I have an arduino nano 33 ble that sends data from sensors every second or so. Right now I also have an app, written with Flutter and flutter_reactive_ble that receives the data. The problem is that I need to continue receiving data in background, but I am too inexperienced with Dart/Flutter, so I have trouble to do it.
For now I am using am implementation found on
https://github.com/ubiqueIoT/flutter-reactive-ble-example
I am using the subScribeToCharacteristic method.
My understanding is that it checks for new data only when there is something on the screen (using StreamBuilder<List>).
Is there a way to perform some basic math operations on the data in background and to dump it all to an array? I read about isolates and some other interesting techniques, but I was wondering whether there is a simpler solution, because those look a bit too difficult. I saw that apparently if I don't close my app completely (iOS) there is a way for it to continue to receive data for some time like 15-30 minutes, which is enough for my purpose, but there was no code/examples/etc.
I tried to put Stream out of StreamBuilder and to receive data just in the main body of Widget build, but nothing worked, I stopped receiving any data.
Thanks in advance!
here is a good starting point for you just use this plugin
import 'package:flutter_background_geolocation/flutter_background_geolocation.dart'
as bg;
// Initialize the plugin.
bg.BackgroundGeolocation.ready(bg.Config(
desiredAccuracy: bg.Config.DESIRED_ACCURACY_HIGH,
distanceFilter: 10.0,
stopOnTerminate: false,
startOnBoot: true,
));
// Start tracking location in the background.
bg.BackgroundGeolocation.start();
// Subscribe to the BLE device's characteristic to receive data.
bg.BackgroundGeolocation.subscribeToCharacteristic(
'DEVICE_ID',
'CHARACTERISTIC_ID',
(bg.CharacteristicEvent event) {
// Do some basic math operations on the data.
double data = event.value;
double result = data * 2 + 3;
// Dump the data to an array.
List<double> dataArray = [result];
// Do something with the data (e.g., save it to a database, send it to a server, etc.).
},
);
Related
I'm writing a flutter app which sends commands via BlueTooth (FlutterBlue) to a device. The device controlls some LEDs.
The communication is working in general quite well but:
On the UI I have a slider controlling the light intensity. When I pull the slider there are more values generated than the bluetooth backend can handle.
In my first implementation I was sending the data directly to the bluetooth characteristic, resulting in exceptions from the bluetooth backend and some values get lost. It's hard to fade light down to zero.
In my second approach I'm using a stream and an await for loop to send the data. Now all values are send without any exceptions but it takes several seconds after releasing the slider until all values are send. Since I want direct visual feedback on the LEDs, this is not an option.
Since there are multiple commands of the same type to be send, I can skip all commands of the same type which were added while the bluetooth send routine was processing a write event.
I saw that there is a Stream.Distinct method but: It returns a new stream. So I have to exit my await for loop and handle the new stream.
Is there a way of removing undesired events from an existing stream without creating a new stream where I have to listen to?
Here is what I'm doing:
class MyBlueToothDevice {
BluetoothDevice _device;
List<BluetoothCharacteristic> _characteristics =
List<BluetoothCharacteristic>();
final _sendStream = StreamController<Tuple2<SendCommands, List<int>>>();
MyBlueToothDevice(this._device) {
_writeNext();
}
Future<void> write(SendCommands command, List<int> value) async {
if (isConnected) {
_sendStream.add(Tuple2<SendCommands, List<int>>(command, value));
// await _characteristics[command.index].write(value).catchError((value) {
// print("Characteristics.write error: $value");
// });
}
}
Future<void> _writeNext() async {
await for (var tuple in _sendStream.stream) {
await _characteristics[tuple.item1.index]
.write(tuple.item2)
.catchError((value) {
print("Characteristics.write error: $value");
});
}
}
}
The best solution is to use application state management to receive all the events from your slider. The state manager will then rate-limit the messages to the device to something it can handle, and also ensure that the most recent message is not lost.
A very basic solution would receive the slider value and update the value in the state manager. A periodic timer with a suitable rate could then update that value to the device; possibly only if the value actually changed since the last time it was sent.
In short
We have a mobile app that streams fairly high volumes of data to and from a server through various bidirectional streams. The streams need to be closed on occasion (for example when the app is backgrounded). They are then reopened as needed. Sometimes when this happens, something goes wrong:
From what I can tell, the stream is up and running on the device's side (the status of both the GRPCProtocall and the GRXWriter involved is either started or paused)
The device sends data on the stream fine (the server receives the data)
The server seems to send data back to the device fine (the server's Stream.Send calls return as successful)
On the device, the result handler for data received on the stream is never called
More detail
Our code is heavily simplified below, but this should hopefully provide enough detail to indicate what we're doing. A bidirection stream is managed by a Switch class:
class Switch {
/** The protocall over which we send and receive data */
var protocall: GRPCProtoCall?
/** The writer object that writes data to the protocall. */
var writer: GRXBufferedPipe?
/** A static GRPCProtoService as per the .proto */
static let service = APPDataService(host: Settings.grpcHost)
/** A response handler. APPData is the datatype defined by the .proto. */
func rpcResponse(done: Bool, response: APPData?, error: Error?) {
NSLog("Response received")
// Handle response...
}
func start() {
// Create a (new) instance of the writer
// (A writer cannot be used on multiple protocalls)
self.writer = GRXBufferedPipe()
// Setup the protocall
self.protocall = Switch.service.rpcToStream(withRequestWriter: self.writer!, eventHandler: self.rpcRespose(done:response:error:))
// Start the stream
self.protocall.start()
}
func stop() {
// Stop the writer if it is started.
if self.writer.state == .started || self.writer.state == .paused {
self.writer.finishWithError(nil)
}
// Stop the proto call if it is started
if self.protocall?.state == .started || self.protocall?.state == .paused {
protocall?.cancel()
}
self.protocall = nil
}
private var needsRestart: Bool {
if let protocall = self.protocall {
if protocall.state == .notStarted || protocall.state == .finished {
// protocall exists, but isn't running.
return true
} else if writer.state == .notStarted || writer.state == .finished {
// writer isn't running
return true
} else {
// protocall and writer are running
return false
}
} else {
// protocall doesn't exist.
return true
}
}
func restartIfNeeded() {
guard self.needsRestart else { return }
self.stop()
self.start()
}
func write(data: APPData) {
self.writer.writeValue(data)
}
}
Like I said, heavily simplified, but it shows how we start, stop, and restart streams, and how we check whether a stream is healthy.
When the app is backgrounded, we call stop(). When it is foregrounded and we need the stream again, we call start(). And we periodically call restartIfNeeded(), eg. when screens that use the stream come into view.
As I mentioned above, what happens occasionally is that our response handler (rpcResponse) stops getting called when server writes data to the stream. The stream appears to be healthy (server receives the data we write to it, and protocall.state is neither .notStarted nor .finished). But not even the log on the first line of the response handler is executed.
First question: Are we managing the streams correctly, or is our way of stopping and restarting streams prone to errors? If so, what is the correct way of doing something like this?
Second question: How do we debug this? Everything we could think of that we can query for a status tells us that the stream is up and running, but it feels like the objc gRPC library keeps a lot of its mechanics hidden from us. Is there a way to see whether responses from server may do reach us, but fail to trigger our response handler?
Third question: As per the code above, we use the GRXBufferedPipe provided by the library. Its documentation advises against using it in production because it doesn't have a push-back mechanism. To our understanding, the writer is only used to feed data to the gRPC core in a synchronised, one-at-a-time fashion, and since server receives data from us fine, we don't think this is an issue. Are we wrong though? Is the writer also involved in feeding data received from server to our response handler? I.e. if the writer broke due to overload, could that manifest as a problem reading data from the stream, rather than writing to it?
UPDATE: Over a year after asking this, we have finally found a deadlock bug in our server-side code that was causing this behaviour on client-side. The streams appeared to hang because no communication sent by the client was handled by server, and vice-versa, but the streams were actually alive and well. The accepted answer provides good advice for how to manage these bi-directional streams, which I believe is still valuable (it helped us a lot!). But the issue was actually due to a programming error.
Also, for anyone running into this type of issue, it might be worth investigating whether you're experiencing this known issue where a channel gets silently dropped when iOS changes its network. This readme provides instructions for using Apple's CFStream API rather than TCP sockets as a possible fix for that issue.
First question: Are we managing the streams correctly, or is our way of stopping and restarting streams prone to errors? If so, what is the correct way of doing something like this?
From what I can tell by looking at your code, the start() function seems to be right. In the stop() function, you do not need to call cancel() of self.protocall; the call will be finished with the previous self.writer.finishWithError(nil).
needsrestart() is where it gets a bit messy. First, you are not supposed to poll/set the state of protocall yourself. That state is altered by itself. Second, setting those state does not close your stream. It only pause a writer, and if app is in background, pausing a writer is like a no-op. If you want to close a stream, you should use finishWithError to terminate this call, and maybe start a new call later when needed.
Second question: How do we debug this?
One way is to turn on gRPC log (GRPC_TRACE and GRPC_VERBOSITY). Another way is to set breakpoint at here where gRPC objc library receives a gRPC message from the server.
Third question: Is the writer also involved in feeding data received from server to our response handler?
No. If you create a buffered pipe and feed that as request of your call, it only feed data to be sent to server. The receiving path is handled by another writer (which is in fact your protocall object).
I don't see where the usage of GRXBufferedPipe in production is discouraged. The known drawback about this utility is that if you pause the writer but keep writing data to it with writeWithValue, you end up buffering a lot of data without being able to flush them, which may cause memory issue.
I'm not sure why, but for some reason when using the observable that is created via concat I will always get all values that are pushed from my list (works as intended). Where as with the normal subscribe it seems that some values never make it to those who have subscribed to the observable (only in certain conditions).
These are the two cases that I am using. Could anyone attempt to explain why in certain cases when subscribing to the second version not all values are received? Are they not equivalent? The intent here is to rewind the stream. What are some reasons that could explain why Case 2 fails while Case 1 does not.
Replay here is just a list of the ongoing stream.
Case 1.
let observable =
Observable.Create(fun (o:IObserver<'a>) ->
let next b =
for v in replay do
o.OnNext(v.Head)
o.OnNext(b)
o.OnCompleted()
someOtherObs.Subscribe(next, o.OnError, o.OnCompleted))
let toReturn = observable.Concat(someOtherObs).Publish().RefCount()
Case 2.
let toReturn =
Observable.Create(fun (o:IObserver<'a>) ->
for v in replay do
o.OnNext(v.Head)
someOtherObs.Subscribe(o)
).Publish().RefCount()
Caveat! I don't use F# regularly enough to be 100% comfortable with the syntax, but I think I see what's going on.
That said, both of these cases look odd to me and it greatly depends on how someOtherObs is implemented, and where (in terms of threads) things are running.
Case 1 Analysis
You apply concat to a source stream which appears to work like this:
It subscribes to someOtherObs, and in response to the first event (a) it pushes the elements of replay to the observer.
Then it sends event (a) to the observer.
Then it completes. At this point the stream is finished and no further events are sent.
In the event that someOtherObs is empty or just has a single error, this will be propagated to the observer instead.
Now, when this stream completes, someOtherObs is concatenated on to it. What happens now is a little unpreditcable - if someOtherObs is cold, then the first event would be sent a second time, if someOtherObs is hot, then the first event is not resent, but there's a potential race condition around which event of the remainder will go next which depends on how someOtherObs is implemented. You could easily miss events if it's hot.
Case 2 Analysis
You replay all the replay events, and then send all the events of someOtherObs - but again there's a race condition if someOtherObs is hot because you only subscribe after pushing replay, and so might miss some events.
Comments
In either case, it seems messy to me.
This looks like an attempt to do a merge of a state of the world (sotw) and a live stream. In this case, you need to subscribe to the live stream first, and cache any events while you then acquire and push the sotw events. Once sotw is pushed, you push the cached events - being careful to de-dupe events that may been read in the sotw - until you are caught up with live at which point you can just pass live events though.
You can often get away with naive implementations that flush the live cache in an OnNext handler of the live stream subscription, effectively blocking the source while you flush - but you run the risk of applying too much back pressure to the live source if you have a large history and/or a fast moving live stream.
Some considerations for you to think on that will hopefully set you on the right path.
For reference, here is an extremely naïve and simplistic C# implementation I knocked up that compiles in LINQPad with rx-main nuget package. Production ready implementations I have done in the past can get quite complex:
void Main()
{
// asynchronously produce a list from 1 to 10
Func<Task<List<int>>> sotw =
() => Task<List<int>>.Run(() => Enumerable.Range(1, 10).ToList());
// a stream of 5 to 15
var live = Observable.Range(5, 10);
// outputs 1 to 15
live.MergeSotwWithLive(sotw).Subscribe(Console.WriteLine);
}
// Define other methods and classes here
public static class ObservableExtensions
{
public static IObservable<TSource> MergeSotwWithLive<TSource>(
this IObservable<TSource> live,
Func<Task<List<TSource>>> sotwFactory)
{
return Observable.Create<TSource>(async o =>
{
// Naïve indefinite caching, no error checking anywhere
var liveReplay = new ReplaySubject<TSource>();
live.Subscribe(liveReplay);
// No error checking, no timeout, no cancellation support
var sotw = await sotwFactory();
foreach(var evt in sotw)
{
o.OnNext(evt);
}
// note naive disposal
// and extremely naive de-duping (it really needs to compare
// on some unique id)
// we are only supporting disposal once the sotw is sent
return liveReplay.Where(evt => !sotw.Any(s => s.Equals(evt)))
.Subscribe(o);
});
}
}
My CoreMIDI connection on iOS is apparently fast enough to handle ANYTHING that hits it... if I'm just doing some simple object creation and NSLog. In the UI, I don't have time to handle everything that comes in. The UI would blow up, or just finish processing too late.
However, I need to do real processing and UI display in response to CoreMIDI inputs. What I'd like is to process the latest messages every, say, 1ms or 2ms. I've been doing this with a collection that gets emptied by a timer-fired method every 1ms (processFromServerAsync). One problem is that some messages might fall through the cracks, I think, if I grab and substitute:
NSDictionary *queueCopy = [self.queue copy];
// here the dictionary could get messages not in the queue copy!
self.queue = [NSMutableDictionary dictionary];
I realize that I could handle this by synchronizing with a lock, which is easy to screw up:
-(NSMutableDictionary *)messageQueue {
#synchronized(self) {
if (!messageQueue_)
self.messageQueue = [NSMutableDictionary dictionary];
return messageQueue_;
}
}
-(NSDictionary*)clearMessageQueueAndReturnCopy {
#synchronized(self) {
if (!messageQueue_)
return [NSDictionary dictionary];
NSDictionary *retVal = [messageQueue_ copy];
self.messageQueue = [NSMutableDictionary dictionary];
return retVal;
}
}
However, I'm not convinced that I'm even handling this in the correct way. How is throttling typically done (even outside of Obj-C)? I surely cannot process all those messages in the UI nor the program.
There are some well-established patterns for throttling streams of incoming data. This comes up a lot in finance, where you might have a data feed throwing 100K messages/sec at a system.
You employ a sliding window mechanism to discard redundant messages while ensuring that the client has the latest possible copy of the data. You set your window up over some time period (a few milliseconds) then set up a queue for each data stream (meaning a particular CC, midi note etc.) You start a global timer when the first message comes in. You send that message to the client immediately. If anything else comes in during the window you push it to its queue. The queue has just one entry - the latest value - so you overwrite the queued value with each subsequent update. When the timer ticks (the window is over) you send the latest message out to the client. Then, you send the next message out as soon as it comes in, start a new window and repeat. This gives a reasonable balance between swamping the client and avoiding aliasing of update intervals to the timer window. Aliasing is less of an issue with 1-2ms intervals so a cruder rigid timer approach might work for you.
The critical thing is ensuring that you have separate windows for each data stream. You can't risk overwriting or ignoring, say, a note off because a control change came in. One timer, one single-entry queue per Midi message number.
I have an iPhone app which is pulling just about all it's data from ASP.NET MVC services.
Basically just returning JSON.
When I run the app in the simulator, the data is pulled down very fast etc.. however when I use the actual device (3G or WiFi) it's extremely slow. To the point that the app crashes for taking too long.
a) Should I not be calling a service from the FinishedLaunching method in AppDelegate?
b) Am I calling the service incorrectly?
The method I'm using goes something like this:
public static JsonValue GetJsonFromURL(string url) {
var request = (HttpWebRequest)WebRequest.Create (url);
request.AutomaticDecompression = DecompressionMethods.GZip | DecompressionMethods.Deflate;
using(var response = (HttpWebResponse)request.GetResponse()) {
using(var streamReader = new StreamReader(response.GetResponseStream())) {
return JsonValue.Load(streamReader);
}
}
}
Is there a better or quicker way I should be querying a service? I've read about doing things on different threads or performing async calls to not lock the UI, but I'm not sure what the best approach or how that code would work.
a) Should I not be calling a service from the FinishedLaunching method in AppDelegate?
You get limited time to get your application up and running, i.e. returning from FinishedLaunching or the iOS watchdog will kill your application. That's about 17 seconds total (but could vary between devices/iOS versions).
Anything that takes some time is better done in another thread, launched from FinishedLaunching. It's even more important if you use networking services as you cannot be sure how much time (or even if) you'll get an answer.
b) Am I calling the service incorrectly?
That looks fine. However remember that the simulator has a faster access to the network (likely), much more RAM and CPU power. Large data set can take a lot of memory / CPU time to decode.
Running from another thread will, at least, cover the extra time required. It can be as simple as adding the code (below) inside your FinishedLaunching.
ThreadPool.QueueUserWorkItem (delegate {
window.BeginInvokeOnMainThread (delegate {
// run your code
});
});
You can have a look at how Touch.Unit does it by looking at its TouchRunner.cs source file.
note: you might want to test not using (asking) for compressed data since the time/memory to decompress it might not be helpful on devices (compared to the simulator). Actual testing needed to confirm ;)