IoT Cloud Market Development, Market Trends, Key Driven Factors, Segmentation and Forecast to …

Top Key Players Covered in this report: Intel Corporation,Ayla Networks,Artik Cloud,AWS IOT,GE Predix,Google,Microsoft,IBM Watson IoT,ThingWorx …

IoT Cloud

The IoT Cloud Market report gives Analysis of incomes, limits and benefits of Key Manufacturers including the market holdings, offers of units, income dispersion, and the measures that have been taken to overcome the issues faced.

IoT Cloud Market analyses the report based on customer demand, supply and demand status, competitive market scenario and industry policies The IoT Cloud Market report covers all the minute details related to the industry like technological developments, growth opportunities, threats to market growth, innovative strategies and futuristic market trends.

IoT Cloud market report provides the comprehensive analysis of the market, based on leading players of present, past of IoT Cloud Industry and resourceful data that will act as a supportive guide for leading players.

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Top Key Players Covered in this report: Intel Corporation,Ayla Networks,Artik Cloud,AWS IOT,GE Predix,Google,Microsoft,IBM Watson IoT,ThingWorx,Salesforce IoT Cloud,Telit DeviceWise,Xively,Zebra Zatar Cloud,WebNMS,Oracle

IoT Cloud Market Segment by ProductTypes considering Production, Revenue (Value), Price Trends:

  • Information Processing
  • Signal Communication
  • Other
  • IoT Cloud Market Segment by Applications considering Consumption Growth Rate and Market Share:

  • Manufacturing
  • Energy & Power
  • Oil & Gas
  • Metals & Mining
  • Healthcare
  • Agriculture
  • Others
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    In this study, the years considered to estimate the market size of IoT Cloud are as follows:

    • History Year: 2014-2018
    • Base Year: 2018
    • Estimated Year: 2019
    • Forecast Year 2019 to 2025

    Scope of IoT Cloud Market: Geographically, this IoT Cloud report is split into crucial positions, size, production, consumption, revenue (Mn/Bn USD), and also market share and increase space of IoT Cloud industry in these regions, by 2025, covering United States, Japan, China, India, Southeast Asia, Europe as well as its share and also CAGR for its forecast interval.

    TOC of IoT CloudMarket Report Contains: –

    1. Market Overview: Product Overview, Classification, Applications, Regional Analysis, Industry Development Factors Analysis, Consumer Behaviour Analysis.
    2. IoT Cloud Market Analysis by Region: Consumption of IoT Cloud Industry at Present Situation Analysis in USA, Europe, Japan, China, India, Southeast Asia regions.
    3. IoT Cloud Market Upstream and Downstream Analysis: Key Raw Materials Suppliers and Price Analysis, Key Raw Materials Production and Consumption Analysis, Manufacturing Process Analysis, Downstream Buyers Analysis, Industry Chain Analysis, Procurement Method Analysis, Customs Tariff Analysis.
    4. IoT Cloud Market Forecast (2019-2025)

    What Report exactly offers to the buyers?

    • To gain insightful analyses of the IoT Cloud Industry and have comprehensive understanding of the global market and its commercial landscape.
    • Market strategies that are being adopted by leading respective organizations
    • Get a detailed representation of the IoT Cloud market.
    • The assessed growth rate, together with IoT Cloud Market size and share over the forecast period 2019-2025.

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    In a word, the IoT Cloud Market report provides major statistics on the state of the IoT Cloud industry and is a valuable source of guidance and direction for companies and individuals interested in the market.

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    IoT panel to startups: Demystify emerging tech and take risks, but prepare to fail fast

    “People seem mystified by new technology. Some of it is our own darn fault — we speak in so many acronyms, the only place worse would be the …

    Entrepreneurs often get lost in the hype of emerging technologies like the Internet of Things, failing to effectively integrate new tech into their startups, said Don Sharp.

    “Whether it’s the latest, greatest thing or not — it’s no different than any other tool,” said Sharp, CEO of St. Louis-based Coolfire Solutions and panelist at RUMBLE’s Friday IoT: Beyond the Hype event. “People seem mystified by new technology. Some of it is our own darn fault — we speak in so many acronyms, the only place worse would be the United States military.

    “When it comes to any kind of technology, especially in IoT, it starts with anything else you do in business: ‘What am I trying to achieve?’ and ‘What is ultimately the business value I’m trying to drive?’”


    Terri Foudray, RUMBLE

    Hosted at Polsinelli, end-to-end IoT designer and integrator firm RUMBLE brought together four IoT experts — Sharp, Nadine Manjaro, Stephanie Atkinson and RUMBLE co-founder Perry Lea — to deliver insight to KC business leaders on the importance of effectively implementing the innovative tech, said Terri Foudray.

    “Very few people know how to design and implement end-to-end IoT solutions,” said Foudray, RUMBLE co-founder and CEO. “IoT creates advantages for adopters and we want to ensure regional organizations have the information that will help them move forward successfully.”

    Click here to learn more about Overland Park-based RUMBLE.

    Implementing IoT solutions or any new technology is difficult if the task is outside the scope of the organization, said Manjaro, IoT consultant and CEO of Beyond Machine to Machine Communications in New Jersey.

    “Definitely bring in people who have expertise,” she advised. “Don’t try to do everything yourself because I’ve seen this with so many large companies who say, ‘Yeah, we can do this ourselves,’ but $20 or $30 million dollars later — it failed. The technology didn’t fail, they just didn’t understand the pitfalls.

    “Get help early and start small,” she added.

    The biggest hurdles for startup: being risk averse and failing to force needed change, said Sharp.

    Perry Lea, RUMBLE, Microsoft

    “It’s the number one thing I consistently see,” he said. “When you’re innovating, it’s about failing fast and learning quickly. By definition, you have to fail. That flies in the face of every mature organization’s compensation structures, performance reviews, all those kinds of things.”

    “If you don’t have that culture of innovation, your competitor does,” added Lea, co-founder of RUMBLE and a Microsoft principal. “You have to embrace these new technologies. We talked about a lot of hype today, but you have to go beyond that, and say, ‘How do these technologies work for me and my customers?’”

    Lea recently published “The Internet of Things for Architects.” Click here to learn more about the book.

    Fog Computing Market Segmentation & Market Analysis Research Report 2019

    Crystal Market Research has added the report on Fog Computing Market for the forecast till 2023, the report comprises of the estimation of the global …

    Crystal Market Research has added the report on Fog Computing Market for the forecast till 2023, the report comprises of the estimation of the global Fog Computing Market. The following Industry is shown to progress with a noteworthy rise in the Compound Annual Growth Rate (CAGR) during the forecasted period owing to various factors driving the market. The Fog Computing report provides the data related to the market segmentation, regional outlook, market trends, regional outlook, and competitive outlook.

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    The study of the Fog Computing report is done based on the noteworthy research methodology that provides the analytical inspection of the global market based on various segments the market is divided into also the summary and latest size of the market owing to the various outlook possibilities. The report also gives information about the key players of the Fog Computing Industry by different features that include the Fog Computing overview of the companies, the portfolio of the product and also the revenue facts from the foreseen period.

    Segmentation by Key Players:

    • Nebbiolo Technologies
    • FogHorn Systems
    • Cisco Systems
    • Cisco Systems
    • IBM and PrismTech

    Major Types:

    • Type 1
    • Type 2
    • Type 3

    Major Applications:

    • Smart grids
    • Connected healthcare
    • Connected vehicles
    • Smart cities
    • Smart manufacturing

    Regional Overview:

    The report gives an overview of the Fog Computing Market mainly in the regions of North America, Europe, Asia-Pacific, South America and the Middle East and Africa.

    Report Highlights:

    1. Fog Computing business report produces value for global level playing competition, which delivers the same position for both the existing giants as well as the new entrees.

    2. This report will give you the overall outlook of the entire Fog Computing Industry helps in improving your knowledge.

    3. It prepares you a go-to-market strategy to improve Fog Computing organizations among other competitors which makes it completely a helpful research report.

    4. Fog Computing Reports helps you to understand the present scenario of the Industry as the report offers past data regarding the market space and makes future projections.

    5. You not only get a look at the customized Fog Computing industry segments according to geographical regions but also country or even different manufacturers in the market.

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    Wearables firm partners with IBM on safety offerings

    … wearables, infrastructure and software platforms, plans to work with the IBM Watson “internet of things” platform to further modernize safety offerings, …

    A workplace safety wearables technology firm has announced a collaboration with IBM to integrate solutions for smart personal protection equipment.

    Guardhat Inc., a Detroit-based industrial safety technology company specialized in developing wearables, infrastructure and software platforms, plans to work with the IBM Watson “internet of things” platform to further modernize safety offerings, the company said in a statement.

    International Business Machines Corp.’s Watson platform is designed to help clients improve the operational efficiency of their physical assets and address potential risk through insights driven by artificial intelligence. Its factory and construction industries solution, Maximo Worker Insights, monitors biometric and environmental data in near real-time from wearables and other connected devices to help employers identify potential hazards in the workplace, according to the statement.

    Guardhat’s proprietary software actively monitors a user’s location, pulse, body temperature and work environment. This provides a holistic view of every user’s work environment and instant alerts in the event of a fall, exposure to toxic gases, lockout zones and proximity to moving equipment, according to the statement.

    The application of such technologies allows industrial leaders to recognize and respond to potential risks in near real-time, leading to a reduction of injuries and accidents in the workplace, according to the statement.

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    Big Data, IoT And AI, Part One: Three Sides Of The Same Coin

    Big Data, IoT And AI, Part One: Three Sides Of The Same Coin … Big Data, AI, and IoT are three of the most widely misappropriated terms of recent … Venkat Viswanathan, the co-founder and chairman of LatentView Analytics, had …

    Big Data, AI and IoT are all parts of the same system – and nothing will improve unless we think of them holisticallyYukokusamurai, Shutterstock

    Since computers were first invented people have been looking for ‘the next big thing.’ Now, as close to half the world owns a phone faster than the earliest supercomputers, it is difficult to keep track of what we should pay attention to. While many of the inventions that utilize powerful technology barely make it past the headlines (has anyone ever seen a smart fridge?), the advances that made them possible often fall victim to the same hype.

    Big Data, AI, and IoT are three of the most widely misappropriated terms of recent times, and many do not know how these technologies are linked, or how they have paved the way for the technological progress we have come to expect. This article will shed some light on these concepts, and further articles will delve into their importance in industry, obstacles that they face, and what lies ahead.

    The big bang

    In the years following the launch of The World Wide Web in 1989, there was a huge growth in the number of machines connected to each other, and when GPS became viable between 1994 and 2000 the amount of data being generated by computers and connected devices skyrocketed. The potential of this network of devices was soon realized, and in 1999 the term ‘Internet of Things’ was first coined by Kevin Ashton of MIT, postulating: ‘If we had computers that knew everything there was to know about things, using data they gathered without any help from us, we would be able to track and count everything and greatly reduce waste, loss and cost.’

    With GPS technology taking off, RFID tags being used in loyalty card systems, and the PDA market heating up, businesses were able to ‘see’ into their processes and the conditions were perfect for an information explosion. In 2005, the term ‘Big Data’ was first used by Roger Mougalas as the amount of data generated became too much for existing tools to process. The launch of the iPhone in 2007 marked Big Data’s move into the consumer sphere, and since then the rise of smartphones, wearables, tablets, and all number of smart devices have changed our perception of the physical and digital world.

    Big Data, big changes

    At the same time, the rise of social media and e-commerce led to the idea of a digital persona, and the incredible value of data became apparent. The ’00s also saw the emergence of the data sector, with companies forming specifically to help enterprises manage organizational data and use it to improve processes. Venkat Viswanathan, the co-founder and chairman of LatentView Analytics, had experienced the power of data in the consumer marketing sphere and saw interest in business environments too. ‘What enabled a lot of this transformation is that digital data is so much more granular,’ said Viswanathan, ‘companies were taking ideas from the consumer sector and applying it in industry.’

    Industrial environments were already used to technology and data collection, as they used data to inform decisions only in real-time – checking pressure levels, temperatures, etc. Storing that data for later analysis and use was not even considered until specialized sensor data became refined enough and the cost of storage dropped: ‘As storage costs came down and cloud storage came into use over the last 5-8 years, the opportunity to look back at historical data and identify patterns arose,’ Viswanathan said.

    AI’s time to shine

    Once data storage became a viable option for businesses of all kinds, and the cloud made it possible to collect huge and detailed sets of data, AI finally had a solid foundation to build on. Over the years, AI research has gone through several AI winters, in which the development of algorithmic techniques hit a wall due to lack of interest or investment. As more and more data became available, and AI research branched off into narrower and narrower applications, the latest generation of algorithms has made huge progress in fundamental areas – such as natural language processing, computer vision, and machine translation – because of the massive amount of information available to learn from.

    This surge in available training data from a huge range of sources has led to a vast improvement in AI systems, a phenomenon known as the unreasonable effectiveness of data, which states that even simple algorithms, given enough data, can reach accurate conclusions. Combined with decades of work perfecting these algorithms to perform specific tasks with near-human performance, AI finally has something to sink its teeth into to achieve meaningful results. But Big Data has not just given an added boost to AI – it is required, as, in the words of the Devin Gharibian-Saki the CSO of Redwood Software, ‘AI systems run on statistical models, so you can’t run AI without a lot of data to feed it.’

    A holistic view

    With a host of sensors, IoT devices and even user data available to provide data to teach an AI system, predictions and decisions can now be made across all areas of a business, provided that users understand what the data means and where it comes from. ‘You have to have an idea of what you want in the end, otherwise all that data, technology and sensors are useless,’ says Gharibian-Saki, especially in an environment where anything is measurable and the risk of being lost in data is greater than ever. Companies must remember that there is no quick-win from using IoT, Big Data, or AI in isolation: ‘we always look for one thing that will make all our problems go away,’ says Gharibian-Saki, ‘but IoT devices, sensors, robotics, and AI are all components in a system – without that holistic view it will take a long time to achieve a big win.’

    IoT, Big Data, and AI all feed into each other and create an ecosystem of automation – IoT devices collect data on millions of criteria, which is then collated in the cloud, and used to train and improve AI algorithms. As such, ensuring that people understand how IoT, Big Data and AI interact and improve each other is the most important thing we can do to bring real improvements to our lives.

    Subsequent articles in this series will focus on the intersection of IoT, Big Data and AI in industry, obstacles that this ecosystem faces now, and what the future holds for this group of technologies.

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