The Future Of The Industrial Internet Of Things

Industrial IoT, IoT Security

Article | July 12, 2023

The biggest IoT trends are likely to manifest in 2022 and beyond. 1. BLOCKCHAIN The term blockchain is a new concept and is known as a single registry; agreed and distributed in several nodes of a network that will continue in force in the coming years in various activities. 2. MOBILE COMMERCE OR M-COMMERCE It is part of electronic commerce that is carried out exclusively through mobile devices such as smartphones or tablets. The processes will be specialized. With mobile commerce comes the need for device management. Device management is a vital step to ensure security is promptly implemented.Mobile Device Management (MDM) solutions, also known as MDM, offer brilliant benefits across all areas. 3. TELEWORK AND DISTANCE EDUCATION Academic and work activities that are carried out remotely, preferably from home, will continue to be applied in a fixed or hybrid way. A smart device(s) can be used from a remote location and therefore enable workers to more effectively manage time. 4. ROBOTIC PROCESSING AUTOMATIZATION It is all technology-oriented to the use of software, with the aim of reducing human intervention in the use of computer applications, especially in repetitive tasks. This reduces the risk of human error and will also cut down management costs. 5. ARTIFICIAL INTELLIGENCE It is the combination of algorithms proposed with the purpose of creating machines that have the same capabilities as humans, with the aim of doing a variety of tasks. If we decide to develop an Artificial Intelligence that has greater intelligence, responsibility and scalability, we can make the most of learning algorithms and interpretation systems. In this way, we are able to create value more quickly and with a greater business impact. It is essential to have new techniques that achieve smarter AI solutions, that require less data, with greater ethical responsibility and more resilience.” Gartner 6. DIGITAL TRANSFORMATION Digital transformation is the change associated with the application of digital technologies in all aspects of human society, and especially in organizations. Accelerating business digital transformation requires entrepreneurs to step back and re-evaluate their plans. It’s about aligning the customer experience strategy with coordinated and detailed digitization plans of what needs to be done, by whom and when. To do this, having precisely identified the customer journey of your digital customer allows you a complete approach, for which tools such as the customer journey map is key. 7. FINTECH It is a nascent industry in which companies use technology to provide financial services in an efficient, agile, comfortable and reliable way. They aim to expand bank penetration. 8. DATA ANALYSIS It is the process by which raw data is analyzed in order to answer questions and reach practical conclusions that support an organization’s decision-making. Using predictive models and AI tools, we can run simulations that are based on real scenarios and information. Thanks to this, we obtain data on contexts that would be difficult, very expensive or impossible to test in physical environments. Big data is big money. 9. SOFTWARE DEVELOPMENT Software development is generally considered part of the agile family of approaches, and is often used in combination with one or more other methods. Softwareon a smart device can also be upgraded to include better connectivity. In addition to its development, there is likely to be more outsourcing also. 10. ADVANCED MANUFACTURING OR INDUSTRY 4.0 It refers to a new business model in which the interconnection of integrated ICT systems both with each other and with the internet is key. The adoption of Industry 4.0 technologies and the training of personnel will be the greatest opportunities that industries, companies and governments will have in the next decade. Therefore, 2022 represents the next step to embrace technological transformation as an indispensable element for competitiveness, resilience, and development.

IoT Security

Article | October 11, 2023

5G trends are shaping the future of various technologies, from the Internet of Things to virtual reality. Learn more about the top trends in 5G to stay ahead of the competition in this sector. Contents 1 The Current State of IoT Data Security 2 Top Trends in IoT Data Security in 2023 2.1 Emergence of AI-powered Security Solutions 2.2 Potential of Blockchain Technology 2.3 Growing Use of Zero-trust Security Frameworks 2.4 Greater Emphasis on End-to-end Encryption 2.5 Industry and Government Collaboration 3 Conclusion As the Internet of Things (IoT) continues to rapidly expand, data security has become a critical concern for businesses and consumers alike. With recent high-profile breaches and cyberattacks, the latest trends in IoT data security focus on implementing stronger encryption and authentication protocols, as well as enhancing device-level security measures to protect sensitive data from potential threats. 1 The Current State of IoT Data Security The growing adoption of IoT has led to a digital transformation in the way businesses operate. IoT technology has enabled organizations to collect and analyze vast amounts of data in real-time, allowing for improved decision-making, increased operational efficiency, and enhanced customer experiences. Despite these benefits, organizations are currently facing significant IoT data security challenges that must be addressed to fully realize the potential of this technology. Companies recognize unauthorized access (43%), data privacy (38%), and data integrity (31%) as top IoT security challenges. (Source: Statista) Businesses are actively addressing these security challenges by investing in IoT and data security solutions. The global market for IoT data security continues to grow, and companies are increasingly investing in strategies for data security in the IoT. To secure access to mission-critical connected devices and sensitive data, it is imperative for businesses to keep track of IoT trends in data security. 2 Top Trends in IoT Data Security in 2023 2.1 Emergence of AI-powered Security Solutions AI-powered security systems can rapidly detect and respond to attacks, reducing the likelihood of significant damage to IoT devices or networks. In particular, its ability to analyze vast amounts of data in real-time and identify anomalies or potential security threats makes AI a vital component of an IoT data security strategy. Detecting an IoT security breach in progress is possible with AI security systems, which identifies unusual behavior by analyzing data patterns from IoT devices. AI can also be used to diagnose potential vulnerabilities in IoT devices and networks, allowing organizations to take proactive measures to address them before they are exploited. The pattern recognition capabilities of AI also help secure IoT technology through predictive analytics. By analyzing past data breaches and attacks, AI systems detect potential cyberattacks and develop predictive models to detect and respond to them proactively. AI-driven security systems have the potential to streamline incident response by lessening the load on cybersecurity teams and reducing response time. The ability to adapt and learn from a previous cyberattack allows machine learning (ML) algorithms to create novel strategies that prevent similar attacks in the future. AI represents a significant development in addressing IoT security concerns since it provides sophisticated capabilities to protect IoT networks and devices that conventional security measures cannot provide. AI-enabled security systems deliver immediate identification, reaction, and deterrence of possible threats, which is why they will be critical in ensuring data security in the IoT. 2.2 Potential of Blockchain Technology Blockchain's unique features, such as decentralization, immutability, and cryptographic security, provide a robust framework for secure communication and data sharing among IoT devices. By leveraging blockchain technology, businesses can ensure their IoT data's integrity, confidentiality, and authenticity. One of the key advantages of using blockchain for IoT data security is its decentralized nature. Blockchain networks are distributed and run on a peer-to-peer basis, making it difficult for attackers to compromise the network. This also makes it an ideal solution for recording and securing data from multiple access points, such as IIoT systems. Additionally, blockchain networks are designed to be immutable, making them an ideal solution for IoT data security and providing a tamper-proof and transparent ledger for recording data flow. This can help enterprises identify and mitigate security threats more quickly and efficiently, reducing the risk of cybersecurity incidents. A research paper published in Wireless Networks highlights the advantage of using a Blowfish Blockchain Model to enable IoT data sharing security, particularly for multimedia content. Blockchain technology is a promising solution for securing IoT data. Its unique features, including decentralization, immutability, and cryptographic security, make it an ideal candidate for many IoT use cases. This technology can potentially transform data security for IoT devices by offering the IoT sector the solution it requires. 2.3 Growing Use of Zero-trust Security Frameworks Zero-trust frameworks ensure that only authorized devices and users can access sensitive data and systems, protecting against insider threats and external attacks. This is especially important in IoT environments, where devices may lack traditional security measures like firewalls and antivirus software. Device identity management is a critical component of zero-trust security for IoT data. Only recognized devices are allowed access to a network or data by leveraging processes and technologies that authenticate device identity. With Zero Trust, any connected device must be authorized before accessing any resources, including data. By closely monitoring and managing access, businesses can maintain the security of the IoT. This protects against threats that exploit weak device identity management. Overall, zero-trust security frameworks are essential for safeguarding IoT data from malicious actors and protecting the integrity of IoT ecosystems. 2.4 Greater Emphasis on End-to-end Encryption IoT poses a threat to data security when users do not take proper measures to protect the data generated. End-to-end encryption provides a strong layer of protection against unauthorized access, interception, and other cyber threats by encrypting data at the source, during transmission, and at rest. IoT devices collect and process a wide range of sensitive data, from personal information and financial data to critical infrastructure and medical records. This data is often transmitted over networks and shared with cloud services, and the risk of cyberattacks during transmission cannot be ignored. End-to-end encryption can provide a strong layer of protection by encrypting data at the source, working to improve the limited data security of the IoT. As the use of IoT devices continues to grow, implementing end-to-end encryption will become increasingly important for ensuring the security and privacy of sensitive IoT data. 2.5 Industry and Government Collaboration In late 2021, the UK and Singapore governments became the first to announce obligatory security requirements for specific categories of IoT devices. Due to IoT data security risks, other countries have also defined guidelines, best practices, certifications, or labeling efforts for IoT devices. However, adoption among IoT device makers and vendors has been slow. The National Institute of Standards and Technology (NIST) has been working on establishing cybersecurity guidelines for IoT devices. In June 2022, NIST incorporated consumer IoT cybersecurity criteria into its family of IoT cybersecurity guidance. NIST is also working with the IoT industry to design, standardize, and test solutions for IoT security controls. By discussing IoT device security concepts and establishing guidelines in collaboration, the industry and the government can foster adoption of general methods to protect IoT devices from cybersecurity breaches. Such cooperation can be crucial in ensuring that IoT devices are secure from cyber threats and that IoT device makers and vendors adopt best practices for IoT device security. 3 Conclusion The trends in IoT data security showcase several proactive measures that can be taken to protect sensitive data in a rapidly evolving technological landscape. In addition, organizations are moving towards a more comprehensive approach to IoT data security with the emergence of AI-powered security solutions, blockchain technology, and the shift to zero-trust security frameworks. As IoT devices continue to proliferate, organizations must prioritize security and data protection to prevent data breaches and cyberattacks. This emphasizes the need for collaboration between industry and government to strengthen security measures and improve IoT device security by building with a ‘secure by design’ approach.

Security, IoT Security

Article | July 13, 2023

Discover the crucial role of big data capabilities in unlocking the potential of IoT for businesses. This article covers their synergy, challenges, and value in decision-making and revenue generation. Contents 1 Why Big Data and IoT Matter for Businesses 2 Understanding Synergy of Big Data and IoT 2.1 How IoT generates Big Data 2.2 Challenges of Processing Big Data from IoT Devices 2.3 Importance of Big Data in IoT Applications 3 The Value of Big Data and IoT for Businesses 3.1 Improved Decision-making for Businesses 3.2 Generate New Revenue Streams 4 Final Thoughts 1. Why Big Data and IoT Matter for Businesses The internet of things (IoT) is connecting all types of physical assets to the internet, from smart wearables that track wearer’s vitals to connected industrial units that can report any malfunctions automatically. Big data in IoT is a natural outcome with the growth of IoT devices, with an immense surge in the amount of data being generated. There are currently over 13 billion connected IoT devices worldwide. (Source – Techjury) This data is extremely valuable to businesses as it can help streamline operations, predict trends, and diagnose device issues. Certain functions of IoT devices that are crucial for modern businesses, such as enabling predictive maintenance, depend on the analysis of the data generated every second. However, to maximize the ROI from their IoT ecosystem, businesses must first manage and process the vast amounts of unstructured data they produce. This is where big data capabilities come in. 2. Understanding Synergy of Big Data and IoT Big data and the IoT are fundamentally different concepts, but are closely connected. Big data is a term that is used for a great amount of data that is characterized by volume, velocity, variety and veracity (or the ‘trustworthiness’ of data). The IoT is a term for physical devices or objects linked to the internet using an assortment of technologies. Understanding the synergy between these two technologies will be critical for businesses looking to leverage their full potential. 2.1 How IoT generates Big Data IoT is one of the primary drivers of big data growth. The vast number of interconnected devices in the IoT ecosystem generates a massive amount of data every second. This data includes information on user behavior, device performance, and environmental conditions, among others. The nature of this data makes it challenging to store, process, and analyze using traditional data management tools. This is where big data technologies such as Hadoop, Spark, and NoSQL databases come in, providing the ability to manage massive amounts of data in near-real-time, enabling critical applications of big data in IoT. For businesses, processing IoT data is synonymous with processing big data, due to the nature of the data generated by an IoT ecosystem. 2.2 Challenges of Processing Big Data from IoT Devices IoT data processing is a complex and challenging task due to several reasons. Firstly, the sheer volume of data generated by these devices is enormous and is only increasing. This requires a robust infrastructure and specialized tools to store, manage, and analyze the data efficiently. This data is also generally unstructured, heterogeneous, and complex, making it difficult to process using traditional data management and analysis techniques. Moreover, it is often noisy and may contain errors or outliers, which can impact the accuracy of data analysis. Businesses also face a challenge when securing such vast amounts of data. Since IoT devices collect sensitive information such as personal and financial data at scale, it is critical to ensure that data is encrypted, transmitted securely, and stored safely. Additionally, IoT devices often operate in remote locations with limited connectivity, making it challenging to transmit data to the cloud for storage and analysis. As IoT devices continue to proliferate and generate increasingly large amounts of data, businesses must adopt big data technologies to gain actionable insights from this data. 2.3 Importance of Big Data in IoT Applications There are several use cases of the IoT where processing large amounts of data is essential. It plays a critical role in IoT applications, providing businesses with valuable insights that can be used to optimize processes, reduce costs, and improve overall efficiency. By collecting and analyzing large amounts of data from IoT devices, businesses can gain a better understanding of customer behavior, machine performance, and other critical metrics. For example, big data in IoT can be used to identify patterns in customer behavior, allowing businesses to tailor their marketing efforts and improve customer engagement. Additionally, IoT devices can be used to collect data on machine performance, allowing businesses to identify potential problems before they occur, minimize downtime, and optimize maintenance schedules. The value of big data in IoT applications lies in its ability to provide businesses with real-time insights that can be used to drive growth, reduce costs, and improve overall efficiency. 3. The Value of Big Data and IoT for Businesses Businesses looking to integrate big data in IoT must first consider their data storage and analytics capabilities. By understanding the value of big data technology in capturing and analyzing IoT-generated data, businesses can unlock insights that can help them make better decisions, optimize processes, and create new business opportunities. 3.1 Improved Decision-making for Businesses IoT and big data technologies offer businesses a wealth of data that can be used to make better-informed decisions. By integrating IoT sensors and devices with their operations, businesses can collect real-time data on customer behavior, operational performance, and market trends. This data can then be analyzed using big data analytics tools to generate valuable insights that can inform decision-making. For example, operational data can be analyzed to identify inefficiencies and areas for optimization, helping businesses reduce costs and improve efficiency. With the right data storage and analytics capabilities, businesses can leverage the power of IoT and big data to gain a competitive advantage and make better-informed decisions that drive growth and success. 3.2 Generate New Revenue Streams By leveraging the vast amount of data generated by IoT devices and analyzing it with big data analytics tools, businesses can gain insights into customer behavior, market trends, and operational performance. These insights can be used to create new revenue streams and business models, such as subscription-based services, pay-per-use models, and predictive maintenance services. For example, IoT sensors can be used to collect data on equipment performance, allowing businesses to offer predictive maintenance services that help prevent equipment breakdowns and reduce downtime. Similarly, customer data can be analyzed to identify new revenue opportunities, such as personalized product recommendations and targeted advertising. With the right strategy and investment in IoT and big data technologies, businesses can unlock new revenue streams and create innovative business models that drive growth and success. 4. Final Thoughts Big data in IoT is becoming increasingly important for businesses, and the future prospects are bright. As IoT continues to grow and generate more data, businesses that can effectively analyze it will gain a competitive advantage, leading to increased efficiency, reduced costs, and higher ROI. To fully realize the benefits of IoT, businesses must develop big data analytics and IoT devices in tandem, creating a feedback loop that drives continuous improvement and growth. By embracing these technologies, businesses can make data-driven decisions and unlock new insights that will help them thrive in the years ahead.

Article | April 10, 2020

As development teams race to build out AI tools, it is becoming increasingly common to train algorithms on edge devices. Federated learning, a subset of distributed machine learning, is a relatively new approach that allows companies to improve their AI tools without explicitly accessing raw user data. Conceived by Google in 2017, federated learning is a decentralized learning model through which algorithms are trained on edge devices. In regard to Google’s “on-device machine learning” approach, the search giant pushed their predictive text algorithm to Android devices, aggregated the data and sent a summary of the new knowledge back to a central server. To protect the integrity of the user data, this data was either delivered via homomorphic encryption or differential privacy, which is the practice of adding noise to the data in order to obfuscate the results.