Yazar "Almohamad, Tarik Adnan" seçeneğine göre listele

Listeleniyor 1 - 7 / 7
  • Küçük Resim Yok
    Öğe
    Adaptive Trust-Based Framework for Securing and Reducing Cost in Low-Cost 6LoWPAN Wireless Sensor Networks
    (Mdpi, 2022) Ahmad, Rami; Wazirali, Raniyah; Abu-Ain, Tarik; Almohamad, Tarik Adnan
    Wireless Sensor Networks (WSNs) are the core of the Internet of Things (IoT) technology, as they will be used in various applications in the near future. The issue of security and power consumption is still one of the most important challenges facing this type of network. 6LoWPAN protocol was developed to meet these challenges in networks with limited power and resources. The 6LoWPAN uses a hierarchical topology and the traditional method of encryption and key management, keeping power consumption levels high. Therefore, in this paper, a technique has been developed that helps in balancing security and energy consumption by exploiting the Trust technique between low-cost WSN nodes called Trust-Cluster Head (Trust-CH). Trust between nodes is built by monitoring the behavior of packet transmission, the number of repetitions and the level of security. The Trust-CH model provides a dynamic multi-level encryption system that depends on the level of Trust between WSN nodes. It also proposes a dynamic clustering system based on the absolute-trust level in the mobile node environment to minimize power consumption. Along with a set of performance metrics (i.e., power consumption and network lifetime), the Cooja simulator was used to evaluate the Trust-CH model. The results were compared to a static symmetric encryption model together with various models from previous studies. It has been proven that the proposed model increases the network lifetime by 40% compared to previous studies, as well as saves as much as 28% power consumption in the case of using a static encryption model. While maintaining the proposed model's resistance to many malicious attacks on the network.
  • Küçük Resim Yok
    Öğe
    Computationally Efficient Stochastic Algorithm Supported by Deterministic Technique: A Futuristic Approach
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Gul, Faiza; Mir, Imran; Almohamad, Tarik Adnan
    The challenge of creating a map of the territory solely based on information collected from one or more sensors without any prior knowledge is addressed by simultaneous localization and mapping. Most of the time, a human operator controls the robot, but certain systems can navigate autonomously while mapping; this process is known as active simultaneous localization and mapping. The locomotion mechanism is frequently the primary design consideration for Exploration Robots because of the difficult conditions in which they are typically deployed. Strategies for locomotion that are based on biological systems are frequently advantageous. A common focus is on overall platform design and system integration to build robots that can endure harsh settings long enough to complete their tasks. The aim of the paper is to present the integration of the deterministic method (MAE) with the biologically inspired method for robotic space exploration purposes. The method is called the Multi-Agent Exploration Adaptive Aquila Optimizer (MAE-AAO). The occupancy grid is used as a map for exploration. The algorithms run by first calculating the cost & utility values of all the adjacent surrounding cells of the agent. To increase the rate of exploration, adaptive aquila is used. Upon comparing with other contemporary algorithms, the proposed method outshines in terms of rate of exploration, execution time, and number of aborted simulation runs. The proposed algorithm offers an average of 98% exploration rate with a mean time of only 29 seconds. The method has another distinct feature: zero failed simulation runs, which is the added advantage in the exploration rate.
  • Küçük Resim Yok
    Öğe
    Data detection in decentralized and distributed massive MIMO networks
    (Elsevier, 2022) Albreem, Mahmoud A.; Alhabbash, Alaa; Abu-Hudrouss, Ammar M.; Almohamad, Tarik Adnan
    In order to meet the user demands in performance and quality of services (QoS) for beyond fifth generation (B5G) communication systems, research on decentralized and distributed massive multiple-input multiple output (M-MIMO) is initiated. Data detection techniques are playing a crucial role in realization and implementation of M-MIMO networks. Although most of detection techniques were proposed for centralized M-MIMO, there is a notable trend to propose efficient detection techniques for decentralized and distributed M-MIMO networks. This paper aims to provide insights on data detection techniques for decentralized and distributed M-MIMO to generalists of wireless communications. We garner the detection techniques for decentralized and distributed M-MIMO and present their performance, computational complexity, throughput, and latency so that a reader can find a distinction between different algorithms from a wider range of solutions. We present the detection techniques based on the following architectures: decentralized baseband processing (DBP), feedforward fully decentralized (FD), and feedforward partially decentralized (PD), FD based on coordinate descent (FD-CD), and FD based on recursive methods. In addition, the role of expectation propagation algorithm (EPA) in decentralized architectures is comprehensively reviewed. In each section, we also discuss the pros, cons, throughput, latency, performance, and complexity profile of each detector and related implementations. Moreover, the energy efficiency of several decentralized M-MIMO architectures is also illustrated. The cell-free M-MIMO (CF-M-MIMO) architecture is discussed with an overview of deployed detection schemes. This paper also illustrates the challenges and future research directions in decentralized and distributed M-MIMO networks.
  • Küçük Resim Yok
    Öğe
    Dual-Determination of Modulation Types and Signal-to-Noise Ratios Using 2D-ASIQH Features for Next Generation of Wireless Communication Systems
    (Ieee-Inst Electrical Electronics Engineers Inc, 2021) Almohamad, Tarik Adnan; Salleh, Mohd Fadzli Mohd; Mahmud, Mohd Nazri; Karas, Ismail Rakip; Shah, Nor Shahida Mohd; Al-Gailani, Samir Ahmed
    In order to pursue rapid development of the new generation of wireless communication systems and elevate their security and efficiency, this paper proposes a novel scheme for automatic dual determination of modulation types and signal to noise ratios (SNR) for next generations of wireless communication systems, fifth-generation (5G) and beyond. The proposed scheme adopts unique signatures depicted in two-dimensional asynchronously sampled in-phase-quadrature amplitudes' histograms (2D-ASIQHs)-based images and applies the support vector machines (SVMs) tool. Along with the estimation of the instantaneous SNR values over 0-35 dB range, the determination of nine modulation types that belong to different modulation categories i.e., phase-shift keying (Binary-PSK, Quadrature-PSK, and 8-PSK), amplitude-shift keying (2-ASK and 4-ASK) and quadrature-amplitude modulation (4-QAM, 16-QAM, 32-QAM, and 64-QAM) could be achieved by this scheme. The application of this scheme has been simulated using a channel model that is impaired by additive white Gaussian noise (AWGN) and Rayleigh fading, covering a broad range of SNRs of 0-35 dB. The performance of this dual-determination scheme shows high modulation recognition accuracy and low mean SNR estimation error. Therefore, it can be a better alternative for designers of next generation wireless communication systems.
  • Küçük Resim Yok
    Öğe
    An Efficient Internet Traffic Classification System Using Deep Learning for IoT
    (Tech Science Press, 2022) Umair, Muhammad Basit; Iqbal, Zeshan; Bilal, Muhammad; Nebhen, Jamel; Almohamad, Tarik Adnan; Mehmood, Raja Majid
    Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper feature selection. In this research, an efficient multi-layer deep learning based classification system is presented to overcome these challenges that can classify internet traffic. To examine the performance of the proposed technique, Moore-dataset is used for training the classifier. The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network (DNN). In particular, the maximum entropy classifier is used to classify the internet traffic. The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification, i.e., 99.23%. Furthermore, the proposed algorithm achieved the highest accuracy compared to the support vector machine (SVM) based classification technique and k-nearest neighbours (KNNs) based classification technique.
  • Küçük Resim Yok
    Öğe
    Enhancing Road Safety: Real-Time Distracted Driver Detection Using Nvidia Jetson Nano and YOLOv8
    (Ieee, 2024) Neamah, Osamah N.; Almohamad, Tarik Adnan; Bayir, Raif
    This study introduces an innovative approach that combines cutting-edge technology and advanced models for real-time applications. Leveraging the performance of the Nvidia Jetson Nano, alongside an integrated camera and GSM/GPS module, our innovative system demonstrates both its practicality and versatility. Specifically, by employing the YOLOv8 classification model for handling State Farm Distracted Driver Detection data which underscores its adaptability and effectiveness in this critical domain. Additionally, our research thoroughly assesses computational efficiency, exploring both hardware and software-based analysis methods. This work is a cornerstone in harnessing technology for real-world impact, merging innovation with practicality and comprehensive evaluation.
  • Küçük Resim Yok
    Öğe
    Stochastic Computing Convolutional Neural Network Architecture Reinvented for Highly Efficient Artificial Intelligence Workload on Field-Programmable Gate Array
    (Amer Assoc Advancement Science, 2024) Lee, Yang Yang; Halim, Zaini Abdul; Wahab, Mohd Nadhir Ab; Almohamad, Tarik Adnan
    Stochastic computing (SC) has a substantial amount of study on application-specific integrated circuit (ASIC) design for artificial intelligence (AI) edge computing, especially the convolutional neural network (CNN) algorithm. However, SC has little to no optimization on field-programmable gate array (FPGA). Scaling up the ASIC logic without FPGA-oriented designs is inefficient, while aggregating thousands of bitstreams is still challenging in the conventional SC. This research has reinvented several FPGA-efficient function generator, and binary rectified linear unit, and successfully scaled and implemented a fully parallel CNN model on Kintex7 FPGA. The proposed SC hardware only compromises 0.14% accuracy compared to binary computing on the handwriting Modified National Institute of Standards and Technology classification task and achieved at least 99.72% energy saving per image feedforward and 31x more data throughput than modern hardware. Unique to SC, early decision termination pushed the performance baseline exponentially with minimum accuracy loss, making SC CNN extremely lucrative for AI edge computing but limited to classification tasks. The SC's inherent noise heavily penalizes CNN regression performance, rendering SC unsuitable for regression tasks.