As the COVID-19 hit our lives, businesses have had a lashing impact on them and have transformed from the usual to virtual. In these times, they are leveraging technology to overcome the everyday challenges, to provide quality services to the customers, and to gear up for the future with future business solutions. Yet, the digital transformation has been amazing over the times and even so Businesses are eager to use AI and ML to the best of its capability to increase efficiency and productivity. 

The AI infrastructure

The advancements in artificial intelligence has opened the doors for development and innovation. It is contributing multifolds to improvement in production and quality work by precisely discovering opportunities, patterns and themes in real time with large amounts of data and input sets. Of course with the speed at which these AI algorithms operate, they are paving the better ways of getting business done.

There are large amounts of data generated everyday that gives us a great deal of information about our customers preferences. Having said that, businesses understand the need to rely on the modern methods to drive growth. Intelligent computer softwares and actionable insights drawn from customer’s data help boost revenue, increase productivity, improve customer experience, and drive growth. Let’s see how.

Customer service

Approximately 70% of buying decisions are made based on how clients feel they are being treated, superior customer service has the power to turn any company into an industry juggernaut. (Source: Salesforce)

Companies have started taking utmost care of their customers by putting effort into giving them the best experience with their services. 

For instance chat-bots allow the customers to interact with the company in real-time so that their complaints are resolved, queries are answered, and other information is shared quickly and seamlessly. Artificial customer support helps give faster answers by handling commonly asked questions through live chat experiences which is available 24 hours a day providing continuous customer support. On the other hand, customer support teams spend a lot of time researching the answers to the customer’s questions increasing the wait time for the customers. AI can save these answers and use them for the frequently asked questions that customers have. 

AI leverages information from CRM solutions and highlights the key customer details so that the approach towards the customer’s is intelligent and AI-powered. With predictive insights, AI is able to suggest products to the customer based on their previous experience and the product’s availability in the inventory. And, sentiment analysis helps the customer executives by categorizing the tickets raised by customers into different categories like neutral, positive, or negative. This helps the agents understand how to prioritize their work. Artificial intelligence makes the work for the customer’s more incisive and responsive. Strategic and smart work.

Business Intelligence

AI-powered natural processing language and automated predictive insights are doing wonders for businesses. Employing AI in business intelligence is turning into everyday business with many adopting the technology. The plethora of data and the need to understand that data are the factors that are contributing towards the growth of Business Intelligence. This is a system that is designed to structure the large volumes of data that is collected, processed, and analyzed so that the data can be used to strengthen their business processes by making smart business decisions. Then again, digital AI amplifies the functionality of Business Intelligence by splitting large chunks of data into detailed insights that could be used in advantage for their business.

With benefits of Machine learning coming into the scenario, the issue of real-time insights was able to be resolved by ML algorithms predicting trends and generating real-time insights. This has influenced the value of Business Intelligence in a constructive manner.

You may also read: Scope of AI on gaming business

AI-driven personalized and targeted marketing

To be able to communicate with the target markets is extremely essential for every business and AI helps you do just that. It is behaviour-based algorithms and predictive analysis that brings together all the data that makes this communication possible. Also, there are several AI-powered intelligent multisensory systems that monitor different types of sensors in an environment and accordingly interpret the activities that are taking place in that monitored environment. This monitoring and interpreting has come together as a solution to several challenges. They help businesses learn about their customers’ requirements and expectations which helps them provide the best services possible. 

You may also read: Predictive analytics & machine learning across 5 industries

The other facets that come with integrating AI into brand marketing is Ad targeting that are Ads that target specific users based on their previous online shopping behaviour. To serve these various purposes, there are advertising intelligent apps like Match2One, ReFUEL4, and others that prospect and retarget customers. Then there’s personalized messaging that again uses behavioural data to send messages to the customers that are of some relevance to them. Yoochoose, Dynamic Yield are some of the companies that use data to provide the customers with personalized emails and messages.

AI-enabled product recommendation and predictive analysis

Product recommendations engines are leveraged by companies to improve customer’s experience. There are many AI development companies that offer AI-enabled products and services and advertise more and more products in front of the customers. The fundamental point is to put out the right product in front of a customer and that is how AI recommendations work. The  scads of customer data like the customer’s profile, product metadata, the products purpose, etc. that is present on the internet is used by the AI system (machine learning algorithm) to predict and match products for the customers that are most likely to be purchased by them. Well, there are a number of popular recommendation approaches that subsist. Most of the product recommendation operates on predictive analysis that helps train the algorithm with all the product and customer data. The relevant product ads then appear to the customers on the sidebar, top banner, and other areas of the retailer’s choice through designated channels.

Blockchain and AI development 

Blockchain and AI development individually are preeminent technologies across almost every industry and also together they are proving to be quite an influential duo. Integration of Artificial intelligence in Blockchain is imperative as they both deal with data and value and they collectively improve the capability of a machine learning algorithm. Not to mention that Blockchain ensures secure storage and exchange of data and AI analyzes and draws insights from the data to derive value. Both the technologies mutually benefit one another and enhance each other’s capabilities which multifolds the grade of efficiency, security, and accountability for business processes.

Natural language processing

What this means is the capability of an AI system to read, understand and recognize several human languages and an NLP technique converts the written or spoken language into a form that computers will understand. In business, the NLP technology is put forth using sentiment analysis that helps the businesses earn a broad public viewpoint on their products and services. A few applications of NLP technology are Email filters that are used to stop spam emails from entering their inbox, smart voice driven interfaces to understand and take better care of user’s concerns and queries, extraction of information from texts, infographics, and images (unstructured data) to understand human conversation and improve the ML program. There are some popular NLP softwares and tools like Amazon comprehend, Gensim, Google Cloud translation, IBM WatsonTone Analyzer, and others that are used to build chatbots, voice assistants, predictive text applications, python programs and libraries, cloud based solutions to work with human language data, etc.

The number of AI-powered voice assistants is forecast to reach 8 billion by 2023—a 146 percent increase from 2019’s 3.25 billion. (Source: Oberlo)

For those not in the know, one such company that provides AI solutions to businesses is Logic Simplified, an artificial intelligence development company in India. We understand the importance of data processing in AI for businesses and its capability to solve long standing business challenges. Every business is different but some of the challenges that they come across are age old and quite common among all. And, the team of AI developers at LS are well-positioned to bring to you AI-enabled solutions and approaches that are effective and capable of allowing businesses to manage data, satisfy customers, improve customer interactions, ensure secure transactions. For the most part, make business highly productive. For more information get in touch with us or email at enquiry@logicsimplified.com

Source: Statista

There are countless physical devices around the world that are connected via the internet. There is interchanging of data going on every second. It is pretty much adding digital intelligence to devices with sensors and no human assistance. With times changing, this innovation is getting bigger, better, and impactful on our everyday lives. Businesses are  integrating the Internet of Things into their various work models and are observing impactful outcomes for themselves. Nevertheless, there are still some obstacles that come the developers way and they are doing their best to overcome them. 

Data security and Privacy concerns

IoT is doing things in wonderful ways around the world. There are more and more devices being added to the IoT network and there are also vulnerabilities associated with every other device. Besides, there’s also tonnes of data being produced every minute that is harnessed, distributed, stored, and processed by all big and small businesses using the Internet of Technology. This massive amount of data that is produced is difficult to manage even with technology such as Artificial Intelligence and its algorithms. 

As long as there exists inadequate IoT security standards, the developers will continue to create devices with poor security systems. Weak passwords, hardware shortcomings, an unsafe update mechanism, insecure data transfer and storage, and unpatched softwares and operating systems are the underlined obstacles that are seen in IoT devices and are seen to exist from the manufacturers end.

Other than that, the importance of being aware of the ways that can help us stay safe on the world wide web is also crucial. The internet users must know how to avoid spam and phishing mails, execute virus scans on their computer systems, keep strong passwords, and to know how to have a secure wifi system, and good understanding of the IoT functionality. And, on the other hand the manufacturers need to carry out proper testing before bringing out the product in the market. Or else there are of course potential threats to the security of the user but such a breach on the company’s end is capable of destroying their reputation.

The report states, “Through the first six months of 2019, SonicWall has registered 2.4 million encrypted attacks, almost eclipsing the 2018 full-year total in half the time. This marks a 76 percent year-to-date increase.”

Source: Forbes

The devices connecting to the IoT infrastructure are on the rise but so are the number of malware and ransomware that are exploiting the system. These attacks to the system have the potential to affect the device’s functionality but they also steal user’s sensitive data like credentials, location, personal data, and more. There are also several Blockchain and IoT technology companies that work together where IoT applications and IoT development platforms rely on Blockchain to monitor and update the applications round the clock, the whole purpose being prevention of cryptocurrency exploitation. 

Connectivity and Communication

Connectivity and communication has changed our lives and made people’s life comfortable in unbelievable ways. Though, there is a seamless flow of data between the devices, cloud, and applications that seems challenging. The sensors, actuators, and the people are all interconnected in an Internet of Things system. There is transfer of streams of data between the three all the time which makes connectivity an important part of IoT.

You may also read the IoT data collection and reporting

It becomes difficult for software developers to keep up with the latest technologies and to ensure that the devices work smoothly despite the changes and upgradations. Developers come up with versatile solutions to confront the connectivity problems. The solutions that should be simple, inexpensive, and able to be used in both R&D and manufacturing to leverage code and minimize measurement correlation issues across the different phases of development. Here, connectivity is evaluated on various terms like the frequency bands, MAC protocols, other network protocols, mobility, and more. At the global level, the network becomes more complex with other heterogenous networks with slow connectivity, fast connectivity, proxy servers, firewalls and more disrupting the connection. This makes it difficult for developers to control the connectivity. The process of transferring data majorly depends on good connectivity. Nevertheless, there are several factors that impact good connectivity.

Factor_1. In a big IoT setup, there are processors, user and communication interfaces involved with immense amounts of power which make the networking complex. Hence, there is a need to efficiently manage the power usage or else it can in some or the other way affect connectivity.

Factor_2. Bandwidth consumption is anyways expensive and like in most cases, when there is  an outsized amount of IoT devices involved with server issues, the connectivity issues rise.

Factor_3. Over the times, there are different connectivity standards that have been introduced in the industry. And, to avoid any sort of connectivity issue, it is necessary to choose the appropriate and compatible standard/IoT protocol as per the requirement.

Factor_4. For transportation of data between devices, a credible bidirectional signalling is essential to abstain from any problem in the connectivity.

Factor_5. It is important to ensure immediate detection of IoT devices going online and offline so that any problem that has arised in the network can be fixed which is again to avoid connectivity problems.

The global Internet of Things (IoT) market size stood at USD 250.72 billion in 2019 and is projected to reach USD 1,463.19 billion by 2027, exhibiting a CAGR of 24.9% during the forecast period.

Source: Fortunebusinessinsights

Compatibility Issues

As the technology is evolving and expanding in different paths, the IoT challenges in compatibility are increasing simultaneously. There are rising compatibility issues coming from operating systems, varied firmwares, the non-unified cloud services, poor standardized M2M protocols, and others. Other than that, there are several technologies competing to make a mark and become definitive and this is calling for deployment of any additional hardware or software devices.

Hardware (sensors, IoT gateways, actuators, and more) and device compatibility together build an IoT network. Therefore, this challenge can be a roadblock for successful IoT implementation. Any device discontinuation by its manufacturer, device failure on the developer’s end, or not being able to replace a specific device  should not be the area of concern and this is where compatibility of devices needs to be taken care of so that IoT appliances can function without any hurdle.

Undoubtedly, the internet of things has opened the doors to a galore of opportunities and intriguing applications for many industries in the market. But again, there are varying complexities also that come our way. Indeed, it is a tough nut to crack but there is the need to deeply understand the problems and look for solutions that are the right fit to build a robust cornerstone and get our way. One such company that is contributing in its own way to overcome these technical challenges and present state-of-the-art IoT services to its clients is Logic Simplified. An IoT app development company based out of Dehradun that works towards using the right design, validation, testing, protocols/standards, manufacturing and IoT security solutions to ensure that they deliver what is expected of them with utmost attention to detail and quality.

There’s tonnes of data being generated every minute of our life. Thereafter, the data is provided to a machine learning model so that it is calculated and analyzed thousands of times to make progressive decisions and recommendations. With no human assistance, machine models learn, identify patterns, and make accurate and efficient decisions removing any possibility of human error. Abundance data availability, affordable data storage and processing marks the expansion of this prospective technology. This is also the reason that motivates companies to build compelling machine learning models that are able to analyze bigger and complex data in less time with improved results. 

Countless industries like healthcare, government, marketing and sales, e-commerce, social media, transportation, logistics, manufacturing, etcetera are embracing the technology as the best way to walk the path of expansion and development. They understand the value that it can bring to business of all sizes.

75% of enterprises using AI and machine learning enhance customer satisfaction by more than 10% (Source: Forbes)

Making informed decisions 

This is a very crucial aspect for business organizations and they rely on good information available at the right time for that. Business organizations take advantage of ML technology and put other intelligent technologies to use to extract large data sets to integrate them into everyday processes and develop actionable predictions. This futuristic technology in the 20th century offers improved business models and services.

You may also read Benefits of AI wearable devices for healthcare industry

AI-driven personal assistants reduce man’s efforts

These computer systems perform tasks based on human commands. Machine learning here uses natural language processing (NLP)  to program a system to process and assess the human language. The speech or text recognition is looked into that allows humans to communicate with computers in their own language and the computers to understand the command and act on it and perform likewise.

Machine learning improves logistics 

ML improves logistics (a business that runs on data) by enhancing the processes like buying raw materials, manufacturing, selling the end product, shipment, etc. It allows the service providers to study large frameworks of data making its management system intelligent and improved. From optimizing equipment costs, and increasing transparency across the supply chain, accounting for unusual activity, this technology is proving productive. 

Maintaining manufacturing for companies 

Predictive analysis and ML apps let businesses save and make money. It stores data, analyses it, predicts outcomes, generates insights, and brings solutions for the business. There are specific algorithms that can also analyse the working of a machine and when it requires maintenance and this avoids unexpected machinery problems and shutdowns.

According to a report, the global machine learning market was valued at around USD 1.58 billion in 2017 and is expected to reach approximately USD 20.83 billion in 2024, growing at a CAGR of 44.06% between 2017 and 2024.

Source: Globe News Wire

Analysing customer’s data

Now think of it this way, let’s say a company has 1 petabyte of data which is trust me a lot and calculatively, it is humanly impossible for one person to process all of this data without any errors. So, by using a machine learning software, the company will be able to analyze this large volume of unstructured data in less time and without errors. Now you tell me, won’t this give the company a competitive edge over the others?   

Business competition is overwhelming and for any business to do better in the market and earn profits, it is important for them to understand their customers and their ever-changing needs. There is large amounts of data to make the process a tad bit easier. And, when it comes to processing that data, machine learning comes next to help with the process. 

Customer’s data like purchasing patterns, preferences, demographics, their reviews, complaints, and more are analyzed to make predictions and machine learning will explain all the data that can be used to build business strategies and increase business by providing better customer experience. This can be achieved by building individual customer profiles, improving user interface and user experience and optimizing search results on search engines.

57% of enterprise executives believe the most significant growth benefit of AI and machine learning will be improving customer experiences and support.

Source: Forbes

Cybersecurity

Unidentified threats that are capable of causing harm to any business should be recognized and taken care of and having said that, machine learning in business is capable of doing so and within no time.

“If not now then when?” is the question here. It’s high time we discontinue manual data entry. There are errors and duplication of data likely to be found with a manual approach. Also, let’s not forget about the increased time consumption the process demands. Machine learning and predictive algorithms encourage to make the data entry process less arduous and also reduces the chances of glitches.

Financial Analysis

Investors, financers, traders, stock marketers use ML algorithms to analyse the market and make desired profits. Algorithm trading to make better trade decisions, fraud detection and prevention, portfolio management, loan underwriting and credit scoring are all taken care of with the use of developed machine learning systems. Oodles of data are accessed, studied, different parameters are adjusted to improve the overall experience by providing accurate insights and calculative predictions, streamlined processes, reduced risks, and better-optimized portfolios.

You may also read about the Development Sets in Machine Learning

Machine learning has become an integral part of all businesses and that is because entrepreneurs understand its value and how it can work in great ways for them, if used to its full potential. The flourishing internet, the booming online presence along with the outsized number of connected devices, will make businesses reliable on algorithms to bring solutions to their expansive problems. This discipline of artificial intelligence will increase performance and enhance security with a substantial booth in ROI and revenue. The key is to know how to take advantage of ML to solve business problems effectively.

Be that as it may, Logic Simplified, an artificial intelligence development company in Dehradun, India has aspiring and focused machine learning programmers that are innovative and share their experience with their admirable work. The real-world cases of machine learning are heard by many if not all and we as a team walk slowly but with heavy steps to bring to the world the unheard and unseen with our unparalleled work in the field of artificial intelligence and machine learning. To know more, drop in your queries at enquiry@logicsimplified.com

The internet world is blooming. It is not only about laptops, computers, tablets, and smartphones anymore. Numerous devices are now internet-connected. Door locks, washing machines, robotic vacuum cleaners, toys, and toasters are part of the list of “smart” devices. 

There is no denying the fact that IoT is a powerful tool for business sectors to optimise their operations, increase profits, and minimize the overheads as it has now been incorporated with AI and data analytics software. You can now find the smartest technologies around you, be it on smartphones, security systems, smart appliances, or cars.

Before we go further, let me give you a brief about this growing technology that has taken over the world and the impact of IoT2020. 

What is IoT?

IoT refers to a wide array of internet-connected devices that can interact with other devices and networks. They can perform many activities but are most commonly used to collect data and perform specific actions. Basically, a network of connected gadgets that allows sharing of data within the network. All the gadgets are constrained by sensors that are built in it. IoT provides a typical platform for dumping their data and a common language to communicate with each other. 

You may also read: IoT data collection & reporting in the light of cloud computing

2020: The year in review

With the COVID-19 crisis, we can assume that there have not been many major advances in the technology field. The global pandemic has caused a significant setback in the technology sector. Many advancements, including 5G and IoT, which were predicted in 2020, had to be moved to later years. 

But many improvements were made in the employment and business sectors, as in health care during the year 2020. The global pandemic has forced all the business organizations to change their working practices and goals in a few weeks. For example, 2020 was the year of working from home, and the use of numerous remote-work platforms and technologies that exploded suddenly. Connectivity and the capability to perform home activities from purchasing items like groceries to attending international conferences became more crucial than before. So, 2020 drove companies and employees to become more focused on technology for professional and personal benefit. 

As businesses strive to develop digital innovations into the post-pandemic world, it is anticipated that 2021 will meet sustainable development trends. As physical interactions across the globe are constrained, industry sectors like restaurants, shopping malls are in the course of becoming more digital. Thus, there is no wrong in assuming 2021 and 2022 as the “digital year”.

According to a report study by SafeAtLast, there were around 15.41 billion IoT connected devices in 2015, which have risen to 26.66 billion. In 2021, an estimated 35 billion IoT devices will be installed worldwide, and it is expected to reach 75 billion by 2025. This increasingly growing number of IoT devices would create additional opportunities for large and small companies to reap the smart technologies’ benefits. 

Looking ahead to IoT in 2021 and beyond, the technology is all set to be the heart of every enterprise. IoT is a technology of hope. This year, few of the trends will come to the forefront, increasing its significance, right from basic health-and-safety needs to data-intensive experiences, edge computing, etc. So, let’s have a look at the future of IoT trends in 2021 that will change your life.    

IoT and Big Data 

IoT and big data - these two major innovations have developed independently, but they are now interrelated. They compliment each other well. In addition, several IoT devices are based on chips, and the main objective of this is to monitor the user’s activity. 

The concept behind integrating these two technologies is to obtain valuable information that can assist in making informed decisions. This is the primary reason big data development and the IoT are essential in making effective business moves. This is the new trend that is emerging and will surely boom in the year ahead.  

IoT and Machine Learning

IoT brings more smart tools into our lives and therefore contact from machine to machine becomes more advanced. Machine learning allows you to predict the outcomes of various scenarios better. Analysis of users is constant, whereas the algorithms of machine learning get better over time. 

You may also read: Predictive data analytics & machine learning applications across industries

As the IoT devices communicate with other devices, training the smart devices has become more straightforward: you can train them all by training just one device. For example, Apple introduced the Apple watch 4 series in 2018, redesigned and re-engineered with an advanced health monitor. It measures the heart rate of the user and includes a new accelerometer and gyroscope. These features detect hard falls and an electric heart rate sensor that can take an electrocardiogram (ECG) using the ECG app.

IoT and Artificial Intelligence

When combined with AI, IoT can do wonders for your business, and be used to develop highly smart and robust applications. In the past decade, AI has already received a lot of hype, and it continues to be a 2021 trend due to its notable influence on how we live, work, and play. 

From 2021 onwards, AI algorithms will need fewer data to generate more efficient results. As IoT devices generate higher and increased quality data, the amalgamation of these two technologies offers increasingly beneficial insights. In industrial companies, IoT and AI will automate processes to minimise the operational costs and reduce downtime. On a commercial level, these two in a combination will help wearable devices as well as other gadgets to understand human behaviour in an automated way better.  

Blockchain for IoT security

What does Blockchain technology mean? Is this technology the new internet? It is a technology that allows a company and individual to make instant transactions without any involvement of the third party. It works in the same way as the financial bank works. These days security is the main concern for businesses that deal with digital money transfer. And what can be a better way to transfer the money securely. 

The blockchain and IoT technology encrypts the information and stores it in the form of a chain. The transactions are highly encrypted and cannot be changed or hacked. Blockchain IoT solutions are extensively used by financial companies and government institutions, consumers, entrepreneurs, and industrialists. This is one of the most prominent IoT trends that will bring a considerable difference in the technology field and promote its advanced application to create technological devices. 

Fast and smooth 5G experience

The introduction of 5G, after the 2020 pandemic, opens several doors for future emerging technologies. As 4G is combined with office work and other internet browsing, whereas, for immersive technology such as AI robotics, self-driving cars, cloud computing, virtual reality, etc. 5G gives more importance to instant communication to work effectively with low latency. 

Also, reduced latency will allow the connected IoT devices to send and receive the data much faster than before, enabling the analysis and data management to run at a level that is impossible in 4G networks. With the global pandemic, video streaming and telecommunications became a much-needed component. 

The surge from 4G to the upcoming 5G comes down to fast communication and swift response. This instant communication will create new possibilities for AI technologies or IoT. According to the study, with the rise in 5G, IoT sectors have over 50% better gains. Thus, telecom firms have said to roll out 5G networks in countries like India and the US in the second half of 2021.

Prominence of edge computing over cloud computing

In the coming years, edge computing will see a great deal of prominence over cloud computing. The number of advantages it provides makes more and more people and industries will be inclined to use edge computing. IoT devices were previously based on the cloud for data storing needs. But now, rather than sending your entire data to the cloud from the IoT device, it is first transferred to a local device located closer to the device or the edge of the network. 

Now, the local storage device(edge) will send a certain part of the data to the cloud directly once sorting and calculating the data. This helps in reducing the traffic to the network. This process manages a large amount of information sent by each device. Also, reduced dependency on the cloud enables apps to run and minimise latency.

IoT and healthcare industry

One of the most crucial IoT applications that will rule the IoT app development in the year ahead is the healthcare industry. The global pandemic and constant lockdowns have forced everyone to consider the importance of the IoT. People have taken a step forward in using connected healthcare applications in the form of smart wearables to monitor their health and manage their illness. The IoT medical devices are expected to reach $72.02 billion by 2021 equivalent to the compound annual growth rate of 26.2%. 

Source: Aabme.asme.org

Healthcare devices that are excellently well set up with IoT makes it easier to track the patients’ health status. The 2020 pandemic has significantly increased telemedicine resources. In April 2020, 43.5% of people used telehealth facilities. One of telemedicine’s main advantages is that it eliminates the interaction between healthcare workers, patients, and other patients. 

Source: Mobidev.biz

Even after the pandemic is over, telemedicine is expected to continue. Healthcare is now using several sensors and wearable sensors, tracking, indoor navigation technology for healthcare facilities. Mobile health apps as well as digital assistants to monitor the health of the patients at home and a variety of other connected devices will help in reshape the medical world. 

Increase in smart city solutions

Over the last five years, we have witnessed many government entities implementing IoT projects that impact entire cities. For instance, Singapore uses a Smart Nation Sensor Platform (SNSP) to collect, process, and transmit data from connected sensors and devices to strengthen transportation, public safety, and urban planning on the island. 

Amsterdam’s city government uses a public Wi-Fi network, smart lighting with dimmable LED lights, and cameras in the squares of the city. 

As these great projects commence to generate enormous amounts of data, governments have the chance to introduce several intelligent solutions in order to improve the safety of their people, reduce traffic congestion, unleash sustainable development, and foster economic growth. Thanks to the implementation of artificial intelligence. 5G and edge computing innovations can advance data processing to a higher level as cities are turned into hubs for development.

IoT is all set to integrate with other technologies to make life simple and smart. As per market experts, the IoT industry will expand rapidly in the coming years and transform how we see things around us.  Whether we talk about the role of the Internet of Things in healthcare services or the great prominence of edge computing, developments in this technology will continue to significant success in the whole technological ecosystems across the globe. These IoT trends will favour both entrepreneurs and consumers, and almost all the industries will experience an increase in their business.   

Have any IoT projects in mind? IoT development experts at Logic Simplified offer a wide range of IoT app development services. Our IoT developers use advanced platforms for IoT developments like Amazon Web Series, Google Cloud IoT, openHAB and more. Throughout your business idea, we will guide you and create a next-gen IoT solution that actually benefits your business and your end customers. For any query related to IoT development, please contact us here or write to us at enquiry@logicsimplified.com

In 2020, consumer AR/VR spending is expected to reach around seven billion U.S. dollars (Source: Statista).

Having said that, over the years, the scope of AR has soared and the technology has been incorporated into different businesses to simplify daily tasks and business actions. It is no longer the time when the use of Augmented reality was just limited to the technical world but now it is integrated into various sectors & industries and this integration is benefiting businesses in many ways. 

The AR apps are used for staff training, quick referencing, and cooperative work in such businesses. Moreover, such apps can help you seamlessly track inventory and offer maximum support in business workflows. Over and above, the AR development process involves steps like testing, deployment, and post release that have its own expenses. Other than that, the app maintenance includes app’s update, security, addition of new features, third party charges, etc. Not to forget, the main code architecture and application design are the two closely related processes that comprise steps like 3rd party API integration, providing access to enterprise data, data storage, basic controls, encryption, scalability and the designing part includes 3D graphic designing, UI/UX designing, animation, visual design, and so much more.

AR-powered features are used to strengthen applications and games and thus, businesses. And moreover, mobile devices and good internet speed has made AR more accessible by being available on regular devices. AR has entered several industries - architecture, navigation, and healthcare to name a few.

There are several factors that influence the time and cost of developing a fully functional and well developed augmented reality app. And, the cost hinges upon a few subsidiary aspects like the type of app, scope of work, timeline, the AR development team, customization in the app, and more but the design, features, and the functionality are the primary factors that most importantly determine the pricing. With every feature a developer wants to implement, the complexity of an app increases. A few factors determining the cost of developing an augmented reality app are jotted down below.

1. There are three kinds of AR apps one can choose from for development. The types are Marker-based AR apps, Location-based AR apps, and Slam based AR apps. The costing differs for these different types of AR apps. The location-based and Slam based tracking AR app are way costlier to develop than the marker-based apps. The marker-based AR apps offer the flexibility to scan physical images called markers to provide images and a 3D model. Augmented reality business cards are an example. They also provide the opportunity to develop and integrate. 

2. Unlike the former, Location-based AR apps collect the user’s information like the location from the GPS and not by scanning any physical object. Example being the 2016’s famous augmented reality mobile game, Pokemon Go. 

3. Simultaneous localization and mapping (SLAM) technology is used in self-driving cars, drones, robots, and more. With the help of multiple sensors, algorithms integrate complex data to calculate the user’s position, place 3D objects in real surroundings, and create a map of the unknown environment. This technique is also used to build 3D models of random real objects by simply moving the camera around it. This approach has extensively received great acceptance in the field of Augmented reality as it uses feature points to understand the physical world around and allows AR applications to overlay digital interactive augmentations. 

4. Then again, augmented reality apps fall into one of the three categories, that are Augmented reality 3D viewers, Augmented reality browsers, and Augmented reality game apps. These complex categories of apps also contribute in regulating the cost of augmented reality app development.

5. AR Viewer development apps are intricate and its complexity depends on the AR types and the client’s requirements. Augmented reality browsers employ market based AR features and location-based, along with an internet connection for a functional GPS, gyroscope, device APIs and compass. Augmented reality content can also be incorporated into its pages. It also provides the feasibility to purchase such products from the browser using a payment gateway integration, 3D models of the  products, and security measures. AR game development cost also depends on in-game mechanics, and appropriate content as these are the factors that make the game interesting, fun, and provides the player’s a better gaming experience. Storytelling, music and sound effects, real-time multiplayer, and tiny elements like these are what grabs all the attention.

The typical cost of an Augmented reality 3D viewers app is estimated to range between $35,000 to $70,000. Augmented reality browsers will range between $50,000 to $85,000 and Augmented reality game apps would cost between $50,000 to $200,000.

6. Augmented reality apps are accessible in all sorts and support several platforms that include Apple iOS, Android, PC, and Windows Phone. The platform is chosen keeping in mind the preferences of the target audience and it is suggested to create an app that is platform specific. The AR development cost varies based on the platform.       

7. Augmented reality SDK (software development kit) allows developers to use packages of software development tools and libraries to develop augmented reality applications instead of creating everything from scratch. Examples are Vuforia, Wikitude, Kudan, and more that provide powerful augmented reality tools and impact the cost of development.

8. There are a lot of requirements that an app is expected to meet and planning that is believed to help us meet those expectations. An accurate estimation of the cost cannot be made because of the several factors involved in the process. One of the important determining factors includes the project size and the members involved in a development team that are crucial to complete their roles and tasks of the augmented reality project. 

9. Augmented Reality apps customization with diverse functions and features based on the business type and its requirements and more such features increase complexity. And, adding such features, functionalities, and customizations affect the app development cost but satisfy user’s requirements.

The type of app the client may want might be a simple gyroscope based app or an app using SLAM technology, with 3D models and simple user interfaces, apps with different number of models used and with a different number of screens and screen sizes it could lie on, and contrasting compatibility for various platforms. There are various challenging factors and complexity involved in the process and as discussed earlier, the final cost of an AR app is affected by several factors. Nonetheless, AR applications hold enormous capability to skyrocket productivity and profitability for a business.

However, with time passing, we get to see more advancements in mobile devices and the outsized integration of augmented reality in our daily lives. Therefore, it is extremely necessary for Augmented reality development companies to offer AR products and services that meet the customer’s interests. On the related note, Logic Simplified, an Augmented reality game development company in India builds AR solutions for both small and enterprise-scale businesses. They have a team of highly experienced and innovative game app developers that create AR applications that have the potential to build brand recognition, increase customer engagement and accessibility by providing support and receiving customer feedback. So, for businesses looking for solutions and expansion, the robust features of AR will help you to not just keep up in the competitive market but will make sure you hit that ball of progression right out of the park.

Security Breach Risk because of Lack of Compliance from Manufacturers

Manufacturers release new IoT devices into the market each day but most of them have undiscovered vulnerabilities. One of the major reasons IoT security issues is not spending enough time and resources while manufacturing IoT devices. For example, most fitness trackers that come with bluetooth remain visible after the first pairing. A smart refrigerator can give hackers an opportunity to access gmail credentials. Because of no universal IoT security standard being set as yet, manufacturers tend to create devices with poor security and do not include security as an important element in their product design process. The biggest IoT risks from manufacturing include weak or guessable passwords, unsecured hardware, absence of a secure update mechanism, lack of patching mechanism, insecure data transfer and storage

Security Issues because of No Patching and Update Management

Even if a manufacturer created a secure hardware and software for an IoT product, new vulnerabilities would eventually be discovered. This is the top reason why updates are so important to protect IoT devices from new vulnerabilities. Other devices like computers and smartphones get automatic updates, but some IoT devices lack necessary updates. An important point to consider here is that during an update, a device will be tasked to keep a backup on the cloud causing a short downtime. If encryption technique is not used for the connection and the update files are not protected, a hacker can stand a chance to steal sensitive information. Also, the nature and use of IoT devices make it difficult to release updates regularly. Think of sensors sprawling over hundreds of acres of farmland or of IoT devices on a factory floor that need to go offline for updates resulting in production loss.

Also read 7 factors companies must consider for IoT software app development.

Botnet Attacks

A real threat does not come from just a single IoT device infected with malware, but from many of those infected IoT devices to bring down anything.  A hacker can perform a botnet attack by creating an army of bots by using malware and then sending numerous requests per second to bring down targets like electricity grids, transportation systems, water treatment facilities, and manufacturing plants and more. Serious concerns over IoT security were raised after the Mirai bot attack in 2016. A  massive distributed denial of service (DDoS) attack brought down the DNS that provided services to platforms like GitHub, Twitter, Reddit, Netflix, and Airbnb. IoT devices can easily fall prey to malware attacks majorly because of no regular software security updates that a computer usually gets. So, IoT devices can be easily turned into infected zombies and can be used to send incredibly vast amounts of traffic.

Lack of Physical Security

Lack of physical hardening can also lead to security breaches. Many IoT devices do not require human intervention but these devices are sometimes installed in remote locations which makes them prone to outer threats. Criminals can physically temper them, for example, by using a USB flash drive with malware. Users must take the responsibility of physically securing their IoT devices. Without adequate protection, a smart motion sensor or a video camera outside a building or house can be tampered with and render any data it collects or relays, unreliable.

Also read how Blockchain and IoT Technology empower each other.

Lack of User Knowledge and Awareness Around IoT

Internet users have learned over the years how to avoid phishing emails, run virus scans on their PCs, and create strong passwords to secure their WiFi networks. But things are not the same with IoT as it’s still a new technology and even many seasoned IT professionals have lack of knowledge and awareness of IoT devices. The 2010 Stuxnet attack against a nuclear facility in Iran strongly shows how the user’s ignorance and lack of awareness can create severe IoT security risks.  The target of the attack was industrial programmable logic controllers (PLCs), which function similar to IoT devices. The attack happened after a worker plugged a USB flash drive into one of the plant’s computers, causing physical damage to around 1,000 centrifuges. It’s often a doddle to trick a human to gain access to sensitive information and this IoT risk should not be overlooked in social engineering attacks.

Ransomware can Hijack IoT Devices

Ransomware is an evolving IoT attack which stops access to sensitive files. Although a ransomware attack does not cause any harm to your sensitive files, it can lock down the entire functionality of your device. IoT devices with poor security can become targets of ransomware and may put a user in a situation where a hacker asks them to pay a ransom to start their car or unlock their house or for the decryption key to unlock their sensitive files. Just eight days before Trump inauguration speech, cybercriminals attacked police surveillance cameras with ransomware in Washington DC. The ransomware attack infected 70% of the CCTVs and the police couldn’t record for several days. IoT devices like wearables, healthcare gadgets, smart homes, and other smart devices will be at risk of ransomware attacks if there’s cutting corners in security.

Espionage & Eavesdropping

Some IoT devices give unnecessary access to others and put your sensitive information at risk. The interactive IoT doll is a good example which gave access to the toy’s microphone and speaker to anyone within the 25-30 meter radius. It was banned and labeled as an espionage device in Germany. Many IoT devices like health equipment, smart toys, wearables, etc. record user information and hackers can get access to sensitive information if your device behaves like an espionage. Some countries have already started banning IoT devices with security issues.

The security risks discussed above show how IoT devices are at more risk than other computing devices. Whether it’s IoT software app development or IoT hardware manufacturing, strong security protocols should be followed at all levels to avoid hack attacks.

Logic Simplified offers IoT app development services and keeps security as a top priority. Our IoT app development company builds secure IoT solutions for platforms like Amazon Web Services IoT, Microsoft Azure IoT, Google Cloud IoT, openHAB, IBM Bluemix IoT and more. We are experts in programming languages, development frameworks, communication protocols, sensor technology and 3 party APIs for IoT development. For any query related to IoT development, please write to us at enquiry@logicsimplified.com.

What Does an IoT Developer Do?

IoT related work is carried out by three types of professionals

1. The network specialists who manage connectivity

2. The data analysts who collect data from the devices and interpret it

3. The engineers who develop the platforms, software, hardware, and systems that allow these devices to work.

IoT developers fall into the last category; these individuals are responsible for overseeing the production of the devices or sensors. It includes most prominent programming software that allows the device at hand to both connect with other systems as well as function accurately on its own. Though responsibilities will vary considerably depending on the industry, other job roles may include designing, coding, and testing features of products that are meant for connecting to other devices. Some projects may also require developing embedded software that’s cloud-compatible, to allow products to integrate accurately with one another. We will now list the skills and know-how that you should look for in a good IoT developer.

AI and Machine Learning

AI and IoT together are reinventing the way businesses used to operate. AI with its powerful subset of machine learning has paved the way for smarter task execution with real-time analysis and better interaction between people and machines. IoT, on the other, has raised the level of communication between devices and humans via effective intelligent technology. The convergence of the Internet of Things and Artificial Intelligence makes each other’s applications more diverse and robust.

An IoT developer maintains the skills of analyzing and gathering a huge amount of data for interpreting the pattern and predicting the result. With the rise in complexity, AI is used for managing the tasks and autonomous decisions are made with  AI as well. The algorithms of machine learning, on the other hand,  are used for creating smarter devices with the help of data sensors. 

IoT developers possess the skills of machine learning and big data management that helps them in making predictions based on the identification of data patterns. Every company needs skilled IoT developers IoT software app development who can harvest the data from the IoT sensors and connected devices.

Application design and development

The IoT market is widespread, and it has something to offer to many other markets. Internet of things application development services are becoming more popular. Web and mobile applications provide user interfaces for communicating with and consuming data from IoT devices.  IoT devices, however, are equipped with their user interfaces. Voice-based and gesture-based interfaces are gaining popularity within IoT, especially for home automation, while augmented reality interfaces provide opportunities for extending IoT data over the physical world. As a result, UI and UX design skills are some of the trending skills in the IoT industry.

Web and mobile applications are created with the help of high-level languages, with Java, Swift, and Node.js among the top languages for IoT app development services. GPS programming skills are in high demand, as are various IoT applications, including wearables and smart vehicles, that track location.  IoT developers keep track of emerging frameworks and developer kits that they can utilize for rapid prototyping, as well as IoT platforms to provide foundation and means to automate developing, deploying, maintaining, and operating IoT applications.

Security Knowledge

In the last decade or so, we have observed the explosion in the data industry. We have seen security issues at the top of the list of priorities of every business. Especially in the IoT industry, because consumers want to be sure if their information and data is safe. To keep the data private and safe, they have to be familiar with public key infrastructure,  ethical hacking, vulnerability assessment, network security etc. A specialization in cybersecurity is another significant role for an IoT developer. Whether it is IoT data collection or processing, it's an essential part of developing IoT devices and the key to the success of the business. 

Cloud Computing

The underlying idea behind IoT and Cloud computing is to increase efficiency in the day to day tasks, without interrupting the quality of the data being collected or transferred.  Since the relationship is shared, both the services complement each other perfectly. The IoT becomes the source of the data, while the Cloud becomes the destination for it to be stored. 

Due to the interconnected network in IoT, there is also a large amount of data to handle. It requires secure data storage which poses a huge challenge for IoT developers. This is the reason why the IoT industry has professionals who are expert in or have a previous history of working with cloud computing technologies for better analysis-ready data storage and management solutions. 

Business Intelligence

BI (business intelligence), is changing, as a result of the IoT. Data has become a new kind of currency in the modern-day world, and about everything we do in life and business generates data. We see every connected machine and smart devices generate incomprehensible amounts of information every second of the day. With so many devices consuming and sending and receiving terabytes of new information, the true potential of "big data” will be accomplished. Organizations try to collect, store and analyze smart device data streams for actionable intelligence business. IoT professionals with skills in sensor data analysis, data centre management, predictive analytics, PaaS, as well as programming skills in popular big data platforms like Apache Hadoop and NoSQL are ideally suitable to meet the needs. 

Networking

IoT sensors and applications can be retrieved with the help of smart devices. There are several networking tools and techniques needed to connect them efficiently with the apps. IoT professionals generally acquire networking expertise. The sensors associated with the internet of things environment connect with the ecosystem around them. The collected information is sent to get examined. The communication network design needs to be sound, reliable and secure. Therefore, internet Protocol networking is one of the essential skills sets  acquired by IoT developers

In addition to network design, the developer has practical knowledge of network standards, protocols, and technologies. It includes Wi-Fi, Low Energy Bluetooth, Zigbee,  RFID technologies used in consumer applications, and in Low Power Wide-Area Network (LPWAN) technologies like LoRa.

UX/UI Design

Designing a valuable and intuitive user experience for an IoT system in itself can be challenging given the complicated nature and modernity of this technology. Creating UI/UX design for IoT mobile application development is particularly sensitive. So it becomes necessary that the design of the interface between user and device is user friendly and equally efficient.

This skill set of IoT developers comes in handy to make the quality of product efficient. Some of the skill sets they have as a UX/UI designer on the internet of things development are responsive design and service design. If anything goes wrong, UI must be furnished enough to supervise users. Hence, one of the challenges for IoT developers is to develop skills to build intuitive and sophisticated UI with easy-to-understand and interactive elements. 

Consider the Critical Role of Sensors

In most of the automated solution, sensors are responsible for exchanging the live data to a digitally connected system. Developers working with IoT development platforms have detailed knowledge and precise understanding of how sensors operate and integrate into the IoT-powered architecture. IoT developers are skilled in wireless solutions and embedded systems and related functionalities.

LOGIC SIMPLIFIED FOR IOT DEVELOPMENT

Logic Simplified, an IoT app development company based in Dehradun, India, is committed to delivering high-end IoT solutions. Logic Simplified focuses on vision technology, home automation & smart office, connected cars & traffic, healthcare, retail, smart grid appliances, and education. Our IoT developers use sophisticated platforms for IoT development, such as Amazon Web Services IoT, Microsoft Azure IoT, Google Cloud IoT, and more. Third-party APIs we use include Google Assistant, Google Home, Google Vision, Apple HomeKit, MI Light, Cortana, Alexa Voice Service, Philips Hue and Android Things. 

Our team of programmers use C/C++, Python, Ruby and JavaScript to program IoT systems. We have already built various IoT apps of international standards. The experience of our offshore team in the domain can significantly help you save development time and cut expenses. We can help you to build a top-notch IoT solution too. If you want to discuss your IoT development plan with us, reach us at enquiry@logicsimplified.com, and our authorities will get back to you shortly to tell you how we can be your perfect technology partner to build a smart IoT app.

Surveys of machine learning developers and data scientists have shown that the data collection and preparation steps can take up to 80% of a machine learning project's time. 

Source: SearchEnterpriseAI

A machine learning model creation step involves training the model and then testing it. It starts with an idea, according to which the raw data is collected for the model and then data processing for AI and ML algorithms takes place which converts the data into a form that can be used by the model to learn. Once the model is built to solve a specific problem, it is tested until the model gives satisfying results.

Building a model can be time consuming and asks for the right approach and methods. For that, it is essential to understand the requirements that are expected of the model and the problem that is trying to be solved.

First things first, the data collected is prepared for use by employing algorithms and appropriate techniques. It is distributed into three categories - structured, unstructured, and semi-structured data. The model is thereafter trained by using the good quality and prepared data. This exhaustive process involves selecting the right technique, hyperparameters, algorithm, whereupon configuring and tuning hyperparameters, identifying the appropriate features for best results, finally testing and evaluation of different versions of models for optimum performance and whether it meets the objective. The evaluation is done applying the validation technique and using a validation dataset which determines the model’s capability of performing once ready. Operationalizing the model is what comes next which involves measuring and monitoring its performance. Then finally what is left is to make the model adjustable so that it works best in all circumstances and iterations are also made to attain the desired results.

The technique

The Hold-out cross validation technique is a cross validation technique used to  build a computational machine learning model and it divides the algorithm into three variant subsets on which the training, tuning, model selection, and testing is carried out. Those three sets are Training set, Validation set, and Testing set. As the name suggests, the machine learning algorithm is trained using a training dataset, the trained model is validated using the validation or development dataset, and the testing dataset tests the trained and validated model. 

Based on the algorithm and the type of data the model consumes, Machine Learning has two basic types of important learning methods - Unsupervised and Supervised. Supervised learning follows predictive data analysis while on the other hand unsupervised learning works on finding data patterns.

The dataset that does carry weight

The development set is a significant dataset in the process of developing a ML model and it forms the basis of the whole model evaluation procedure. A machine learning algorithm has two parameters -  model parameters that define individual models and hyperparameters define high-level structural settings for algorithms. The development set is used to select the parameters, tune them and then use them to choose the best model of a training algorithm. Nevertheless, it also helps in avoiding or minimizing overfitting and simultaneously controls the learning rate. 

It is the quantity and quality of the dataset that determines the picking of the best performance model and it’s precision. Development sets develop machine learning solutions and help one find the best model of all the different models. It allows one to choose the number of layers (Depth), neurons per layer (width), activation function (ReLU, ELU, etc.), optimizer (SGD, Adam, etc.), learning rate, batch size, and more in the algorithm.

60-20-20 rule of thumb

The size of the dev set is 20% of the whole and that sums up to a large amount of data which is used for training and teaching the model more diverse features. The three sets that are the training set, development set and test set split the algorithm into the ratio of 60 : 20 : 20.

Errors

While the model is computationally trained, there are chances of error arising, just like in any other process. And here, the error value on the training set is called Bias while the difference between the error value on dev set and training set is called Variance. And, error is analysed by identifying  Bias and Variance. 

To choose the best model that aligns with the needs of the objective, it is necessary that there are reduced possible errors in the process. The different sort of errors that arise in the path (bias-variance trade-off) are the training error and development error. Focusing on the latter, it is measured by analysing the divergence from the value predicted. 

Different data is spent to train and test your model by feeding it to the algorithm. If you use the same data to train and test the model, in that case the model could be overfit and then the model could perform well on the training data subset but poorly on the test data and vice versa.

The development error should be the lowest so that the model comes out good. Taking that into account, the errors are analysed time and again until they are reduced to minimal. This paradigm is used to pick the best model (algorithm) that can later be used to find accuracy on the test set. It is the development set that is used to choose and tune the AI model. 

A model’s performance should have low bias and variance. And, Cross validation is a common technique that is used to balance the Bias and Variance of a model. It contributes to achieving a stable estimate of the model performance. If the dataset is not split appropriately, it can lead to extremely high Variance of the model performance. Cross-validation techniques can also be used when evaluating and mutually comparing more models, various training algorithms, or when seeking for optimal model parameters. The model must work in training as well as validation and should not be overfitted. And, a validation set or development set can be taken as a part of the training set that helps find the accuracy and efficiency of the algorithm.

Logic Simplified, a machine learning and artificial intelligence development company in India has developers with qualities that are looked for, like precision, accuracy,  someone who understands the ML ecosystem well and has the capability to build machine learning models that meet the interests of the industries and create diverse possibilities and opportunities for people. Get in touch with us and enquire all you want to avail the best services in town. Share your thoughts, enquiries and suggestions by writing to us at enquiry@logicsimplfied.com, and we will get back to you shortly to provide you with high-end Artificial Intelligence and Machine Learning solutions.

A typical interactive VR application can generate about one terabyte of data per hour, which is the equivalent of approximately 200,000 songs and 17,000 hours of music (assuming the size of one song is 5 MB). Storing such huge piles of VR data is a big and expensive task. Cloud may not be a practical solution for storing VR content as there is a constant need for retrieving, editing and enhancing VR data based on user interactions. Any delay in data access will hamper the user experience and break the immersion because of hiccups and outright loading freezes. Slow-loading or lag-heavy experiences not just frustrate users, but also pose the risk of motion sickness due to disorientation or other motion-induced conditions. Also, the initial cost of the cloud is nominal, but recurring cost increases sharply as data volumes grow. The cost of recalling data from the cloud also goes on a higher side when we take into consideration the type of files as large as VR.

One of the best ways to store VR data is to find a solution that makes it possible for companies to access and tinker with the files as per their needs. Plus, it should also allow to archive VR data files economically in order to protect and store the data for as long as needed. VR developers can achieve this by following a two-tier system approach, with network-attached storage (NAS) on the front end, and tape storage on the backend. This system not just significantly cuts storage costs but also works perfectly for VR content. VR data can be easily accessed through tier 1 storage, such as NAS disk systems. Once the data is edited, VR developers can move it to a low-cost second-tier storage medium, such as tape. A two-tier storage infrastructure will go a long way in saving time and money for your VR business.

Also read: What goes in virtual reality game development

Solid state drives (SSDs) are considered to be the best for building VR applications as they allow for faster read/write data access. However, as SSDs are more expensive than mechanical hard disk drives (HDDs), most VR developers opt for HDDs because of far greater storage ability at a much lower cost. Yes, the cost of SSDs have started to decline, but it will still take some more time to get ahead of HDDs.

Edge computing is also a very good solution for storing VR content. As data storage technology advances and becomes less expensive, we will see more and more VR apps distributing much of their VR content away from the core of the network at the edge facilities that are closer to their end-users. Edge data centres can eliminate much of the latency issue caused by distance.

Also read: The good and the bad of Sony PlayStation VR gaming

Since VR applications are about delivering an immersive experience, it also becomes important to use the highest quality displays, especially because the screen is held so close to the user’s eyes. A typical 1080p display is good, but far from ideal for VR content. Many VR developers now use more detailed 4k displays, but 8K displays are also available in the market now. Video footage in such high resolution on 8K displays will create an even bigger challenge for network infrastructure.

It’s not an easy task to create and store 3D content for VR apps, but you can save a lot of time, money and effort by outsourcing VR game app development and 3D modeling to experts in India. That said, I would like to introduce you to Logic Simplified, a virtual reality game development studio in India, with over a decade of experience in building video games for multiple platforms.

Build Your VR Game with Logic Simplified

Our virtual reality game developers do a high level of brainstorming and have great prototyping skills. It’s very important to handle data storage issues from the beginning of VR development and we, at Logic Simplified, ensure that every time. You can hire VR game developers from us to build VR games for best technologies, such as Oculus Rift DK2, Oculus Samsung Gear VR, HTC VIVE, Google Cardboard (Mobile VR), Google Daydream VR and more. Maintaining a proper frame rate of 60 FPS is quite a challenge in VR game development as rendering is done twice for each eye perspective. If your device’s frame rate does not match with user head motion, it will cause motion sickness. Our VR developers use various optimization techniques, such as object pooling, dynamic and static batching, baked lighting, VR optimized shading and more, to provide an immersive experience of looking around, without triggering the events of motion sickness. Our developers also use specialized tools and libraries, such as gvr-unity-sdk-master (Google VR SDK)/Leap Motion Orion SDK (HTC VIVE SDK)/Unity Embedded Oculus DK2 SDK, to speed up the overall VR game development cycle. We are a reputed game app developer in India you can trust to save a lot of time and money that go in building a highly immersive VR game. Please share your VR game idea by writing to us at enquiry@logicsimplified.com, and we promise to get back to you shortly to provide you with gaming solutions we are proud of.

There are various new revolutionary technologies also backing the burgeoning growth of IoT devices that are now much more intelligent and useful than Romkey’s toaster. And we know them as  AI/ML, Big Data Analytics, Cloud Computing, Digital Twins, Edge Computing, AR/VR, Blockchain, and the next generation mobile connection technology 5G. But, it’s not an easy task to build a complex application, especially for IoT hardware. It requires tech expertise and knowledge of cutting-edge platforms to come up with the best practices when building an IoT solution. That said, I am listing down 7 best platforms for IoT development that most IoT companies are using in 2020 to build impeccable IoT solutions.

1. Microsoft Azure IoT

Azure IoT offers a collection of managed and platform services from edge to cloud that connect and handle billions of IoT assets. Azure IoT also includes security and operating systems for devices and equipment, along with data analytics to help you build, deploy and control IoT applications. Azure IoT Edge, Azure Stack and Azure Stack Edge allow you to handle push applications and workloads to the edge. Azure IoT Hub and Azure IoT Central are very popular services that an IoT app development company can use for connecting and managing device data with flexibility. You can also create digital models of entire environments using Azure Digital Twins. Azure Stream Analytics helps process a large amount of data generated by sensors, whereas Azure Time Series Insights allows you to explore and gain insights from time-series IoT data in real-time. Microsoft is making continuous efforts to build more IoT products and its new IoT business solution focuses on removing waste through AI and ML and boosting business productivity. Azure is a very popular platform which many businesses are using for easier and productive IoT development.

2. Amazon Web Services (AWS)

AWS is another popular platform available today for IoT development. AWS is also a managed cloud-based platform that connects your IoT device to other devices and AWS cloud services. AWS offers FreeRTOS (an open-source, real-time operating system for microcontrollers) and AWS IoT Greengrass to connect your device and operate them at the edge. So, your edge devices can act locally on the data they generate, while the cloud will still be used for management, analytics, and durable storage. Using FreeRTOS, you can easily program, deploy, secure, connect, and manage small, low-powered edge devices. For robust security, control and management of your devices from the cloud, AWS IoT suite includes AWS IoT Core, AWS IoT Device Defender and  AWS IoT Device Management. AWS IoT Analytics and AWS IoT Events allow you to run sophisticated analytics on massive amounts of IoT data. Several companies globally use AWS for IoT development as it provides an exclusively solid and easy-to-use framework in the cloud, along with versatility, adaptability and cost-effectiveness.

3. IBM Watson

IBM Watson is an API offering centralized service for connecting sensors, service transceivers, and backend. Watson also brings Blockchain and IoT technology together and enable data analytics that allow businesses (especially manufacturing, electronics management, and infrastructures that require lots of maintenance)  to capture and explore data for devices, equipment, and machines, and get executable insights for better decision-making. IBM Edge Application Manager helps scale run edge solutions anywhere with autonomous management to act on insights closer to where data is created. 

The portfolio of edge-enabled applications and services include  IBM Visual Insights, IBM Production Optimization, IBM Connected Manufacturing, IBM Asset Optimization, IBM Maximo Worker Insights and IBM Visual Inspector. With IBM Waston, you get the flexibility to deploy AI and cognitive applications and services at scale. Watson’s advanced machine learning solutions help analyze data and detect anomalies in the historical data. You can dynamically retrain models, automatically generate APIs to build AI-powered applications, and streamline model management and deployment end-to-end with an easy-to-use interface. IoT data collection and processing play a big role in the success of IoT projects to which IBM Watson provides some brilliant services to use easily and for high accuracy.

4. Home Assistant

It is an open-source tool for home automation that puts local control and privacy first. Home Assistant is designed after combining Home Assistant Core and tools which allow users to run it easily on a Raspberry Pi and other platforms without setting up an operating system first. Home Assistant functions with a Python-based coding system. You can set up your own Home Assistant server with MQTT support. Using Home Assistant, you can build an IoT system that can be easily controlled with mobile or desktop browsers. 

Though Home Assistant lacks cloud components, it’s well trusted by customers for operations, security and privacy in this Internet age. The recommended hardware for Home Assistant is a Raspberry Pi 4 Model B, but you can also use it on an existing, more traditional Linux host by using Docker. Home Assistant addresses the security concerns over sending private data to centralized servers for processing, let’s say simply for turning on lights of your home. This platform provides a private centralized hub for automation and communication between a home's various IoT devices. Home Assistant is a popular platform, approaching its 600th release from nearly 2,000 contributors on GitHub.

5. Arduino

Arduino is an IT company based in Italy and builds microcontroller boards, interactive objects and kits for IoT development. Arduino is the most preferred IDE for IoT and provides a full-blown, mature and very well optimized platform to interconnect different hardware systems. It acts as the brain of the system and processes the data from sensors and comes with an ATMEGA microcontroller that processes the data and facilitates the proper working of the IoT system. The beauty of Arduino is that it can be programmed ‘n’ number of times, which means you can use it for various types of IoT projects just by making changes in a simple code. C++ is used for Arduino programming and an IDE software for Arduino based IoT projects. You can build hardware using Arduino by feeding a logic to take input from the environment, process it, and produce a desirable output. For example, your garden sprinkler to start pumping water when the temperature outside is greater than 50 degrees or automatically pulling out your window blinds at a fixed time in the morning.

6. Eclipse IoT

Eclipse IoT provides a set of services and frameworks for IoT software app development. It allows developers to build M2M and IoT applications and enable features such as device management, wired/wireless communication, and vertical solutions. Eclipse IoT is a collaboration of various companies and individuals who are committed to build a set of open source IoT technologies. Among its major services is SmartHome which helps create a framework for building smart home solutions. SmartHome facilitates interaction between devices by providing uniform device and information access. Eclipse SCADA makes it possible to connect various industrial instruments to a shared communication system. Besides, it post-processes data and sends data visualizations to operators.

Eclipse IoT also embraces important standards and its libraries  are superb for M2M/IoT device communication via MQTT, CoAP or ETSI M2M. Eclipse Ditto is where IoT devices and their digital twins get together. This framework enables you to manage the state of digital twins. By providing search functionality on meta data and state data, Eclipse Ditto also allows you to organize your set of digital twins, building a bridge between real-world IoT devices via their digital representations and applications. Eclipse Ditto is where IoT devices and their digital twins get together. This framework enables you to manage the state of digital twins. By providing a search functionality on meta data and state data. Eclipse Ditto brings IoT devices and their digital twins together. You can organize your set of digital twins and build a bridge between real-world IoT devices through their digital representations and applications.

7. Contiki

Contiki is a very popular open-source IoT operating system, especially for low power microcontrollers and other IoT devices to run effectively using Internet protocol IPv6, and IPv4. Contiki is written using C to provide a rapid environment for development in a single download. It also supports wireless standard CoAP, 6lowpan, and RPL. You need only 10kb of RAM and 30 kb of ROM to run this IoT operating system. Contiki programming model uses Protothread memory-efficient programming. Contiki has broken many myths about the smallest footprint in which an OS can be stored and made to function. It also has ports available on other platforms such as Arduino and Atmel. Contiki’s functions include process and memory management, communication management, file system management, and more.

With this, I have covered 7 top platforms for IoT development. From tech giants (like Google, Amazon, IBM Corporation, Cisco and Microsoft) to many startups with great IoT ideas and backed by big funding (like Alert Media, Armis Security and Element Analytics) are now leveraging IoT development to create a smarter world where people don’t need to wait as devices communicate. We are also witnessing IoT applications such as driverless cars, smart homes, smart healthcare, smart grids, and many more turning into reality. If you are also looking forward to building an IoT solution to solve a real-world problem, Logic Simplified can help shape your idea into a reality.

Why Logic Simplified for IoT Development?

Logic Simplified, an IoT application development service company based in Dehradun, India, is committed to deliver high-end IoT solutions. Logic Simplified’s focus areas in IoT are vision technology, home automation & smart office, connected cars & traffic, healthcare, retail, smart grid appliances, and education. Our IoT developers use sophisticated platforms for IoT development, such as Amazon Web Services IoT, Microsoft Azure IoT, Google Cloud IoT, and more. Third party APIs we use include Google Assistant, Google Home, Google Vision, Apple HomeKit, MI Light, Cortana, Alexa Voice Service, Philips Hue and  Android Things. Our team of programmers use C/C++, Python, Ruby and JavaScript to program IoT systems. We have already built various IoT apps of international standards and the experience of our offshore team in the field can significantly help you save development time and cut costs. For example, we have built a mobile application that correctly suggests the best fit of any garment or footwear. Another is a security device which notifies users through their smartphones when any vehicle comes in a defined radius on the rear side of their vehicles. We can help you too to build a top-notch IoT solution. If you want to discuss your IoT development idea with us, write to us at enquiry@logicsimplified.com, and our experts will get back to you shortly to tell you how we can be your perfect technology partner to build a smart IoT app.