I’ve logged the CCS811 eCO2 values over 24 h, using the 10s acquisition PULSE mode of the MEAS_MODE.DRIVE_MODE register bit field. It looks like the internal algorithm is not so well able to cope with humidity changes, as soon as humidity drops (when opening a window), the eCO2 values start drifting. The hardware/firmware specs:
The air quality in the room is somewhat constant and should normally range between 400 and 800 ECO2 pseudo-ppm.
In the examples below, no environmental on-sensor compensation (using the ENV_DATA register) is used.
In all cases where too much drifting was registered, the sensor DRIVE_MODE was reset to Pulse (2). You can then also see that the RAW value is then at a different offset.
The sudden humidity changes seem to be problematic, obviously. Since the raw values don’t change much during the continuous measurement interval (without restarting/rewriting DRIVE_MODE) , this would suggest that the firmware is somewhat mislead and keeps adapting its values.
The app note AN369 mentions the handling of the BASELINE register in paragraph 17. This is to be experimented with next.
T/H Environment compensation
The Write-Only (!) ENV_DATA register should, according to the HW reference, allow compensation of the humitidy. However, first attempts made the measurements go completely haywire and oscillating. This is currently under scrutiny.
It looks like that only a more or less constant humidity makes the sensor operate in a sane range. Otherwise, the high eCO2 values can not be trusted whenever the humidity rises rapidly. I guess this is a somewhat unwanted property of any SnO2 gas sensor and it can be quite difficult to adjust without knowing the parameters. However in this case it seems that the firmware is doing some internal adjustment that is still somewhat not understood.
The BASELINE register is somewhat obscure and undocumented: Some MSBs seem to be flags, the LSB some offset value. The AN tells you to just write back a ‘known’ baseline from clean air. However, I’d assume that for a proper measurement you’d have to record a set of baselines corresponding to environment T/H values to figure out which to write. Not there yet…
Conclusion / possible way out
Whenever the humidity is changing rapidly, the current strategy is:
Wait until humidity has settled and try resetting the BASELINE to a ‘known good’ state
Rewrite DRIVE_MODE to the previous value and wait a certain settling time
Resetting the baseline according to the AN sometimes works, sometimes produces completely unusable results. It is still a bit of a mystery why the ENV_DATA compensation does not work as ‘promised’.
Since scalability of the netpp node solution was advertised, one issue has turned into a FAQ: How to handle errors on loaded networks and traffic between multiple nodes?
Especially with UDP, plenty of scenarios can occur which can confuse all higher protocol layers: lost packets, reverse packet order, duplicate packets…
How to handle these errors, does the netpp layer take care of it all?
It doesn’t. The current strategy with UDP is: We want to see all errors. If we don’t, we’d rather switch to a TCP implementation.
In our test scenario we have, connected by a Gigabit Hub:
One Gigabit Ethernet capable client, one 100M client
Six netpp nodes
If the network is not completely jammed, the usual you would see is timeouts. In Python, these are simple IOError exceptions. If an illegal packet sequence is detected, a SystemError exception will be raised.
As a simple example, the script below will poll all detected hosts at highest frequency possible and cover IOError and SystemError exceptions with different recovery timings.
Note that there is no particular finer control for specific errors in Python. If required, these have to be handled on the C-API level.
hostlist = range(8, 15)
targets = 
for h in hostlist:
ip = "192.168.0.%d" % h
d = netpp.connect("UDP:%s:2016" % ip)
print "Target %s alive" % ip
print "Target %s down" % ip
for h, t in targets:
rev = t.SysCtrl.ReleaseTag.get()
print "Node %d : Class '%s' rev %s" % (h, t.name(), rev)
l = t.LED.Red.get()
l = not l
print 40 * "-"
l = True
for h, t in targets:
dataready = t.UART.RXREADY.get()
print "> %s" % t.UART.RxData.get()
print "Node %d: %s" % (h, sys.exc_info())
print "Node %d: %s" % (h, sys.exc_info())
l = not l
targets = init(hostlist)
The script will just toggle the Green LED for each target and check if there’s input available on the UART. If you have the corresponding netpp node connected via USB serial, you can type in a character at the terminal and see it reported from the script.
Timeouts and sessions
UDP is session-less, therefore netpp handles the connectivity from peer to peer. By default, only two simultaneous connections (two clients) are supported. If a connection is lost, the netpp node will terminate it after a certain timeout, if a new connection is detected. This is signalled on the netpp node UART console by:
If the connection is lost from the netpp node side, for example via a reboot or long cable disconnect, the client may not detect that and keep sending queries. In this case, you might see the following error on the netpp node console:
QRY 55 NAK
(the 55 could be any other code).
In this case, the session would have to be reopened from the client:
d = netpp.connect(...)
FPGA goes cloudy
If you’re collecting data as simple as Temperature and Humidity, for instance, you might want to push the data to the cloud. This is also done using a simple Python script doing a HTTP get request to ThinkSpeak. Note the netpp node does not push the data, the script is running on an embedded Linux module.
Run the example server via Start Menu->netpp->Run example server. A warning will appear and request you to unblock the service, possibly. Then, a small example netpp server is listening on your local machine.
Start the IDLE environment via Start Menu->Python 2.x->IDLE. Read more in detail below…
If you did not download Python before installing netpp, the netpp installer will throw a warning, but still continue.
When you have started IDLE, first try to import the netpp module as shown in the screen shot. Then make a connection to your local example server using the .connect() method.
The .sync() command creates a local property tree with root node ‘r’. When first called, the device server is queried for all available properties, so this can take a long time on some systems. Once the query has completed, the tree is stored in a cache and is only reloaded, if the device properties have changed.
Note the message “using PWD for storage”. If you have no cache directory created, the cache will be installed in the current program’s working directory, which may not be desired. Create the folders ‘.netpp/cache’ in your home directory, then the warning will go away. If you ever need to manually delete the cache files, you will find them there.
Next, we are going to look at the properties. This is simply done the pythonish way using ‘dir’.
And last, we obtain a property value using the .get() method. Simple as that.
Using the Power shell
There are two command line utilities:
master: Simple command line demo tool for netpp access
netpp-cli: interactive netpp client
Open up a Power shell (or a legacy cmd.exe) and change directory to where your netpp binaries are installed, like
cd c:\Program Files (x86)\section5\netpp\bin
The master.exe is a very primitive command line tool for netpp device query. When run without arguments, it will display the available hubs and send out a broadcast on the local network for attached netpp devices. If your example server is running, you will see it listed. Try accessing it:
and it will list the device’s properties.
The netpp-cli.exe is an interactive console with a bit more caching functionality and session character, i.e. when you made a connection, the device will reserve a session with you until you exit the CLI. This operation mode may be required on some more complex devices that work session based.
Make a connection to a device:
At the netpp prompt, type ‘?’. Now you can just get a property value by typing its property name, like Container.Test. When you append a value, you can change a property, likewise.
Process viewer/browser support
To use the predefined GUI process control based on the free pvbrowser (see github repository), you need a demo setup based on a modhub or another Linux embedded demo setup running a pvhub server. This assumes a netpp node setup with default design.
Start the pvbrowser on your PC
Enter the URL
into the pvbrowser for a direct connection to the netpp node (assuming default IP at 192.168.0.5). Replace ‘modhub’ by the IP address of your pvhub server host.
The process viewer should display something like below:
I am glad to announce a new user evaluation platform module called ‘netpp node’. Its motto is ‘IoT on FPGA done right’. See detailed specs and preliminary order information here: [ refdesigns/netpp_node ].
The netpp node engineering samples v0.0 have just passed the long term burn-in. Running since approx. 11 weeks non-stop, the units are flooded with netpp requests from an embedded PC and have shown no failure in the hardware, except a reboot resulting from a power outage.
v0.1 series [18.1]:
Received the series! So finally we can ramp up with the delivery to beta developers…(thanks for being so patient).
For analog I/O, U3 on the board is by default populated with a MSP430G2553, functioning as a smart ADC that is controlled from the ZPUng ‘dagobert’ SoC via i2c. All relevant ADC configuration registers are directly accessible via netpp. For instance, we access the low level registers through a process browser panel as shown above to play with the parameters. The process view panel automatically updates the volatile properties from the netpp peer device. The ADC10 variant of the netpp node provides up to six analog channels internally sampled at up to 200ksps. When in synchronous acquisition configuration (SPI master), only five channels can be used.
Differential 16 bit sigma-delta ADC
The alternate population option with a MSP430F2013 provides a Sigma-Delta 16 bit ADC with differential inputs and programmable gain amplifier. This variant provides three different input channel configurations using the provided analog input pins on this board. Moreover, the internal temperature is available in a separate channel.
‘Push on demand’ data streaming
By default, the analog sensors are polled, i.e. a measurement value is delivered upon request by the master. For synchronous sampling however, a ‘push’ strategy might be desired, where a netpp node delivers a value stream to a data logger or database. This can be netpp (where the netpp node acts as a master), however for high speed data transfers (‘network scope’), a low overhead UDP stream is more desirable. The dagobert SoC features a data port option with programmable slots to stream I/O channels as well as analog values using a standard real time protocol with 90 kHz time stamps.
Monitoring netpp packet performance
Packet behaviour in a real network is measured using the Wireshark protocol analyzer.
The figure below shows some example netpp transaction log that the netpp node handles at a very low CPU overhead based on direct register accesses.The red bars is the effective number of query responses using somewhat ineffective ping-pong requests. The performance can be increased by accumulating data into larger buffer properties.
For i2c or SPI transactions however, the packet rate is expected way lower.
For high speed performance like MJPEG video streaming, a separate UDP/RTP queue can be set up within the firmware to reach maximum throughput. However, there is no handshaking using this method.
The image below shows a repeated property query from within Python. The pauses are introduced by external disturbance (stress test) that causes a packet drop – and the netpp engine to timeout and re-synchronize.
Improved RX/TX queue
With an improved packet FIFO on FPGA, I was able to crank up the number of netpp requests per second, as shown in the Wireshark trace below. This test makes sure that several netpp clients can poll the netpp node at high frequencies without disturbing each other. The blue trace is a repeated poll of the full property tree, the red bars are the timed queries from a process viewer daemon. With no other disturbance, we get the occasional drops (e.g. at 45s, 101.5s) due to the queue running full
The default boot loader firmware supports self-programming over the cable. That means, the netpp_node can be supplied remotely with a new firmware image via a simple upgrade procedure over netpp. If the uploaded image is faulty, the system will fall back to the default boot loader. However, if the new design itself has errors, the system will be unable to recover unless the reset button is pressed.
As the full model of this design is available for simulation, we can verify the system effectively against stress situations. In particular, network safety is of outmost importance. The test procedure check list of the dagobert SoC:
ARP and ping flooding
netpp packet performance test
Broken packet handling
Lost interrupt scenario (packet queue desynchronization)
Jumbo packet flooding was tested, however support can not be enabled on this platform for the receiver queue. It is however possible under certain circumstances to generate (TX) Jumbo packets for experimental purposes. The performance gain is however minimal.
Extended RTOS support
Currently, the netpp node runs a simply bare metal main loop without particular RTOS functionality, i.e. all user code must be designed such that there are no blocking wait statements. Let me just put the FAQ together:
There is FreeRTOS and eCos support code for the ZPU architecture. However, I have no plans in going down that road, you’d be on your own.
A NuttX port is currently under evaluation and may likely be released in a few months time. NO PROMISES!
A simple ‘netpp OS’ with very basic task management is in experimental stage:
Guaranteed latency time from driver interrupt to queue handler task
‘User space’ context switch when sleep() called
Very cheap context switches due to ZPU architecture improvements
Code size is an issue on this particular platform, larger programs (TCP stacks) need to move to the SPI flash overlay program space. Since this involves caching, the program timing is no longer fully deterministic and the RTOS functionality can only apply to program code running in the L1 memory.