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All Journal EMITTER International Journal of Engineering Technology Indonesian Journal of Electrical Engineering and Computer Science
Fuad Bajaber
King Abdulaziz University
Computer Science & IT

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Comparison by simulation of PEGASIS and IEEPB routing protocols Samah Alnajdi; Fuad Bajaber
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 3: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i3.pp1569-1576

Abstract

As the applications of wireless sensor networks (WSNs) became widely used throughout the years the importance of advanced sensor networks techniques increased as well. One of the main techniques used in WSNs is hierarchical routing which mainly aims to reduce the consumption of sensor nodes energy by assigning different roles to the sensor nodes to create multi-layer scheme for data transmission. This paper embraces a simulation for two known hierarchical routing protocols: Power-Efficient Gathering in Sensor Information Systems (PEGASIS) protocol and an Improved Energy-Efficient PEGASIS-Based (IEEPB) protocol. Both protocols aim to reduce the transmission distance in order to save the nodes energy by performing chain-based clustering. For evaluation, we measured the residual energy and control overhead throughout the network operation time and the results showed major flaws in both protocols such as long link problem and poor leader selection method in PEGASIS. Moreover, high nodes density problem in IEEPB.
Determination of the K-optimal number of chains-based routing protocol formed by the K-Means algorithm for the WSN Samah Alnajdi; Fuad Bajaber
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 2: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i2.pp917-930

Abstract

Wireless sensor networks comprise of a large number of lightweight and relatively low-cost computational nodes which their main task is to sense the surrounding environment and collect the information to send it wirelessly to a central point to take the appropriate actions. Although these networks had been used in various applications, achieving this task is challenging due to the many constraints of sensor nodes including their limited processing power, communication bandwidth, and power supply. Therefore, an energy efficient routing protocols had to be developed specifically for sensor networks to insure longer lifetime and reasonable performance of the network. In this work, we propose an energy efficient hierarchical routing protocol using chain-based clustering. By simulation on MATLAB, the proposed protocol proved to enhance the performance as it prolongs the lifetime of the network and decreases the energy consumption, the transmission delay, and the overhead compared to other existing protocols as it depends on some advanced methods including dynamic selection of number of chains method, k-means clustering method, advanced greedy chain construction method, and multi-factor based leader selection method.
Machine Learning Approaches for Subcluster in IoT Sensor Networks with Hierarchical Clustering and Dendrograms Fuad Bajaber
EMITTER International Journal of Engineering Technology Vol 13 No 2 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i2.906

Abstract

This research focuses on optimizing IoT Sensor Networks (ISNs) by implementing hierarchical clustering algorithms. Traditional clustering methods often lead to imbalanced energy consumption, impacting network lifetime and performance. Our approach leverages hierarchical clustering to partition the network into a set of clusters. Each cluster has a cluster head and a set of sensor nodes. To enhance data aggregation and energy efficiency, we introduce subclustering within clusters using dendrograms. We assessed performance metrics using simulation, including energy consumption and scalability. The proposed hierarchical clustering methodology significantly improves network lifetime, energy efficiency, and data aggregation.
Co-Authors Samah Alnajdi