Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence

One of the popular candidates in wireless technology for indoor positioning is Bluetooth Low Energy (BLE). However, this technology faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations due to the behavior of the different advertising channels and the effect of human body shadowing among other effects. In order to mitigate these effects, the paper proposes and implements a dynamic Artificial Intelligence (AI) model that uses the three different BLE advertising channels to detect human body shadowing and compensate the RSSI values accordingly. An experiment in an indoor office environment is conducted. 70% of the observations are randomly selected and used for training and the remaining 30% are used to evaluate the algorithm. The results show that the AI model can properly detect and significantly compensate RSSI values for a dynamic blockage caused by a human body. This can significantly improve the RSSI-based ranges and the corresponding positioning accuracies.

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In-vehicle localization based on multi-channel Bluetooth Low Energy received signal strength indicator

International Journal of Distributed Sensor Networks ◽

10.1177/1550147719900093 ◽

2020 ◽

Vol 16 (1) ◽

pp. 155014771990009

Author(s):

Gao Yuan ◽

Zhao Ze ◽

Huang Changcheng ◽

Han Chuanqi ◽

Cui Li

Keyword(s):

Standard Deviation ◽

High Precision ◽

Signal Strength ◽

Received Signal Strength ◽

Low Energy ◽

Support Vector ◽

Time Sharing ◽

Received Signal Strength Indicator ◽

Bluetooth Low Energy ◽

Vehicle Localization

High-precision in-vehicle localization is the basis for both in-vehicle location-based service and the analysis of the driver or passengers’ behaviors. However, interferences like effects of multipath and reflection of the signals significantly raise great challenges to the positioning accuracy at in-vehicle environment. This article presents a novel high-precision in-vehicle localization method, namely, the LOC-in-a-Car, based on functional exploration and full use of multi-channel received signal strength indicator of Bluetooth Low Energy. To achieve higher positioning precision, a hierarchical computation algorithm based on Adaboost and support vector machine is proposed in our method. In particular, we also proposed a device calibration method to deal with the heterogeneity of different smartphone terminals. We developed an Android app as a component in which the channel time-sharing acquisition method is fulfilled, enabling smartphones to distinguish data from multi-channels. The system performance is verified via intensive experiments, of which the results show that our method can distinguish the locations of driver or passengers with an accuracy ranging from 86.80% to 92.02% for each seat on Nexus phone, and the overall accuracy is 89.86%, with standard deviation of 2.64%. On Huawei phone, the accuracy ranges from 85.43% to 93.33% with overall accuracy of 89.75% and standard deviation of 3.07%. Both outperform the existing methods.

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Pendeteksi Kehadiran menggunakan ESP32 untuk Sistem Pengunci Pintu Otomatis

JTT (Jurnal Teknologi Terapan) ◽

10.31884/jtt.v7i1.318 ◽

2021 ◽

Vol 7 (1) ◽

pp. 37

Author(s):

Anton Prafanto ◽

Edy Budiman ◽

Putut Pamilih Widagdo ◽

Gubtha Mahendra Putra ◽

Reza Wardhana

Keyword(s):

Smart Home ◽

Detection System ◽

Signal Strength ◽

Received Signal Strength ◽

Low Energy ◽

Experimental Results ◽

Received Signal Strength Indicator ◽

Bluetooth Low Energy ◽

Door Lock ◽

Lock In

The design of the detection system in this study using ESP32 module that includes Wi-Fi: 802.11 b / g / n and Bluetooth: v4.2 BR / EDR and Bluetooth Low Energy (BLE). In general, the automatic door lock system using a fingerprint or RFID card in its implementation, but this study using BLE which is a component of iBeacon which has a function as a result of the fingerprint and RFID card. ESP32 have a duty to control the door lock in order to open and lock the door using relays and solenoid system, in addition to the BLE on ESP32 serves to detect the presence of individuals where Bluetooth Device Address (BD_ADDR) smartwatch or individual gadget is already registered on ESP32. Based on the experimental results, the system can detect a user's presence smartwatch on the Received Signal Strength Indicator (RSSI), which is determined on the program was uploaded to ESP32. This system can also be developed in the future and applied to a variety of special purposes such as absenteeism, indoor mapping, and smart home.

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Multi-Slot BLE Raw Database for Accurate Positioning in Mixed Indoor/Outdoor Environments

Data ◽

10.3390/data5030067 ◽

2020 ◽

Vol 5 (3) ◽

pp. 67 ◽

Cited By ~ 2

Author(s):

Fernando J. Aranda ◽

Felipe Parralejo ◽

Fernando J. Álvarez ◽

Joaquín Torres-Sospedra

Keyword(s):

Signal Strength ◽

Received Signal Strength ◽

Low Energy ◽

Location Based Services ◽

Positioning System ◽

Positioning Accuracy ◽

Bluetooth Low Energy ◽

Outdoor Environments ◽

The University

The technologies and sensors embedded in smartphones have contributed to the spread of disruptive applications built on top of Location Based Services (LBSs). Among them, Bluetooth Low Energy (BLE) has been widely adopted for proximity and localization, as it is a simple but efficient positioning technology. This article presents a database of received signal strength measurements (RSSIs) on BLE signals in a real positioning system. The system was deployed on two buildings belonging to the campus of the University of Extremadura in Badajoz. the database is divided into three different deployments, changing in each of them the number of measurement points and the configuration of the BLE beacons. the beacons used in this work can broadcast up to six emission slots simultaneously. Fingerprinting positioning experiments are presented in this work using multiple slots, improving positioning accuracy when compared with the traditional single slot approach.

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Indoor Location Tracking System Based on Android Application Using Bluetooth Low Energy Beacons for Ubiquitous Computing Environment

Journal of Communications ◽

10.12720/jcm.15.7.572-576 ◽

2020 ◽

pp. 572-576

Author(s):

Khamla NonAlinsavath ◽

◽

Lukito Edi Nugroho ◽

Widyawan Widyawan ◽

Kazuhiko Hamamoto

Keyword(s):

Radio Frequency Identification ◽

Tracking System ◽

Signal Strength ◽

Smartphone Application ◽

Indoor Positioning ◽

Low Energy ◽

Positioning System ◽

Location Awareness ◽

Android Application ◽

Bluetooth Low Energy

Indoor positioning and tracking systems have become enormous issue in location awareness computing due to its improvement of location detection and positioning identification. The locations are normally detected using position technologies such as Global Positioning System, radio frequency identification, Bluetooth Beacon, Wi-Fi fingerprinting, pedometer and so on. This research presents an indoor positioning system based on Bluetooth low energy 4.0 Beacons; we have implemented Bluetooth signal strength for tracking the specific location and detect the movement of user through Android application platform. Bluetooth low energy was addressed to be an experiment technique to set up into the real environment of interior the building. The signal strength of beacons is evaluated and measured the quality of accuracy as well as the improvement of provide raw data from Beacons to the system to get better performance of the direction map and precise distance from current location to desire’s positioning. A smartphone application detects the location-based Bluetooth signal strength accurately and can be achieved the destination by provided direction map and distance perfectly.

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PENGARUH KEBERADAAN OBJEK MANUSIA TERHADAP STABILITAS RECEIVED SIGNAL STRENGTH INDICATOR (RSSI) PADA BLUETOOTH LOW ENERGY 4.0 (BLE)

Telematika ◽

10.31315/telematika.v13i1.1715 ◽

2016 ◽

Vol 13 (1) ◽

pp. 11

Author(s):

Budy Santoso

Keyword(s):

Poor Performance ◽

Signal Strength ◽

Wide Band ◽

Received Signal Strength ◽

Low Energy ◽

Location Based Services ◽

Received Signal Strength Indicator ◽

Simple Method ◽

The Status ◽

Fixed Area

There are many systems with diverse technologies such as GPS, Wi-Fi, Bluetooth, Zigbee, Ultra Wide Band, Ultrasound, Infrared can be used for location-based services. Of these technologies can be developed several applications for positioning purposes such as monitoring patients in hospitals or elderly people who are undergoing treatment at home. This paper proposes a simple method to estimate the presence of the object / user in a fixed area using parameter Received Signal Strength Indicator (RSSI) on Bluetooth 4.0 Low Energy (BLE). To determine the performance of the RSSI, conducted two experiments in a room scenario dimensions 3 x 2.80 x 2.5 m (present and not present). Two experiments were conducted to test the performance of the RSSI signal. The first experiments with conditions not present showed a good performance. However, in the second experiment (present) with the status of various objects that are in the same room, resulting in poor performance of RSSI, where there is a shift in the RSSI value at the first measurement was obtained average RSSI -73 dBm with a range distance of 2 m, the second measurement obtained an average RSSI value of -85 dBm at a distance of 3 m range. With these results it can be concluded that the human presence in the area of research is very influential on the performance positioning signal strength (RSSI) and the significant impact that the shift distance of up to 1 m.

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Penerapan Kalman Filter Pada Metode Trilaterasi Untuk Peningkatan Akurasi Estimasi Perhitungan Jarak Di Dalam Ruangan

Jurnal Ilmiah Betrik ◽

10.36050/betrik.v9i02.35 ◽

2018 ◽

Vol 9 (02) ◽

pp. 96-102

Author(s):

Fahrudin Wibowo ◽

Aulia Burhanudin

Keyword(s):

Kalman Filter ◽

Signal Strength ◽

Received Signal Strength ◽

Low Energy ◽

Bluetooth Low Energy ◽

Received Signal Strength Indication

Penelitian tentang posisi maupun jarak suatu obyek di dalam ruangan telah banyak dilakukan. Metode trilaterasi adalah salah satu metode yang dapat dipergunakan untuk menghitung nilai estimasi jarak atau posisi suatu obyek di dalam ruangan, berdasarkan nilai RSSI (Received Signal Strength Indication) yang diterima suatu receiver. Namun, nilai RSSI yang diterima tidak dapat stabil dikarenakan sinyal yang diterima oleh receiver sangat dipengaruhi kondisi lingkungan pada ruangan yang pada umumnya memiliki nilai noise yang cukup tinggi. Sehingga dapat berakibat pada nilai estimasi jarak yang diperoleh menjadi kurang akurat. Sehubungan dengan hal tersebut maka setelah dilakukan perhitungan dengan trilaterasi, dilanjutkan dengan menambahkan metode Kalman Filter untuk meningkatkan nilai akurasi. Penelitian ini menggunakan BLE (Bluetooth Low Energy) sebagai transmitter, sedangkan receiver menggunakan smartphone yang sudah ter-install aplikasi untuk menerima nilai RSSI. Setelah menggunakan Kalman Filter diperoleh peningkatan nilai akurasi sebesar 0, 1 meter dari nilai perhitungan trilaterasi

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Indoor Positioning System (IPS) Berdasarkan Kekuatan Received Signal Strength Indicator (RSSI)

Journal on Information Technology and Computer Engineering ◽

10.25077/jitce.2.01.27-33.2018 ◽

2018 ◽

Vol 2 (01) ◽

pp. 27-33

Author(s):

Budi Rahmadya Rahmadya

Keyword(s):

Signal Strength ◽

Indoor Positioning ◽

Received Signal Strength ◽

Shopping Mall ◽

Positioning System ◽

Received Signal Strength Indicator ◽

Indoor Positioning System

Shopping Mall merupakan area pusat perbelanjaan yang besar dan memiliki sistem keamanan seperti sistem layanan informasi yang dapat dimanfaatkan oleh konsumen untuk mendapatkan informasi yang dibutuhkan. Penggunaan sistem layanan informasi pada area Shopping Mall bagi konsumen terkadang sangat tidak efektif. Hal ini dikarenakan konsumen membutuhkan waktu yang lama dalam mendapatkan informasi, dimana konsumen terlebih dahulu harus mencari lokasi tempat sistem layanan informasi tersebut. Hal ini menjadikan sistem keamanan pada Shopping Mall menjadi lemah. Indoor Positioning System (IPS) merupakan sistem yang dapat digunakan untuk mengetahui posisi pengguna melalui kekuatan sinyal Wi-Fi yang didapat dalam gedung. Pada penelitian ini, penulis membuat suatu aplikasi android yang dapat digunakan untuk mengetahui posisi konsumen pada area Shopping Mall tersebut.

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Precise At-sea Ship System for Indoor Outdoor Navigation (PASSION) Augmentation Using Bluetooth Low Energy

International Journal of Wireless Networks and Broadband Technologies ◽

10.4018/ijwnbt.2014100103 ◽

2014 ◽

Vol 3 (4) ◽

pp. 36-68

Author(s):

Bereket Tanju ◽

Shahram Sarkani ◽

Thomas Mazzuchi ◽

Joseph Perkowski ◽

Zachary Brong ◽

...

Keyword(s):

Environmental Factors ◽

Situational Awareness ◽

Signal Strength ◽

Received Signal Strength ◽

Low Energy ◽

Tracking Problem ◽

Qr Codes ◽

Bluetooth Low Energy ◽

Smartphone Sensors ◽

Outdoor Navigation

The employment of smartphone sensors for navigation applications and situational awareness on naval ships and in battlefield applications has been demonstrated. The PASSION application uses Wi-Fi, accelerometers, gyroscopes, QR codes, and manual entry on Android and iPhone tracking applications for ships. This paper primarily discusses supplementing the existing implementation with a new augmentation using Bluetooth Low Energy (BTLE version 4.0). The characteristics of BTLE segregated ranging using Received Signal Strength measurements are explored. The sensitivity of BTLE RSS measurements to environmental factors such as placement, cases, orientation of the device, orientation of the Smartphone and intervening materials is quantified. Algorithms that employ BTLE measurements within the framework of the overall navigation positioning and tracking problem are discussed. Their possible utilization for situational awareness and collaborative navigation is discussed.

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Fingerprint Database Variations for WiFi Positioning

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v11i10.2979 ◽

2013 ◽

Vol 11 (10) ◽

pp. 3101-3107

Author(s):

Nelson Acosta ◽

Juan Toloza ◽

Carlos Kornuta

Keyword(s):

Mobile Device ◽

Signal Strength ◽

Indoor Positioning ◽

Received Signal Strength ◽

Relative Precision ◽

Received Signal Strength Indicator ◽

Positioning Systems ◽

Fingerprinting Technique ◽

Euclidian Distance ◽

Fingerprint Database

Indoor positioning systems calculate the position of a mobile device (MD) in an enclosed environment with relative precision. Most systems use WiFi infrastructure and several positioning techniques, where the most commonly used parameter is RSSI (Received Signal Strength Indicator). In this paper, we analyze the fingerprinting technique to calculate the error window obtained with the Euclidian distance as main metric. Build variations are presented for the Fingerprint database analyzing various statistical values to compare the precision achieved with different indicators.

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

Sensors ◽

10.3390/s20020527 ◽

2020 ◽

Vol 20 (2) ◽

pp. 527 ◽

Cited By ~ 4

Author(s):

Hani Ramadhan ◽

Yoga Yustiawan ◽

Joonho Kwon

Keyword(s):

Real World ◽

Indoor Environment ◽

Experimental Studies ◽

Signal Strength ◽

Indoor Positioning ◽

Low Energy ◽

Hotspot Detection ◽

Received Signal Strength Indicator ◽

Positioning Technique ◽

Short Time

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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