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All Journal Buletin Ilmiah Sarjana Teknik Elektro International Journal of Robotics and Control Systems
Al-Tahir, Sarah O.
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Control & Systems Engineering Electrical & Electronics Engineering

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A Comparative Study of Fuzzy Logic Controller, ANFIS, and HHOPSO Algorithms in the LEACH Protocol for Optimising Energy Efficiency and Network Longevity in Wireless Sensor Networks Shafeeq Bakr, Zaid; Hassan, Reem Falah; Al-Tahir, Sarah O.; Basil, Noorulden; Ma'arif, Alfian; Marhoon, Hamzah M.
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.1918

Abstract

This research provides a thorough analysis of the algorithms used in the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol for Wireless Sensor Networks (WSNs) to apply Fuzzy Logic, Adaptive Neuro-Fuzzy Inference System (ANFIS), and Harris Hawks Optimisation-Particle Swarm Optimisation (HHOPSO). The primary aim of this paper is to compare and measure these methods by how they save energy, prolong the network’s lifetime and choose the best cluster heads. We look at major indicators such as First Node Death (FND) and the number of rounds when 80% and 50% of nodes are still working, by testing 100 simulated network nodes. The HHOPSO is shown to do a better job at keeping node batteries alive and, at length the network in operation than both Fuzzy Logic and ANFIS. Moreover, ANFIS is more effective than Fuzzy Logic, because it can learn better from data. It is found that HHOPSO helps LEACH become more efficient and effective, contributing new information about how to manage energy and network performance in Wireless Sensor Networks. The document shows the effectiveness of advanced algorithms in keeping sensor networks running longer and offers ideas on how to evaluate them in various network settings.
Metaheuristic-Driven Optimisation of Support Vector Regression Models for Precision Control in Unmanned Aerial Vehicle Systems Marhoon, Hamzah M.; Omar, Rasha Khalid; Al-Rammahi, Hussein; Al-Tahir, Sarah O.; Basil, Noorulden; Tarik, Benmessaoud Mohammed; Agajie, Takele Ferede
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.14251

Abstract

Unmanned Aerial Vehicle (UAV) systems are deployed in dynamic and uncertain environments where many traditional control structures, including Proportional–Integral–Derivative (PID) and Linear Quadratic Regulator (LQR) controllers, are unable to provide stability and adaptation. In order to overcome these shortcomings, this work presents a hybrid Support Vector Regression (SVR) model optimised with the Eagle Strategy-Particle Swarm Optimisation (ES-PSO). The proposed framework is tested with high-fidelity simulated flight data on a quadcopter platform, in which throttle, pitch, roll and yaw are provided as control variables and altitude, velocity and orientation are provided as outputs. The ES-PSO algorithm is an algorithm that optimises the global and local hyperparameters of the SVR and makes it more effective at capturing nonlinear dynamics of the input-output process under both nominal and perturbed flight conditions. To compare with benchmarking, standalone SVR, Neural Networks, Decision Trees, Naive Bayes and K-Nearest Neighbour models were executed using the same simulation parameters with no metaheuristic optimisation, and it was made fair. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Percentage Error (MPE) quantitative assessments illustrate that the ES-PSO-SVR model has the lowest error in prediction and the highest tracking accuracy compared to all baseline techniques. These results demonstrate how metaheuristic-based learning systems can be used to drive forward the creation of adaptive and intelligent UAV control systems that can perform effectively in challenging operational conditions.
Co-Authors Agajie, Takele Ferede Al-Rammahi, Hussein Alfian Ma’arif Basil, Noorulden Hassan, Reem Falah Marhoon, Hamzah M. Omar, Rasha Khalid Shafeeq Bakr, Zaid Tarik, Benmessaoud Mohammed