A Systematic Review on Recent Trends, Challenges, Privacy and Security Issues of Underwater Internet of Things

Seamless integration of Internet of Things, miniaturization, and environmental chemical surveillance

IoT is a game-changer across all fields, including chemistry. Embracing sustainable practices and green chemistry, the miniaturization and automation of systems, and their integration into IoT is key to achieving these principles, as a rising trend with momentum. Particularly, IoT and analytical chemistry are linked in the rapid exchange of analytical data for environmental, industrial, healthcare, and educational applications. Meanwhile, cooperation with other fields of science is evident, and there is a prompt and subjective analysis of information related to analytical systems and methodologies. This paper will review the concepts, requirements, and architecture of IoT and its role in the miniaturization and automation of analytical tools using electronic modules and sensors. The aim is to explore the standards and perspectives of IoT and its interaction with different aspects of analytical chemistry. Additionally, it aimed to explain the basics and applications of IoT for chemists, and its relevance to different subfields of analytical chemistry, particularly in the field of environmental chemical surveillance. The article also covers updating IoT devices and creating DIY-based degradation devices to enhance the educational aspect of chemistry and reduce barriers to lab facilities and equipment. Lastly, it will explore how IoT is really important and how it's going to significantly impact analytical chemistry.

Joint Model-Order and Robust DoA Estimation for Underwater Sensor Arrays

The direction-of-arrival (DoA) estimation algorithms have a fundamental role in target bearing estimation by sensor array systems. Recently, compressive sensing (CS)-based sparse reconstruction techniques have been investigated for DoA estimation due to their superior performance relative to the conventional DoA estimation methods, for a limited number of measurement snapshots. In many underwater deployment scenarios, the acoustic sensor arrays must perform DoA estimation in the presence of several practical problems such as unknown source number, faulty sensors, low values of the received signal-to-noise ratio (SNR), and access to a limited number of measurement snapshots. In the literature, CS-based DoA estimation has been investigated for the individual occurrence of some of these errors but the estimation under joint occurrence of these errors has not been studied. This work investigates the CS-based robust DoA estimation to account for the joint impact of faulty sensors and low SNR conditions experienced by a uniform linear array of underwater acoustic sensors. Most importantly, the proposed CS-based DoA estimation technique does not require a priori knowledge of the source order, which is replaced in the modified stopping criterion of the reconstruction algorithm by taking into account the faulty sensors and the received SNR. Using Monte Carlo techniques, the DoA estimation performance of the proposed method is comprehensively evaluated in relation to other techniques.

IoT malware: An attribute-based taxonomy, detection mechanisms and challenges

During the past decade, the Internet of Things (IoT) has paved the way for the ongoing digitization of society in unique ways. Its penetration into enterprise and day-to-day lives improved the supply chain in numerous ways. Unfortunately, the profuse diversity of IoT devices has become an attractive target for malware authors who take advantage of its vulnerabilities. Accordingly, enhancing the security of IoT devices has become the primary objective of industrialists and researchers. However, most present studies lack a deep understanding of IoT malware and its various aspects. As understanding IoT malware is the preliminary base of research, in this work, we present an IoT malware taxonomy with 100 attributes based on the IoT malware categories, attack types, attack surfaces, malware distribution architecture, victim devices, victim device architecture, IoT malware characteristics, access mechanisms, programming languages, and protocols. In addition, we have mapped these categories into 77 IoT Malwares identified between 2008 and 2022. Furthermore, To provide insight into the challenges in IoT malware research for future researchers, our study also reviews the existing IoT malware detection works.