Robust Floor Determination Algorithm for Indoor Wireless Localization Systems under Reference Node Failure

Maneerat, Kriangkrai; Kaemarungsi, Kamol; Prommak, Chutima

doi:10.1155/2016/4961565

Cited by 8 publications

(16 citation statements)

References 19 publications

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“…For example, the objective of the Uniform is to place the RNs in a service area, in which the whole service area must be able to receive signals from at least one RN. In order to estimate the target location inside the multi-story buildings, the RMoS floor algorithm [37] and the active Euclidean distance technique based on the location fingerprinting approach are used. Instead of matching the RSS patterns obtained from all the RNs in the service area as occurs with the traditional Euclidean distance technique, the active Euclidean distance technique only considers the RSS values that are transmitted from the available RNs (i.e., active RNs) for a matching RSS pattern process.…”

Section: Experimental Setupsmentioning

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: Experimental Setupsmentioning

confidence: 99%

Robust System Design Using BILP for Wireless Indoor Positioning Systems

Maneerat

Kaemarungsi

2018

Mobile Information Systems

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“…e first step of the online estimation phase is to determine the floor number on which the target node is located. We use the robust floor determination algorithm called the Robust Mean of Sum-RSS (RMoS) floor algorithm, which was developed in our previous works [32,33]. e RMoS floor algorithm can accurately determine the floor on which the target nodes are located and can work under either the fault-free scenario or the RN-failure scenarios.…”

Section: Online Estimation Phasementioning

confidence: 99%

“…In this case, the proposed RMoS floor algorithm correctly reports that the target node is on the second floor of the building. Detailed descriptions of the RMoS floor algorithm can be found in [33].…”

Section: Online Estimation Phasementioning

confidence: 99%

“…In this section, we evaluate the oor determination performance in the three-story Building A. We compare the performance of two di erent oor determination algorithms without the use of the location ngerprinting database: the Group variance oor algorithm [37] and the proposed RMoS oor algorithm [33]. Two system design structures that have the same number of RNs installed are considered, consisting of the proposed R-MSMR with R 2 and the PhI-Uni as shown in Figures 13(c) and 13(d), respectively.…”

Section: Percentage Of Correct Floor Determinationmentioning

confidence: 99%

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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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“…Therefore, for this work, we used the first 26 reachable features only. In [26,27], missed values were filled using average values. However, it is unrealistic to represent all missed APs with similar values when there will be more than one missed AP.…”

Section: Proposed Systemmentioning

confidence: 99%

Applying Deep Neural Network (DNN) for Robust Indoor Localization in Multi-Building Environment

et al. 2018

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Featured Application: This work can be applied to track mobile users, manage indoor navigations, provide alarms in secured areas, such as unacceptable hospital areas, military systems and mass rapid transit (MRT) inside enclosed areas. In general, this work is applicable to inside enclosed areas where the specific location is mandatory.Abstract: In the Internet of Things (IoT) era, indoor localization plays a vital role in academia and industry. Wi-Fi is a promising scheme for indoor localization as it is easy and free of charge, even for private networks. However, Wi-Fi has signal fluctuation problems because of dynamic changes of environments and shadowing effects. In this paper, we propose to use a deep neural network (DNN) to achieve accurate localization in Wi-Fi environments. In the localization process, we primarily construct a database having all reachable received signal strengths (RSSs), and basic service set identifiers (BSSIDs). Secondly, we fill the missed RSS values using regression, and then apply linear discriminant analysis (LDA) to reduce features. Thirdly, the 5-BSSIDs having the strongest RSS values are appended with reduced RSS vector. Finally, a DNN is applied for localizing Wi-Fi users. The proposed system is evaluated in the classification and regression schemes using the python programming language. The results show that 99.15% of the localization accuracy is correctly classified. Moreover, the coordinate-based localization provides 50%, 75%, and 93.10% accuracies for errors less than 0.50 m, 0.75 m, and 0.90 m respectively. The proposed method is compared with other algorithms, and our method provides motivated results. The simulation results also show that the proposed method can robustly localize Wi-Fi users in hierarchical and complex wireless environments.

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Robust Floor Determination Algorithm for Indoor Wireless Localization Systems under Reference Node Failure

Cited by 8 publications

References 19 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

Applying Deep Neural Network (DNN) for Robust Indoor Localization in Multi-Building Environment

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