Industrial IoT, IoT Security
Article | July 12, 2023
Every major industry, including retail, transportation, banking, healthcare, and energy, has significantly benefited from the Internet of Things. Processes like supply chains are where the Internet of Things best demonstrates its promise. Applications for management, forecasting, and oversight aid fleet managers in increasing distribution's operational effectiveness and decision-making openness.
Some of the primary goals for IoT deployment in supply chain management include tracking and monitoring. For example, warehouse and fleet managers can use technology to keep an eye on their stock and freight.
Reasons to Use IoT in Supply Chain Management
Real-time Location-tracking
Thanks to the Internet of Things, managers have access to a consistent stream of real-time data on the product's location and the environment surrounding transportation. You may keep track of the delivery of both finished items and raw materials, and you will be informed if the product is transported in the wrong direction.
Monitoring of Storage Conditions
Environmental sensors allow management to monitor cargo conditions and take immediate action when something changes. One of the most popular IoT supply chain systems, for example, collects data on pressure, humidity, the temperature inside vehicles, and other factors that could harm the goods and then automatically adjusts the environment.
Enhance Your Emergency Planning
Supply chain managers can design routes with the use of IoT and data analytics, taking into account traffic, weather, potential accidents, and other delay-causing events that may occur along the way. The Internet of Things collects all the data required to create adaptable backup plans and identify the source of any current delays. Also, supply chain managers can get alerts from the system in real time, which speeds up reducing risks.
Forecast Goods Arrival and Movement
IoT devices and data analytics systems are used by managers to enhance the decision-making process and boost the accuracy of delivery estimates. Real-time tracking lets businesses keep track of products as they are shipped, predict when they will arrive, and plan for and reduce the risk of delays.
Conclusion
There are many different IoT applications for supply chain management. For example, it improves communication between parties, makes it easier to track and monitor commodities, and makes planning more precise.
As long as you have a clear goal for what you need the technology to achieve for you, an IoT-based platform is an excellent investment for both small and large organizations. It's also essential to bring on a talented team for the design and development phase.
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IoT Security
Article | October 11, 2023
Edge computing enables the IoT to move intelligence out to the edge. If organizations have a lot of data and need to use it, they should do so in end-to-end paths, environments with lots of sensors, or environments where a lot of data is generated at the edge, thanks to the Internet of Things (IoT) and edge data sensing. Additionally, traditional methodologies fall short of the necessary standards when dealing with real-time information and the growing amount of unstructured data, which includes a sensor and IoT data. For management, power concerns, analytics, real-time needs, and other IoT situations, speed and high-speed data are essential elements. This enables edge computing to handle data.
The Internet of Things (IoT) benefits from having compute capacity close to the location of a physical device or data source. IoT device data needs to be processed at the edge rather than traveling back to a central site before that analysis can be done in order to react quickly or prevent concerns. For the data processing and storage requirements of IoT devices, edge computing serves as a local source.
Benefits of Using IoT and Edge Together
The connection between IoT devices and the main IT networks has less latency.
Greater operational efficiency and quicker response times.
Network bandwidth improvement.
When a network connection is lost, the system continues to run offline.
Utilizing analytics algorithms and machine learning, local data processing, aggregation, and quick decision-making are possible.
Industrial IoT, often known as IIoT, is the application of IoT in an industrial setting, such as factory machinery. Consider the lifespan of the large, factory-used machinery. Equipment may be stressed differently over time depending on the user, and malfunctions are a regular aspect of operations.
The parts of the machinery that are most prone to damage or misuse can be equipped with IoT sensors. Predictive maintenance can be performed using the data from these sensors, cutting down on overall downtime.
Because IoT devices can be used as Edge Computing, the line between IoT and Edge Computing can occasionally be razor-thin. However, the most significant difference is the ability not only to compute data locally (in real-time) but also to sync that data to a centralized server at a time when it is safe—and feasible—to send.
IoT and edge computing are both here to stay since they fulfill crucial societal and commercial needs.
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IoT Security
Article | June 27, 2023
Explore the world of readings on IoT security, to address complex cyber security challenges and privacy issues. It caters to a wide range of readers including industrialists, students & enthusiasts.
The Internet of Things (IoT) has revolutionized industries, enabling innovative applications and improved efficiency. However, along with the numerous benefits of the IoT comes the pressing need for robust security measures. As IoT devices become more prevalent and interconnected, their risks and vulnerabilities also increase. The experts in the domain must stay updated with the latest security practices and techniques to ensure IoT systems' integrity, confidentiality, and availability. A wide range of books has been explicitly tailored address these security concerns.
1. Analytics for the Internet of Things (IoT)
Author: Andrew Minteer
Analytics for the Internet of Things (IoT): Intelligent analytics for your intelligent devices provides a comprehensive guide for businesses aiming to make informed decisions and gain greater control over their IoT infrastructure. Written by an expert in the field, this book equips readers with the essential knowledge and techniques to solve the unique challenges associated with IoT and extract valuable insights from vast amounts of data. The book begins by tackling the complex task of extracting value from large volumes of often complex IoT data, empowering readers to make data-driven decisions. Strategies to address data quality concerns are discussed, ensuring that readers are equipped to handle the inherent challenges. It offers readers approaches to optimize business value and bring down costs. Scaling both data storage and analytics is a critical consideration in IoT deployments, and the book provides practical insights into handling scale effectively. The book covers a range of topics, including transmission protocols, data flow, value extraction, geospatial analytics, machine learning, and optimizing business value.
2. Industrial Internet of Things (IIoT)
Editors: R. Anandan, Suseendran Gopalakrishnan, Souvik Pal, Noor Zaman
One of the essential IoT security books, Industrial Internet of Things (IIoT): Intelligent Analytics for Predictive Maintenance comprehensively explores how the industrial internet is transforming through increased network agility and the ability to deploy, automate, integrate artificial intelligence, orchestrate, and secure diverse use cases at hyperscale. The adoption of industrial automation on a large scale is revolutionizing business processes, with the market for industrial robots projected to reach $73.5 billion by 2023. The book highlights how IoT industrial automation provides numerous advantages, including enhanced efficiency, high accuracy, cost-effectiveness among others. This book presents real-world case studies in IIoT, robotic and intelligent systems, and web-based applications. The content is tailored to appeal to a broad audience, including working professionals, educators, and researchers in various technical disciplines. The book provides industry leaders with valuable insights by proposing business models that revitalize the workforce.
3. IoT and OT Security Handbook
Authors: Smita Jain, Vasantha Lakshmi, Foreword: Dr Rohini Srivathsa
IoT and OT Security Handbook: Assess risks, manage vulnerabilities, and monitor threats with Microsoft Defender for IoT is a comprehensive guide that equips industrial security, IoT security, and IT security professionals with the knowledge and tools to effectively address cybersecurity challenges in the rapidly evolving world of IoT and OT. In the era of the Fourth Industrial Revolution, where digital transformation and connected industries dominate, the book sheds light on the pressing security concerns that must be addressed to ensure data protection and operational resilience. Through a deep dive into the Purdue model of reference architecture, readers gain a solid understanding of common cyber-attacks prevalent in IoT and OT environments. The centerpiece of the book revolves around Microsoft Defender for IoT, a powerful security solution specifically designed to safeguard IoT and OT ecosystems. Furthermore, the concept of zero trust, which is crucial for establishing a robust security foundation, is thoroughly explored with practical insights on its implementation in the context of IoT devices.
4. Practical Internet of Things Security
Author: Brian Russell, Drew Van Duren
Practical Internet of Things Security: Design a security framework for an Internet connected ecosystem is an indispensable guide that navigates the complex realm of securely building and deploying systems in our IoT-connected world. The book primarily targets IT security professionals, security engineers, and individuals responsible for ensuring the security of their organization's data in the IoT landscape. However, it also serves as a valuable resource for business analysts and managers seeking to understand and address the security challenges associated with IoT deployments. Readers will gain a wealth of knowledge and practical skills, including breaking down cross-industry barriers, building a rock-solid security program, applying systems security engineering and privacy-by-design principles, and harnessing cloud-based systems. It delves into the unique security challenges associated with IoT and provides practical guidelines for architecting and deploying a secure IoT ecosystem within an enterprise.
5. IoT: Security and Privacy Paradigm (Internet of Everything (IoE))
Editors: Souvik Pal, Vicente García Díaz, Dac-Nhuong Le
IoT: Security and Privacy Paradigm is a comprehensive and authoritative resource that explores the evolution of security and privacy issues within the realm of the IoT. This book serves as a single reference point for students, researchers, and practitioners seeking to better understand the IoT security platforms and privacy landscape. The book adopts security engineering and privacy-by-design principles to design and implement robust cyber-security solutions within IoT ecosystems. It takes readers on a journey, starting with exploring security issues in IoT-enabled technologies and their practical applications. The book provides practical guidance on tackling security challenges and constructing a secure infrastructure for IoT devices. The book thoroughly discusses security challenges and solutions in areas such as RFID, WSNs, and IoT. The primary audience for this book includes specialists, researchers, graduate students, designers, experts, and engineers focused on security-related issues and research.
6. IoT Security Issues
Author: Alasdair Gilchrist
IoT Security Issues addresses the rapid proliferation of internet-connected devices, where security often takes a backseat to product development. This book delves into the inherent vulnerabilities and IoT security challenges, offering insights on how to address and mitigate these issues. By examining the root causes of these problems and emphasizing the importance of programming and security best practices, the author presents practical solutions to combat the lax security processes prevalent in the IoT landscape. This book caters to a wide range of readers, including programmers who have yet to focus on the IoT, security professionals, and individuals with a keen interest in hacking and making. While a basic programming background would be beneficial for certain chapters later in the book, the core content is explained in a manner that is approachable for readers from various backgrounds.
7. Security and Privacy Issues in IoT Devices and Sensor Networks
Editors: Sudhir Kumar Sharma, Bharat Bhushan, Narayan C. Debnath
This book, of all the IoT security books, delves into the critical aspects of security breaches in IoT and sensor networks, offering a comprehensive exploration of potential solutions. The book takes a two-fold approach, thoroughly examining the fundamentals and theoretical foundations of sensor networks and IoT security. It then explores the practical IoT security solutions that can be implemented to enhance the security of these elements, providing illuminating case studies to reinforce understanding. The book is an invaluable resource for industry professionals working with wireless sensor networks (WSN) and IoT systems, enabling them to elevate the security of these interconnected systems. Additionally, researchers, material developers, and technology specialists grappling with the intricate nuances of data privacy and security enhancement will find the book's comprehensive information highly beneficial.
Final thoughts
IoT security for professionals involves implementing secure communication protocols, strong authentication, device management, data encryption, access control, and regular security audits. It is crucial to stay updated, maintain a security-aware culture, and prioritize the ongoing monitoring and adaptation of security measures to address emerging threats.
The above listed books delve into various aspects of IoT security, providing insights, strategies, and practical solutions to mitigate risks and protect IoT ecosystems. This article highlights some essential IoT security books that are indispensable resources for IoT professionals striving to enhance the security posture of their organizations. They also provide real world case studies, best practices and strategies to minimize risks.
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Enterprise Iot, Infrastructure
Article | May 31, 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.
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