arXiv subjects

Saif Al-Kuwari

Publications and source records attributed to Saif Al-Kuwari.

112 records · Page 7Linked to original sources

arXiv2022

Full-Duplex Magnetic Induction Communication: Opportunities and Challenges

The demand for high data rates is rapidly increasing as the interest in Magnetic Induction (MI) communication-based underwater applications grow. However, the data rate in MI is limited by the use of low operational frequency in generating a quasi-static magnetic field. In this paper, we propose the use of full-duplex (FD) MI communication to efficiently utilize the available bandwidth and instantly double the data rate. We propose a two-dimensional transceiver architecture to achieve full-duplex communication by exploiting the directional nature of magnetic fields. We further evaluate the proposed end-to-end FD MI communication against self-interference (SI), its impact on communication distance, and robustness in view of orientation sensitivity. Finally, we conclude by discussing typical challenges in the realization of FD MI communication and highlight a few potential future research directions.

eess.SP↗

arXiv2022

Underwater and Air-Water Wireless Communication: State-of-the-art, Channel Characteristics, Security, and Open Problems

We present a first detailed survey on underwater and air-water (A-W) wireless communication networks (WCNs) that mainly focuses on the security challenges and the countermeasures proposed to date. For clarity of exposition, this survey paper is mainly divided into two parts. The first part of the paper focuses on the state-of-the-art underwater and A-W WCNs whereby we outline the benefits and drawbacks of the four promising underwater and A-W candidate technologies: radio frequency (RF), acoustic, optical and magnetic induction (MI), along with their channel characteristics. To this end, we also describe the indirect (relay-aided) and direct mechanisms for the A-W WCNs along with their channel characteristics. This sets the stage for the second part of the paper whereby we provide a thorough comparative discussion of a vast set of works that have reported the security breaches (as well as viable countermeasures) for many diverse configurations of the underwater and A-W WCNs. Specifically, we provide a detailed literature review of the various kinds of active and passive attacks which hamper the confidentiality, integrity, authentication and availability of both underwater and A-W WCNs. Finally, we highlight some research gaps in the open literature and identify security related some open problems for the future work.

eess.SP↗

arXiv2022

Physical Unclonable Functions (PUF) for IoT Devices

Physical Unclonable Function (PUF) has recently attracted interested from both industry and academia as a potential alternative approach to secure Internet of Things (IoT) devices from the more traditional computational based approach using conventional cryptography. PUF is promising solution for lightweight security, where the manufacturing fluctuation process of IC is used to improve the security of IoT as it provides low complexity design and preserves secrecy. It provides less cost of computational resources which prevent high power consumption and can be implemented in both Field Programmable Gate Arrays (FPGA) and Application-Specific Integrated Circuits (ASICs). In this survey we provide a comprehensive review of the state-of-the-art of PUF, its architectures, protocols and security for IoT.

cs.CR↗

arXiv2022

Deep-Disaster: Unsupervised Disaster Detection and Localization Using Visual Data

Social media plays a significant role in sharing essential information, which helps humanitarian organizations in rescue operations during and after disaster incidents. However, developing an efficient method that can provide rapid analysis of social media images in the early hours of disasters is still largely an open problem, mainly due to the lack of suitable datasets and the sheer complexity of this task. In addition, supervised methods can not generalize well to novel disaster incidents. In this paper, inspired by the success of Knowledge Distillation (KD) methods, we propose an unsupervised deep neural network to detect and localize damages in social media images. Our proposed KD architecture is a feature-based distillation approach that comprises a pre-trained teacher and a smaller student network, with both networks having similar GAN architecture containing a generator and a discriminator. The student network is trained to emulate the behavior of the teacher on training input samples, which, in turn, contain images that do not include any damaged regions. Therefore, the student network only learns the distribution of no damage data and would have different behavior from the teacher network-facing damages. To detect damage, we utilize the difference between features generated by two networks using a defined score function that demonstrates the probability of damages occurring. Our experimental results on the benchmark dataset confirm that our approach outperforms state-of-the-art methods in detecting and localizing the damaged areas, especially for novel disaster types.

cs.CV↗