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

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 527 ◽  
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.

scholarly journals An Enhanced Indoor Positioning Technique Based on a Novel Received Signal Strength Indicator Distance Prediction and Correction Model

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 719
Author(s):  
Mohammed Nagah Amr ◽  
Hussein M. ELAttar ◽  
Mohamed H. Abd El Azeem ◽  
Hesham El Badawy
Keyword(s):  
Signal Strength ◽  
Indoor Positioning ◽  
Low Energy ◽  
Indoor Environments ◽  
Positioning Accuracy ◽  
Correction Model ◽  
Positioning Technique ◽  
Unknown Node ◽  
Positioning Algorithm ◽  
Distance Prediction

Indoor positioning has become a very promising research topic due to the growing demand for accurate node location information for indoor environments. Nonetheless, current positioning algorithms typically present the issue of inaccurate positioning due to communication noise and interferences. In addition, most of the indoor positioning techniques require additional hardware equipment and complex algorithms to achieve high positioning accuracy. This leads to higher energy consumption and communication cost. Therefore, this paper proposes an enhanced indoor positioning technique based on a novel received signal strength indication (RSSI) distance prediction and correction model to improve the positioning accuracy of target nodes in indoor environments, with contributions including a new distance correction formula based on RSSI log-distance model, a correction factor (Beta) with a correction exponent (Sigma) for each distance between unknown node and beacon (anchor nodes) which are driven from the correction formula, and by utilizing the previous factors in the unknown node, enhanced centroid positioning algorithm is applied to calculate the final node positioning coordinates. Moreover, in this study, we used Bluetooth Low Energy (BLE) beacons to meet the principle of low energy consumption. The experimental results of the proposed enhanced centroid positioning algorithm have a significantly lower average localization error (ALE) than the currently existing algorithms. Also, the proposed technique achieves higher positioning stability than conventional methods. The proposed technique was experimentally tested for different received RSSI samples’ number to verify its feasibility in real-time. The proposed technique’s positioning accuracy is promoted by 80.97% and 67.51% at the office room and the corridor, respectively, compared with the conventional RSSI trilateration positioning technique. The proposed technique also improves localization stability by 1.64 and 2.3-fold at the office room and the corridor, respectively, compared to the traditional RSSI localization method. Finally, the proposed correction model is totally possible in real-time when the RSSI sample number is 50 or more.

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

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1350 ◽  
Author(s):  
Sharareh Naghdi ◽  
Kyle O’Keefe
Keyword(s):  
Artificial Intelligence ◽  
Human Body ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Low Energy ◽  
Wireless Technology ◽  
Received Signal Strength Indicator ◽  
Bluetooth Low Energy ◽  
Office Environment

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.

scholarly journals Fast Signals of Opportunity Fingerprint Database Maintenance with Autonomous Unmanned Ground Vehicle for Indoor Positioning

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3419 ◽  
Author(s):  
Yitang Peng ◽  
Xiaoji Niu ◽  
Jian Tang ◽  
Dazhi Mao ◽  
Chuang Qian
Keyword(s):  
Indoor Environment ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Kriging Interpolation ◽  
Unmanned Ground Vehicle ◽  
Received Signal Strength Indicator ◽  
Ground Vehicle ◽  
Fingerprint Database ◽  
Signals Of Opportunity

Indoor positioning technology based on Received Signal Strength Indicator (RSSI) fingerprints is a potential navigation solution, which has the advantages of simple implementation, low cost and high precision. However, as the radio frequency signals can be easily affected by the environmental change during its transmission, it is quite necessary to build location fingerprint database in advance and update it frequently, thereby guaranteeing the positioning accuracy. At present, the fingerprint database building methods mainly include point collection and line acquisition, both of which are usually labor-intensive and time consuming, especially in a large map area. This paper proposes a fast and efficient location fingerprint database construction and updating method based on a self-developed Unmanned Ground Vehicle (UGV) platform NAVIS, called Automatic Robot Line Collection. A smartphone was installed on NAVIS for collecting indoor Received Signal Strength Indicator (RSSI) fingerprints of Signals of Opportunity (SOP), such as Bluetooth and Wi-Fi. Meanwhile, indoor map was created by 2D LiDAR-based Simultaneous Localization and Mapping (SLAM) technology. The UGV automatically traverse the unknown indoor environment due to a pre-designed full-coverage path planning algorithm. Then, SOP sensors collect location fingerprints and generates grid map during the process of environment-traversing. Finally, location fingerprint database is built or updated by Kriging interpolation. Field tests were carried out to verify the effectiveness and efficiency of our proposed method. The results showed that, compared with the traditional point collection and line collection schemes, the root mean square error of the fingerprinting-based positioning results were reduced by 35.9% and 25.0% in static tests and 30.0% and 21.3% respectively in dynamic tests. Moreover, our UGV can traverse the indoor environment autonomously without human-labor on data acquisition, the efficiency of the automatic robot line collection scheme is 2.65 times and 1.72 times that of the traditional point collection and the traditional line acquisition, respectively.

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

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.

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

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.

scholarly journals IPSCL: An Accurate Indoor Positioning Algorithm Using Sensors and Crowdsourced Landmarks

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2891 ◽  
Author(s):  
Beakcheol Jang ◽  
Hyunjung Kim ◽  
Jong Wook Kim
Keyword(s):  
Signal Strength ◽  
Indoor Positioning ◽  
Reference Points ◽  
Sensor Technology ◽  
Indicator Values ◽  
Received Signal Strength Indicator ◽  
Surrounding Environment ◽  
Heading Direction ◽  
Positioning Algorithm ◽  
Additional Device

Indoor positioning technology has attracted the attention of researchers due to the increasing pervasiveness of smartphones and the development of sensor technology, along with the increase of indoor time. Sensor technology, which is one of the most commonly used data sources for indoor positioning, has the advantage that sensors can receive data from a smartphone without installing any additional device. However, the readings of built-in sensors are easily affected by the surrounding environment and are even occasionally different from each other which adversely influence the accuracy of indoor positioning. Moreover, once an error occurs, it can accumulate because there is not any reference point in the sensor, only in indoor positioning. In this paper, we present an accurate indoor positioning technology, which uses smartphone built-in sensors and Bluetooth beacon-based landmarks. Our proposed algorithm chooses proper one between values of sensors alternately based on their characteristics. It exploits landmarks as the reference points of indoor positioning. It also allows individuals to add the location where they repeatedly detect the same and special beacon received signal strength indicator values as a crowdsourced landmark. Extensive experimental results show that our proposed algorithm facilitates the acquisition of accurate heading direction and coordinates of the user.

scholarly journals An Improved Indoor Positioning Technique Based on Receiving Signal’s Strength

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xingsi Xue ◽  
Xiaoquan Lin ◽  
Chaofan Yang ◽  
Xiaojing Wu
Keyword(s):  
Indoor Environment ◽  
Signal Transmission ◽  
Mobile Station ◽  
Optimal Solution ◽  
Indoor Positioning ◽  
Centroid Method ◽  
Indoor Space ◽  
Wireless Signal ◽  
Complex Procedure ◽  
Positioning Technique

Wireless signal-transmitting process is a complex procedure, to improve the indoor positioning accuracy, and this work proposes a novel indoor positioning technique based on receiving signal’s strength. First, the indoor environment of the building is regionalized in the training phase of indoor positioning. Then, the adjacent points of the indoor space with the same wireless signal transmission characteristics are gathered into the same area, and the corresponding parameter sets and decision domains of each area are constructed. After that, during the positioning stage, the regional confidence and receiving signal’s strength are used to predict the indoor area where the mobile station is located. Finally, the ranging and solution results of the traditional three-sided positioning process are constrained to obtain the optimal solution. Comparing with the traditional positioning techniques that regard the entire complex indoor environment as an entirety, the proposed indoor space regionalization preprocessing method can effectively reduce the ranging error. Compared with the indiscriminate data fusion of the centroid method, the data filtering method based on regional confidence is more targeted. In the experiment, a practical office area is used to test our proposal’s performance, and the experimental results show that our approach can effectively improve the accuracy of indoor positioning results.

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

Telematika ◽  
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.

scholarly journals DL-IPS: Deep Learning Based Indoor Positioning System for Improved Accuracy

2019 ◽  
Vol 8 (4) ◽  
pp. 10797-10801
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Signal Strength ◽  
Low Energy ◽  
Positioning System ◽  
Received Signal Strength Indicator ◽  
Indoor Tracking ◽  
And Control ◽  
Improved Accuracy ◽  
Indoor Positioning System

Indoor tracking has evolved with various methods and well known these days. There are diverse types of solutions that concentrate on exactness, low cost, and control utilization within the field. Particularly in recent years, Received Signal Strength Indicator based positioning estimation have been getting popular. Still, the accuracy are not adequate, and there's no correct way chosen to overcome this issue. In this paper, we propose a strategy that leverage Deep Learning and Wi-Fi/BLE (Bluetooth Low Energy) Fingerprinting strategy to produce superior precise accuracy.

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