Internet of Things (IoT) smart sensors: bridging the technology gap….

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The internet of things (IoT) has gained tremendous popularity over the last few years, as many organisations strive to add IoT smart sensor technologies to their product portfolios. The basic paradigm centres around connecting everything to everything, and exchanging all data. This could be house hold appliances to more blue sky applications, such as smart cities. But what does this particularly mean for you?

Almost all IoT applications involve the use of sensors. But how do SME and even multi-national organisations transform their legacy product offering into a 21st century IoT application? One the first challenges that many organisations face is how to migrate to an IoT application while balancing design time, time to market, budget and risk.

Sounds interesting? Then read further….

We recently completed a project for a client who manufactured their own sensors, but wanted to improve their sensor measurement accuracy from ±10% to better than ±0.5% without going down the road of a massive re-design project.

 

The question that they asked us was simply: “Is it possible to get high measurement accuracy performance from a signal that is corrupted with all kinds of interference components without a hardware re-design?”

Our answer: “Yes, but the winning recipe centres around knowing what architectural building blocks to use”.

Traditionally, many design bureaus will evaluate the sensor performance and try and improve the measurement accuracy performance by designing new hardware and adding a few standard basic filtering algorithms to the software. This sort of intuitive approach can lead to very high development costs for only a modest increase in sensor performance. For many SMEs these costs can’t be justified, but perhaps there’s a better way?

Algorithms: the winning recipe

Algorithms and mathematics are usually regarded by many organisations as ‘academic black magic’ and are generally overlooked as a solution for a robust IoT commercial application. As a consequence very few organisations actually take the time to analytically analyse a sensor measurement problem, and those who do invent something tend to come up with something that’s only useable in the lab. There has been a trend over the years to turn to Universities or research institutes, but once again the results are generally too  academic and are based more on getting journal publications, rather than a robust solution suitable for the market.

Our experience has been that the winning recipe centres around the balance of knowing what architectural blocks to use, and having the experience to assess what components to filter out and what components to enhance.  In some cases, this may even involve some minor modifications to the hardware in order to simplify the algorithmic solution. Unfortunately, due to the lack of investment in commercially experienced, academically strong (Masters, PhD) algorithm developers and the pressure of getting a project to the finish line, many solutions (even from reputable multi-national organisations) that we’ve seen over the years only result in a moderate increase in performance.

Despite the plethora of commercially available data analysis software, many organisations opt to do basic data analysis in Microsoft Excel, and tend to stay away from any detailed data analysis as it’s considered an unnecessary academic step that doesn’t really add any value.   This missed opportunity generally leads to problems in the future, where products need to be recalled for a ‘round of patchwork’ in order solve the so called ‘unforeseen problems’. A second disadvantage is that performance of the sensors may be only satisfactory, whereas a more detailed look may have yielded clues on how make the sensor performance good or in some cases even excellent.

 Algorithms can save the day!

 “Although many organisations regard data analysis as a waste of money, our experience and customers prove otherwise.”

Investing in detailed data analysis at the beginning of a project usually results in some good clues as to what needs to be filtered out and what needs to be enhanced in order to achieve the desired performance.   In many cases,  these valuable clues allow  experienced algorithm developers to concoct a combination of signal processing building blocks without re-designing any hardware – which is very desirable for many organisations! Our experience has shown that this fundamental first step can cut project development costs by as much as 75%, while at the same time achieving the desired smart sensor measurement performance demanded by the market.

So what does this all mean in the real world?

Returning the story of our customer, after undertaking a detailed data analysis of their sensor data, our developers were able design a suitable algorithm achieving a ±0.1% measurement accuracy from the original ±10% with only minor modifications to the hardware. This enabled the customer to present his IoT application at a trade show and go into production on time, and yes, we stayed within budget!

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IoT Security – 21st century solutions based on 9th century concepts

The internet of things (IoT) devices have been around for a number of years now, but very few smart sensors have any decent level of data security. For many organisations the issue of data security and secure remote updates to legacy products has become of paramount importance. Unfortunately, many of the engineers who design sensor products have little or no understanding a security algorithms, leading to systems that can be easily hacked – the fiasco of the UK smart meter system is a good example.

 Algorithms to the rescue

Algorithms and mathematics are usually regarded by many organisations as ‘academic black magic’ and are generally overlooked as a solution for a robust IoT commercial application. Nevertheless, some of you may be surprised by how old the concept of algorithms actually are in solving real world problems.

A few weeks ago, I looked through my old PhD thesis and stumbled across a reference to one of world’s first documented algorithms from the 9th century mathematician, Al-Khwarizmi (where, the word ‘algorithm’ is derived from al-Khwarizmi’s name).

Al-Khwarizmi undertook pioneering work in algebra, which was popularized in his book, “al-Mukhtasar fi Hisab al-Jabr wa l-Muqabala” and altered society’s perspective of analyzing problems, be they a simple domestic chore or a complex mathematical concept.

 An excerpt from “Al-Mukhtasar fi Hisab al-Jabr wa l-Muqabala” for the solution to x^2 + 10x = 39.

Translation: For the squares and roots equal to a number, it is as saying: a square and ten of its roots is equal to thirty-nine dirhams. The solution is to halve roots, equal to five in this problem, then, multiplying the root by itself then this will be twenty-five. Then add it to thirty-nine and this will be sixty-four. Then take the square root, which will be eight and subtract from it half the root, which is five. The remainder is three and that is the root you are seeking and the square is nine.

I had forgotten (well, it was 14 years ago!) how elegant Al-Khwarizmi work actually was, and how I’m sure he would probably smile at the challenges that we’re facing today. Nevertheless, without his pioneering work, we wouldn’t have any of the IoT and security algorithms that we take for granted today.

Solutions in the 21st century

We’ve been pleasantly surprised by the rich offering from commercial IC vendors, such as: Atmel, NXP and Analog Devices in producing secure micro-controllers for the IoT market. Many of these micro-controllers include all of the necessary hardware encryption building blocks needed for building a secure IoT sensor, and some even offer a decent amount of processor power for data analytics algorithms.

Sounds ideal, right?

The Achilles heel of all of these solutions is how engineers implement them in a system. The micro-controller itself may be ‘secure’, but what about the system architecture (i.e. the algorithmic building blocks and and how they interact with each other). And what about encryption keys? How are they stored and updated? For the UK smart meter system mentioned above, the system just used one key for the whole system – not very secure ! It is this fact that is painfully overlooked by many, and as such, which eventually leads to the system being hacked and rendered useless.

In short, hardware based encryption technology is a great step in right direction for IoT device security, but without good understanding of encryption technology as part of the system architecture the solution is doomed to failure.

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How to get the best performance from your IoT smart sensor

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.

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2018: The death of big data for IoT

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.

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Successful product development: Follow the yellow brick road…

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When looking at undertaking a NPD (new product development) it’s always tempting to take short cuts in order make the development more attractive to management or a client. I’ve lost count as to the number of times that I’ve heard,

 If we had the budget and time, of course we’d do it properly.

The problem with this, is that after doing this several times, it becomes the norm, and taking short cuts and in order to potentially save money usually leads to more problems in the long run.

 Follow the yellow brick road

For those of you who remember the Wizard of Oz, following the yellow brick road led Dorothy to Emerald city. The NPD process is the same, and is nicely described on Wiki and many other great resources, so there’s no reason to ignore it. If you can’t get the information out of clients, propose a workshop or private session, where you can discuss all requirements and get a clear picture of the clients vision/expectations. Remember that before taking on any new development, you should undertake the task of defining specifications:

  •  User specifications: a document that specifies what the user(s) expects the product (e.g. software) to be able to do.
  •  Functional specifications: are requirements that define what a system is supposed to do. A functional specification does not define the inner workings of the proposed system, and does not include the technical details of how the system function will be implemented.
  • Technical specifications: based on the Workshops, User and Functional requirements, you can finally construct the technical specifications documentation. This document(s) will contain all technical details of how the system will be implemented, and usually includes tables, equations and sketches of GUI layouts and hardware block diagrams.
  • Review: the specifications and findings with the client, and make sure that they understand what they will be getting (i.e. the deliverables).

Although it’s tempting to ignore these steps and start playing with software, following the aforementioned steps keeps you focused and almost always leads to shorter development times.

About the author: Sanjeev Sarpal is director of algorithms and analytics at Advanced Solutions Nederland BV. He holds a PhD in signal processing and has over 20 years commercial experience with the design and deployment of algorithms for smart sensor applications.

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R&D – which one does your company do?

Many hi-tech companies that we have spoken with over the years have struggled to clarify this question, and almost all say that they fall into the D category. Where many say,

we’re not a University.

However, upon closer examination, it would appear that many companies are actually engaged in applied research activities but don’t even realise it or don’t like advertising it. From our experience, research activities can be broken up into two distinct categories:

  • Applied research
  • Fundamental research

Fundamental research is where Universities and research institutes are primarily focused, with the sole purpose of advancing theoretical knowledge in that field via publications and conferences.

Applied research on the other hand, takes the fundamental research findings and applies them to real world application for commercial exploitation. Many companies are constantly on the lookout for new concepts and methods to put them in front of their competition. This could take the form of implementing a radically new scientific method, or even improving their internal processes in order to reduce waste and boost profits.

The latter (also applied research) is currently where many companies are focused (mainly due to the current financial situation), but for the companies who can afford it, there is still some room for the first point.

How do they do it?

Many companies generally hire academics to help them with the translation of these concepts into products, but experience has shown that academics are not the best choice as product developers, so companies tend to use a mix of skills sets, i.e. academic and non-academics in order to get to their products to the finish line.

Academics generally prove to be invaluable at analysing problems and processes and highlighting any areas of weakness, while at the same time taking a pivotal role in the definition of technical specifications.

What about culture and egos?

There are the ego and cultural aspects to consider too, which is especially prevalent with academics. The mismatch with the academic and commercial models unfortunately leads to many focusing on providing the best solution (the academic world), rather than focusing on what the client actually needs (the commercial world).

Many academics consider management as too dumb to understand their fantastic idea.

Good leadership and emotional intelligence are needed to keep the R&D team focused on the big picture, and avoiding the department from turning into a playground for hobby projects. Many engineers and academics struggle with the concept of knowing when a product is good enough, and keep on fiddling with it until it’s perfect.

From our experience, getting clear and realistic customer requirements and then coming up with a careful plan, led by experienced developers is the only way of reaching a successful conclusion.

R, D, or both ?

Whether your company falls into R or D or a mixture of both depends on your business model, but perhaps you actually do more R than you realise. In all cases, selection of a good team that works well together is paramount for success.

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Steps to successfully market and release your new software product

So, you’ve had a great idea for a new software product, you’ve scribbled something down on paper and have a majority of the details in your head, and started coding – Right??

Not so fast!

” make sure that you do your homework first!”

All successful products (software is no exception) are based on the company/coders doing their homework first. Although, it’s boring and sometimes very frustrating, find out why people would buy or even use your product, and what it is they miss or find unhandy with the existing products. Don’t send out an email and ask people, ‘why’ – most people struggle at explaining things, but use a short to the point questionnaire, focusing on the core functionalities.

Also, make sure that you have fully understood what your competition offers, by producing a sheet of pros and cons of each product. You should generally find that a competitor’s product is better at certain things, and worse others. Try to read any customer reviews too, as these can be invaluable. Collecting these pieces of information should help you fine-tune your ideas about your new product. Remember: the key to a product’s success lies in “giving them what they want”, not what you think that they want.

After completing this phase, you can objectively see if there is a business case for your product.

 Beginning the design

Great! You’ve decided that there is a business case.

Don’t start coding yet! Make sure that summarise the homework phase and use it to produce your product specifications and system architecture documentation. Get colleagues or peers to review that key concepts in order to see if you’ve missed something or if there is flaw in the concept.

 Now for the exciting bit

You can now start coding! Try and split the design phase up into manageable chunks, and don’t be afraid to leave out features for your first release.

Try and build up relationships with prospective clients by showing them your progress (posting short videos to blogs are good), and listening to any feedback that they may have. The key here is building up your mailing list of prospective clients before you release.

Sometime later, you’ve gone through the Alpha and Beta testing and are now ready to show the world what you’ve done, and start earning some money for your endeavours.

 Selling your product

Believe or not, this is actually the difficult bit!

Using the mailing list that you should have hopefully built up, you can email your prospective clients. Don’t just send one email and sit back, you need to be a little more proactive than that, and chase up leads. Remember, most people don’t care that you’ve created a new product or even share the same level of enthusiasm that you have. The solution: you need to convince them with examples, but how?

This is can get very tricky, as everybody hates receiving a, “hey, buy my stuff” email. The best advice here is don’t even mention price, focus on the ‘why’ question, i.e. just like your homework phase, why is this useful for solving their problems? Give practical examples (videos, whitepapers or even presentations) of how you can use your product in solving real world issues/problems – hopefully there will be some overlap with a problem that they’re currently facing, which arouses interest.

For many people, price becomes less important if the product solves their problem. However, money is money, and price is always important, so don’t be afraid to directly answer their question if they ask, “how much does it cost?”

 Uninterested clients

If you hear, “It’s too expensive” , “let me think about it” or “I’ve haven’t had the time to look at it yet” then the client has their serious doubts, so the practical examples (case studies) are essential. However, don’t overdo it, and remain polite at all times, even if the client is being rude.

If these hints helped you, please click on like or leave a comment below.

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Some points to bear in mind when designing and implementing algorithms

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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.

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