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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Ganiyu Adesola Aderounmu
Obafemi Awolowo University
Computer Science & IT Electrical & Electronics Engineering

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An Enhanced Cluster-Based Routing Model for Energy-Efficient Wireless Sensor Networks Rotimi Alagbe Gbadebo; Mistura Laide Sanni; Bodunde Odunola Akinyemi; Temitope Omotosho Ajayi; Ganiyu Adesola Aderounmu
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 2: June 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i2.4566

Abstract

Energy efficiency is a crucial consideration in wireless sensor networks since the sensor nodes are resource-constrained, and this limited resource, if not optimally utilized, may disrupt the entire network's operations. The network must ensure that the limited energy resources are used as effectively as possible to allow for longer-term operation. The study designed and simulated an improved Genetic Algorithm-Based Energy-Efficient Routing (GABEER) algorithm to combat the issue of energy depletion in wireless sensor networks. The GABEER algorithm was designed using the Free Space Path Loss Model to determine each node's location in the sensor field according to its proximity to the base station (sink) and the First-Order Radio Energy Model to measure the energy depletion of each node to obtain the residual energy. The GABEER algorithm was coded in the C++ programming language, and the wireless sensor network was simulated using Network Simulator 3 (NS-3). The outcomes of the simulation revealed that the GABEER algorithm has the capability of increasing the performance of sensor network operations with respect to lifetime and stability period.
A contrived dataset of substation automation for cybersecurity research in the smart grid networks based on IEC61850 John Edet Efiong; Jide Ebenezer Taiwo Akinsola; Bodunde Odunola Akinyemi; Emmanuel Ajayi Olajubu; Ganiyu Adesola Aderounmu
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 5: October 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i5.26000

Abstract

Relevant datasets that depict and/or emulate the smart grid networks (SGN) are key to developing cybersecurity models that can effectively provide security for mission-critical infrastructure. The difficulty in obtaining relevant SGN real-life datasets presents a considerable challenge for researchers in the field and, the existing datasets lack representation of the IEC61850 protocol for modelling substation automation processes for cybersecurity solutions. This paper presents a dataset simulated from a fully virtual testbed, intended to provide researchers with the necessary datasets for research and experiments that require massive amounts of data close to the real-world scenario. Experimentally, the dataset was used to develop an intrusion detection model based on gated recurrent unit (GRU), deep belief network (DBN), long-short term memory (LSTM), and evaluated using accuracy, precision, recall, F1-score, detection rate, false alarm rate (FAR), missed alarm rate (MAR), mean squared error (MSE), mean absolute error (MAE), and loss. Results show that the standalone deep learning (DL) algorithms outperformed the hybridized ones and are more suitable for developing models for securing substation automation systems that support generic object-oriented substation events (GOOSE), and manufacturing message specification (MMS) and run on IEC61850, distributed network protocol version 3 (DNP3), and Modbus-transmission control protocol (ModbusTCP).
Co-Authors Bodunde Odunola Akinyemi Emmanuel Ajayi Olajubu Jide Ebenezer Taiwo Akinsola John Edet Efiong Mistura Laide Sanni Rotimi Alagbe Gbadebo Temitope Omotosho Ajayi