23 October 2017

Hands-on with a Cloud Platform

In our previous blogpost on Cloud, we looked at the different types of Cloud services in the form of Platorm as a Service (PaaS), Software as a Service (SaaS) and Infrastructure as a Service (IaaS). We also demonstrated the features offered by cloud platforms which include storage space, application framework for processing stored data, visualization of data and event-based triggers among others.

In this post, our research engineer Koustabh illustrates an example of how to push data to a cloud platform and visualize the data. An open source gateway framework AGILE is used to read data from sensors of a TI Sensor Tag device over BLE. Furthermore, this data is then pushed to the cloud platform Xively to store the data and visualize the data over HTTP. This application flow is developed on Node-RED, a visual tool to build application logic with flows with 2 customized nodes, one for fetching the data from the TI SensorTag and the other to push the data to Xively. We would like to cover the AGILE framework and more examples of cloud platforms in our following videos. Let us know what you thought of this video!


04 October 2017

Swarm Intelligence



In this blog post, our Engineer Tanmay Chakraborty writes about Swarm Intelligence (SI). It can be applied in many IoT use cases for consumer benefits.

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Anything in a flock is termed as swarms. Large number of simple organisms performing some simple task in order to get a complex or greater task accomplished with ease is what we understand by swarm intelligence. Its a collective behavior of the group that help in performing complex tasks which would be hard for an individual to perform alone. All the individuals coordinate using decentralized control and self-organization. The focus in mainly on local interactions of the individuals with each other and their environment. Swarm Intelligence can be found widely in Colonies of ants , school of fishes, flock of birds, honey bees etc.

Swarm Intelligence is the collaborative behavior of a self-organized and decentralized system that may be natural or artificial. This concept has been put to work in Artificial Intelligence systems. This is an emerging field of biologically-inspired systems that are based on behavioral models of social insects such as ants, bees, wasps etc. The expression Swarm intelligence was first introduced by Gerardo Beni, Hackwood and Jing Wang in 1989, in cellular robotics context. These systems typically consists of a large number of simple agents or boids interacting locally with one another and with their environment. The inspiration is especially natural biological systems. 

Agents are simple with limited capabilities by interacting with other agents of their own kind they achieve a task. The agents follow simple rules, although there is a lack of centralized control structure controlling the behavior of individual systems, local, and to a certain extent random, interactions between such agents leads to the emergence of intelligence and collective behavior unknown to the individual systems. Application to swarm principles to robots is called swarm robotics. Which include multi-robot systems and their coordination. Using of a distributed form of control makes a system more efficient, effective and scalable. The main aim of swarm intelligence is to increase the performance and robustness. Swarm algorithms are faster and more robust solutions to solve complex set of problems. The interaction between the different swarms can be direct or indirect. Direct interactions are achieved by audio or video. And indirect interaction is done via environment. Here one agent cause a change in the environment and the others respond to that change. This type of communication is called stigmergy, interaction through environment. These collective behavior of the swarms and the capability of the swarms to solve very complex problems in nature has inspired many researchers to investigate this phenomenon. They came up with different algorithms like artificial fish swarm algorithm, dynamic optimization with which different SI systems were built which could solve various problems. 


ANT COLONY OPTIMIZATION (ACO)
Ants are one of the best natural example of swarm. They live in colonies. They lay pheromone (i.e volatile chemical substance) on the way from their nest to the food source. Single ants do not have enough intellect to find shortest path to their food. But when in colonies they can perform a lot complex tasks with ease. Ant Colony Optimization was first proposed by Macro Darigo in 1991. The initial algorithm was based on the path optimization done by ant colonies while finding the shortest route to the food source. ACO algorithms find its usage in optimization problems, scheduling problems, vehicle routing problems etc. Its application in SI is as a class of algorithm which inspires the forging behavior of the ant. The main iteration in the ant system ACO is the updating of the pheromone level.


BEE COLONY OPTIMIZATION 
Bees are another good example of natural swarms. Swarms of bees are dynamic. They display their intelligence by dividing their work among other bees. Bees perform tasks like foraging, storing, honey distribution, collecting pollen, retrieving, communication and adapting themselves to changes. Bees are very good in organizing their colonies. They are social and live together in colonies. Bees communicate with each other through waggle dance.


ARTIFICIAL FISH SWARM ALGORITHM (AFSA)
AFSA is one of the best options for optimization among available swarm intelligence algorithms. This is a biologically inspired algorithm made from the observations of the collective movements of the fish and their social behavior.

CONCLUDING NOTES
In this article, we presented some Swarm Intelligence algorithms that are most common and successfully implemented so far. Algorithms described here mainly focuses on optimization. The large number of use cases makes these algorithms very popular. Optimization algorithms like the AFSA, BCO, ACO find a lot of real world uses. All of them are good at solving real world complex problem sets.

21 August 2017

Lab-X Foundation Android App

Mobile apps are one of the main pillars of Digital Transformation. Future Tech Lab  is proud to present an Android app for Lab-X Foundation.

Lab-X Foundation is a Boston based non-profit committed to bringing world class education to developing countries. We provide opportunities for undergraduate students in Science, Technology, Engineering and Mathematics (STEM) with hands-on opportunities, global exposure 

In this app, you will be able to :

  • Be informed about one new international internship opportunity every week!
  • Learn more about Lab-X's programs, get international internships 
  • Win CASH prizes by participating in Lab-X’s poster-prototype competitions
  • Participate in panel discussions, webinars from renowned experts to get global exposure
  • Get inspired from the stories of our past interns and learn from their journey
  • Get notifications about upcoming Lab-X programs
  • Find mentors, grow your network
  • Contribute to Lab-X Foundation, create positive impact, make world a better place one student at a time!


Get it on Google Play
Note: Google Play and the Google Play logo are trademarks of Google Inc.

13 August 2017

What do Cloud Platforms offer?

We are back with another video from our A4IoT initiative. Our research engineer Koustabh Dolui talks about what cloud platforms offer in the Internet of Things context.



In the previous blog post and video, Koustabh pointed out the gaps in the IoT device ecosystem in terms of data processing and data storage. Due to the constrained nature of the IoT devices the storage and processing of data on a longer time span and from multiple devices can be a difficult ask. 

In this video Koustabh talks about the different cloud services offered for the Internet of Things. Termed as X-aaS as the different types of services can be defined as the following - 

Platform-as-a-Service: The provider offers storage of data and an application framework to write programs to process the stored data.

Software-as-a-Service: The cloud provider offers subscription to a software running on the cloud specific to a use-case or application area of IoT.

Infrastructure-as-a-Service: The provider offers cloud infrastructure with more flexibility as well as, in some cases, the end devices for IoT.

Among these types of services, the focus of the videos is on PaaS, or the cloud platforms for the Internet of Things. Cloud platforms have the following three features which are absolutely necessary. They are:
  • Storage and accumulation of data from the IoT end devices.
  • Application framework for writing applications to process the data.
  • Remote access to the data stored on the cloud platform.

Cloud platforms may also offer - 
  • Libraries to write code on the cloud as well as end devices.
  • Rule engines to set triggers based on criteria defined on the data.
  • Visualization of the data stored on the cloud platform.

08 August 2017

A4IoT - Cloud Platforms for IoT

In this blog post, we are back with a new video for our A4IoT initiative. This video once again features our Research Engineer Koustabh Dolui, who talks about why cloud platforms have become indispensable in the IoT ecosystems. Do check out his talk below!


This post is a follow up on our previous blog post and video by Soumya, who explained the IoT ecosystem in brief with the roles of the IoT devices, the network connecting these devices, IoT gateways and the cloud platforms for IoT. 

Koustabh focus on to the Cloud platforms in the IoT ecosystem and talks about the gap in the IoT devices in terms of storage and processing of data generated from these devices. Generally IoT devices are resource constrained both in terms of the processing power and storage capabilities. This leads to a dilemma on how to store and process the data generated from these devices! Data generated are either - 
  1. Textual: generated from sensors like accelerometer, light sensor, pressure sensor
  2. Multimedia: generated from camera or video recorders for surveillance applications

Depending on the storage and the bandwidth of connectivity available on the end devices, the data is offloaded in raw form or processed to some extent before offloading to the cloud. The data processing on the stored data might be performed based on - 
  1. Period of the data: Based on the time span of the data, the processing can be performed on transient data collected over a short period of time or on historic data to extract patterns from the data.
  2. Devices from which data is collected: Data can processed from a single device or from multiple devices.

In the above cases, the processing of historic data can be overwhelming for a resource constrained device whereas a single device may not have visibility of data from other devices when data from multiple devices are considered. In these cases, M2M applications running in the cloud come handy.

Watch and subscribe to our YouTube Channel for more such videos.

05 August 2017

A4IoT - IoT Devices, Networks and Systems

In this blog post, our Co-Founder, Soumya Kanti Datta talks about IoT devices, networks and systems. This is second in a series of introductory videos on the IoT and Cloud Platforms from our Academy for IoT. Take a look at our Youtube Video below.



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In our previous blog post and video, Koustabh introduced the IoT as a network of smart objects (sensors, actuators) and applicability of IoT in home automation, connected car and other domains. In this blog post, we focus on the IoT systems, their components and interconnection (shown in the picture below).

Figure 1: IoT Systems and Components (Source: ETSI)

From a high level perspective, Figure 1 shows four types of the IoT systems. On the left, there are simple systems where one or a collection of M2M devices (often called IoT devices or smart objects) exchange data with M2M application(s) over communication network. The M2M application(s) typically run in a cloud infrastructure. On the right, you see a gateway and a service capabilities layer are introduced to handle much more complex scenarios. These IoT systems typically have following six components - 
  • M2M Device - It is capable of replying to a query, e.g. sensor, actuator.
  • M2M Area Network - It connects the M2M or IoT devices to an IoT Gateway, e.g. Bluetooth, LoRa.
  • IoT Gateway - It provides many functionalities including - (i) connecting IoT devices to the Internet, (ii) providing protocol translation, (iii) ensuring IoT device interworking and (iv) local data processing in an Edge Computing scenario.
  • Core Network - It connects the IoT Gateways to the Cloud.
  • M2M Application - It contains middleware for sensor data processing.
  • Cloud - It has become indispensable for IoT data processing, service provisioning, long term data storage and many more functionalities.
Watch the video above to learn more on these devices, networks and IoT ecosystem along with some interesting statistics on IoT devices and market dynamics.


03 August 2017

Unmanned Aerial Vehicle - An Introduction

In this blog post, our Hardware Engineer Tanmay Chakraborty writes about Unmanned Aerial Vehicles (UAV). It represents an enormous market segment that spans IoT, emergency response and many more with promising growth for near future.

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INTRODUCTION

UAV- Unmanned Aerial Vehicles which is commonly termed as drones, is an aircraft which has no human pilot on board, it can be remotely controlled via a remote from a ground base station or may have autonomous algorithms for auto pilot. Historically UAVs were mostly used for military applications for missions that were too dangerous for humans to perform. In today’s world UAVs find a number of use cases military, civil, scientific, creative, business survey, agriculture, recreational and the list goes on. In fact civilian UAVs now vastly outnumber military UAVs With over a million sold by 2017, they have emerged as an early commercial application of autonomous applications.


HISTORY
Innovation in the field of UAVs first started as early as 1900. In 1916 an attempt to develop a powered UAV was taken up by A.M. Low. He developed a model “Aerial Target”. The first remote controlled aircraft was developed by model-air plane enthusiast Reginald Denny in 1935. During the world war II UAV development got a boost. Nazi Germany manufactured and used various UAVs during the war. In 1959, the U.S. Air Force began using the UAVs to protect their pilots from flying into hostile territories. With the improvement in technology in 1980s and 1990s the advancement in UAVs were encountered.

UAVs evolved as a possibility of cheap and capable flying machines, deployable without risk to aircrews. Initial uses were surveillance but soon they emerged as a tool for aerial photography.


CLASSIFICATION
UAVs can be broadly classified into six categories.
  • Target and decoy – These UAVs provide ground and aerial gunnery a target to be shot at.
  • Aerial Reconnaissance – providing battlefield intelligence to the intelligence bureau.
  • Aerial Combat – These UAVs provide attack capability for missions that have high-risk factor.
  • Logistics – These types of UAVs are meant for delivering cargoes.
  • Scientific Research and Development – improve UAV technologies for the future of drones.
  • Civil and commercial UAVs – agriculture, aerial photography, data collection, construction, surveillance.

GENERAL COMPONENTS OF AN UAV
Generally, the UAVs consists of the following basic components.
  • Chassis – The body of the UAV. Initially the chassis were designed like the air crafts but with introduction to quad rotors, octo rotors chassis design changed too.
  • Sensors – To achieve autonomy a number of sensors have been placed in an UAV. Most basic and important sensors being gyroscope and accelerometer, barometer, telemetry, GPS, magnetometer, LIDAR etc.
  • Communication – For controlling an UAV remotely communication between the UAV and the base station is the most important thing. Radio Frequency is widely used for such purposes. Nevertheless, wireless technologies like Wi-Fi, LTE are also in testing phase.
  • Data Collection Unit – This unit consists of cameras. They are the eyes of the UAV. They can be used for aerial photography or to have a track of where the UAV is heading. With improvements in the field of computer vision they are now being used for obstacle avoidance systems for the UAV.
  • Power Supply unit – Most UAVs are powered by Li-Po cells.
  • Flight Controller Unit – This unit is the brain of the UAV. It consists of a system on chip board with a fast microprocessor. Capable enough to process the Inertial measurement and data processing in real time.
  • Actuators – The actuators involve a digital Electronic Speed Controller (ESC) connected to a brushed DC motor/engine. The propellers are connected to the motors. The specification of the motors and propellers change according to the job the UAV needs to function. For lifting a high payload high torque motors are used with longer propellers.


SWARMS
Improvement in technology had a great effect in almost every field. One of the most interesting technology in the field of aerial robotics is “Swarms of Drones”. Swarm as the name suggest, it consists of a number of drones, coordinated together to perform a given task. Each drone can sense its surroundings and react to its surroundings according to the stimulus. Swarms are a biological inspiration taken from swarms of bees. One of the best example is the World Record created by INTEL where they used around 500 quad copters to form patterns in the sky.


AUTONOMY
The autonomous features that are common among the present UAVs are listed below.
  • Self-level function – This is a feature that helps the UAV to maintain a particular altitude on the pitch and roll axis.
  • Altitude hold feature - The UAV maintains its altitude using barometric or ground sensors.
  • Hover - Keep the pitch and roll level, stable the yaw heading and altitude while maintaining position using GPS or inertial measurement unit.
  • Headless mode - Pitch is controlled relative to the position of the controller rather than relative to the vehicle's axes.
  • Care-free mode - Automatic control for roll and yaw while moving horizontally.
  • Failsafe for drones - Automatic landing or return-to-home upon loss of control signal from the base station.
  • Return-to-base - Fly back to the point from where the drone took off.
  • Follow-me function - Maintain relative position to a moving controller or other object using GPS, image recognition or homing beacon.
  • GPS waypoint navigation - Using GPS to navigate to an intermediate location on a travel path.

CONCLUSION
UAV is the area where the future belongs. Recent development has enabled UAVs to be a part of the common human life. UAVs as distributed sensor networks for IoT, pizza delivery units, postal delivery systems, aerial photography and videography units, surveillance units, construction reviewing units, agricultural units, disaster management units are already being used at large. The wide variety of use cases is the reason for such popularity of the UAVs. UAVs that could be used as public transport vehicles are still under test phase and will be in market sooner than we can imagine. Possibilities with UAVs are enormous and is only bounded by imagination.

REFERENCE

01 August 2017

Horizontal Axis Wind Turbine

Future Tech Lab (FTL) is launching IoT and digital transformation services for the renewable energy market. Our hardware engineer Debayan Paul continues his first post with more detailed discussion about Horizontal Axis Wind Turbine (HAWT). You have already seen one deployed in country-sides when you take an intercity train or a flight. In this blog, you will learn the about different parts of HAWT and how IoT, Big Data and Machine Learning are changing the wind energy market.

Historical Development
Wind has served mankind as a primary source of power for over 3000 years now. Before the inception of steam engines, wind power was primarily utilized for sailing ships. Wind power was probably used in Persia (present-day Iran) about 500–900 A.D. The wind wheel of Hero of Alexandria marks one of the first recorded instances of wind powering a machine in history. However, the first known practical wind power plants were built in Sistan, an Eastern province of Iran, from the 7th century. In the next few centuries, with the advent of wind mills, wind power was being converted to mechanical power through wind mills for grinding grains and pumping water. Wind mills have also been known to drive water through pipes for irrigation. With the development of the steam engine, the dependence on wind energy dropped drastically which also resulted in lower interest in research into the field of wind power. In the late nineteenth century, electricity had become the currency of energy, and thermal and hydroelectric power plants became the favored sources of electricity. But not every country had the luxury of fossil fuel or water resources. Denmark, being one of those, invested in the development of wind turbines to provide for its electricity demand. The 1890s saw Denmark lead the path in the development of wind turbines. Wind energy is fast becoming a preferable alternative to conventional sources of electric power. Owing to the perennial availability of the wind, and the considerable range of power control, wind turbines are now coming up in almost all parts of the world. In the early days of development, wind turbines were designed to rotate at a constant speed through pitch control or stall control. The modern wind turbines implement pitch control in order to tap maximum energy at wind speeds lower than rated wind speed.


Horizontal Axis Wind Turbine

A wind turbine is a device that converts the wind's kinetic energy into electrical power. Wind turbines can rotate about either a horizontal or a vertical axis, the former being both older and more common. We will discuss about the Horizontal Axis Wind Turbine (HAWT) in the upcoming discussion.

The main rotor shaft and electrical generator are generally at the top of a tower for a horizontal axis wind turbine (HAWT). A horizontal axis wind turbine has a design which demands that it should be pointed to the wind to capture maximum power. This process is called yawing. The turbine shaft is generally coupled to the shaft of the generator through a gearbox which turns the slow rotation of the blades into a quicker rotation that is more suitable to drive an electrical generator.

Different parts of Horizontal Axis Wind Turbine

Anemometer: - Measures the wind speed and transmits wind speed data to the controller.

Brake: - Stops the rotor mechanically, electrically, or hydraulically, in emergencies.

Controller: - Starts up the machine at wind speeds of about 8 to 16 miles per hour (mph) and shuts off the machine at about 55 mph. Turbines do not operate at wind speeds above about 55 mph because they may be damaged by the high winds.

Gear box: - Connects the low-speed shaft to the high-speed shaft and increases the rotational speeds from about 30-60 rotations per minute (rpm), to about 1,000-1,800 rpm; this is the rotational speed required by most generators to produce electricity. The gear box is a costly (and heavy) part of the wind turbine and engineers are exploring "direct-drive" generators that operate at lower rotational speeds and don't need gear boxes.

Generator: - Produces 60-cycle AC electricity; it is usually an off-the-shelf induction generator.

High-speed shaft: - Drives the generator at about 900-1500 rpm.

Low-speed shaft: - Turns the low-speed shaft at about 30-60 rpm.

Nacelle: - Sits atop the tower and contains the gear box, low- and high-speed shafts, generator, controller, and brake. Some nacelles are large enough for a helicopter to land on.

Pitch: - Turns (or pitches) blades out of the wind to control the rotor speed, and to keep the rotor from turning in winds that are too high or too low to produce electricity.

Blades & Rotor: -

(a)    The lifting style wind turbine blade. These are the most efficiently designed, especially for capturing energy of strong, fast winds. Some European companies manufacture a single blade turbine.

(b)    The drag style wind turbine blade, most popularly used for water mills, as seen in the Old Dutch windmills. The blades are flattened plates which catch the wind. These are poorly designed for capturing the energy of heightened winds.

(c)    The rotor is designed aerodynamically to capture the maximum surface area of wind in order to spin the most ergonomically. The blades are lightweight, durable and corrosion-resistant material. The best materials are composites of fiberglass and reinforced plastic.

Tower: - Made from tubular steel (shown here), concrete, or steel lattice. Supports the structure of the turbine. Because wind speed increases with height, taller towers enable turbines to capture more energy and generate more electricity.

Wind vane: - Measures wind direction and communicates with the yaw drive to orient the turbine properly with respect to the wind.

Yaw drive: - Orients upwind turbines to keep them facing the wind when the direction changes. Downwind turbines don't require a yaw drive because the wind manually blows the rotor away from it.

           Yaw motor: - Powers the yaw drive.

Figure 1: Different parts of a Horizontal Axis Wind Turbine (HAWT)

How the Wind energy sector is changing

Alternative energy technologies are quickly becoming globally accepted and reliable source of electrical power. With a growing installed capacity of renewable energy plants comes a growing number of remote monitoring solutions to track the performance of these plants. Enormous amounts of data are being generated by these renewable energy plants and it is becoming ever important to create valuable insights from this data. Big data analytics performed on the data collected from these plants, enables owners and O&M crews to operate the renewable plants at the plants maximum potential. Among all the types of big data analytics that could be performed on the plant data, predictive analytics holds the most promising of providing insights by leveraging performance data to create correlations and outcomes. Predictive analytics when used deftly on renewable energy power plants can provide accurate energy production forecasts. One study estimates that a good predictive model can increase the power generating capacity of a wind farm by about 10%, which practically revitalizes the entire business. It is also important to note that Predictive Analytics doesn’t only improve operational efficiencies but also improves the lifespan of the valuable renewable energy technology assets. The current growth of renewable energy technologies could be amplified if there is enough data to prove that they are credible investment options. Numerous renewable energy power projects still lack appropriate funding because of the lack of historic data that raises suspicions on the long-term viability of the projects. Predictive analytics can address this problem by accurately forecasting energy generation based on historic performance, weather and other parameters. These quantifiable results associated with revenues generated from the future performance can improve the bankability of renewable energy projects. People working in the renewable energy field are increasingly turning their attention to the "Industrial Internet" to maximize the efficiency of existing equipment.  Such as Offshore wind energy power plants are remotely located - and so it's not only investment costs that need to be considered, it's also about the operating costs. Based on this data gathered, they can see how wind and wave conditions are affecting the base of the turbine. Even not all the wind sites are the same. Some locations can wear out the turbines quicker than others because of harsher power surges, greater wake effects or sporadic surges in power. Using big data, the company can recreate the conditions experienced by the turbine - creating a replay of certain events to learn how the turbine responds to them.

Machine Learning helps make complex systems more efficient. Regardless of whether the systems in question are steel mills or gas turbines, they can learn from collected data, detect regular patterns, and optimize their own operations. Siemens engineers have been studying machine learning for the past 25 years. They have used machine learning to optimize industrial facilities such as steel mills and gas turbines. Machine learning can also be used to reliably forecast the prices of energy and raw materials or to predict energy demand in entire regions. Sensors in and on such systems routinely record data regarding the direction and speed of the wind, temperatures, electric currents and voltages, as well as vibrations produced by major components such as the generator and the rotor blades. Based on past measurement data, software calculates the optimal settings for various weather scenarios that involve a variety of factors such as sunshine duration, hazy conditions, and thunderstorms. The data is transmitted to the wind turbines’ control units, which take it into account from then on as they adjust the functions. If familiar wind conditions arise, the control units immediately use the optimal settings that were ascertained as a result of machine learning.

Figure 2: Big Data platform for a wind energy firm.

References
  1. http://www.turbinesinfo.com/horizontal-axis-wind-turbines-hawt/ (Figure 1)
  2. https://en.wikipedia.org/wiki/Wind_turbine
  3. https://macaulay.cuny.edu/eportfolios/alternativeenergyinnewyork/wind-turbines/
  4. https://machinepulse.wordpress.com/2014/11/14/how-predictive-analytics-can-make-the-renewable-energy-industry-grow/
  5. http://www.dw.com/en/big-data-is-about-to-transform-renewable-energy/a-36189374 (Figure 2)
  6. http://algoengines.com/2014/07/29/data-generated-by-wind-and-solar-plan/
  7. https://www.siemens.com/innovation/en/home/pictures-of-the-future/digitalization-and-software/from-big-data-to-smart-data-machine-learning-in-windturbines.html


30 July 2017

Academy for Internet of Things

Future Tech Lab (FTL) is a Digital Transformation Provider. The Internet of Things (IoT), Cloud Computing and Cyber Security are at the heart of the Digital Transformation. SMEs and Large Enterprises (LE) understand the its benefits but find it challenging to grasp the fast moving technologies. To help them, we have launched an initiative - Academy for Internet of Things (A4IoT) since early 2016. A4IoT is a training and certification program with on-demand tutorials on IoT, Cloud Computing, Big Data, Smart City and Cyber Security. Participants not only learn about architectures, protocols, communication technologies used in these areas, but also benefit from hands-on training. Our A4IoT face to face workshops have attracted 2000+ participants from 125+ organizations around the world (statistics till 10 May 2017). In this blog, our Co-Founder Soumya Kanti Datta writes about A4IoT program while our Research Engineer Koustabh Dolui gives an introduction to the IoT.

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During my R&D and paper presentation, I interacted with a lot of SMEs and LEs participants in IoT conferences and events. They would ask me after my talk about how to start an IoT project to turn an idea into prototype and later into a commercial product. After several such interactions during 2013-2015, I decided to create the A4IoT program with an aim to educate students and train SME/LE human resources about the ongoing development around - IoT, Cloud Computing, Cyber Security etc.

Starting from January 2016, FTL has organized 10 physical events (workshops, conferences, tutorials) and 8 webinars. A4IoT programs and courses are developed in collaboration with experts on sensors, middleware, security, network operator, cloud provider, application developer and standardization experts. The tutorial lectures and hands-on training materials are designed in accordance with - 
  • Current market requirement in IoT, Cloud Computing, Cyber Security.
  • Consumer centric scenarios.
  • Balanced and on demand approach.

A4IoT Program Values for Individuals
  • Keep updated with evolving ecosystems of IoT, Cloud Computing, Smart City etc.
  • Pursue your ideas and turn them into prototype through hands-on training sessions.
  • Learn latest IoT community news.
  • Give a boost to your career with IoT, Cloud Computing and Smart City skill sets.
  • Earn certification for each module completion and distinction.
A4IoT Program Values for Enterprises
  • Focused training to maintain a skilled workforce.
  • Flexible and customized delivery mechanism and schedule.
  • Reduced time to prepare and execute training sessions.
  • Content available through webinars and physical workshops.
  • Balanced approach with theory and hands-on learning.

Since January 2016, A4IoT workshops were organized in many countries around the world. They attracted more than 2015 participants (including students and professionals) from 129 organizations around the world. Our distinguished keynote speakers were from - ABB, Altiux, Cisco, IEEE Consumer Electronics Society, Intel, The Mitre Corporation, W3C, Telecom Italia, QCRI, Oracle and Hydroswarm.

Fig. 1 - Previous A4IoT physical event locations.

For a list of our upcoming event, visit - http://www.iotappslab.com/events/upcoming-events.html

We a launching a series of MOOCs on IoT, Cloud Computing, Cyber Security. Here is a video giving an introduction to the IoT. 


Stay tuned at our A4IoT web page for further announcements.

If you want us to conduct an workshop at your organization, contact Soumya Kanti Datta at skd@future-tech-lab.com.

22 July 2017

Various Forms of Renewable Energy

Future Tech Lab (FTL) is launching IoT and digital transformation services for the renewable energy market. In this blog, our hardware engineer Debayan Paul writes about various forms of renewable energy. In future blogs we will outline our Paradise Cloud Platform based solutions for renewable energy market. If you are curious, contact Soumya Kanti Datta to know more.


Introduction

The year 1973 brought an end to the era of secure and cheap oil. In October of that year, OPEC (Organization of Petrol Exporting Countries) put an embargo on oil production and started the oil-pricing control strategy. Oil prices skyrocketed causing a severe energy crisis all over the world which also resulted in spiraling price rise of various commercial energy resources, further leading to global inflation. The government of all countries took this matter very seriously, and for the first time, an imminent need for developing alternative energy sources was felt. Alternate energy sources were given serious consideration, and huge funds were allocated for the development of these resources. Thus, the year 1973 is considered as the year of the first ‘Oil Shock’. In the same decade, one more ‘Oil Shock’ jolted the world in 1979, which further expedited the focusing of attention on alternate energy sources.

Various forms of Renewable Energy

Solar Energy: -  The Sun radiates energy uniformly in all directions in the form of electromagnetic waves. Solar energy can be utilized directly in two ways, either by collecting and converting it directly to electrical energy using the Photovoltaic system or by collecting the radiant heat and using it in a thermal system. The solar radiation received on the surface of the earth on a bright sunny day at noon is approximately 1 kW/sq m. The earth continuously intercepts solar power of 178 billion MW, which is about 10,000 times the world’s energy demand.

Fig. 1 - Solar Photovoltaic Panel

Fig. 2 - Solar Thermal Parabolic Trough
Wind Energy: - Wind energy is the kinetic energy associated with movement of large masses of air where these motions result from uneven heating of the atmosphere by the sun, creating temperature and pressure differences. Wind energy is harnessed as mechanical energy with the aid of a wind turbine which could be further utilized for operating farm appliances and water pumping or could be converted into electrical power using an aero-generator. The power available in the winds flowing over the earth surface is estimated to be 1.6x107MW, which is more than the present energy requirement of the world.
Fig. 3 - Horizontal Axis Wind turbine

Fig. 4 - Offshore Wind Turbine


Hydroelectric Energy: - Hydropower or water power is derived from the potential energy of falling energy or fast running water, which may be harnessed for useful purposes. Since ancient times, hydropower from many kinds of watermills has been used as a renewable energy source for irrigation and operation of various mechanical devices. Primary hydropower generating methods include conventional dams, pumped-storage, run-of-the-river and micro hydroelectric plants.

Fig. 5 - Hydroelectric Power Plant

Biomass Energy: - Biomass is a generic term for living material- plants, animals, fungi, bacteria. The earth’s biomass represents an enormous store of energy which is also a potential non-exhaustible resource. Biomass energy harnessing mainly involves transformation of crude biomass into intermediate bio-fuels such as methane, ethanol, producer gas by chemical or biological processes.

Geothermal Energy: - Geothermal energy is derived from huge amounts of stored thermal energy in the interior of the earth, through its economic recovery on the surface of the earth is not feasible everywhere. Though the Geothermal energy is restricted to some specific geographical area, it is useful for number of applications like direct heat use and electrical power generation harvesting the high temperature.

Ocean Energy: -  Oceans cover about 71% of the earth’s surface. They receive, store and dissipate energy through various physical processes. As per present technological status, recoverable energy in oceans exists mainly in the form of waves, tidal and temperature difference (between surface and deep layers).  Tidal energy is a form of hydro power that converts ocean tides into electricity or other useful forms of power. Other two forms are still in its nascent stages.

India Energy Status: -
India is presently the world’s fourth largest economy as far as Purchasing Power Parity (PPP) terms as concerned and the fifth largest energy consumer in all over the world. However, due to its enormous population of approximately 1.3 billion, the per-capita consumption of most energy related products is extremely low as per the world standard. It roughly stands around to be a very modest 530 Kg of Oil Equivalent (kgoe), while the world average is approximately 1800 kgoe. India currently has stupendous prospects in the field of sustainable energy, especially in Solar, Hydel, Biomass and Wind Power generation. A current worldwide share of sustainable energy generation by region is given below.

Fig. 6 - Proportion of renewable power generation by region (in Million kgoe and %)

References: -
Images are taken from www.google.co.in

Other Sources
  1. Non-Conventional Energy Resources by B.H.Khan
  2. https://en.wikipedia.org/wiki/1973_oil_crisis
  3. https://www.theguardian.com/environment/2011/mar/03/1970s-oil-price-shock
  4. https://www.slideshare.net/MisterKhan/ppt-on-present-energy-scenario



15 July 2017

Automotive Industry, Internet of Things and Smart City

The automotive industry has come a long way since 1886 when the commercial production of automobiles began. For the Auto 1.0 [1] ecosystem, cars were truly novelties, expensive and time consuming to produce. During Auto 2.0 (post 1950s), cultural and economic forces shaped the auto industry. It shifted its focus on performance, dealer diagnostics and basic infotainments. The technology was still invisible to the customers. Right now, we are at Auto 2.5 (Fig.1) which is a transition period to Auto 3.0 ecosystem. Well established auto OEMs (e.g. BMW, Audi) and new market entrants (e.g. Tesla) has been looking into the true potential of software beyond infotainment. For example, customers can now avail OTA software updates for some repair problems and add new software features without going to a dealer. The "always connected" aspect is one of the driving forces behind this. 

Figure 1: Auto 2.5: Connected Cars [2]
In parallel, the Internet is evolving too. The Internet of Things (IoT) promises to revolutionize the automotive industry. Cars (depicted in Fig.1) have the capabilities of exchanging sensor data with an OEM or third party Cloud Computing infrastructure over 3G/4G networks. The Cloud backends can provide services like (i) the quickest route to a destination, (ii) finding the nearest fuel station, (iii) searching for an empty parking, (iv) automatic diagnostics of cars and more. The IoT is also assisting in making Smart City initiatives a reality. Several cities around the world are deploying infrastructure for better road safety, co-operative mobility management, reduce pollution etc. 

The Auto 1.0 and 2.0 can not be a part of the IoT and Smart Cities due to a lack of - (i) powerful On Board Units (OBU), (ii) vehicle to vehicle and infrastructure communication looks, (iii) standards and (iv) integration with next-gen ICT. To fully benefit from the ongoing technological evolution, the auto industry is responding with Auto 3.0 ecosystem. The focus of Auto 3.0 are - 
  • Support Intelligent Transportation System (ITS) through V2X Communications.
  • Expose vehicular resources for data collection, processing, management and storage.
  • Seamless communication and information exchange among vehicular gateways, Edge & Cloud platforms and consumer devices.
  • Seamless interoperability among vehicles, external computing platforms and consumers.
The technological evolution leading to Auto 3.0 is exploiting the IoT and in turn enabling - 

  • Automatic vehicle information discovery and exchange with computing systems and other vehicles.
  • Enhanced access and core networking.
  • Computing on vehicular sensor data.
As a result, connected cars are truly becoming resources for the IoT ecosystem [3]. Thus, on-board sensor data are used to understand pollution level, traffic flow in a city and even manage road intersections. This can effectively reduce the number of sensors needed to be deployed in emerging smart cities. 

In a nutshell, the combined (Auto 3.0, IoT and Smart City) ecosystem integrates - (i) vehicular resources (sensors, actuators), (ii) ITS & V2X technologies, (iii) Edge and Cloud Computing platforms to perform sensor data analysis and (iv) consumer centric services. It will potentially lead to connected cars as an IoT service which is frequently called - "Automotive IoT".

[1] http://www.ntti3.com/wp-content/uploads/Automotive_as_a_Digital_Business_V1.03-1.pdf

[2] http://design.avnet.com/axiom/autorama-connecting-your-car-to-the-internet-of-tomorrow

[3] S. K. Datta, J. Haerri, C. Bonnet and R. Ferreira Da Costa, "Vehicles as Connected Resources: Opportunities and Challenges for the Future," in IEEE Vehicular Technology Magazine, vol. 12, no. 2, pp. 26-35, June 2017.

About Future Tech Lab | Digital Transformation Provider

Future Tech Lab is a R&D company that transforms customers legacy business processes, operations and assets into a truly digital platform. ​Our Paradise IoT Platform provides secure, scalable and easy to use software solutions that power consumer centric Mobile Apps, IoT and Smart City products and services. We provide "Prototype as a Service" and Consultancy on Digital Transformation, IoT and Smart City areas to shape our customer's ideas into prototypes and then turn them into finished products. 

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