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Fleet-wide analysis and debugging with nRF Cloud

In this topic, we will look at how to use the nRF Cloud dashboard to analyze data from deployed Bluetooth LE devices. We will cover how to navigate the dashboard, interpret per-device metric timelines, identify fleet-wide trends, and use crash data to debug firmware faults remotely.

The goal is to connect the data points from Topic 2 and the integration from Topic 3 to practical debugging workflows. By the end of this topic, you should have a better understanding of how to interpret a device’s metric timeline and relate it to the power budget expectations from earlier lessons.

Per-device metric timelines

When you select a device in the nRF Cloud dashboard, you can view its metric timeline. This is a chronological view of all heartbeat data reported by the device. Each heartbeat appears as a data point on the timeline, and you can select individual heartbeats to inspect the full set of metric values at that point in time.

For a Bluetooth LE peripheral with the Memfault SDK configured as described in Topic 3, each heartbeat will include the automatically collected metrics: connection interval, peripheral latency, supervision timeout, PHY, data length, RSSI, peer vendor and version, disconnect count, connection event count, and connected time.

The timeline view is useful for tracking how a device’s behavior changes over time. For example, you might observe that a device starts with a 1000 ms connection interval as expected, but after a firmware update on the user’s phone, the interval drops to 30 ms because the updated phone app requests different connection parameters. The metric timeline makes this change visible, along with the relative time it occurred.

You can also overlay multiple metrics on the same timeline. Plotting connection interval alongside battery state of charge lets you visually correlate shorter connection intervals with faster battery drain. Plotting RSSI alongside disconnect count lets you see whether disconnections correspond to periods of weak signal.

Reboot and crash tracking

Every time a device reboots, the Memfault SDK captures the reset reason and reports it in the next heartbeat. The nRF Cloud dashboard displays reboot events on the device timeline, making it easy to see when and how often a device is resetting.

Reboots are categorized by cause, such as pin reset, power-on reset, watchdog timeout, hard fault, and application assert. This categorization helps you quickly distinguish between expected reboots (such as a user-initiated reset or a firmware update) and unexpected ones (such as a hard fault or watchdog timeout).

When a crash occurs, the Memfault SDK captures a coredump containing the CPU register state, active thread stacks, and key memory regions. This coredump is stored on the device and transmitted to the cloud the next time a central connects. In the nRF Cloud dashboard, you can inspect the coredump to see the full backtrace at the time of the fault, including the function name, source file, and line number for each frame in the call stack.

This is particularly valuable for Bluetooth LE devices where crashes may be difficult to reproduce. A fault that only occurs after a specific sequence of GATT operations, or only when connected to a particular phone model, may never appear in lab testing. With remote coredump capture, you get the same level of debug information you would have with a debugger attached, but from a device in the field.

Correlating crashes with Bluetooth LE activity

When you see a crash on the device timeline, you can look at the surrounding heartbeats to understand what was happening at the time of the fault.

For example, suppose you see an assert in a GATT notification send function. By looking at the heartbeat data around the crash, you might find that the device had just experienced a data length update, or that the peer vendor was a specific manufacturer. This context narrows the investigation significantly compared to a standalone crash report with no surrounding data.

Similarly, if a device shows a watchdog timeout, the surrounding metrics might reveal that the connection event count was unusually high, suggesting the CPU was spending too much time servicing Bluetooth LE events and starving other threads. Or the metrics might show that RSSI was very low, indicating the device was struggling with retransmissions in a noisy RF environment.

Fleet-wide metric charts

Beyond individual device analysis, the nRF Cloud dashboard provides fleet-wide views that aggregate metrics across all devices or across devices running a specific firmware version. This is where you can identify systemic issues that affect a subset of your fleet.

A common workflow is to compare a key metric across firmware versions. For example, you might track the Bluetooth LE sync success rate (the ratio of successful connection synchronizations to total attempts) across three firmware releases. If version 1.1.0 shows a significantly lower sync success rate than version 1.0.0, you know that a regression was introduced in that release. You can then drill down into the devices running version 1.1.0 to look at their individual metric timelines and crash data to find the root cause.

Fleet-wide views are also useful for understanding the distribution of peer devices your product connects to in practice. If 80% of your fleet connects to Apple devices and 15% connects to Samsung devices, you know where to focus your interoperability testing. If disconnection rates are disproportionately high for a specific peer vendor, you have a clear signal to investigate compatibility with that vendor’s Bluetooth stack.

Debugging workflow example

To tie these concepts together, here is a typical debugging workflow using the nRF Cloud dashboard.

A customer reports that their device’s battery is draining faster than expected. You look up the device by its serial number in the dashboard. On the device timeline, you see that battery state of charge is dropping roughly twice as fast as your power budget predicted.

You overlay the connection interval metric and notice that the device is running at a 15 ms connection interval instead of the 1000 ms interval your firmware requests. Looking at the peer information, you see the device is connected to an older Android phone running Bluetooth link layer version 4.2.

You check the connection parameter update metrics and find that the data length is at the default 27 bytes, confirming that DLE negotiation did not succeed with this peer. The combination of a short connection interval and no DLE explains the higher-than-expected power consumption.

With this information, you can update your firmware to handle the case where the central rejects the parameter update request. For example, you might implement a retry strategy, adjust the advertising interval to reconnect less aggressively if parameters cannot be updated, or accept the shorter interval but increase peripheral latency to compensate.

How remote observability complements other tooling

Remote observability is a complement to the bench-level tools and techniques covered in earlier lessons, not a replacement. The PPK2 and oscilloscope give you detailed, high-resolution current profiles that are essential for understanding exactly what happens during a single advertising event or connection interval. The nRF Cloud dashboard gives you a higher-level view over hours, days, and weeks across many devices.

The best approach is to use bench tools during development to optimize and validate your power budget under controlled conditions, and then use remote observability to verify that those optimizations hold up across the diversity of real-world conditions your product will encounter.

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