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The Internet of Things has grown in popularity over the previous few decades. It has converted robots into lifelike assets, and its influence is expanding by the day. Today, communication gadgets outnumber humans, and they make our lives more convenient and intelligent than ever before. So, if you're thinking of developing an IoT app and want to know how much it will cost, this article is for you.

Furthermore, research forecasts that by 2025, there will be around 21 billion linked gadgets. However, this isn't the only reason that IoT app development is growing in popularity; there are other reasons as well. It emphasizes every aspect of the cost of producing an IoT app.

Market Statistics of IoT App Development

  • In the next years, 65 percent of marketers feel that firms that do not have IoT-based apps would fall behind.
  • According to Gartner, there will be around 20 billion connected devices in 2020, which will more than quadruple by 2024-25.
  • Approximately 95% of decision-makers anticipate their companies to use IoT applications by the end of 2025.
  • The base installation for 5G IoT endpoints was 3.5 billion in 2020 and is expected to reach 49 billion by 2024.
  • By 2025, the number of internet-connected devices is expected to reach 28 billion.
  • These figures demonstrate the ever-increasing need for high-quality IoT applications, as well as their relevance in driving company development and productivity. So, now that you know why you should go into IoT mobile app development, let's get started.

Most Significant Applications Of IoT By Industry 

  • IoT apps For the Retail Sector

IoT apps have a promising future in the retail business. Retailers are investing in IoT solutions, namely to track and manage the supply chain and inventory operations.The need for IoT-powered mobile apps for supply chain management tracking is increasing. Retailers can remotely monitor and measure certain areas of supply chain activities. Such an automated procedure will assure high-level security, lower operating costs, and improve business outcomes.

On the other hand, IoT is rapidly being integrated into the creation of mobile apps to track and manage stocks. Warehouse management or inventory management software based on IoT tracks stock levels digitally, assisting shops in balancing demand and supply and improving sales outcomes.

  •  IoT Mobile Apps For Smart Home Automation

One of the key reasons for the increased demand for IoT mobile app development is smart home automation. Sensor-enabled smart home gadgets make people's lives more pleasant, convenient, easy, and intelligent. The incorporation of IoT in mobile apps allows users to operate all compatible smart home gadgets, such as LEDs, cameras, refrigerators, and so on, while on the road.

Here's a popular IoT app that allows users to control smart home devices from afar.

Amazon Alexa is an intelligence program that uses voice commands to operate smart home products. Users may use the Alexa mobile app to control switches, thermostats, and any Alexa-enabled smart electrical home products.

Applications Of IoT Technology For Healthcare Apps Development

IoT technology is quickly becoming a critical component of the healthcare app development market. IoT integration is becoming common in fitness applications, wearables, and other patient monitoring apps.

Yes. The development of IoT-based mobile apps for tracking and monitoring health is a market trend. As a result, the future of IoT mobile apps for smartwatches or wearables seems promising.

Leading enterprise-level software development firms, for example, are already on their way to providing futuristic IoT apps for wearables to measure pulse rate, body temperature, calories burned, sleep quality, steps walked, and so on. This information provided by sensor-equipped wearables is easily accessible via IoT smartphone apps.

What is the typical cost to build an Internet of Things app?

A typical Internet of Things app costs $20,167 to develop. The entire cost, however, might be as little as $5,000 or as high as $35,000. An Internet of Things app with fewer functionalities (also known as a "minimum viable product," or MVP) will be less expensive than an app with all planned capabilities.

For example, these are some current Crowdbotics Internet of Things app price quotes:

$27,500

$23,000

$10,000

However, the cost of IoT mobile app development or solution will be determined by a number of factors, including:

  • Types of applications that are long with the complicity
  • The cost of IoT app development is determined by the number of developers on your app application development team.
  • Depends on how long it takes to design, create, test, and post-develop an IoT app.
  • The cost of developing a mobile application varies depending on where you live. As an example:
  • South-East Asian custom mobile app development firms would charge between $20 and $40 per hour.
  • A mobile app development business in Eastern Europe will charge between $30 and $50.
  • The top mobile app development business in India would charge between $50 and $25 per hour.

IoT mobile application development is becoming popular due to the rising demand for IoT-based mobile apps from organizations across sectors. IoT app development has a promising future in areas such as automotive, healthcare, and smart home automation, as well as security.

 

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By Bee Hayes-Thakore

The Android Ready SE Alliance, announced by Google on March 25th, paves the path for tamper resistant hardware backed security services. Kigen is bringing the first secure iSIM OS, along with our GSMA certified eSIM OS and personalization services to support fast adoption of emerging security services across smartphones, tablets, WearOS, Android Auto Embedded and Android TV.

Google has been advancing their investment in how tamper-resistant secure hardware modules can protect not only Android and its functionality, but also protect third-party apps and secure sensitive transactions. The latest android smartphone device features enable tamper-resistant key storage for Android Apps using StrongBox. StrongBox is an implementation of the hardware-backed Keystore that resides in a hardware security module.

To accelerate adoption of new Android use cases with stronger security, Google announced the formation of the Android Ready SE Alliance. Secure Element (SE) vendors are joining hands with Google to create a set of open-source, validated, and ready-to-use SE Applets. On March 25th, Google launched the General Availability (GA) version of StrongBox for SE.

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Hardware based security modules are becoming a mainstay of the mobile world. Juniper Research’s latest eSIM research, eSIMs: Sector Analysis, Emerging Opportunities & Market Forecasts 2021-2025, independently assessed eSIM adoption and demand in the consumer sector, industrial sector, and public sector, and predicts that the consumer sector will account for 94% of global eSIM installations by 2025. It anticipates that established adoption of eSIM frameworks from consumer device vendors such as Google, will accelerate the growth of eSIMs in consumer devices ahead of the industrial and public sectors.


Consumer sector will account for 94% of global eSIM installations by 2025

Juniper Research, 2021.

Expanding the secure architecture of trust to consumer wearables, smart TV and smart car

What’s more? A major development is that now this is not just for smartphones and tablets, but also applicable to WearOS, Android Auto Embedded and Android TV. These less traditional form factors have huge potential beyond being purely companion devices to smartphones or tablets. With the power, size and performance benefits offered by Kigen’s iSIM OS, OEMs and chipset vendors can consider the full scope of the vast Android ecosystem to deliver new services.

This means new secure services and innovations around:

🔐 Digital keys (car, home, office)

🛂 Mobile Driver’s License (mDL), National ID, ePassports

🏧 eMoney solutions (for example, Wallet)

How is Kigen supporting Google’s Android Ready SE Alliance?

The alliance was created to make discrete tamper resistant hardware backed security the lowest common denominator for the Android ecosystem. A major goal of this alliance is to enable a consistent, interoperable, and demonstrably secure applets across the Android ecosystem.

Kigen believes that enabling the broadest choice and interoperability is fundamental to the architecture of digital trust. Our secure, standards-compliant eSIM and iSIM OS, and secure personalization services are available to all chipset or device partners in the Android Ready SE Alliance to leverage the benefits of iSIM for customer-centric innovations for billions of Android users quickly.

Vincent Korstanje, CEO of Kigen

Kigen’s support for the Android Ready SE Alliance will allow our industry partners to easily leapfrog to the enhanced security and power efficiency benefits of iSIM technology or choose a seamless transition from embedded SIM so they can focus on their innovation.

We are delighted to partner with Kigen to further strengthen the security of Android through StrongBox via Secure Element (SE). We look forward to widespread adoption by our OEM partners and developers and the entire Android ecosystem.

Sudhi Herle, Director of Android Platform Security 

In the near term, the Google team is prioritizing and delivering the following Applets in conjunction with corresponding Android feature releases:

  • Mobile driver’s license and Identity Credentials
  • Digital car keys

Kigen brings the ability to bridge the physical embedded security hardware to a fully integrated form factor. Our Kigen standards-compliant eSIM OS (version 2.2. eUICC OS) is available to support chipsets and device makers now. This announcement is a start to what will bring a whole host of new and exciting trusted services offering better experience for users on Android.

Kigen’s eSIM (eUICC) OS brings

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The smallest operating system, allowing OEMs to select compact, cost-effective hardware to run it on.

Kigen OS offers the highest level of logical security when employed on any SIM form factor, including a secure enclave.

On top of Kigen OS, we have a broad portfolio of Java Card™ Applets to support your needs for the Android SE Ready Alliance.

Kigen’s Integrated SIM or iSIM (iUICC) OS further this advantage

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Integrated at the heart of the device and securely personalized, iSIM brings significant size and battery life benefits to cellular Iot devices. iSIM can act as a root of trust for payment, identity, and critical infrastructure applications

Kigen’s iSIM is flexible enough to support dual sim capability through a single profile or remote SIM provisioning mechanisms with the latter enabling out-of-the-box connectivity, secure and remote profile management.

For smartphones, set top boxes, android auto applications, auto car display, Chromecast or Google Assistant enabled devices, iSIM can offer significant benefits to incorporate Artificial intelligence at the edge.

Kigen’s secure personalization services to support fast adoption

SIM vendors have in-house capabilities for data generation but the eSIM and iSIM value chains redistribute many roles and responsibilities among new stakeholders for the personalization of operator credentials along different stages of production or over-the-air when devices are deployed.

Kigen can offer data generation as a service to vendors new to the ecosystem.

Partner with us to provide cellular chipset and module makers with the strongest security, performance for integrated SIM leading to accelerate these new use cases.

Security considerations for eSIM and iSIM enabled secure connected services

Designing a secure connected product requires considerable thought and planning and there really is no ‘one-size-fits-all’ solution. How security should be implemented draws upon a multitude of factors, including:

  • What data is being stored or transmitted between the device and other connected apps?
  • Are there regulatory requirements for the device? (i.e. PCI DSS, HIPAA, FDA, etc.)
  • What are the hardware or design limitations that will affect security implementation?
  • Will the devices be manufactured in a site accredited by all of the necessary industry bodies?
  • What is the expected lifespan of the device?

End-to-end ecosystem and services thinking needs to be a design consideration from the very early stage especially when considering the strain on battery consumption in devices such as wearables, smart watches and fitness devices as well as portable devices that are part of the connected consumer vehicles.

Originally posted here.

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The advent of the internet of things on Metaverse is expected to change its overall market outlook in the future. The IoT Includes a plethora of features which, in turn, will highly benefit the Metaverse Market in the upcoming years. With a growth rate of 38.25 per cent CAGR, the metaverse market size was estimated to be worth USD 124.04 billion in 2022 and USD 1655.29 billion in 2030.

The IoT, which was first launched in 1999, links hundreds of devices, including thermostats, voice-activated speakers, and medical equipment, to a variety of data. IoT is now poised to revolutionize the Metaverse as it effortlessly connects the 3D environment to a wide range of physical objects. One of the renowned & largest private software firms in the UK, IRIS Software Group, offers software solutions and services that significantly improve operational compliance, efficiency, and accuracy.

The identity environment will expand enormously as the Metaverse takes traction and new applications and access points emerge alongside it, creating additional entry points for potential bad market players. Already, 84% of corporate executives concur that their company now manages significantly more digital identities than it did ten years ago (up to 10x). Additionally, 95% of firms say they have trouble keeping track of all the identities that are currently a part of their organization (human and machine). We have a perfect storm of rising complexity and expanding threat vectors that may be exploited, which can lead to breaches, business disruption, and material expenses when we add in the Metaverse and the rise in IoT usage that will accompany it.

Top features of IoT:

 a.) A 360-degree enhanced and real-world training: 

Using the IoT, we are able to develop and test training methods in situations where we are unable to do so in the real world due to the scope and authenticity of training on extreme real-world situations (such as severe weather or cyber events) that can be done through virtual simulations using digital twins in the Metaverse. Io Train-sim will aid in preparing people and AI/software to cooperate to better recognize issues and lessen the impact in real life as virtual metaverse environments develop to more closely resemble reality.

b.) Smarter and better long-term planning along with its near-term response: 

The metaverse system will increasingly closely resemble our real world as it fills up with digital duplicates of real-world objects (such as cars, buildings, factories, and people). We will be able to run different long-term planning scenarios, identify the most optimal designs for our energy, transportation, and healthcare systems, and dynamically operate these techniques as the real world evolves thanks to this system-of-systems complicated virtual simulation (e.g., more renewable sources, new diseases, population migrations or demographic changes). These simulations will assist teams of humans in responding to current events and solving an issue utilizing monthly, weekly, or day-ahead planning, in addition to long-term planning. AI will then be used to learn from the outcome and enhance the response during the next event.

Conclusion

Brands are utilizing a variety of cutting-edge technologies to fuel the Metaverse with the aim of making the virtual as real-time and authentic as possible. These technologies include AR, VR, Blockchain, AI, and IoT. Sensors, cameras, and wearables are already implemented and in use due to the present IoT development. These gadgets are the engines that make it possible for the Metaverse to reflect the real world in real-time when they are connected to it. A metaverse representation of a physical site, such as Samsung's 837x recreation of its 837 Washington St. experience centre in New York City's Meatpacking District, might, for instance, be updated continuously and in real-time as objects enter and exit the physical location

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Against the backdrop of digital technology and the industrial revolution, the Internet of Things has become the most influential and disruptive of all the latest technologies. As an advanced technology, IoT is showing a palpable difference in how businesses operate. 

Although the Fourth Industrial Revolution is still in its infancy, early adopters of this advanced technology are edging out the competition with their competitive advantage. 

Businesses eager to become a part of this disruptive technology are jostling against each other to implement IoT solutions. Yet, they are unaware of the steps in effective implementation and the challenges they might face during the process. 

This is a complete guide– the only one you’ll need – that focuses on delivering effective and uncomplicated IoT implementation. 

 

Key Elements of IoT

There are three main elements of IoT technology:

  • Connectivity:

IoT devices are connected to the internet and have a URI – Unique Resource Identifier – that can relay data to the connected network. The devices can be connected among themselves to a centralized server, a cloud, or a network of servers.

  • Data Communication:

IoT devices continuously share data with other devices in the network or the server. 

  • Interaction

IoT devices do not simply gather data. They transmit it to their endpoints or server. There is no point in collecting data if it is not put to good use. The collected data is used to deliver IoT smart solutions in automation, take real-time business decisions, formulate strategies, or monitor processes. 

How Does IoT work?

IoT devices have URI and come with embedded sensors. With these sensors, the devices sense their environment and gather information. For example, the devices could be air conditioners, smart watches, cars, etc. Then, all the devices dump their collected data into the IoT platform or gateway. 

The IoT platform then performs analytics on the data from various sources and derives useful information per the requirement

What are the Layers in IoT Architecture?

Although there isn’t a standard IoT structure that’s universally accepted, the 4-layer architecture is considered to be the basic form. The four layers include perception, network, middleware, and application.

  • Perception:

Perception is the first or the physical layer of IoT architecture. All the sensors, edge devices, and actuators gather useful information based on the project needs in this layer. The purpose of this layer is to gather data and transfer it to the next layer. 

  • Network:

It is the connecting layer between perception and application. This layer gathers information from the perception and transmits the data to other devices or servers. 

  • Middleware

The middleware layer offers storage and processing capabilities. It stores the incoming data and applies appropriate analytics based on requirements. 

  • Application

The user interacts with the application layer, responsible for taking specific services to the end-user. 

Implementation Requirements

Effective and seamless implementation of IoT depends on specific tools, such as:

  • High-Level Security 

Security is one of the fundamental IoT implementation requirements. Since the IoT devices gather real-time sensitive data about the environment, it is critical to put in place high-level security measures that ensure that sensitive information stays protected and confidential.  

  • Asset Management

Asset management includes the software, hardware, and processes that ensure that the devices are registered, upgraded, secured, and well-managed. 

  • Cloud Computing

Since massive amounts of structured and unstructured data are gathered and processed, it is stored in the cloud. The cloud acts as a centralized repository of resources that allows the data to be accessed easily. Cloud computing ensures seamless communication between various IoT devices. 

  • Data Analytics

With advanced algorithms, large amounts of data are processed and analyzed from the cloud platform. As a result, you can derive trends based on the analytics, and corrective action can be taken. 

What are the IoT Implementation Steps?

Knowing the appropriate IoT implementation steps will help your business align your goals and expectations against the solution. You can also ensure the entire process is time-bound, cost-efficient, and satisfies all your business needs. 

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Set Business Objectives 

IoT implementation should serve your business goals and objectives. Unfortunately, not every entrepreneur is an accomplished technician or computer-savvy. You can hire experts if you lack the practical know-how regarding IoT, the components needed, and specialist knowledge. 

Think of what you will accomplish with IoT, such as improving customer experience, eliminating operational inconsistencies, reducing costs, etc. With a clear understanding of IoT technology, you should be able to align your business needs to IoT applications. 

Hardware Components and Tools

Selecting the necessary tools, components, hardware, and software systems needed for the implementation is the next critical step. First, you must choose the tools and technology, keeping in mind connectivity and interoperability. 

You should also select the right IoT platform that acts as a centralized repository for collecting and controlling all aspects of the network and devices. You can choose to have a custom-made platform or get one from suppliers. 

Some of the major components you require for implementation include,

  • Sensors
  • Gateways
  • Communication protocols
  • IoT platforms
  • Analytics and data management software

Implementation

Before initiating the implementation process, it is recommended that you put together a team of IoT experts and professionals with selected use case experience and knowledge. Make sure that the team comprises experts from operations and IT with a specific skill set in IoT. 

A typical team should be experts with skills in mechanical engineering, embedded system design, electrical and industrial design, technical expertise, and front/back-end development. 

Prototyping

Before giving the go-ahead, the team must develop an Internet of Things implementation prototype. 

A prototype will help you experiment and identify fault lines, connectivity, and compatibility issues. After testing the prototype, you can include modified design ideas. 

Integrate with Advanced technologies

After the sensors gather useful data, you can add layers of other technologies such as analytics, edge computing, and machine learning. 

The amount of unstructured data collected by the sensors far exceeds structured data. However, both structured and unstructured, machine learning, deep learning neural systems, and cognitive computing technologies can be used for improvement. 

Take Security Measures

Security is one of the top concerns of most businesses. With IoT depending predominantly on the internet for functioning, it is prone to security attacks. However, communication protocols, endpoint security, encryption, and access control management can minimize security breaches. 

Although there are no standardized IoT implementation steps, most projects follow these processes. But the exact sequence of IoT implementation depends on your project’s specific needs.

Challenges in IoT Implementation

Every new technology comes with its own set of implementation challenges. 

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When you keep these challenges of IoT implementation in mind, you’ll be better equipped to handle them. 

  • Lack of Network Security

When your entire system is dependent on the network connectivity for functioning, you are just adding another layer of security concern to deal with. 

Unless you have a robust network security system, you are bound to face issues such as hacking into the servers or devices. Unfortunately, the IoT hacking statistics are rising, with over 1.5 million security breaches reported in 2021 alone. 

  • Data Retention and Storage 

IoT devices continually gather data, and over time the data becomes unwieldy to handle. Such massive amounts of data need high-capacity storage units and advanced IoT analytics technologies. 

  • Lack of Compatibility 

IoT implementation involves several sensors, devices, and tools, and a successful implementation largely depends on the seamless integration between these systems. In addition, since there are no standards for devices or protocols, there could be major compatibility issues during implementation. 

IoT is the latest technology that is delivering promising results. Yet, similar to any technology, without proper implementation, your businesses can’t hope to leverage its immense benefits. 

Taking chances with IoT implementation is not a smart business move, as your productivity, security, customer experience, and future depend on proper and effective implementation. The only way to harness this technology would be to seek a reliable IoT app development company that can take your initiatives towards success.

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 The internet-based global digital landscape comprises a plethora of complex software and hardware systems spread on-premise and across the cloud. Also, there are software applications within embedded devices that are connected to the internet a la the Internet of Things (IoT). When we envision the future of the digital world, the IoT, along with other technologies, seems to be the harbinger. It has the potential to usher in a world driven by smart technologies to make lives more convenient and qualitatively superior. According to statistics, the number of IoT devices is likely to surpass 25.4 billion by 2030. Also, the IoT can generate an economic value ranging from $4 - $11 trillion by 2025 (Source: dataprot.net). The data shows how the Internet of Things (IoT) is going to define the digitized future of the world. 

However, notwithstanding the tremendous potential of IoT as a technology to drive the next digital revolution, it offers several challenges as well. IoT testing has become critical given that the success of the IoT ecosystem depends on the seamless functioning of its associated software and hardware systems. Let us discuss the challenges in some detail in the below-mentioned segment: 

Digitalization Challenges with IoT

The Internet of Things QA testing ensures IoT devices function safely and reliably. However, this type of testing has a host of challenges to grapple with, as mentioned below:

Testing in an omnichannel environment: The IoT ecosystem comprises various devices, platforms, and systems spread across on-premise and cloud environments. To ensure effective utilization of such systems, IoT testing should be conducted rigorously. Since IoT devices generate data at high velocity, their veracity needs to be ensured in real-time. However, this can be easier said than done, for the data generated is mostly unstructured. Also, IoT testing services need to test several devices with varying capabilities across platforms. Hence, creating a real IoT environment for testing can be a challenge, for there are many devices that require testing on the platform they operate upon. Besides, there are device upgrades in terms of software and firmware, which need to be considered by IoT device testing solutions for effective test outcomes. Thus, cross testing for IoT devices in an omnichannel environment comprising various versions and platforms can be an uphill task.

Cybersecurity risks: Given that IoT devices generate a large quantum of data (structured and unstructured), they may be vulnerable to hacking. Even so, statistics suggest that around seventy percent of IoT devices have security-related issues. Therefore, such devices should be subjected to rigorous IoT security testing. It involves identifying vulnerabilities in the architecture of devices using IoT penetration testing and fixing them. Testers should focus on checking and verifying the devices’ passwords and authorization policies.

Different protocols of IoT communications: IoT devices follow a range of protocols when it comes to communicating among themselves and with the server. These may include AMPQ, XMPP, CoAP, and MQTT. Besides, various components in an IoT ecosystem can use different protocols for communication. Hence, such components need to be tested over communication protocols to preclude functional and security risks. For instance, when embedded software within devices runs on low memory due to higher loading requests, they balance load requests among components using an IoT gateway. Testing IoT applications can verify the load balance among different components, thereby ensuring their smooth functioning.

Lack of standardization: Creating standards for IoT devices can be a challenge across four levels - application, business model, connectivity, and platform. The lack of a uniform standard across the IoT landscape makes it a difficult case for testers. This is because different companies build devices with competing and often conflicting standards. The common IoT testing approach is based on the intended use of the system or the use case. The best way to wriggle out of the situation is to establish uniform standardization across the above-mentioned levels.

Battery life: A large number of IoT devices are powered by batteries, which need to function at their optimum at all times. To ensure IoT devices are energy efficient, they need to have low-power components. Thus, the battery needs to be tested under different conditions and scenarios to maximize the life of such devices. Also, testers should check whether the device is able to report the low-battery status to the cloud platform properly.

Conclusion

The quality of an IoT ecosystem can only be ensured if the above-mentioned challenges are addressed by stringently testing IoT applications. Business enterprises building and utilizing IoT devices can look at various benefits by implementing stringent IoT testing. These include driving innovation and speeding up risk-free initiatives; facilitating time-to-market; improving interoperability; and achieving a higher ROI.

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PYNQ is great for accelerating Python applications in programmable logic. Let's take a look at how we can use it with OpenMV camera.

Things used in this project

Hardware:

  • Avnet Ultra96-V2 (Can also use V1 or V3)
  • OpenMV Cam M7
  • Avnet Ultra96 (Can use V1 or V2)

Software:

  • Xilinx PYNQ Framework

Introduction

Image processing is required for a range of applications from vision guided robotics to machine vision in industrial applications.

In this project we are going to look at how we can fuse the OpenMV camera with the Ultra96 running PYNQ. This will allow out PYNQ application to offload some image processing to the camera. Doing so will provide a higher performance system and open the Ultra96 using PYNQ to be able to work with the OpenMV ecosystem.

 

What Is the OpenMV Camera 

The OpenMV camera is a low cost machine vision camera which is developed using Python. Thanks to this architecture of the OpenMV Camera we can therefore offload some of the image processing to the camera. Meaning the image frames received by our Ultra96 already have faces identified, eyes tracked or Sobel filtering, it all depends on how we set up the OpenMV Camera.

As the OpenMV camera has been designed to be extensible it provides 10 external IO which can be used to drive external sensors. These 10 are able to support a range of interfaces from UART to SPI, I2C and PWM. Of course the PWM is very useful for driving servos.

On very useful feature of the OpenMV camera is its LEDs mine (OpenMV M7) provides a tri-colour LED which can be used to output Red, Green, Blue and a separate IR LED. As the sensor is IR sensitive this can be useful for low light performance.

8100406101?profile=RESIZE_400xOpenMV Camera

How Does the OpenMV Camera Work

OpenMV Cam uses micro python to control the imager and output frames over the USB link. Micro python is intended for use on micro controllers and is based on Python 3.4. To use the OpenMV camera we need to first generate a micro python script which configures the camera for the given algorithm we wish to implement. We then execute this script by uploading and running it over the USB link.

This means we need some OpenMV APIs and libraries on a host machine to communicate with the OpenMV Camera.

To develop the script we want to be able to ensure it works, which is where the OpenMV IDE comes into its own, this allows us to develop and test the script which we later use in our Ultra96 application.

We can develop this script using either a Windows, MAC or Linux desktop.

 

Creating the OpenMV Script using the OpenMV IDE

To get started with the OpenMV IDE we frist need to download and install it. Once it is installed the next step is to connect our OpenMV camera to it using the USB link and then running a script on it.

To get started we can run the example hello world provided, which configures the camera to outputs standard RGB image at QVGA resolution. On the right hand side of the IDE you will be able to see the images output from the camera.

 

We can use this IDE to develop scripts for the OpenMV camera such as the one below which detects and identifies circles in the captured image.

Note the frame rate is lower when the camera is connected to the IDE.

 

We can use the scripts developed here in our Ultra96 PYNQ implementation let's take a look at how we set up the Ultra96 and PYNQ

Setting Up the Ultra96 PYNQ Image

The first thing we need to do if we have not already done it, is to download and create a PYNQ SD Card so we can run the PYNQ framework on the Ultra96.

As we want to use the Xilinx image processing overlay we should download the Ultra96 PYNQ v2.3 image.

Once you have this image creating a SD Card is very simple, extract the ISO image from the compressed file and write it to a SD Card. To write the ISO image to the SD Card we need a program such a etcher or win32 disk imager.

With a SD Card available we can then boot the Ultra96 and connect to the PYNQ framework using either

  • Use a USB Ethernet connection over the MicroUSB (upstream USB connection).
  • Connect via WiFi.
  • Use the Ultra96 as a single-board computer and connect a monitor, keyboard and mouse.

For this project I used the USB Ethernet connection.

The next thing to do is to ensure we have the necessary overlays to be able to accelerate image processing functions into the programmable logic. To to this we need to install the PYNQ computer vision overlay. 

Downloading the Image Processing Overlay

Installing this overlay is very straight forward. Open a browser window and connect to the web address of 192.168.3.1 (USB Ethernet address). This will open a log in page to the Jupyter notebooks, the password is Xilinx

 

Upon log in you will see the following folders and scripts

 

Click on new and select terminal, this will open a new terminal window in a browser window. To download and use the PYNQ Computer Vision overlays we enter the following command

sudo pip3 install --upgrade git+https://github.com/Xilinx/PYNQ-ComputerVision.git
 

Once these are downloaded if you look back at the Jupyter home page you will see a new directory called pynqOpenCV.

 

Using these Jupyter notebooks we can test the image processing performance when we accelerate OpenCV functions into the programmable logic.

 

Typically the hardware acceleration as can be seen in the image above greatly out performs implementing the algorithm in SW.

Of course we can call this overlay from our own Jupyter notebooks

 

Setting Up the OpenMV Camera in PYNQ

The next step is to configure the Ultra96 PYNQ instance to be able to control the OpenMV camera using its APIs. We can obtain these by downloading the OpenMV git repo using the command below in a terminal window on the Ultra96.

git clone https://github.com/openmv/openmv
 

Once this is downloaded we need to move the file pyopenmv.py

From openmv/tools

To /usr/lib/python3.6

This will allow us to control the OpenMV camera from within our Jupyter applications.

To be able to do this we need to know which serial port the OpenMV camera enumerates as. This will generally be ttyACM0 or ttyACM1 we can find this out by doing a LS of the /dev directory

 

Now we are ready to begin working with the OpenMV camera in our applications let's take a look at how we set it up our Jupyter Scripts

 

Initial Test of OpenMV Camera

The first thing we need to do in a new Jupyter notebook is to import the necessary packages. This includes the pyopenmv as we just installed.

We will alos be importing numpy as the image is returned as a numpy array so that we can display it using numpy functionality.

import pyopenmvimport timeimport sysimport numpy as np 

The first thing we need to do is define the script we developed in the IDE, for the "first light" with the PYNQ and OpenMV we will use the hello world script to obtain a simple image.

script = """

# Hello World Example

#

# Welcome to the OpenMV IDE! Click on the green run arrow button below to run the script!

import sensor, image, time

import pyb

sensor.reset()                      # Reset and initialize the sensor.

sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)

sensor.set_framesize(sensor.QVGA)   # Set frame size to QVGA (320x240)

sensor.skip_frames(time = 2000)     # Wait for settings take effect.

clock = time.clock()                # Create a clock object to track the FPS.

red_led = pyb.LED(1)

red_led.off()

red_led.on()

while(True):

   clock.tick() 

   img = sensor.snapshot()         # Take a picture and return the image.

"""

Once the script is defined the next thing we need to do is connect to the OpenMV camera and download the script.

 

portname = "/dev/ttyACM0"

connected = False

pyopenmv.disconnect()

for i in range(10):

   try:

       # opens CDC port.

       # Set small timeout when connecting

       pyopenmv.init(portname, baudrate=921600, timeout=0.050)

       connected = True

       break

   except Exception as e:

       connected = False

       sleep(0.100)

if not connected:

   print ( "Failed to connect to OpenMV's serial port.\n"

           "Please install OpenMV's udev rules first:\n"

           "sudo cp openmv/udev/50-openmv.rules /etc/udev/rules.d/\n"

           "sudo udevadm control --reload-rules\n\n")

   sys.exit(1)

# Set higher timeout after connecting for lengthy transfers.

pyopenmv.set_timeout(1*2) # SD Cards can cause big hicups.

pyopenmv.stop_script()

pyopenmv.enable_fb(True)

pyopenmv.exec_script(script)

Finally once the script has been downloaded and is executing, we want to be able to read out the frame buffer. This Cell below reads out the framebuffer and saves it as a jpg file in the PYNQ file system.

 

running = True

import numpy as np

from PIL import Image

from matplotlib import pyplot as plt

while running:

   fb = pyopenmv.fb_dump()

   if fb != None:

       img = Image.fromarray(fb[2], 'RGB')

       img.save("frame.jpg")

       img = Image.open("frame.jpg")

       img

       time.sleep(0.100)

 

When I ran this script the first light image below was received of me working in my office.

 

Having achieved this the next step is to start working with advanced scripts in the PYNQ Jupyter notebook. using the same approach as above we can redefine scripts which can be used for different processing including

script = """

import sensor, image, time

sensor.reset() # Initialize the camera sensor.

sensor.set_pixformat(sensor.GRAYSCALE) # or sensor.RGB565

sensor.set_framesize(sensor.QQVGA) # or sensor.QVGA (or others)

sensor.skip_frames(time = 2000) # Let new settings take affect.

sensor.set_gainceiling(8)

clock = time.clock() # Tracks FPS.

while(True):

   clock.tick() # Track elapsed milliseconds between snapshots().

   img = sensor.snapshot() # Take a picture and return the image.

   # Use Canny edge detector

   img.find_edges(image.EDGE_CANNY, threshold=(50, 80))

   # Faster simpler edge detection

   #img.find_edges(image.EDGE_SIMPLE, threshold=(100, 255))

   print(clock.fps()) # Note: Your OpenMV Cam runs about half as fast while

"""

For Canny edge detection when imaging a MiniZed Board

 

Alternatively we can also extract key points from images for tracking in subsequent images.

script = """

import sensor, time, image

# Reset sensor

sensor.reset()

# Sensor settings

sensor.set_contrast(3)

sensor.set_gainceiling(16)

sensor.set_framesize(sensor.VGA)

sensor.set_windowing((320, 240))

sensor.set_pixformat(sensor.GRAYSCALE)

sensor.skip_frames(time = 2000)

sensor.set_auto_gain(False, value=100)

def draw_keypoints(img, kpts):

   if kpts:

       print(kpts)

       img.draw_keypoints(kpts)

       img = sensor.snapshot()

       time.sleep(1000)

kpts1 = None

# NOTE: uncomment to load a keypoints descriptor from file

#kpts1 = image.load_descriptor("/desc.orb")

#img = sensor.snapshot()

#draw_keypoints(img, kpts1)

clock = time.clock()

while (True):

   clock.tick()

   img = sensor.snapshot()

   if (kpts1 == None):

       # NOTE: By default find_keypoints returns multi-scale keypoints extracted from an image pyramid.

       kpts1 = img.find_keypoints(max_keypoints=150, threshold=10, scale_factor=1.2)

       draw_keypoints(img, kpts1)

   else:

       # NOTE: When extracting keypoints to match the first descriptor, we use normalized=True to extract

       # keypoints from the first scale only, which will match one of the scales in the first descriptor.

       kpts2 = img.find_keypoints(max_keypoints=150, threshold=10, normalized=True)

       if (kpts2):

           match = image.match_descriptor(kpts1, kpts2, threshold=85)

           if (match.count()>10):

               # If we have at least n "good matches"

               # Draw bounding rectangle and cross.

               img.draw_rectangle(match.rect())

               img.draw_cross(match.cx(), match.cy(), size=10)

           print(kpts2, "matched:%d dt:%d"%(match.count(), match.theta()))

           # NOTE: uncomment if you want to draw the keypoints

           #img.draw_keypoints(kpts2, size=KEYPOINTS_SIZE, matched=True)

   # Draw FPS

   img.draw_string(0, 0, "FPS:%.2f"%(clock.fps()))

"""

Circle Detection

 

import sensor, image, time

sensor.reset()

sensor.set_pixformat(sensor.RGB565) # grayscale is faster

sensor.set_framesize(sensor.QQVGA)

sensor.skip_frames(time = 2000)

clock = time.clock()

while(True):

   clock.tick()

   img = sensor.snapshot().lens_corr(1.8)

   # Circle objects have four values: x, y, r (radius), and magnitude. The

   # magnitude is the strength of the detection of the circle. Higher is

   # better...

   # `threshold` controls how many circles are found. Increase its value

   # to decrease the number of circles detected...

   # `x_margin`, `y_margin`, and `r_margin` control the merging of similar

   # circles in the x, y, and r (radius) directions.

   # r_min, r_max, and r_step control what radiuses of circles are tested.

   # Shrinking the number of tested circle radiuses yields a big performance boost.

   for c in img.find_circles(threshold = 2000, x_margin = 10, y_margin = 10, r_margin = 10,

           r_min = 2, r_max = 100, r_step = 2):

       img.draw_circle(c.x(), c.y(), c.r(), color = (255, 0, 0))

       print(c)

   print("FPS %f" % clock.fps())

 

 

 

This fusion of ability to offload processing to either the OpenMV camera or the Ultra96 programmable logic running Pynq provides the system designer with maximum flexibility.

 

Wrap Up

The ability to use the OpenMV camera, coupled with the PYNQ computer vision libraries along with other overlays such as the klaman filter and base overlays. We can implement algorithms which can be used to enable us to implement vision guided robotics. Using the base overlay and the Input Output processors also enables us to communicate with lower level drives, interfaces and other sensors required to implement such a solution.

Originaly posted here.

 

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How Doews IoT help in Retail? Continuous and seamless communication is now a reality between people, processes and things.  IoT has been enabling retailers to connect with people and businesses and gain useful insight about product performance and engagement of people with such products. 

Importance of IoT in Retail

  • It helps improve customer experience in new ways and helps brick and mortar shops compete with their online counterparts by engaging customers in different ways.
  • IoT can track customer preferences, analyze their habits and share relevant information with the marketing teams and help improve the product or brand features and design and keep the customer updated on new products, delivery status etc.
  • Using IoT retailers can increase efficiency and profitability in various ways for their benefit.
  • IoT can significantly improve the overall customer experience, like automated checkouts and integration with messaging platforms and order systems.
  • It helps increase efficiency in transportation and logistics by reducing the time to deliver goods to market or store. It helps in vehicle management, and tracking deliveries. This helps in reducing costs, improving the bottom line and increasing customer satisfaction.
  • Inventory management becomes easier with IoT. Tracking inventory is much easier and simpler from the stocking of goods to initiating a purchase.
  • It helps increase operational efficiency in warehouses, by optimizing temperature controls, improving maintenance, and managing the warehouse. 

Use Cases of IoT in Retail

  1. IoT is used in Facility management to ensure day-to-day areas are clean and can be used to monitor consumable supplies levels. It can be used to monitor store environments like temperature, lighting, ventilation and refrigeration. IoT can identify key areas that can provide a complete 360 degrees view of facility management.
  2. It can help in tracking the number of persons entering a facility. This is especially useful because of the pandemic situation, to ensure that no overcrowding takes place.
    Occupancy sensors provide vital data on store traffic patterns and also on the time spent in any particular area. This helps retailers with better planning and product placement strategies. This helps in guided selling with more effective display setups, layouts, and space management.
  3. IoT helps in a big way for Supply chain and logistics, by providing information on the stock levels. 
  4. IoT helps in asset tracking in items like shopping carts and baskets. Sensors can ensure that location data is available for all carts making retrieval easy. It can help lock carts if they are taken out of location.
  5. IoT devices can and are being used to personalize user experience. Bluetooth beacons are used to send personalized real-time alerts to phones when the customer is near an aisle or a store. This can prompt a customer to enter the store or look at the aisle area and take advantage of offers etc. IoT-based beacons, helps Target, collect user data and also send hyper-personalized content to customers.
  6. Smart shelves are another example of innovative IoT ideas. Maintaining shelves to refill products or ensure correct items are placed on the right shelves is a time-consuming task. Smart shelves automate these tasks easily. They can help save time and resolve manual errors.

Businesses should utilize new technologies to revolutionize the retail sector in a better way. Digitalization or digital transformation of brick and mortar stores is not a new concept. With every industry wanting to improve its services and facilities and trying to stay ahead of the competition, digitalization in retail industry is playing a big role in this transformation. To summarize, digitalization helps in enhanced data collection, helps data-driven customer insights, gives a better customer experience, and increases profits and productivity. It encourages a digital culture.

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Arm DevSummit 2020 debuted this week (October 6 – 8) as an online virtual conference focused on engineers and providing them with insights into the Arm ecosystem. The summit lasted three days over which Arm painted an interesting technology story about the current and future state of computing and where developers fit within that story. I’ve been attending Arm Techcon for more than half a decade now (which has become Arm DevSummit) and as I perused content, there were several take-a-ways I noticed for developers working on microcontroller based embedded systems. In this post, we will examine these key take-a-ways and I’ll point you to some of the sessions that I also think may pique your interest.

(For those of you that aren’t yet aware, you can register up until October 21st (for free) and still watch the conferences materials up until November 28th . Click here to register)

Take-A-Way #1 – Expect Big Things from NVIDIAs Acquisition of Arm

As many readers probably already know, NVIDIA is in the process of acquiring Arm. This acquisition has the potential to be one of the focal points that I think will lead to a technological revolution in computing technologies, particularly around artificial intelligence but that will also impact nearly every embedded system at the edge and beyond. While many of us have probably wondered what plans NVIDIA CEO Jensen Huang may have for Arm, the Keynotes for October 6th include a fireside chat between Jensen Huang and Arm CEO Simon Segars. Listening to this conversation is well worth the time and will help give developers some insights into the future but also assurances that the Arm business model will not be dramatically upended.

Take-A-Way #2 – Machine Learning for MCU’s is Accelerating

It is sometimes difficult at a conference to get a feel for what is real and what is a little more smoke and mirrors. Sometimes, announcements are real, but they just take several years to filter their way into the market and affect how developers build systems. Machine learning is one of those technologies that I find there is a lot of interest around but that developers also aren’t quite sure what to do with yet, at least in the microcontroller space. When we hear machine learning, we think artificial intelligence, big datasets and more processing power than will fit on an MCU.

There were several interesting talks at DevSummit around machine learning such as:

Some of these were foundational, providing embedded developers with the fundamentals to get started while others provided hands-on explorations of machine learning with development boards. The take-a-way that I gather here is that the effort to bring machine learning capabilities to microcontrollers so that they can be leveraged in industry use cases is accelerating. Lots of effort is being placed in ML algorithms, tools, frameworks and even the hardware. There were several talks that mentioned Arm’s Cortex-M55 architecture that will include Helium technology to help accelerate machine learning and DSP processing capabilities.

Take-A-Way #3 – The Constant Need for Reinvention

In my last take-a-way, I eluded to the fact that things are accelerating. Acceleration is not just happening though in the technologies that we use to build systems. The very application domain that we can apply these technology domains to is dramatically expanding. Not only can we start to deploy security and ML technologies at the edge but in domains such as space and medical systems. There were several interesting talks about how technologies are being used around the world to solve interesting and unique problems such as protecting vulnerable ecosystems, mapping the sea floor, fighting against diseases and so much more.

By carefully watching and listening, you’ll notice that many speakers have been involved in many different types of products over their careers and that they are constantly having to reinvent their skill sets, capabilities and even their interests! This is what makes working in embedded systems so interesting! It is constantly changing and evolving and as engineers we don’t get to sit idly behind a desk. Just as Arm, NVIDIA and many of the other ecosystem partners and speakers show us, technology is rapidly changing but so are the problem domains that we can apply these technologies to.

Take-A-Way #4 – Mbed and Keil are Evolving

There are also interesting changes coming to the Arm toolchains and tools like Mbed and Keil MDK. In Reinhard Keil’s talk, “Introduction to an Open Approach for Low-Power IoT Development“, developers got an insight into the changes that are coming to Mbed and Keil with the core focus being on IoT development. The talk focused on the endpoint and discussed how Mbed and Keil MDK are being moved to an online platform designed to help developers move through the product development faster from prototyping to production. The Keil Studio Online is currently in early access and will be released early next year.

(If you are interested in endpoints and AI, you might also want to check-out this article on “How Do We Accelerate Endpoint AI Innovation? Put Developers First“)

Conclusions

Arm DevSummit had a lot to offer developers this year and without the need to travel to California to participate. (Although I greatly missed catching up with friends and colleagues in person). If you haven’t already, I would recommend checking out the DevSummit and watching a few of the talks I mentioned. There certainly were a lot more talks and I’m still in the process of sifting through everything. Hopefully there will be a few sessions that will inspire you and give you a feel for where the industry is headed and how you will need to pivot your own skills in the coming years.

Originaly posted here

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Will We Ever Get Quantum Computers?

In a recent issue of IEEE Spectrum, Mikhail Dyakonov makes a pretty compelling argument that quantum computing (QC) isn't going to fly anytime soon. Now, I'm no expert on QC, and there sure is a lot of money being thrown at the problem by some very smart people, but having watched from the sidelines QC seems a lot like fusion research. Every year more claims are made, more venture capital gets burned, but we don't seem to get closer to useful systems.

Consider D-Wave Systems. They've been trying to build a QC for twenty years, and indeed do have products more or less on the market, including, it's claimed, one of 1024 q-bits. But there's a lot of controversy about whether their machines are either quantum computers at all, or if they offer any speedup over classical machines. One would think that if a 1K q-bit machine really did work the press would be all abuzz, and we'd be hearing constantly of new incredible results. Instead, the machines seem to disappear into research labs.

Mr. Duakonov notes that optimistic people expect useful QCs in the next 5-10 years; those less sanguine expect 20-30 years, a prediction that hasn't changed in two decades. He thinks a window of many decades to never is more realistic. Experts think that a useful machine, one that can do the sort of calculations your laptop is capable of, will require between 1000 and 100,000 q-bits. To me, this level of uncertainty suggests that there is a profound lack of knowledge about how these machines will work and what they will be able to do.

According to the author, a 1000 q-bit machine can be in 21000 states (a classical machine with N transistors can be in only 2N states), which is about 10300, or more than the number of sub-atomic particles in the universe. At 100,000 q-bits we're talking 1030,000, a mind-boggling number.

Because of noise, expect errors. Some theorize that those errors can be eliminated by adding q-bits, on the order of 1000 to 100,000 additional per q-bit. So a useful machine will need at least millions, or perhaps many orders of magnitude more, of these squirrelly microdots that are tamed only by keeping them at 10 millikelvin.

A related article in Spectrum mentions a committee formed of prestigious researchers tasked with assessing the probability of success with QC concluded that:

"[I]t is highly unexpected" that anyone will be able to build a quantum computer that could compromise public-key cryptosystems (a task that quantum computers are, in theory, especially suitable for tackling) in the coming decade. And while less-capable "noisy intermediate-scale quantum computers" will be built within that time frame, "there are at present no known algorithms/applications that could make effective use of this class of machine," the committee says."

I don't have a dog in this fight, but am relieved that useful QC seems to be no closer than The Distant Shore (to quote Jan de Hartog, one of my favorite writers). If it were feasible to easily break encryption schemes banking and other systems could collapse. I imagine Blockchain would fail as hash algorithms became reversable. The resulting disruption would not be healthy for our society.

On the other hand, Bruce Schneier's article in the March issue of IEEE Computing Edge suggests that QC won't break all forms of encryption, though he does think a lot of our current infrastructure will be vulnerable. The moral: if and when QC becomes practical, expect chaos.

I was once afraid of quantum computing, as it involves mechanisms that I'll never understand. But then I realized those machines will have an API. Just as one doesn't need to know how a computer works to program in Python, we'll be insulated from the quantum horrors by layers of abstraction.

Originaly posted here

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SSE Airtricity employees Derek Conty, left, Francie Byrne, middle, and Ryan Doran, right, install solar panels on the roof of Kinsale Community School in Kinsale, Ireland. The installation is part of a project with Microsoft to demonstrate the feasibility of distributed power purchase agreements. Credit: Naoise Culhane

by John Roach

Solar panels being installed on the roofs of dozens of schools throughout Dublin, Ireland, reflect a novel front in the fight against global climate change, according to a senior software engineer and a sustainability lead at Microsoft.

The technology copmpany partnered with SSE Airtricity, Ireland's largest provider of 100% green energy and a part of FTSE listed SSE Group, to install and manage the internet-connected solar panels, which are connected via Azure IoT to Microsoft Azure, a cloud computing platform.

The software tools aggregate and analyze real-time data on energy generated by the solar panels, demonstrating a mechanism for Microsoft and other corporations to achieve sustainability goals and reduce the carbon footprint of the electric power grid.

"We need to decarbonize the global economy to avoid catastrophic climate change," said Conor Kelly, the software engineer who is leading the distributed solar energy project for Microsoft Azure IoT. "The first thing we can do, and the easiest thing we can do, is focus on electricity."

Microsoft's $1.1 million contribution to the project builds on the company's ongoing investment in renewable energy technologies to offset carbon emissions from the operation of its datacenters.

A typical approach to power datacenters with renewable energy is for companies such as Microsoft to sign so-called power purchase agreements with energy companies.The agreements provide financial guarantees needed to build industrial-scale wind and solar farms and connections to the power grid.

The new project demonstrates the feasibility of agreements to install solar panels on rooftops distributed across towns with existing grid connections and use internet of things, or IoT, technologies to aggregate the accumulated energy production for carbon offset accounting.

"It utilizes existing assets that are sitting there unmonetized, which are roofs of buildings that absorb sunlight all day," Kelly said.

New Business Model

The project is also a proof-of-concept, or blueprint, for how energy providers can adapt as the falling price of solar panels enables distributed electric power generation throughout the existing electric power grid.

Traditionally, suppliers purchase power from central power plants and industrial-scale wind and solar farms and sell it to consumers on the distribution grid. Now, energy providers like SSE Airtricity provide renewable energy solutions that allow end consumers to generate power, from sustainable sources, using the existing grid connection on their premises.

"The more forward-thinking energy providers that we are working with, like SSE Airtricity, identify this as an opportunity and industry changing shift in how energy will be generated and consumed," Kelly noted.

The opportunity comes in the ability to finance the installation of solar panels and batteries at homes, schools, businesses and other buildings throughout a community and leverage IoT technology to efficiently perform a range of services from energy trading to carbon offset accounting.

Kelly and his team with Azure IoT are working with SSE Airtricity to develop the tools and machine learning models necessary to unlock this opportunity.

"Instead of having utility scale solar farms located outside of cities, you could have a solar farm at the distribution level, spread across a number of locations," said Fergal Ahern, a business energy solutions manager and renewable energy expert with SSE Airtricity.

For the distributed power purchase agreement, SSE Airtricity uses Azure IoT to aggregate the generation of all the solar panels installed across 27 schools around the provinces of Leinster, Munster and Connacht and run it through a machine learning model to determine the carbon emissions that the solar panels avoid.

The schools use the electricity generated by the solar panels, which reduces their utility bills; Microsoft receives the renewable energy credits for the generated electricity, which the company applies to its carbon neutrality commitments.

The panels are expected to produce enough energy annually to power the equivalent of 68 Irish homes for a year and abate more than 2.1 million kilograms, which is equivalent to 4.6 million pounds, of carbon dioxide emissions over the 15 years of the agreement, according to Kelly.

"This is additional renewable energy that wouldn't have otherwise happened," he said. "Every little bit counts when it comes to meeting our sustainability targets and combatting climate change."

Every little bit counts

Victory Luke, a 16 year old student at Collinstown Park Community College in Dublin, has lived by the "every little bit counts" mantra since she participated in a "Generation Green" sustainability workshop in 2019 organized by the Sustainable Energy Authority of Ireland, SSE Airtricity and Microsoft.

The workshop was part of an education program surrounding the installation of solar panels and batteries at her school along with a retrofit of the lighting system with LEDs. Digital screens show the school's energy use in real time, allowing students to see the impact of the energy efficiency upgrades.

Luke said the workshop captured her interest on climate change issues. She started reading more about sustainability and environmental conservation and agreed to share her newfound knowledge with the younger students at her school.

"I was going around and talking to them about energy efficiency, sharing tips and tricks like if you are going to boil a kettle, only boil as much water as you need, not too much," she explained.

That June, the Sustainable Energy Authority of Ireland invited her to give a speech at the Global Conference on Energy Efficiency in Dublin, which was organized by the International Energy Agency, an organization that works with governments and industry to shape sustainable energy policy.

"It kind of felt surreal because I honestly felt like I wasn't adequate enough to be speaking about these things," she said, noting that the conference attendees included government ministers, CEOs and energy experts from around the world.

At the time, she added, the global climate strike movement and its youth leaders were making international headlines, which made her advocacy at school feel even smaller. "Then I kind of realized that it is those smaller things that make the big difference," she said.

SSE Airtricity and Microsoft plan to replicate the educational program that inspired Luke and her classmates at dozens of the schools around Ireland that are participating in the project.

"When you've got solar at a school and you can physically point at the installation and a screen that monitors the power being generated, it brings sustainability into daily school life," Ahern said.

Proof of concept for policymakers

The project's education campaign extends to renewable energy policymakers, Kelly noted. He explained that renewable energy credits—a market incentive for corporations to support renewable energy projects—are currently unavailable for distributed power purchase agreements.

For this project, Microsoft will receive genuine renewable energy credits from a wind farm that SSE Airtricity also operates, he added.

"And," he said, "we are hoping to use this project as an example of what regulation should look like, to say, 'You need to award renewable energy credits to distributed generation because they would allow corporates to scale-up this type of project.'"

For her part, Luke supports steps by multinational corporations such as Microsoft to invest in renewable energy projects that address global climate change.

"It is a good thing to see," she said. "Once one person does something, other people are going to follow.

Originaly posted HERE

Read more…

An edge device is the network component that is responsible for connecting a local area network to an external or wide area network, which can be accessed from anywhere. Edge devices offer several new services and improved outcomes for IoT deployments across all markets. Smart services that rely on high volumes of data and local analysis can be deployed in a wide range of environments.

Edge device provides the local data to an external network. If protocols are different in local and external networks, it also translates this information, and make the connection between both network boundaries. Edge devices analyze diagnostics and automatic data populating; however, it is necessary to make a secure connection between the field network and cloud computing. In the event of loss of internet connection or cloud crash edge device will store data until the connection is established, so it won’t lose any process information. The local data storage is optional and not all edge devices offer local storage, it depends on the application and service required to implement on the plant.

How does an edge device work?

An edge device has a very straightforward working principle, it communicates between two different networks and translates one protocol into another. Furthermore, it creates a secure connection with the cloud.

An edge device can be configured via local access and internet or cloud. In general, we can say an edge device is a plug-and-play, its setup is simple and does not require much time to configure.

Why should I use an edge device?

Depending on the service required in the plant, the edge devices will be a crucial point to collect the information and create an automatic digital twin of your device in the cloud. 

Edge devices are an essential part of IoT solutions since they connect the information from a network to a cloud solution. They do not affect the network but only collect the data from it, and never cause a problem with the communication between the control system and the field devices. by using an edge device to collect information, the user won’t need to touch the control system. Edge is one-way communication, nothing is written into the network, and data are acquired with the highest possible security.

Edge device requirements

Edge devices are required to meet certain requirements that are to meet at all conditions to perform in different secretions. This may include storage, network, and latency, etc.

Low latency

Sensor data is collected in near real-time by an edge server. For services like image recognition and visual monitoring, edge servers are located in very close proximity to the device, meeting low latency requirements. Edge deployment needs to ensure that these services are not lost through poor development practice or inadequate processing resources at the edge. Maintaining data quality and security at the edge whilst enabling low latency is a challenge that need to address.

Network independence

IoT services do not care for data communication topology.  The user requires the data through the most effective means possible which in many cases will be mobile networks, but in some scenarios, Wi-Fi or local mesh networking may be the most effective mechanism of collecting data to ensure latency requirements can be met.

Good-Edge-IOT-Device-1024x576.jpg

Data security

Users require data at the edge to be kept secure as when it is stored and used elsewhere. These challenges need to meet due to the larger vector and scope for attacks at the edge. Data authentication and user access are as important at the edge as it is on the device or at the core.  Additionally, the physical security of edge infrastructure needs to be considered, as it is likely to hold in less secure environments than dedicated data centers.

Data Quality

Data quality at the edge is a key requirement to guarantee to operate in demanding environments. To maintain data quality at the edge, applications must ensure that data is authenticated, replicated as and assigned into the correct classes and types of data category.

Flexibility in future enhancements

Additional sensors can be added and managed at the edge as requirements change. Sensors such as accelerometers, cameras, and GPS, can be added to equipment, with seamless integration and control at the edge.

Local storage

Local storage is essential in the event of loss of internet connection or cloud crash edge device will store data until the connection is established, so it won’t lose any process information. The local data storage is optional and not all edge devices offer local storage, it depends on the application and service required to implement on the plant

Originaly Posted here

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by Singapore University of Technology and Design

Internet-of-Things (IoT) such as smart home locks and medical devices, depend largely on Bluetooth low energy (BLE) technology to function and connect across other devices with reduced energy consumption. As these devices get more prevalent with increasing levels of connectivity, the need for strengthened security in IoT has also become vital.

A research team, led by Assistant Professor Sudipta Chattopadhyay from the Singapore University of Technology and Design (SUTD), wit team members from SUTD and the Institute for Infocomm Research (I2R), designed and implemented the Greyhound framework, a tool used to discover SweynTooth—a critical set of 11 cyber vulnerabilities.

Their study was presented at the USENIX Annual Technical Conference (USENIX ATC) on 15 to 17 July 2020 and they have been invited to present at the upcoming Singapore International Cyber Week (SICW) in October 2020.

These security lapses were found to affect devices by causing them to crash, reboot or bypass security features. At least 12 BLE based devices from eight vendors were affected, including a few hundred types of IoT products including pacemakers, wearable fitness trackers and home security locks.

The SweynTooth code has since been made available to the public and several IoT product manufacturers have used it to find security issues in their products. In Singapore alone, 32 medical devices reported to be affected by SweynTooth and 90% of these device manufacturers have since implemented preventive measures against this set of cyber vulnerabilities.

Regulatory agencies including the Cyber Security Agency and the Health Sciences Authority in Singapore as well as the Department of Homeland Security and the Food and Drug Administration in the United States have reached out to the research team to further understand the impact of these vulnerabilities.

These agencies have also raised public alerts to inform medical device manufacturers, healthcare institutions and end users on the potential security breach and disruptions. The research team continues to keep them updated on their research findings and assessments.

Beyond Bluetooth technology, the research team designed the Greyhound framework using a modular approach so that it could easily be adapted for new wireless protocols. This allowed the team to test it across the diverse set of protocols that IoTs frequently employ. This automated framework also paves new avenues in the testing security of more complex protocols and IoTs in next-generation wireless protocol implementations such as 5G and NarrowBand-IoT which require rigorous and systematic security testing.

"As we are transitioning towards a smart nation, more of such vulnerabilities could appear in the future. We need to start rethinking the device manufacturing design process so that there is limited reliance on communication modules such as Bluetooth to ensure a better and more secure smart nation by design," explained principal investigator Assistant Professor Sudipta from SUTD.

Originally posted HERE.

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When you’re in technology, you have to expect change. Yet, there’s something to the phrase “the more things change, the more they stay the same.” For instance, I see in the industrial internet of things (IIoT) a realm that’ll dramatically shape the future - how we manufacture, the way we run our factories, workforce needs – but the underlying business goals are the same as always.

Simply put, while industrial enterprise initiatives may change, financial objectives don’t – and they’re still what matter most. That’s why IIoT is so appealing. While the possibilities of smart and connected operations, sites and products certainly appeal to the dreamer and innovator, the clear payoff ensures that it’s a road even the most pragmatic decision-maker will eagerly follow.

The big three
When it comes to industrial enterprises, IIoT addresses the “big three” financial objectives head on. The technology maximizes revenue growth, reduces operating expense and increases asset efficiency.

IIoT does this in numerous ways. It yields invaluable operational intelligence, like real-time performance management data, to reduce manufacturing costs, increase flexibility and enable agility. When it comes to productivity, connected digital assets can empower a workforce with actionable insights to improve productivity and quality, even prevent safety and compliance issues.

For example, recognizing defects in a product early on can save time, materials, staff hours and possibly even a company’s reputation.

Whether on or off the factory floor, IIoT can be used to optimize asset efficiency. With real-time monitoring, diagnostics and analytics, downtime can be reduced or avoided. Asset utilization can also be evaluated and maximized. Think applications like equipment health monitoring, predictive maintenance, the ability to provide augmented 3D instructions for complex repairs. And, you can also scale production more precisely via better control over processes and inventory.

All of this accelerates time to market; another key benefit of IIoT and long held business goal.

Why is 5G important for IIoT and augmented reality (AR)?
As we look at the growing need to connect more devices, more sensors and install things like real-time cameras for doing analytics, there is growing stress and strain that is brought into industrial settings. We have seen the need to increase connectivity while having greater scalability, performance, accessibility, reliability, and broader reach with a lower cost of ownership become much more important. This is where 5G can make a real difference.

Many of our customers have seen what we are doing with augmented reality and the way that PTC can help operators service equipment. But in the not so distant future, the way that people interact with robotics, for example, will change. There will be real-time video to do spatial analytics on the way that people are working with man and machines and we’ll be able to unlock a new level of intelligence with a new layer of connectivity that helps drive better business outcomes.

Partner up
It sounds nice but the truth is, a lot of heavy lifting is required to do IIoT right. The last thing you want to do is venture into a pilot, run into problems, and leave the C-suite less than enthused with the outcome. And make no mistake, there’s a lot potential pitfalls to be aware of.

For instance, lengthy proof of concept periods, cumbersome processes and integrations can slow time to market. Multiple, local integrations can be required when connectivity and device management gets siloed. If not done right, you may only gain limited visibility into devices and the experience will fall short. And, naturally, global initiatives can be hindered by high roaming costs and deployment obstacles.

That said, you want to harness best of breed providers, not only to realize the full benefits of Industry 4.0, but to set yourself up with a foundation that’ll be able to harness 5G developments. You need a trusted IoT partner, and because of the sophistication and complexity, it takes an ecosystem of proven innovators working collaboratively.

That’s why PTC and Ericsson are partners.

Doing what’s best
Ericsson unlocks the full value of global cellular IoT connectivity and provides on-premise solutions. PTC offers an industrial IoT platform that’s ready to configure and deploy, with flexible connectivity and capabilities to build solutions without manual coding.

Drilling down a bit further, Ericsson’s IoT Accelerator can connect and manage billions of devices and millions of applications easily, seamlessly and globally. PTC’s IoT solutions digitalize processes and products, combining the physical and digital worlds seamlessly.

And with wireless connectivity, we can deploy a lot of new technology – from augmented reality to artificial intelligence applications – without having to think about the time and cost of creating fixed infrastructures, running wires, adding network capacity and more.

According ABI Research, organizations that embrace Industry 4.0 and private cellular have the potential to improve gross margins by 5-13% in factory and warehouse operations. Manufacturers can expect a 10x return on their investment. And with 4.3 billion wireless connections in smart factories anticipated by 2030, it’s clear where things are headed.

By focusing on what we each do best, PTC and Ericsson is able to do what’s best for our customers. We can help them build and scale global cellular IoT deployments faster and gain a competitive advantage. They can reap the advantages of Industry 4.0 and create that path to 5G, future-proofing their operations and enjoying such differentiators as network slicing, edge computing and high-reliability, low latency communications.

Further, with our histories of innovation, customers are assured they’ll be supported in the future, remaining out front with the ability to adapt to change, grow and deliver on financial objections.

Editor's Note: This post was originally published by Steve Dertien, Chief Technology Officer for PTC, on Ericsson's website, and is part of a joint content effort with Kiva Allgood, head of IoT for Ericsson. To view Steve's original, please click here. To read Kiva's complementary post, please click here.

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A scientist from Russia has developed a new neural network architecture and tested its learning ability on the recognition of handwritten digits. The intelligence of the network was amplified by chaos, and the classification accuracy reached 96.3%. The network can be used in microcontrollers with a small amount of RAM and embedded in such household items as shoes or refrigerators, making them 'smart.' The study was published in Electronics.

Today, the search for new neural networks that can operate on microcontrollers with a small amount of random access memory (RAM) is of particular importance. For comparison, in ordinary modern computers, random access memory is calculated in gigabytes. Although microcontrollers possess significantly less processing power than laptops and smartphones, they are smaller and can be interfaced with household items. Smart doors, refrigerators, shoes, glasses, kettles and coffee makers create the foundation for so-called ambient intelligece. The term denotes an environment of interconnected smart devices. 

An example of ambient intelligence is a smart home. The devices with limited memory are not able to store a large number of keys for secure data transfer and arrays of neural network settings. It prevents the introduction of artificial intelligence into Internet of Things devices, as they lack the required computing power. However, artificial intelligence would allow smart devices to spend less time on analysis and decision-making, better understand a user and assist them in a friendly manner. Therefore, many new opportunities can arise in the creation of environmental intelligence, for example, in the field of health care.

Andrei Velichko from Petrozavodsk State University, Russia, has created a new neural network architecture that allows efficient use of small volumes of RAM and opens the opportunities for the introduction of low-power devices to the Internet of Things. The network, called LogNNet, is a feed-forward neural network in which the signals are directed exclusively from input to output. Its uses deterministic chaotic filters for the incoming signals. The system randomly mixes the input information, but at the same time extracts valuable data from the information that are invisible initially. A similar mechanism is used by reservoir neural networks. To generate chaos, a simple logistic mapping equation is applied, where the next value is calculated based on the previous one. The equation is commonly used in population biology and as an example of a simple equation for calculating a sequence of chaotic values. In this way, the simple equation stores an infinite set of random numbers calculated by the processor, and the network architecture uses them and consumes less RAM.

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The scientist tested his neural network on handwritten digit recognition from the MNIST database, which is considered the standard for training neural networks to recognize images. The database contains more than 70,000 handwritten digits. Sixty-thousand of these digits are intended for training the neural network, and another 10,000 for network testing. The more neurons and chaos in the network, the better it recognized images. The maximum accuracy achieved by the network is 96.3%, while the developed architecture uses no more than 29 KB of RAM. In addition, LogNNet demonstrated promising results using very small RAM sizes, in the range of 1-2kB. A miniature controller, Atmega328, can be embedded into a smart door or even a smart insole, has approximately the same amount of memory.

"Thanks to this development, new opportunities for the Internet of Things are opening up, as any device equipped with a low-power miniature controller can be powered with artificial intelligence. In this way, a path is opened for intelligent processing of information on peripheral devices without sending data to cloud services, and it improves the operation of, for example, a smart home. This is an important contribution to the development of IoT technologies, which are actively researched by the scientists of Petrozavodsk State University. In addition, the research outlines an alternative way to investigate the influence of chaos on artificial intelligence," said Andrei Velichko.

Originally posted HERE.

by Russian Science Foundation

Image Credit: Andrei Velichko

 

 

 

 

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Impact of IoT in Inventory

Internet of Things (IoT) has revolutionized many industries including inventory management. IoT is a concept where devices are interconnected via the internet. It is expected that by 2020, there will be 26 billion devices connected worldwide. These connections are important because it allows data sharing which then can perform actions to make life and business more efficient. Since inventory is a significant portion of a company’s assets, inventory data is vital for an accounting department for the company’s asset management and annual report.

Inventory solutions based on IoT and RFID, individual inventory item receives an RFID tag. Each tag has a unique identification number (ID) that contains information about an inventory item, e.g. a model, a batch number, etc. these tags are scanned by RF reader. Upon scanning, a reader extracts its IDs and transmits them to the cloud for processing. Along with the tag’s ID, the cloud receives location and the time of reading. This data is used for updates about inventory items’, allowing users to monitor the inventory from anywhere, in real-time.

Industrial IoT

The role of IoT in inventory management is to receive data and turn it into meaningful insights about inventory items’ location, status, and giving users a corresponding output. For example, based on the data, and inventory management solution architecture, we can forecast the number of raw materials needed for the upcoming production cycle. The output of the system can also send an alert if any individual inventory item is lost.

Moreover, IoT based inventory management solutions can be integrated with other systems, i.e. ERP and share data with other departments.

RFID in Industrial IoT

RFID consist of three main components tag, antenna, and a reader

Tags: An RFID tag carries information about a specific object. It can be attached to any surface, including raw materials, finished goods, packages, etc.

RFID antennas: An RFID antenna receives signals to supply power and data for tags’ operation

RFID readers: An RFID reader, uses radio signals to read and write to the tags. The reader receives data stored in the tag and transmits it to the cloud.

Benefits of IoT in inventory management

The benefits of IoT on the supply chain are the most exciting physical manifestations we can observe. IoT in the supply chain creates unparalleled transparency that increases efficiencies.

Inventory tracking

The major benefit of inventory management is asset tracking, instead of using barcodes to scan and record data, items have RFID tags which can be registered wirelessly. It is possible to accurately obtain data and track items from any point in the supply chain.

With RFID and IoT, managers don’t have to spend time on manual tracking and reporting on spreadsheets. Each item is tracked and the data about it is recorded automatically. Automated asset tracking and reporting save time and reduce the probability of human error.

Inventory optimization

Real-time data about the quantity and the location of the inventory, manufacturers can reduce the amount of inventory on hand while meeting the needs of the customers at the end of the supply chain.

The data about the amount of available inventory and machine learning can forecast the required inventory which allows manufacturers to reduce the lead time.

Remote tracking

Remote product tracking makes it easy to have an eye on production and business. Knowing production and transit times, allows you to better tweak orders to suit lead times and in response to fluctuating demand. It shows which suppliers are meeting production and shipping criteria and which needs monitoring for the required outcome.

It gives visibility into the flow of raw materials, work-in-progress and finished goods by providing updates about the status and location of the items so that inventory managers see when an individual item enters or leaves a specific location.

Bottlenecks in the operations

With the real-time data about the location and the quantity, manufacturers can reveal bottlenecks in the process and pinpoint the machine with lower utilization rates. For instance, if part of the inventory tends to pile up in front of a machine, a manufacturer assumes that the machine is underutilized and needs to be seen to.

The Outcomes

The data collected by inventory management is more accurate and up-to-date. By reducing these time delays, the manufacturing process can enhance accuracy and reduce wastage. An IoT-based inventory management solution offers complete visibility on inventory by providing real-time information fetched by RFID tags. It helps to track the exact location of raw materials, work-in-progress and finished goods. As a result, manufacturers can balance the amount of on-hand inventory, increase the utilization of machines, reduce lead time, and thus, avoid costs bound to the less effective methods. This is all about optimizing inventory and ensuring anything ordered can be sold through whatever channel necessary.

Originally posted here

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After so many years evangelizing the Internet of Things (IoT) or developing IoT products or selling IoT services or using IoT technologies, it is hard to believe that today there are as many defenders as detractors of these technologies. Why does the doubt still assail us: "Believe or Not Believe in the IoT"? What's the reason we keep saying every year that the time for IoT is finally now?

It does not seem strange to you that if we have already experienced the power of change that involves having connected devices in ourselves (wearables), in our homes, in cities, in transportation, in business, we continue with so many non-believers. Maybe, because the expectations in 2013 were so great that now in 2020 we need more tangible and realistic data and facts to continue believing.

In recent months I have had more time to review my articles and some white papers and I think I have found some reasons to continue believing, but also reasons not to believe.

Here below there are some of these reasons for you to decide where to position yourself.

Top reasons to believe

  • Mackinsey continue presenting us new opportunities with IoT
    • If in 2015 “Internet of Things: Mapping the value beyond the hype” the company estimated a potential economic impact as much as 11,1 US trillions per year in 2025 for IoT applications in 9 settings.
    • In 2019 “Growing opportunities in the Internet of Things” they said that “The number of businesses that use the IoT technologies has increased from 13 percent in 2014 to about 25 percent today. And the worldwide number of IoT connected devices is projected to increase to 43 billion by 2023, an almost threefold increase from 2018.”
  • Gartner in 2019 predicted that by 2021, there will be over 25 Billion live IoT endpoints that will allow unlimited number of IoT use cases.
  • Harbor Research considers that the market opportunity for industrial internet of things (IIoT) and industry 4.0 is still emergent.
    • Solutions are not completely new but are evolving from the convergence of existing technologies; creative combinations of these technologies will drive many new growth opportunities;
    • As integration and interoperability across the industrial technology “stack” relies on classic IT principles like open architectures, many leading IT players are entering the industrial arena;
  • IoT regulation is coming - The lack of regulation is one of the biggest issues associated with IoT devices, but things are starting to change in that regard as well. The U.S. government was among the first to take the threat posed by unsecured IoT devices seriously, introducing several IoT-related bills in Congress over the last couple of years. It all began with the IoT Cybersecurity Improvement Act of 2017, which set minimum security standards for connected devices obtained by the government. This legislation was followed by the SMART IoT Act, which tasked the Department of Commerce with conducting a study of the current IoT industry in the United States.
  • Synergy of IoT and AI - IoT supported by artificial intelligence enhances considerably the success in a large repertory of every-day applications with dominant one’s enterprise, transportation, robotics, industrial, and automation systems applications.
  • Believe in superpowers again, thanks to IoT - Today, IoT sensors are everywhere – in your car, in electronic appliances, in traffic lights, even probably on the pigeon outside your window (it’s true, it happened in London!). IoT sensors will help cities map air quality, identify high-pollution pockets, trigger alerts if pollution levels rise dangerously, while tracking changes over time and taking preventive measures to correct the situation. thanks to IoT, connected cars will now communicate seamlessly with IoT sensors and find empty parking spots easily. Sensors in your car will also communicate with your GPS and the manufacturer’s system, making maintenance and driving a breeze!. City sensors will identify high-traffic areas and regulate traffic flows by updating your GPS with alternate routes. These IoT sensors can also identify and repair broken street lamps. IoT will be our knight in shining, super-strong metallic armor and prevent accidents like floods, fires and even road accidents, by simply monitoring fatigue levels of truck drivers!. Washing machines, refrigerators, air-conditioners will now self-monitor their usage, performance, servicing requirements, while triggering alerts before potential breakdowns and optimizing performance with automatic software updates. IoT sensors will now help medical professional monitor pulse rates, blood pressure and other vitals more efficiently, while triggering alerts in case of emergencies. Soon, Nano sensors in smart pills will make healthcare super-personalized and 10x more efficient!

Top reasons not to believe

  1. Three fourths of IoT projects failing globally. Government and enterprises across the globe are rolling out Internet of Things (IoT) projects but almost three-fourths of them fail, impacted by factors like culture and leadership, according to US tech giant Cisco (2017). Businesses are spending $745 billion worldwide on IoT hardware and software in 2019 alone. Yet, three out of every four IoT implementations are failing.
  2. Few IoT projects survive proof-of-concept stage - About 60% of IoT initiatives get stalled at the Proof of Concept (PoC) stage. If the right steps aren’t taken in the beginning, say you don’t think far enough beyond the IT infrastructure, you end up in limbo: caught between the dream of what IoT could do for your business and the reality of today’s ROI. That spot is called proof-of-concept (POC) purgatory.
  3. IoT Security still a big concern - The 2019 annual report of SonicWall Caoture Labs threat researchers analyzing data from over 200,000 malicious events indicated that 217.5 percent increase in IoT attacks in 2018.
  4. There are several obstacles companies face both in calculating and realizing ROI from IoT. Very few companies can quantify the current, pre-IoT costs. The instinct is often to stop after calculating the cost impact on the layer of operations immediately adjacent to the potential IoT project.  For example, when quantifying the baseline cost of reactive (versus predictive or prescriptive) maintenance, too many companies would only include down time for unexpected outages, but may not consider reduced life of the machine, maintenance overtime, lost sales due to long lead times, supply chain volatility risk for spare parts, and the list goes on.
  5. Privacy, And No, That’s Not the Same as Security. The Big Corporations don’t expect to make a big profit on the devices themselves. the Big Money in IoT is in Big Data. And enterprises and consumers do not want to expose everything sensors are learning about your company or you.
  6. No Killer Application – I suggest to read my article “Worth it waste your time searching the Killer IoT Application?"
  7. No Interoperable Technology ecosystems - We have a plethora of IoT vendors, both large and small, jumping into the fray and trying to establish a foothold, in hopes of either creating their own ecosystem (for the startups) or extending their existing one (for the behemoths).
  8. Digital Fatigue – It is not enough for us to try to explain IoT, that now more technologies such as Artificial Intelligence, Blockchain, 5G, AR / VR are joining the party and of course companies say enough.

You have the last word

We can go on forever looking for reasons to believe or not believe in IoT but we cannot continue to deny the evidence that the millions of connected devices already out there and the millions that will soon be waiting for us to exploit their full potential.

I still believe. But you have the last word.

Thanks in advance for your Likes and Shares

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