Article Per Year (5 Year)
p-Index From 2019 - 2024
1.091
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Perfecting a Video Game with Game Metrics Bulletin of Electrical Engineering and Informatics
Sangsoon Lim
Sungkyul University
Computer Science & IT Electrical & Electronics Engineering

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Design and implementation of heterogeneous surface gateway for underwater acoustic sensor network Youngjun Jo; Sangsoon Lim
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 2: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (479.078 KB) | DOI: 10.11591/ijece.v9i2.pp1226-1231

Abstract

Underwater Acoustic Sensor Networks (UASNs) are used for diverse purposes such as pollution monitoring, disaster prevention and industrial sensing in the oceans. Especially, UASNs are mainly focusing on monitoring various underwater environmental data and delivering the data to a monitoring center where nearby or far from the deployed area. To reliably deliver the data, a surface gateway should convert acoustic signal to RF (Radio Frequency) signal. In this paper, we devise a multiple interfaces-based surface gateway that can connect both a cellular network and a Zigbee network. Depends on the service requirement, the surface gateway can easily adopt each wireless interface and relay the data to a low power ZigBee network or a long range CDMA network.
IACR: an interference-aware channel reservation for wireless sensor networks Sangsoon Lim
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 2: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (299.23 KB) | DOI: 10.11591/ijece.v9i2.pp1220-1225

Abstract

In battery-based wireless sensor networks, energy-efficient operation is one of the most important factors. Especially, in order to improve energy efficiency in wireless sensor networks, various studies on low power operation have been actively conducted in the MAC layer. In recent years, mutual interference among various radio technologies using the same radio frequency band has become a serious problem. Wi-Fi, ZigBee, and Bluetooth use the same frequency band of 2.4GHz at the same time, which causes various signal interference problems. In this paper, we propose a novel channel reservation scheme, called IACR, to improve the energy efficiency of wireless sensor networks in an environment where interference occurs between various wireless technologies. The proposed scheme inserts a PN code into a long preamble for exchanging transmission status information between a transmitting node and a receiving node, thereby improving the transmission success probability while receiving less influence on transmission of other radio technologies. We performed an event-driven simulation and an experiment to measure the signal detection rate. As a result, it can be seen that the proposed technique reduces the packet drop rate by 15% and increases the discoverable distance of the control packet for channel reservation.
An energy-efficient cluster head selection in wireless sensor network using grey wolf optimization algorithm Kaushik Sekaran; R. Rajakumar; K. Dinesh; Y. Rajkumar; T. P. Latchoumi; Seifedine Kadry; Sangsoon Lim
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 6: December 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i6.15199

Abstract

Clustering is considered as one of the most prominent solutions to preserve theenergy in the wireless sensor networks. However, for optimal clustering, anenergy efficient cluster head selection is quite important. Improper selectionofcluster heads(CHs) consumes high energy compared to other sensor nodesdue to the transmission of data packets between the cluster members and thesink node. Thereby, it reduces the network lifetime and performance of thenetwork. In order to overcome the issues, we propose a novelcluster headselection approach usinggrey wolf optimization algorithm(GWO) namelyGWO-CH which considers the residual energy, intra-cluster and sink distance.In addition to that, we formulated an objective function and weight parametersfor anefficient cluster head selection and cluster formation. The proposedalgorithm is tested in different wireless sensor network scenarios by varyingthe number of sensor nodes and cluster heads. The observed results conveythat the proposed algorithm outperforms in terms of achieving better networkperformance compare to other algorithms.
Selection of optimal hyper-parameter values of support vector machine for sentiment analysis tasks using nature-inspired optimization methods Lakshmana Kumar Ramasamy; Seifedine Kadry; Sangsoon Lim
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2098

Abstract

Sentiment analysis and classification task is used in recommender systems to analyze movie reviews, tweets, Facebook posts, online product reviews, blogs, discussion forums, and online comments in social networks. Usually, the classification is performed using supervised machine learning methods such as support vector machine (SVM) classifier, which have many distinct parameters. The selection of the values for these parameters can greatly influence the classification accuracy and can be addressed as an optimization problem. Here we analyze the use of three heuristics, nature-inspired optimization techniques, cuckoo search optimization (CSO), ant lion optimizer (ALO), and polar bear optimization (PBO), for parameter tuning of SVM models using various kernel functions. We validate our approach for the sentiment classification task of Twitter dataset. The results are compared using classification accuracy metric and the Nemenyi test.
An efficient apriori algorithm for frequent pattern mining using mapreduce in healthcare data M. Sornalakshmi; S. Balamurali; M. Venkatesulu; M. Navaneetha Krishnan; Lakshmana Kumar Ramasamy; Seifedine Kadry; Sangsoon Lim
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2096

Abstract

The development for data mining technology in healthcare is growing today as knowledge and data mining are a must for the medical sector. Healthcare organizations generate and gather large quantities of daily information. Use of IT allows for the automation of data mining and information that help to provide some interesting patterns which remove manual tasks and simple data extraction from electronic records, a process of electronic data transfer which secures medical records, saves lives and cuts the cost of medical care and enables early detection of infectious diseases. In this research paper an improved Apriori algorithm names enhanced parallel and distributed apriori (EPDA) is presented for the health care industry, based on the scalable environment known as Hadoop MapReduce. The main aim of the work proposed is to reduce the huge demands for resources and to reduce overhead communication when frequent data are extracted, through split-frequent data generated locally and the early removal of unusual data. The paper shows test results, whereby the EPDA performs in terms of the time and number of rules generated with a database of healthcare and different minimum support values.
Co-Authors K. Dinesh Kaushik Sekaran Lakshmana Kumar Ramasamy M. Navaneetha Krishnan M. Sornalakshmi M. Venkatesulu R. Rajakumar S. Balamurali Seifedine Kadry T. P. Latchoumi Y. Rajkumar Youngjun Jo