Cross-Platform Fault Reporting Architecture for Scalable and Reliable Wearable Systems

1. Introduction

Wearable devices have moved from niche tools to common health and activity platforms. They run continuous sensing and interactive apps on small batteries and different operating systems. At scale, silent faults and rare crashes can harm user trust and safety [1]. Fault reporting is therefore essential. It must work across vendors and handle weak networks, tight energy budgets, and privacy rules [2]. Recent studies cover parts of this need. Mobile analytics and crash logging improved symbol capture, stack collection, and app triage on phones [3]. IoT telemetry added light protocols, store-and-forward buffers, and rate control for intermittent links [4]. On-device anomaly detection reduces data volume and flags sensor faults near the source [5]. Privacy-preserving upload and local redaction support compliance in health and fitness settings [6]. Work on open tracing and common schemas moves systems toward shared event formats [7]. For wearables, previous research presented one of the first cross-platform solutions capable of supporting millions of terminals while maintaining stability and efficiency [8]. Important gaps remain. Many papers target one platform or one vendor stack. Cross-OS support for Wear OS, watchOS, RTOS, and custom firmware is still rare [9]. Event schemas often differ across products, which blocks correlation and slows response [10]. Several studies rely on small lab traces instead of long field logs, so tail delay and burst faults are under-measured [11]. End-to-end metrics such as p95/p99 time-to-report, loss rate, and mean time to detect (MTTD) under load are often missing [12]. Energy and thermal costs of always-on reporting are also seldom measured together with delivery success [13]. Finally, security is often treated apart from telemetry; designs that join trusted runtime with safe reporting remain limited [14]. This paper addresses these gaps with a cross-platform fault reporting system for wearables at million-device scale. The design has three parts. First, a unified event model defines faults across app, OS, radio, and sensor layers with a compact schema and versioning [15]. Second, a lightweight on-device reporter manages memory use, applies adaptive sampling, and performs local redaction to protect user data [16]. Third, an edge–cloud path uses store-and-forward, backoff, and priority queues over MQTT/HTTPS to keep delivery working on weak networks [17].

We evaluate the system on heterogeneous devices and operating systems using fault injection and field traffic. We report p50/p95/p99 time-to-report, loss and duplication rates, and energy per delivered report. We compare with platform-specific loggers and other baselines. Our goals are to show stable operation under burst load, to quantify energy and thermal cost, to demonstrate schema portability across platforms, and to document privacy and security controls needed for health-grade deployments.

2. Materials and Methods

2.1. Study Area and Sample Description

We analyzed fault logs and field data from 60,412 wearables over 180 days on three systems: Wear OS, watchOS, and an RTOS used in fitness bands. The fleet covered 8 hardware models deployed in North America, Europe, and East Asia. We also ran 1,200 lab sessions with controlled fault injection. Lab tests were done in a chamber at 22 ± 2 °C and 40–55% relative humidity. Networks included Wi-Fi (2.4/5 GHz), LTE, and BLE; link quality was set with attenuators from −30 to −85 dBm. We targeted four fault classes: app (crash, ANR), OS (watchdog reset), radio (drop, queue overflow), and sensor (stuck values, time drift). Devices buffered events locally and uploaded when the link was available.

2.2. Experimental Design and Control Setup

We used an A/B design with blocking at the device level. The baseline was the native vendor logger. The cross-platform setup added a unified event schema, compact encoding, local redaction, store-and-forward, and priority queues. Each device alternated the two setups in 48-hour epochs; the order was randomized within hardware and region blocks. In the lab, fault type and link setting followed a balanced schedule; each condition was repeated 10 times per model. This design supports within-device comparison and limits bias from region, hardware, and time of day. For energy tests, 96 devices (32 per OS) were instrumented with an inline USB power monitor (watches) or a battery tap (bands).

2.3. Measurement Procedure and Quality Control

On device, events were time-stamped with a monotonic clock and signed with a per-device key. Reports were sent to gateways over MQTT or HTTPS with TLS 1.2+. Servers used NTP/PTP; target clock skew was < 1 ms. Time-to-report (TTR) was defined as the time from the event stamp to server receipt. Loss and duplication were computed from fingerprints (device ID, monotonic time, counter, hash). Energy per delivered report was measured during scripted fault bursts. Quality checks were: (i) drop records with clock skew > 2 s vs server time; (ii) deduplicate identical fingerprints; (iii) exclude sessions with gateway packet loss > 1%; (iv) verify schema version and signature. Instruments were calibrated weekly and zeroed before each session. Data handling used on-device masking for user identifiers.

2.4. Data Processing and Model Equations

For each setup we computed medians, p95, and p99 of TTR, plus per-device loss rate and duplication rate. Energy per report equaled total burst energy divided by the number of unique reports delivered. Two metrics and one model were applied.

(1) On-time reporting rate at threshold τ

where Ti is the TTR of event i and N is the number of events. We report OTR30s , OTR60s , and OTR300s .

(2) Logistic model for success within 60 s

log RSSI_i+β₂ Battery_i+β₃ OS_i+β₄ Model_i+β₅ QueueDepth_i

where pi= Pr ( Ti≤60 s) . Predictors were received signal strength (RSSI), battery level, OS and hardware indicators, and local queue depth at send time. Coefficients were estimated by maximum likelihood with cluster-robust standard errors at the device level. Model checks used ROC curves, calibration plots, and bootstrap confidence intervals (1,000 resamples).

These procedures provide a clear and repeatable way to compare delay, loss, duplication, and energy across platforms, devices, and network conditions.

3. Results and Discussion

3.1. End-to-End Latency, Loss, and Tail Behavior

Across 60,412 devices over 180 days, the cross-platform reporter reduced median time-to-report (TTR) from 74 s to 41 s (−44.6%). The p95 and p99 TTR fell from 212 s to 97 s and from 531 s to 226 s, respectively. The on-time reporting rate within 60 s rose from 0.61 to 0.83. Loss per device dropped from 1.9% to 0.6%, and duplication fell from 0.7% to 0.2%. These gains came from a compact event schema, bounded queues, and store-and-forward under weak links. The shape of the latency distribution matches results reported for real-time MQTT paths in which architectural controls cut average and worst-case delays roughly in half, supporting our use of priority channels for time-sensitive reports [18].

Figure 1. MQTT-based fault reporting path from wearable device to cloud service.

Figure 1. MQTT-based fault reporting path from wearable device to cloud service.

3.2. Factor Effects and Model Interpretation

The logistic model for success within 60 s explained 38–45% of variance across platforms. Signal strength (per +10 dB RSSI) increased the odds by 1.47 (95% CI 1.42–1.52). Added queue depth (per extra slot at send time) reduced the odds to 0.82 (0.79–0.85). Battery below 20% lowered the odds to 0.76 (0.73–0.80). OS and hardware indicators were significant but smaller in effect. These results show that radio quality and local back-pressure dominate reporting speed; compute capacity was rarely the limiting factor. This agrees with controlled studies of MQTT timing that highlight network path control and admission as the main levers for predictable delivery.

3.3. Cross-Platform Schema, Fault Grouping, and Triage

Unifying event fields across app, OS, radio, and sensor layers reduced distinct incident clusters by 35–39% through better fingerprinting and deduplication, which cut median time-to-detect by 28%. On devices with intermittent coverage, store-and-forward plus priority upload preserved ordering and kept duplication under 0.3% during bursts. This behavior is consistent with wearable pipelines that couple on-device detection with a cloud path and notification channel; Fig. 2 illustrates a representative wearable architecture in which events are detected locally and pushed to the cloud and messaging endpoints, similar to our production path.

Figure 2. Fall detection pipeline with on-device detection and cloud alerts.

Figure 2. Fall detection pipeline with on-device detection and cloud alerts.

3.4. Energy, Overhead, and Limits

On an instrumented subset (n = 96), the energy per delivered report fell by 23% (IQR 18–27%) under mixed Wi-Fi/LTE conditions. The largest savings came from batching, dictionary-based encoding, and backoff that avoided futile retries at low RSSI. CPU time for encoding dropped by 31%, and DRAM traffic during bursts fell by 29%. However, benefits were smaller on watchOS where OS-level queue policies limited batching. Two limits remain: first, we did not test very low-power radios (e.g., NB-IoT PSM/eDRX) at scale; second, our field logs did not include long rural outages. Future work should extend to wide-area low-duty-cycle radios and add joint tests of secure boot and attestation so that integrity guarantees are measured together with latency and energy.

4. Conclusion

This study developed a cross-platform fault reporting system for wearables and tested it on a large device fleet and in controlled labs. Across 60,412 devices over 180 days, median time-to-report fell from 74 s to 41 s (−44.6%), p95/p99 dropped to 97 s and 226 s, the share of reports delivered within 60 s rose from 0.61 to 0.83, and per-device loss and duplication declined to 0.6% and 0.2%. A unified event schema, compact encoding, store-and-forward, and priority queues improved stability under weak links; incident clusters were reduced by 35–39% and median time-to-detect by 28%. On an instrumented subset, energy per delivered report decreased by 23%, with 31% less CPU time for encoding and 29% lower DRAM traffic. The main contribution is to show that common event fields and a simple on-device reporter can give lower delay and lower overhead across Wear OS, watchOS, and RTOS, while keeping report checks in place. Scientifically, the results clarify that radio quality and local queue depth are the key drivers of reporting delay and offer clear metrics—p95/p99, loss, duplication, and energy per report—for future comparison. In practice, the system supports large-scale deployments in health and safety settings that need timely and reliable fault reports. Limits include limited testing on low-duty-cycle wide-area radios and no coverage of long rural outages. Future work will add NB-IoT/eDRX cases, longer field runs, and joint tests with secure boot and attestation.