An Iterative Weighted KNN (IW-KNN) Based Indoor Localization Method in Bluetooth Low Energy (BLE) Environment

Peng, Yiran; Fan, Wentao; Dong, Xiao; Zhang, Xing

doi:10.1109/uic-atc-scalcom-cbdcom-iop-smartworld.2016.0127

Cited by 55 publications

(40 citation statements)

References 13 publications

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“…The variation of the localization error in the above cases defined the maximum tolerance

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parameter value at

T o l

= 2 m. In this way, it was ensured that the user’s actual location was falling within the candidate fingerprints chosen in the optimized dataset. Our findings were consistent with [38], where BLE RSS indication fluctuated up to 10 dB. Error factors covered by the introduction of the

T o l

parameter included the actual number of active BLEs, user/device orientation, body and multipath effects.…”

Section: Test Environmentsupporting

confidence: 91%

Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization

Kanaris

Kokkinis

Liotta

et al. 2017

Sensors

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Indoor user localization and tracking are instrumental to a broad range of services and applications in the Internet of Things (IoT) and particularly in Body Sensor Networks (BSN) and Ambient Assisted Living (AAL) scenarios. Due to the widespread availability of IEEE 802.11, many localization platforms have been proposed, based on the Wi-Fi Received Signal Strength (RSS) indicator, using algorithms such as K-Nearest Neighbour (KNN), Maximum A Posteriori (MAP) and Minimum Mean Square Error (MMSE). In this paper, we introduce a hybrid method that combines the simplicity (and low cost) of Bluetooth Low Energy (BLE) and the popular 802.11 infrastructure, to improve the accuracy of indoor localization platforms. Building on KNN, we propose a new positioning algorithm (dubbed i-KNN) which is able to filter the initial fingerprint dataset (i.e., the radiomap), after considering the proximity of RSS fingerprints with respect to the BLE devices. In this way, i-KNN provides an optimised small subset of possible user locations, based on which it finally estimates the user position. The proposed methodology achieves fast positioning estimation due to the utilization of a fragment of the initial fingerprint dataset, while at the same time improves positioning accuracy by minimizing any calculation errors.

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“…The variation of the localization error in the above cases defined the maximum tolerance

T o l

parameter value at

T o l

T o l

parameter included the actual number of active BLEs, user/device orientation, body and multipath effects.…”

Section: Test Environmentsupporting

confidence: 91%

Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization

Kanaris

Kokkinis

Liotta

et al. 2017

Sensors

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“…In this article, the authors describe the main features and potential applications for BLE technology [178]. This data set shows BLE related applications such as home automation [179][180][181] and indoor location [182][183][184] and health care [185][186][187]. WiFi is the other network protocol used for IoT research, with a total of 85 publications, and applications related to: home automation [188,189], indoor localization [190].…”

Section: Communication Protocols According To Open Systems Interconnementioning

confidence: 99%

Internet of Things: A Scientometric Review

et al. 2017

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Abstract:Internet of Things (IoT) is connecting billions of devices to the Internet. These IoT devices chain sensing, computation, and communication techniques, which facilitates remote data collection and analysis. wireless sensor networks (WSN) connect sensing devices together on a local network, thereby eliminating wires, which generate a large number of samples, creating a big data challenge. This IoT paradigm has gained traction in recent years, yielding extensive research from an increasing variety of perspectives, including scientific reviews. These reviews cover surveys related to IoT vision, enabling technologies, applications, key features, co-word and cluster analysis, and future directions. Nevertheless, we lack an IoT scientometrics review that uses scientific databases to perform a quantitative analysis. This paper develops a scientometric review about IoT over a data set of 19,035 documents published over a period of 15 years (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) in two main scientific databases (Clarivate Web of Science and Scopus). A Python script called ScientoPy was developed to perform quantitative analysis of this data set. This provides insight into research trends by investigating a lead author's country affiliation, most published authors, top research applications, communication protocols, software processing, hardware, operating systems, and trending topics. Furthermore, we evaluate the top trending IoT topics and the popular hardware and software platforms that are used to research these trends.

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“…Indoor trajectories [1] support these services for several applications, such as people behavior analytics, movement patterns extraction, next-to-visit recommendations, and hotspot detection. Positioning devices, such as Wi-Fi [2], BLE (Bluetooth Low Energy) [3][4][5][6][7][8], and RFID (radio frequency identification) [9], provide the trajectories using indoor positioning techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Then, the methods estimate the current user position using the knowledge from the reference set. Some popular indoor positioning techniques are the Hidden Markov Model (HMM) [4,5,[9][10][11][12], k-nearest neighbors (kNN) [6][7][8], and Deep Neural Networks (DNN) [3,13]. Those methods (HMM, kNN, and DNN) utilize machine learning as their core.…”

Section: Introductionmentioning

confidence: 99%

“…To acquire this semantic trajectory in an indoor space, we can simply get the semantic positions by associating certain areas in 2D space as semantic locations. Figure 1b, we see an extracted 2D indoor trajectory that denotes the movements of a user from position (6,7) at t 1 to position (8,3) at t 2 and then to position (9,7.5) at t 3 in a short time in Figure 1c. Thus, if we know that position (6,7) is at the Corridor AB, position (8,3) is at Corridor-BD, and position (9,7.5) is at Room B-North, we can define a more understandable trajectory such as Corridor AB → Corridor BD → Room B-North.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Applying Movement Constraints to BLE RSSI-Based Indoor Positioning for Extracting Valid Semantic Trajectories

Ramadhan

Yustiawan

Kwon

2020

Sensors

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Indoor positioning techniques, owing to received signal strength indicator (RSSI)-based sensors, can provide useful trajectory-based services. These services include user movement analytics, next-to-visit recommendation, and hotspot detection. However, the value of RSSI is often disturbed due to obstacles in indoor environment, such as doors, walls, and furnitures. Therefore, many indoor positioning techniques still extract an invalid trajectory from the disturbed RSSI. An invalid trajectory contains distant or impossible consecutive positions within a short time, which is unlikely in a real-world scenario. In this study, we enhanced indoor positioning techniques with movement constraints on BLE (Bluetooth Low Energy) RSSI data to prevent an invalid semantic indoor trajectory. The movement constraints ensure that a predicted semantic position cannot be far apart from the previous position. Furthermore, we can extend any indoor positioning technique using these movement constraints. We conducted comprehensive experimental studies on real BLE RSSI datasets from various indoor environment scenarios. The experimental results demonstrated that the proposed approach effectively extracts valid indoor semantic trajectories from the RSSI data.

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An Iterative Weighted KNN (IW-KNN) Based Indoor Localization Method in Bluetooth Low Energy (BLE) Environment

Cited by 55 publications

References 13 publications

Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization

Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization

Internet of Things: A Scientometric Review

Applying Movement Constraints to BLE RSSI-Based Indoor Positioning for Extracting Valid Semantic Trajectories

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