With digital transformation in full swing, the number of connected devices is increasing at a fast pace. IDC Data predicts 152,200 connected IoT devices every minute by the year 2025. While this translates to more data and, subsequently, more avenues to improve efficiency, a robust network is necessary for this data exchange. The fifth-generation wireless technology has features that will not only support high-speed mobile communication but also make IoT data transfer more efficient. Let’s look at these features in contrast with the existing 4G network:
- A higher band throughput
- 100 times faster than the current network
- 25 times lower latency (lag)
- The ability to support a massive scale for IoT communications. It is estimated to connect about 1 million IoT devices per square kilometer which is a thousand times more than now.
- A 90% reduction in power consumption, guaranteeing up to 10 years of battery life in low-powered IoT devices.
- Provide network slicing ability. In simple terms, network slicing means running multiple logical networks as virtually independent operations on shared physical infrastructure. Network slicing will give businesses the flexibility to design their network system according to their requirements, such as low latency over a higher bandwidth or more device connections over low latency.
All these features make the 5G network adaptable to the external environment, unlike its predecessors, which has limited network flexibilities.
Now that we know the capabilities of 5G let’s check how it will revolutionize various sectors when combined with the Internet Of Things.
5G will take Industry 4.0 to the next level. The extensive real-time data collected can improve quality control by identifying defects faster. The wide mobility, low latency, and high (mission-critical) reliability required in production automation and autonomous vehicle control in open-pit mining are only possible in the ultra-reliable 5G network. Also, in manufacturing setups, often radio links from traditional network layouts get blocked because of metal structures and barriers. A cellular-based positioning system deploys multiple Transmission/Reception Points (TRPs) within a facility which can interact with the machine from various directions. So, even if one path gets blocked, signals can come from other directions, creating a signal redundancy and preventing loss of communication.
In the wind and solar power sectors, IoT sensors, and devices, along with AI, are helping increase productivity through advanced weather forecasting and analysis. The intelligent tuning mechanisms automatically adjust control settings for varied weather.
Smart grids will manage energy loads more efficiently. A planned energy infrastructure based on regular data collected from IoT devices can help minimize downtime and energy costs.
All this involves the seamless transfer of an enormous amount of data through a network with minimal lag. Maintenance (both predictive and preventive) will also see higher efficiency with more data collection and lower latency.
The supply chain requires a significant amount of manual supervision, especially during transportation to the warehouse and movements inside it. 5G will enable individual product level information through IoT enabled sensors as opposed to vehicle level details available today. This will make tracking and tracing of shipments easier. For perishable products, remote monitoring of trailer conditions like temperature and humidity will be possible. Inventory disposition, location, and re-routing will now happen in real-time with 5G enabled IoT trackers. 5G and IoT combined will provide an end-to-end view of the entire supply chain.
5G and IoT can improve the retail shopping experience by making it more personalized and contextual. An AI-powered fitting room with a screen could suggest similar outfits and matching accessories at the touch of a button. The customers will also be able to speak to the sales clerk and ask for other products instead of awkwardly shouting over the door. IoT enabled smart shelves can automate inventory management. They can provide real-time inventory data, build omnichannel customer relationships, and dynamically adjust prices depending on demand. The 5G network’s high speed and throughput will enable the processing of such massive data.
To get a glimpse of IoT-enabled automated stores, let’s look at the Amazon Go store. It has no cashiers or checkout system. Instead, shoppers have to download an app and scan on entry. One can pick up any product and simply walk out. Once out, the app’s automatic payment activates, and it emails a receipt of the purchase. The store has computer vision, deep learning, and shelf sensors, among other technologies to track what one picks up and what they keep back on the shelf.
The basic premise of a smart city is connectivity and automation of its various resources, operations, and services. Today we use IoT devices in homes and commercial buildings. For smart cities, this technology should extend to public departments like water, electricity, gas, waste management, traffic monitoring, and even environmental services. 5G network will process this data quickly. It will also help streamline the autonomous car industry by enabling machine to machine communication with low-latency and high-reliability.
In 2012, the city of Barcelona in Spain started deploying IoT technologies across its urban systems, including transportation, energy, water, and waste management. By 2013, the number of Wifi hotspots had increased by 62% to 670 hotspots with a maximum distance of 100 meters from point to point, and the number of Wifi users doubled. In the energy sector, they installed 19,500 smart meters. The waste management system was updated with smart bins to monitor waste levels deposited by households and optimize the collection routes.
They modernized the transportation sector with digitized bus stops and parking systems. The amenities include real-time updates on bus locations, USB charging stations, free Wifi, and information on tools and apps related to the city. They embedded the vehicle parking spots with sensor systems that has helped reduce congestion and emissions significantly. The street lights have motion sensors to conserve energy. They remain dim and only brighten up when they pick up motion. The lamp posts also provide free internet and collect air quality data of the city.
For watering of parks, they use sensors to monitor rain and humidity and determine how much water it requires. A system of remotely controlled electro valves delivers that quantity of water across the city.
For self-driving vehicles to function correctly, it requires a lot of data exchange. Besides tracking temperature, traffic, weather, and GPS location, information about pedestrians and street fixtures (like lamp posts) also play a vital role. While IoT enabled sensors and devices will collect data, the 5G network will ensure efficient transfer of these to the vehicles with no lag. In fact, in the future, vehicles will communicate among themselves, eliminating the need for traffic signals. All this requires the development of Smart Cities and an extensive 5G network.
For healthcare, the amount of data generated is enormous. A single MRI scan or PET scan can be a huge file. Apart from this, there are other records like medication details, vitals, patient response to ongoing treatment, and so on. Often these records need to be shared among doctors and other medical professionals, which requires a network with high bandwidth and download speeds.
Telemedicine is steadily gaining popularity. A study by Market Research Future predicts telemedicine to grow by 16.5% from 2017 to 2023.
While this is a boon for people with limited access to medical facilities, it requires a network with high reliability and low latency to ensure the transfer of patient records and uninterrupted tele connection.
Even for patients with chronic illness or post-discharge diagnosis, remote monitoring is possible with IoT enabled wearables connected through the 5G network. This will provide mobility to chronic illness patients, free up hospital resources, and relieve doctors while still actively monitoring them in real-time.
Augmented Reality, Virtual Reality, and spatial computing scope will further improve with the combination of 5G and IoT. It will stimulate sophisticated medical sensors to generate more information.
For most service providers, end to end 5G infrastructure won’t be available on public networks until 2025–2030. But companies have begun building their 5G networks for specific uses and individual facilities to take advantage of the technology before the competition sets in. So, it may not be long before we see them in our everyday lives in retail stores, groceries, vehicles, and even public infrastructures.
Top 10 Big Data trends of 2020
During the last few decades, Big Data has become an insightful idea in all the significant technical terms. Additionally, the accessibility of wireless connections and different advances have facilitated the analysis of large data sets. Organizations and huge companies are picking up strength consistently by improving their data analytics and platforms.
2019 was a major year over the big data landscape. In the wake of beginning the year with the Cloudera and Hortonworks merger, we’ve seen huge upticks in Big Data use across the world, with organizations running to embrace the significance of data operations and orchestration to their business success. The big data industry is presently worth $189 Billion, an expansion of $20 Billion more than 2018, and is set to proceed with its rapid growth and reach $247 Billion by 2022.
It’s the ideal opportunity for us to look at Big Data trends for 2020.
Chief Data Officers (CDOs) will be the Center of Attraction
The positions of Data Scientists and Chief Data Officers (CDOs) are modestly new, anyway, the prerequisite for these experts on the work is currently high. As the volume of data continues developing, the requirement for data professionals additionally arrives at a specific limit of business requirements.
CDO is a C-level authority at risk for data availability, integrity, and security in a company. As more businessmen comprehend the noteworthiness of this job, enlisting a CDO is transforming into the norm. The prerequisite for these experts will stay to be in big data trends for quite a long time.
Investment in Big Data Analytics
Analytics gives an upper hand to organizations. Gartner is foreseeing that organizations that aren’t putting intensely in analytics by the end of 2020 may not be ready to go in 2021. (It is expected that private ventures, for example, self-employed handymen, gardeners, and many artists, are excluded from this forecast.)
The real-time speech analytics market has seen its previously sustained adoption cycle beginning in 2019. The idea of customer journey analytics is anticipated to grow consistently, with the objective of improving enterprise productivity and the client experience. Real-time speech analytics and customer journey analytics will increase its popularity in 2020.
Multi-cloud and Hybrid are Setting Deep Roots
As cloud-based advances keep on developing, organizations are progressively liable to want a spot in the cloud. Notwithstanding, the process of moving your data integration and preparation from an on-premises solution to the cloud is more confounded and tedious than most care to concede. Additionally, to relocate huge amounts of existing data, organizations should match up to their data sources and platforms for a little while to months before the shift is complete.
In 2020, we hope to see later adopters arrive at a conclusion of having multi-cloud deployment, bringing the hybrid and multi-cloud philosophy to the front line of data ecosystem strategies.
Actionable Data will Grow
Another development concerning big data trends 2020 recognized to be actionable data for faster processing. This data indicates the missing connection between business prepositions and big data. As it was referred before, big data in itself is futile without assessment since it is unreasonably stunning, multi-organized, and voluminous. As opposed to big data patterns, ordinarily relying upon Hadoop and NoSQL databases to look at data in the clump mode, speedy data mulls over planning continuous streams.
Because of this data stream handling, data can be separated immediately, within a brief period in only a single millisecond. This conveys more value to companies that can make business decisions and start processes all the more immediately when data is cleaned up.
Continuous Intelligence is a framework that has integrated real-time analytics with business operations. It measures recorded and current data to give decision-making automation or decision-making support. Continuous intelligence uses several technologies such as optimization, business rule management, event stream processing, augmented analytics, and machine learning. It suggests activities dependent on both historical and real-time data.
Gartner predicts more than 50% of new business systems will utilize continuous intelligence by 2022. This move has begun, and numerous companies will fuse continuous intelligence during 2020 to pick up or keep up a serious edge.
Machine Learning will Continue to be in Focus
Being a significant innovation in big data trends 2020, machine learning (ML) is another development expected to affect our future fundamentally. ML is a rapidly developing advancement that used to expand regular activities and business processes
ML projects have gotten the most investments in 2019, stood out from all other AI systems joined. Automated ML tools help in making pieces of knowledge that would be difficult to separate by various methods, even by expert analysts. This big data innovation stack gives faster results and lifts both general productivity and response times.
Abandon Hadoop for Spark and Databricks
Since showing up in the market, Hadoop has been criticized by numerous individuals in the network for its multifaceted nature. Spark and managed Spark solutions like Databricks are the “new and glossy” player and have accordingly been picking up a foothold as data science workers consider them to be as an answer to all that they disdain about Hadoop.
However, running a Spark or Databricks work in data science sandbox and then promoting it into full production will keep on facing challenges. Data engineers will keep on requiring more fit and finish for Spark with regards to enterprise-class data operations and orchestration. Most importantly there are a ton of options to consider between the two platforms, and companies will benefit themselves from that decision for favored abilities and economic worth.
In-memory computing has the additional advantage of helping business clients (counting banks, retailers, and utilities) to identify patterns rapidly and break down huge amounts of data without any problem. The dropping of costs for memory is a major factor in the growing enthusiasm for in-memory computing innovation.
In-memory innovation is utilized to perform complex data analyses in real time. It permits its clients to work with huge data sets with a lot more prominent agility. In 2020, in-memory computing will pick up fame because of the decreases in expenses of memory.
IoT and Big Data
There are such enormous numbers of advancements that expect to change the current business situations in 2020. It is hard to be aware of all that, however, IoT and digital gadgets are required to get a balance in big data trends 2020.
The function of IoT in healthcare can be seen today, likewise, the innovation joining with gig data is pushing companies to get better outcomes. It is expected that 42% of companies that have IoT solutions in progress or IoT creation in progress are expecting to use digitized portables within the following three years.
Digital Transformation Will Be a Key Component
Digital transformation goes together with the Internet of Things (IoT), artificial intelligence (AI), machine learning and big data. With IoT connected devices expected to arrive at a stunning 75 billion devices in 2025 from 26.7 billion presently, it’s easy to see where that big data is originating from. Digital transformation as IoT, IaaS, AI and machine learning is taking care of big data and pushing it to regions inconceivable in mankind’s history.
Sberbank’s Pay with QR and Take&Go offerings recognized among world’s best
Sberbank has won Future Digital Awards in two categories, securing gold accolades for the innovativeness and user-friendliness of its Pay with QR (Best QR Code Payment Offering) and Take&Go (Best In-Store Technology) services.
Pay with QR is an innovative cashless method of accepting payments for products or services, which is available to Sberbank customers, as well as to clients of other banks who use Sberbank’s QR Platform. To make a payment, you only need to show your personal QR code generated by the mobile application or scan the code posted at the outlet. For businesses, this type of payment is much more affordable than merchant acquiring, with the fee being only 0.6% to 1.5%. On top of that, outlets don’t need any special equipment to use the service.
Take&Go is a revolutionary computer vision technology to make purchases at stores. The first store without checkouts in Russia was opened at Azbuka Vkusa. To make a purchase, you just need to scan a QR code using the Take&Go mobile app, take the groceries you need off the shelf and leave. The money will be debitedautomatically.
Dmitry Malykh, Acquiring and Bank Cards Director, Sberbank:
“Sberbank constantly develops and introduces innovative payment solutions that become an integral part of our customers’ lives over time. They pay for their purchases every day in the most convenient and secure ways. The Pay with QR and Take&Go offerings are already very popular among our individual customers while opening up new opportunities for businesses of any size. The international experts’ recognition once again proves that we are moving in the right direction.”
Future Digital Awards is an international award, founded in 2008. Its participants are companies that operate in fintech and payment, digital service provision, technology and innovation such as smart devices, cities and houses, IoT, etc. Juniper Research analytical group assesses applications, draws up short lists of potential winners based on criteria such as product innovation, its peculiar features and advantages, product partnerships, certification and compliance, and future business outlooks. Amazon, Visa, Google, Microsoft, Apple, Mastercard, IBM and other globally recognized tech companies won the award in 2016–2019.
How Artificial Intelligence is Changing the eLearning Environment
Artificial intelligence (AI) is everywhere these days, making inanimate objects increasingly smart. It’s designed by humans for humans, to enrich and facilitate our everyday lives. As a matter of fact, AI is now the brain behind your smartphone, car, music streaming service, banking app, freezer, and travel agency. It’s not only omnipresent, it’s omniscient as well.
If the main keyword for AI is “smart”, then how come we don’t talk more about this burgeoning technology in the context of knowledge, learning, and education? AI has everything we need to revolutionize the industry, enrich and facilitate the learning experience of students and adult learners, and boost the knowledge retention rates across the board.
It’s a whole new chapter in the history of the classroom. The change has already begun, with AI reshaping eLearning as we know it. Find out how.
AI Makes Personalized Learning a Child’s Play
Every student is different. Educators have known this for a long time, but it wasn’t until LMS software solutions that they were able to actually accommodate the various needs of different types of learners.
The introduction of technology to the traditional classroom established a framework for blended learning, which is now the dominant model for educating modern-day students. Essentially, blended learning allows students to choose how they access educational content, and how they acquire knowledge in terms of learning styles and speed. But AI brings personalization to a whole new level.
Unlike teachers, artificial intelligence can recognize students’ learning needs, interests, preferences, habits, and capacities automatically and in real-time, by analyzing their performance. This powerful insight helps create highly personalized learning environments and paths where every aspect – from content type to delivery method – is adjusted to the students’ individual needs.
The result? Optimum performance of every learner.
AI is at Learners’ Disposal Anytime, Anywhere
In combination with personalized learning paths, unlimited connectivity makes it possible for students to access educational content from anywhere and at any given moment.
Adaptive learning environments enable and encourage self-paced learning, thus allowing students to make the best out of their peak productivity hours. But what happens if one learner’s productivity peaks in the middle of the night, when there’s no one around to help solve a dilemma or address points of confusion?
Since blended learning is a combination of instructor-led training and online courses, tutors should remain available 24/7, whenever students may need their guidance. This may not be possible with human tutors, but it is with digital assistants.
Thanks to machine learning and natural language processing, AI assistants can learn to provide necessary guidance to students, answer their questions, and solve their problems. And since machines never sleep, this digital mentorship is available 24/7.
Digital Mentorship Can Encourage Engagement
Usually, AI assistants work with students individually. Extending the concept of student-centered education, smart technology caters not only to different learning styles but also to different personalities and temperaments. This is significant to introverted students, who usually don’t feel comfortable expressing themselves in front of their peers in the traditional classroom environment.
Faceless AI is a saving grace for such learners as it allows them to voice their dilemmas without the fear of being judged, while the eLearning environment itself encourages them to participate in social learning. Of course, AI has the potential to motivate and engage all students, be they introverts or extroverts, visual learners or auditory learners.
Higher engagement is a natural consequence of personalization – by facilitating the learning path, AI makes knowledge acquisition easier, thus eliminating all frustration from the process. All students thrive in such a learning and development (L&D) environment when they are motivated by their own achievements.
AI Allows Continual Real-Time Improvement
Thanks to cutting-edge software technology and authoring tools, everyone can learn to create an online course that encourages engagement and equips students for success.
It’s maintaining this eLearning environment that’s hard, especially in today’s fast-paced world. Technology is a powerful knowledge acquisition tool – the more potent it becomes, the more we understand about the world we live in. The human race is learning on a daily basis.
The road from tacit to explicit knowledge was a long one in the pre-digital age. At times, students had to wait years before new findings were added to their old textbooks. Today, we have the Internet – the biggest and fastest knowledge base in history.
With AI, we can now comb through this knowledge base in matters of minutes, update our online courses with cutting-edge information in real time, and keep educational content relevant. Having said that, up-to-date content is not enough to ensure a rich learning experience. Educators and online course creators must learn from their students as there are always challenges that need to be addressed. Because of this, student feedback is precious.
Real-Life Examples of AI in Learning
Let’s now see a few companies who are working behind the scenes to bring the benefits of AI-powered technologies to people.
Massachusetts-based software technology company, Nuance builds speech recognition software. The technology can transcribe a maximum of 160 words per minute. It is useful especially to learners with mobility challenges, including writing. Instructors too can rely on it to dictate classroom lectures and expedite repetitive tasks such as emails.
As a provider of quality math education, Carnegie Learning brings together artificial intelligence and machine learning to its platform. It puts emphasis on personalized learning experiences to turn learners into critical thinkers, creative problem solvers, and lifelong learners. By combining cognitive and learning science with research and practical instructions, CL helps students gain a deeper understanding of what they are learning.
Century is a provider of an AI-based teaching and learning platform located in London. It provides adaptive learning solutions for learners according to their strengths and weaknesses, while helping instructors simplify their routine tasks such as grading. For this, it combines AI, neuroscience, and data analytics.
Artificial intelligence is already making waves in the eLearning industry by unlocking exciting new opportunities for educators and students alike. But deep personalization, 24/7 availability, increased engagement, and real-time improvement are only a fragment of what AI may mean for the future of education. Experts predict that the use of this smart technology will grow by 47.5% in the next two years. We can only imagine the possibilities.
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