The industrial internet of things IIoT

techmanga June 27, 2021 0 Comments

Big Data

DescriptionBig Data Engineers develop, maintain, organize and evaluate big data. Theseare so complex and large that traditional methods of handling data cannot beapplicable. So the challenges involve huge data storing, analysis, searchingand transferring.Hence, Big Data is among the top tech careers and is currently being preferredby a number of individuals. Most importantly those who are already pursuingjobs in technology are upgrading their qualifications by going forcertification or training in this career field.Application of Big DataBig Data is enormously used to understand customer performance and preference.For example, has become the largest online retail store in theeCommerce market. It started off as an online bookstore and later expandedinto sales and production of consumer goods, electronics, and household items.Amazon uses product recommenders to personalize the customer experience inwhich the products are customized to suit their taste and interest. And sinceAmazon uses larger data and customer base, Big Data comes into play. Amazonmakes use of clustering algorithms and collaborative filtering to groupcustomers based on preferences. Their product recommender system group’scustomers into groups based on similar search and item to item collaborativefiltering.So basically when we open amazon’s app and are confused as to how an app wasable to predict a product of our interest, that’s where Big Data was able toassist the servers in proving information of the data based on our previoussearches and activities in the app.Education QualificationMinimum educational Qualification for Big Data engineer is to have sufficientexperience in software engineering along with object-oriented designs, coding,and testing. Certifications in various languages may be added as additionalqualifications.Some certification courses for up-gradation in tech careers are as follows:Name of certification| Mode| Website —|—|— IBM Certified Data Engineer – Big Data| Online| Link- here Microsoft Certified Solutions Expert (MCSE): Data Management and Analytics|Online| Link- here Big Data Hadoop Certification| Offline| Link- here Bits PilaniPost Graduate Programme in Big Data Engineering| Offline| Link-here

Data Science

DescriptionData Science is an integrated field that uses scientific methods, algorithms,and systems to extract knowledge and insights from data in various organizedand unorganized forms. So basically a Data Scientist is responsible forevaluating and analyzing large amounts of data to identify ways of improvisingbusiness systems and operations. Data Science is one of the emerging techcareers and gaining significant preference by those who are looking for thelatest careers in technology.Application of Data ScienceData Science is a subcategory in Big Data engineering and often the jobincludes cleaning and validating the data to ensure accuracy, completeness,and uniformity, Devising and applying models and algorithms to mine the storesof big data, and also analyzing the data to identify patterns and trends,Interpreting the data to discover solutions and opportunities.Education QualificationMinimum Educational qualification for Data Science is having a bachelor’sdegree in computer science or a software engineering background.Some certification courses for up-gradation in tech careers are as follows:Name of certification| Mode| Website —|—|— Microsoft Professional Program for Data Science| Online| Link- here Data Science Specialization| Online| Link- here Data Science Essentials| Online| Link- here

Internet Of Things (IoT)

DescriptionInternet of things is the interconnection of devices, home appliances,security cameras, etc. where all devices become smart and interconnected toeverything around us. Internet of things is expected to create opportunitiesfor more direct integration of the physical world into computer-based systemswhich can result in improvements in efficiency, economic benefits, and reducehuman efforts. If you are looking for the latest in tech careers, then youshould consider IoT.Application of Internet of thingsWe are all set to enter an age where more advanced connectivity optionsamongst networks, systems, devices are going to change the way we live whichwill go beyond the human to machine communications. The Internet of things hasalready begun changing the world by providing an excess of options to operateand live a life full of ease.One such great development is Home Automation. A home automation system willcontrol lighting, temperature, entertainment systems, and appliances alsoinclude home security such as access control and alarm system. When connectedwith the Internet, home devices are an important constituent of the Internetof Things.One of the many benefits that IoT provides is the ability to control roomtemperature just before reaching home. The systems work in such a way that thedevice can be controlled by connecting your smartphone with the devices. Thisgives free access to monitor and control the devices. These have been seen asenergy conservers as they automatically turn off when no one is in the room.Education QualificationThe basic aspects of becoming an Internet of Things engineer is a computerscience degree followed by in-depth knowledge about hardware’s and software’sas the job includes part hardware and part software qualities. Understandingof microcontrollers (Arduino, Raspberry Pi, etc.) along with Programminglanguages like C++, C, Python, Node JS, and others are a must.Some certification courses for up-gradation in tech careers are as follows:Name of course/ certification| Mode| Website —|—|— Bits Pilani Post Graduate Programme in the Internet Of Things| Offline| Link-here Internet of Things – Advance level| Online| Link-here GSTF Training & Certification Program| Online| Link-here

· Big data

Big data refers to extremely large amounts of data (such as that collected bybusinesses and institutions). It has become one of the most popular techbuzzwords as companies obtain more and more customer data, which they need tostore, utilise and keep safe. While this is one of the more meaningful terms,be wary of companies selling ‘data’ as automatically synonymous with‘intelligence’.

· Tech industry

The term ‘tech industry’ has become just another of the tech buzzwords thatholds no real meaning. It might refer to software and computing businesses,but it could also be financial tech, medical tech, or even taxi companies. Thepervasiveness of technology means that every business is now part of thepopular ‘tech industry’. And unfortunately, this generic labelling can createa smokescreen for unscrupulous companies to hide poor practices behind.

· Internet of Things (IoT)

This brings us to the next buzzword: the Internet of Things. This is the namegiven to the ability for an increasing number of devices to connect andcommunicate with each other via the internet. Given its rather vague name,you’d be forgiven for not immediately extracting meaning from the IoT term.

Tech buzzwords

Some of these tech buzzwords have lost all meaning, others serve to highlightthe most popular topics of today’s tech climate.Whether you love them, hate them, or love to hate them, tech buzzwords willcontinue to thrive. When it comes to the ever-progressing tech industry,buzzwords that finally fade into obscurity are bound to be replaced by newtech terms claiming their own time in the spotlight.* * *

8. AI Integrated Surveillance Systems (CCTV Security Cameras):

Electronic surveillance is the most reliable security system. Most criminaland theft cases are solved based on CCTV footage. In offices, schools,streets, and malls our every move is recorded and tracked. But, yet there aresome limitations with traditional CCTV.Conventional surveillance systems are not smart. They need human interventionand a huge time consumptive while investigation.VIDEO

The industrial internet of things (IIoT)

The IIoT is one of the most promising and talked-about aspects of Industry4.0. In fact, it’s essentially the backbone of the entire endeavor.Broadly, it describes a way to connect digital and physical systems to producean intelligent, transparent and efficient type of infrastructure that’s farmore than the sum of its parts. You’ve probably heard the term “smartfactory.” If you understand the concept, you know it’s the future ofmanufacturing. However, we won’t get there without significant investments indigital-physical systems and IIoT devices.IIoT tools are what allow factories to monitor their own environmentalconditions—humidity, temperature, lighting control, etc.—and make changesbased on occupancy or operational needs, either automatically or with remotehuman interaction. That’s hardly the end of it, though.Factories that invest in IIoT technologies also enjoy the benefit of greaterinsight into their operational data. Connected material handling equipment canact as a governor for product throughput when it detects a slowdown or abottleneck—on a conveyor belt moving products, for example.IIoT devices also make maintenance efforts smarter and more proactive. Factoryequipment is increasingly able to gauge its own performance and signal whenmaintenance is required, usually long before a total equipment failure thatcould leave your operation crippled.Industry 4.0 is much bigger than these few examples. In fact, it stretchesinto each of the following technology areas, too, and provides a sort ofcentral nervous system for other investments to work together.

Big data and analytics

Stated simply, big data is the process of analyzing information gathered froma variety of sources. Industrial control systems and connected machinery aretwo potential sources. Others include customer relationship managementsoftware, enterprise planning platforms, and even data gleaned from webtraffic, search engine results, social media, customer service interactionsand more.The ultimate goal of big data and analytics is to move toward making moredecisions in or near real-time. Not surprisingly, 70 percent of the mostsuccessful distribution companies have some kind of analytical capabilitiesbaked right into their enterprise planning systems.

AI and autonomous robots

As we’ve seen already, most of these technologies are nested within oneanother like Russian matryoshka dolls. IIoT devices feed real-time informationinto the cloud. The cloud distributes this data to analytical platforms andwherever else it’s needed. Big data provides the means for multiplefacilities, partners and even industries to engage in closer collaboration andinformation-sharing.Artificial intelligence is helping smart manufacturers and factories bringtogether each of the aforementioned technologies and move us closer to a worldwithout the burden of human error and unnecessary labor. As we speak,artificial intelligence is becoming indispensable for predicting customerbehaviors, anticipating machine failures, automating inventory processes andraw material reorders, and much more.The future holds even more potential. Generative design is emerging as a wayto create ever-more-efficient product designs within certain fixed parameters.It works like this: A human engineer uses generative design software tospecify qualities like material usage, desired tolerances of the final designand even cost requirements. Then, the AI within the program generates one ormore physical designs that meet the desired criteria.As AI comes of age before our eyes, we’re witnessing a proliferation ofautonomous technologies, including robotics. Collaborative robots, also knownas cobots, are an appealing bridge technology toward fully robotic factories.Cobots work alongside human workers and ease the burden of physicallydemanding processes.In assembly plants, collaborative robots can lift and hold heavy objects, suchas engine parts or automobile panels, while human workers perform work thatrequires finesses and dexterity, such as welding it in place.In other factory environments, we can expect cobots to perform a larger shareof inspection duties and other tasks that require considerable attention todetail and where errors can be costly.

Horizontal and vertical systems integration

Ultimately, each of these areas of technological development serves the samegoal: cross-company cohesion between departments and employee functions, andacross multiple companies and supply-chain partners. Reaching this level ofhorizontal and vertical systems integration requires robust cyber-physicalinfrastructure. A true smart factory, for example, would likely need each ofthe following types of system integrations: * Incoming freight items, such as unfinished goods, are loaded from trucks onto automated rollers and pass under RFID scanners. These scanners automatically verify the count and send that information to an intelligent facility system. The system diverts the freight to wherever it’s needed — whether to temporary storage or directly to the assembly floor. * Multiple businesses working in unison within a shared supply chain can engage in systems integration to automate parts reordering and to sync their pickup and delivery schedules. * Multiple industrial systems come together in an emerging trend known as “digital twins.” Manufacturing companies live and die according to the leanness with which they conduct their operations, and that means having no more inventory on hand at a time than is necessary. Enterprise-planning software draws conclusions about future inventory levels based on past and current partner and customer data. Automated manufacturing systems call up digital schematics — digital twins — and send them to factory equipment. The computerized assembly and handling equipment then works until demand is met.Vertical systems integration within factories, and horizontal systemsintegration across value-chain partners, are the inevitable future ofmanufacturing. This network of interconnected technology systems meanscleaner, leaner and more efficient production. It also delivers significantsavings in the form of lower error rates, as well as fewer transmissionmistakes between departments and partners.Achieving this future involves compatibility between industrial controlsystems and digital management platforms. This, in turn, requirescollaboration between partners or the use of APIs. However, these are easyobstacles to overcome once the benefits are made plain to each party.

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