What you need for IoT security

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.

IoT Security

Article | July 17, 2023

Organizations around the world are coping with a variety of challenges related to the COVID-19 outbreak. Many companies are struggling to convert their processes from ‘in-office’ to ‘remotely accessible’. And, they’re scrambling to find new ways to “remote” tasks – with “remote” now becoming a verb. For example, we’ve heard from many customers that adding or expanding remote employee access capabilities is a hot topic. One such customer told us that they went from 9% of their workforce working remotely, to 52%. Wow! That’s not only a substantial change to operations and processes – it also directly impacts the company’s security posture. The challenge facing OT security practitioners is daunting. We absolutely must secure the people and systems responsible for saving mankind from an alien super-virus pandemic. But, while the bad guys are lobbing attacks from afar, the good guys are acting behind the scenes like NPCs (non-player characters). They’re bypassing the security systems we developed through years of hard work, like using Gmail or Zoom, or turning off anti-virus, in the name of getting things done.

IoT Security

Article | June 27, 2023

Tech companies are stepping up Internet of Things technologies to protect against COVID-19 and future viruses by using LiDAR and infrared cameras to detect a person’s body temperature from a distance or even handwashing. Keeping the data secure in such detection is also going to be a challenge. One approach is to put a chip inside an IoT device when it is manufactured to enable strong authentication and secure communication, mainly to guard against device counterfeiting. Hitachi Vantara has touted forward looking infrared cameras (FLIR) cameras to detect the temperature of a person from a distance. That way a passenger on a train or a worker or a customer in a store can be non-intrusively screened, according to a blog from Mark Jules, global vice president of smart spaces and video intelligence.

Article | April 17, 2020

Pharma is big business, but what it’s not generally recognized is, in large part, a manufacturing business with complex supply chains, finicky chemical processes and products that have to meet stringent quality controls. Few of those outside the industry think about how drugs are made safely, efficiently and at scale with reliable quality and in precisely measured doses. Even more interesting is the simple fact that pharma often produces sophisticated drugs using manufacturing processes that are decades out of date, and which are being phased out in comparable industries, such as chemical manufacturing.