A. Taufiq Asyhari

Bio: A. Taufiq Asyhari is an academic researcher from Birmingham City University. The author has contributed to research in topics: MIMO & Fading. The author has an hindex of 14, co-authored 53 publications receiving 485 citations. Previous affiliations of A. Taufiq Asyhari include Coventry University & University of Bradford.

Transposition Errors in Diffusion-Based Mobile Molecular Communication

Abstract: In this letter, we investigate diffusion-based molecular communication between two mobile nano-machines. We derive a closed-form expression for the first hitting time distribution by characterizing the motion of the information particles and the nano-machines via Brownian motion. We validate the derived expression through a particle-based simulation. For the information transfer we consider single particles of different types, where transposition errors are the dominant source of errors. We derive an analytical expression for the expected bit error probability and evaluate the error performance for the static and the mobile case by means of computer simulations.

IMPACT: Impersonation attack detection via edge computing using deep autoencoder and feature abstraction

Abstract: An ever-increasing number of computing devices interconnected through wireless networks encapsulated in the cyber-physical-social systems and a significant amount of sensitive network data transmitted among them have raised security and privacy concerns. Intrusion detection system (IDS) is known as an effective defence mechanism and most recently machine learning (ML) methods are used for its development. However, Internet of Things (IoT) devices often have limited computational resources such as limited energy source, computational power and memory, thus, traditional ML-based IDS that require extensive computational resources are not suitable for running on such devices. This study thus is to design and develop a lightweight ML-based IDS tailored for the resource-constrained devices. Specifically, the study proposes a lightweight ML-based IDS model namely IMPACT (IMPersonation Attack deteCTion using deep auto-encoder and feature abstraction). This is based on deep feature learning with gradient-based linear Support Vector Machine (SVM) to deploy and run on resource-constrained devices by reducing the number of features through feature extraction and selection using a stacked autoencoder (SAE), mutual information (MI) and C4.8 wrapper. The IMPACT is trained on Aegean Wi-Fi Intrusion Dataset (AWID) to detect impersonation attack. Numerical results show that the proposed IMPACT achieved 98.22% accuracy with 97.64% detection rate and 1.20% false alarm rate and outperformed existing state-of-the-art benchmark models. Another key contribution of this study is the investigation of the features in AWID dataset for its usability for further development of IDS.

World energy outlook

Abstract: This chapter discusses leading problems linked to energy that the world is now confronting and to propose some ideas concerning possible solutions. Oil deserves special attention among all energy sources. Since the beginning of 1981, it has merely been continuing and enhancing the downward movement in consumption and prices caused by excessive rises, especially for light crudes such as those from Africa, and the slowing down of worldwide economic growth. Densely-populated oil-producing countries need to produce to live, to pay for their food and their equipment. If the economic growth of the industrialized countries were to be 4%, even if investment in the rational use of energy were pushed to the limit and the development of nonpetroleum energy sources were also pursued actively, it would be extremely difficult to prevent a sharp rise in prices. It is evident that it is absolutely necessary to pursue actively the development of coal, natural gas, and nuclear power if a physical shortage of energy is not to block economic growth.

Population and Housing Census

Abstract: The questionnaires from the field were received, checked and stored by the data processing personnel. They checked: 1. The completeness of the questionnaires 2. The correct bubbling 3. The correct number of questionnaires per household, if total males + total females > 8 as the questionnaire ONLY accommodated maximum of 8 household members. 4. The reference number appears in all the 10 pages of the questionnaires.

UAV Communications for 5G and Beyond: Recent Advances and Future Trends

Abstract: Providing ubiquitous connectivity to diverse device types is the key challenge for 5G and beyond 5G (B5G). Unmanned aerial vehicles (UAVs) are expected to be an important component of the upcoming wireless networks that can potentially facilitate wireless broadcast and support high rate transmissions. Compared to the communications with fixed infrastructure, UAV has salient attributes, such as flexible deployment, strong line-of-sight (LoS) connection links, and additional design degrees of freedom with the controlled mobility. In this paper, a comprehensive survey on UAV communication towards 5G/B5G wireless networks is presented. We first briefly introduce essential background and the space-air-ground integrated networks, as well as discuss related research challenges faced by the emerging integrated network architecture. We then provide an exhaustive review of various 5G techniques based on UAV platforms, which we categorize by different domains including physical layer, network layer, and joint communication, computing and caching. In addition, a great number of open research problems are outlined and identified as possible future research directions.