Federated ensemble-learning for transport mode detection in vehicular edge network

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.

Chi tiết về thư mục
Những tác giả chính:	Alam, MD. Mustakin, Ahmed, Tanjim, Hossain, Meraz, Emo, Mehedi Hasan, Islam Bidhan, Md. Kausar
Tác giả khác:	Rabiul Alam, Dr. Md. Golam
Định dạng:	Luận văn
Ngôn ngữ:	English
Được phát hành:	Brac University 2023
Những chủ đề:	Transport mode detection Artificial intelligence Internet of things Intelligent transportation system Vehicular edge network Deep learning Federated learning Federated ensemble-learning Decentralized Majority voting XG Boost Random forest Multi-layer perceptron Wireless communication systems.
Truy cập trực tuyến:	http://hdl.handle.net/10361/20229

id	10361-20229
record_format	dspace
spelling	10361-202292023-08-30T21:02:46Z Federated ensemble-learning for transport mode detection in vehicular edge network Alam, MD. Mustakin Ahmed, Tanjim Hossain, Meraz Emo, Mehedi Hasan Islam Bidhan, Md. Kausar Rabiul Alam, Dr. Md. Golam Reza, Mr. Md. Tanzim Department of Computer Science and Engineering, Brac University Transport mode detection Artificial intelligence Internet of things Intelligent transportation system Vehicular edge network Deep learning Federated learning Federated ensemble-learning Decentralized Majority voting XG Boost Random forest Multi-layer perceptron Wireless communication systems. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 47-50). Transport Mode detection has become a crucial part of Intelligent Transportation Systems (ITS) and Traffic Management Systems due to the recent advancements in Artificial Intelligent (AI) and the Internet of Things (IoT). Accurately predicting a person’s mode of transportation was challenging for many years until the computational power of smartphones and smartwatches expanded dramatically over time. This is a result of the numerous sensors built within smart devices, which enable the worldwide cloud server to acquire sensory data and anticipate a person’s method of transport using multiple machine learning models. Currently, all smart devices and vehicular edge devices are interconnected by Vehicular Edge Networks (VEN). However, as the data are shared globally, the security of an individual’s data is questioned, and hence a significant portion of the population is still unwilling to share their sensory data with the global cloud server. Also, the processing time for the massive amount of sensory data should be considered. In this paper, we present a distributed method, Federated Ensemble-Learning in VEN, in which a vast amount of data is used to train the model while the training data is kept decentralized. Federated Ensemble-Learning (FedEL), a hybrid approach, is proposed to enhance the performance of federated strategies. In addition, a majority voting ensembling method has been developed as part of the federated strategy to determine the mode of transportation of local customers. Two machine learning algorithms, XGBoost and Random Forest, and one deep learning technique Multi-Layer Perceptron (MLP) are trained with data from each local client. A prediction is then maintained based on a majority vote among the three models. The class with the most votes is taken into account, while the others are discarded. The FedEL technique has been shown to be highly effective on the TMD dataset, with an accuracy of 94-95% for the 5- second window dataset and 98-99% for the half-second window dataset, based on extensive testing. MD. Mustakin Alam Tanjim Ahmed Meraz Hossain Mehedi Hasan Emo Md. Kausar Islam Bidhan B. Computer Science and Engineering 2023-08-30T08:08:12Z 2023-08-30T08:08:12Z 2023 2023-01 Thesis ID: 19301105 ID: 22241192 ID: 19301152 ID: 19301245 ID: 19301156 http://hdl.handle.net/10361/20229 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 50 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Transport mode detection Artificial intelligence Internet of things Intelligent transportation system Vehicular edge network Deep learning Federated learning Federated ensemble-learning Decentralized Majority voting XG Boost Random forest Multi-layer perceptron Wireless communication systems.
spellingShingle	Transport mode detection Artificial intelligence Internet of things Intelligent transportation system Vehicular edge network Deep learning Federated learning Federated ensemble-learning Decentralized Majority voting XG Boost Random forest Multi-layer perceptron Wireless communication systems. Alam, MD. Mustakin Ahmed, Tanjim Hossain, Meraz Emo, Mehedi Hasan Islam Bidhan, Md. Kausar Federated ensemble-learning for transport mode detection in vehicular edge network
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
author2	Rabiul Alam, Dr. Md. Golam
author_facet	Rabiul Alam, Dr. Md. Golam Alam, MD. Mustakin Ahmed, Tanjim Hossain, Meraz Emo, Mehedi Hasan Islam Bidhan, Md. Kausar
format	Thesis
author	Alam, MD. Mustakin Ahmed, Tanjim Hossain, Meraz Emo, Mehedi Hasan Islam Bidhan, Md. Kausar
author_sort	Alam, MD. Mustakin
title	Federated ensemble-learning for transport mode detection in vehicular edge network
title_short	Federated ensemble-learning for transport mode detection in vehicular edge network
title_full	Federated ensemble-learning for transport mode detection in vehicular edge network
title_fullStr	Federated ensemble-learning for transport mode detection in vehicular edge network
title_full_unstemmed	Federated ensemble-learning for transport mode detection in vehicular edge network
title_sort	federated ensemble-learning for transport mode detection in vehicular edge network
publisher	Brac University
publishDate	2023
url	http://hdl.handle.net/10361/20229
work_keys_str_mv	AT alammdmustakin federatedensemblelearningfortransportmodedetectioninvehicularedgenetwork AT ahmedtanjim federatedensemblelearningfortransportmodedetectioninvehicularedgenetwork AT hossainmeraz federatedensemblelearningfortransportmodedetectioninvehicularedgenetwork AT emomehedihasan federatedensemblelearningfortransportmodedetectioninvehicularedgenetwork AT islambidhanmdkausar federatedensemblelearningfortransportmodedetectioninvehicularedgenetwork
_version_	1814308488372813824

Federated ensemble-learning for transport mode detection in vehicular edge network

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