Datafication has emerged as a key driver of the digital economy, enabling businesses, governments, and individuals to extract value from the growing flood of data. In this comprehensive survey, we explore the various dimensions of datafication, including the technologies, practices, and challenges involved in turning information into structured data for analysis and decision-making. We begin by providing an overview of the historical context and the rise of big data, and then delve into the latest developments in artificial intelligence and machine learning. We examine the key drivers of datafication across industries and sectors, and explore the ethical, legal, and social implications of the data revolution. Finally, we consider the challenges and opportunities presented by datafication, including issues of data privacy and security, the need for new skills and competencies, and the potential for data to drive innovation and social change. Overall, this survey provides a comprehensive and up-to-date overview of the datafication landscape, helping readers to better understand and navigate the rapidly-evolving world of data.

The Internet of Things (IoT) has brought about significant changes to various domains such as healthcare, transportation, and manufacturing. However, security remains a critical challenge in IoT due to the large number of connected devices with varying processing capabilities and memory constraints. Traditional cryptographic algorithms are not well-suited for IoT devices due to their high computational and memory requirements. Lightweight cryptography algorithms have emerged as a promising solution for securing IoT devices with limited resources. In this abstract, we provide an overview of lightweight cryptography algorithms for IoT, including their design principles, security properties, and performance evaluation.

As the need for machine learning models continues to grow, concerns about data privacy and security become increasingly important. Federated learning, a decentralized machine learning approach, has emerged as a promising solution that allows multiple parties to collaborate and build models without sharing sensitive data. In this comprehensive survey, we explore the principles, techniques, and applications of federated learning, with a focus on its privacy-preserving aspects

Matlab has become a popular choice for researchers across various fields due to its versatility, ease of use, and powerful analytical capabilities. In this paper, we explore the role of Matlab as the ultimate solution for research challenges. We first discuss the benefits of using Matlab in research, including its ability to handle complex mathematical computations, data visualization, and simulation of complex systems.

The Internet of Things (IoT) is rapidly growing and becoming an integral part of our daily lives. However, the increasing use of IoT devices also raises significant security concerns. One of the most pressing threats to IoT security is the blackhole attack, where an attacker can selectively drop or discard packets to disrupt communication between IoT devices. In this paper, we conduct a comprehensive survey on blackhole attacks in IoT networks. We explore the types of blackhole attacks, the methods attackers use to exploit vulnerabilities in IoT devices, and the potential impact of these attacks. We also review existing solutions and strategies for mitigating the effects of blackhole attacks in IoT networks. Through our survey, we provide a deeper understanding of the blackhole attack's nature and the potential implications for the security and reliability of IoT networks. Ultimately, our findings highlight the need for increased awareness of this type of attack and the implementation of robust security measures to protect IoT devices and networks.