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

Abstract

Uneven distribution is common in setting up access points where some areas collide and others have no signals (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 clustering is the mapping of dense regions (traffic) and loose regions to determine the coverage areas of each access point (AP). This approach is meant to optimize the placement of access points in terms of their number and specifications. The experimentation indicates that the use of K-Means clustering method significantly optimized the distribution model of access points on a Wi-Fi network.