Components of IoT
IoT is becoming the trend of nowadays. Sooner or later it will not just take over the industrial sector but also impact the daily household. When designing an IoT ecosystem, there are certain factors which we need to bear in mind. Security, devices to be used i.e. sensors, microcontroller, gateways and cloud computing. The devices or “things” play a vital role. Right from a refrigerator door to a coffee machine; will be connected to the Internet. With rapid advancement in technology and 5G’s demand in the future, it is believed by the year 2021, approximately 20 billion devices will be connected to the internet.
What is IoT basically? A network where not only a vehicle, a home appliance like fan or air conditioner but also embedded systems like electronics, software, sensors which enable exchange or transfer of data over wireless technologies like Wi-Fi, ZigBee, Bluetooth etc. IoT in short will reduce human effort and increase our dependency on machines.
In IoT, the major components are Sensors, Gateway or microcontrollers, connectivity, analytics or data processing and cloud computing.
- Sensors: They are the “things” in an IoT system. They are responsible for collecting and transmitting real-time data to the microcontroller. Sensors are used to detect physical changes for example, temperature, humidity or pressure. Here are some features which should be the basis of a good sensor:
- It should not be sensitive to the phenomena it measures
- It should not detect other physical changes apart from its designation for example, DHT11 is designed to sense the temperature and humidity of its surroundings. If it starts determining the luminosity then its problem which needs attention.
- It should not modify the readings during the measurement process.
There are several properties or characteristics one should keep in mind while selecting a sensor:
|Characteristics of a sensor|
|Response and Recovery Time|
- Microcontrollers or Gateways as they are commonly known. Like in a human body the brain controls actions and movements, similarly microcontroller acts as the brain of an IoT system.
Why should we use a microcontroller in an IoT system?
- Simplicity: Programming a microcontroller or a setting up is no difficult job. Also, interfacing sensors with a microcontroller is easy.
- Security: Code is written on a “bare metal” in a microcontroller which results in very little attack and maintains a secure environment.
- Cost: Microcontroller are very cost-effective. With minimum cost they offer you simplicity and security.
Raspberry Pi, Arduino Uno, NodeMCU are a few examples of microcontrollers.
The factors one should keep in mind while selecting a Microcontroller:
- Compatibility: Will our Microcontroller support the sensors/ actuators? Depending upon how sensors are being, you should decide the number ports required.
- Architecture: If your Microcontroller will be able to handle complex programming, what are the functional requirement of your application and also the power it should compute for the application to run.
- Memory: Choosing the microcontroller with enough memory size is of utmost importance, in order to save time and money.
- Availability: A thorough research is a must about the availability of the microcontroller and the quantity. Selecting a correct microcontroller during the initial stages of your project is important which can help scale your application.
- Power: Energy efficiency plays a key role in designing an IoT system. How much power does it require, will it need to be wired or whether batteries are required.
Hence, this component of the IoT needs to be the most secured part as it analyses, processes data from thousands of sensors and acts a gateway to the cloud. Microcontroller (MCU) should also have the capability to host the process, store data i.e. act like a memory and provide a secured operating system.
- Analytics or data processing or Data Analytics play another significant role in an IoT system. Drawing conclusions from big or small data is basically data analytics. It will play an integral part in the development of IoT. The following points enlist the effect it will have on the businesses.
Volume: The shear amount of data to analysed will be huge in an IoT application. Real-time data from many sensors or actuators will require data analytics.
Structure: The data coming from sensors will be structured, semi-structured or unstructured. This will require data to be analysed on a bigger scale.
Revenues: Data analytics will help us provide an insight of what the customer demands and expects from the analytics. This will increase or generate revenues for the same.
Competition: As we know IoT is going to be the future. It provides freedom and better performance. Hence, by offering data analytics, one can upgrade his or her business.
- Cloud computing: If Microcontroller is the brain of an IoT system then Internet is the heart of an IoT system. There are many ways to connect to the Internet for example Wi-Fi, Bluetooth. Cloud computing is a sector vital in the evolution of IoT. If IoT provides the data then cloud provides the path for the data to travel.
Cloud computing’s sole motto is to enable IoT or users to access data from remote parts of the world through its storage option. Also, for the developers to work from different parts of the world. Cloud computing is also economically viable as it has minimum pay charges which depends on the cloud model. For example, Microsoft Azure Cloud Services, in their free trial provide upto 8,000 messages per day. This will encourage IoT companies or start-ups and in turn reduce costs.
Some of the cloud computing platforms are:
Book: Internet of Things by Dr. Jeeva Jose
We’ve contributed to a multi-stakeholder report by 58 co-authors at 30 organizations, including the Centre for the Future of Intelligence, Mila, Schwartz Reisman Institute for Technology and Society, Center for Advanced Study in the Behavioral Sciences, and Center for Security and Emerging Technologies. This report describes 10 mechanisms to improve the verifiability of claims made about AI systems. Developers can use these tools to provide evidence that AI systems are safe, secure, fair, or privacy-preserving. Users, policymakers, and civil society can use these tools to evaluate AI development processes.
While a growing number of organizations have articulated ethics principles to guide their AI development process, it can be difficult for those outside of an organization to verify whether the organization’s AI systems reflect those principles in practice. This ambiguity makes it harder for stakeholders such as users, policymakers, and civil society to scrutinize AI developers’ claims about properties of AI systems and could fuel competitive corner-cutting, increasing social risks and harms. The report describes existing and potential mechanisms that can help stakeholders grapple with questions like:
- Can I (as a user) verify the claims made about the level of privacy protection guaranteed by a new AI system I’d like to use for machine translation of sensitive documents?
- Can I (as a regulator) trace the steps that led to an accident caused by an autonomous vehicle? Against what standards should an autonomous vehicle company’s safety claims be compared?
- Can I (as an academic) conduct impartial research on the risks associated with large-scale AI systems when I lack the computing resources of industry?
- Can I (as an AI developer) verify that my competitors in a given area of AI development will follow best practices rather than cut corners to gain an advantage?
The 10 mechanisms highlighted in the report are listed below, along with recommendations aimed at advancing each one. (See the report for discussion of how these mechanisms support verifiable claims as well as relevant caveats about our findings.)
Institutional Mechanisms and Recommendations
- Third party auditing. A coalition of stakeholders should create a task force to research options for conducting and funding third party auditing of AI systems.
- Red teaming exercises. Organizations developing AI should run red teaming exercises to explore risks associated with systems they develop, and should share best practices and tools.
- Bias and safety bounties. AI developers should pilot bias and safety bounties for AI systems to strengthen incentives and processes for broad-based scrutiny of AI systems.
- Sharing of AI incidents. AI developers should share more information about AI incidents, including through collaborative channels.
Software Mechanisms and Recommendations
- Audit trails. Standard setting bodies should work with academia and industry to develop audit trail requirements for safety-critical applications of AI systems.
- Interpretability. Organizations developing AI and funding bodies should support research into the interpretability of AI systems, with a focus on supporting risk assessment and auditing.
- Privacy-preserving machine learning. AI developers should develop, share, and use suites of tools for privacy-preserving machine learning that include measures of performance against common standards.
Hardware Mechanisms and Recommendations
- Secure hardware for machine learning. Industry and academia should work together to develop hardware security features for AI accelerators or otherwise establish best practices for the use of secure hardware (including secure enclaves on commodity hardware) in machine learning contexts.
- High-precision compute measurement. One or more AI labs should estimate the computing power involved in a single project in great detail and report on lessons learned regarding the potential for wider adoption of such methods.
- Compute support for academia. Government funding bodies should substantially increase funding for computing power resources for researchers in academia, in order to improve the ability of those researchers to verify claims made by industry.
Angular offers the following features:
- Angular CLI
Angular providers a command line tool to create, test, run and build project. This CLI tool provides a rich set of commands that can also be used to generate your Angular components, services, directives, pipes, modules, classes and more. Use the CLI command to run the project in watch mode during development. You can also run the test files using CLI command. A single CLI command can also produce code that can be deployed in your web server.
- Open-source and cross-platform development
Angular is an open-source web framework for developing Single Page Applications. It provides cross-platform support, so you can develop your web application from and OS using your favorite IDEs such as VS code, Atom, JetBrains WebStorm, NetBeans, IntelliJ IDEA etc.
- MVC or MVVM Architecture
MVC refers to Model-View-Controller and MVVM refers to Model-View-ViewModel. You can develop you application in MVC or MVVM architecture using Angular. Angular allows you to create reusable components. A component provides and HTML view and code file. The code file contains the event handling code and other functions. The HTML file contains the markup and Angular directives and pipes. You can also create injectable services for reusable code logic.
- Performance and fast view rendering
Next version of Angular framework will come with a new compilation and rendering engine. This next generation rendering engine is named as Ivy. With the version 9 release of Angular, the new compiler and runtime instructions are used by default instead of the older compiler and runtime, known as View Engine.
- TypeScript for development
- Built-in Dependency Injection (DI) support
To increase the efficiency and modularity of your application you can create reusable service classes in Angular. These service classes can be injected in to any component, directive, pipe or other services using Dependency Injection. Angular uses its own DI framework to handle it. With DI application manages the number of instances, scope and life time of your service objects.
- Event handling and Two-way data binding support
Angular offers built-in two-way data binding which helps us to bind the objects to form controls. Angular also also provides event-handling functionality that helps to invoke function on various events of the UI elements.
- Built-in form validation and error handling Angular provides two ways for creating and managin forms- Template Driven and Reactive. The template driven using the FormsModule and directives such as ngModel and ngForm. Reactive forms uses ReactiveFormsModule and directives and services suchas FormGroup, FormControl, Validators, FormBuilder etc.
- Enahanced and simple routing
Angular uses built-in routing module that uses the HTML 5 routing paths. You can use route parameters and query parameters to the routes. Angular uses the RouterModule to enable routing in your applciation. Angular routing also offers the following features:
- Lazy loading
- Route guards
- Data resolvers
- Http Interceptors
- Component Development Kit (CDK) and support for Angular Material
The Component Dev Kit (CDK) is a set of tools that implement common interaction patterns whilst being unopinionated about their presentation. Angular CDK provides a feature called Virtual Scrolling that loads only a set of data that fits the screen. When you scroll down it loads the data dynamically and load into the page component. The latest version of Angular provides support for Material themes using Angular Material that is used as the backbone of the Angular CDK.
- Differential loading
Features of ReactJS
- Open-source and cross-platform support
npm install -g create-react-appcommand.
- Virtual DOM support
React uses a concept called Virtual DOM for fast rendering of UI elements. Virtual DOM is an exact copy of the Browser DOM that is updated frequently based on the data changes. It is quick to update the Virtual DOM than the browser DOM since it is a memory object.
- One-way data binding
ReactJS is introduced as a UI development library for rendering data quickly on the web pages. For that, it used the one-way data bidning to update the data on the UI element. React does not support two-way data binding by default. But you can use the events and properties to achieve this.
- Easy integration with other web frameworks Since it is a UI development library you can easily integrate React with any of your web frameworks such as PHP, JSP and Servlets, ASP.NET, Angular etc. You can use the CDN links or downloadable JS files in your applications.
- Ideal for mobile app development
You can create native apps for you Android and iOS devices using the React Native. React Native is a custom renderer that runs on the React platform. It uses the native components instead of the web components.
- Rich set of libraries
- Better community support
ReactJS is now driven by a community and individual developers. You can contribute to the React through the community.
Which one to choose – React or Angular?
One of the major question asked by developers and project managers to me about is ‘Which one to choose- React or Angular?’. Every one have their own reason for choosing Angular and React for their projects. If you closely look into the capabilities of the Angular and React, you will find the solution for it. You may read about Angular and React from many blogs and forums, and you may come with an answer ‘React’- because uses Virtual DOM for fast rendering of the UI elements.
If you read the above description about Angular and React you will notice one important point that Angular is a complete framework for SPA development and React is just a library. Angular is a complete web framework for developing an end-to-end web application. It provides all the features for developing a complete web application such as routing, two-way data binding, form validation, Dependency injection, CLI tool, asynchrounous functions using Observables and promises and more. But, React is just a library like jQuery which can be easily integrated with any other web framework. It is used just to increase the speed of the view rendering.
If you are looking for a complete web application such as HR management application, E-commerce applications, Financial applications etc you need to choose Angular. Such applciations are very large and they use multiple pages, data entry forms and reusable code logics. Angular offers built-in routing module that provides features such as lazy loading of modules, http interceptors for reqeust and response processing, data resolvers for loading data when a route is activated, guards for conditionally activating and deactivating routes and more. It also provides built-in form handling modules control binding and validation, event handing etc. The builting DI engine helps to control the scope and lifetime of the services.
But, if you are developing a web application which is mostly used for presenting data to users than entry forms such as Dashboards, social media applications, online newspaper websites etc then you can choose ReactJS for it. Because these kind of applications are mostly using data representation UI components than the data entry forms. React’s Virtual DOM with one-way data binding helps to render the data quickly on the web pages. You can also develop end-to-end web application using React but you may need to use a large set of external libraries for state-management (Redux or Flux), routing (React router), responsive design (React Bootstrap/Material-UI) and form validation (React Form).
I hope this will help you to understand the differences of two promising JS technologies for Web development.
Author: Sonu Sathyadas
Carbon-free technologies like renewable energy help combat climate change, but many of them have not reached their full potential. Consider wind power: over the past decade, wind farms have become an important source of carbon-free electricity as the cost of turbines has plummeted and adoption has surged. However, the variable nature of wind itself makes it an unpredictable energy source—less useful than one that can reliably deliver power at a set time.
In search of a solution to this problem, last year, DeepMind and Google started applying machine learning algorithms to 700 megawatts of wind power capacity in the central United States. These wind farms—part of Google’s global fleet of renewable energy projects—collectively generate as much electricity as is needed by a medium-sized city.
Using a neural network trained on widely available weather forecasts and historical turbine data, we configured the DeepMind system to predict wind power output 36 hours ahead of actual generation. Based on these predictions, our model recommends how to make optimal hourly delivery commitments to the power grid a full day in advance. This is important, because energy sources that can be scheduled (i.e. can deliver a set amount of electricity at a set time) are often more valuable to the grid.
Although we continue to refine our algorithm, our use of machine learning across our wind farms has produced positive results. To date, machine learning has boosted the value of our wind energy by roughly 20 percent, compared to the baseline scenario of no time-based commitments to the grid.
We can’t eliminate the variability of the wind, but our early results suggest that we can use machine learning to make wind power sufficiently more predictable and valuable. This approach also helps bring greater data rigor to wind farm operations, as machine learning can help wind farm operators make smarter, faster and more data-driven assessments of how their power output can meet electricity demand.
Our hope is that this kind of machine learning approach can strengthen the business case for wind power and drive further adoption of carbon-free energy on electric grids worldwide. Researchers and practitioners across the energy industry are developing novel ideas for how society can make the most of variable power sources like solar and wind. We’re eager to join them in exploring general availability of these cloud-based machine learning strategies.
Google recently achieved 100 percent renewable energy purchasing and is now striving to source carbon-free energy on a 24×7 basis. The partnership with DeepMind to make wind power more predictable and valuable is a concrete step toward that aspiration. While much remains to be done, this step is a meaningful one—for Google, and more importantly, for the environment.
As the modern world seeks innovation and convenience, retail providers are faced with the new challenge — to keep up with the trend or fall behind.
Due to this, many retailers are delving into the latest technologies that seek to address the new needs of their businesses, and that may mean looking toward enterprise software development. Let’s look at how retailers are innovating and dive deeper into their artificial intelligence and robotics solutions.
Why Do Retailers Need to Modernize?
According to Statista, by 2021 online e-commerce sales are set to total a record of $4.8 trillion (USD). Meanwhile, in 2018 this amount was estimated at a lower $2.8 trillion. What this shows is an industry in rapid growth, and there are no signs of it slowing down.
This growth makes one factor exceptionally clear — if you want to stay competitive in the retail business, no matter whether you have a small corner shop or a multinational enterprise, you need to consider optimizing your operations with new technology. Across web, mobile, and in-store, such technology is poised to include AI and robotic process automation (RPA), and here’s why:
The Value Driven by AI and Robotics in Retail
- Better insights into inventory and supply planning
- No or fewer employees required in physical location management and delivery tracking
- Predictive analytics of customer-tailored demands
- Personalization of customer support
- Cashier-less checkout operations
- Better product categorization of both local and global stock units
How AI and Robotics Solutions Boost Retail Businesses
Now that we know the benefits, let’s look at how these solutions work. To begin, let’s consider retail business processes as divided into two parts:
- Back-office operations — consisting of paperwork, staff and product management
- Shop-front operations — serving customers and addressing their issues
Across all of these functions, AI and robotics help retailers achieve better results.
Improving Planning and Strategy
AI technologies allow retailers to gather, rework and standardize data, automatically enter it into spreadsheets, and transform it into understandable visuals such as charts. In turn, this helps build efficient business plans, reduces the time on report compilations, forecasts sales figures, generates customer profiles, and understand customers’ shopping preferences.
Equipped with these reports on customer and market behavior, marketing and sales professionals can efficiently plan campaigns and target them toward real consumers. For managers, this aids in ensuring certain products remain stocked as they know which are in demand.
Optimizing Logistics and Inventory
AI programs stock, process and analyze significant amounts of information, resulting in a prediction of the outcomes and even applying them to discover new revenue channels. This can be helpful in back-office operations such as accounting and business planning, but is not limited to these areas.
For example, when paired with IoT, AI applications have already begun to improve the transportation of goods by managing their provenance and shipping conditions data. This can be tracked through the entire journey, ensuring better food security and enabling logistics enterprises to make more informed decisions.
In addition, cloud technologies assist retailers in restocking the shelves and tracking customers’ movement in-store, gathering information on the demand and forecasting the popularity of certain products.
Personalization and Customer Experience Management
According to McKinsey & Company, the retail sector is one of the foremost industries that has benefited from AI and robotics implementation. One of the reasons is that this can transform retail businesses by making them more customer-oriented.
AI-equipped systems can collect exceptionally accurate data about buyers’ preferences and habits. Relying on this data, retailers can grow their sales by recommending suitable items to customers. This is something that a few big names have already tried out with visible results:
- NY-based company Caper has recently developed a handy computerized shopping cart. This cart helps customers to learn more about products by simply scanning them; the details then show up on the screen. In addition to this, buyers can “checkout” their goods online to avoid standing in a line.
- Ocado, a grocery company, uses the Google technology based on speech recognition to deal with customer complaints. Google Cloud AI speeds up the process of complaint analysis, helping Ocado to promptly fix and improve their services.
In addition, robotics proves beneficial for in-store service, too. For example, robots can provide retailers with the information on the shelf inventory, price tag changes and consumer preferences, personalizing the products in stock. Robotized call-centers can help cut on the expenses while ensuring customer support is available 24 hours a day.
Finally, the buyers themselves can benefit from machine learning systems by using automated checkouts, avoiding long queues or getting quick support through digital kiosks.
Challenges of AI Adoption and Their Solutions
Despite these numerous benefits, it is an undoubtable fact that any business seeking to integrate new technologies, AI in particular, will be faced with certain challenges:
1. New working practices
As IT integrations advance, we are likely to see more changes in how we work. The current trend sees manual labor activities increasingly performed by robots, while “mental” work is performed by humans. But even this could be set to change as AI programs are gaining skills and are able to effectively work with data.
Recent research by McKinsey & Company has shown that out of 2,000 labor activities, about 800 occupations can be automated to some extent. For society, in general, this will mean a new drive in skill building and a changing job market in the future.
However, for retailers, this means having to both reconsider their staffing needs and their technology firepower to be able to keep up with the competition.
2. Costs of new software
For retail businesses that are just starting to introduce technology, the initial costs may seem off-putting. Usually, this means developing customized software and products to improve the business, and this may be more costly than off-the-shelf products. In addition, companies may need to consider hiring specialists to maintain and service such systems.
While initial roll-outs of such developments come at a price, companies should look at their long-term benefits and the overall effect on the business.
Finally, retail providers will find new challenges in dealing with security. For many of these systems to work effectively, a large amount of information has to be collected and stored. This means that companies will be ever more responsible for data security, in the areas of individual privacy and the privacy of their whole businesses.
Safe data storage and consent management is one aspect; another is protection from hackers. This is essential to keep data from being exploited and systems from becoming corrupted.
For retailers to adapt and thrive in the new era, they will need to undertake changes to how they do business, and this may mean involving AI and robotics technologies.
These changes have both advantages and disadvantages for the retail sector and its employees. Personalization and robots taking over routine operations may be seen as positives, while the changing roles within an organization may be a negative. It will take flexibility and thought-out strategies for retailers to go with this AI flow without major disruptions to their modus operandi.
AI chatbots and recognition technology are seeping into every segment of society. It is making new waves everywhere. This intelligent technology is making to every corner of our lives, right from our homes, businesses to our relationships.
Now it is not just about home experiences and trending gadgets anymore. The current market has already seen chatbot therapists, chatbot educators, chatbot lawyers, and chatbot customer service representatives. Let us see to what extent AI chatbots and Recognition Technology, is impacting our lives.
AI Chatbot Success
As AI technologies proliferate, they are becoming integral for businesses globally. They are giving businesses a competitive edge over others. A strategically designed and implemented chatbot can work wonders for businesses worldwide.
AI chatbots and recognition technology is a brilliant way to outsource manual work and non-judgmental work. This technology saves up time, effort, and money. With AI in place, businesses can concentrate and invest in skilled work.
It also substantially reduces staff workload. As per the Grand View Research, the chatbot market is expected to hit a whopping $1.25 Billion by the year 2025 at a CAGR of 24.3%.
As digital transactions are becoming the standard norm of purchasing goods and services, leading eCommerce firms are using AI to enhance their customer loyalty and brand competitiveness. Some of the leading e-Commerce brands using AI technology include eBay, Alibaba, Amazon, ASOS, and JD.com.
As per an Oracle survey, 80% of businesses want chatbots by 2020. Companies such as Nitro Café, Sephora, 1–800 Flowers, Coca Cola, Snap Travel, and Marriott have started seeing returns. Here are a few AI chatbot success stories.
Nitro Café: Nitro Café’s messenger chatbot designed for direct payments, easy ordering, and instant 2-way communication has led to an increase in Nitro Café’s sales by 20%.
Sephora: Sephora’s facebook messenger chatbot has increased its makeover appointments by 11%.
ASOS: ASOS’s Messenger chatbots helped reach 3.5x more people, increased returns by 250% and increased its number of orders by 300%.
1–800 Flowers: 1–800 Flowers reported that 70% of its messenger orders were derived from new customers.
Uses of AI Recognition Technology
- Voice Recognition Technology
Voice recognition technology has revolutionized our lives in multiple ways. It is already being used in live subtitling on television, for offline note making systems or offline speech to text conversion and in dictation tools for the legal and medical profession.
Virtual assistants such as Amazon’s Alexa, Google’s Google Home, and Apple’s HomePod use voice recognition technology. These virtual assistants can control your smart home.
They can control thermostats, TVs, garage doors, lights, fans, locks, sprinklers, and switches. They can also play music, make calls, send texts, help you watch the footage from your security cameras, let you listen to audio books, make food orders, create alarms and reminders for you and give you News information.
You can also browse the internet for information about gazillion things as per your discretion. You can do all of this with just your voice.
With “OK Google” and “Hey Siri” making it to our smartphones, voice recognition technology has largely impacted the way we function.
With the help of voice recognition technology, you can also solve crimes, secure your bank accounts, and buy products and services.
- Facial Recognition Technology
For long now AI facial recognition technology has been associated with the security sector. However, today, you can see its active expansion into other industries such as marketing, retail, and health.
Some of the common uses of AI facial recognition technology include unlocking of phones, prevention of retail crimes, smarter advertising, helping the blind, finding missing persons and pets, protection of law enforcement, facilitating forensic investigations, identifying people on social media platforms, diagnosing diseases, tracking attendance at school, college and workplace, facilitate secure transactions, validate identities at ATMs and control access to sensitive areas.
AI-based recognition technology has also revolutionized the photography industry. An example of the same would be Accent AI 2.0, which is an AI recognition technology implemented in Luminar 3.
It features object and facial recognition technology that helps photographers to improve different part of the photo instantly, for instance, make the sky more expressive by applying a brighter color or replace portrait background.
Chris Burkard — a well-known photographer and artist, has in-length appreciated the fascinating and diverse use of the distinguishing AI facial recognition technology in the field of photography. He thinks the AI recognition technology has amplified accuracy and acts as a significant support system for an artist’s creativity.
Pioneering applications such as AiCure and ePAT are dramatically improving the health care setting. While AiCure uses facial recognition technology to improve medication adherence practices on a mobile device, ePAT can detect facial nuances associated with pain and help in prudent pain management.
AI chatbots and recognition technology have become decidedly mainstream. This radical technology is here to stay and evolve.