Chameth.com - posts like android-espresso-spoon, g15-ram-upgrade, monitoring-power-with-wemo but not why-you-should-be-using-https

Home Automation Without the Megacorps

2025-05-21T00:00:00Z

I first experimented with home automation in 2016, by picking up a Samsung “SmartThings” hub. It was terrible. The UI to configure things was slow and clunky, firmware updates were applied whether you wanted them or not, and everything stopped working if their cloud services stopped. You were also locked into whatever integrations they deigned to support, of course. After that broke for the umpteenth time I scaled back and for years the closest I got to home automation was a couple of Hue bulbs.

Recently I’ve been building it out again, though. This time using off-the-shelf components that interop using Zigbee, open-source software, and some code I wrote myself. It’s great; it runs entirely locally and has had basically zero downtime. The Zigbee ecosystem lets me integrate all sorts of things without having to spend lots of money on “smart” alternatives. I think I’ve spent less on this incarnation than I did on the original SmartThings hub all those years ago (even without adjusting for inflation!).

My current setup

I run everything on a Raspberry Pi 4, with a Sonoff USB Zigbee adapter based on the CC2652P chipset¹. Interfacing with the Zigbee stack is handled by zigbee2mqtt (z2m for short), an open-source project that basically bridges your devices to an MQTT broker². When a device reports some data, it will send a new message over MQTT; when you want to make a device do something you just post a message back. It’s incredibly lightweight, but supports a huge array of devices out of the box. And as it’s just using MQTT, it’s trivial to integrate with other software or build on top of.

I know a lot of people building out this kind of home automation use Home Assistant, but I don’t get on with it terribly well. It felt incredibly sluggish, and the entire project is just a bit… much. I want a binary or package or docker image I can just run, not an entire operating system. That’s not how computers are meant to work! In contrast, z2m is simple to set up, super light weight and responsive.

Anyway. z2m exposes Zigbee devices over MQTT, so I wrote some code in Go to connect to the MQTT broker, and listen to the messages. It’s grown a bit beyond this now, and I’m skipping some boring bits like error handling and JSON parsing, but at first I had something like:

for {
    message, topic, err := c.ReadSlices()
    
    if string(topic) == "zigbee2mqtt/desk-button" {
        if strings.Contains(message, "single") {
            c.Publish(nil, []byte("{\"state\": \"TOGGLE\"}"), "zigbee2mqtt/room-lights/set")
        }
    }
}

I think I actually made this harder to read by trying to simplify it here, but hopefully you can follow that it was relatively straight forward to listen for a particular action to happen (in this case me “single”-pressing on the device called “desk-button”) and then make another device do something in response (toggling the state of the “room-lights” device).

So what actually are these devices? At present in z2m I have the following:

Some generic buttons
An air quality sensor (USB powered), and a separate temperature sensor (battery powered)
A light switch
Some 240V relay modules, that I use to turn non-smart devices on and off
Some USB relay modules, for the same purpose
Some “smart” plugs that I mostly use to monitor power usage
A motorised blind roller

I think all of these are either Tuya or Moes branded, and were all sourced from AliExpress. You can get the same things from Amazon or elsewhere, but they tend to be 3-4x more expensive for the same product.

One of the cool things about Zigbee devices is that the powered ones work together to create a mesh network, so you don’t have to worry about network repeaters or signal strength like you do with Wi-Fi networks. z2m even makes a map showing the connections:

The network map produced by z2m. You can see how all the powered devices form a mesh that the lower power ones can connect to.

Some interesting automations

A Zigbee relay spliced into the power cable for the fan

So what do I actually do with all of this? Some of it is just what I’ll call “laziness automation”: I want to be able to turn on the lights without getting up and walking to the light switch. So there’s a Zigbee button on my desk that does it. Then there are some less manual automations: my blinds are automatically closed at sunset³, for example. It feels a bit trivial, but it’s surprisingly nice just not to have to think about that at all.

Another nice quality of life automation is the light I have above my monitor. It’s USB powered, and I now have it going through a Zigbee USB relay that lets it be toggled on and off. Then I have a small agent running on my desktop that turns the light on when the computer is unlocked, and off when it locks or shuts down. I keep meaning to make a “film mode” that detects when I’m watching a film, and turning all the lights and blinds down, but I haven’t got around to it yet.

The most complex automation is probably for a window fan. It’s not smart in any way, so I cut the power cable and inserted a Zigbee relay. The whole thing is housed in a little project box to keep it secure. The relay basically acts as a switch: the live wire leading to the fan runs to the “normally open”⁴ contact, while the upstream power goes to “common”. When the relay is told to turn the fan on, the two get connected and the fan gets power. Oh, it’s also not actually a window fan, but I designed and 3D printed some adapters so that it sits right in the window.

So now we have a window fan that can be turned off and on automatically. But I don’t want to have to press a button to do that. I’m lazy, remember? Instead, I made it so that the Go code constantly monitors the temperature reported by the air quality sensor, and queries the free OpenWeatherMap API to get the rough temperature outside. It can then turn the fan on if the room is too hot, and outside is cool enough to make a difference (there’s no point in blowing hotter air in!).

A graph of room temperature, showing the effect of the fan being turned on and off

I don’t want it to do that all the time though. The room doesn’t need to be cooled if I’m not around. There are various Zigbee presence sensors you can get, but the cheaper ones seem to be of dubious quality. Instead, I wrote some code to guess whether I’m present. It uses the state of the monitor light as a proxy for “is the computer in use”, and then makes some guesses based on the last time the computer was used and the current time (if I turn the computer off at midnight it probably means I’m going to sleep; if I turn it off at 2pm it probably means I’m going out somewhere).

You can see in the graph that the fan has a decent impact on temperature. I coded it to cool the room to 19 degrees, but then not turn on again until it was above 20. This prevents it flip-flopping on and off constantly. You can clearly see the pattern in the graph, as it actively cools and then slowly the room warms back up. This arrangement is much better than being woken up at 5am because it’s painfully cold. Trust me.

Bonuses: metrics and 3D printer

One of the things my custom Go code does is collate all the various stats reported by the Zigbee devices, and inserts them into a VictoriaMetrics database. I originally hosted this on the Pi itself, and it performed fine, but I’ve since moved it onto a server so that I can use it for some other things as well.

I set up Grafana to point to VM, and can create dashboards showing power usage, what devices are turned on when, and a bunch of environmental conditions. This also makes it easy to spot how good the data coming from the devices are. For example, the air quality sensor reports a figure for the amount of Carbon Dioxide in the air, as well as the amount of Volatile Organic Compounds (VOCs). The graphs are basically identical, but on a different scale. It turns out this particular device has no actual way of detecting CO₂, so it just “calculates” it from the VOCs figure. Useful to know if you want to actually use that data.

Recently I came across a go library for interacting with Bambu Lab printers, so I’ve also hooked that into my automation. It exports metrics about the printer, so I now have a way of seeing what’s going on when I’m not physically present⁵. I even added a HTTP endpoint (exposed over Tailscale) that shows pictures from the built-in camera.

On the automation front, I’ve made it automatically turn off the printer’s light when it finishes a print, and also added push notifications via PushOver whenever the state changes. No longer will I be sat in another room blissfully unaware it ran out of filament seconds after I walked out the door!

Was it worth it?

These days you could probably just buy some kind of ‘smart home’ hub that works well enough, and do most of what I’ve done with a lot less effort and no coding. I’m still of the opinion that for something so essentially local, it should itself be managed entirely locally. I don’t trust companies like Google or Amazon not to kill their products, or change or remove an API I rely on.

Obviously the “Not Invented Here” approach of coding everything yourself doesn’t suit everyone, but as someone who enjoys coding and enjoys having things work just so it works very well for me.

It turns out the chipset is important. I initially used a similar Sonoff dongle that used a CC2531 chipset, and its performance wasn’t great. It often failed to pair with new devices, and dropped links to existing ones. The zigbee2mqtt docs do explicitly advise against the CC2531 chips for that reason. At the time they recommended CC2652Ps, so that’s what I went with. If you’re starting new I’d go with whatever their latest recommendation was. ↩︎
MQTT is basically a network protocol for publishing and subscribing to arbitrary messages. A broker is the thing that sits in the middle and routes the messages. I use Mosquitto but any will do. ↩︎
They originally also re-opened at sunrise but that was a terrible mistake. Who knew the sun rose so early?! ↩︎
“open” meaning “there’s a gap so it doesn’t work” not “open for business”. This confusion in terminology also extends to drawbridges. ↩︎
I use the printer in LAN mode, which means there’s no way to monitor it from a phone, even if you’re connected to the same network still. ↩︎

Upgrading the RAM in a Dell G15 laptop

2023-07-29T00:00:00Z

The Dell G15

I currently use a Dell G15 laptop for work. It has served me well for a little over a year, but recently it has been struggling a little with my day-to-day workload. It came with 32GB of RAM — the highest possible specification at the time¹ — but that is apparently no longer enough for me.

For a recent project, I was working on a Rust library used in an Android app. That meant running the usual glut of Android tools (Android Studio, an emulator and at least one Gradle daemon) alongside a normal IDE (IntelliJ IDEA). Throw in a web browser and a couple of electron apps, and I often managed to use all 32GB.

When you start swapping memory out to an encrypted disk — even an SSD — it doesn’t make for great performance. At first, I tried to work around this by enabling the Linux out-of-memory (OOM) killer, but it turns out that it’s not too good with Electron apps: it will kill the large browser process, but then the small Electron wrapper will just respawn it.

Can it be upgraded or not?

The obvious solution to not having enough RAM is to add more RAM. A quick look in the manual showed this might not be possible, though. The manual includes the following “Memory specifications” table:

Description	Values
Memory slots	Two SODIMM slots
Memory type	DDR5
Memory speed	4800
Maximum memory configuration	32GB
Minimum memory configuration	8GB
Memory size per slot	8GB or 16GB
Memory configurations supported	8 GB, 1 x 8 GB, DDR5, 4800 MHz 16 GB, 1 x 16 GB, DDR5, 4800 MHz 16 GB, 2 x 8 GB, DDR5, 4800 MHz, dual-channel 32 GB, 2 x 16 GB, DDR5, 4800 MHz, dual-channel

That unambiguously says that an upgrade from 32GB is not possible. I gave up.

Later though, I was complaining about memory issues to a friend, and he pointed out a Dell forum thread where a couple of people claim to have successfully installed dual-channel 32GB modules. Since the alternative was getting an entire new PC after only a year, I decided to give it a go.

The upgrade attempt

I ordered a pair of Crucial 32GB DDR5-4800 SODIMMs, and after they turned up dismantled the laptop. The G15 comes apart pretty normally: there are uncovered screws on the bottom holding the lower part of the case on. With those removed and some gentle prying, it pops off, and you get access to the battery, GPU and motherboard.

The first thing I saw was this:

The G15 motherboard adamantly proclaiming that it doesn’t want 32GB SODIMMs

Not one but two labels that indicate it will only accept 8GB or 16GB modules. Oh well, what’s the worst that can happen?

Oops?

I dutifully installed the new modules, reconnected the battery and put the case back together. I pressed the power button, and… nothing. None of the usual garish lights that immediately come on, no screen output, just a dead laptop. After reading some more of the user manual, I found that there is a tiny status LED on the side next to the ethernet port. Forcing the laptop to power off and back on again, the status LED blinked a distress code at me: 2 amber blinks, 4 white blinks. The manual says that is — unsurprisingly — a memory fault.

I figured at this point that the manual and labels on the motherboard were probably right. I took the laptop apart again, reinstalled the original 2x16GB modules, reassembled it, and pressed the power button. It didn’t boot. I don’t spend a lot of time fiddling inside computers, but I’ve done it enough that I’m reasonably confident I can’t entirely break a computer while swapping some RAM modules. I took to Googling², and found an interesting article that said Dell laptops don’t like to boot after RAM changes unless you clear the CMOS by popping out the battery for 15 minutes.

I opened the laptop up, and looked around for the CMOS battery. There wasn’t one. Turns out they don’t exist any more. I left the main battery disconnected for a while to see if it would help, and it didn’t.

Unexpected success

I started to get worried: if I couldn’t fix this, I wouldn’t be able to work until I got a new PC, and that wasn’t really in my budget at the minute. I sat reading old forum threads and help guides³, none of which were actually useful. Out of nowhere, though, the laptop booted up.

Naturally, I immediately shut the laptop down again, opened it up, and switched back to the new RAM modules. Then I turned it on again and sat waiting. After about 15 minutes of it looking totally dead, it turned on and showed a BIOS warning about the hardware configuration being changed. It then booted perfectly normally, and all 64GB of RAM was visible and usable.

My theory is that the forum threads were right: Dell laptops are funny about RAM upgrades. But somehow in removing the physical CMOS battery, they’ve kept the same “you have to wait 15 minutes” behaviour just without any indication that’s what’s happening. Regardless, I now have enough RAM even for the greediest of IDEs and Electron apps.

Bizarrely, the maximum spec has decreased to 16GB since then. ↩︎
In the generic sense. I use Kagi these days. ↩︎
On my phone because, y’know, the laptop was busted. ↩︎

Android testing with Espresso and Spoon

2017-05-16T00:00:00Z

I’ve been spending some time recently setting up automated testing for our collection of Android apps and libraries at work. We have a mixture of unit tests, integration tests, and UI tests for most projects, and getting them all to run reliably and automatically has posed some interesting challenges.

Running tests on multiple devices using Spoon

Spoon is a tool developed by Square that handles distributing instrumentation tests to multiple connected devices, aggregating the results, and making reports.

As part of our continuous integration we build both application and test APKs, and these are pushed to the build server as build artefacts. A separate build job then pulls these artefacts down to a Mac Mini we have in the office, and executes Spoon with a few arguments:

java -jar spoon-runner.jar \
    --apk application.apk \
    --test-apk applicationTests.apk \
    --fail-on-failure \
    --fail-if-no-device-connected

Spoon finds all devices, deploys both APKs on them, and then begins the instrumentation tests. We use two physical devices and an emulator to cover the form factors and API versions that are important to us; if any test fails on any of those devices, Spoon will return an error code and the build will fail.

For library projects, you only have a single APK containing both the tests and the library itself. The current version of Spoon requires both --apk and --test-apk to be specified, so we simply pass in the same APK to both. It looks like future versions of Spoon will be more flexible in this regard.

Spoon produces HTML reports, showing the status of each test run on each device. We have the report output folder collected as a build artefact, so the reports can be seen right from the build server:

Spoon output summary, showing results of 171 tests run on 3 devices

Flake-free UI testing with Espresso

Espresso is an Android library that provides an API for interacting with and making assertions about the UI of Android applications. Espresso has a very simple interface, and does lots of clever things under the hood to ensure that your tests only execute code when the UI is idle (and hence stable). You shouldn’t ever need to make your code sleep or wait.

In order for Espresso’s magic to work, it needs to know whenever some background activity is going on that it needs to wait for. By default, it hooks in to Android’s AsyncTask executor so it can wait for those to finish. In our apps, there were a few cases where we used an explicit Thread to do some background work, which caused tests to fail intermittently (depending on whether the thread performed its UI update before or after Espresso executed the test code). Rewriting these cases to use an AsyncTask enabled Espresso to figure out what was happening and the tests started passing reliably.

Another, similar, problem occurred where we were using RxJava to load some data. There are two possible ways to deal with this… Espresso has a concept of an idling resource, which provides a way of telling Espresso when a resource is busy so it can belay interacting with or testing the UI until the resource is finished. In our case, the code in question was going to be rewritten soon, so we went for a quicker and dirtier option: force RxJava to use the same executor as AsyncTask.

To do this, we added a simple test utility class that registers an RxJavaPlugin that overrides the Schedulers used by Rx:

/**
 * Hooks in to the RxJava plugins API to force Rx work to be scheduled on the
 * AsyncTask's thread pool executor. This is a quick and dirty hack to make
 * Espresso aware of when Rx is doing work (and wait for it).
 */
public final class RxSchedulerHook {

    private static final RxJavaSchedulersHook javaHook =
            new RxJavaTestSchedulerHook();

    private RxSchedulerHook() {
        // Should not be insantiated
    }

    public static void registerHooksForTesting() {
        if (RxJavaPlugins.getInstance().getSchedulersHook() != javaHook) {
            RxJavaPlugins.getInstance().reset();
            RxJavaPlugins.getInstance().registerSchedulersHook(javaHook);
        }
    }

    private static class RxJavaTestSchedulerHook extends RxJavaSchedulersHook {
        @Override
        public Scheduler getComputationScheduler() {
            return Schedulers.from(AsyncTask.THREAD_POOL_EXECUTOR);
        }

        @Override
        public Scheduler getIOScheduler() {
            return Schedulers.from(AsyncTask.THREAD_POOL_EXECUTOR);
        }

        @Override
        public Scheduler getNewThreadScheduler() {
            return Schedulers.from(AsyncTask.THREAD_POOL_EXECUTOR);
        }
    }
}

With the hook registered Rx does all of its work on the same thread pool as AsyncTask, which Espresso already knows about. It’s not the best long-term solution, but it means we don’t have to spend time integrating IdlingResource for code that doesn’t have long to live. With the hook in place, the tests that were flaking because of Rx started passing reliably as well.

Getting automatic screenshots of failures

Spoon provides a client library to, among other things, take a screenshot of the device. Espresso provides a hook that can be used to change how errors are handled. Putting the two together is very simple:

final FailureHandler defaultHandler =
        new DefaultFailureHandler(
                InstrumentationRegistry.getTargetContext());

Espresso.setFailureHandler(new FailureHandler() {
    @Override
    public void handle(Throwable throwable, Matcher<View> matcher) {
        try {
            Spoon.screenshot(
                    getActivity(),
                    "espresso-failure",
                    description.getClassName(),
                    description.getMethodName());
        } catch (Exception ex) {
            Log.e(TAG, "Error capturing screenshot", ex);
        }
        defaultHandler.handle(throwable, matcher);
    }
});

In our new error handler we simply ask Spoon to take a screenshot, then call Espresso’s original handler so that it can output its debugging information and fail the test. The Spoon runner automatically picks up the screenshot and adds it to the report:

Spoon output details, showing a screenshot captured of the failure

Having the screenshot, error message and logs all presented in a clean UI makes debugging failures much, much easier than searching through a huge build log to try and find the exception.

Filtering tests based on device capabilities

Some tests won’t work on every device you throw at them. We had two problems: some tests require a higher API version than some of our devices, and some of the UIs under test were designed to run only on certain resolutions.

The main cause for our dependence on newer API versions was the use of WireMock, a brilliant library for stubbing out web services. WireMock requires API 19, while our physical devices tend to run versions older than that. Stopping these tests running is simply a case of applying an annotation to the class:

@SdkSuppress(minSdkVersion=19)

Screen resolution is a bit more complicated. One of our apps is designed for a specific tablet device (and will never be used on anything else), and trying to render the UI on smaller screens results in items overlapping, important parts ending up below the fold, and other problems.

We’d still like all the other tests for that app to run on all of the devices, though, so we can test them on a variety of API versions and in other conditions. We just need to suppress the UI tests. To do this, we subclassed the Android ActivityTestRule and overrode the apply method:

@Override
public Statement apply(Statement base, final Description description) {
    if (!canRunUiTests(InstrumentationRegistry.getContext())) {
        // If we can't run UI tests, then return a statement that does nothing
        // at all.  With normal JUnit tests we'd just throw an assumption
        // failure and the test would be ignored, but that makes the Android
        // runner angry.
        return new Statement() {
            @Override
            public void evaluate() throws Throwable {
                // Do nothing
            }
        };
    }

    return super.apply(base, description);
}

/**
 * Checks that the screen size is close enough to that of our tablet device.
 */
@TargetApi(Build.VERSION_CODES.HONEYCOMB_MR2)
private boolean canRunUiTests(Context context) {
    if (Build.VERSION.SDK_INT < Build.VERSION_CODES.HONEYCOMB_MR2) {
        return false;
    }
    int screenWidth = dpToPx(context,
            context.getResources().getConfiguration().screenWidthDp);
    return screenWidth >= 600;
}

private int dpToPx(Context context, int dp) {
    DisplayMetrics displayMetrics = context.getResources().getDisplayMetrics();
    return Math.round(dp * displayMetrics.density);
}

When the rule is run, we check if the screen width meets a minimum number of pixels. If it doesn’t, an empty Statement is returned that turns the test into a no-op. These show up as passed rather than skipped in the output, but there doesn’t seem to be a nice way to signal to the Android JUnit runner that the test is being ignored programatically.

Monitoring power draw with WeMo Insight Switches

2016-05-02T00:00:00Z

A WeMo Insight Switch

I recently picked up a couple of Belkin’s WeMo Insight Switches to monitor power usage for my PC and networking equipment. WeMo is Belkin’s home automation brand, and the switches allow you to toggle power on and off with an app, and monitor power usage.

The WeMo Android app is pretty dismal. It’s slow, doesn’t look great, and crashed about a dozen times during the setup process for each of my two switches. It also doesn’t provide much information at all about power: you can see average power draw and current power draw, and that’s basically it.

Belkin has provided an option to e-mail yourself a spreadsheet with historical power data, and can even do it on a regularly scheduled basis, but that’s not really a nice solution if you want up-to-date power stats. Even if you were happy with data arriving in batch, having to get hold of an e-mail attachment and parse out a weirdly formatted spreadsheet doesn’t make for easy automation. It also relies on Belkin supporting the service indefinitely, which isn’t necessarily going to happen.

Enter the SOAP API

Fortunately, the devices themselves expose an API to get the data. Once a switch is setup with the WeMo app, it connects to a WiFi network. You can discover WeMo devices on the network using a UPnP broadcast, and then from there a list of supported services. Each service has a number of SOAP actions, with nice obvious names, and parameters documented in the XML service description.

For the insight switches, there’s a service at /upnp/control/insight1 which has a number of actions including GetPower and GetTodayKWH. Sending a SOAP request isn’t too difficult (albeit nowhere near as nice as a REST JSON API) — here’s a sample request I made using the DHC chrome extension:

POST /upnp/control/insight1 HTTP/1.1
Accept: */*
Accept-Encoding: gzip, deflate
Accept-Language: en-GB,en;q=0.8
Content-Type: text/xml
Origin: chrome-extension://aejoelaoggembcahagimdiliamlcdmfm
SOAPACTION: "urn:Belkin:service:insight:1#GetPower"
User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/51.0.2704.29 Safari/537.36

<s:Envelope xmlns:s="http://schemas.xmlsoap.org/soap/envelope/" s:encodingStyle="http://schemas.xmlsoap.org/soap/encoding/">
    <s:Body>
        <u:GetPower xmlns:u="urn:Belkin:service:insight:1"/>
    </s:Body>
</s:Envelope>

The name of the action (in the SOAPACTION header) and the XML namespace in the body are both constructed from information in the service definition. The result that comes back is:

<s:Envelope xmlns:s="http://schemas.xmlsoap.org/soap/envelope/" s:encodingStyle="http://schemas.xmlsoap.org/soap/encoding/">
    <s:Body>
        <u:GetPowerResponse xmlns:u="urn:Belkin:service:insight:1">
            <InstantPower>92170</InstantPower>
        </u:GetPowerResponse>
    </s:Body>
</s:Envelope>

The current power draw in milliwatts is returned in the <InstantPower> argument.

Putting the data to work

So now we have a way to pull the current power draw, a nice thing to do would be to plot a graph of it to see the variation over time. This is the short of thing you’d expect to see in the Belkin app, but it’s sadly missing. After a bit of research I settled on the tried and true rrdtool to store data and generate graphs. I created a new database to store the values from the two switches:

rrdtool create power.rrd \
    --start now \
    --step 60 \
    DS:wemoComputer:GAUGE:120:U:U \
    DS:wemoNetworking:GAUGE:120:U:U \
    RRA:AVERAGE:0.5:1:1440 \
    RRA:AVERAGE:0.5:10:1008 \
    RRA:AVERAGE:0.5:30:1488 \
    RRA:AVERAGE:0.5:120:1488 \
    RRA:AVERAGE:0.5:360:1488 \
    RRA:AVERAGE:0.5:1440:36500

This creates a database file which expects values to be given every 60 seconds for two data series: ‘wemoComputer’ and ‘wemoNetworking’. These are gauge types (i.e., a value we read off a gauge, rather than a counter that keeps going up) with no minimum and maximum (‘U’).

It then defines a series of round-robin archives. Each of these stores a fixed number of entries, and the oldest is overwritten when they become full. The interesting arguments are the step count (second to last) and number of samples (last argument). The first one takes every sample and keeps 1,440 of them (i.e., a full day at 1-minute resolution); the last one has a step of 1,440 and keeps 36500 of them (i.e., 100 years at 1-day resolution). The use of RRAs ensures that rrdtool can retain enough data to create historical graphs, while providing a fixed, finite maximum file size. With sufficient RRA coverage you can graph most timescales and have data available at a reasonable resolution.

Next, I created a small python script to retrieve the power using SOAP:

#!/usr/bin/python3

import requests
from xml.etree import ElementTree

def get_power(ip_and_port):
    headers = {'Content-type': 'text/xml', 'SOAPACTION': '"urn:Belkin:service:insight:1#GetPower"'}
    payload = '''<s:Envelope xmlns:s="http://schemas.xmlsoap.org/soap/envelope/"
                             s:encodingStyle="http://schemas.xmlsoap.org/soap/encoding/">
                  <s:Body>
                   <u:GetPower xmlns:u="urn:Belkin:service:insight:1"/>
                  </s:Body>
                 </s:Envelope>'''
    r = requests.post("http://%s/upnp/control/insight1" % ip_and_port, headers=headers, data=payload)
    et = ElementTree.fromstring(r.text)
    return et.find('.//InstantPower').text

I then have a dictionary of IP addresses to data series names, and the script polls each one in turn and then executes an rrdtool update query to add the items to the database. I have this script running every minute via cron.

After leaving the script to run for a bit and gather data, it’s time to make some graphs. I use the following to create a graph with a background gradient:

rrdtool graph desk-1d.png \
        -o -X0 -w800 -h500 \
        -u 2000 -l 20 -r \
        DEF:raw=power.rrd:wemoComputer:AVERAGE \
        CDEF:power=raw,1000,/ \
        CDEF:powerz=power,2000,LT,power,2000,IF CDEF:powerzNoUnk=power,UN,0,powerz,IF AREA:powerzNoUnk#ff0000 \
        CDEF:powery=power,1500,LT,power,1500,IF CDEF:poweryNoUnk=power,UN,0,powery,IF AREA:poweryNoUnk#ff0000 \
        CDEF:powerx=power,1000,LT,power,1000,IF CDEF:powerxNoUnk=power,UN,0,powerx,IF AREA:powerxNoUnk#ff0000 \
        CDEF:powerw=power,900,LT,power,900,IF CDEF:powerwNoUnk=power,UN,0,powerw,IF AREA:powerwNoUnk#ff0000 \
        CDEF:powerv=power,800,LT,power,800,IF CDEF:powervNoUnk=power,UN,0,powerv,IF AREA:powervNoUnk#ff1b00 \
        CDEF:poweru=power,700,LT,power,700,IF CDEF:poweruNoUnk=power,UN,0,poweru,IF AREA:poweruNoUnk#ff4100 \
        CDEF:powert=power,600,LT,power,600,IF CDEF:powertNoUnk=power,UN,0,powert,IF AREA:powertNoUnk#ff6600 \
        CDEF:powers=power,400,LT,power,400,IF CDEF:powersNoUnk=power,UN,0,powers,IF AREA:powersNoUnk#ff8e00 \
        CDEF:powerr=power,200,LT,power,200,IF CDEF:powerrNoUnk=power,UN,0,powerr,IF AREA:powerrNoUnk#ffb500 \
        CDEF:powerq=power,180,LT,power,180,IF CDEF:powerqNoUnk=power,UN,0,powerq,IF AREA:powerqNoUnk#ffdb00 \
        CDEF:powerp=power,160,LT,power,160,IF CDEF:powerpNoUnk=power,UN,0,powerp,IF AREA:powerpNoUnk#fdff00 \
        CDEF:powero=power,140,LT,power,140,IF CDEF:poweroNoUnk=power,UN,0,powero,IF AREA:poweroNoUnk#d7ff00 \
        CDEF:powern=power,120,LT,power,120,IF CDEF:powernNoUnk=power,UN,0,powern,IF AREA:powernNoUnk#b0ff00 \
        CDEF:powerm=power,100,LT,power,100,IF CDEF:powermNoUnk=power,UN,0,powerm,IF AREA:powermNoUnk#8aff00 \
        CDEF:powerl=power,90,LT,power,90,IF CDEF:powerlNoUnk=power,UN,0,powerl,IF AREA:powerlNoUnk#65ff00 \
        CDEF:powerk=power,80,LT,power,80,IF CDEF:powerkNoUnk=power,UN,0,powerk,IF AREA:powerkNoUnk#3eff00 \
        CDEF:powerj=power,70,LT,power,70,IF CDEF:powerjNoUnk=power,UN,0,powerj,IF AREA:powerjNoUnk#17ff00 \
        CDEF:poweri=power,60,LT,power,60,IF CDEF:poweriNoUnk=power,UN,0,poweri,IF AREA:poweriNoUnk#00ff10 \
        CDEF:powerh=power,50,LT,power,50,IF CDEF:powerhNoUnk=power,UN,0,powerh,IF AREA:powerhNoUnk#00ff36 \
        CDEF:powerg=power,40,LT,power,40,IF CDEF:powergNoUnk=power,UN,0,powerg,IF AREA:powergNoUnk#00ff5c \
        CDEF:powerf=power,30,LT,power,30,IF CDEF:powerfNoUnk=power,UN,0,powerf,IF AREA:powerfNoUnk#00ff83 \
        CDEF:powere=power,20,LT,power,20,IF CDEF:powereNoUnk=power,UN,0,powere,IF AREA:powereNoUnk#00ffa8 \
        CDEF:powerd=power,0,LT,power,0,IF CDEF:powerdNoUnk=power,UN,0,powerd,IF AREA:powerdNoUnk#00ffd0 \
        LINE:power#080

This seems a bit unweildy, but it’s fairly straight forward. The options tell rrdtool to create a graph with a canvas size of 800x500 pixels, a lower limit of 20W, upper limit of 2kW, and a log scale. The first CDEF divides our raw value (which was in millwatts) by 1000 to get a value in watts. rrdtool uses reverse polish notation like some calculators, so you provide the arguments before the operator.

The big block of CDEF/CDEF/AREA parameters creates a series of area plots in different colours according to the power level. They’re in descending order so the smaller areas are drawn on top of the larger layers. This results in a graph that looks like this:

Graph of power usage over a day

This graph shows the total power for all the things plugged in at my desk. You can see the idle power draw is around 60W. When I’m using the computer it jumps up to around 130W, and when the computer is under heavy load (playing games, for example) it goes up even further to the 200W mark. With a couple of small tweaks to the rrdtool command, I also have a graph showing the entire week:

Graph of power usage over a week

The two huge spikes near the start of the data are caused by a heater under my desk. They’re also one of the main reasons I chose to plot the graphs with a logarithmic scale. With a linear scale between 20W and 2kW, the graph would be completely dominated by these large spikes — in fact the difference in height between the two spikes would take almost half the graph! The relative difference between the normal values of 60-200W and the heater’s value of 1.8kW isn’t actually that interesting, and certainly not worth using about 80% of the graph to demonstrate. The log scale helps to compress this, and emphasises the difference in the smaller values more.

Reverse engineering the Sense API

2016-04-10T00:00:00Z

A Sense unit and its two pillow sensors

Sense is a little device that sits by your bedside and, in conjunction with a little ‘pill’ attached to your pillow, monitors your sleeping patterns and any environmental conditions that might hamper them. Android and iOS apps show you your sleep history, and offer suggestions for improvements.

Sense was Kickstarted in August 2014, raising over 2.4 million US dollars, and shipped to backers in mid 2015. The campaign blurb included this snippet:

Building with Sense

You’ll always have access to your data via our API. Take it, play with it, graph it, do whatever you want with it. It’s yours. That’s important to us.

We enjoy tinkering with and building on-top of other products we like. Sense will let you have that experience.

We’d love to hear your thoughts on what you might want to build with Sense, and how you could directly interact with the hardware, and the data it collects.

Sounds great! But a year after shipping, there’s no sign of an API, and some of us who enjoy tinkering are getting a bit restless…

Reverse engineering to the rescue

Data from the Sense hardware is transmitted off onto the Internet somewhere, and then pulled down by the mobile apps. If we can snoop that traffic, we can probably figure out how to grab the data from the Sense.

So the first step is to capture the raw network traffic while the Sense and the app are in use. I crafted an extremely over-the-top setup that involves my phone and the Sense being on a new, separate wireless network that gateways through a Linux server that can look in on all the traffic (because you never know when a dedicated capturing network will come in handy, right?…)

Running tshark while the app and device are active showed they were making HTTPS requests an Amazon Elastic Cloud instance. That’s not really much use, as we can’t see the content of the requests. The next step is to move up the network stack and target the HTTPS traffic specifically. Step in, mitmproxy. This automates man-in-the-middle attacks on HTTPS requests. When it receives such a request, it cooks up its own certificate and sends that to the client, and then sits in between while the client and the server communicate.

The certificates generated by mitmproxy aren’t issued by a trusted source, so (well behaved) browsers and apps will throw up lots of warnings and refuse to communicate with it for the most part. This is the mechanism that stops anyone impersonating your banking website when you access it over HTTPS, so is generally a very good thing. In this case, both the Sense itself and the Android app refused to talk with the blatant attacker.

We control the Android phone, though, so can just tell it that we trust the dodgy certificate issuer. This involves grabbing the certificate generated by mitmproxy, and adding it to Android’s key store. The process is documented nicely in the mitmproxy docs and is pretty straightforward. With Android trusting our certificate authority, the Sense app starts talking through the proxy and we can look at the plain HTTP requests.

The API

Logging in to the Android app and navigating through the various screens shows that the API is actually pretty nice. It’s RESTful, and uses an OAuth bearer token for authorisation, as you can see in the mitmproxy output of the authentication and first few requests:

POST https://api.hello.is/v1/oauth2/token
    ← 200 application/json 151B 1.17s
GET https://api.hello.is/v2/account/preferences
    ← 200 application/json 124B 109ms
GET https://api.hello.is/v1/account
    ← 200 application/json 181B 388ms
GET https://api.hello.is/v2/devices
    ← 200 application/json 235B 126ms
POST https://api.hello.is/v1/app/checkin
    ← 200 application/json 106B 119ms
GET https://api.hello.is/v2/insights
    ← 200 application/json 1.4kB 157ms
POST https://api.hello.is/v1/notifications/registration
    ← 204 [no content] 364ms
GET https://api.hello.is/v2/timeline/2016-04-09
    ← 200 application/json 160B 550ms
GET https://api.hello.is/v1/questions?date=2016-04-10
    ← 200 application/json 284B 132ms
PATCH https://api.hello.is/v1/app/stats
    ← 202 [no content] 126ms
GET https://api.hello.is/v1/app/stats/unread
    ← 200 application/json 71B 171ms

Authorisation

To get a bearer token, you send a POST request to /v1/oauth2/token, supplying the username and password, and a client ID and secret. Presumably if the API is ever opened up you’ll be able to register clients and get your own ID and secret, but for now reusing the ones from the Android app works fine. [I’ve prettified and linewrapped the content to make it a bit easier to read.]

POST /v1/oauth2/token HTTP/1.1
Host: api.hello.is
Accept: */*
Content-Length: 123
Content-Type: application/x-www-form-urlencoded

grant_type=password
 &client_id=8d3c1664-05ae-47e4-bcdb-477489590aa4
 &client_secret=4f771f6f-5c10-4104-bbc6-3333f5b11bf9
 &username=USERNAME
 &password=PASSWORD

HTTP/1.1 200 OK
Cache-Control: no-cache
Content-Type: application/json
Date: Sun, 10 Apr 2016 17:25:16 GMT
transfer-encoding: chunked
Connection: keep-alive

{
  "token_type":"Bearer",
  "expires_in":31536000,
  "account_id":"1234",
  "access_token":"2.xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",
  "refresh_token":"2.yyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyy"
}

Sleep timeline

Getting the timeline for one night is just a straight up GET request. It gives a detailed log of events, including different ‘depths’ of sleep (I’ve cut out a whole bunch here for simplicity). It also shows metrics relating to the entire night, and their condition (‘warning’ or ‘ideal’).

GET /v2/timeline/2016-04-08 HTTP/1.1
Host: api.hello.is
Accept: */*
Authorization: Bearer 2.xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

HTTP/1.1 200 OK
Cache-Control: no-cache
Content-Type: application/json
Date: Sun, 10 Apr 2016 17:29:00 GMT
transfer-encoding: chunked
Connection: keep-alive

{
  "score":76,
  "score_condition":"WARNING",
  "message":"You were asleep for **8.2 hours**, and sleeping soundly for 3.2 hours.",
  "date":"2016-04-08",
  "events":[
    {
      "timestamp":1460168700000,
      "timezone_offset":3600000,
      "duration_millis":60000,
      "message":"You went to bed.",
      "sleep_depth":0,
      "sleep_state":"AWAKE",
      "event_type":"GOT_IN_BED",
      "valid_actions":["ADJUST_TIME","VERIFY","INCORRECT"]
    },{
      "timestamp":1460169600000,
      "timezone_offset":3600000,
      "duration_millis":60000,
      "message":"You fell asleep.",
      "sleep_depth":100,
      "sleep_state":"SOUND",
      "event_type":"FELL_ASLEEP",
      "valid_actions":["ADJUST_TIME","VERIFY","INCORRECT"]
    },{
      "timestamp":1460177040000,
      "timezone_offset":3600000,
      "duration_millis":60000,
      "message":"You were moving around quite a bit.",
      "sleep_depth":2,
      "sleep_state":"AWAKE",
      "event_type":"GENERIC_MOTION",
      "valid_actions":["VERIFY","INCORRECT"]
    },{
      "timestamp":1460199000000,
      "timezone_offset":3600000,
      "duration_millis":60000,
      "message":"Good morning.",
      "sleep_depth":0,
      "sleep_state":"AWAKE",
      "event_type":"WOKE_UP",
      "valid_actions":["ADJUST_TIME","VERIFY","INCORRECT"]
    }
  ],
  "metrics":[
    { "name":"total_sleep", "value":490, "unit":"MINUTES", "condition":"IDEAL" },
    { "name":"sound_sleep", "value":190, "unit":"MINUTES", "condition":"IDEAL" },
    { "name":"time_to_sleep", "value":15, "unit":"MINUTES", "condition":"IDEAL" },
    { "name":"times_awake", "value":2, "unit":"QUANTITY", "condition":"IDEAL" },
    { "name":"fell_asleep", "value":1460169600000, "unit":"TIMESTAMP", "condition":"IDEAL" },
    { "name":"woke_up", "value":1460199000000, "unit":"TIMESTAMP", "condition":"IDEAL" },
    { "name":"temperature", "value":null, "unit":"CONDITION", "condition":"IDEAL" },
    { "name":"humidity", "value":null, "unit":"CONDITION", "condition":"IDEAL" },
    { "name":"particulates", "value":null, "unit":"CONDITION", "condition":"WARNING" },
    { "name":"light", "value":null, "unit":"CONDITION", "condition":"IDEAL" },
    { "name":"sound", "value":null, "unit":"CONDITION", "condition":"IDEAL"}
  ]
}

Other interesting resources

Pretty much everything displayed in the app is available as a REST resource. They clearly put some thought into API design, which makes it even more of a shame that they haven’t made it public yet. Some of the other interesting resources are:

/v1/alarms: the alarms set on the Sense.
/v1/room/current?temp_unit=c: gives the latest environmental readouts from the Sense.
/v1/room/all_sensors/hours?quantity=2&from_utc=<timestamp>: sensor history.
/v2/devices: the paired devices, including battery status of pills.
/v2/insights: JSON representation of the cards displayed on the main screen (recommendations, analysis, etc).
/v2/trends/LAST_WEEK (or MONTH or 3_MONTHS): gives graph data for sleep scores, duration and depths.

Now the API is figured out, all that’s left is to build something that uses it…