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

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.

When compared to desktops and mobile devices, IoT devices have less power, memory, processing and size. It is thus important to choose an IoT operating system that fits the capabilities of the device and the requirements for its functionality. The architecture of IoT systems involves a large number of sensors connected to gateways, which in turn are connected to remote cloud platforms. IoT OS is critical to connectivity, security, networking, storage, remote device management, protocol support, security and other IoT system needs. Some IoT systems have the capability to process data in real time and are referred to as real-time operating systems (RTOS). As every IoT device has its perspective, the choice comes down to the careful assessment of all the requirements and capability of different IoT operating systems available in the market. However, according to the IoT Developer Survey, Linux remains the top IoT OS choice for IoT microcontrollers, constrained devices and gateways.

Source: IoT Developer Survey

What makes the Internet of Things work is interaction between sensors, devices, gateways, servers, and user applications. But, there are IoT standards and protocols to follow to make an IOT system function and transfer information in the online mode only when devices are safely connected to a communication network. General protocols used for personal computers, smartphones or tablets may have IoT devices constraints such as bandwidth, range, and power consumption, which is why multiple IoT network protocols have been developed and new ones are still evolving. In addition to wired (USB, Serial, Ethernet, etc.) and wireless (Wi-Fi, BT, ZigBee, LoRA, etc.) protocols, IoT systems have multiple IoT protocols that typically have low transmission overheads like MQTT, CoAP, AMQP, XMPP or UDP. These IoT protocols allow to produce smaller data overheads and optimize for use in restrictive device and network environments. However, which one to choose to fully meet your requirements can be tricky. Also, there are at least 10 implementations of each IoT protocol, thus confusion is inevitable. But, you can easily find the resolve by consulting an expert IoT developer and service provider to get things going right for your IoT system. 

Since IoT applications generate huge amounts of data, data collection and processing play a big role in making IoT systems work well.  So, a lot of planning needs to be done for the way data is collected, stored or processed within an environment. Data size stored on the cloud and compliance with platform requirements are other two important factors to consider for effective IoT data collection and processing. It’s only good to involve data experts, analytics engineers and machine learning experts to get vital insights from data stored. In IoT software development, data plays a big role but what’s even more important is how the collected data is processed and used.  

Cross-platform compatibility is a very crucial aspect of IoT development as the IoT ecosystem includes devices with different architectures, protocols and operating systems. The IoT development team must achieve a right balance between hardware and software functions so that the IoT platform delivers the best performance despite heavy OS, device updates and bug fixings. IoT applications run on the web and mobile devices as well, hence need to be compatible.  There are SDKs and APIs available from many vendors to add new functionalities to an already developed IoT application.

As IoT is all about numerous connected devices, IoT network can be vulnerable to hacking and other cyber attacks. As more and more IoT applications are being built for homes and other personal spaces, hackers may have multiple juicy targets to scan for vulnerability and spy on people. In fact, security is one of the most important concerns of IoT development and the 2016 Dyn cyber attack is worth a reminder about the vulnerability posed by IoT. To deal with these precarious situations, developers are currently using communication-layer security (TLS or DTLS) and data encryption as the best remedies, but blockchain or distributed ledger technology is likely to surpass them as it matures in the future and the barriers making it sometimes impractical for constrained/embedded devices gradually disappear. Learn how Blockchain and IoT Technology empower each other.

Quality assurance is another important aspect of IoT software development as IoT is inherently a complex shared system with numerous integrated network components, applications and resources. Without proper testing, there may occur constraints in communication, computation and energy. To develop a reliable and optimized IoT device, quality assurance is a must and all underlying processes and each small entity should be tested in detail. Among various types of testing, some are component testing, exception testing, compatibility testing, performance testing and security testing. The QA team plays a very important role in detecting errors that hinder the ability of an IoT platform to work efficiently and the ability of the entire network of IoT devices to work well with each other and deliver as expected. 

No matter how complicated it is to build an IoT application, users must not experience any trouble using it. So, an IoT app should be design-driven and as simple as possible for use. No user wants to read a manual to install and set a new device or update a smartwatch. This is why it’s important that IoT developers and designers work in tandem from the beginning to ensure:

• • Secure but simple onboarding

• • Seamless transition between devices and systems

• • User experience personalization and adjusting products to behavior patterns

• Unified environment for the entire IoT system

The top programming languages used for IoT software development are Java, C/C++, Python, Javascript, Swift, Kotlin and Ruby. Amazon AWS and Microsoft Azure are the top 2 cloud services available for IoT. But you don’t have to worry about the technical requirements and implementations for your IoT project, as Logic Simplified shares all the technology burden to convert your IoT idea into a reality.

Logic Simplified is a game app developer and an  IoT app development company which offers services ranging from consulting to designing, development, testing, integration, launch and post-launch support. Our team of developers has the expertise in using machine-learning algorithms and Big Data solutions to empower your IoT devices to make intelligent decisions in real-time. We also offer IoT cloud services to ensure quality data storing, processing, and transferring in the cloud. We are a go-to company for IoT software development as we have hands-on experience of using various programming languages, development frameworks, platforms, communication protocols, sensor technologies, 3 party-APIs and other tech solutions. Get in touch with us or discuss your IoT project by writing to us at enquiry@logicsimplfied.com, and we promise to get back to you shortly to provide you with tailor-made solutions that will precisely meet all your IoT project needs.

IDC says, by 2025 worldwide data will grow 61% to 175 zettabytes, with as much of the data residing in the cloud as in data centers.

Source: NetworkWorld

There's tons of data generated every minute of our lives. And with the rise of IoT, the quantum has only increased multifold. If we come to think of it, it's astonishing how IoT connects or has potential to connect anything to everything - from a pin so tiny to buildings so gigantic, of course, ensuing tons of data. No matter where you are, what you do, or where you go - whether you are using social media or finding a restaurant nearby. Your every action over the internet produces trails of information from different sources overflowing the systems whilst making operations complex. And with IoT, when it doesn’t even require a human action, the data generation is literally 24X7. And, this overwhelming massive and complex data called the Big Data and the complications that arose with it gave rise to cloud storage and computing. 

Before I curb your curiosity and enlighten you on what goes on with all that data, let’s read a bit about the contribution IoT is making during this tough phase of COVID-19.

IoT, in the light of COVID-19:

The history in making

To make no bones about the relevance of cloud-based data processing, let’s take you through some real life instances. We’re all well aware of the outbreak that the whole world is going through but are we aware of the role that cloud data processing and IoT technology are playing along? I think not. How about I give you a walk through it?

1. Initially, as the infectious virus was on its spread, robot assistance was put to use to disinfect hospitals and perform delivery of medicines. Telehealth consultations through video conferencing during the lockdown and using IoT devices to perform digital diagnostics is also on the rise.

2. IoT is being utilized to monitor internet or social media data that can be used to detect the early stages of the virus.

3. Timely analysis of widespread data-sets (on the virus and people & places affected) generated through the Internet of Things and mobile devices is being carried out through data processing.

4. AI computing power is throughout being used for public research institutions around the world so as to accelerate the development process of new virus drugs and vaccines based on the massive scale of data coming in.

5. As coronavirus forced the world’s largest work from home, the demand for cloud-based video conferencing and online teaching skyrocketed.

These applications listed above are just a few of many contributions that this technology is showering upon the world during the tough times and the usuals.

Now, coming back to the question of how do machines grab hold of all the relevant data? It starts with the Sensors. That’s right!

Sensors and Power - run the IoT devices

The data input process involves data collection, encoding, transmission, and communication. There are innumerable smart wireless sensors (camera, accelerometer, GPS, etc) and actuators embedded in physical devices such as security systems, smart appliances, smart TVs, and wearable health meters, etc. that are connected through the internet. The sensors respond to specific types of conditions or changes in the physical world by generating a signal that represents the enormity of the situation being observed. The different sensing parameters here are light, heat, sound, distance, pressure, etc. Their primary purpose being data collection in the IoT architecture.

Nevertheless, the potential of IoT sensors generating ideas and possibilities is way beyond one can imagine. It’s about connecting the dots and understanding how a particular type of sensor can collate specific data in different ways. The process sets off by first understanding the environment and data. Then the relevant and structured data is accepted to achieve overarching objectives. For the use of this, there is a staggering range of sensors, depending on the demand of the IoT device. Few of them are mentioned below.

Water Quality sensors 

They sense the water quality by monitoring mechanisms like chemical presence, oxygen levels, electrical conductivity, pH level, turbidity levels, etc.

Motion sensors 

They are controlled via a smartphone or a computer, in cases of automatic door, automatic parking systems, energy management systems, etc. These sensors use one of any technologies like passive infrared (PIR), microwave detection, or ultrasonic, which uses sound to detect objects. 

Acceleration sensors 

They sense movement and displacement of objects. These make use of technologies like hall-effect sensors, capacitive sensors, piezoelectric sensors, etc. 

Proximity sensors 

They are used to detect the presence and absence of objects near the sensors using several technology designs like inductive, capacitive, photoelectric, ultrasonic. 

Temperature sensors 

They measure the heat energy released by an object or substance like soil, machines, etc. These include thermocouples, thermistors, resistor temperature detectors (RTDs), and integrated circuits (ICs).

There are many others like Digital sensors, Image sensors, IR sensors, Smoke sensors, Infrared sensors, etc. using different mechanisms and serving different purposes. All the data captured by the sensors is integrated into a form that makes sense, only after which sensible Data Analytics can be carried out.

No matter how big or small an IoT device is, or whatever the character differences it may hold, it operates on power. In this case, the power sources could be electricity used in Home automation and industrial appliances, batteries used in wearables or portable IoT devices, or harvesting electrical energy from ambient forms. Ex. Solar cells convert light energy into electrical energy.

Data reporting

“To measure is to know”. Advanced Data Analysis techniques are creating a footprint  in a business sector, healthcare, sports, banking, transportation, energy, etc. Using data analytic tools, algorithms, and supercomputers, large data-sets are analysed to provide meaningful and actionable insights to the end-user. That data is then converted to a format like patterns, statistics, graphs, etc. to create a report. 

For instance, a data analysis report can help a company understand customer preferences, point out revenue opportunities, more effective marketing, and demands that would make them work on their products for better services, products, and customer experience which will result in increased growth, profits and give them a competitive edge in the market. Also, with the ever-evolving technology, businesses and sectors are experiencing increased security and surveillance abilities through video sensors and application of data analytics.

Then there is Predictive Analysis building smart devices, spaces, and cities. These analyzed predictions help control damage, save time, reduce the risk of downtime, prevent road accidents, prevent traffic on roads, track variables like temperature, pressure, vibrations, etc. in industries by using, measuring, and feeding the collected information into an analytics platform.

The setbacks 

Improvement calls for challenges and limitations. And, to come up with better techniques of data processing, it was important to identify the shortcomings. Having said that, while getting familiarised with the technique, there were certain challenges that people came across.

1. Collecting the appropriate or correct data from different sources.

2. Duplicacy of Data - As the data is collected from different sources, there are chances of duplicate data which can lead to incorrect analysis and results.

3. Inconsistent Data - Again, as the sources are different, there are chances of receiving heterogenous data. 

4. Inconsistent data collection standards - At present, there are many data collection standards in different states and countries, some of which are inconsistent. The reason being the difference in the scope of information collected for various services. This inconsistency makes it difficult for services to compare data sets.

5. Data integration - Lack of knowledge or skills, technological advancements, data problems can make collecting data and organizing it difficult.

Turning setbacks to progress

And so came the solutions which were mostly about being precise and particular with every step involved in the process. And, if you get that right then those setbacks can most certainly be turned to progress. 

1. Direct and careful observation while collecting data. 

2. Surveys can help researchers precisely structure the data collection plan.

3. Deduplication technology should be used for the elimination of redundant data, which will also free storage space and help in achieving precise and high accuracy data. This is where Blockchain and IoT Technology together can turn ugly things on their head.

4. Inconsistency and the variety in data can be checked by Indexing and Meta description. Data profiling helps with identifying abnormalities, and using a universally accepted format for data collection like Extensible markup language (XML) maintains consistency.

The steps involved in Data integration are - 

1. Consolidating the data collected from multiple sources into a single persistent data set.

2. Federation of that data so that one can have a single virtual view of data received from multiple sources. 

3. And, Data propagation copies data from one source to another. 

At a pace at which the capabilities of smart devices is growing, experts say it won’t be long before we carry “supercomputers” in our pockets with unparalleled processing power and secure storage capacity. It is expected that in the coming years, more than a trillion sensors will be connected to the internet. It means the world will see a transformation in business operations, how schools operate, homes will be automated with smart appliances, the healthcare and industrial sector will revolutionize. 

With the world growing so fast, there is a need to keep up with the momentum. There is a need for businesses to make the most of the technology trends. And, the expertise that comes with software outsourcing companies can benefit businesses. That being said, I would like to introduce you to Logic Simplified, a promising IoT app development company based out of Dehradun. Logic Simplified offers IoT software app development services that help businesses leverage innovative technologies to gain a competitive advantage, enhance customer engagement, and improve revenues. Let’s hear it from one of their client’s himself. 

“Great team of expert programmers to work with - Results oriented!

I have worked with Logic Simplified on multiple projects over the last several years. In each case, they have worked hard to accomplish the exact results I asked them for. From commercial apps to augmented reality apps, from Websites to virtual reality and even IoT cloud based projects - Logic Simplified has provided the expertise to accomplish our requirements from concept to prototype to finished products ready to launch. In each case, their work has reflected positively on us for our global customer base. I would not hesitate to recommend Logic Simplified for your coding projects.”

Data is what that most businesses of today rely on to make critical decisions. But, is just having piles of data available at your disposal enough to be worth your salt? Naaah! The secret sauce is the way you do data processing and analysis to get structured and meaningful information so as to actually be able to act on actionable insights. Even using the new revolutionary technologies such as Artificial Intelligence (AI) and Machine Learning (ML) for smart decision making and driving business growth are like flogging a dead horse without applying the right data processing techniques. This is what today’s businesses are learning, though, slowly. That said, I am making an attempt to help you understand the importance of data processing in ML and AI algorithms so that they can do correct analysis and furnish you with information you can comprehend and use to bring that proverbial midas touch in your way of doing business.

When done right, data processing teaches ML and AI algos to work as intended

After you extract data that can be in structured, semi-structured, and unstructured form, you transform it into a usable form so that ML algorithms can understand it. But, what’s more critical here is the relevance. If the data itself is not relevant, you can’t expect from your ML algorithms to learn what would eventually make them  smart and bring value to your business.

Phases of Data processing

The graphic above explains and simplifies the phenomenon of data processing for machine learning algorithms through sequential steps, elaborated below - production of actionable motive being the sole purpose of this procedure.

1. DATA SELECTION

This step involves collecting data from available sources that are trustworthy and then selecting the highest quality of the whole. In this case, remember that less is more because the focus here has to be on quality and not quantity. The other parameter to take into consideration is the objective of the task.

2. DATA PREPROCESSING

Preprocessing here means getting the data into a format that the algorithm will understand and accept. It involves -

• Formatting - There are different formats in which data could be found, such as a proprietary file format and a Parquet file format, to name a few. Data formatting makes it convenient for learning models to effectively work with data.
• Cleaning - At this step, you remove the unwanted data and fix the instances of missing data by removing them also.
• Sampling - This step is essential to save time and memory space. You need to understand that instead of picking the whole dataset, you can use a smaller sample of the whole that will be faster for exploring and prototyping solutions.

3. DATA TRANSFORMATION 

Lastly, the specific algorithm you are working with and the solution that one's looking for influence the process of transformation of preprocessed data. After you upload the dataset in the library, the next step  is the Transformation process. A few of the many are mentioned below.

Scaling: Scaling means the transformation of the value of numeric variables in a way that helps it fit in a specific scale like 0 - 1 or 0 - 100. This procedure ensures the data we receive has similar properties, and no odds, thus makes the outcome meaningful.

Decomposition: This process uses a decomposition algorithm to transform a heterogeneous model into a triple data model. The transformation rules here will categorize the data set into structured data, semi-structured data, and unstructured data. Subsequently, we can pick the category that suits our model's ML algorithm.

Data Aggregation Process (DAP): The raw dataset is aggregated through an aggregator with the purpose of locating, extracting, transporting, and normalizing it. This process may undergo multiple aggregations to bring up aggregated data, which may either be stored or carried out further for other operations. This process directly impacts the quality of the software system.

4. DATA OUTPUT & INTERPRETATION 

In this, meaningful data is obtained as an output in various forms as one prefers. It could be a graph, video, report, image, audio, etc. The process involves the following steps:

• Decoding the data to an understandable form, that earlier was encoded for the ML algorithm.
• Then, the decoded data is communicated to various locations that are accessible to any user at any time. 

5. DATA STORAGE

The final step of the entire process is where data or metadata is stored for future use.   

Difference between a regular computing program and AI

Let’s take you through a simple example:

Let’s say, an AI is given marks of 10 students in a class (1, 3, 5, 6, 8, 9, 12, 7, 13, 100). Based on that, I ask it a question, "How will you rate the overall class on a scale from A-E (based on slabs like 0-20 is E, 21-40 is D and so on)?".

The difference between a regular computer program and AI is the same as the two men in this saying, "Give a man a fish and he'll eat for a day. Teach a man to fish, and he'll eat for his lifetime.” The first man is like a regular program that does not learn on its own and will give an output only on providing input data. Still, on the other hand, AI is the man you teach "how" once, and then it learns and  improves on its own and gives the desired output with rules and methods of what to do with certain kinds of data and how. The way it learns is ML (Machine Learning or Machine Intelligence).

A regular program may take an average of 10 marks and rate it based on that. Nevertheless, an AI will be able to identify the outlier (100 in this case) and then give us the answer, which clearly shows the world of difference between the two computer programs and how Artificial Intelligence gets ahead of all with the help of Machine Learning.

GIGO

We train a machine learning model based on the output we expect from it. And, the data that we provide to the AI algorithm determines this. If the data provided is inappropriate, then the information it would give us would be worthless. The strict Logic that computers work on is the compatibility between the input and the output. The quality of data provided (input) determines the quality of information we will receive (output). In other words, Garbage in, garbage out (GIGO). 

Logic Simplified has done much work in Artificial Intelligence and Machine Intelligence, and understands the critical role and importance of data processing. Being the driver for different other technologies, we know that AI and ML will impact the future of every industry and humans in many expected and also unexpected ways. Let our AI programmers help you make an impact in your world - to ensure enhanced productivity, escalating profits, reduced time consumption, enhanced security throughout the process, prevention of unauthorized access, and so much more - by making your systems smarter.

AI in video games may sound as a new innovation, but one of the very first attempts to use game AI had been made in 1950s when Arthur Lee Samuel, an American pioneer in the field of computer gaming and AI, built a self-learning Checkers-playing program. AI has come a long way since then, from IBM’s Deep Blue that defeated a reigning world chess champion, Garry Kasparov, on 11 May 1997 to Google’s AlphaGo AI Go player that defeated the world’s best human Go player.

However, the future of AI in video games is not just to outsmart humans, but to generate a user experience that is better and more unique.

Before we proceed further to understand how AI is a boon to video games, let’s understand in a nutshell what AI basically is.

Artificial Intelligence is a science that makes a computer program or a machine capable of thinking, learning and solving problems the way human brain does. The sole reason why “Artificial” is used in “Artificial Intelligence” is that such intelligence is not acquired naturally as we humans do, but by using learning algorithms that assess vast amounts of data and make logical sense out of it to behave or respond intelligently like humans. Machine learning (ML) is a subset of AI that uses certain algorithms to learn and make smart decisions.  

Facebook’s image recognition, Amazon’s shopping recommendations, Apple’s Siri and Netflix’s personalized video streaming service are some of many examples of AI people come across in their daily lives.

As far as AI development for games is concerned, you can think of F.E.A.R, The Last of Us, Far Cry 2 and First Person Shooter (FPS) like Call of Duty: Black Ops II. Let’s dive deeper into this.

How AI was used In those Games

If we talk about F.E.A.R, the reaction from enemies is not predictable at all. The game AI makes them capable of reacting to each other’s situations and learning from their mistakes and never repeating them. As a result, the players need to keep devising new strategies and never sit in the same position. Many video game companies are now looking to hire AI game developers as AI and ML in game development are quickly gaining ground to meet the expectation of today's modern gamers.

In The Last of US, Ellie is a companion AI of Joel, the player’s character. She accompanies and supports Joel throughout most of the game. All her moves, dodging style, taking cover, runtime cover, combat performance, fire rate and accuracy look natural and believable, making the game awe-inspiring.

The enemy AI of Far Cry 2 amazed players with its brutality and unforgiving nature. The players never saw such a chaotic and unpredictable AI behavior before. Even the veteran players hard a very time to win the game.

Call of Duty: Black Ops II displays one of the best AI bots behaviours. The commendable AI algorithm enables each bot in the game to use different tactics, like running, gunning, knifing, camping and drop-shooting/jump-shooting.

How AI enhances User Experience and makes Video Games Better?

Makes Non Player Characters (NPCs) Smarter

One of the best uses of AI that Artificial Intelligence game developers make  is controlling the behavior of NPCs. The games without AI often become boring after playing for sometime as they become easy to beat due to their predictable behavior. The real fun of playing video games comes from competing with NPCs that react in unpredictable way and surprise you.

Imagine an FPS game in which enemies are capable of analyzing their environments so that they can find what’s important for their survival or take actions that preempt your intelligent moves to increase their chances of victory. Not only this, what if they can learn from their own actions and are able to take cover, recognize sounds and patterns, communicate with each other and maneuver in a way you never saw or predicted before. Having new experiences despite playing such a game several times keeps players excited and enticed to keep coming back, isn’t it? An expert artificial intelligence game development company can help you build such games that player love to play for long long hours.

AI is also used for Pathfinding in real-time strategy games. Pathfinding means that NPCs are adept at moving from one point on a map to another after analyzing the terrain, obstacles and possibly "fog of war”. AI provides enemies the ability to safely navigate in a dynamic environment without colliding with other entities. It also enables group navigation by allowing an NPC to collaborate with other NPCs.

A few games that have smart AI-powered NPCs are Tom Clancy's Splinter Cell: Blacklist, XCOM: Enemy Unknown and Halo: Reach.

AI Makes the Gaming World more Realistic

AI has a huge potential to improve visual quality of video games, making them appear more realistic and natural than ever before. Game environments and game characters can mimic the real world through deep learning and by using algorithms that make sense of the ever-growing amounts of game data. Video games look more realistic when NPCs behave like humans, be it walking, moving, running, expressing themselves or making a decision.

Combining AI with Virtual or Augmented Reality further opens the gates to add reality factor to video games. Pokémon Go, an augmented reality-based game, has already proved that immersive and interactive video games are the future, be it on mobiles, computers, Xbox or Playstation.

Real-time customization to Enhance Overall Gaming Experience

AI overhauls the overall gaming experience by real-time customization of scenarios. EA Sports’ FIFA 17 is a good example to understand how.

The game gives you one of the five player choices to pick for each position in your team. However, you have no idea what the chemistry between the players you have chosen for your team is. But don’t worry, the AI of the game is so designed that it automatically determines that for you and increases the chances of your team performing well.

Besides, the AI makes the game more interactive by boosting your playing experience. For instance, if you’re losing a game, it will encourage the fans to cheer for your team louder, so as to lift the morale of your team and make your players perform better. Such an ability of AI takes the overall gaming experience to an all new level.

Adjusts Difficulty Level as Per Player’s Ability

Another virtue of AI-designed video games is player-experience modeling, which means providing tailor made experience to players as per their level of expertise in real time. So, if a player is a noob, the AI will adjust the difficulty level to easy mode so that the player doesn’t get frustrated or exapserated for not being competent enough to progress in the game. On the contrary, if a player is an expert, the AI will make the game difficult so that the player doesn’t get bored or jaded. This ability of AI is called dynamic game difficulty balancing.

Crash Bandicoot, Archon: The Light and the Dark and Flow are some video games that use dynamic game difficulty balancing. Game AI can also determine player intent through gesture recognition which enables players to communicate and interact with video games naturally without any mechanical devices.

Procedural Content Generation

Game AI also enables game app developers to automatically generate creative content, like landscapes, items, levels, rules, automated music and quests. Employing procedural content generation in quest-driven games can automatically generate weapons and armor based on the player-character's level.

There are also many open world or survival games that use procedural content creation to create a game world from a random seed or one provided by a player, making each playthrough unique with high visual appeal. This technique has already been used in many video games, including Rogue, Elite, Diablo, Diablo II, Dwarf Fortress, etc.

Conclusion

The most beautiful part of AI in video games is incredible environment creation and presenting unpredictable scenarios by altering the flow and intensity of the gameplay, which makes gaming a lot more fun. There’s nothing better for a player than getting a satisfying and challenging experience, right? As the future unfolds, we will see more and more games with AI controllers to optimize user experience like never before. Besides, AI will also provide a testing ground to game developers to improve their code and design to finally build a game that rocks the game charts.

Logic Simplified is a top game development company which always believes in embracing new technologies to keep the pace with ever-changing market demands. And, AI is no different! You can hire game app developers from us to get advatage of our expertise in AI game development and build games that offer gamers personalized and highly interactive experiences. Please write to us at enquiry@logicsimplified.com to discuss your AI game idea, and we will get back to your shortly to tell you how we can help shape it into a reality.