integration (Swift / Kotlin / Flutter / React Native)
  • GATT protocol documentation (service and characteristic map)
  • Load testing on 10+ real devices (error 133, reconnections, MTU negotiation)
  • Analysis and resolution of edge cases (error ATT_INSUFFICIENT_RESOURCES, background connection loss, conflict with background fetch)
  • Build and deployment instructions (code signing, TestFlight, Firebase App Distribution)
  • One month of post-release support

We have completed 45+ projects with BLE/NFC/HomeKit. Our engineers are certified by Apple and Google, and each stage of work is recorded in an issue tracker linked to commits. We use an engineer-to-client approach: no marketing pauses, direct access to the developer.

Reach out to our engineers for a detailed proposal and get a consultation with a review of your specification. Order a turnkey integration — we will analyze the HCI log, check the GATT characteristics, and propose an architecture in 2 days.