Random Forest and Interpolation Techniques for Fingerprint-based Indoor Positioning System in Un-ideal Environment

Suroso, Dwi Joko; Rudianto, Alvin S. H.; Arifin, Muhammad; Hawibowo, Singgih

doi:10.12785/ijcds/100166

Cited by 10 publications

(6 citation statements)

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“…They compared the classic pattern-matching algorithm and the minimum Euclidean distance algorithm. The latter performed better in accuracy and precision, while the random forest algorithm performed better in reducing the maximum estimation error 14 .…”

Section: Related Workmentioning

confidence: 99%

Research on indoor positioning method based on LoRa-improved fingerprint localization algorithm

Chen,

Yang,

Hao

et al. 2023

Sci Rep

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Traditional fingerprint localization algorithms need help with low localization accuracy, large data volumes, and device dependence. This paper proposes a LoRa-based improved fingerprint localization algorithm-particle swarm optimization-random forest-fingerprint localization for indoor localization. The first improvement step involves creating a new exceptional fingerprint value (referred to as RSSI-RANGE) by adding the Time of Flight ranging value (referred to as RANGE) to the Received Signal Strength Indication (RSSI) value and weighting them together. The second improvement step involves preprocessing the fingerprint data to eliminate gross errors using Gaussian and median filtering. After noise reduction, the particle swarm optimization algorithm is used to optimize the hyper parameters of the random forest algorithm, and the best RSSI-RANGE value is obtained using the random forest algorithm. The Kriging method is then used for interpolation to establish an offline fingerprint database, and the final online recognition and localization are performed. Experimental results demonstrate that the first improvement step improves localization accuracy by 53–57% in different experimental scenarios, while the second improves localization accuracy by 25–31%. When both steps are combined, the localization accuracy is improved by 58–63%. The effectiveness of this method is demonstrated through experiments.

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Section: Related Workmentioning

confidence: 99%

Research on indoor positioning method based on LoRa-improved fingerprint localization algorithm

Chen,

Yang,

Hao

et al. 2023

Sci Rep

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“…In the next step, we added artificial data into the measurement database to have a new database with a 0.5 × 0.5 m 2 grid database from the fingerprint database that we created using bilinear interpolation, polynomial regression, and polynomial interpolation and named as fp+intBil_db, fp+regPoly, and fp+intPoly_db. The method to make the database denser is applied based on the authors' works in [53,54].…”

Section: The 2d Measurement Database Enhancementmentioning

confidence: 99%

Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-based Indoor Localization

et al. 2022

Self Cite

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Most applied indoor localization is based on distance and fingerprint techniques. The distance-based technique converts specific parameters to a distance, while the fingerprint technique stores parameters as the fingerprint database. The widely used Internet of Things (IoT) technologies, e.g., Wi-Fi and ZigBee, provide the localization parameters, i.e., received signal strength indicator (RSSI). The fingerprint technique advantages over the distance-based method as it straightforwardly uses the parameter and has better accuracy. However, the burden in database reconstruction in terms of complexity and cost is the disadvantage of this technique. Some solutions, i.e., interpolation, image-based method, machine learning (ML)-based, have been proposed to enhance the fingerprint methods. The limitations are complex and evaluated only in a single environment or simulation. This paper proposes applying classical interpolation and regression to create the synthetic fingerprint database using only a relatively sparse RSSI dataset. We use bilinear and polynomial interpolation and polynomial regression techniques to create the synthetic database and apply our methods to the 2D and 3D environments. We obtain an accuracy improvement of 0.2m for 2D and 0.13m for 3D by applying the synthetic database. Adding the synthetic database can tackle the sparsity issues, and the offline fingerprint database construction will be less burden. Doi: 10.28991/esj-2021-SP1-012 Full Text: PDF

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“…Indoor Positioning System (IPS) is a tracking system that usually uses a set of network devices to locate people or objects within a building, or a particular room [1] where GPS would fail entirely [2] or lack of high accuracy [3]. An indoor tracking system is one of the most helpful features of a smart building [4] or smart environment [5] that can usually be achieved by working with Internet of Things (IoT) [6,7] techniques, e.g.…”

Section: Introductionmentioning

confidence: 99%

Seen to Unseen: When Fuzzy Inference System Predicts IoT Device Positioning Labels That Had Not Appeared in Training Phase

Han

Zuo

et al. 2022

Proceedings of the 7th International Conference on Internet of Things, Big Data and Security

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Situating at the core of Artificial Intelligence (AI), Machine Learning (ML), and more specifically, Deep Learning (DL) have embraced great success in the past two decades. However, unseen class label prediction is far less explored due to missing classes being invisible in training ML or DL models. In this work, we propose a fuzzy inference system to cope with such a challenge by adopting TSK+ fuzzy inference engine in conjunction with the Curvature-based Feature Selection (CFS) method. The practical feasibility of our system has been evaluated by predicting the positioning labels of networking devices within the realm of the Internet of Things (IoT). Competitive prediction performance confirms the efficiency and efficacy of our system, especially when a large number of continuous class labels are unseen during the model training stage.

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Random Forest and Interpolation Techniques for Fingerprint-based Indoor Positioning System in Un-ideal Environment

Cited by 10 publications

References 0 publications

Research on indoor positioning method based on LoRa-improved fingerprint localization algorithm

Research on indoor positioning method based on LoRa-improved fingerprint localization algorithm

Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-based Indoor Localization

Seen to Unseen: When Fuzzy Inference System Predicts IoT Device Positioning Labels That Had Not Appeared in Training Phase

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