Blockchain Empowerment for Securing IoT Sensory Data in Next-Gen Intelligent Systems

Blockchain Empowerment for Securing IoT Sensory Data in Next-Gen Intelligent Systems

R. Logambigai, C. V. Suresh Babu
Copyright: © 2024 |Pages: 34
DOI: 10.4018/979-8-3693-5643-2.ch001
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Abstract

This chapter, titled “Blockchain Empowerment for Securing IoT Sensory Data in Next-Gen Intelligent Systems,” systematically investigates the pivotal role of blockchain in safeguarding IoT sensory data, crucial for next-generation intelligent systems. Beginning with an elucidation of IoT sensory data and its significance, the chapter establishes a foundation for comprehending blockchain's role in ensuring the security of this sensitive information. Subsequent sections explore how blockchain technology offers a decentralized and secure framework, overcoming challenges posed by traditional centralized data management systems. Real-world examples shed light on the practical implications of immutable data records, emphasizing the contribution of Blockchain to advancing next-gen intelligent systems. Furthermore, the chapter explores additional use cases, emphasizing privacy-preserving techniques that enhance the secure management of IoT sensory data, providing a comprehensive understanding of the intersection between Blockchain and IoT security.
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1. Introduction To Iot Sensory Data Management

This section have explore the definition and significance of IoT sensory data, setting the stage for understanding the role of Blockchain in securing this critical and sensitive information. The next sections will delve into how Blockchain technology can empower the protection of IoT sensory data in next-generation intelligent systems.

1.1 Overview of IoT Sensory Data

The Internet of Things (IoT) has revolutionized the way devices communicate and share data, leading to an unprecedented increase in the volume and complexity of sensory data. IoT devices, equipped with various sensors, generate massive datasets that provide valuable insights for applications ranging from smart homes to industrial automation.

1.1.1 Importance of IoT Sensory Data

IoT sensory data is pivotal for enhancing decision-making, optimizing resource utilization, and improving overall system efficiency. Nevertheless, the vast scale and sensitive nature of this data expose it to security threats, leading to concerns about data integrity, confidentiality, and privacy. In addressing these challenges, Akhbar et al. (2016) present a tailored big data architecture for IoT, emphasizing the data-information and information-knowledge layers. Their approach integrates nano data centers and cloud computing models, offering solutions to data transfer and storage challenges, as validated through an IT professionals' poll. Concurrently, Mohammadi et al. (2017) provide a comprehensive overview of advanced machine learning techniques, specifically Deep Learning (DL), for IoT analytics. Distinguishing between IoT big data analytics and IoT streaming data analytics, their work establishes a foundational understanding of these intricacies.

1.1.2 Security Challenges in IoT Sensory Data Management

The centralized nature of traditional data storage systems presents security challenges for IoT sensory data, with threats such as unauthorized access, data tampering, and denial-of-service attacks compromising the reliability and trustworthiness of information from IoT devices. To tackle the crucial issue of secure identity management in IoT, Cheng et al. (2016) offer a solution that integrates physically unclonable functions (PUFs) with channel-based key agreement (CBKA). Their work rectifies a flaw in a PUF-based authentication protocol, bolstering overall security. Simultaneously, Tomar et al. (2019) propose a multi-attribute decision-making scheme to enhance the continuous flow of sensory data, considering network attributes like residual energy, distance to MC, neighborhood criticality, and charging significance. This addresses energy limitations in IoT nodes, contributing to a more robust and secure IoT ecosystem.

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