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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Rashid, Rozeha A.
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Electrical & Electronics Engineering

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Energy efficiency scheme for relay node placement in heterogeneous networks As’ari, Aziemah Athirah; Apandi, Nur Ilyana Anwar; Muhammad, Nor Aishah; Rashid, Rozeha A.; Sarijari, Mohd Adib; Salleh, Jamaliah
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4050

Abstract

Relay node (RN) placement expands the network coverage and capacity and significantly reduces the energy consumption of heterogeneous networks (HetNets). Energy efficiency is the system design parameter in HetNets as it determines network operators' energy consumption and economic value. Relay is one of the energy-saving methods, where it can reduce the transmit power by breaking a long transmission distance into several short transmissions. However, placing an RN without a proper transmission distance may lead to a waste of energy. Thus, investigating an optimum RN placement in HetNets is crucial to ensure energy efficiency and maintain network performance. This paper presents an energy efficiency scheme for the RN based on four commonly used network topologies of indoor HetNets. The minimum energy consumption algorithm is proposed based on a comparison of distance and links of the RN. The results show that the circular network topology is an optimal network model with an efficiency factor increase of 6% that can be used to design the energy efficiency indoor HetNet.
A machine learning for environmental noise classification in smart cities Ali, Yaseen Hadi; Rashid, Rozeha A.; Abdul Hamid, Siti Zaleha
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1777-1786

Abstract

The sound at the same decibel (dB) level may be perceived either as annoying noise or as pleasant music. Therefore, it is necessary to go beyond the state-of-the-art approaches that measure only the dB level and also identify the type of the sound especially when the sound is recorded using a microphone. This paper presented a case study that considers the ability of machine learning models to identify sources of environmental noise in urban areas and compares the sound levels with the recommended levels by the World Health Organization (WHO). The approach was evaluated with a dataset of 44 sound samples grouped in four sound classes that are highway, railway, lawnmowers, and birds. We used mel-frequency cepstral coefficients for feature extraction and supervised algorithms that are Support vector machine (SVM), k-nearest neighbors (KNN), bootstrap aggregation (Bagging), and random forest (RF) for noise classification. We evaluated performance of the four algorithms to determine the best one for the classification of sound samples in the data set under consideration. The findings showed that the noise classification accuracy is in the range of 95%-100%. Furthermore, all the captured data exceeded the recommended levels by WHO which can cause adverse health effects.
Co-Authors Abdul Hamid, Siti Zaleha Ali, Yaseen Hadi Apandi, Nur Ilyana Anwar As’ari, Aziemah Athirah Muhammad, Nor Aishah Salleh, Jamaliah Sarijari, Mohd Adib