Sunday, 30 December 2018

Machine Learning



  1.       What is Machine Learning – algorithms point of view
  2.        Application Areas of Machine Learning
  3.        Popular Machine Learning Algorithms
  4.       Types of Machine Learning Algorithms
  5.       Supervised Learning
  6.       Unsupervised Learning
  7.       Reinforcement Learning
  8.       Classification Supervised learning
  9.       Regression Supervised learning
  10.      Clustering
  11.      Recommendation Systems / Association
  12.      Machine Learning Process

What is Machine Learning


Simply to say:

Machine Learning is teaching computers to learn to perform task from past experiences ie., data. Machine Learning can find relationships and patterns within volumes of data that the human mind is incapable of processing (Eg: IRIS Dataset – 3 clusters) 

Machine learning is everywhere

– influencing nearly everything we do. You’ve likely heard that Uber is world’s largest taxi company, yet owns no vehicles. Facebook, the world’s most popular media owner, creates no content. Alibaba, the most valuable retailer, has no inventory. And Airbnb, the world’s largest accommodation provider, owns no real estate. But what you haven’t explicitly heard is that all of these companies are machine learning companies at their very core. Companies like Netflix use machine learning to recommend movies for us to watch. Navigation apps like Waze use machine learning to help optimize our driving experience. Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data. 


Machine learning is the semi-automated extraction of knowledge from data. Breaking the above definition into three components:

Knowledge from data:

  Machine learning always starts with data and our goal is to extract knowledge /insight from that data. We have a question and we hypothesize (to give a possible but not yet proved explanation for something) that our question might be answerable by the data.

Automated extraction:

Machine learning needs some automation. We apply some process/algorithm to the data using a computer so that the computer can provide us the insight.


Machine learning is not fully automated process. Machine learning requires us to make many smart decisions in order for the process to be successful. 

What is Machine Learning – algorithms point of view

Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data. Eg: For example, one kind of algorithm is a classification algorithm. It can put data into different groups. The same classification algorithm used to recognize handwritten numbers could also be used to classify emails into spam and not-spam without changing a line of code. It’s the same algorithm but it’s fed different training data so it comes up with different classification logic.  

Application Areas of Machine Learning

·         YouTube Video Recommendations
·         E-commerce recommendation engines
·         Image/Face/Smile Recognition   Bike Number recognition and alarm at Signal Junction for the Traffic Police.o   Face Recognition at Railway stations for a criminal with a known photo.o   Face Recognition at a mass gathering for a known criminal with a known photo.o   Open Google’s Mobile App, open camera, focus on a Shop/Business’s logo/name and get its reviews or more details from google servers. (more data it already has, more the accuracy would be).
·         Voice Recognition
·         Email Spam Detection
·         Teaching a Computer how to play Chess
·         Self-Driving Cars
·         Detecting Credit Card fraud
·         Detecting which insurance customer is likely to file a claim
·         Sentiment Analysis / Opinion Mining
·         Predict the price of a house
·         Character Recognition (Recognizing Signatures)
·         Computer Games
·         Customer Segmentation.

Popular Machine Learning Algorithms

  •     Linear Regression
  •     Logistic Regression
  •     Decision Trees
  •     Naïve Bayes Classification
  •     Support Vector Machines
  •     KNN (K-Nearest Neighbours)
  •     K-Means
  •     Random Forest
Categories of machine learning Algorithms

There are 3 main types of Machine Learning algorithms
  • Supervised Learning
  • Un-Supervised Learning
  • Reinforcement Learning

Supervised Learning (based on historical data) (related to prediction): 
Supervised learning is also known as predictive modeling. It is the process of making future predictions using data.

Ex1: online shopping trends
Ex2: stock shares values
Ex3: email message is spam or ham (not spam)
Ex4: customer churning
Ex5: optimal price determination

Here the baby is already taught (data is labeled) about apple and banana. Later when a different colored banana is shown it can label it (mapping)
Here predictions are made on new data for which the label is unknown.
Let’s say you are a real estate agent. Your business is growing, so you hire a bunch of new trainee agents to help you out. But there’s a problem — you can glance at a house and have a pretty good idea of what a house is worth, but your trainees don’t have your experience so they don’t know how to price their houses.
To help your trainees (and maybe free yourself up for a vacation), you decide to write a little app that can estimate the value of a house in your area based on it’s size, neighborhood, etc, and what similar houses have sold for.
So you write down every time someone sells a house in your city for 3 months. For each house, you write down a bunch of details — number of bedrooms, size in square feet, neighborhood, etc. But most importantly, you write down the final sale price:

This is our training data (for which labels/price is already known)
Using that training data, we want to create a program that can estimate how much any other unsold house in your area is worth:
We want to use the training data to predict the prices of other houses.
This is called supervised learning. You knew how much each house sold for, so in other words, you knew the answer to the problem and could work backward from there to figure out the logic.
To build your app, you feed your training data about each house into your machine learning algorithm. The algorithm is trying to figure out what kind of math needs to be done to make the numbers work out.
Unsupervised Learning (Categorized learning)
Extracting structure from data or learning how to best represent data.
The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data.

Here the baby is not yet taught about dogs and cats (data is not labeled). But still, the baby recognizes that they can be categorized into two groups – dogs group and cats group without labeling them like dogs and cats based on their looks and heights.

Unsupervised Learning
  •      Unsupervised learning is used against data that has no historical labels.
  •      The System is not told the “Right Answer”. The algorithm must figure out what is being shown.
  •      The goal is to explore the data and find some structure within.

Ex2 of Supervised Learning:

Note: 1 indicates customer has responded to the email and purchased a product/service
Let’s go back to our original example with the real estate agent. What if you didn’t know the sale price for each house? Even if all you know is the size, location, etc of each house, it turns out you can still do some really cool stuff. This is called unsupervised learning.
Even if you aren’t trying to predict an unknown number (like price), you can still do interesting things with machine learning.
So what could do with this data? For starters, you could have an algorithm that automatically identified different market segments in your data. Maybe you’d find out that home buyers in the neighborhood near the local college really like small houses with lots of bedrooms, but home buyers in the suburbs prefer 3-bedroom houses with lots of square footage. Knowing about these different kinds of customers could help direct your marketing efforts.
Another cool thing you could do is automatically identify any outlier houses that were way different than everything else. Maybe those outlier houses are giant mansions and you can focus your best salespeople on those areas because they have bigger commissions.
Classification Supervised learning (for discrete values)
Taking some kind of input (pictures) and mapping it to the discrete number of labels like:
  •     True or False
  •     Male or Female(whether an image is of Male or Female)
  •     Yes or No (whether a candidate would get a university seat or not, whether a customer would buy this product or not)

Classification, also known as categorization, is a machine learning technique that uses known data to determine how the new data should be classified into a set of existing categories. A classification is a form of supervised learning.
  • Mail service providers such as Yahoo! and Gmail use this technique to decide whether a new mail should be classified as spam. The categorization algorithm trains itself by analyzing user habits of marking certain emails as spams. Based on that, the classifier decides whether a future mail should be deposited in your inbox or in the spams folder.
  • iTunes application uses classification to prepare playlists.
Classification Supervised Learning Algorithms examples:
  •     Decision Trees
  •     Naive Bayes Classifier Algorithm (Detecting Spam e-mails)
  •     Logistic Regression (Student Admitted or Rejected)

Regression Supervised learning (for continuous values)

It is used to predict continuous values.

Ex: Finding Price of a House:
Sachin has a house with W rooms, X bathrooms, Y square-footage and Z lot-size. Based on other houses in the area that have recently sold, how much (rupees) can he sell his house for?

It can be some 'numerical value' (which can be continuous): this relates to regression.

So we would use regression for this kind of problem.

Other Applications of Regression (check):
  •     Loan repayment – based on credit scores (How much should be the credit score)
  •     Grades – getting a job (How much should be the grade value)
  •     Grades – getting a seat in a top university (How much should be the grade value)

Regression Supervised Learning Algorithms examples:
  •     Regression (detecting weight by knowing height)
  •     Multiple Linear Regression Analysis (detecting mileage by knowing hp, wt etc)

Classification and Regression difference with another example:

Suppose from your past data (trained data) you come to know that your best friend likes the above movies.

Now one new movie (test data) has released. Hopefully, you want to know your best friend like it or not. If you strongly confirmed about the chances of your friend like the move, you can take your friend to the movie this weekend.

Now one new movie (test data) has released. Now you are going to find how many times this newly released movie will your friend watch? It could be 5 times, 6 times, 10 times etc…

If you clearly observe the problem is about finding the count, sometimes we can say this as predicting the value.

  •     If forecasting target class ( Classification )
  •     If forecasting a value ( Regression )

Clustering – Unsupervised learning

Clustering is used to form groups or clusters of similar data based on common characteristics. Clustering is a form of unsupervised learning.
  • Search engines such as Google and Yahoo! use clustering techniques to group data with similar characteristics.
  • Newsgroups use clustering techniques to group various articles based on related topics – technology, politics, sports etc.

Recommendation Systems /Association – Unsupervised learning
The recommendation is a popular technique that provides close recommendations based on user information such as previous purchases, clicks, and ratings.
  • Amazon uses this technique to display a list of recommended items (“customers who bought this item also bought”) that you might be interested in, drawing information from your past actions. There are recommender engines that work behind Amazon to capture user behavior and recommend selected items based on your earlier actions.
  • Facebook uses the recommended technique to identify and recommend the “people you may know list”.
  • Google Search Engine uses the recommended technique to recommend the “People also search for”

Ex1: Amazon E-commerce
Ex2: Google Search

Explaining Supervised Vs unsupervised Machine Learning In other words:
Supervised Learning
  •     is like learning with a teacher
  •     training dataset is like a teacher
  •     the training data set is used to train the machine

UnSupervised Learning
  •     is like learning without a teacher
  •     the machine learns through observation & find structures in data

Reinforcement Learning
It is often used for Robotics, Gaming, and Navigation
With Reinforcement Learning, the algorithm discovers through trial and error which actions yield the greatest rewards.
It has three primary components:
  • The Agent: The learner or decision maker (Eg: The Driverless Car)
  • The Environment: Everything the Agent interacts with (Eg: The Roads, Trafic Signal lights, Humans, traffic lines on roads, Parking area etc)
  • The Actions: What the Agent can do (Eg: Drive the car, stop, park, horn, start the engine, stop the engine, open doors etc)

Note: The Goal in Reinforcement learning is to learn the best policy.

Machine learning process

---Knowledge acquired from various resources online and our take on this subject

Monday, 17 December 2018

Internet Of Things (IoT)


  1.       What is IoT
  2.       Why should we use IoT
  3.       The Age of Smart
  4.       Birth of IoT
  5.       The “thing” in IoT
  6.       Applications of IoT in Different Domains
  7.       Applications of IoT for Smart Cities
  8.       Prerequisites to become a IoT Professional
  9.       IoT Key Features
  10.       IoT Disadvantages
  11.       IoT Sensors
  12.       Different types of Sensors
  13.       Wearable Electronics
  14.       Standard Devices
  15.       IoT Software
  16.       Technologies and Protocols
  17.       What is Internet of Everything (IoE)
  18.       IOT Components
  19.       IOT Platforms, Apps and Services
  20.       IOT and the Cloud
  21.       IOT Architecture
  22.       IOT Predictions 2020
  23.       IOT Companies
  24.       Most In-Demand IoT Skills for IoT Solution Developers
  25.       IOT Job Market / Future of IoT

What is IoT

The Internet is now 20 years old and over 2 billion people are connected to it using computers, smartphones and tablets.

The Internet currently connects people to people (P2P) and is now being called Internet Phase 1. The next Phase of the Internet is just beginning and will connect people to everyday devices (M2P), and everyday devices to each other (M2M).
                        Internet of People                                                                                                                                                                                                                  Internet of Things

Def by Wiki:

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

Def:IoT (Internet of Things) is an advanced automation and analytics system which exploits networking, sensing, big data, and artificial intelligence technology to deliver complete systems for a product or service.

Why should we use IoT

IoT is creating a giant network where all the devices are connected to each other and providing them with the capability to interact with each other. This is driving the automation to a next level where devices will communicate with each other and make decisions on their own without any human interventions.

These systems allow greater transparency, control, and performance when applied to any industry or system.


Assume that it is a Mid night, and a person is sleeping with a band on his hand. Suddenly his heart beat becomes abnormal, the band detects this, vibrates aggressively and starts alarming the person by sending msg to the Phone about his medical condition and suggest him to take Aspirin Heart medicine.

The band automatically sends this information to a nearby Heart Hospital.

The Hospital sends an OTP to the band, the band sends it the Door sensor, An Ambulance arrives and the door gets opened automatically (As the ambulance driver shows his RFID tag and enters the OTP generated by the Hospital and the door identifies that he has come from his medical provider Hospital and opens up), the Ambulance personal picks him up and takes him to the Hospital for Medication.

The person is saved…😊 all because the things are talking to other things – wrist band, mobile phone, smart door etc.

The Age of Smart

It started with the smart phone. Now everyday devices like lights, cars, TVs etc. are being made smart by connecting them together over networks and to the Internet.

These devices will not only be able to send data to the Internet but they will also be controlled over the Internet.

These devices will become “things” on the “Internet of things”.


A room temperature sensor gathers the data and send it across the network, which is then used by multiple device sensors to adjust their temperatures accordingly.  For example, refrigerator’s sensor can gather the data regarding the outside temperature and accordingly adjust the refrigerator’s temperature. Similarly, your air conditioners can also adjust its temperature accordingly. This is how devices can interact, contribute & collaborate.

Birth of IoT

The term “The Internet of Things” (IoT) was coined by Kevin Ashton in a presentation to Proctor & Gamble in 1999. He is a co-founder of MIT’s Auto-ID Lab. He pioneered RFID (used in bar code detector) for the supply-chain management domain. He also started Zensi, a company that makes energy sensing and monitoring technology.

So, let me first take you through a quote by Kevin Ashton, which he wrote in 2009 for RFID journal. This will help you in understanding IoT from its core.

“If we had computers that knew everything there was to know about things—using data they gathered without any help from us—we would be able to track and count everything, and greatly reduce waste, loss and cost. We would know when things needed replacing, repairing or recalling, and whether they were fresh or past their best.

We need to empower computers with their own means of gathering information, so they can see, hear and smell the world for themselves, in all its random glory.”

The “thing” in IoT

The ‘Thing’ in IoT can be any device with any kind of built-in-sensors with the ability to collect and transfer data over a network without manual intervention. The embedded technology in the object helps them to interact with internal states and the external environment, which in turn helps in decisions making process.

Applications/Benefits of IoT in Different Domains

Better Policing:

Ex: Rupa lives in a small city. She’s heard about a recent spike in crime in her area, and worries about coming home late at night.

Police has been alerted about the new “hot” zone through Mobile Apps and Websites, and they’ve increases their presence. Area Monitoring Devices have detected suspicious behavior, and law enforcement has investigated these leads/complaints to prevent crimes.

Personalized Experience at Home and Office:
Ex: Sai enters his office cabin, and the IoT System recognizes his face & opens the door. It puts on the required number of lights and adjusts AC or Puts on the fan according to his preference on certain temperatures Eg: In winter no AC, fan at low speed.

Improved Customer Engagement:
IoT improves customer experience by automating the action. For e.g. any issue in the car will be automatically detected by the sensors. The driver, as well as the manufacturer, will be notified about it. Till the time driver reaches the service station, the manufacturer will make sure that the faulty part is available at the service station.

Technical Optimization:
IoT has helped a lot in improving technologies and making them better. The manufacturer can collect data from different car sensors and analyze them to improve their design and make them much more efficient.

Manufacturing Industry:
Our current insights are superficial, but IoT provides real-time information leading to effective decision making & management of resources. For example, if a manufacturer finds fault in multiple engines, he can track the manufacturing plant of those engines and can rectify the issue with manufacturing belt.

Energy Industry / Electrical Department:
Smart Street lights system in colony roads and national highways

Residential Energy:
The rise of technology has driven energy costs up. Consumers search for ways to reduce or control consumption. IoT offers a sophisticated way to analyze and optimize use not only at device level, but throughout the entire system of the home.

This can mean simple switching off or dimming of lights, or changing device settings and modifying multiple home settings to optimize energy use.

IoT can also discover problematic consumption from issues like older appliances, damaged appliances, or faulty system components. Traditionally, finding such problems required the use of often multiple professionals (electricians, plumbers, pest control etc).

Commercial Energy:         

A smart-meter still requires a reader to visit the site. This automated meter reader makes visits unnecessary, and also allows energy companies to bill based on real-time data instead of estimates over time.
Emergency Services:Anusha is a nurse in an emergency room. A call has come in for a man injured and unconscious in a Road accident. The system recognized the patient by his photo (already in records maintained by network of hospitals database) (photo taken by the caller and uploads it to the Hospital App) and pulls his record like blood group, chronic diseases, BP, Diabetic or not etc. On the scene, paramedic equipment captures critical information automatically sent to the receiving parties at the hospital. The system analyzes the new data and current records to deliver a guiding solution. The status of the patient is updated every second in the system during his transport.

Healthcare Industry:
Smartwatches and fitness devices have changed the frequency of health monitoring. People can monitor their own health at regular intervals. Not only this, now if a patient is coming to the hospital by ambulance, by the time he or she reaches the hospital his health report is diagnosed by doctors and the hospital quickly starts the treatment. The data gathered from multiple healthcare applications are now collected and used to analyze different disease and find its cure.

Transportation:Ex1: APSRTC buses, School buses, Government Officials Vehicles to find out whether the Vehicle is used for Official use or personal use – by using GPS Tracking
Ex2: Managing traffic congestion and guiding the commuters less congestion paths
Ex3: Now, we have self-driving cars with sensors, traffic lights that can sense the traffic and switch automatically, parking assistance, giving us the location of free parking space etc.
Ex4: Also, various sensors in your vehicle indicate you about the current status of your vehicle, so that you don’t face any issues while travelling.
Ex5: Respond to Ambulances


A school in Richmond, California, embeds RFID chips in ID cards to track the presence of students. Even if students are not present for check-in, the system will track and log their presence on campus.

Waste Management System:
Ex: Waste Management System, sensors to GVMC(Greater Visakhapatnam Municipal Corporation) Dustbins and GVMC Trucks which dispatch them to the city outskirts

Smart trashcans in New York tell garbage collectors when they need to be emptied. They optimize trash service by ensuring drivers only make necessary stops, and drivers modify their route to reduce fuel consumption.
Law Enforcement Agencies:
Ex: IoT systems shave costs by reducing human labor in certain areas such as certain traffic violations like Red Signal Jumps, Over speed etc.

Applications of IOT for Smart Cities

Waste Management
Existing Problems:
Unnecessarily the waste collection trucks going to empty or half filled Dustbins

IOT Solution:
                Sensors on Dustbins (GVMC Tanks) to sends information like:
Full or Not

Traffic ManagementExisting Problems:
Unfair distribution of green signal time in a Rush Hour : Eg at Isukathota Junction, more time should be allotted to traffic going towards Madhurawada and traffic going towards Gurudwara.
IOT Solution:
Modify green signal duration based on traffic congestion at rush hours with the help of CC cameras at Signal Points or Drones near important Signal points.
Finding Parking spots easily at public places Eg: Big Bazaar, Datapro MVP  (to avoid wastage of fuel and time in searching for a parking spot)

Electricity ManagementExisting Problems:
For the top level Electricity officials no idea on whether the LED lights are functioning or not at a particular remote place
IOT Solution:
Sensors on LED lights on Highways, main roads and street roads which receive and send information like:
ON or OFF, Working or not, ON and OFF automatically at certain time like ON at 6pm in winter and 6:30pm in summer, OFF at 6am in winter and 5am in summer
Receive information: LED ON when there is some vehicle or pedestrian movement otherwise keep the LED off like in auto water flow taps when we put our palms under it

Public Transport ManagementEx1:
Existing Problems:
No idea on whether a particular bus has already started at its source or not Eg: 68K
No idea on whether empty seats are available or not to go to Srikakulam in an Non-Stop JNNURM Bus
No idea on whether the route is congested or not due to any accident or strike etc

IOT Solution:
Cabs going to a particular destination
Buses available to a particular destination in the next 30 minutes, seats available, book and pay online by smartphone
Status of particular Bus Routes Ex1: 900, 68K, 222 etc.  via GPS to Smartphones of customers via Cloud storage
For Bus drivers show alternative routes if traffic jam on a particular regular route

If there is any accident in a certain route, smart camera shall send the data to a server, the server then diplays the same information on the display boards to take diversion well ahead and inform the Airports Authority about the same and delay the flights, inform the school/college authorities and defer their class timings

Health Care

Cars themselves calling Nearby Hospital and Ambulance (Eg: Accident happened at night time at a remote place, Only driver is present in the Car and is in unconscious state)

Energy Industries
New oil and gas pipelines are fitted with sensors that detect leaks and alert repair teams, so issues are fixed before they can cause problems

Construction Industry
In the construction industry, determining concrete quality is very important. The Embedded Data Collector, or EDC, from Smart Structure, works by embedding sensors in the concrete during the pouring and curing process. This way, the sensors become a permanent part of the structure. They provide vital information about concrete strength and quality directly to the Smart Structures Work Station.

Prerequisites to become a IoT Professional

General knowledge of:
  •     Networking
  •     Electronics
  •     Sensors
  •     Databases
  •     Programming
  •     Server Side technologies like Node.js
  •     Cloud technologies

IoT Key Features

Artificial Intelligence:IoT essentially makes virtually anything “smart”, meaning it enhances every aspect of life with the power of data collection, artificial intelligence algorithms, and networks. This can mean something as simple as enhancing your refrigerator and cabinets to detect when milk and your favorite cereal run low, and to then place an order with your preferred grocer.

Connectivity:New enabling technologies for networking, and specifically IoT networking, mean networks are no longer exclusively tied to major providers. Networks can exist on a much smaller and cheaper scale while still being practical. IoT creates these small networks between its system devices.
Enhanced Data Collection:Modern data collection suffers from its limitations and its design for passive use. IoT breaks it out of those spaces, and places it exactly where humans really want to go to analyze our world. It allows an accurate picture of everything.

IoT Disadvantages

Security: IoT creates an ecosystem of constantly connected devices communicating over networks. The system offers little control despite any security measures. This leaves users exposed to various kinds of attackers.

Privacy: The sophistication of IoT provides substantial personal data in extreme detail without the user's active participation.Eg: your medical data (operation on any organ you wanted to keep it secret) is shared with other hospitals and used when you visit another hospital for any operation and your previous operation details may be revealed to your friends/relatives

Complexity: Some find IoT systems complicated in terms of design, deployment, and maintenance given their use of multiple technologies and a large set of new enabling technologies.

IoT Sensors

The most important hardware in IoT might be its sensors.

Different types of Sensors

Wearable Electronics

Wearable electronic devices are small devices worn on the head, neck, arms, torso, and feet.

Smartwatches not only help us stay connected, but as a part of an IoT system, they allow access needed for improved productivity.

Smart glasses help us enjoy more of the media and services we value, and when part of an IoT system, they allow a new approach to productivity.

Current smart wearable devices include:
Head − Helmets, glasses
Neck − Jewelry, collars
Arm − Watches, wristbands, rings
Torso − Clothing, backpacks
Feet − Socks, shoes

Standard Devices

The desktop, tablet, and cellphone remain integral parts of IoT as the command center and remotes:

The desktop provides the user with the highest level of control over the system and its settings.
The tablet provides access to the key features of the system in a way resembling the desktop, and also acts as a remote.
The cellphone allows some essential settings modification and also provides remote functionality.

Other key connected devices include standard network devices like routers and switches.

IoT Software

IoT software addresses its key areas of networking and action through platforms, embedded systems, partner systems, and middleware. These individual and master applications are responsible for data collection, device integration&real-time analytics within the IoT network. They exploit integration with critical business systems (e.g., ordering systems, robotics, scheduling, and more) in the execution of related tasks.

Data Collection:The system transmits all collected data from the sensors to a central server.

Device Integration:They manage the various applications, protocols, and limitations of each device to allow communication.

Real-Time Analytics:These applications take data or input from various devices and convert it into viable actions or clear patterns for human analysis.

Technologies and Protocols

NFC (Near Field Communication): consists of communication protocols for electronic devices, typically a mobile device and a Computer
RFID (Radio Frequency Identification): This technology employs 2-way transmitter-receivers to identify and track tags associated with objects like Vehicles – RFID IDCards at School/College Gates/Classrooms, RFID enabled VIP Vehicles identification at factory/Company Gates
Low-Energy Bluetooth: Bluetooth Low Energy hit the market in 2011 as Bluetooth 4.0. When talking about Bluetooth Low Energy vs. Bluetooth, the key difference is in Bluetooth 4.0's low power consumption. It is extremely useful when talking about M2M communication. With Bluetooth LE's power consumption, applications can run on a small battery for four to five years. It is vital for applications that only need to exchange small amounts of data periodically.
Low-Energy Wireless: This technology replaces the most power hungry aspect of an IoT system. Though sensors and other elements can power down over long periods, communication links (i.e., wireless) must remain in listening mode. Low-energy wireless not only reduces consumption but also extends the life of the device through less use.
Radio Protocols: ZigBee, Z-Wave, and Thread are radio protocols for creating low-rate private area networks. These technologies are low-power but offer high throughput, unlike many similar options. This increases the power of small local device networks without the typical costs.
LTE-A (Long Term Evolution- Advanced): It delivers an important upgrade to LTE technology by increasing not only its coverage but also reducing its latency and raising its throughput. It gives IoT a tremendous power through expanding its range, with its most significant applications being a vehicle, UAV(Unmanned Aerial Vehicle), and similar communication.
Wifi-Direct: WiFi-Direct eliminates the need for an access point. It allows P2P (peer-to-peer) connections with the speed of WiFi, but with lower latency.

What is Internet of Everything

The Internet of things refers to the devices like sensors and actuators, but the term The Internet of Everything used by Cisco is broader and encompasses the devices, data, people and processes/software.

The devices Eg: sensors will send data. This data will then be processed and used by people or by machines to control the devices or other devices.

For example: A temperature sensor sends temperature data to a process which determines that the room temperature is too hot and so sends a signal to turn on the air conditioning.

IOT Components

An IOT system comprises of three basic Components:

  •     The Things -Sensors & Devices
  •     The Network
  •     Data Processing

First, sensors or devices help in collecting very minute data from the surrounding environment.

That collected data is sent to a cloud infrastructure but it needs a medium for transport.

The sensors can be connected to the cloud through various mediums of communication and transports such as cellular networks, satellite networks, Wi-Fi, Bluetooth, wide-area networks (WAN), low power wide area network and many more.
Note: To turn an everyday object like a house or a car into a smart house or car or a “thing” will require that the object has:
A unique address – IPv6 address
A way to connect to a network – Wireless
Sensors Eg: temperature, light, speed etc.

Data Processing
Once the data is collected and it gets to the cloud, the software performs processing on the acquired data.

This can range from something very simple, such as checking that the temperature reading on devices such as AC or heaters is within an acceptable range. It can sometimes also be very complex, such as identifying objects (such as intruders in your house) using computer vision on video. But there might be a situation when a user interaction is required, example- what if when the temperature is too high or if there is an intruder in your house? That’s where the user comes into the picture.

There are also cases where some actions perform automatically. By establishing and implementing some predefined rules, the entire IOT system can adjust the settings automatically and no human has to be physically present. Also in case if any intruders are sensed, the system can generate an alert not only to the owner of the house but to the concerned authorities.

IOT Platforms, Apps and Services

An IOT platform combines several IOT functions in one.

It can collect and distribute data, convert data between protocols, store and analyze data.

They are available as cloud-based and standalone platforms and are available from many companies -large and small.

  •      Amazon Web services (AWS)
  •      IBM Watson Bluemix
  •      Microsoft Azure
  •      ThingWrox

IoT and the Cloud

The cloud will have an important role to play in the IOT as it will enable companies to create networks, store data, automate processes without having to build the infrastructure themselves.

This will enable IOT services to be developed much quicker, and at a lower cost than using traditional in-house systems and services.

IoT Architecture



IoT Predictions 2020

IoT Companies

Tech Giants

Mid-Size companies
SkyBell: provides a smart video doorbell
R-Style Lab: Smart Home IoT solutions, Healthcare & Fitness IoT apps, Industrial IoT sol.s, s/w for wearables etc.
Deako:  smart light switches
June: provides Intelligent convention oven
Particle: IoT Hardware company
MyMDBand: Medical Emergency Band
Root: IoT Device company
Jasper: cloud-based IoT SaaS platform
Samsara: energy monitoring, asset utilization, and vehicle tracking.
Placemeter: smart sensors and computer vision in real time for transportation, retail, real estate, Their biggest pride are smart city projects, video streaming, and processing in particular.
HQSoftware: Industrial, Healthcare, Automotive, Smart City, etc.
TP-Link: automation, security, energy management, its means are day/night cameras, wi-fi light bulbs, smart plugs, and switches.
Invoxia: provides a unique GPS tracker to help secure personal belongings
Verizon: smart cities, telematics (route planning for delivery trucks), mobile commerce, asset tracking, and management
and many more…

Most In-Demand IoT Skills for IoT Solution Developers

  •      Big Data: Hadoop, Spark
  •      Data Analytics Tools: R, Python, Tableau, PoweBI
  •      Machine Learning: using R, Python
  •      Mobile App Development: Android, IOS etc
  •      UI/UX Design: Angular2, Front End, Javascript, CSS, Bootstrap, Basic colors, Photoshop
  •      Information Security
  •      Hardware Engineering
  •      Networking

IoT Job Market / Future of IoT

Internet of Things, or IoT as it is popularly known, has gone on to become one of the hottest buzzwords in the tech industry for 2017. For anything to become a buzzword, three things should be in place — applications, jobs, and global recognition. IoT over the last few months has clearly met these criteria. There is an uncountable number of IoT applications floating around in the world. Consequently, jobs are mushrooming and companies — big and small — are embracing IoT for the long term.

IoT has pervaded into pretty much every job role remotely connected with IT, but you need to acquire specific skills on top of your regular tech competency in order to ride the IoT career wave. Some of the highest paying and most sought-after job titles these days are; IoT Solutions Architect, IoT Cloud Architect, IoT Systems Engineer, IoT Security Developer, among others. For the more experimental and R&D driven folks, there are jobs like IoT Research Analyst, IoT Development Lead and the likes.

When we mention IoT, we are staring at something that will add $300 billion to the global technology industry in less than three years. It is also estimated that the three big cats of the industry — manufacturing, logistics, and utilities — will contribute to 50% of all IoT innovation during this time. This, besides the more consumer-driven applications and offerings that are being created by the ecosystem every single day. Indeed, we are in for good times with IoT!

---Knowledge acquired from various resources online and our take on this subject


CUSTOMER RELATIONSHIP MANAGEMENT This booklet is designed to help small and medium business owners understand the basic...