A Comprehensive Beginners Guide to Tensorflow

An article by Ashish Mokalkar (Software Engineer, IOT department, Mobiliya)

Hey Deep Learners/Dear Readers,

Recently, Deep Learning has become that hot girl in the class whom everyone wants to impress and show off their superiority to other classmates. And why not? Deep Learning has been able to give seemingly impossible breakthroughs over the past 3 years. May it be tech giants like Google, Amazon and Microsoft or start-ups like Niki.ai and Snapshopr, everyone is trying to harness the power of deep learning to prove their dominance in the AI industry. Google Trends also proves this by showing a steady increase in the search term “deep learning” over the past few years, with an even more noticeable uptick since late 2014.

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As a data science enthusiast, experiencing such breakthroughs in my field inspired me to jump into this flourishing field. I came across Tensorflow and have gone through its official documentation. It was abstruse and diverting for a beginner like me. Moreover I couldn’t find potential tutorials on understanding Tensorflow’s core functionality. Everyone would claim to know Tensorflow by showing off mnist problems, image classifiers, music generation, object detection etc. Consequently I faced difficulties in applying deep learning concepts in my projects. Therefore, after working with Tensorflow in my professional career for the past 16 months, I aim to develop an easy to comprehend guide about Tensorflow which would be extensively used by data scientists wishing to add a badge of deep learning to their prospects.

So, what is Tensorflow??

During my early learning period, I asked this question to many folks and I have never ever received the same answer twice. At the core, Tensorflow is just a computational library by Google which first generates the graph of computations and actually runs them when instructed.

In other words, it’s a form of lazy computing. At heart, it is neither a deep learning framework nor a computer vision library. All these functionalities are built using its computational representations.

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Cool, but why Tensorflow?

Python uses numpy as computation library. So why did Googlers come up with Tensorflow which performs the same task? Let’s understand it using an example python code.

X = 5

Y = 10

Z = x + y

W = z – 5

Print W

The above python script basically says “create two variables x with value 5 and y with value 10, add both and set it as the value of a new variable z, subtract 5 from it and create new variable w and print it out”. When running this without Tensorflow, every operation is performed as the lines get executed. When executed using Tensorflow, the computation of z and w is never actually performed.  Instead, it is effectively an equation that means “when z is computed, take the value of z (as it is then) and subtract 5 from it”. It just generates the computational graph with variables as nodes.

The main advantages of using such mechanism are as follows:

1)    Reduced redundancy in some computations

2)    Faster computation of complex variables

How do I get Tensorflow on my PC?

For ubuntu:

Step 1:  Install or upgrade pip package

sudo apt-get install python-pip python-dev

Step 2: Install TensorFlow

$ pip install tensorflow     # Python 2.7; CPU support (no GPU support)

$ pip3 install tensorflow  # Python 3.n; CPU support (no GPU support)

$ pip install tensorflow-gpu  # Python 2.7;  GPU support

$ pip3 install tensorflow-gpu # Python 3.n; GPU support

Step 3: Upgrade tensorflow

$ sudo pip  install –upgrade TF_BINARY_URL   # Python 2.7
$ sudo pip3 install –upgrade TF_BINARY_URL   # Python 3.N

For Windows:

Step 1: TensorFlow only supports version 3.5.x of Python on Windows. If its not installed, install it now. It comes with the pip3 package

https://www.python.org/downloads/release/python-352/

Step 2: Install Tensorflow

$ pip install tensorflow     # Python 2.7; CPU support (no GPU support)

$ pip3 install tensorflow  # Python 3.n; CPU support (no GPU support)

$ pip install tensorflow-gpu  # Python 2.7;  GPU support

$ pip3 install tensorflow-gpu # Python 3.n; GPU support

Step 3 : Upgrade Tensorflow

$ sudo pip  install –upgrade TF_BINARY_URL   # Python 2.7
$ sudo pip3 install –upgrade TF_BINARY_URL   # Python 3.N

Know about Tensors

 As it is important to know swimming before diving into river, it is important to know about tensors before doing magic using Tensorflow programming. You can think of a tensor as an n-dimensional array or list. Only tensors may be passed between nodes in the computation graph. These tensors flow from one node to another in the computational graph. Hence the name

“TensorFlow”. For example,

[[1, 2, 3], [4, 5, 6], [7, 8, 9]] is a tensor of rank 2.

[[[2], [4], [6]], [[8], [10], [12]], [[14], [16], [18]] is a tensor of rank 3.

3 core data managing structures in Tensorflow

Every Tensorflow program will contain these data managing structures:

  • Constants: Constants are the data structure which are initialized when declared and their value doesn’t change. A constant’s value is stored in the graph.

Import tensorflow as tf

X = tf.constant(10, name = ‘X’)

  • Variables: Variables are the data structure whose value can change. They require initial value to be provided while declaration.

Import tensorflow as tf

y = tf.Variable(x + 5, name = ‘y

  • Placeholders: A placeholder is simply a variable that we will assign data to at a later date. It allows us to create our operations and build our computation graph, without needing the data. We don’t have to provide an initial value and we can specify it at run time.

Import tensorflow as tf

x = tf.placeholder(“float”, None)

‘None’ indicates our program to allow x to take on any length.

Computational Graphs:

A computational graph defines the computation. It doesn’t compute anything nor does it hold any values. It just defines the operations that you specified in your code. Below is a sample computation graph for addition of a constant and a placeholder.

b = tf.constant(1,name = “b”)

x = tf.placeholder(“float”, None)

         Y = tf.add(x,b)

Picture3.pngSessions:

The actual execution of the tensorflow operations is performed using sessions. It allows to execute graphs or part of graphs. It allocates resources (on one or more machines) for that and holds the actual values of intermediate results and variables. For example,

sess = tf.Session()                 #initiating a session

result = sess.run(product)      #running the tensorflow operations in session

sess.close()

The flow of Tensorflow programs

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The typical flow of every tensorflow program you will write

Sample tensorflow program

import tensorflow as tf

X = tf.constant(10, name = “X”)

Y = tf.Variable(5, name = “Y”)

a = tf.add(X,Y)

b = tf.multiply(Y, a)

f = tf.div(b, 15)

model = tf.initialize_all_variables()

with tf.Session() as sess:

    writer = tf.summary.FileWriter(“output1”, sess.graph)

    sess.run(model)

    print(sess.run(f))

    writer.close()

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Computational Graph Visualization

End Notes

Not a day goes by when I don’t hear of an Artificial Intelligence program gone wrong. The AI community is coming up with critical breakthroughs every day. There is a lot of confusion about what is actually possible today using deep learning. One can get easily lost in the hype around AI, leading to misconceptions. Understanding the core functionality is very important in order to do magic using Tensorflow.

After reading this article, you can now understand the flow of any Tensorflow program. Also, you can go on to implement your own neural network in Tensorflow. In fact, you can now actually unbox the secret behind the AI magic like MNIST problems, Image classifiers, music generation etc.

Resources :

https://www.tensorflow.org/

http://learningtensorflow.com/

– ASHISH MOKALKAR (Software Engineer, IOT department, Mobiliya)

A global software engineering company enabling digital transformation for world’s leading organizations by leveraging emerging technology areas like Deep Learning, Augmented Reality and Internet-of-Things along with core capabilities in software engineering and security. Headquartered in Texas, US, the company also has global R&D centres based out of Canada, India, China and S. Korea. For more information, visit: https://www.mobiliya.com/

 

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Building Security into the Development Process

Every year, about $9.1 billion in potential online sales revenue is lost—all because shoppers are afraid of buying online, according to FirstData. While this figure may be upsetting for owners of ecommerce portals or other online businesses, what’s more distressing is that so many consumers are fearful that their personal or financial information is at-risk for online theft, fraud and data breaches. And they have every right to feel that way.

Each year, more and more organizations, large and small, fall prey to cyberattacks and data fraud. The number of data breaches reported in the U.S. continue to rise: from 2012 to 2014, data breaches increased from 447 annual occurrences to 783, and the trend will continue. With so many companies suffering massive data breaches impacting millions of consumers, modern enterprises require a comprehensive approach to data protection and security.

Make Security Integral, Not an Afterthought

Whether it’s a simple app designed for a local start-up or a mega-project for a large corporation—a product, app or system usually goes through a specific development cycle (SDLC = Software Development Life Cycle), which includes design, coding, testing and deployment. However, a major miss in this process is that it typically considers security at the end of development. It is imperative that companies instead switch to S-SDLC (Secure Software Development Life Cycle), which closely incorporates security and involves security assessment at every phase of software development.

Instead of the usual cycle of testing-patching-retesting that runs into multiple iterations, the S-SDLC process addresses security issues very early in the development cycle—saving time and money. Organizations can follow the simple steps outlined below to ensure that their critical data is never susceptible to hackers and can be recovered during any disaster.

Onboarding Security Team from Day One: Instead of having the routine, one-time security check before going live, development teams must ensure that they have software security experts who can analyze the threat perception at every level and suggest necessary security patches that must be done early in the development cycle.

Think like a Hacker: “To combat a hacker, you need to think like a hacker,” which is why ethical hacking techniques and security assessment measures like ‘penetrative testing’ become so critical. Penetration testing, or pen testing, consists of an authorized simulated attack on a computer system that looks for security weaknesses, potentially gaining access to the system’s features and data. The process typically identifies the target systems and a particular goal – then reviews available information and undertakes various means to attain the goal. Pen testing involves conducting physical security assessments of servers, systems and network devices, probing for vulnerabilities in web and thin/fat client applications to pinpoint methods that attackers could use to exploit weaknesses and logic flaws. Depending upon the scope of the project, organizations can choose between Black Box, White Box or Grey Box Penetration testing. The result of conducting such testing can be discussed with IT teams as well as management to finalize the necessary measures required to plug the security flaws.

Dedicated Time Slot for Security Analysis: The typical product development cycle is a frantic rush of deadlines, and project managers may often become hard-pressed to spare time for security checks, opening up the opportunity for error. While meeting deadlines is a must, it is important to follow adequate security measures, especially as the project increases in scope and complexity.

Spending on Security is Worth It: Security teams are often charged by project managers or top management executives for adding to project costs in their bid to buy special security software or solutions. While it indeed adds to the spiralling cost, the true value-add of these solutions makes onboarding worthwhile. It is only then that the circle is complete and the idea of involving security experts in the project truly works. Restricting their role to mere security reviews is a half-hearted measure with inadequate results.

While software security assessment is still considered a time-consuming exercise, organizations must try to create seamless channels that will enable faster assessment and swift deployment of security measures.

Krish Kupathil is the founder and CEO of Mobiliya, which provides device-to-cloud software engineering and system integration services with specialization in Internet-of-Things, enterprise software, augmented reality, embedded systems, security and automotive. With a track record of over 25 years in building and growing companies and new markets, he has carried out strategic exits, pioneered enterprise mobility, cross-OS communication and collaboration services. 

Website url: http://www.mobiliya.com

AR Drives Field Service Improvements

Mobiliya is a product engineering services company that helps other enterprises create products in the area of mobile-to-cloud system integration, IoT (Internet of Things), Deep Learning and other key practices.

Mobiliya’s AR solutions have several use cases based on helping field workers improve their performance, efficiency, and safety.

Telco field workers are frequently maintaining and troubleshooting stations and towers in remote locations. To reduce errors in the field, minimize incomplete tasks, and reduce the need for re-work, companies will use Mobiliya’s solution that can be accessed via tablet, smartphone, or wearable smart glasses. Information is up to date and customized for each version of the system in the field. Workers’ capacity is improved with interactive, user-friendly information and reference material that provides step-by-step guidance on the actions that need to be taken. It’s much easier for the technician to access information online than with paper manuals, which are frequently dated when printed.

Workers can perform the operations required using the application, which can also record each step to automatically document compliance. The solution can also be customized to access IoT device data from the field for predictive and preventive maintenance and verification the system is performing as planned.

Similar use cases apply in the healthcare field, where medical equipment needs to be set up, configured and maintained. Mobiliya´s solution for medical device makers is able to tell a clinician/technician if a equipment is ready to be used by a surgeon or another medical professional.

This is critical on a couple of fronts:

  1. Earning the trust of the medical professional
  2. Maximizing the uptime and use of expensive, revenue-generating equipment.

Whereas, if the equipment is down for longer than necessary, or not ready when the medical professional needs it, the perception of the brand is damaged and may be ripe for replacement by a competitor.

One of the promising directions of AR in the enterprise is to integrate it with IoT systems. This can allow a field worker to access combined information pulled directly from the IoT devices, and information coming from the cloud. This would, for example, allow verification whether tests or maintenance have passed correctly, or to access alarm notifications, or visualize current conditions of a system.

While AR seems like a technology of the future, it is ready for prime time and adopted with good return-on-investment in many of the leading enterprises today. Usually, the current AR solutions are used on Smartphones and Tablets, due to the ubiquity of those the devices. However, with advancements in smart glasses, ultimately field workers will have a hands-free device with all the information they need to do their job, in their field of vision.

This can include:

  1. Labels with text
  2. Labels with numbers (e.g., valve settings)
  3. 3-D and interactive 3-D information displayed
  4. 3-D animation of the actual assembly task.

Another successful area for enterprise adoption of AR and VR is training. While training services often leverage VR with a 3-D model of the product to train on, there are a lot of legacy in industrial companies without a correct representation of their complex product. When they want training done in a real environment, we have the ability to create specific animation and then let them train on the real product thanks to augmented reality.

For safety in hazardous environments, solutions are able to remind the technician to take safety precautions (e.g., depressurizing the fuel injection system before working on it). Alarms can be set to remind people to take safety actions, and show the actions that need to be taken, with 3-D animation or 2D graphics, whatever is more suitable for the scenario.

According to Gerald, it’s best for companies to start adopting Augmented Reality via a small and efficient Proof-of-Concept project. Identify a specific business case, develop and implement a proof of concept, then a solution, and move up to more challenging/demanding business cases. He advises building proofs of concept quickly, with the right data and input from the customer, and clear and shared objectives.

Mention AR to a lot of people and they think entertainment, like Pokemon Go. However, some of the most successful projects for AR take place in a commercial/industrial environment. Research and development will continue to look for use cases which will generate more value, more interest in and traction across verticals, as well as the B2C market.

In time, we will see if and when Facebook’s Oculus Research’s prediction that “AR glasses will replace smartphones,” and Apple’s claim that “AR is the future” will come to fruition.

For more information visit our site on: https://www.mobiliya.com/

Deep Learning and the Future of Business

Featured article by Krish Kupathil, founder and CEO of Mobiliya

Artificial intelligence (AI) has been regarded as the stuff of science-fiction movies. From self-driving cars to image classification and face recognition, AI has transformed machines into self-thinkers, replete with human-like intelligence and reasoning. Deep learning, a subset of AI, has allowed for several practical applications of AI in our daily lives. Gaming has been one of the foremost areas where deep learning has made a huge impact: 2016 saw the defeat of South Korean Go master Lee Sedol by Google DeepMind’s AlphaGo program. Deep learning even promises to transform global businesses. It has already made a promising start in solving complex business needs and problems, which were otherwise beyond human capabilities. According to a Forrester research report, investment in AI will increase by 300 percent in 2017, a sign of things to come.

Deep learning will transform the modern workplace. Some of the first areas that will see major deep learning applications are:

Manufacturing:

Manufacturing is one of the most intensive and loss-prone verticals, with even a minor systemic error or manual lapse capable of triggering faulty products and causing massive losses. Fine-tuning manufacturing assembly lines with deep learning or deep neural networks will enable a system to produce a much greater number of finished products that pass quality control tests, making manufacturing increasingly profitable.

Healthcare:

Deep learning algorithms can spot early patterns among patients who are likely to develop life-threatening diseases like cancer in the next one to two years. It can even help in suggesting timelines for conducting and reviewing PET or PET-CT (Positron Emission Tomography–Computed Tomography) scans to check the probability of cancer. Deep learning algorithms can also identify critical parameters predicting hospitalization among other chronic diseases like diabetes.

Finance:

Most finance firms utilize proprietary systems to accurately predict stock market happenings and execute trades. However, these systems are primarily based on the concept of probability in determining the highest and lowest performing stocks. That said, sometimes high-volume trading at great speeds can turn even the least probable stocks profitable. Deep learning systems can better predict such variations while processing enormous quantities of data and trades at breakneck speeds. Likewise, deep learning-powered fraud models can also help credit companies accurately determine who to lend credit to and identify likely future defaulters.

Automotive

Advanced Driver Assistance Systems or ADAS is an area that leverages deep learning to provide robust driver assistance. Popular use cases are object detection, pedestrian detection, and traffic sign detection. These are the very core applications of deep learning which are required by autonomous or driverless cars as well. In addition, deep learning is also required for critical scenarios like detecting driver drowsiness and triggering alert, lane departure warning, blind spot detection and predictive braking. Thus, the next generation of vehicles have to be deep learning ready to deliver solid assistance to consumers.

Customer Service:

The future belongs to brands that deliver enhanced and highly personalized customer service. With deep learning, companies can personalize the emails, coupons and offers that every customer receives—all designed to serve customers better and build lasting customer relationships. Deep learning models can compare the previous buying trends of an individual with an enormous database of millions of other users, and from there provide relevant or allied product purchase suggestions. It can even recognize whether customers are looking to buy certain products as gifts rather than for themselves, which adds a new dimension to customer service and product recommendation.

New Business Models:

The idea of using drones for package delivery has been making rounds. Global ecommerce giant Amazon is already seriously considering this idea by applying machine learning and deep learning mechanisms, making supply chain systems faster, more accurate and efficient. Such drone-based delivery mechanisms allow retail, supply chain and logistics companies to reach areas that were otherwise beyond reach or too expensive.

For business owners and top executives of global businesses, the writing on the wall is clear. Embracing AI through practical deep learning models is the way ahead. While it need not necessarily be in the form of investing millions for sophisticated deep learning applications, even a modest start can go a long way toward eventually changing business for the better.

For more information you can visit our website on: https://www.mobiliya.com/

Augmented Reality Set to Revolutionize Global Medical Education Methodologies

If you are a medical student, chances are your days are filled with reams of printed data explaining the fundamentals of surgery procedures or human anatomy and reliance on imagination or visualization skills to get the perfect understanding. However, what if instead of learning from a simple graphic of the human brain, students could actually see a 3-D form of the human brain that literally shows how the brain processes information and even walk around it to see bits of data flowing through the brain? This incredible transformation in the way students are educated is driven by Augmented Reality (AR), which is rapidly becoming the preferred method of research, training and education. While AR has been subject to a great amount of initial inhibitions in terms of its usability, app deployment and hardware, 2016 has seen it come to the forefront and become a mainstream technology. The global success of Pokémon Go underscores this fact. The level of interactivity that AR provides medical students and professionals in understanding the workings of the human body by superimposing digital information onto human skeletons, textbook graphics and diagrams is unrivalled. Recognizing the possibilities of this trend to revolutionize the industry has medical institutes across the world swiftly moving towards upgrading their training courses, teaching pedagogies and study materials to AR-powered resources.

Augmented Reality in Today’s Operating Rooms Traditional vs. AR Learning: An Overview

Medical students, prior to internships, learn from live examples only during dissection sessions or occasional patient visits, which often limits real-life experience and learning. AR helps close the gap between theoretical information and the real world by creating the biggest impact in learning the basics of human anatomy. A simple textbook with static images is no match to 3-D models in action that allow students to see the way the heart, lungs or diaphragm moves and sounds. For students, AR provides an X-ray vision that helps them pierce through the skin and see the internal workings of the body. This makes learning more effective and efficient.

AR-powered training methods also deliver precision. For example, students can learn from an accurate reconstruction of surgery procedures and 3-D models offering the virtual experience of being in an actual operation room where a live surgery is in progress. This makes them aware of the intricacies of achieving the highest level of medical precision.

Additional benefits AR-based training offers include better understanding of spatial relationships and concepts, easy understanding of complex theories, higher retention of information and a shortened learning curve.

The Future of Medical Imaging and Minimally Invasive Treatment Procedures

AR-based healthcare training has received a further boost due to the continuous enhancements in medical learning apps and the availability of better HMDs (head-mounted displays) such as Google Glass and Microsoft HoloLens. This is gradually changing the way in which minimally invasive surgeries such as laparoscopic procedures are conducted. In a typical endoscopy, a camera is placed under the patient’s skin to display images and critical parameters on many different monitors making it difficult for the surgeon to focus during the surgery. With AR-powered HMDs, these images can be projected directly on the patient’s body, reducing the risk and making it easier for doctors as well as students to diagnose the exact ailments. Laparoscopic surgery and procedures are particularly difficult for students to learn and gain accuracy. AR apps for mobile devices and smart glasses ensure that students can learn these complex processes in an efficient and interactive way.

Not just students, but even practicing doctors and medical staff are increasingly using AR during treatment procedures. Often patients are unable to describe their symptoms accurately, thereby making it harder for the doctors to provide treatment. With AR apps, doctors can simulate the impact of specific conditions, thereby helping patients in better understanding their symptoms and their actual medical state. While administering IV injections and fluids, nurses often struggle to find the right vein with almost 40% missing the vein in the first attempt. New AR-powered scanners can project the vein on top of the skin, making it easy for the nurses to administer IV medicines.

With more and more sophisticated AR healthcare apps being developed almost every day, medical students and professionals can look forward to accessing improved training techniques providing enhanced healthcare facilities and eventually saving more lives.

For more information you can visit our website on: https://www.mobiliya.com/

 

Fix It Faster, Save Lives: Augmented Reality for Medical Equipment

The last 15 years have seen a phenomenal change in the way we work, communicate, shop, explore and generally go about our everyday lives. And the technological transformation is far from over. The same technology that gave us the Googles, Facebooks, innumerable mobile apps and cloud platforms of the world is now set to save human lives. While the past few years have seen tremendous innovations in the field of healthcare, 2016 has been one of the biggest years for medical IT. Prominent among those include apps driving interoperability between healthcare systems, remote patient monitoring, robotic nurse assistants, electronic underwear to prevent bed sores and anti-aging drugs. However, the technology that has truly been in the spotlight for transforming the healthcare and medical industry is Augmented Reality, or AR. AR offers a seamless connection between the virtual and the real world, making it the ideal tool to be used for a range of healthcare applications, equipment and treatment procedures.

AR for Complex Medical Machine Maintenance

Augmented Reality Set to Revolutionize Global Medical Education Methodologies 

Most medical equipment today is highly sophisticated, feature-rich and has a combination of advanced electronics and software, making it increasingly complex. Thus, any operational or functional failure of such medical equipment can be difficult and expensive to detect, troubleshoot or repair. While in a normal industrial scenario such downtimes mean financial or productivity losses, in the healthcare sector it can even mean loss of precious human lives. It is in such situations that technologies like AR can enable easy and accurate tracking of equipment failures enabling quick repair, and curbing costs and increasing efficiency. In a non-AR scenario, repairing or troubleshooting such machines typically involves calling upon service technicians who rely upon large printed manuals, installation guides or maintenance manuals to repair the fault. Often if the problem is too complex, these field service technicians may need to consult experts or engineers who may be distantly located. This going back and forth may further lengthen the repairing or troubleshooting process, aggravating the inconvenience for the clinic or hospital team.

Conversely, with AR, this process can pan out differently and flawlessly. During servicing of the machine, service technicians will just need a mobile phone or a smartglass that can convert plain text or image into an interactive audio-visual that gives a real feel of the machine with intricate details clearly labeled and explained. The technician needs to then punch in the nature of error to see the repair process played “live” in front of him, making it the ideal tutorial. Apart from this, field technicians can remotely connect with experts back at the office to collaborate on the status of the machine. Both can collaborate through on-site video feeds, annotate on top of the shared videos and communicate through an audio call, all at once, ensuring that the field technician does not have to schedule another visit and can complete all troubleshooting and maintenance work in one go. It is estimated that AR can reduce installation and maintenance time by up to 30%, with 20% fewer errors in setup and repair and up to a 70% reduction in travel for field technicians—all of which eventually contributes to reducing cost for the medical facility.

AR: A Win-Win for Medical and Tech Organizations

For a critical segment like healthcare, AR’s multi-pronged benefits make it a natural fit for medical centers and healthcare technology professionals. For hospitals, it minimizes time, cost and resources needed to maintain complex systems and mechanisms, while for the service technicians and professionals it closes the service-knowledge gap effectively. Moreover, technology companies can maintain a much smaller team of service technicians that can handle a range of equipment instead of having dedicated resources for specific equipment and machines.

While a few years back, AR and VR were a target of great scepticism due to a dearth of useful healthcare apps, heavy and unfashionable smartglasses, HMDs and poor quality graphics; 2016 has seen things come a full circle with AR and VR coming back strongly to change the face of all industries and enterprises. Today, the healthcare industry is leading the pack as one of the earliest adopters of AR technology.

For more information you can visit our website on: https://www.mobiliya.com/

Top 5 Capabilities that IoT Providers Can’t Do Without

In the last five years, the Internet of Things (IoT) has been a topic of great discussion, speculation, prediction and a lot of hype. The IoT market consistently grew in 2015 and 2016 and is expected to continue its growth through 2020. While the research firm Gartner has a conservative estimate projecting a growth of 20.8 billion connected by 2020, Cisco’s estimate posts a figure at a whopping 50 billion with the scope of connections including tires, roads, supermarket shelves and even cattle. While we can only wait and hope for cows to send data about how much milk they produce each day, for now we can safely say that even by conservative estimates, the IoT market projects growth of 30 percent each year. For IoT companies, this means ramping up their offerings and building a mature model or framework that brings in the maximum value to its customers with minimum disruption and risk.

IoTKey Capabilities Required for IoT Success

1. Defined Approach to Solution Design:
This is critical for companies offering IoT consulting, specifically for industrial and enterprise customers. To provide a robust, scalable and targeted IoT solution design it is important to have a clear road map based on the customer requirements, the existing hardware or infrastructure, protocols, standards and cloud framework required. This typically includes a detailed analysis of areas like the number of things to be connected, how they are powered, whether they need a gateway or the Internet, what is the range of connectivity required, security essentials, application design, data storage type needed and collaboration requirements of people and processes, among several others. The ability to create a precise and detailed design documentation that forms the basis of the subsequent IoT project plan and the eventual solution development is a key feature that most IoT manufacturers must possess.

2. Integration Across Technology and Business:
In an IoT connected office environment there may be thousands of sensors and endpoints for a range of functions such as controlling lighting, measuring temperature, building systems operations, work systems, factory machines, security systems, etc. Each of these devices may use different protocols like Wi-Fi, Bluetooth, ZigBee, Z-Wave, etc. With so much variety in hardware and software, it is essential that an IoT solution connects all of them so that the data coming from them is not siloed. System integrators fill this gap by connecting sensors, devices, platforms, external data, back-end systems and analytics. Apart from connectivity, system integrators like IoT gateways perform critical functions such as protocol translation, data filtering and processing, security alerts and notifications, device management, etc. Such integration of data can further help in automating a range of enterprise and industrial functions and business processes, like predicting machine failures, managing assets, ordering parts and more.

3. Analytics:
Another key component necessary for IoT vendors and service providers is providing analytics applications that aggregate data from different connected devices and convert it into actionable insights driven by flexible dashboards. These IoT analytics applications score over periodic reporting essentially because of the real-time updates provided for every connected device. IoT analytics are key for core industrial functions like predictive maintenance, real time status of goods and materials at the warehouse and process issues, if any.

Secured data transfer4. Security:
This is arguably the biggest threat that IoT growth faces. Hence, all IoT providers must have a strong capability in enabling security across the spectrum of devices, data, network and cloud. This essentially includes authentication of connected devices and encrypting data transmitted throughout IoT systems and networks. Security systems must provide mutual authentication powers that allow only trusted and authorized systems to connect to devices, blocking any possibility of malicious attacks or hacks.

5. Service:
Last and certainly not least, delivering superior customer service through connected and remote service solutions. IoT service providers must be capable of delivering swift service responsiveness capabilities to customers spanning key features of remote monitoring, remote asset management, service agents login and ability to manage and troubleshoot workforce and resource problems remotely and without deploying on-site personnel.

For IoT solution providers, it is critical to effectively bridge the gap between the physical and digital worlds and create new opportunities for clients across industries. Only when service providers deliver strategic differentiation to their customers will they be able to emerge successful in the IoT business.

For more information you can visit our website on: https://www.mobiliya.com/