GO LOOK IMPORTANTBOOK: Amnimarjeslow Goverment to open flash connecvtivity electronic machines ; 91220017 and 02096010014 relationship ; Gen . Mac Flash on the way of speed star create machine paths through past and future space and time .

Digital communication technology in wise control space and time must be to have got Quality efficiency of IPOTimer machines (Input, Process, Output, Transfer Function setup and Timer) Electronic computing tools to other electronic devices such as satellites, TVs, electronic cars and others (Smart home, Smart Office, Smart City): 1. Parallel Port , 2. Serial Port , 3. ISA , 4. PCI , 5. USB and HDMI , 6. Wireless , 7. IOT ( Internet of Things ) , 8 Cloud and Machine Learning by AI 9. Metaverse Speed , 10. Inteligent Machine integrated , All possible equipment connectivity needs for synchronization and computational analysis with integrated electronic machines continue to increase not only computers with printers but also with other external devices. Usually this connectivity is through an electronic communication device which we call an interface. There are many methods in electronics to connect the 4th and 5th generation electronic machines with the host system connected to external devices using an interlock, that is, each control signal transition will be recorded and analyzed at the opposite end of the interface. the interface works like a transistor system in analog circuits, namely electronic switches which are divided into 2 groups, namely data communication equipment and data terminal equipment. all electronic communication systems and their controls must be accompanied by improvements in IC (integrated circuit) technology, namely the IC input, processor, output and memory devices which we usually call Chips and their installation can be plug and play, making it easier to use and apply, connectivity and spectrum fast analysis in an integrated electronic machine network is necessary for the efficiency and effectiveness as well as the quality of a service product and high technology industry . allows integrating intelligent electronic machines . a series of electronic machines is an arrangement of many modules, namely software modules, hardware modules, analog and digital communication modules, brainware modules, modules for the relationship between electronic machines and the environment and humans as well as integration modules between electronic machines. Electronic equipment must be controlled both from the input, process and output as well as the setting and timing. In the current era, electronic machine tools must be integrated with artificial intelligence systems and the internet of things so that electronic systems are integrated and smart inteligence . Smart inteligence meaning smart control automation ; Automation in control is an important moment in various fields of human science in the form of science and technology, the science of control and automation is a science that learns for the stages of the human productivity process, humans develop from work productivity processes with manual control then develop into control of electric machines and now with digital electronic control with advanced development of microcontroller control and cloud engine based, namely the IOTX network. Control systems and automation models then and now are a comparison of human life in the era of electric machines into the era of digital electronic machines that integrate feedback networks with a collection of neural networks in Artificial intelligence. the manual control system is an open control system, while the automatic control system is a closed control. closed control system, namely automation control or networked digital electronic control that uses digital sensors and transducers in its technological progress, digital era technology is widely applied to automation products in manufacturing with Robotic PID control (ie a mixture of manual, setting and timer control). control automation systems in integrated network digital electronics produce many 21st century products such as drones, driverless cars and efficiency and effectiveness in working capital and capital investment, in other words automation control enters the era of Smart home, Smart Manufacture, Smart System, Smart Production , Smart City , Smart Planet .

I. Smart Description Electronic Machines Networking ___________________________________________________ Exposure to future technology synchronization which synchronizes Artificial Intelligence with Machine Learning and Deep learning with various forms of good, efficient and high quality smart electronic networks. Machine learning Machine learning is a branch of computer science with a focus on developing a system that is able to learn on its own without having to be repeatedly programmed by humans. However, before producing a data result from object behavior, Machine Learning requires initial data as material to be studied. Machine learning (ML) is a learning machine designed to be able to learn without human direction. Machine learning is a branch of artificial intelligence (AI) or artificial intelligence. Machine learning is often used for various purposes. Machine learning also has the ability to be able to obtain its own data and then study it so that it can perform certain tasks. This machine learning is based on the sciences of mathematics, statistics, data mining, and others The initial role of data is very important as the first step in Machine Learning to produce output. It is used as an initial exercise or trial of Machine Learning. After passing the initial trials, Machine Learning will be able to solve problems without being explicitly programmed. Deep Learning Deep Learning is a part of machine learning where the algorithm is able to understand patterns with high accuracy based on very large data through various complex variables. Deep Learning on the other hand is one of the implementation methods of Machine Learning which aims to imitate how the human brain works using Artificial Neural Network or artificial reasoning network. Deep Learning with a number of algorithms as "neurons" will work together in determining and digesting certain characteristics of a data set. Programs in Deep Learning usually use more complex capabilities in learning, digesting, and also classifying data. One of the main differences between Machine Learning and Deep Learning is performance as the amount of data increases and how to solve problems. Deep Learning algorithms are used to create artificial neural networks that are not capable of optimally processing small amounts of data. This is because Deep Learning algorithms require large amounts of data and are able to solve the problem as a whole from start to finish without the need to separate it into several parts. Meanwhile, Machine Learning algorithms are capable of processing smaller amounts of data. And to solve the problem, it is recommended to break it into several parts so that it can be solved separately, and the solutions are combined to get a complete result.

Every technological sophistication is designed to make human work easier. Likewise with machine learning, machine learning has its own way of working that varies according to the technique to be used. The main concept of machine learning remains the same, which includes data collection, data cleaning, data exploration, data selection, technique selection. provide training on models, and evaluate Machine learning results. We often encounter the application of machine learning in everyday life for various purposes. Some examples of the application of machine learning include: marketplace recommendations in the online shopping system, where one of the data is obtained from search history categorization of email, whether it is included in the category of updates, social, promotions, spam, and others. facial recognition, often used in security systems search engine, provides search suggestions in the google search engine Machine learning in its application has penetrated into various fields. Things like transportation applications, financial services, education, health, and social media are examples of machine learning in everyday life. https://youtu.be/bkqcKJHE7mw

Life is inseparable from production , production produces what we call goods , services and one more quality . The three spheres of added value in this life are mutually synergistic, each other must exist and each other is interrelated and needed in human social life. The internet of things increases the ability of the human senses to be able to remotely monitor and control goods and services and quality products, we know this as the increase in industry 4.0. The programming stages in Industry 4.0 are supported by the availability of appropriate smart electronic technology to support IOT programming in human social life in the future, where many analysis and data collection processes can be accessed anywhere and under any conditions. on the internet of things programming and planning starts from everything based on connected microcontroller electronics based on IOT systems with Cloud engines or in other words the machine learning process on smart electronics and deep learning analysis in the cloud engine learning stage like what happens on social media: Google , Facebook , Whatsapp , Instagramm , Truthleak , YouTube , TikTok , Chat AI bot and others , of course the artificial intelligence program in the form of machine learning will add added value to goods and services as well as the quality of human life in the future . welcome we studying to make machine learning simplicity programme look like google engine : Electronic Machine Learning and deep learning processing : machine learning is a subset of artificial intelligence that involves the development of computer algorithms that access large amounts of data to create models for information. These models are then used to predict specific behavior. The three machine learning types are supervised, unsupervised, and reinforcement learning. Machine learning with Artificial inteligent a science discipline group in Artificial intelligence moving, it is where electronics and computer science meet. It involves custom-designed hardware with complex algorithms and software. On this base brainware , we 'll design computer systems that can learn from data, recognise speech and images, and solve problems. The 7 Steps of Machine Learning 1. Data Collection. → The quantity & quality of your data dictate how accurate our model is. 2. Data Preparation. → Wrangle data and prepare it for training. 3. Choose a Model. . 4. Train the Model. 5. Evaluate the Model. 6. Parameter Tuning. 7. Make Predictions Human life in the era of timelines continues to experience changes both in terms of financial transactions and technology transfer as well as energy storage techniques that are not easily stolen or destroyed in terms of material but can be stored virtually. This increase in change is made possible by the development of IOT technology and machine learning as well as artificial intelligence era future line . IoT devices are built with software that contains instructions for them and is coded using programming languages. They might seem like devices, but they're essentially computers, and every computer needs to be instructed, and programming language is the way to do it. IoT is a digital technology revolution that is even bigger than the industrial revolution. The Internet of Things is one of the most palpable consequences of the Fourth Industrial Revolution, of which we are currently in the early stages. Just as it happened during the previous revolutions, early adopters, professionals are able to create or adapt their business around the new technologies, will ensure their competitive edge for the following decades. As always, knowledge is power. The Ultimate Guide To Implementing IoT and Challenges : Requirements Implementation steps : Step 1: Clearly set your business objectives Step 2: Research tested IoT use cases Step 3: Decide on the correct hardware Step 4: Selecting IoT tools Step 5: Selecting an IoT platform Step 6: Prototyping and implementing Step 7: Gather useful data Step 8: Apply cold and hot path analytics Step 9: Implement Machine Learning Step 10: Think about security, security, security ( Privacy ) risks Risk 1: Failure of Implementation Risk 2: Internet Failure Risk 3: Security Risk 4: Doing nothing IoT is the extension of Internet connectivity into physical devices and everyday objects. Embedded with electronics, Internet connectivity, and other forms of hardware (such as sensors), these devices can communicate and interact with others over the Internet, and they can be remotely monitored and controlled. https://youtu.be/usSPMfyc2So Let's break that down: First, IoT is about connectivity. All your things are connected via the internet. Things refer to any physical object that can be uniquely identified (by URI or Unique Resource Identifier) and that can send/receive data by connecting to a network. Examples are buildings, vehicles, smartphones, shampoo bottles, cameras, etc. They can be connected among themselves, with a central server, with a network of servers, with the cloud, or a mix of all this and more. Second, IoT is about information and communication. Everything is sharing information to their designated endpoints either other things or servers. They are constantly sending information about status, actions, sensor data and more. All of them with their unique ID attached, so that it is possible to know where the data came from. And finally, IoT is all about action and interaction. These last two concepts define the core of what IoT is: connection and information sharing. However, all that data isn't generated just to be stored somewhere and forgotten. It has to be used for something. And that use can be automation: computers using the data to automatically (or even autonomously) make decisions and, for example, with the help of Machine Learning, act. And that usage can also be monitoring: letting people know the state of something or some process. The people may be the users of a product or the overseers of for example a production line.

IOT program security and processing techniques: 1. Challenge: Data processing The volume of data collected through IoT presents challenges for rapid cleaning, processing and interpretation. Edge computing addresses this challenge by shifting most of the data processing away from centralized systems to the edge of the network, closer to the devices that need the data. However, the decentralization of data processing presents new challenges, including the reliability and scalability of edge devices and the security of data in transit. 2. IoT security, safety and privacy IoT security and privacy are important considerations in any IoT project. While IoT technology can transform your business operations, IoT devices can pose a threat if not properly secured. Cyber attacks can compromise data, damage equipment, and even cause harm. 3. Strong IoT cybersecurity (IOTX) goes beyond standard secrecy measures to include threat modeling. Understanding the different ways an attacker can harm your system is the first step to preventing attacks. 4. When planning and developing an IoT security system, it is important to choose the right solution for every step of the platform., from OT to IT. A software solution that provides the necessary protection for a given system. II . IOT Application to Proof _____________________________ IoT applications of today's technology AI and IoT IoT systems collect large amounts of data, so it's often necessary to use AI and machine learning to sort and analyze that data so you can detect patterns and take action based on the insights. For example, AI can analyze data collected from manufacturing equipment and predict maintenance needs, reducing costs and downtime from unforeseen breakdowns. Blockchain and IoT Currently, there is no way to confirm that data from IoT has not been manipulated before being sold or shared. Blockchain and IoT work together to break down data silos and build trust so data can be verified, tracked, and relied on. Kubernetes and IoT With a zero-downtime deployment model, Kubernetes helps IoT projects stay updated in real time without impacting users. Kubernetes scales easily and efficiently using cloud resources, providing a common platform for edge deployments. Open source and IoT Open source technologies accelerate IoT, enabling developers to use the tools of their choice in IoT technology applications. Quantum computing and IoT The massive amounts of data generated by IoT are naturally suitable for quantum computing capabilities to accelerate heavy computing. Additionally, quantum cryptography helps add a level of security that is needed but is currently hindered by the low computational power inherent in most IoT devices. Serverless and IoT Serverless computing allows developers to build applications faster by removing the need for them to manage infrastructure. With serverless applications, cloud service providers automatically provision, scale, and manage the infrastructure needed to run code. With the variable traffic of IoT projects, serverless provides a cost-effective way to scale dynamically. Virtual reality and IoT Used together, virtual reality and IoT can help you visualize complex systems and make decisions in real time. For example, using a form of virtual reality called augmented reality (also known as mixed reality), you can display important IoT data as a graphic on top of real-world objects (such as your IoT devices) or workspaces. This combination of virtual reality and IoT has inspired technological advances in industries such as healthcare, field services, transportation, and manufacturing. Digital Twins and IoT Testing your system before execution can be a dramatic cost and time saving measure. Digital Twins take data from multiple IoT devices and integrate it with data from other sources to offer a visualization of how the system will interact with devices, people, and spaces. IoT data and analytics IoT technologies generate such high volumes of data that special processes and tools are needed to turn data into actionable insights. Typical IoT technology applications and challenges: Application: Predictive maintenance IoT machine learning models designed and trained to identify signals in historical data can be used to identify similar trends in current data. This allows users to automate preventive service requests and order new parts early so they are always available when needed. Application: Real time decisions A variety of IoT analytics services are available, designed for real-time and end-to-end reporting, including: High-volume data stores use formats that can be queried by analytical tools. Processing of high volumes of data streams to filter and aggregate data prior to analysis. Low latency analytics turnaround using real time analytics tools that report and visualize data. Use of real time data using message intermediaries. Challenge: Data storage Large data collection implies large data storage requirements. Several data storage services are available that have varying capabilities in organizational structure, authentication protocols, and size limits. Data link layer The data layer is part of the IoT protocol that transfers data within the system architecture, identifies and corrects errors found at the physical layer. IEEE 802.15.4 Radio standard for low power wireless connections. It is used with Zigbee, 6LoWPAN, and other standards for building wireless embedded networks. LPWAN A low power wide area network (LPWAN) allows communications across a range of 500 meters to over 10 km in some places. LoRaWAN is an example of an LPWAN optimized for low power consumption. Physical layer The physical layer is a communication channel between devices in a given environment. Bluetooth Low Energy (BLE) BLE dramatically reduces power consumption and costs while maintaining the same range of connectivity as classic Bluetooth. BLE works natively across mobile operating systems and is quickly becoming a favorite with consumer electronics due to its low cost and long battery life. Ethernet This wired connection is a less expensive option that provides a fast data connection and low latency. Long term evolution (LTE) A wireless broadband communication standard for mobile devices and data terminals. LTE increases the capacity and speed of wireless networks and supports broadcast and multicast streaming. Near field communication (NFC) A collection of communication protocols using electromagnetic fields that allow two devices to communicate within four centimeters of each other. NFC-enabled devices function as identity key cards and are commonly used for contactless mobile payments, tickets, and smart cards. Power Line Communication (PLC) Communications technology that allows sending and receiving data over existing power cables. This allows you to power and control IoT devices over the same cable. Radio frequency identification (RFID) RFID uses electromagnetic fields to track unsupported electronic tags. Compatible hardware provides power and communicates with this tag, reading its information for identification and authentication. Wi-Fi/802.11 Wi-Fi/802.11 is standard in homes and offices. While an inexpensive option, it may not suit all scenarios due to limited ranges and 24/7 energy consumption. Z wave The grid network uses low energy radio waves to communicate from appliance to appliance. zigbees IEEE 802.15.4-based specification for a suite of high-level communications protocols used to create personal area networks with small, low-power digital radios. IoT technology stack part 3: IoT Platforms The IoT platform makes it easy to build and launch IoT projects by providing a single service that manages your deployments, devices and data. The IoT platform manages hardware and software protocols, offers security and authentication, and provides user interfaces. The exact definition of an IoT platform varies as more than 400 service providers offer features ranging from software and hardware to SDKs and APIs. However, most IoT platforms include: IoT cloud gateways Authentication, device management and APIs Cloud infrastructure Third party application integration Managed service IoT managed services help businesses proactively operate and maintain their IoT ecosystem. Various IoT managed services, such as Azure IoT Hub, are available to help simplify and support the process of creating, deploying, managing, and monitoring your IoT projects. IoT protocol: How IoT devices communicate with the network IoT devices communicate using IoT protocols. Internet protocol (IP) is a set of rules that define how data is sent across the internet. The IoT protocol ensures that information from one device or sensor is read and understood by other devices, gateways and services. Different IoT protocols have been designed and optimized for different scenarios and uses. Given the wide array of IoT devices available, it's important to use the right protocol in the right context. What IoT protocol is right for the required situation? The type of IoT protocol you need depends on the layer of the system architecture through which the data will be traversed. The Open Systems Interconnection (OSI) model provides a map of the different layers that transmit and receive data. Each IoT protocol in the IoT system architecture enables device-to-device, device-to-gateway, gateway-to-data center, or gateway-to-cloud communication, as well as inter-data center communication. Application layer The application layer works as an interface between users and devices in a given IoT protocol. Advanced Message Queuing Protocol (AMQP) The software layer that creates interoperability between messaging middleware. This helps a range of systems and applications work together, making messaging the industry standard. Restricted Application Protocol (CoAP) Limited bandwidth and limited network protocol designed for devices with limited capacity to connect in computer-to-machine communication. CoAP is also a document transfer protocol that runs over the User Datagram Protocol (UDP). Data Distribution Services (DDS) Versatile peer-to-peer communication protocol, from running small devices to connecting high-performance networks. DDS simplifies deployment, increases reliability, and reduces complexity. Message Queuing Telemetry Transport (MQTT) A messaging protocol designed for lightweight computer-to-machine communication and primarily used for low-bandwidth connections to remote locations. MQTT uses a publisher-subscriber pattern and is ideal for small devices that require efficient bandwidth and battery usage. Transport layer In any IoT protocol, the transport layers enable and protect data communication as it moves between layers. Transmission Control Protocol (TCP) The dominant protocol for most internet connectivity. The application offers host-to-host communication, breaks large data sets into individual packets, and resends and reorders packets as needed. User Datagram Protocol (UDP) A communications protocol that enables process-to-process communication and runs over IP. UDP increases the data transfer rate over TCP and best suited applications that require lossless data transmission. Network layer The network layer of the IoT protocol helps each device communicate with the router. IP Many IoT protocols use IPv4, while newer executions use IPv6. This recent update to IP routes traffic across the internet and identifies and discovers devices on the network. 6LoWPAN This IoT protocol works best with low-power devices that have limited processing capabilities. IoT X2 technology stack: IoT protocol and connectivity Connecting IoT devices A key aspect of planning an IoT technology project is determining the device's IoT protocol—in other words, how the device connects and communicates. In the IoT technology stack, devices are connected via gateways or built-in functionality. What are IoT gateways? Gateways are part of IoT technology that can be used to help connect IoT devices to the cloud. While not all IoT devices require a gateway, they can be used to establish device-to-device communications or connect devices that are not IP based and cannot connect to the cloud directly. Data collected from IoT devices moves through gateways, is pre-processed at the edge, and then sent to the cloud. Using an IoT gateway can lower latency and reduce transmission size. Having a gateway as part of the IoT protocol also allows you to connect devices without direct internet access and provides an additional layer of security by protecting data moving in both directions. How to connect IoT devices to the network? The type of connectivity you use as part of the IoT protocol depends on the device, its functionality, and the user. Typically, the distance that data must travel—both short and long range—determines the type of IoT connectivity required. IoT network type Low power and short range networks Low-power, short-range networks are perfect for homes, offices, and other small environments. Such networks tend to require only small batteries and are usually inexpensive to operate. Typical example: bluetooth Good for high-speed data transfer, Bluetooth transmits voice and data signals up to 10 meters. NFC A collection of communications protocols for communication between two electronic devices that are 4 cm (1 ⁄2 in) or less apart. NFC offers a low-speed connection with a simple setup that can be used to bootstrap a more supportive wireless connection. Wi-Fi/802.11 Wi-Fi's low operating costs make it standard throughout homes and offices. However, it may not be the right choice for all scenarios due to its limited range and 24/7 energy consumption. Z wave The grid network uses low energy radio waves to communicate from appliance to appliance. zigbees IEEE 802.15.4-based specification for a suite of high-level communications protocols used to create personal area networks with small, low-power digital radios. Low power and wide area network (LPWAN) LPWAN allows communication between a minimum of 500 meters, requires minimal power, and is used for most IoT devices. Common examples of LPWANs are: IoT LTE 4G With high bandwidth and low latency, this network is a great choice for IoT scenarios that require real time information or updates. IoT 5G While not yet available, 5G IoT networks are expected to enable further innovation in IoT by providing significantly faster download speeds and connectivity to more devices in a given area. Cat-0 This LTE based network is the lowest cost option. This network laid the foundation for Cat-M, the technology that will replace 2G. Cat-1 This standard for cellular IoT will replace 3G eventually. Cat-1 networking is easy to set up and offers a great solution for applications that require a voice or browser interface. LoRaWAN Long-term wide area networks (LoRaWANs) connect mobile devices, devices that are secure and battery operated two-way. LTE Cat-M1 The network is fully compatible with LTE networks optimizing cost as well as power on the second generation of LTE chips specifically designed for IoT applications. Narrowband or NB-IoT/Cat-M2 NB-IoT/Cat-M2 uses direct sequence spread spectrum modulation (DSSS) to send data directly to servers, eliminating the need for gateways. While NB-IoT costs more, not requiring a gateway makes it cheaper to run. Sigfox This global IoT network provider offers a wireless network to connect low-power objects that transmit continuous data. IoT technology and protocols The Internet of Things is the convergence of embedded systems, wireless sensor networks, control systems, and automation that makes industrial manufacturing factories, smart retail, next-generation healthcare, smart homes and cities, and connected wearables possible. IoT technology empowers you and me to transform business with data-driven insights, refined and controlled operational processes, new business lines, and more efficient and effective use of quality materials. IoT technology is constantly evolving, with countless service providers, multiple platforms, and millions of new devices emerging every year, leaving developers with many decisions to make before entering the IoT ecosystem. Understand common IoT protocol, power and connectivity requirements. The IoT science and technology ecosystem consists of the following layers: device, data, connectivity, and technology users. 1. Device layer A combination of sensors, actuators, hardware, software, connectivity, and gateways which are the devices that connect to and interact with the network. 2 . Data layer Data collected, processed, transmitted, stored, analyzed, presented and used in a business context. 3. Business layer and R&D IoT technology business functions, including billing management and marketplace data. 4. User layer ( share ) People interacting with IoT devices and technologies. X1 IoT technology stack: I . IoT devices 1. Actuators Actuators perform physical actions when the control center gives instructions, usually in response to changes identified by sensors. They are a type of transducer. 2. Embedded system Embedded systems are microprocessor or microcontroller based systems that manage specific functions within a larger system. The system includes both hardware and software components. 3. Smart device Devices that have capabilities for computing. These devices often include microcontrollers and cloud engines that can best spread a given workload across devices. 4. Microcontroller unit (MCU) This small computer is embedded on a microchip and contains a CPU, RAM, and ROM. Although they contain the elements necessary to carry out simple tasks, microcontrollers have more limited power than microprocessors. 5. Microprocessor unit (MPU) The MPU performs CPU functions on one or more integrated circuits. Although a microprocessor requires peripherals to complete tasks, it greatly reduces processing costs because it contains only the CPU. 6. Non-computing devices A device that only connects and transmits data and has no computing capability. 7. Transducer In general, a transducer is a device that converts one form of energy into another. In IoT devices, this includes internal sensors and actuators that transmit data when the device engages with its environment. 8. Sensors Sensors detect changes in their environment and create electrical impulses to communicate. Sensors usually detect environmental shifts such as changes in temperature, chemicals, and physical position and are a type of transducer.

III . How AI is changing IoT ** _______________________________ Just when we needed it most, the internet of things is delivering gobs of data and remote device control across almost every industry : Electronic Industry , healthcare industry , Agriculture Industry , Inteligence Industry and Military Industry . Today’s growing multitude of IoT endpoints is tying the digital and physical worlds ever closer together, improving the accuracy of predictions and delivering event-driven messages that can be acted on without human intervention. To examine the impact of the IoT and provide implementation advice, Network World, Computerworld, CSO, CIO, and InfoWorld each bring their own view of the most pervasive trend in tech. IoT + Artificial intelligence unlocks the true potential of IoT by enabling networks and devices to learn from past decisions, predict future activity, and continuously improve performance and decision-making capabilities. Businesses have been built or optimized using IoT devices and their data capabilities, ushering in a new era of business and consumer technology. Now the next wave is upon us as advances in AI and machine learning unleash the possibilities of IoT devices utilizing “artificial intelligence of things,” or AIoT. Consumers, businesses, economies, and industries that adopt and invest in AIoT can leverage its power and gain competitive advantages. IoT collects the data, and AI analyzes it to simulate smart behavior and support decision-making processes with minimal human intervention. ***Why IoT needs AI ?*** IoT allows devices to communicate with each other and act on those insights. These devices are only as good as the data they provide. To be useful for decision-making, the data needs to be collected, stored, processed, and analyzed. This creates a challenge for organizations. As IoT adoption increases, businesses are struggling to process the data efficiently and use it for real-world decision making and insights. This is due to two problems: the cloud and data transport. The cloud can’t scale proportionately to handle all the data that comes from IoT devices, and transporting data from the IoT devices to the cloud is bandwidth-limited. No matter the size and sophistication of the communications network, the sheer volume of data collected by IoT devices leads to latency and congestion. Several IoT applications rely on rapid, real-time decision-making such as autonomous cars. To be effective and safe, autonomous cars need to process data and make instantaneous decisions (just like a human being). They can’t be limited by latency, unreliable connectivity, and low bandwidth. Autonomous cars are far from the only IoT applications that rely on this rapid decision making. Manufacturing already incorporates IoT devices, and delays or latency could impact the processes or limit capabilities in the event of an emergency. In security, biometrics are often used to restrict or allow access to specific areas. Without rapid data processing, there could be delays that impact speed and performance, not to mention the risks in emergent situations. These applications require ultra-low latency and high security. Hence the processing must be done at the edge. Transferring data to the cloud and back simply isn’t viable. ***Benefits of AIoT *** Every day, IoT devices generate around one billion gigabytes of data. By 2025, the projection for IoT-connected devices globally is 42 billion. As the networks grow, the data does too. As demands and expectations change, IoT is not enough. Data is increasing, creating more challenges than opportunities. The obstacles are limiting the insights and possibilities of all that data, but intelligent devices can change that and allow organizations to unlock the true potential of their organizational data. With AI, IoT networks and devices can learn from past decisions, predict future activity, and continuously improve performance and decision-making capabilities. AI allows the devices to “think for themselves,” interpreting data and making real-time decisions without the delays and congestion that occur from data transfers. AIoT has a wide range of benefits for organizations and offers a powerful solution to intelligent automation. ***Avoiding downtime *** Some industries are hampered by downtime, such as the offshore oil and gas industry. Unexpected equipment breakdown can cost a fortune in downtime. To prevent that, AIoT can predict equipment failures in advance and schedule maintenance before the equipment experiences severe issues. Increasing operational efficiency AI processes the huge volumes of data coming into IoT devices and detects underlying patterns much more efficiently than humans can. AI with machine learning can enhance this capability by predicting the operational conditions and modifications necessary for improved outcomes. Enabling new and improved products and services Natural language processing is constantly improving, allowing devices and humans to communicate more effectively. AIoT can enhance new or existing products and services by allowing for better data processing and analytics. ***Improved risk management*** Risk management is necessary to adapt to a rapidly changing market landscape. AI with IoT can use data to predict risks and prioritize the ideal response, improving employee safety, mitigating cyber threats, and minimizing financial losses. ***Key industrial applications for AIoT *** AIoT is already revolutionizing many industries, including manufacturing, automotive, and retail. Here are some common applications for AIoT in different industries. ***Manufacturing*** Manufacturers have been leveraging IoT for equipment monitoring. Taking it a step further, AIoT combines the data insights from IoT devices with AI capabilities to offer predictive analysis. With AIoT, manufacturers can take a proactive role with warehouse inventory, maintenance, and production. Robotics in manufacturing can significantly improve operations. Robots are enabled with implanted sensors for data transmission and AI, so they can continually learn from data and save time and reduce costs in the manufacturing process. ***Sales and marketing*** Retail analytics takes data points from cameras and sensors to track customer movements and predict their behaviors in a physical store, such as the time it takes to reach the checkout line. This can be used to suggest staffing levels and make cashiers more productive, improving overall customer satisfaction. Major retailers can use AIoT solutions to grow sales through customer insights. Data such as mobile-based user behavior and proximity detection offer valuable insights to deliver personalized marketing campaigns to customers while they shop, increasing traffic in brick-and-mortar locations. ***Automotive*** AIoT has numerous applications in the automotive industry, including maintenance and recalls. AIoT can predict failing or defective parts, and can combine the data from recalls, warranties, and safety agencies to see which parts may need to be replaced and provide service checks to customers. Vehicles end up with a better reputation for reliability, and the manufacturer gains customer trust and loyalty. One of the best-known, and possibly most exciting, applications for AIoT is autonomous vehicles. With AI enabling intelligence to IoT, autonomous vehicles can predict driver and pedestrian behavior in a multitude of circumstances to make driving safer and more efficient. ***Healthcare*** One of the prevailing goals of quality healthcare is extending it to all communities. Regardless of the size and sophistication of healthcare systems, physicians are under increasing time and workload pressures and spending less time with patients. The challenge to deliver high-quality healthcare against administrative burdens is intense. Healthcare facilities also produce vast amounts of data and record high volumes of patient information, including imaging and test results. This information is valuable and necessary to quality patient care, but only if healthcare facilities can access it quickly to inform diagnostic and treatment decisions. IoT combined with AI has numerous benefits for these hurdles, including improving diagnostic accuracy, enabling telemedicine and remote patient care, and reducing the administrative burden of tracking patient health in the facility. And perhaps most importantly, AIoT can identify critical patients faster than humans by processing patient information, ensuring that patients are triaged effectively. ***Prepare for the future with AIoT *** AI and IoT is the perfect marriage of capabilities. AI enhances IoT through smart decision making, and IoT facilitates AI capability through data exchange. Ultimately, the two combined will pave the way to a new era of solutions and experiences that transform businesses across numerous industries, creating new opportunities altogether. IV . IoT, AI, and the future battlefield ________________________________________ Powered by artificial intelligence (AI), a massive military Internet of Things (IoT) promises a host of battlefield benefits in such areas as unmanned surveillance and targeting, situational awareness, soldier health monitoring, and other critical applications. However, major data and communications challenges must be overcome first. Future conflicts will require critical decisions made within hours, minutes, or seconds – not days – that entail analyzing an operating environment and issuing commands, according to a Congressional Research Service publication on the Joint All-Domain Command and Control (JADC2) initiative. One way the Department of Defense (DoD) aims to speed up and automate decision-making is through a massive military Internet of Things (IoT) and artificial intelligence (AI). A major DoD initiative, JADC2 aims to collect data streams from thousands of battlefield vehicles, environmental sensors, and other intelligent devices across every military branch. AI and machine learning (ML) can then be used to deliver relevant information enabling quick decision-making at the front lines – even down to identifying military targets and recommending the optimal weapon to engage them. Military IoT includes many different “things” – everything from battlefield sensors and weapons systems to tracking devices, communications equipment, wearables, drones, ships, planes, tanks, and even body sensors. Together they stream unprecedented volumes of real-time information to the battlefield. Each branch of the military has its IoT-related initiatives. For the Air Force, IoT is an essential component of its evolving Advanced Battlefield Management System (ABMS). For the Army, it’s the Army Futures Command, and for the Navy, Project Overmatch. The overall goal of JADC2 is to tie all these initiatives together and make them work as a single force successfully on the battlefield. ***Big challenges ahead*** The success of this massive IoT initiative depends of course on the ability to collect and store huge volumes of streaming data from thousands of “things” in real time. A much greater challenge, however, is actually making sense of all that information instantly and getting the results to warfighters fast enough that they can use it to their advantage. The technical obstacles are formidable and include: Merging, integrating, and sharing huge volumes of streaming IoT data generated from devices residing in siloed military branches with scores of different data formats and communications networks. Ideally, the goal is a single data format and data store that can be processed rapidly. Deciding on a common high-bandwidth, low-latency network to serve as the connective tissue between military IoT devices and edge and cloud processing and AI environments. There are numerous possibilities, including satellite and specialized proprietary military network solutions, but 5G is envisioned by many as the eventual connective-tissue solution. Dividing data processing and storage intelligently between a massively scalable centralized environment such as the cloud when feasible, and fast-performing systems lying at the network edge. These solutions get systems much closer to the battlefield where data connections can deliver the fast network performance, low latency, and availability to enable quick decisions on the front lines. Resilient data storage, communications, synchronization, and processing at the network edge, even in remote locations or at times when there are no traditional communication capabilities such as 5G available, often for weeks. Battlefield personnel can’t be forced to rely on less-than-reliable distant cloud connections, plus critical data can’t be lost due to a connection or power lapse, even if it’s just for a few minutes. Airtight cyberattack prevention, detection, and remediation for all this data communications and storage. ***Compelling military IoT use cases*** The DoD is in the very early stages of planning and implementing JADC2 and IoT, with many of these decisions still to be made and only a few limited demonstrations of IoT’s potential to date. Assuming most of these IoT challenges can be met, the use cases for manned and unmanned applications are compelling and many. Following are several examples. Autonomous weapons systems: Human beings continue to be the principal battlefield agents and drivers of success. However, autonomous surveillance and weapons systems such as military drones, smart missiles, and unmanned ground vehicles can conduct advanced battlefield surveillance, enhance battle intelligence, and even engage targets to preserve soldiers’ lives. They can also bring precision to the battle via AI and technologies such as facial recognition that can target enemy combatants more accurately than humans and avoid friendly fire and civilian casualties. Deciding on the division between human and autonomous decision-making will be one of the big moral and technical challenges linked to the success of autonomous systems. Soldier-borne sensors and devices: Often called the Internet of battlefield things, a network of intelligence-gathering and biometric body sensors embedded in soldiers’ combat uniforms, helmets, weapons systems, and transports can convey valuable battlefield information together with soldier location, health stats, and mental state. This knowledge can be used to decide when to move soldiers out of the battlefield in the most adverse situations or administer medical aid proactively on a timely basis to reduce casualties. Situational awareness: Situational awareness is critical for quick and effective decision-making on the battlefield. Not only is merging IoT with AI a way to enhance and automate situational awareness – including battleground layout, squad and enemy locations, assets, and objectives – it has the potential to provide that awareness faster than ever before without having to rely on centralized command and control. Leveraging resilient connections and the power of network edge processing, unmanned systems and other IoT surveillance devices can share and merge data to deliver superior intelligence, surveillance, and reconnaissance (ISR) information directly to the front lines. The use of AI to assist and automate many surveillance functions can lighten the stress and cognitive load on soldiers on the battlefield . Connecting drones, sensors, and other devices to local edge database/AI/ML servers via 5G or another common fabric makes information available when the cloud is not accessible or too distant to deliver information quickly. When cloud connections are feasible, IoT can take advantage of the cloud’s massive scalability and processing power. Even in remote situations where 5G is not available or cyberattacks render it infeasible, alternative available peer-to-peer networks such as WiFi, Bluetooth, or private proprietary communications solutions can synchronize distributed databases and provide the network and data resiliency needed on the battlefield. A solution is available for harnessing peer-to-peer connections and synchronizing data across them, then connecting and synchronizing data with local, regional, and cloud servers when they are available. There are numerous other IoT use cases, such as supply-line vehicle monitoring, military-base security, preventive maintenance on the battlefield, and even inventory management. As the battlefield becomes more complex and unpredictable, IoT and AI will become an increasingly valuable strategy for accelerating and automating critical decision-making, outthinking the enemy, and minimizing combat and civilian casualties. V . energy storage on the IOT and AI network using a battery with controlled free energy . Today, increasing numbers of batteries are installed in Automatic car ( example tesla cars ) , telecommunication operation ( Sattelite operation ) ,residential and commercial buildings; by coordinating their operation, it is possible to favor both the exploitation of renewable sources and the safe operation of electricity grids. However, how can this multitude of battery storage systems be coordinated? Using the Application Programming Interfaces of the storage systems’ manufacturers is a feasible solution, but it has a huge limitation: communication to and from storage systems must necessarily pass through the manufacturers’ cloud infrastructure. Therefore, this schematic concept presents an IoT-based solution which allows monitoring/controlling battery storage systems, independently from the manufacturers’ cloud infrastructure. More specifically, a home gateway locally controls the battery storage using local APIs via Wi-Fi on the condition that the manufacturer enables them. If not, an auxiliary device allows the home gateway to establish a wired communication with the battery storage via the SunSpec protocol. Validations tests demonstrate the effectiveness of the proposed IoT solution in monitoring and controlling . In the absence of free APIs, the adoption of the SunSpec protocol is a valid option, but SunSpec alliance members (Tesla, General Electric, Jinko, LG, SMA, Sonnen, Solax Power, to name a few) must define a standard for the messages’ bodies, sent via the SunSpec protocol. The integration of the IoT in power systems is rapidly growing today as IoT supports measurement, communication, data processing and command implementation in smart grids. However, the literature is not very generous with contributions on IoT applications in battery storage systems monitoring and control, at residential and commercial levels . the battery storage system is part of a microgrid that also includes a photovoltaic system, loads and a hydrogen-based storage system. For that microgrid, the authors proposed an innovative multi-layered architecture to deploy heterogeneous automation and monitoring systems. A Controller Area Network (CAN) bus was used to interconnect the battery management unit with a central controller which acts as a data and command exchanger. How does energy storage use the IoT? Large-scale battery storage facilities are becoming a widespread solution to energy storage challenges. Digitalised battery storage solutions, connected via the IoT, can store and dynamically distribute energy exactly as it is needed, either locally or from a central distribution hub. Battery storage enables consumers and businesses to store and consume what they generate. It can also serve as a primary or backup power source at industrial/commercial sites or hospitality events. Secure, resilient cellular connectivity enables service providers to remotely control and monitor battery assets for operational, safety, environmental and efficiency reasons. The IoT collects and communicates real-time data, giving asset managers unparalleled visibility into devices and operations. For the energy sector, the IoT exchanges data to assist with asset monitoring, metering measurement, equipment maintenance, performance optimisation, demand and capacity management and identifying cost saving opportunities.

Conclusion : the internet of things is also projected to work on an operating system, a cloud base visual work operating system that consists of modular building blocks used ans assembled to create software applications and work management tools. IOTX can also function privately as state and international cyber security, namely for early detection with singular communication formulas, as well as quick responses for cyber handling, by sending information on artificial neural network analysis from cyber data so that IOTX plus AI can be used for cyber crime prevention and response. fast handling by checking and analyzing organizational data and cyber crime work areas both nationally and internationally to cloud workloads which enables seamless and automatic protection against a spectrum of a cyber threats. ex problem sample cyber security + AI + IOTX = https://youtube.com/shorts/L_yb7S9Irb4?feature=share

_______________________________________________________________________ Created , structured and analyzed in a thinking structure by Agustinus Manguntam Siber Wiper Glock as Thinker , providing investments in time and space in the Smart Electronics business for a more capable life across Earth and space .