A Two-Nearest Wireless Access Point-Based Fingerprint Clustering Algorithm for Improved Indoor Wireless Localization

Abstract

Fingerprint database clustering is one of the methods used to reduce localization time and improve localization accuracy in a fingerprint-based localization system. However, optimal selection of initial hyperparameters, higher computation complexity, and interpretation difficulty are among the performance-limiting factors of these clustering algorithms. This paper aims to improve localization time and accuracy by proposing a clustering algorithm that is extremely efficient and accurate at clustering fingerprint databases without requiring the selection of optimal initial hyperparameters, is computationally light, and is easily interpreted. The two closest wireless access points (APs) to the reference location where the fingerprint is generated, as well as the labels of the two APs in vector form, are used by the proposed algorithm to cluster fingerprints. The simulation result shows that the proposed clustering algorithm has a localization time that is at least 45% faster and a localization accuracy that is at least 25% higher than the k-means, fuzzy c-means, and lightweight maximum received signal strength clustering algorithms. The findings of this paper further demonstrate the real-time applicability of the proposed clustering algorithm in the context of indoor wireless localization, as low localization time and higher localization accuracy are the main objectives of any localization system. Doi: 10.28991/ESJ-2023-07-05-019 Full Text: PDF

Enhancing Fingerprint Localization Accuracy With Inverse Weight-Normalized Context Similarity Coefficient-Based Fingerprint Similarity Metric

“…Rather than calculating the distances between fingerprint vectors to determine their similarity, the authors in [16], [5], and [17] used the wireless APs closest to each fingerprint vector as fingerprint similarity metrics. In [16] and [17], the wireless APs that are closest to each fingerprint are used to cluster the fingerprint vectors.…”

“…The closest wireless AP is the one with the highest RSS value in the fingerprint vector. The authors of [5] expand on the work of [16] by utilizing the two closest wireless APs. Table 1 gives a summary of the fingerprint similarity metrics used in clustering fingerprint databases in related works.…”

“…The density of the fingerprint database is one of the factors that affects the localization accuracy of the fingerprint-based localization system [4], [5]. The higher the density, the higher the localization accuracy; however, the longer the localization time.…”

Outlier Detection Performance of a Modified Z-Score Method in Time-Series RSS Observation With Hybrid Scale Estimators

“…Fingerprint-based localization, which uses received signal strength (RSS) measurements obtained from spatially deployed wireless access points (APs), is an emerging technology that has found applications in a variety of areas, including indoor localization [1], [2]. Due to the dynamic nature of the indoor environment, such as the presence and absence of crowds and furniture, as well as variations in temporal and ambient conditions, the signals from the wireless APs fluctuate, causing the RSS measurements to fluctuate, resulting in poor localization accuracy [3], [4].…”

Improvement of Low Energy Adaptive Clustering Hierarchical Protocol Based on Genetic Algorithm to Increase Network Lifetime of Wireless Sensor Network

“…Similar to other applications, the genetic algorithm (GA) has been utilized in the LEACH protocol to enhance its performance. Researchers have also conducted studies on enhancing the LEACH CH selection algorithm to improve further the routing protocol's efficiency and network lifetime of WSN (7), A genetic algorithm is proposed that randomly selects nodes in a network to serve as cluster heads (8). It then determines the number of predefined independent clusters to reduce and minimize communication distance.…”