IWKNN: An Effective Bluetooth Positioning Method Based on Isomap and WKNN

Wang, Qi; Feng, Yingying; Zhang, Xiangde; Sun, Yanrui; Lu, Xiaojun

doi:10.1155/2016/8765874

Cited by 16 publications

(12 citation statements)

References 24 publications

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“…e sample vectors of RSS values are matched with the patterns in the database during the online phase to estimate the location of the target [4,18]. e algorithms used in the Scene Analysis approach are the probabilistic methods [18], the Weighted K-Nearest Neighbor (WKNN) [19,20], and the neural networks [21].…”

Section: Introductionmentioning

confidence: 99%

Robust System Design Using BILP for Wireless Indoor Positioning Systems

Maneerat

Kaemarungsi

2018

Mobile Information Systems

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In wireless indoor positioning system designs, reference node (RN) failures during the online phase cause received signal strength values to be unavailable. is leads to accuracy performance degradation and a lack of system reliability in smart office systems. Moreover, the major design concern in the reliability of indoor positioning systems under the faulty RNs during the online phase has not been yet investigated in previous works. To address these gaps, we propose a novel mathematical formulation using a Binary Integer Linear Programming (BILP) approach that employs the Simulated Annealing (SA) solution technique. e proposed robust system design aims to put in place a suitable number of RNs and to determine their optimum locations, which may be located on a single floor or on multiple floors. In particular, the proposed system design provisions to support robust operation both during a normal situation and when there are some RN failures. Experimental results and comparative performance evaluation revealed that the proposed robust system design outperformed other system designs and was able to achieve the highest location accuracy performance in both fault-free and RN-failure scenarios. Specifically, when nine of the RNs in a three-story building failed, the proposed system design achieved 84.6%, 54.7%, and 32.9% more accurate performance than the Uniform, the MSMR, and the PhI-Uni, respectively.

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Section: Introductionmentioning

confidence: 99%

Robust System Design Using BILP for Wireless Indoor Positioning Systems

Maneerat

Kaemarungsi

2018

Mobile Information Systems

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“…is closeness fingerprint location is called the nearest neighbor location. en, those restriction distance metrics of the k-nearest neighbor's locations are used to compute the weighting factor [34]. e calculation of the WKNN method is written in the following equations:…”

Section: Online Estimation Phasementioning

confidence: 99%

RoC: Robust and Low-Complexity Wireless Indoor Positioning Systems for Multifloor Buildings Using Location Fingerprinting Techniques

Maneerat

Kaemarungsi

2019

Mobile Information Systems

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Most existing wireless indoor positioning systems have only success performance requirements in normal operating situations whereby all wireless equipment works properly. There remains a lack of system reliability that can support emergency situations when there are some reference node failures, such as in earthquake and fire scenarios. Additionally, most systems do not incorporate environmental information such as temperature and relative humidity level into the process of determining the location of objects inside the building. To address these gaps, we propose a novel integrated framework for wireless indoor positioning systems based on a location fingerprinting technique which is called the Robust and low Complexity indoor positioning systems framework (RoC framework). Our proposed integrated framework consists of two essential indoor positioning processes: the system design process and the localization process. The RoC framework aims to achieve robustness in the system design structure and reliability of the target location during the online estimation phase either under a normal situation or when some reference nodes (RNs) have failed. The availability of low-cost temperature and relative humidity sensors can provide additional information for the location fingerprinting technique and thereby reduce location estimation complexity by including this additional information. Experimental results and comparative performance evaluation revealed that the RoC framework can achieve robustness in terms of the system design structure, whereby it was able to provide the highest positioning performance in either fault-free or RN-failure scenarios. Moreover, in the online estimation phase, the proposed framework can provide the highest reliability of the target location under the RN-failure scenarios and also yields the lowest computational complexity in online searching compared to other techniques. Specifically, when compared to the traditional weighted k-nearest neighbor techniques (WKNN) under the 30% RN-failure scenario at Building B, the proposed RoC framework shows 74.1% better accuracy performance and yields 55.1% lower computational time than the WKNN.

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“…Their results have shown that -NN reports the best mean positioning error, approximately 4 m. In [30] better results are presented by using a combination of BLE4.0 beacon and Wi-Fi technologies and the same classification algorithms. A more in-depth analysis on the parameters of different classification algorithms is presented in [31], where the best results have been obtained using a weighted distance (WD) for -NN. In [32], Peng et al have obtained similar results using -NN, testing different values for " .…”

Section: Ble4mentioning

confidence: 99%

“…In this work, we explore the use of two popular SLAs, namely, the -Nearest Neighbour ( -NN) [21,[29][30][31] and the Support Vector Machine (SVM) [28,29] algorithms. A brief description of these two algorithms is included in the following:…”

Section: Supervised Learning Algorithmsmentioning

confidence: 99%

An Analysis of Multiple Criteria and Setups for Bluetooth Smartphone-Based Indoor Localization Mechanism

et al. 2017

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Bluetooth Low Energy (BLE) 4.0 beacons will play a major role in the deployment of energy-efficient indoor localization mechanisms. Since BLE4.0 is highly sensitive to fast fading impairments, numerous ongoing studies are currently exploring the use of supervised learning algorithm as an alternative approach to exploit the information provided by the indoor radio maps. Despite the large number of results reported in the literature, there are still many open issues on the performance evaluation of such approach. In this paper, we start by identifying, in a simple setup, the main system parameters to be taken into account on the design of BLE4.0 beacons-based indoor localization mechanisms. In order to shed some light on the evaluation process using supervised learning algorithm, we carry out an in-depth experimental evaluation in terms of the mean localization error, local prediction accuracy, and global prediction accuracy. Based on our results, we argue that, in order to fully assess the capabilities of supervised learning algorithms, it is necessary to include all the three metrics.

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IWKNN: An Effective Bluetooth Positioning Method Based on Isomap and WKNN

Cited by 16 publications

References 24 publications

Robust System Design Using BILP for Wireless Indoor Positioning Systems

Robust System Design Using BILP for Wireless Indoor Positioning Systems

RoC: Robust and Low-Complexity Wireless Indoor Positioning Systems for Multifloor Buildings Using Location Fingerprinting Techniques

An Analysis of Multiple Criteria and Setups for Bluetooth Smartphone-Based Indoor Localization Mechanism

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