M
Mahmood Safaei
Researcher at National University of Malaysia
Publications - 13
Citations - 363
Mahmood Safaei is an academic researcher from National University of Malaysia. The author has contributed to research in topics: Wireless sensor network & Anomaly detection. The author has an hindex of 6, co-authored 10 publications receiving 134 citations. Previous affiliations of Mahmood Safaei include Universiti Teknologi Malaysia & University of Surrey.
Papers
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Journal ArticleDOI
A Systematic Literature Review on Outlier Detection in Wireless Sensor Networks
Mahmood Safaei,Shahla Asadi,Maha Driss,Wadii Boulila,Abdullah Alsaeedi,Hassan Chizari,Rusli Abdullah,Mitra Safaei +7 more
TL;DR: The current paper presents an improved taxonomy of outlier detection techniques that will help researchers and practitioners to find the most relevant and recent studies related to outlier Detection in WSNs.
Journal ArticleDOI
Standalone noise and anomaly detection in wireless sensor networks: A novel time‐series and adaptive Bayesian‐network‐based approach
Mahmood Safaei,Abul Samad Ismail,Hassan Chizari,Maha Driss,Maha Driss,Wadii Boulila,Wadii Boulila,Shahla Asadi,Mitra Safaei +8 more
TL;DR: The proposed local outlier detection algorithm (LODA) is a decentralized noise detection algorithm that runs on each sensor node individually with three important features: reduction mechanism that eliminates the noneffective features, determination of the memory size of data histogram to accomplish the effective available memory, and classification for predicting noisy data.
Proceedings ArticleDOI
SmartSim: Graphical Sensor Network Simulation Based on TinyOS and Tossim
TL;DR: A new simulation environment, SmartSim, has been developed to provide an useful all-in-one solution for WSN and has new, unique features such as detailed graphical presentation of topology, ability to proceed through network events either forward or backward and a comprehensive power usage report generation.
Journal ArticleDOI
Global outliers detection in wireless sensor networks: A novel approach integrating time-series analysis, entropy, and random forest-based classification.
TL;DR: In this article, a global outlier detection approach based on time-series analysis and entropy techniques is proposed to find and select appropriate neighbors to ensure an adaptive collaborative detection in WSNs.
Visualization, Data Analyzing and Energy Usage Analysis in Wireless Sensor Network Based on TinyOs and PowerTossimZ
TL;DR: This paper produces and develop some unique feature in plotting based on PowerTossim and SmartSim that can help to SmartSim to be unique all-in-one simulation for TinyOs and Tossim.