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Hoe ASN Filter Designer ingenieurs helpt met digitale filters

DSP voor ingenieurs: de ASN Filter Designer is de ideale tool om de sensordata snel te analyseren en te filteren. Maak een algoritme binnen enkele uren in plaats van dagen. Wanneer u met sensorgegevens werkt, herkent u deze uitdagingen waarschijnlijk:

  • Mijn sensordatasignalen zijn te zwak om zelfs maar een analyse te maken. Daarom heb ik versterking van de signalen nodig
  • Waar ik een vlakke lijn zou verwachten, zien de gegevens eruit als een puinhoop door interferentie en andere vervuiling. Ik moet de gegevens eerst opschonen voordat ik ze analyseer.
Sensor data: wanted components, desired signals (DC components), and unwanted components (50HZ sine powerline interference, white noise). Filter sensor data DSP

Waarschijnlijk heb je tot nu toe dagen of zelfs weken gewerkt aan signaalanalyse en filtering. Het ontwikkelingstraject is over het algemeen langzaam en zeer pijnlijk. Denk maar eens aan het aantal uren dat je had kunnen besparen als je een ontwerptool had gehad die alle algoritmische details voor jou beheerde. ASN Filter Designer is een standaardoplossing voor de industrie die wordt gebruikt door duizenden professionele ontwikkelaars die wereldwijd aan iot-projecten werken.

Onze nauwe samenwerking met Arm en ST zorgt ervoor dat alle ontworpen filters 100% compatibel zijn met alle Arm Cortex-M processoren, zoals de populaire STM32-familie van ST.

Uitdagingen voor ingenieurs

  • 90% van IoT smart sensors zijn gebaseerd op Arm Cortex-M processor technologie
  • Sensor signal processing is moeilijk
  • Sensoren hebben moeite met interferentie en allerlei ongewenste componenten
  • Hoe ontwerp ik een filter dat voldoet aan mijn requirements?
  • Hoe kan ik mijn ontworpen filter controleren op testdata?
  • Voor betere product performance is schone sensor data nodig
  • Tijdrovend proces om een filter op een embedded processor te implementeren
  • Tijd is geld!

Ontwerpers verzanden vaak met traditionele tooling. Deze vereist meestal een iteratieve, trial and error aanpak of deskundige kennis. Met deze aanpak gaat kostbare tijd verloren. ASN Filter Designer helpt u met een interactieve ontwerpmethode. Hierbij voert de tool automatisch de technische specificaties in op basis van eisen die de gebruiker grafisch heeft ingevoerd.

Snelle ontwikkeling van het DSP-algoritme

  • Volledig gevalideerd filterontwerp: geschikt voor toepassing in DSP, Arm microcontroller, FPGA, ASIC of PC-toepassing
  • Automatische gedetailleerde ontwerpdocumentatie: de Filter Designer helpt je met documenatie, waardoor je de peer review kunt versnellen en projectrisico’s verlaagt
  • Eenvoudige overdracht: projectdossier, documentatie en testresultaten bieden een gemakkelijk manier voor overdracht aan collega’s of andere teams
  • Gemakkelijk in te passen in nieuwe scenario’s: het ontwerp kan eenvoudig worden aangepast aan andere eisen en scenario’s, zoals 60Hz interferentieonderdrukking op de voedingslijn, in plaats van de Europese 50Hz.

ASN Filter Designer: de snelle en intuitieve filter designer

De ASN Filter Designer is het ideale hulpmiddel om sensorgegevens snel te analyseren en filteren. Indien nodig kun je jouw gegevens eenvoudig naar tools als Matlab en Python exporteren voor verdere analyse. Daarom is het ideaal voor ingenieurs die een krachtige tool voor signaalanalyse nodig hebben en een datafilter voor hun IOT-toepassing moeten maken. Zeker als je af en toe een datafilter moet maken. Vergeleken met andere tools creeer je een algoritme binnen enkele uren in plaats van dagen.

Exporteer jouw algoritmes naar Matlab, Python of een Arm microcontroller

Je kunt veel tijd besparen doordat je met ASN Filter Designer algoritmes eenvoudig kunt implementeren in Matlab, Python of direct op een Arm-microcontroller omdat de Filter Designer automatisch code generateert.

Onmiddelijke verlichting

Denk eens aan het aantal uren dat je had kunnen besparen als je een ontwerptool had gehad die alle algoritmische details voor je beheerde.

ASN Filter Designer is een standaardoplossing in de sector die wordt gebruikt door duizenden professionele ontwikkelaars die wereldwijd aan ivd-projecten werken. Onze nauwe samenwerking met Arm en ST zorgt ervoor dat alle filters 100% compatibel zijn met alle Arm Cortex-M processoren.

Hoeveel pijnverzachting kun je voor 145 Euro kopen?

Omdat veel technici onze ASN Filterontwerper voor korte tijd nodig hebben, is een licentie van 145 euro voor slechts 3 maanden mogelijk!

Vraag jezelf maar af: is 145 Euro een eerlijke prijs om te betalen voor onmiddellijke pijnverlichting en resultaat? Wij denken van wel. Bovendien hebben we een licentie voor 1 jaar en zelfs een eeuwigdurende licentie. Download de demo om het zelf te zien of neem contact met ons op voor meer informatie

 

 

Download demo

Prijzen en licenties

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How drones fuel ‘smart air’: air control and congestion

While the developments in the use of drones are going very fast, most of its use is still in an experimenting phase. Besides, the technique is working on an individual basis. From start-ups to big companies like Google, Amazon and UPS. Companies are experimenting by delivering pizzas on the beach. In the future, when drones have become widely adapted, a new form of air control must be developed. In crowded areas in particular. When drones might take up from anyplace and land anywhere anytime, air control is far more difficult than control of normal airplanes. And of course, delivery drones are supposed to work without human interference, even beyond sight from the owner of these drones.

Communication

Drones need to communicate with each other, and with other participants of air traffic. Furthermore, questions about prevention from flying over fires and forbidden areas must be solved. For instance airports, strategic points as driveways and military zones. Taking in consideration they might fly of its course due to wind.

Congestion

When pizza delivery will be just as common as delivery by scooter nowadays, a form of air congestion is going to take place as well. Companies are already proposing to use different ‘airlines’ for speedy delivery and slower registering traffic. But then, there must come a solution how to handle the event when delivery drones are in each other environment without colliding. Or to prevent that the whole traffic gets stuck because every device is waiting on other drones.

The ‘congestion’ takes also place in the use of frequencies. Drones use the same frequencies as a lot of other uses. For instance, airlines and military.

Standardization and legislation

That means standardization and legislation is needed. Standardization, to make certain that drones from different users/companies can communicate with each other. And to make decisions to fly safely and as efficiently as possible. Like other kinds of traffic, legislation is needed to set some rules how all devices can participate in drone traffic and traffic in general. And, when industry won’t be able to solve the already issues mentioned on trust and acceptability, legislation might also come in to set restrictions in the use of drones.

Final Thoughts

The application possibilities of drones are very promising for delivery and registration uses. It is still in its experimental phase. But with developments going fast, soon it will reach the maturity phase. For this, there are two-fold kind of challenges.

One of these are challenges on privacy, safety and security. These challenges have to be solved before their use will get widespread trust and acceptance. Besides, there are technical and communication issues: where multiple drones are being used – especially in cities- challenges how drones can and have to behave in traffic has to be solved.

In both challenges, sensors play a pivotal role in solving the technical questions. Find out more about Drones and DC motor Control

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Preventive Maintenance and the challenges of qualified Personnel

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Preventive Maintenance is one of the golden nuggets of IOT. How does this focus affect the deployment of personnel?

  • Efficiencty of personnel: more and better results
  • Challenge of scarcity of personnel
  • The challenges of the aging engineer

Efficiency of personnel: more and better results

There was and is a lot of attention what sensors can do for preventive maintenance: with preventive maintenance, huge costs of big repair costs are avoided by acting on time. One aspect in this way of thinking, was that existing personnel could work more efficiently. In old days, mechanics and engineers did their regular scheduled rounds of maintenance, where every device got similar time of attention, whether the device was in a bad state or not. Sensors measure the state of maintenance of devices real-time. As such, personnel can give attention to devices which really needs it. By using your existing personnel in this more efficient way, high personnel costs are saved because no other personnel would have been hired.

Challenge of Scarcity of personnel

When Preventive Maintenance became popular some years ago as one of the fields of Internet of Things, the world was still in the last phase of the economic crisis. Industry has in some ways still crisis thought: yes, personnel is hard to find. But they don’t make the connection that efficiency has changed in the guise of ‘cost saver’ to ‘benefit most from opportunities’. Because personnel is so hard to find, industry has to use the available personnel as efficient and effectively as possible. Besides, engineering for infrastructure isn’t a popular study any longer. So, engineers are even harder to find.

With preventive maintenance with the aid of sensors, personnel can give attention to the devices which really needs them.

The challenges of the aging engineer

There is more: most infrastructure has been built 20 years ago. Already, there’s the challenge that those engineers have moved on to other jobs. So, it’s very possible indeed that in a company, nobody knows how this infrastructure works exactly any longer. Last years, a new challenge has come up: those engineers are beginning to retire. That means that a pool of this specific knowledge is already decreasing and will even lessen more in the years to come. Therefore, it is very important to have measures for maintenance in place, before this knowledge has disappeared completely.

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Sensor market $87.6 billion by 2025

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How do you get the best performance from your IoT smart sensor?

The global smart sensor market size is projected to grow from USD 36.6 billion in 2020 to USD 87.6 billion by 2025, at a CAGR of 19.0%. At least 80% of these IoT/IIoT smart sensors (temperature, pressure, gas, image, motion, loadcells) will use Arm’s Cortex-M technology.

IoT sensor measurement challenge

The challenge for most, is that many sensors used in these applications require 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. So ther’s no need to redesign your hardware: a massive cost saving!
  • To reduce the data sent off to the cloud by pre-analysing data. So send only the data which 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. Now we are able to estimate 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). So, you’ll be up and running within minutes.

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How to reduce maintenance programme costs, improve safety and improve customer satisfaction?

How to reduce maintenance programme costs, improve safety and improve customer satisfaction? In Preventive maintenance, algorithms are used in many ways to solve their challenges.

What are the challenges?

Typical challenges faced by assets managers include:

  • Measurement of mechanical component fatigue
  • Assess electrical wiring health
  • How to reduce overall operating costs, but not comprise on public safety?
  • Risks posed by hackers & terrorists
  • Asset damage due to vandalism

Preventive Maintenance aims to solve these problems by acting beforehand. This is achieved by constantly monitoring the performance of critical components (usually with sensors). So, the maintenance team can be alerted that a component is about to fail. Then, the asset management team can then schedule maintenance in order to replace the failing component(s) with minimum disruption to the public, and overall lower operational costs.

Reduction of operating costs

Preventive maintenance is one of the golden nuggets of IoT. In IoT, algorithms are found everywhere. Sensors can measure if mechanical component fatigue sets in. Or measure the health of electrical wiring. These are some examples how preventive maintenance can benefit from IoT using sensors. As a result, operation costs are reduced. And even more important: devices will work safe and secure.

Security

Besides, most devices have been built while security was not an issue. With everything being connected, IoT devices are a interesting target for terrorists or other evil-doers. Prevent yourself from being hacked. And if you are being hacked, you know as soon as possible. So you can take action before the hack leads to major damage.

Read more at https://www.advsolned.com/preventative-maintenance/

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Samenwerking staat centraal op KPN Manufacturing Event

Op het KPN Event van 12 september 2019 stond samenwerking centraal. In het bijzonder ten aanzien van 5GDe komende industriële revolutie is onlosmakelijk verbonden met 5G. 

“Met OEM partners zoals Advanced Solutions Nederland, Alcochem, ExRobotics, Semiotic Labs Topcon Agriculture en enkele tientallen start-ups wordt nauw samengewerkt. Het 5G-ecosysteem vereist samenwerking en openheid naar partners. De nieuwe benadering van de zakelijke markt is voor KPN al realiteit. Paul Cobben, sector developer manufacturing bij KPN, besluit: ‘KPN is actief in Smart Industry, in diverse fieldlabs. We geven graag samen met de maakindustrie invulling aan wireless factories en de use cases die dit mogelijk maakt. Daarmee is KPN een echte ‘enabler’ voor de smart industry en kunnen we onze klanten concreet ondersteunen bij hun digitale transformatie.'” Lees het verslag op de KPN site:

https://www.kpn.com/zakelijk/blog/samenwerking-staat-centraal-op-kpn-manufacturing-event.htm
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IoT Real-time dataset analysis with 5G: the big challenges

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5G’s claim of ultra-low latency, and suitability for real-time edge processing has created a fever of interest in the IoT market. But what does Real-time dataset analysis really mean for your IoT application?

It’s estimated that the global smart sensor market will have over 50 billion smart devices in 2020. All of these IoT smart sensors (temperature, pressure, gas, image, motion, loadcells) will be connected to Wifi, 5G, LoRa etc network services via embedded processors performing real-time signal processing on the captured datasets.

But there are a number of challenges….

IoT sensor measurement challenge

A common challenge 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.

Do you have a practical example?

All analog sensor signals need to be sampled by a digital system in order to make them usable for analysis in the digital domain.  The choice of the sampling frequency is primarily goverend by the maximum frequency that needs to be analysed. But what are design rules?

Consider the following application for gas sensor measurement (see the figure below). The requirement is to determine the amplitude of the noisy sinusoid (shown in blue) in order to get an estimate of gas concentration, where the bigger amplitude, the more the gas concentration.

In order to clean the noisy sinusoid with a filtering algorithm (results shown in red), we first need to find what the frequency of the sinusoid is. The Nyquist sampling Theorem is used for determining this value, and states that,

the analog signal must be sampled at a least two times the maximum analog frequency component.

For our gas sensor, the frequency of the blue sinusoid is about 5Hz, so a minimum sampling frequency of 10Hz is required in order to perform valid analysis on the sampled dataset. However, many designers choose a value 10 times higher than Nyquist in order account for the effects of the noise component and not to be on the borderline of the Nyquist-sampling theorem.

The concept of sampling is demonstrated below:

 

What does Real-time really mean?

Many clients ask us to clarify what real-time really means.

Most people assume that an instant response to a button push or event means real-time. However, the reality is a little more complicated, as a real-time system means that the response is deterministic occurring within a known time frame. This could be seconds or even micro-seconds. In all cases, the response or action time is always known.

For the gas sensor discussed above, the sampling frequency must be constant in order to correctly follow the characteristics of the sinusoid. If the sampling rate varied over time, the sampled data wouldn’t match the design criteria of the algorithmic filtering blocks, and the data analysis would be invalid.

In recent years, much has been said about 5G’s potentially ultra-low latency, and suitability for real-time edge processing. Time will tell how far 5G’s low latency claim can be realised. However, latency in network/cloud services, means that no communication channel can be guaranteed to be real-time 100% of the time. This is further complicated by the requirement of meeting the Nyquist-sampling criteria for sampling analog sensors signals.

In light of all of these issues, our experience has shown that real-time sensor processing (especially for critical automotive or industrial control operations) should be performed at the edge on an embedded real-time processor for maximum reliability and safety.

Our close collaboration with leading technology companies, such as: Arm, Texas Instruments and KPN ensure that our 5G IoT solutions are built with the latest design paradigms using the best of today’s sensor and networking technology.

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How drones fuel ‘smart air’: Delivery drones

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Where ‘smart traffic’ has already 417 billion hits on google, I only found ‘smart air’ for a kind of door lock and ‘smart drone’ for an advanced toy drone. But definitely, drones are so hot that they will become part of something called ‘smart air’. The SESAR project predicts that drones will make 250 billion hours of flight in the European Union alone. For comparison: this is far more than the air traffic of ‘normal’ airplanes today.

Because drones are using many sensors, we did some research how the use of drones can grow to maturity and fuel ‘smart air’. Today we talk about challenges for delivery drones.

Delivery drones

No wonder, drones have proven to be very convenient already and have even more promises in store. Soon, it will be commonplace that drones are delivering packages, from hot pizzas to even more urgent medicines. And even humans: the first drone taxis are already being tested. At this moment, drones are already used for drag-and-drop deliveries in some rural and faraway areas. Most articles on the internet talk about the use in drones in big city areas. And there they have the big advantage of an -still- almost empty sky instead of congested roads and overfull parking places. For that, delivery by drones will be faster and more predictable.

But until use of drones are entirely tried and tested, most drone developments will take place on rural environments. Because here the risk of large damage is a lot smaller when something will go wrong. In time, delivery drones will still be used in rural places. Maybe as a standalone, maybe in combination with self-driving trucks. Reach will not be a big problem, since the whole word is getting connected fast. So, reach will almost only depend on battery endurance. And for now, these batteries have only a limited capacity for distance and cargo.

Challenges while travelling

Like all delivery services, drone delivery has to a pick-up a package, travel to the destination and drop-of the package.  While travelling, drones have to know how to reach their destination. Meanwhile, there are some challenges:

  • Risk of colliding, with other drones, birds and other air users. Just like other traffic
  • And at point in time, some traffic rules have to be set in place. Sensors can help to let the drone follow these rules
  • How drones can stay on course, even with wind
  • Preventing drones to cross over forbidden (known) areas and unexpected ‘wrong’ areas (e.g. a building or a wood on fire)
  • How to prevent a package from falling? How to alert that a package will probably fall? Or maybe the drone itself? If so, measurement can be taken. Already, there are experiments with self-destruction. But maybe more practical solutions can be found to let the drone aim for a ‘safe area’, such as a park, river, etc. for an ‘emergency landing’
  • Acceptance of drones beside safety: how to guarentee privacy when drones are flying over peopled areas? Then there is the issue of noise: research shows that people find the noise of drones one of the most annoying forms of noise

Challenges with dropping the cargo

For now, the drop-of is literally done by dropping-of the cargo. Maybe with the aid of a cord which places the package as soft as possible to the ground. But anyhow: the drone stays in the air. So, technology has to get safe: for the package to be delivered undamaged. How does the drone know that the right person gets the package? And we have to prevent dogs from biting the package. And of course, to prevent that the dropped cargo will harm humans, animals or buildings or even worse.

The use of sensors

The application possibilities of drones are very promising for delivery uses. It is still in its experimental phase. But with developments going fast, soon it will reach the maturity phase. For this, there are two-fold kind of challenges.

Some are challenges on privacy, safety and security. These challenges have to be solved before the use of drones will get widespread trust and acceptance. The other are technical and communication issues: where multiple drones are being used – especially in cities- challenges how drones can and have to behave in traffic has to be solved.

In both challenges, sensors play a pivotal role in solving the technical questions. In all cases, ASN Filter Designer can help with sensor measurement with real-time feedback and the powerful signal analyzer. How? Look at ASN Filter Designer or mail ASN consultancy: designs@advsolned.com

Do you agree with this list? Do you have other suggestions? Please let us know!

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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.

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

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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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