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Accurate Location Estimation of Smart Dusts Using Machine Learning

Computers, Materials & Continua 2022年第6期71卷 6165-6181页

作者： Shariq Bashir Owais Ahmed Malik daphne teck ching lai Institute of Applied Data Analytics(IADA) Universiti Brunei Darussalam(UBD)GadongBE1410Brunei Darussalam School of Digital Sciences(SDS) Universiti Brunei Darussalam(UBD)GadongBE1410Brunei Darussalam

Traditional wireless sensor networks(WSNs)are not suitable for rough terrains that are difficult or impossible to access by *** dust is a technology that works with the combination of many tiny sensors which is highly useful for obtaining remote sensing information from rough *** tiny sensors are sprinkled in large numbers on rough terrains using airborne distribution through drones or aircraftwithout manually setting their *** it is clear that a number of remote sensing applications can benefit from this technology,but the small size of smart dust fundamentally restricts the integration of advanced hardware on tiny *** raises many challenges including how to estimate the location of events sensed by the smart *** solutions on estimating the location of events sensed by the smart dusts are not suitable for monitoring rough terrains as these solutions depend on relay sensors and laser patterns which have their own limitations in terms of power constraint and uneven *** study proposes a novel machine learning based localization algorithm for estimating the location of *** approach utilizes timestamps(time of arrival)of sensed events received at base stations by assembling them into a multidimensional vector and input to a machine learning classifier for estimating the *** to the unavailability of real smart dusts,we built a simulator for analysing the accuracy of the proposed approach formonitoring forest *** experiments on the simulator show reasonable accuracy of the approach.

关键词： Smart dust sensor localization remote sensing machine learning algorithms Internet of Things sensor applications

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Identification and Classification of Driving Behaviour at Signalized Intersections Using Support Vector Machine

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International Journal of Automation and computing 2021年第3期18卷 480-491页

作者： Soni Lanka Karri Liyanage Chandratilak De Silva daphne teck ching lai Shiaw Yin Yong Faculty of Science Universiti Brunei DarussalamBandar Seri Begawan BE 1410Brunei Darussalam Faculty of Integrated Technologies Universiti Brunei DarussalamBandar Seri Begawan BE 1410Brunei Darussalam Institute of Applied Data Analytics Universiti Brunei DarussalamBandar Seri Begawan BE 1410Brunei Darussalam

When the drivers approaching signalized intersections(onset of yellow signal),the drivers would enter into a zone,where they will be in uncertain mode assessing their capabilities to stop or cross the ***,any improper decision might lead to a right-angle or back-end *** avoid a right-angle collision,drivers apply the harsh brakes to stop just before the signalized *** this may lead to a back-end crash when the following driver encounters the former's sudden stopping *** situation gets multifaceted when the traffic is heterogeneous,containing various types of *** order to reduce this issue,this study's primary objective is to identify the driving behaviour at signalized intersections based on the driving features(parameters).The secondary objective is to classify the outcome of driving behaviour(safe stopping and unsafe stopping)at the signalized intersection using a support vector machine(SVM)*** moments are used to identify the zones and label them accordingly for further *** classification of 50 instances is identified for training and testing using a 70%-30% rule resulted in an accuracy of 85% and 86%,*** performance is further verified by random sampling using five cross-validation and 30 iterations,which gave an accuracy of 97% and 100% for training and *** results demonstrate that the proposed approach can help develop a pre-warning system to alert the drivers approaching signalized intersections,thus reducing back-end crash and accidents.

关键词： Signalized intersection driving behaviour machine learning support vector machine(SVM) road accidents

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