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