تجاوز إلى المحتوى الرئيسي
User Image

Saad Abdullah AlAhmadi | سعد عبدالله الأحمدي

Professor

Professor in Computer Science - Specialty: Artificial Intelligence (AI), Cybersecurity, and the Internet of Things (IoT)

علوم الحاسب والمعلومات
Building 31 (CCIS Building) - 2nd Floor - Room 2179
المنشورات
مقال فى مجلة
2020

SOFCluster: Safety‐oriented, fuzzy logic‐based clustering scheme for vehicular ad hoc networks

Vehicular ad hoc network (VANET) nodes are characterized by their high mobility
and by exhibiting different mobility patterns. Therefore, VANET clustering
schemes are required to account for the mobility parameters among neighboring
nodes to produce relatively stable clustering schemes. In this article, we
propose a novel cluster-head (CH) selection scheme for VANETs. This scheme
is based on a fuzzy logic-powered, k-hop distributed clustering algorithm. It
deals efficiently with scalability and stability issues of VANETs and is able to
achieve highly stable clustering topologies as compared with other schemes.
Our proposed clustering scheme strives to maintain a safe intervehicle distance
as a one prime metric for CH selection. Moreover, a major contribution of our
work is the proposal of a novel strategy for constructing fuzzy logic-based clustering
algorithms useful for VANETs. This proposed solution is useful in an
Internet of things-based setting that involves controlled vehicle-to-vehicle communication.
We first derive mathematically, a new average distance estimation
formula that is used as a metric for selecting CHs, leading to safer clusters that
avoid collisions with front and rear vehicles. Furthermore, the new proposed
scheme creates stable clusters by reducing reclustering overhead and prolonging
clusters' lifetimes.

نوع عمل المنشور
Article
مجلة/صحيفة
Transactions on Emerging Telecommunications Technologies
الصفحات
1-22
مزيد من المنشورات
publications

Internet of Things (IoT) networks’ wide range and heterogeneity make them prone to cyberattacks. Most IoT devices have limited resource capabilities (e.g., memory capacity, processing power, and…

2025
تم النشر فى:
Sensors
publications

Machine Learning (ML) has been exploited across diverse fields with significant success. However, the deployment of ML models on resource-constrained devices, such as edge devices, has remained…

2025
تم النشر فى:
IEEE Access
publications

One of the most promising applications for electroencephalogram (EEG)-based brain–computer interfaces (BCIs) is motor rehabilitation through motor imagery (MI) tasks. However, current MI training…

2024
تم النشر فى:
Sensors