Transforming E&U operations with IoT

IoT Security

Article | July 5, 2023

Artificial intelligence (AI) has already made headway into becoming a general-purpose technology vastly impacting economies. Yet, the interpretation and estimated trajectory for something remotely close to what we call AI now was first explored in the 1950s. Until this very day, AI keeps on evolving further. Though let’s face it, AI would have been useless without data. With around 2.5 quintillion bytes of data being generated every day, the numbers will shoot up as the Internet of Things (IoT) enters the game. Let’s see what this is all about and where and how exactly IoT crosses paths with AI applications. IoT fundamentals: Where does IoT meet AI The benefits of IoT in AI Challenges of IoT in AI Why implement machine learning in IoT IoT applications for AI Key takeaways IoT fundamentals: Where does IoT meet AI? What is meant by the term internet of things (IoT) is essentially a system of correlated digital and mechanical appliances, computing devices, and sensors embedded often into everyday objects that transfer data over a network. IoT connects the internet to any and every physical thing or place in the world. Modern IoT has advanced from the mere merging of microelectromechanical systems to wireless technologies, and faster data transfer through the internet. This resulted in a confluence of information technology and artificial intelligence, allowing unstructured machine-generated data to be evaluated for insights that could lead to new developments. More and more industries are now referring to IoT to function more proficiently, provide better customer service, escalate the significance of their business, and implement robust decision-making. Machine learning for IoT can be used to identify anomalies, predict emerging trends, and expand intelligence through the consumption of audio, videos, and images. The implication of machine learning in IoT can substitute manual processes and offer automated systems using statistically backed up actions in critical processes. The benefits of IoT in AI and real life IoT offers the following benefits to AI applications: IoT data for business purposes Cost and time savings Task automation and reduction of human intervention Higher quality of life IoT data for business purposes IoT can also be viewed as a data pool. That means by aggregating IoT data, one can extract useful data-driven feedback, which in turn (used properly) may foster effective decision-making. Businesses can also identify new market opportunities, not because of IoT itself but by using the data IoT provides. And since IoT offers companies access to more data, and hence advanced analytics of that data, its usage can eventually result in improved customer outcomes and enhanced service delivery. Cost and time savings When devices get connected, cost reductions come along with it. The gathering of different data allows for advances in efficiency, and it leads to money surplus and low-cost materials. Task automation and reduction of human intervention Nowadays, devices that are internet-connected can be found in every aspect of our lives, and it is safe to say that they make tasks easier. These automation features range from real-time AI-powered chatbots to home automation control systems, and all of it usually takes a click of a button. For businesses offering AI-enabled solutions, similar advancements can be achieved with pipeline automation too. That includes significant cuts in annotation and QA time. By leveraging SuperAnnotate’s platform, hundreds of companies recorded faster task completion and more accuracy in prediction results. Higher quality of life IoT is not only beneficial in the business aspects but it also creates better living circumstances for us. Smart cities and agriculture, intelligent homes, and food waste solutions are some of the most common ways of IoT providing better, more sustainable living conditions for people. Challenges of IoT in AI Despite the numerous benefits and advancements that IoT brings to the table, there have been a few limitations with it. Some of them are listed below: Privacy issues Data overflow Bug issues Compatibility issues Privacy issues With the increased connection between multiple devices or their coexistence for model development purposes, more information is shared between them, which poses vulnerability to your data and makes room for caution. Added layers of protection are needed to prevent risks of data leaks and other threats. Data overflow Eventually, organizations will have to find a way to deal with the large numbers of IoT devices, and that will include the collection and systematic management of all the data from those IoT devices. The proper use of data lakes and warehouses, close governance, and intuitive arrangement of datasets will become an utmost priority. Join hundreds of leading companies who build super high-quality training data up to 5x faster using SuperAnnotate’s intuitive data curation and robust project management features. Bug issues If one IoT device has a bug in its system, there is a large chance that every other connected device will also have it. Compatibility issues Because there are no international standards of compatibility for IoT, it's harder for different devices to communicate with one another. Why implement machine learning in IoT More and more companies are combining IoT with machine learning projects so they can achieve analytical skills on a large variety of use cases which allows their businesses to have access to fresh insights and adopt innovative automation. By implementing machine learning for IoT, they can leverage the following: Convert data into a coherent format Arrange the machine learning model on device, edge, and cloud Enable use of data on edge devices directly for complex decision making IoT applications for AI Although we have covered the basics of IoT, its implications for AI are not as simple. Many corporations are adopting IoT which allows them to have an advanced approach to growing and advancing their business. Novel IoT applications are offering organizations the ability to plan and implement more vigorous risk management strategies. Some of the more common uses of IoT in AI encompass the following: Transport logistics Not only does IoT expand the material flow systems in transport logistics, but it also improves the automatic identification and global positioning of freight. It also increases energy efficiency and consequently declines the consumption of energy. Smart cities Although the term smart city is still incomplete, it mainly refers to an urban area that endorses sustainable enlargement and high quality of life. Giffinger et al.’s model explains the features of a smart city, including the people, the government, the economy, and lifestyle. E-health control The two main objectives of future health care are e-health control and prevention. People nowadays can choose to be monitored by physicians even if they do not live in the same country or place. Tracing and monitoring peoples’ health history makes IoT-assisted e-health extremely useful. IoT healthcare solutions could also benefit the specialists, as they can collect information to advance their medical calculations. Key takeaways Ever since its development, IoT, especially AI-enabled IoT, as discussed, has been enhancing our daily lives and directing us to work smarter while having complete control over the process. Besides having smart appliances to elevate homes, IoT devices can also be essential for providing insights and an actual look for businesses into their systems. Heading forward, IoT will continue to develop as more organizations get to understand its potential usage and tangible benefits.

Security, IoT Security

Article | July 13, 2023

Learn more about IoT data protocols and what makes them essential for a cohesive IoT ecosystem. This article will provide a detailed view of data protocols and their importance for modern businesses. 1 Significance of IoT Data Protocols for Business Operations IoT ecosystems form an integral part of many businesses today, and IoT data protocols serve as the foundation for seamless communication and data exchange between connected devices. IoT protocols ensure the integrity and reliability of data, empowering businesses to make informed decisions, optimize operations, enhance productivity, and drive innovation. With standardized and secure IoT protocols and standards, companies can achieve efficient data transmission and allow for scalability across diverse IoT ecosystems. Understanding and leveraging the right protocols is essential for businesses to benefit from the full potential of their IoT investments and gain a competitive edge in today's interconnected world. 2 Understanding IoT Data Protocols IoT data protocols are standardized rules and formats that ensure efficient and secure data transmission for efficient IoT communication. By adhering to established protocols such as MQTT, CoAP, and AMQP, businesses can maintain interoperability, scalability, and robust data transmission of IoT data, ensuring efficient data storage and management for their IoT ecosystem. This, in turn, empowers organizations to monitor and control critical processes in real-time and make informed decisions. 2.1 Role of IoT Data Protocols in the IoT Ecosystem The seamless functioning of an organization's IoT ecosystem relies on the pivotal role played by IoT data protocols. These protocols, serving as the communication backbone, enable secure transfer and efficient data processing, thereby facilitating the seamless exchange of information within the IoT network. Consequently, businesses operating within the IoT sphere can harness the power of reliable data communication enabled by these protocols to unlock insights that drive innovation. IoT data protocols serve as the vital link that fuels the interconnected landscape of IoT devices, elevating the efficiency and efficacy of businesses as they navigate the complex web of IoT technologies and leverage its immense potential. 2.2 Overview of Common IoT Data Protocols The IoT data protocols come with their own set of applications and challenges. Understanding each protocol's individual use cases will help businesses set up and scale their IoT device ecosystems. MQTT (Message Queuing Telemetry Transport): MQTT is a lightweight and efficient protocol designed for low-power devices and unreliable networks. It uses a publish-subscribe model, making it ideal for IoT applications where bandwidth and power consumption are crucial factors, such as remote monitoring and control systems. CoAP (Constrained Application Protocol): For resource-constrained IoT devices, CoAP is designed to enable smooth communication over the Internet. It uses a client-server model and is suitable for IoT applications where devices have limited processing power and memory, such as smart home automation, environmental monitoring, and healthcare systems. HTTP (Hypertext Transfer Protocol): Although primarily designed for web applications, HTTP is also used in IoT systems for data transmission. The ubiquity and familiarity of HTTP make it a widely supported communication protocol. As a result, it is suitable for IoT devices that require high-level interoperability in applications that involve cloud integration, data analytics, and web-based control interfaces. AMQP (Advanced Message Queuing Protocol): AMQP is a flexible messaging protocol ensuring reliable, secure, and interoperable communication between IoT devices and back-end systems. It supports both publish-subscribe and point-to-point messaging models, making it suitable for IoT scenarios involving complex routing, large-scale deployments, and enterprise integrations. Zigbee: Zigbee is a wireless protocol designed specifically for low-power, short-range communication in IoT networks. It operates on the IEEE 802.15.4 standard and is known for its energy efficiency and mesh networking capabilities, leading to its widespread adoption in home automation, intelligent lighting, and industrial control systems. 3 Considerations for Choosing the Right IoT Data Protocol Selecting a suitable IoT data protocol is essential to maintain smooth interoperability and a unified IoT ecosystem. Compatibility with existing infrastructure is crucial for seamless integration and cost-effective implementation. Security measures must also be robust to protect sensitive data from unauthorized access and potential breaches. Additionally, scalable and flexible data protocols in IoT are vital to accommodate future growth and evolving business requirements. Furthermore, the protocol's reliability and efficiency in transmitting data should align with the use case of IoT systems. Finally, considering the protocol's industry adoption and standardization level will also help minimize risks and enhance interoperability. 4 In Summary IoT data protocols play a significant role in facilitating efficient and secure business operations within the IoT ecosystem. By learning more about the use cases of the most common protocols in the industry, businesses can consider factors such as compatibility, security, scalability, and reliability while choosing the most suitable option for their business. As IoT systems grow, more complex and reliable data protocols will emerge, paving the way for enhanced connectivity, interoperability, and transformative opportunities across various industries.

IoT Security

Article | June 27, 2023

The concept of "never trust, always verify" is the foundation of the relatively new security architecture known as "zero trust." Zero trust requires that all users and devices be verified every time they connect, even from inside the "moat," in contrast to the conventional castle-and-moat security architecture, which automatically trusts users and devices located within a network's perimeter. Companies are being forced to reconsider how they safeguard their networks by the internet of things (IoT). Unmanaged smart gadgets connected to the internet expand the number of potential access points for hackers to compromise your security when they are added to a network. Zero Trust Security Expansion for IoT After establishing it for users and their devices, organizations must extend zero-trust security to cover unmanaged, non-user devices too. To do this, they require zero trust identity management technologies that automatically register devices, issue credentials, and offer password-less authentication. Device Visibility A device may be infected with malware or have a security breach if performance problems or bugs start to appear frequently. In addition, a malfunctioning device may be more vulnerable to attack. Therefore, organizations require device health monitoring that can automatically identify problems and flag them for remedy in order to establish and maintain zero trust security for IoT. Some cutting-edge solutions can also automatically prevent an impacted device from making further connection attempts or carrying out corrective actions without requiring human participation. The Principle of Least Privilege (PoLP) The principle of least privilege (PoLP), which argues that any user or device should only obtain the bare minimum access privileges necessary to perform their job functions, is widely used in conjunction with zero trust security. Therefore, organizations must establish the minimal level of network access required for each device to carry out its functions before limiting its potential privileges in order to deploy PoLP for IoT. Implementing identity and access management (IAM) tools and guidelines that support zero trust and PoLP for devices is one approach to accomplishing this. Security Monitoring There are other zero-trust security monitoring programs created especially for IoT, such as Palo Alto Networks' IoT Security, which was previously discussed. Businesses can also utilize tools to monitor devices and network traffic, such as next-generation firewalls and intrusion detection and prevention systems (IDS/IPS). The zero trust security solution for IoT must include monitoring in addition to as much automation as possible so that threats can be identified, contained, and remedied even when no one is there to press a button or disconnect a device manually. One of the leading causes of zero trust security projects failing over time is that people stop adhering to them once they get complicated. This is especially true for IoT security that operates on zero trust. In addition, it can be logistically challenging to keep remote, unmanaged devices at zero trust.

Article | May 13, 2020

The Industrial Internet of Things changes our view on the classic concept of production today. The largest manufacturing companies are eager to reach for technological solutions that streamline and increase the efficiency of production processes. With the development of subsequent technologies, interest in the concept of industry 4.0 increases, and thus, the number of connected devices. To meet the demand for infrastructure with adequate capacity and speed it was necessary to evolve towards the next generation of networks – 5G.