#iot bigdata challenges

[gis56]

Industrial Embedded Systems: The $118.1B Tech You Didn’t Know You Needed!

Industrial Embedded Systems Market is revolutionizing industries by integrating specialized computing systems into machinery and processes. These systems, comprising microcontrollers, processors, and software, enhance automation, efficiency, and reliability, supporting sectors like manufacturing, energy, automotive, and telecommunications. As digital transformation accelerates, embedded systems are unlocking new possibilities for smart and connected solutions.

To Request Sample Report : https://www.globalinsightservices.com/request-sample/?id=GIS33025 &utm_source=SnehaPatil&utm_medium=Article

📊 Market Growth & Key Insights

✅ Automotive leads, leveraging embedded systems for EVs, ADAS, and vehicle safety. ✅ Healthcare follows, driving advancements in medical diagnostics & smart devices. ✅ North America dominates, fueled by technological innovation and R&D investments. ✅ Europe ranks second, benefiting from IoT adoption and industrial automation. ✅ U.S. & Germany emerge as key players, supported by strong industrial ecosystems.

🔍 Market Segmentation & Trends

🔹 Type: Software, Hardware, Firmware 🔹 Technology: AI, IoT, Machine Learning, Edge Computing, Big Data 🔹 Application: Automotive (35%), Industrial Automation (30%), Consumer Electronics (25%) 🔹 Key Players: Intel, Texas Instruments, NXP Semiconductors

🚀 Future Outlook & Challenges

The future of industrial embedded systems is brighter than ever, with 5G integration, autonomous manufacturing, and AI-driven solutions driving growth. Regulatory standards like EU safety laws push companies toward continuous innovation. However, cybersecurity threats and high implementation costs pose challenges. With edge computing & IoT adoption surging, the market is set for massive expansion in smart factories & real-time analytics.

#industrialautomation #embeddedsystems #smartmanufacturing #iottech #aiintegration #industry40 #automotiveinnovation #5gtechnology #chiptechnology #electronicsengineering #robotics #bigdata #machinelearning #digitaltransformation #realtimedata #semiconductors #evtechnology #autonomoussystems #hardwareengineering #cloudcomputing #techtrends #processautomation #manufacturingtech #energytech #aerospaceengineering #industrialgrowth #automotivedesign #smartindustry #nextgencomputing #datasecurity #innovationtech #industrialiot #hightechsolutions #techindustry

Research Scope:

· Estimates and forecast the overall market size for the total market, across type, application, and region

· Detailed information and key takeaways on qualitative and quantitative trends, dynamics, business framework, competitive landscape, and company profiling

· Identify factors influencing market growth and challenges, opportunities, drivers, and restraints

· Identify factors that could limit company participation in identified international markets to help properly calibrate market share expectations and growth rates

· Trace and evaluate key development strategies like acquisitions, product launches, mergers, collaborations, business expansions, agreements, partnerships, and R&D activities

About Us:

Global Insight Services (GIS) is a leading multi-industry market research firm headquartered in Delaware, US. We are committed to providing our clients with highest quality data, analysis, and tools to meet all their market research needs. With GIS, you can be assured of the quality of the deliverables, robust & transparent research methodology, and superior service.

Contact Us:

Global Insight Services LLC 16192, Coastal Highway, Lewes DE 19958 E-mail: info@globalinsightservices.com Phone: +1–833–761–1700 Website: https://www.globalinsightservices.com/

[lovelypol]

AI is Taking Off! Aviation Market to Soar to $22.1B by 2034 ✈️

AI in Aviation Market is revolutionizing the industry by integrating machine learning, predictive analytics, and automation to enhance operational efficiency, safety, and passenger experience. AI-driven solutions optimize predictive maintenance, air traffic management, and autonomous aircraft systems, reducing costs and improving decision-making.

To Request Sample Report : https://www.globalinsightservices.com/request-sample/?id=GIS33001 &utm_source=SnehaPatil&utm_medium=Article

The market is experiencing strong growth, with the autonomous aircraft segment leading at 45% market share, fueled by advancements in drone technology and pilotless aircraft systems. Predictive maintenance holds 30%, ensuring reduced downtime and cost-efficient operations. Flight operations optimization, at 25%, enhances fuel efficiency and environmental sustainability.

North America dominates AI adoption in aviation, supported by aerospace innovation and regulatory advancements. Europe follows, focusing on sustainability and compliance, while Asia-Pacific emerges as a fast-growing region, driven by expanding commercial aviation markets and AI-supportive government initiatives.

By 2028, the market is projected to expand beyond $10 billion, with major players like Boeing, Airbus, and Lockheed Martin investing heavily in AI-driven innovations. Regulatory bodies like the FAA and EASA shape the landscape, ensuring safe AI adoption while addressing cybersecurity challenges.

#ai #aviation #machinelearning #predictivemaintenance #autonomousaircraft #airtrafficmanagement #computervision #deeplearning #unsupervisedlearning #reinforcementlearning #neuralnetworks #airlines #drones #smartaviation #flightoptimization #cybersecurity #cloudcomputing #airports #automation #aircraftmaintenance #digitalaviation #pilotlessaircraft #airlinesafety #dataanalytics #aviationtechnology #airtransport #bigdata #avionics #intelligentautomation #iot #smartmaintenance #cloudai #aircraftsystems #passengerexperience #aviationregulations #smartairports #aviationsecurity #aircraftrouting #fueloptimization #industry40 #aviationinnovation

Would you like any refinements or additional insights?

[boom3010]

In-Memory Computing Chips: The Next Big Thing? Market to Hit $12.4B by 2034

In-Memory Computing Chips Market is experiencing rapid growth as industries demand faster data processing, real-time analytics, and energy-efficient computing. Unlike traditional architectures, in-memory computing chips store and process data in the same location, eliminating latency and dramatically improving performance. This breakthrough technology is transforming industries such as AI, big data, edge computing, healthcare, finance, and autonomous systems.

To Request Sample Report: https://www.globalinsightservices.com/request-sample/?id=GIS10637 &utm_source=SnehaPatil&utm_medium=Article

Why In-Memory Computing Chips?

✅ Accelerate AI & machine learning applications ✅ Enable real-time big data analytics ✅ Reduce power consumption & latency ✅ Optimize cloud computing & edge AI

Market Growth Drivers:

📈 Growing demand for AI-driven computing & deep learning 📈 Expansion of IoT, 5G, and high-performance computing (HPC) 📈 Rising need for energy-efficient data centers & cloud infrastructure 📈 Advancements in neuromorphic and resistive RAM (ReRAM) technologies

The global in-memory computing chips market is set to expand, with major tech giants and startups investing in AI accelerators, neuromorphic computing, and next-gen memory architectures. As AI, blockchain, and real-time analytics continue to evolve, in-memory computing is emerging as a critical enabler of high-speed, low-latency processing.

With quantum computing and edge AI pushing the limits of traditional computing, in-memory computing chips are paving the way for a faster, smarter, and more efficient digital future.

What are your thoughts on in-memory computing? Let’s discuss! 👇

#InMemoryComputing #AIAccelerators #EdgeAI #HighPerformanceComputing #BigData #RealTimeAnalytics #CloudComputing #MachineLearning #ArtificialIntelligence #NeuromorphicComputing #NextGenChips #TechInnovation #AIChips #DataProcessing #DeepLearning #IoT #5G #QuantumComputing #SmartComputing #ChipTechnology #EnergyEfficientTech #HPC #DataCenters #ReRAM #CloudAI #AIHardware #FutureOfComputing #FastProcessing #LowLatency #NextGenMemory 🚀

Research Scope:

· Estimates and forecast the overall market size for the total market, across type, application, and region

· Detailed information and key takeaways on qualitative and quantitative trends, dynamics, business framework, competitive landscape, and company profiling

· Identify factors influencing market growth and challenges, opportunities, drivers, and restraints

· Identify factors that could limit company participation in identified international markets to help properly calibrate market share expectations and growth rates

· Trace and evaluate key development strategies like acquisitions, product launches, mergers, collaborations, business expansions, agreements, partnerships, and R&D activities

About Us:

Global Insight Services (GIS) is a leading multi-industry market research firm headquartered in Delaware, US. We are committed to providing our clients with highest quality data, analysis, and tools to meet all their market research needs. With GIS, you can be assured of the quality of the deliverables, robust & transparent research methodology, and superior service.

Contact Us:

Global Insight Services LLC 16192, Coastal Highway, Lewes DE 19958 E-mail: info@globalinsightservices.com Phone: +1–833–761–1700 Website: https://www.globalinsightservices.com/

[subornobiswas]

Power of Big Data in Education: Enhancing Learning through Student Analytics

Introduction: 

In today’s data-driven world, the term “Big Data” has become more than just a buzzword. It refers to the collection, processing, and analysis of massive datasets to gain valuable insights and make informed decisions. Beyond its applications in various sectors, Big Data’s impact on education has been profound. Through this blog post, we will explore how Big Data is transforming the educational landscape, enabling educators to track student progress, identify at-risk students, and ultimately improve teaching and learning methods.

The Role of Big Data in Education:

Collecting and Processing Large Datasets: Big Data in education encompasses a vast array of information, including student academic records, assessment scores, attendance records, and even data from classroom activities and digital learning platforms. The ability to collect and store such vast amounts of data efficiently has been made possible by advancements in technology and cloud computing.

Utilizing Big Data for Student Progress Tracking: By harnessing the power of Big Data analytics, educators can gain real-time insights into student performance. This enables them to track individual progress, identify learning patterns, and gain a holistic view of student achievements and challenges.

Identifying At-Risk Students: Big Data has opened new avenues for early intervention. Through predictive analytics, educators can identify at-risk students who may be facing academic or personal challenges. Armed with this knowledge, educational institutions can provide timely support and tailor interventions to ensure every student’s success.

Leveraging Big Data for Improving Teaching Methods:

Personalized Learning Approaches: Big Data empowers educators to personalize learning experiences for each student. Adaptive learning platforms use data analytics to adjust the pace, content, and difficulty of educational materials based on individual progress and learning styles, fostering a more engaging and effective learning environment.

Data-Driven Instructional Design: Big Data helps in shaping better instructional strategies. By analyzing data on teaching methods, educators can identify which approaches yield the most significant impact on student learning outcomes. This continuous improvement cycle leads to more effective curriculum development and teaching practices.

Overcoming Challenges in Big Data Implementation:

Data Security and Privacy Concerns: As valuable as educational data is, it must be handled with utmost care to protect students’ privacy. Educational institutions need to implement robust data security measures and comply with data protection regulations to safeguard sensitive information.

Addressing Ethical Considerations: Big Data analytics must be conducted ethically, avoiding biases and ensuring transparency. The insights derived from Big Data should be used to empower students and educators, not perpetuate inequalities.

Future Prospects and Innovations:

The potential of Big Data in education is ever-evolving, with ongoing advancements in artificial intelligence and machine learning. Additionally, the integration of the Internet of Things (IoT) in educational institutions is set to revolutionize the way data is collected and utilized, providing even more opportunities for enhancing teaching and learning experiences.

Conclusion:

Big Data has emerged as a game-changer in the field of education. By harnessing the power of data analytics, educational institutions can track student progress, identify at-risk students, and implement personalized learning approaches. While Big Data implementation comes with challenges, ethical considerations, and privacy concerns, its potential for continuous improvement in education is undeniable. As we move forward, embracing Big Data analytics responsibly will pave the way for a brighter and more inclusive future in education.

#bigdata #talentserve #education

[microvity-blog]

IOT : INTERNET OF THINGS #iot #bigdata #challenges

The internet of things (IoT) is the network of physical devices, vehicles, buildings and other items—embedded with electronics, software, sensors, and network connectivity that enables these objects to collect and exchange data.

Internet of Things is essentially an architectural framework which allows integration and data exchange between the physical world and computer systems over existing network infrastructure.

As the telecommunication sector is becoming more extensive and efficient, broadband internet is widely available. With technological advancement it is now much cheaper to produce necessary sensors with built-in wifi capabilities making connecting devices less costly.

Most important, the smart phone usage has surpassed all the predicted limits and telecommunication sector is already working on its toes to keep their customers satisfied by improving their infrastructure. As IoT devices need no separate communication than the existing one building IoT tech is very cheap and highly achievable

Internet of Things: 7 Challenges

7. New Use Cases

Remember when the personal computer first emerged, and it was promoted as a place to store recipes? Or when the iPad was released and articles suggested how it might be used? Like the personal computer and the iPad, the IoT is one of those ideas that is being developed because it is possible, not because it can fulfill any specific problem. Although examples of how to use the IoT usually involve timers for turning appliances on and off, the real purposes will probably emerge only after smart devices are everywhere.

That does not mean that the IoT won't be a success, or revolutionize technology. However, it does mean that its consequences are difficult to foresee. Advising everyone to expect the unexpected is probably the only reliable advice -- and that suggestion is hardly accurate for long range planning.

6. The Need for Open Standards

The IoT consists of a lot of individual devices with their own specifications. At this stage, that hardly matters, but a time will arrive soon when further growth will require that smart devices can communicate with each other

5. Energy Demands

Several years ago, Gartner predicted that 4.9 billion smart devices would be used by 2015 -- an increase of thirty percent from 2030. By 2020, Gartner estimated that the number of smart devices would reach 25 billion by 2020, an increase of 100% each year.

Even with improved batteries and green sources like solar and wind, just meeting the demand will be difficult. However, add issues like the wasted energy and pollutants, and powering the IoT could become a major social problem in its own right within the next decade.

4. Waste Disposal

Thanks to planned obsolescence, fifty million tons of e-waste -- the disposal of computers, phones, and peripherals -- are produced each year in the United States alone. As countries like China and India continue to industrialize, and the Internet of Things comes online, the problem is only going to continue. Meanwhile, less than twenty percent of e-waste is recycled, and despite the Basel Convention, much of the rest continues to be shipped overseas to developing nations where it is salvaged in unsafe working conditions

3. Storage Issues

Storage of information generated by smart devices will increase the energy demands required by the Internet of Things. A single corporation like Google, which already has myriad server farms, each occupying tens of thousands of square feet, could be dwarfed by the demands of smart devices.

However, the physical demands are only part of the problem. Much of the data generated by smart devices is needed only briefly to send signals to device, and does not need to be stored. Other data, such as timers for devices, might ordinarily need to be stored for only a week or two at the most.

Yet with such information being available, the demand may arise for storing part of this surge of information for longer periods. Consequently, policies will be needed about what kind of information is stored, and for how long -- to say nothing of who can access it, and the exceptions that might be made to whatever general policies are devised.

2. Lack of Privacy

Potentially, the Internet of Things is a wealth of information about those who use it. Smart phones can already be tracked, but smart devices point to a future where governments supplement census information with the output of smart devices, and manufacturers harvest information about your habits so efficiently that they make Facebook's insights into your interests and buying habits seem vague.

Imagine, too, being stalked by government agencies through your smart devices, or your devices being used against you in a court of law.

1. Lack of Security

When faced with a choice between convenience and security for users, manufacturers almost always choose convenience. Even at this early stage, the Internet of Things is no exception. Already, basic devices such as routers, satellite receivers, network storage and smart TVs are ridiculously easy to hack, and 2015 was marked by the report of the first known successful cracking of a car while it was being operated. Such reports are inevitably greeted with cries of alarm, but, just as inevitably, little is done.

Expecting the Unexpected

None of these challenges is necessarily a reason to oppose the Internet of Things. Nor is the list necessarily complete. Just as purposes for smart devices will be found that we cannot participate today, so challenges are likely to emerge that we cannot anticipate today.

However, the last few decades have seen enormous revolutions produced by everything from the personal computer to the cell phone. If we can extrapolate from the challenges we have seen in earlier revolutions, we can at least mitigate those created by the IoT. If we do, then, if nothing else, we can be better prepared to meet the challenges that we didn't anticipate.

www.microvity.com

[plainfile10-blog]

The Ultimate Solution for Bigdata Analytics

One of many sources of Big Data is logs, and they are able to quickly get out of control with redundant or false alerts. Big data services are getting more popular due to emerging trends like the internet of things. Big Data companies are available in many unique shapes and flavors. The Execute phase is apparently the domain of larger players like multinational businesses. Finding whole new customer segments may lead to tremendous new price. Utica National Insurance Group uses predictive analytics to monitor continuously incoming credit reports that could assess the risk appetite based on a variety of present data rather than simply considering the credit score score alone. The Hidden Truth About Bigdata Analytics Exploratory data analysis needs to be interpreted carefully. Descriptive analyticsor data mining are at the base of the huge data value chain, but they are sometimes valuable for uncovering patterns that provide insight. Predictive analytics isn't a branch of conventional analytics like reporting or statistical analysis. Renewable energy another major grid component that may benefit from Big Data analytics. Data Science has existed for much longer than Big Data, but it was not until the development of information volumes reached contemporary levels that Data Science has turned into a required part of enterprise-level Data Management. It will need to deal with the huge amount of data and analyzing it in real-time becomes a huge challenge as data grows by the minute. The Fight Against Bigdata Analytics Elemento claims that because of all this technology, it is a bright future for being better able to comprehend and treat cancer. The best thing about this system is that we are able to prevent an impending failure that could cost a good deal. On the flip side, there are several poor which can be lent to, but at greater risk. There is continuing resistance to modify by key individuals and they'll need to be addressed, preferably by means of retraining and counseling. Therefore, there's a need of new reforms that are done on the grounds of what is necessary for the students and what has become the prior student's pattern all this isn't only the work of a researcher but this may be accomplished by means of an analyst in a brief time period with the assistance of the tools they have. There are occasions once you simply won't have the ability to watch for a report to run or a Hadoop job to finish. The Do's and Don'ts of Bigdata Analytics The only means to find this high accuracy was supposed to use machine-learning algorithms to combine expression amounts in a means that was nonlinear, states Elemento. All are varieties of information analysis. Predictive analytics permit the company to figure out the probabilities of delays. This proves the simple fact that the quantity of information created each and every day. The present release of Apache Storm is a sound option if you are interested in finding a stream processing framework. On account of the high cost of information acquisition and cleaning, it's well worth considering what you actually will need to source yourself. Numerous machines and parts work together to make the end product. With the arrival of internet, it has become simpler to gather desired information in a portion of seconds. Volume is a huge deal here, but the data velocity may be the largest challenge. Our great data training in Chennai will supply you hands-on experience to fulfill the demands of the industry requirements. This makes it rather important to possess the abilities and infrastructure to handle it intelligently. In the event the upstream and downstream technologies necessary to harness the ability of IoT isn't learnt and used, the greatest potential of IoT wouldn't be realized. Big Data tools also help airline service providers to raise their network connectivity according to the industry demand. Their rise will offer important last mile connectivity and be certain that electricity isn't overused or wasted through the ability of analytics. Hotels and restaurants are an essential part of the hospitality sector and are liable for offering a wide selection of big data analytics services to their clients and clients. Hospitality is, thus, a focus in many nations around the Earth, but it is of specific significance in countries where tourism is an important export market. Most of the main vendors on the market are actively focusing on enhancing their offerings to fulfill the continuing demand for advanced healthcare solutions. Tableau employs a very simple drag and drop interface that anybody can use. In the following article, we will attempt to present a strategy on the best way to develop an analytics system. So, the cloud providers have to be accessed which provides sufficient storage space together with the computing power. This means your users get the ideal data at the correct time. It must be noted however that most dashboard products are made for passive data viewing, in place of interactive action. The grade of the data ought to be checked as early as possible. Predictive Analytics, Big Data, and How to Make Them Work for You can be put into place by doing-it-yourself, utilizing a framework or an item. To predict downtime it might not be necessary to check at all the data but a sample might be sufficient. The opposing side of that, however, is they don't need to flip their own model without making sure the payment is likely to adhere to a different shipping basis. The school is also famous for its focus on business, which makes it a great choice for students that are also seeking to go into management. Fundamentals of Hadoop, challenges connected with it and why it's regarded as game changer by multiple verticals of the business is an important section which will raise the degree of your knowledge bucket.

[clouditeg]

8 Best New Artificial Intelligence Books To Read In 2022

8 Best New #ArtificialIntelligence Books To Read In 2022 ————— #DeepLearning #Datascience #MachineLearning #DataScienceCertification #DataAnalytics #BigData #DigitalTransformation #IoT #100DaysOfCode #Programming #Coding

Books dedicated to Artificial Intelligence are on the rise in 2022. For that reason, we present a selection of the best books recently written by talented authors. These specialists in new technologies present to you the challenges of artificial intelligence (AI) in the world of tomorrow! In the same way, This major discipline feeds more and more debates and fantasies. Some see a very dark future…

[thetechnologylaw]

A large number of students and faculty showed up for my #MasterClass on "Autonomous Output of #AI and Challenges for #IntellectualProperty" organised by the Department of General Science and Culture at Tashkent State University of Law

#aipolicy #aiprinciples #ailaw #aiethics #aiforall #university #law #ethics #ai #tashkent #uzbekistan #centralasia #TSUL #TechLaw #LegalTech #kazakhstan #kyrgyzstan #Tajikistan #Turkmenistan #Europe #China #Beijing #ai #machinelearning #technology #datascience #deeplearning #python #programming #tech #bigdata #robotics #innovation #coding #computerscience #iot #data #dataanalytics #business #datascientist #engineering #automation #software #programmer #robot #ml #pythonprogramming #developer #analytics #coderlife #IP #IPLAW

[planetarduino]

Engineer’s guide to Industrial IoT in Industry 4.0

This is an edited version of a longer piece first published on Wevolver.

In recent years, industrial enterprises are accelerating their digital transformation and preparing themselves for the fourth industrial revolution (Industry 4.0). This digitization of production processes enables industrial organizations to implement agile and responsive manufacturing workflows, which rely on flexible Information Technology (IT) systems rather than on conventional Operational Technology (OT). This flexibility facilitates a shift from conventional Made-to-Stock (MTS) manufacturing to novel customizable production models like Made-to-Order (MTO), Configure-to-Order (CTO) and Engineering to Order (ETO). 

The implementation of Industry 4.0 compliant production systems hinges on the deployment of Cyber-Physical Systems (CPS) in the manufacturing shop floor. In essence, CPS systems comprise one or several internet-connected devices integrated with other production systems in industrial environments. This is the main reason why Industry 4.0 is also referred to as Industrial Internet of Things (IIoT).  IIoT includes the subset of IoT (Internet of Things) systems and applications that are deployed in industrial environments such as the manufacturing, energy, agriculture, and automotive sectors. According to recent market studies, the lion’s share of IoT’s market value will stem from IIoT applications rather than from consumer segments.          

The typical structure of IIoT applications is specified in standards-based architectures for industrial systems such as the Reference Architecture of the Industrial Internet Consortium. It comprises a stack of components that includes sensors and IoT devices, IoT middleware platforms, IoT gateways, edge/cloud infrastructures, and analytics applications. 

The Power of Embedded Sensors in the Manufacturing Value Chain

IT systems, enterprise applications (e.g., ERP and Manufacturing Execution System (MES)), and industrial networks for production automation have been around for decades. The real game-changer in Industry 4.0 is the expanded use of embedded sensors in the value chain. Embedded sensors transform manufacturing assets into cyber-physical systems and enable many optimizations that were hardly possible a few years ago. Overall, embedded sensors and other IIoT technologies empower increased efficiencies by transforming raw digital data to factory floor insights and automation actions. 

Some of the perceived benefits of IIoT and embedded sensors deployments in production operation include:

Flexible Production Lines

Predictive Maintenance 

Quality Management

Supply Chain Management

Zero Defects Manufacturing

Digital Twins

Data analysis options: Edge, Cloud, or combination?

Most IIoT applications include data analytics functionalities such as sensor data analysis based on machine learning techniques. Therefore, they typically collect and process information within cloud computing infrastructures. The latter facilitates access to the required data storage and computing resources. Nevertheless, IIoT deployments in the cloud fall short when it comes to addressing low latency use cases, such as applications involving real-time actuation and control. In such cases, there is a need to execute operations close to the field (i.e., the shopfloor) that cannot tolerate delays for transferring and processing data in the cloud. 

To address real-time, low-latency applications, industrial organizations are deploying IIoT applications based on the edge computing paradigm. The latter involves data collection and processing close to the field, within infrastructures like edge clusters (i.e., local cloud infrastructures), IoT gateways, and edge devices. A recent report by Gartner predicts that by 2023 over 50% of enterprise data will be processed at the edge. 

Edge computing deployments are best suited for real-time control applications while helping to economize on bandwidth and storage resources. Specifically, data processing within edge devices facilitates the filtering of IoT data streams and enables enterprises to selectively transmit to the cloud “data points of interest” only. Furthermore, edge computing provides better data protection than cloud computing, as data remains within local edge devices rather than being transmitted to cloud data centers outside the manufacturing enterprise. Moreover, edge analytics functions like AI algorithms on edge devices are much more power-efficient than cloud-based analytics. 

In practice, industrial enterprises employ both cloud computing and edge computing for their IIoT use cases. Specifically, they tend to deploy real-time functions at the edge and data-savvy industrial automation functions on the cloud. There is always an interplay between cloud and edge functions towards achieving the best balance between analytics accuracy, computational efficiency, and optimal use of bandwidth and storage resources. Thus, IIoT applications are usually deployed in the scope of a cloud-edge environment.

Nowadays, there are many ways to implement edge computing and its interactions with cloud infrastructures. Likewise, there are also many options for employing machine learning at the edge of an industrial network, such as federated machine learning techniques or even deployment of machine learning functions in embedded devices. The latter involves a convergence of embedded programming with machine learning, characterized as embedded machine learning or TinyML. 

State of the art cloud/edge computing paradigms support varying requirements of IIoT use cases in terms of latency, security, power efficiency, and the number of data points needed for training ML algorithms. Future articles in this series will shed light on the technical architecture and the deployment configurations of some of the above-listed cloud/edge paradigms. 

The Scaling of IIoT and the Path towards industry 4.0

Industry 4.0 has been around for over five years, yet we are still quite far from realizing the full potential of embedded sensors and the Industrial IoT. Many enterprises have started their deployment journey by setting up data collection infrastructures and deploying CPS systems and IoT devices on their shop floor. There are also several deployments of operational use cases in areas like asset management, predictive maintenance, and quality control. Nevertheless, many use cases are still in their infancy or limited to pilot deployments in pilot production lines or lab environments. Therefore, there is a need for evolving and scaling up existing deployments to enable industrial enterprises to adopt and fully leverage the fourth industrial revolution.

The scaling up of Industry 4.0 use cases hinges on addressing the following challenges technical and organizational challenges:

Legacy compliance for brownfield deployments. 

Alleviating data fragmentation in industrial environments.

Addressing the IoT, BigData, and AI skills gap.

Ensuring access to pilot lines and experimentation infrastructures. 

Easing IIoT integration end-to-end i.e., from the embedded device to the manufacturing application

Realizing a cultural shift towards Industry 4.0. 

Arduino Pro and Industry 4.0

Driven by these challenges, Arduino has recently created its Arduino Pro solution for Professional Applications. It is an all-in-one IoT platform, which combines:     

Hardware boards for industrial control, robots, and edge AI applications.

End-to-End secure connectivity solutions for deploying cloud-based applications. 

Advanced development environments that enable low code application development. 

Ease of Implementation and significant community support. 

Conclusion

This article has introduced the Industrial Internet of Things, including its main use cases and business value potential for industrial enterprises. It has shed light on how embedded sensors, cloud/edge computing, and Artificial Intelligence provide a sound basis for optimizing production operations in directions that can improve production time, quality, and cost, while at the same time boosting employees’ safety and customers’ satisfaction. 

Read the full version of this article, including references used here, at Wevovler.com.

The post Engineer’s guide to Industrial IoT in Industry 4.0 appeared first on Arduino Blog.

Engineer’s guide to Industrial IoT in Industry 4.0 was originally published on PlanetArduino

[pantechelearning]

IOT security challenges. In this new post, we will review six significant IoT security challenges:  Weak password protection Lack of regular patches and updates and weak update mechanism Insecure interfaces Insufficient data protection  Poor IoT device management The IoT skills gap 1. Weak password protection Hard-coded and embedded credentials are a danger for IT systems and as much hazardous for IoT devices. 2. Lack of regular patches and updates and weak update mechanism  IoT products are developed with ease of use and connectivity in mind.  They may be secure at the time of purchase but become vulnerable when hackers find new security issues or bugs. 3. Insecure interfaces All IoT devices process and communicate data. They need apps, services, and protocols for communication and many IoT vulnerabilities originate from insecure interfaces.  4. Insufficient data protection (communication and storage) The most frequent concerns in the data security of IoT applications are due to insecure communications and data storage. 5. Poor IoT device management A study published in july 2020 analyzed over 5 million IoT, IoMT (Internet of Medical Things), and unmanaged connected devices in healthcare, retail, and manufacturing as well as life sciences @pantechelearning #pantechelearning Any queries regarding major and mini projects contact 📞 us: WhatsApp us:+91 8925533483/82 Website:https://ift.tt/3qgQVo0. . .... . . . . . . . . #pantechelearning#iot #technology #internetofthings #ai #tech #arduino #raspberrypi #robotics #automation #engineering #artificialintelligence #innovation #electronics #programming #bigdata #machinelearning #smarthome #datascience #cybersecurity #security #coding #blockchain #b #business #arduinoproject #python #electrical #arduinouno https://instagr.am/p/CPSDv1RFzNc/

[aksbrillmindz]

What’s trending in digital health ?

Conclusion We one of the best mobile app development companies in dubai have listed what is trending and what could be the innovation in the digital health care and we have a on par development team ,always updated to create the innovative apps be it in health care or any other factor

In order to implement new or enhanced health systems, policies, products, programmes, technology, and delivery methods in the new digital era, continuous and committed efforts in healthcare innovation are needed. Taking a look at one of COVID-19's most notable healthcare outcomes, the advent of telemedicine has emerged as a viable and successful mode of healthcare delivery. The industry's adoption of emerging technology has created an unparalleled climate for digital disruption. Healthcare is set to change our daily lives for the better as the digital revolution accelerates.

What are the innovations in digital health?

On Demand Health Care

Owing to their extremely busy lives, patients pursue on-demand treatment as the healthcare industry enters the age of digital innovation. In healthcare, the term "on-demand" refers to customers who request health or medical services at their leisure, both in terms of time and place. In reality, doctors are increasingly becoming on-demand healthcare providers in order to meet their patients' evolving needs in the most efficient way possible.

Virtual Care For Health The concept of video check-ups has now been adopted by the majority of major health-care organisations in the United States. Virtual treatments are being used to treat a variety of health issues, such as chronic pain, autism, and lazy eye, among others.

Virtual reality (VR), which was once just a fanciful idea for the entertainment industry, is now at the forefront of the digital revolution in the healthcare and wellness industries. Virtual reality has revolutionised almost every industry. It is thought to be extremely promising for pain control, with several studies highlighting the benefits of using technology in post-surgery care and chronic pain treatment. Furthermore, virtual reality (VR) and augmented reality (AR) have been at the forefront of significantly reducing surgeons' training time.

Internet Of Things In Health Care

Doctors and hospitals faced significant challenges in consistently tracking patients' wellbeing and making appropriate recommendations. Remote monitoring in the healthcare sector became possible with the introduction of IoT-enabled devices, which has the potential to motivate doctors to provide superior treatment. Physicians can better track and monitor their patients' health by using IoT-enabled wearables and home monitoring equipment. Physicians may use the data or health insights obtained by IoT devices to determine the appropriate care process for their patients and achieve positive results.

Quick DIagnosis Can Be Done Through AI Artificial Intelligence (AI) has become the epitome of creativity and advances in the healthcare sector, with a slew of major players keen to invest in AI to disrupt the market. With a demand for AI-powered tools in healthcare expected to top $34 billion by 2025, the technology is poised to influence almost every aspect of the industry.

Medical imaging, genomics, precision medicine, drug discovery, and other fields have all benefited from AI. The ability of AI-enabled machines to feel and comprehend data in the same way as humans do has opened up a world of possibilities for health organisations and clinicians that were previously inaccessible or unrecognised.

Wearable Devices For Health Wearable medical devices are gaining a lot of traction because of their user-friendliness, comfortability, and usability of home healthcare. With an impressive compound annual growth rate (CAGR) of 20.4 percent, the global wearable medical devices market is expected to rise from $6.4 billion in 2020 to $16.2 million in 2025. Healthcare companies are investing proactively in wearable technology systems that can provide routine and up-to-date tracking of high-risk patients and predict the probability of a major health incident. The global wearable medical devices market is primarily driven by technological advances and consumer demand for wearables (heart rate monitors, sleep tracking devices, blood pressure monitors, activity monitors, electro-cardiographs, and electroencephalograms).

BigData Will Be Having The Major Role In Health Care

Big data's allure lies in the way it aids businesses in gaining a better understanding of the demand, determining product budgets, and developing a consumer identity based on demographics. Healthcare and pharmaceutical firms are actively investing in improved data organisation in order to take advantage of all of Big data's main advantages.

Via medical record review, big data can have a huge effect on lowering the rate of medication errors. It can detect any discrepancies between the medications administered and the patients' health. Furthermore, the predictive analysis of big data will aid accurate staffing by assisting hospitals in estimating potential admission rates, which not only saves money but also decreases emergency room wait times.

[its-oridoc-world]

Digital transformation is the integration of digital technology into all areas of a business, fundamentally changing how you operate and deliver value to customers. It's also a cultural change that requires organizations to continually challenge the status quo, experiment, and get comfortable with failure.

The process starts at the top-level defining a clear and inspirational digital strategy that aligns with new market and industry trends and is coupled with a company culture based on intelligence-driven processes. Digital transformation requires a change in the way the whole organization works and thinks.

➡️ Here’s our take on how to embrace long-term digital transformation. . .

#digitaltransformation #technology #innovation #digital #business #ai #artificialintelligence #software #machinelearning #iot #tech #covid #automation #industry #cloud #startup #datascience #bigdata #agile #customerexperience #fintech #digitalization #data #blockchain #wednesdaywisdom #oridoc #consultancy #detailsmatter

[boom3010]

DNA Data Storage: $0.25B to $5.5B by 2034

DNA Data Storage Systems Market : As the world generates massive amounts of digital data, the need for more efficient and long-lasting storage solutions has become paramount. DNA data storage systems are emerging as a revolutionary technology, offering unparalleled density, durability, and sustainability for data preservation.

To Request Sample Report: https://www.globalinsightservices.com/request-sample/?id=GIS10577 &utm_source=SnehaPatil&utm_medium=Article

Market Growth and Drivers

The global DNA data storage systems market is expanding rapidly due to the increasing demand for high-capacity and long-term data storage. Researchers and tech giants are investing heavily in this innovative technology to address future data storage challenges.

Key drivers fueling the market include:

Unmatched storage density — DNA can store vast amounts of data in a minuscule space.

Extreme durability — DNA data storage can last thousands of years under optimal conditions.

Growing data generation — The rise of big data, AI, and IoT increases storage demand.

Eco-friendly alternative — DNA storage is highly energy-efficient compared to traditional data centers.

Key Trends in the Industry

The integration of synthetic biology, AI, and automation is accelerating the development of DNA data storage systems. Advances in DNA synthesis and sequencing technologies are reducing costs and increasing accessibility. Additionally, cloud-based DNA storage solutions are being explored to enhance scalability and retrieval efficiency.

Future Outlook

With continuous advancements in biotechnology and data science, the DNA data storage systems market is poised for significant growth. As organizations seek sustainable and high-capacity storage solutions, DNA-based storage is set to revolutionize the future of data management.

#dnadatastorage #futureofstorage #biotechnology #bigdata #cloudcomputing #ai #machinelearning #datasecurity #digitaltransformation #nextgenstorage #syntheticbiology #datapreservation #storageinnovation #bioinformatics #highcapacitystorage #longtermdata #sustainabletech #emergingtech #techrevolution #genometechnology #futuretech #datacenters #storageefficiency #digitaldna #biotechsolutions #datasolutions #aitechnology #futurecomputing #geneticengineering #cuttingedgetechnology #datamanagement

Research Scope:

· Estimates and forecast the overall market size for the total market, across type, application, and region

· Detailed information and key takeaways on qualitative and quantitative trends, dynamics, business framework, competitive landscape, and company profiling

· Identify factors influencing market growth and challenges, opportunities, drivers, and restraints

· Identify factors that could limit company participation in identified international markets to help properly calibrate market share expectations and growth rates

· Trace and evaluate key development strategies like acquisitions, product launches, mergers, collaborations, business expansions, agreements, partnerships, and R&D activities

About Us:

Global Insight Services (GIS) is a leading multi-industry market research firm headquartered in Delaware, US. We are committed to providing our clients with highest quality data, analysis, and tools to meet all their market research needs. With GIS, you can be assured of the quality of the deliverables, robust & transparent research methodology, and superior service.

Contact Us:

Global Insight Services LLC 16192, Coastal Highway, Lewes DE 19958 E-mail: info@globalinsightservices.com Phone: +1–833–761–1700 Website: https://www.globalinsightservices.com/

[thefuturix]

RT ipfconline1: The Internet of Things 2017: Growth & Challengeshttps://t.co/wgkZja9KE5 mushroomnet#IoT #BigData #AI #SmartCities #Infosec pic.twitter.com/Mj4SMToAS2

— Antoine Belleguie (@ABelleguie) February 8, 2018

[idt2020]

This Bigdata Testing tutorial contains a step by step guide to Bigdata testing, Bigdata concepts and Big data testing tools used. Overall, this might be an appropriate Big data testing tutorial for the beginners.

We all know Big data means a large volume of data, an interesting example may be, Facebook generates 4 Petabytes of data every day with 1.9 billion active users and millions of comments, images and videos.

But What about Bigdata Testing, what's that and what about Big data test strategy? I have tried to give an overview of Bigdata Testing in the below article. Hope it woukd add atleast some small bytes to your "Bigdata" of knowledge.

Topic covered:

What is big data

Big data formats

What is Big Data Testing?

Primary characteristics of Big Data

Structured vs Unstructured data testing

Four V's of Bigdata

The Need for Big Data Testing

Essential Necessities in Big Data Testing

7 Big Data Testing Strategies

Challenges in Big Data Testing

Roles and Responsibilities Of A Tester

Benefits of Big Data Testing

Tools for Big Data testing

Big Data Testing Best Practices

Join us:

@Website: http://www.softwaretestingportal.com/

@Youtube: https://www.youtube.com/channel/UCJcfUb4qME_G0-BdfwItt4w?sub_confirmation=1

@Medium: https://medium.com/@theinfographix

@Facebook: https://www.facebook.com/IDT2020

@Twitter: https://twitter.com/InterestingDat1

@ Linkedin: https://www.linkedin.com/in/softwaretesting-portal-21715a171/

Further Readings:

Big-Data Analytics for Cloud, IoT - https://amzn.to/33KPS4G

Handbook of Big Data - https://amzn.to/2xvfypN

Big Data MBA - https://amzn.to/39m5BrY

Please don't forget to subscribe, like and share if you like this video, as always, suggestions are most welcome.

Again, Thanks for reading your own portal - http://www.softwaretestingportal.com/

[dbi-srl]

The Challenge Of Finding Insights in Terabytes of #MachineData. #AI #IoT #BigData #fintech #Insurtech #Industry40 #M2M #PredictiveAnalytics #biomedical #smarthome #selfdriving #DataScience #ethics #MachineLearning @andi_staub @YvesMulkers @jblefevre60 @ipfconline1 via @antgrasso pic.twitter.com/U47myZam8z

— dbi.srl (@dbi_srl) December 18, 2017