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It’s estimated that the global smart sensor market will have over 50 billion smart devices in 2020. At least 80% of these IoT/IIoT smart sensors (temperature, pressure, gas, image, motion, loadcells) will use Arm’s Cortex-M technology – where the largest growth is in smart Image sensors (ADAS) & smart Temperature sensors (HVAC).

IoT sensor measurement challenge

The challenge for most, is that many sensors used in these applications require a little bit of filtering in order to clean the measurement data in order to make it useful for analysis.

Let’s have a look at what sensor data really is…. All sensors produce measurement data. These measurement data contain two types of components:

  • Wanted components, i.e. information what we want to know
  • Unwanted components, measurement noise, 50/60Hz powerline interference, glitches etc – what we don’t want to know

Unwanted components degrade system performance and need to be removed.

So, how do we do it?

DSP means Digital Signal Processing and is a mathematical recipe (algorithm) that can be applied to IoT sensor measurement data in order to clean it and make it useful for analysis.

But that’s not all! DSP algorithms can also help in analysing data, producing more accurate results for decision making with ML (machine learning). They can also improve overall system performance with existing hardware (no need to redesign your hardware – a massive cost saving!), and can reduce the data sent off to the cloud by pre-analysing data and only sending what is necessary.

Nevertheless, DSP has been considered by most to be a black art, limited only to those with a strong academic mathematical background. However, for many IoT/IIoT applications, DSP has been become a must in order to remain competitive and obtain high performance with relatively low cost hardware.

Do you have an example?

Consider the following application for gas sensor measurement (see the figure below). The requirement is to determine the amplitude of the sinusoid in order to get an estimate of gas concentration (bigger amplitude, more gas concentration etc). Analysing the figure, it is seen that the sinusoid is corrupted with measurement noise (shown in blue), and any estimate based on the blue signal will have a high degree of uncertainty about it – which is not very useful if getting an accurate reading of gas concentration!

Algorithms clean the sensor data

After ‘cleaning’ the sinusoid (red line) with a DSP filtering algorithm, we obtain a much more accurate and usable signal which helps us in estimating the amplitude/gas concentration. Notice how easy it is to determine the amplitude of red line.

This is only a snippet of what is possible with DSP algorithms for IoT/IIoT applications, but it should give you a good idea as to the possibilities of DSP.

How do I use this in my IoT application?

As mentioned at the beginning of this article, 80% of IoT smart sensor devices are deployed on Arm’s Cortex-M technology. The Arm Cortex-M4 is a very popular choice with hundreds of silicon vendors, as it offers DSP functionality traditionally found in more expensive DSPs. Arm and its partners provide developers with easy to use tooling and a free software framework (CMSIS-DSP) in order to get you up and running within minutes.

Author

  • Dr. Sanjeev Sarpal

    Sanjeev is an AIoT visionary and expert in signals and systems with a track record of successfully developing over 25 commercial products. He is a Distinguished Arm Ambassador and advises top international blue chip companies on their AIoT solutions and strategies for I4.0, telemedicine, smart healthcare, smart grids and smart buildings.

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With the advent of smart cities, and society’s obsession of ‘being connected’, data networks have been overloaded with thousands of IoT sensors sending their data to the cloud, needing massive and very expensive computing resources to crunch the data.

Is it really a problem?

The collection of all these smaller IoT data streams (from smart sensors), has ironically resulted in a big data challenge for IT infrastructures in the cloud which need to process

massive datasets – as such there is no more room for scalability. The situation is further complicated with the fact, that a majority of sensor data is coming from remote locations, which also presents a massive security risk.

It’s estimated that the global smart sensor market will have over 50 billion smart devices in 2020. At least 80% of these IoT/IIoT smart sensors (temperature, pressure, gas, image, motion, loadcells) will use Arm’s Cortex-M technology, but have little or no smart data reduction or security implemented.

The current state of play

The modern IoT eco system problem is three-fold:

  • Endpoint security
  • Data reduction
  • Data quality

Namely, how do we reduce our data that we send to the cloud, ensure that the data is genuine and how do ensure that our Endpoint (i.e. the IoT sensor) hasn’t been hacked?

The cloud is not infallible!

Traditionally, many system designers have thrown the problem over to the cloud. Data is sent from IoT sensors via a data network (Wifi, Bluetooth, LoRa etc) and is then encrypted in the cloud. Extra services in the cloud then perform data analysis in order to extract useful data.

So, what’s the problem then?

This model doesn’t take into account invalid sensor data. A simple example of this, could be glue failing on a temperature sensor, such that it’s not bonded to the motor or casing that it’s monitoring. The sensor will still give out temperature data, but it’s not valid for the application.

As for data reduction – the current model is ok for a few sensors, but when the network grows (as is the case with smart cities), the solution becomes untenable, as the cloud is overloaded with data that it needs to process.

No endpoint security, i.e. the sensor could be hacked, and the hacker could send fake data to the cloud, which will then be encrypted and passed onto the ML (machine learning) algorithm as genuine data.

What’s the solution?

Algorithms, algorithms….. and in built security blocks.

Over the last few years, hundreds of silicon vendors have been placing security IP blocks into their silicon together with a high performance Arm Cortex-M4 core. These so called enhanced micro-controllers offer designers a low cost and efficient solution for IoT systems for the foreseeable future.

A lot can be achieved by pre-filtering sensor data, checking it and only sending what is neccessary to the cloud. However, as with so many things, knowledge of security and algorithms are paramount for success.

I recently attended a seminar on advanced instrumentation, where algorithms were heavily featured. The project pitches heavily emphasised implementation rather than analysis and design, which started an interesting discussion, and led me to think about providing some hints that we’ve successfully used over the years:

1. What do we want to achieve? This is perhaps obvious, but I’ve seen that many people do over look this step and jump into Matlab or C in order to try something out. I would urge some caution here, and suggest that you think very carefully about what you’re about to undertake before writing a single line of code. Don’t be afraid to ask your colleagues/network for advice, as their suggestions may save you months of development time. Also consider using established techniques such as, MoSCoW.

2. The specifications: After establishing the ‘big picture’, split up the specifications into ‘must haves’ and ‘nice to haves’. This may take some time to work out, but undertaking this step saves a considerable amount of time in the development process, and keeps the client in the loop. The specifications don’t need to be 100% complete at this stage (they’re always minor details to be worked out), but make sure that you’re clear about what you’re about undertake, and don’t be afraid to do some analysis or short experiments if required.

3. Algorithm design: Sketch out the algorithm’s building blocks (Visio is a good tool), and for each idea produce a short list of bullets (pros and cons) and computational complexity. This will allow you easily review each concept with your peers.

4. Test data: arrange for some test vectors data (from clients or design some of your own synthetic signals), and sketch out a simple test plan of test vectors that you aim to use in order to validate your concept.

5. Development: Depending on your programming ability, you may decide to implement in C/C++, but Matlab/Octave are very good starting points, as the dynamic data types, vector math and toolboxes give you maximum flexibility. Use the testplan and vectors that you’ve designed in step 4. However, in the case of how to best design your algorithm for streaming applications, I would say that many aspects of the algorithm can be tested with an offline (data file) approach. For a majority of our radar and audio work, we always begin with data file comprised of 10-30seconds worth of data in order to prove that the algorithm functions as expected. Subsequent implementation steps can be used to make the algorithm streaming, but bear in mind that this may take a considerable amount of time!

6. Avoid a quick fix! Depending on the complexity of your algorithm, there will be certain testvectors that degrade the performance of your algorithm or even cause it to completely fail. Allocate sometime to investigate this behaviour, but remember to prioritise the importance, and don’t spend months looking for a minor bug. Try and avoid looking for a quick fix or a patch, as they generally re-appear in the future and kick you up the backside.

7. Implementation: after verifying that your concept is correct, you can finally consider target implementation. This step couples back to the previous steps, as the algorithm complexity will have direct influence on the implementation platform and development time. Some good questions to ask yourself: Is the target platform embedded? In which case, do I need an FPGA, DSP or microcontroller? Will it be fixed point or floating point? Perhaps it will be PC based, in which case is it for Windows, Linux or Mac or for a tablet? What tools do you need in order to develop and test the algorithm?

8. Validation: Verify that your implemented algorithm works with your test vectors and that look for any difficult cases that you can find – remembering point 6.

9. Documentation: In all of the aforementioned steps, documentation is essential. Make sure that you document your results, and provide a paper trail such that a colleague can continue with your work if you get hit by a bus.

Author

  • Dr. Sanjeev Sarpal

    Sanjeev is an AIoT visionary and expert in signals and systems with a track record of successfully developing over 25 commercial products. He is a Distinguished Arm Ambassador and advises top international blue chip companies on their AIoT solutions and strategies for I4.0, telemedicine, smart healthcare, smart grids and smart buildings.

    View all posts