WiFi Based Indoor Localization: Application and Comparison of Machine Learning Algorithms

Sabancı, Kadir; Yiğit, Enes; Üstün, Deniz; Toktaş, Abdurrahim; Aslan, Muhammet Fatih

doi:10.1109/diped.2018.8543125

Cited by 32 publications

(22 citation statements)

References 30 publications

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“…Many machine learning algorithms such as support vector machine (SVM), K-nearest neighbor (KNN), extreme learning model (ELM), decision tree (DT), Naive Bayes (NB), and Bayesian Network (BN) were used for location estimation in an indoor environment. e results show that KNN and SVM are outperformers [6,7] as compared to others. Moreover, SVM is based on the structural risk minimization principle with good generalization ability and can better solve problems with few samples, nonlinear data, avoid local minima, and so on [2].…”

Section: Introductionmentioning

confidence: 92%

“…e simulation results show that KNN is the best of all. Similarly, Sabanci et al [7] also compare different machine learning algorithms such as ANN, KNN, ELM, SVM, NB, and DT based on Wi-Fi fingerprinting. According to the simulation results, the KNN shows the best performance.…”

Section: Related Workmentioning

confidence: 99%

“…Other researchers have also used machine learning algorithms such as KNN and WNN [19], ELM [20], SVM [7], and SVM and DT [21] for position estimation in an indoor environment. According to the literature two machine learning algorithms, both K-NN and SVM show better performance against the other learning model.…”

Section: Related Workmentioning

confidence: 99%

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Cuckoo Search-based SVM (CS-SVM) Model for Real-Time Indoor Position Estimation in IoT Networks

Khan

Bangash

et al. 2021

Security and Communication Networks

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Internet of Things (IoT), an emerging technology, is becoming an essential part of today’s world. Machine learning (ML) algorithms play an important role in various applications of IoT. For decades, the location information has been extremely useful for humans to navigate both in outdoor and indoor environments. Wi-Fi access point-based indoor positioning systems get more popularity, as it avoids extra calibration expenses. The fingerprinting technique is preferred in an indoor environment as it does not require a signal’s Line of Sight (LoS). It consists of two phases: offline and online phase. In the offline phase, the Wi-Fi RSSI radio map of the site is stored in a database, and in the online phase, the object is localized using the offline database. To avoid the radio map construction which is expensive in terms of labor, time, and cost, machine learning techniques may be used. In this research work, we proposed a hybrid technique using Cuckoo Search-based Support Vector Machine (CS-SVM) for real-time position estimation. Cuckoo search is a nature-inspired optimization algorithm, which solves the problem of slow convergence rate and local minima of other similar algorithms. Wi-Fi RSSI fingerprint dataset of UCI repository having seven classes is used for simulation purposes. The dataset is preprocessed by min-max normalization to increase accuracy and reduce computational speed. The proposed model is simulated using MATLAB and evaluated in terms of accuracy, precision, and recall with K-nearest neighbor (KNN) and support vector machine (SVM). Moreover, the simulation results show that the proposed model achieves high accuracy of 99.87%.

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Section: Introductionmentioning

confidence: 92%

Section: Related Workmentioning

confidence: 99%

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Cuckoo Search-based SVM (CS-SVM) Model for Real-Time Indoor Position Estimation in IoT Networks

Khan

Bangash

et al. 2021

Security and Communication Networks

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“…In the literature, many machine-learning methods have been found to mitigate the impact of RSSI fluctuations [47], [57], [58]. For pattern matching in online phase K-Nearest Neighbor (K-NN), Artificial Neural Network (ANN) [59], Support Vector Machine (SVM) [60] and K-means [61] and Random Forest [62] algorithms have been used. Advance network interface cards (NICs) are required to measure CSI that adds extra cost.…”

Section: Centroidmentioning

confidence: 99%

“…K-NN is a simple and effective ML algorithm. It classifies data in feature space according to distance [60]. This model predicts the value of new data points by comparing the similarity of this value with the training data and finds out K neighbors which have maximum closeness with the new data.…”

Section: Algorithms Description Supervised Learning: K-nearest Neighbmentioning

confidence: 99%

A Survey of Machine Learning for Indoor Positioning

et al. 2020

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Widespread proliferation of wireless coverage has enabled culmination of number of advanced location-based services (LBS). Continuous tracking of accurate physical location is the foundation of these services, which is a challenging task especially indoors. Multitude of techniques and algorithms have been proposed for indoor positioning systems (IPS's). However, accuracy, reliability, scalability and, adaptability to the environment still remain as challenges for widespread deployment. Especially, unpredictable radio propagation characteristics in vastly varying indoor environments plus access technology limitations contribute to these challenges. Machine learning (ML) approaches have been widely attempted recently to overcome these challenges with reasonable success. In this paper, we aim to provide a comprehensive survey of ML enabled localization techniques using most common wireless technologies. First, we provide a brief background on indoor localization techniques. Afterwards, we discuss various ML techniques (supervised and unsupervised) that could alleviate different challenges in indoor localization including Non-line-ofsight (NLOS) issue, device heterogeneity and environmental variations with reasonable complexity. The trade-offs among multitude of issues are discussed using numerous published results. We also discuss how the ML algorithms can be effectively used for fusing different technologies and algorithms to achieve a comprehensive IPS. In essence, this survey will serve as a reference material to acquire a detailed knowledge on recent development of machine learning for accurate indoor positioning. INDEX TERMS Indoor positioning system (IPS), location-based services (LBS), machine learning (ML), non-line-of-sight (NLOS), wireless positioning, indoor tracking.

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Room-Level Indoor Localization with Artificial Neural Networks

Karadeniz

Efe

2019

Pattern Recognition and Artificial Intelligence

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WiFi Based Indoor Localization: Application and Comparison of Machine Learning Algorithms

Cited by 32 publications

References 30 publications

Cuckoo Search-based SVM (CS-SVM) Model for Real-Time Indoor Position Estimation in IoT Networks

Cuckoo Search-based SVM (CS-SVM) Model for Real-Time Indoor Position Estimation in IoT Networks

A Survey of Machine Learning for Indoor Positioning

Room-Level Indoor Localization with Artificial Neural Networks

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