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Thangavel Bhuvaneswari
Multimedia University

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Internet of things (IoT) based smart garbage monitoring system Thangavel Bhuvaneswari; J. Hossen; NurAsyiqinbt. Amir Hamzah; P. Velrajkumar; Oo Hong Jack
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 2: November 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i2.pp736-743

Abstract

Garbage waste monitoring, collection and management is one of the primary concerns of the present era due to its detrimental effects on environment. The traditional way of manually monitoring and collecting the garbage is a cumbersome process as it requires considerable human effort and time leading to higher cost. In this paper, an IoT based garbage monitoring system using Thingspeak, an open IoT platform is presented. The system consists of an Arduino microcontroller, an ultrasonic sensor, a load cell and a Wi-Fi module. The Arduino microcontroller receives data from the ultrasonic sensor and load cell. The depth of the garbage in the bin is measured using ultrasonic sensor and the weight of the bin with garbage is measured from the load cell. The LCD screen is used to display the data. The Wi-Fi module transmits the above data to the internet. An open IoT platform Thingspeak is used to monitor the garbage system. With this system, the administrator can monitor and schedule garbage collection more efficiently. A prototype has been developed and tested. It has been found to work satisfactorily. The details are presented in this paper.
Towards machine learning-based self-tuning of Hadoop-Spark system Md. Armanur Rahman; Abid Hossen; J. Hossen; Venkataseshaiah C; Thangavel Bhuvaneswari; Aziza Sultana
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 2: August 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i2.pp1076-1085

Abstract

Apache Spark is an open source distributed platform which uses the concept of distributed memory for processing big data. Spark has more than 180 predominant configuration parameter. Configuration settings directly control the efficiency of Apache spark while processing big data, to get the best outcome yet a challenging task as it has many configuration parameters.  Currently, these predominant parameters are tuned manually by trial and error. To overcome this manual tuning problem in this paper proposed and developed a self-tuning approach using machine learning. This approach can tune the parameter value when it’s required. The approach was implemented on Dell server and experiment was done on five different sizes of the dataset and parameter. A comparison is provided to highlight the experimented result of the proposed approach with default Spark configuration system. The results demonstrate that the execution is speeded-up by about 33% (on an average) compared to the default configuration.
Co-Authors Abid Hossen Aziza Sultana J. Hossen Md. Armanur Rahman NurAsyiqinbt. Amir Hamzah Oo Hong Jack P. Velrajkumar Venkataseshaiah C