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A new generation of streaming analytics for a connected world

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We live in a world of connected devices. From the watches on our wrists to the doorbells outside our homes, IoT devices are everywhere. According to recent studies, it is estimated that there will be more than 24 billion IoT devices in the next four years, and with a large number of these devices comes a large amount of data. How can the analytics systems that need to manage these devices track and analyze their billions of telemetry messages and respond quickly enough to emerging issues? For example, imagine a healthcare app that monitors thousands of patients through smart watches. This app needs to analyze the data flowing from each smartwatch and compare it to the corresponding patient’s medical condition and history for analysis. If you’re going to spot emerging medical issues, classify their urgency, and respond in seconds, you need to be able to continuously analyze telemetry as soon as it arrives. Current software technologies that only record incoming data for offline consultation or analysis cannot react quickly enough to make proactive, in-the-moment decisions for patients. Further complicating this challenge is the difficulty of writing analytical algorithms to stream data that track dynamic measurements of physical systems. Sensor data, such as EKG data from cardiac monitors or temperature/pressure/RPM data from motors and air compressors, often have complex waveforms that hide patterns that describe emerging issues that require attention. New software technology promises to address these challenges and enable streaming analytics to track large populations of IoT devices quickly and effectively. This technology combines the power of in-memory computing with the digital twin software model to enable incoming telemetry from millions of devices to be analyzed immediately, as it flows, rather than requiring log files or historical databases. and offline analysis. You can immediately flag abnormal events and send alerts to staff. In-memory computing platforms harness the combined computing power of many local or cloud-based servers working together to host information about each IoT device in memory for fast access. They combine this information with incoming telemetry to update their knowledge about the health of each device. In-memory computing can do all of this in milliseconds using the digital twin model, a software technique originally created to build and test new devices. When used for streaming analytics, a digital twin for each device contains information about the device and processes incoming telemetry. This simplifies the design of the analysis code and allows it to run quickly. Although analysis code is typically written in popular programming languages ​​such as Java and C#, creating algorithms that uncover emerging issues hidden within a telemetry stream can be daunting or complex at best. In many cases, the algorithm itself may be unknown because the underlying processes that lead to anomalies and ultimately device failures are not well understood. Machine learning (ML) algorithms can help address this problem by automatically recognizing abnormal patterns in a device’s telemetry messages and associated state information that would otherwise be difficult for humans to detect. After training with historical data that was classified as normal and abnormal, followed by testing and refinement, an ML algorithm can monitor this dynamic information and alert staff when suspicious abnormal behavior is observed. Manual parsing coding is not required. Digital twin models are suitable for hosting machine learning algorithms and running them separately for each IoT device. When hosted on an in-memory computing platform, digital twins can continuously run ML algorithms to process telemetry for large populations of devices. For example, a healthcare app can use ML to track each patient’s smartwatch medical telemetry and combine it with knowledge of the patient’s medical history to make quick, informed decisions about the need for intervention. With dizzying digital transformation and the increased use of IoT devices across all verticals, the need for better situational awareness and timely decision-making is growing rapidly. Streaming analytics systems can no longer rely on log files and offline analytics to track the huge volumes of data flowing from ever-increasing populations of IoT devices. Fortunately, new software technology that combines in-memory computing, digital twins, and ML offers the potential to give operational managers better insights than ever into the torrents of telemetry they must track every day. Want to learn about IoT from industry leaders? Take a look at the IoT Tech Expo taking place in Amsterdam, California and London. Explore other upcoming business technology events and webinars powered by TechForge here. Tags: ScaleOut, streaming analytics

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Hardware Design Leads IoT Deployment Barriers

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A survey of more than 750 IoT professionals finds that hardware design is the top perceived barrier to implementation. The survey, conducted by Eseye in partnership with Kaleido Intelligence, set out to uncover the top pain points for IoT deployments. Here are the key takeaways: 84% said hardware design is the number one barrier 56% of cellular IoT adopters find doing business with multiple vendors “too complex” 51% of implementers of IoT say connectivity performance and quality of service in international markets is not “good enough” 48% believe that robust multi-regional cellular coverage is “lacking” in the IoT connectivity ecosystem “Historically, IoT has been seen as overly complex due to hardware, connectivity, and security challenges; this survey shows that these challenges persist today,” said Nick Earle, CEO of Eseye. “Frankly, customers deserve greater certainty and lower risk when making IoT deployments. They need to be sure that their IoT project will deliver the expected results and an internationally consistent quality of service.” Eseye believes that its Infinity IoT platform addresses many of the operational challenges businesses face. “To deliver the desired levels of trust and return on investment from their IoT projects, companies need to partner with industry specialists who can offer a centrally managed service for their IoT implementation, providing a holistic view of all requirements. of hardware management, connectivity and association in one place,” added Earle. “With the new Infinity IoT platform, our enterprise customers can finally overcome major IoT challenges and successfully implement IoT to meet the connectivity needs of today and tomorrow.” Eseye is also confident that its AnyNet Federation, with its access to more than 700 global networks, solves the connectivity issues highlighted by respondents. “Connectivity is key for IoT to deliver value, but with multiple contracts, combined with roaming restrictions, it’s difficult for organizations to easily control their environment. This is where that business flexibility is really needed,” said Steffen Sorrell, research leader at Kaleido Intelligence. “Similarly, hardware device design and implementation issues were common, and this is where specialist help is critical, especially for new projects, to help the customer from initial design to full implementation. While eSIM has been touted as the answer to many of these problems, it is not a panacea, as many of our respondents reported problems.” (Photo by Matthew Garoffolo on Unsplash) Want to learn about IoT from industry leaders? Take a look at the IoT Tech Expo taking place in Amsterdam, California and London. Explore other upcoming business technology events and webinars powered by TechForge here. Tags: deployments, eseye, internet of things, IoT, kaleido intelligence, report, research, study, survey

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Vodafone’s IoT technology ensures that 1.5 million strawberries arrive at Wimbledon

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Vodafone is deploying IoT technology to ensure that 1.5 million strawberries make it to Wimbledon, the world’s oldest tennis tournament. Wimbledon just wouldn’t be the same without strawberries and cream. Hughe Lowe Farms has supplied Wimbledon strawberries for almost 30 years and will continue to be the exclusive supplier to the tournament this year. Vodafone has partnered with Hughe Lowe Farms to ensure that the strawberries arrive at their destination, the All England Lawn Tennis Club, in optimal condition. A new tracker will allow the farm to monitor every shipment of strawberries heading to Wimbledon. The tracker will report detailed information such as temperature, shock and vibration on the packaging. Marion Regan, MD of Hugh Lowe Farms, said: “We are excited to be working with Vodafone and their support is helping us to optimize the growing conditions for our strawberries. It is a great privilege to be the sole supplier of strawberries to Wimbledon, and that is a role we’ve played for almost 30 years. Now we’re using Vodafone’s cutting-edge technology to be able to offer the best quality strawberries, all produced in a more sustainable way, for fans to enjoy.” benefits of IoT during the farming process The company is using MyFarmWeb, Vodafone’s cloud-based platform for storing, visualizing and comparing all types of maps, geographic and agricultural data generated by IoT, to increase operational insights. data from MyFarmWeb, Hugh Lowe Farms can improve soil and crop health, water use, and enable precise application of fertilizers and pests. icides. Ultimately, this increases production while reducing waste and carbon emissions. Nick Gliddon, Vodafone UK Commercial Director, commented: “Technology has the power to change society for the better and we can see it in action at Hugh Lowe Farms. Our IoT technology not only helps the team make their operations more efficient and produce the best strawberries, it also helps them be as environmentally friendly as possible by reducing excessive water use and minimizing greenhouse gas emissions.” A paper published last year by Vodafone and WPI Economics found that the introduction of IoT technology could help agricultural industries save between 2.4 and 4.8 million tonnes of CO2e per year. Overall, the report found that emerging technologies like IoT and 5G have the potential to help the UK reduce 17.4 million tonnes of CO2 per year. (Image credit: Vodafone) Related: Juniper Research: Vodafone Business Leads IoT Roaming Providers Want to learn about IoT from industry leaders? Take a look at the IoT Tech Expo taking place in Amsterdam, California and London. Explore other upcoming business technology events and webinars powered by TechForge here. Tags: agriculture, environment, farms, hughe lowe farms, internet of things, IoT, myfarmweb, sustainability, vodafone, wimbledon

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DataQube partners with NodeWeaver to provide full cloud capability

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DataQube Global, an edge data center company, has partnered with NodeWeaver, a provider of autonomous and agnostic edge cloud technology, to augment the edge computing capabilities of its modular data center products. The agility of NodeWaver’s edge cloud solution is said to be perfectly aligned with the flexibility and scalability of DataQube, and the new partnership could enable colocation customers to benefit from enterprise-grade cloud functionality at an attractive price point. DataQube’s portfolio of edge data center solutions has been developed for deployment in a wide range of challenging indoor and outdoor locations where traditional data center installations are not feasible or practical due to their sheer scale and expense. initial capital needed. Additionally, the demand for localized processing is growing as companies leverage IoT to streamline processes and gain greater insight into product lifecycles. The associated data generated as a result must be processed at the source and in real time for performance, security, and usability reasons, and edge facilities capable of meeting this demand quickly, cost-effectively, and sustainably are in short supply. NodeWeaver’s ‘edge nano cloud’ operating platform installs on the foundation of almost any hardware and simplifies the deployment, management and orchestration of infrastructure and applications at the distributed edge. One or more NodeWeaver servers are automatically collocated at each edge location, providing a cloud-native experience with reliable and scalable compute and storage for applications. NodeWeaver claims that its simple, autonomous operation dramatically lowers the cost of ownership and reduces the need for IT expertise or human intervention. According to DataQube, integrating NodeWeaver’s intuitive cloud technology into its core design ensures the ultra-high-speed, ultra-low-latency processing power needed for high-bandwidth applications such as industrial IoT (IIoT), digital twins, artificial intelligence ( AI) and artificial vision. . This distributed cloud capability combined with DataQube’s people-free design, minimal fiber requirements, and green credentials make the company’s edge data center system truly unique. Steve Pass, COO of DataQube Global, said: “DataQube is always looking for technology partners to enhance the capabilities of its already unique solution. “The flexible and self-monitoring nature of Nodeweaver’s technology makes it an ideal fit for DataQube and allows us to offer our customers more options. I look forward to developing a fruitful business relationship that benefits both parties.” Carlo Daffara, CEO of NodeWeaver, said: “Dataqube offers a unique proposition in edge data center technology, with a solution that is flexible and adaptable to a variety of deployment requirements. Dataqube’s podular system perfectly complements our scalable nanocloud architecture, allowing users to deploy a cloud-like architecture anywhere, securely and sustainably. We look forward to the use cases this combination enables.” Want to learn about IoT from industry leaders? Take a look at the IoT Tech Expo taking place in Amsterdam, California and London. Explore other upcoming business technology events and webinars powered by TechForge here. Tags: DataQube, edge computing

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