Optimization of Access Point Positioning on Wi-Fi Networks Using the K-Means Clustering Method

Faiz Ainun Karima, Ary Mazharuddin Shiddiqi

Abstract


Uneven distribution is a common problem in setting up access points where some areas have many signals colliding with each other and others have no signals at all (blank spots). As a result, proper access point positioning on the WI-FI network is required to optimize the number of access points used and the signal strength received while maintaining the same coverage area's functionality. In this study, signal strength measurement is used to obtain the estimated distance using the Received Signal Strength Indicator (RSSI) method. The server analyzes using the K-Means Clustering algorithm to cluster the observation area. The output of this classification is the mapping of dense regions (traffic) and loose regions to determine the coverage areas of each access point. This approach aims to optimize the placement of access points in terms of their number and specifications.

Keywords


Clustering; Classification; K-Means; KNN; RSSI

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References


Noviardianto GE, Novel M, Legowo MB. Penggunaan Metode Simulated Annealing untuk Optimasi Penempatan Posisi Access Point pada Jaringan WI-FI. Jurnal Al-Azhar Indonesia Seri Sains dan Teknologi 2019;5:10–18.

Yadav R, Sharma A. Advanced Methods to Improve Performance of K-Means Algorithm: A Review. Global Journal of Computer Science and Technology 2012;12.

Han J, Kamber M, Pei J. Data Mining. Concepts and Techniques, 3rd Edition (The Morgan Kaufmann Series in Data Management Systems). 3rd edition ed. Morgan Kaufmann Publishers; 2012. www.booksite.mkp.com/datamining3e.

Madhulatha TS. An Overview on Clustering Methods. IOSR Journal of Engineering 2012;2:719–725. www.iosrjen.org.

Darmawan AK, Siahaan D, Susanto TD, Hoiriyah, Umam B. Identifying Success Factors in Smart City Readiness using a Structure Equation Modelling Approach. In: Proceedings - 2019 International Conference on Computer Science, Information Technology, and Electrical Engineering, ICOMITEE 2019; 2019. p. 148–153.

The Casagras Partnership. CASAGRAS Final Report: RFID and the Inclusive Model for the Internet of Things; 2009.

Awaludin A. Pengertian dan Cara Kerja Wifi. Ilmu Teknologi Informasi; 2014.

Salamah U. Analisis Kualitas Sinyal WIFI Berdasarkan Halangan dan Lokasi Penempatan Access Point. PhD thesis, Universitas Satya Negara Indonesia; 2020.

Procopio ET, de Assis Mota A, Mota LTM, da Silva LRB. Received Signal Stength Indication Modeling In Indoor Wireless Sensor Networks. American Journal of Applied Sciences 2013 8;10:1043–1049.

Putra TSJ. Analisis Kualitas Signal Wireless Berdasarkan Received Signal Strength Indicator (RSSI) pada. PhD thesis, Universitas Kristen Satya Wacana; 2018.

Kordnoori S, Mostafaei H, Kordnoori S, Ostadrahimi MM. Evaluating the CDMA System Using Hidden Markov and Semi Hidden Markov Models. IPTEK The Journal for Technology and Science 2021;31(3):64–73.

Kordnoori S, Mostafaei H, Kordnoori S, Ostadrahimi M, Blvd V, Sadoughi St S, et al. Evaluating the CDMA System Using Hidden Markov and Semi Hidden Markov Models. IPTEK The Journal for Technology and Science 2021 jan;31(3):295–308. https://iptek.its.ac.id/index.php/jts/article/view/7016.

Triandini E, Djunaidy A, Siahaan D. Determining e-commerce adoption level by SMEs in Indonesia based on customer-oriented benefits. In: 2014 1st International Conference on Information Technology, Computer, and Electrical Engineering: Green Technology and Its Applications for a Better Future, ICITACEE 2014 - Proceedings; 2015. p. 281–285.

Purwitasari D, Priantara IWS, Kusmawan PY, Yuhana UL, Siahaan DO. The use of Hartigan index for initializing K-means++ in detecting similar texts of clustered documents as a plagiarism indicator. Asian Journal of Information Technology 2011;10(8):341–347.

Merliana NPE, Santoso AJ. Analisa Penentuan Jumlah Cluster Terbaik Pada Metode K-Means Clustering. Seminar Nasional Multi Disiplin Ilmu (SENDI_U) 2015;1.

Bholowalia P, Kumar A. EBK-Means: A Clustering Technique based on Elbow Method and K-Means in WSN. International Journal of Computer Applications 2014;105:975–8887.

Rahman AT, Wiranto, Anggrainingsih R. Coal Trade Data Clusterung Using K-Means (Case Study PT. Global Bangkit Utama). ITSMART: Jurnal Ilmiah Teknologi dan Informasi 2017;6:24–3




DOI: http://dx.doi.org/10.12962/j20882033.v33i1.12402

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