scholarly journals Analysis of Received Signal Strength Quantization in Fingerprinting Localization

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3203 ◽  
Author(s):  
Syed Khandker ◽  
Joaquín Torres-Sospedra ◽  
Tapani Ristaniemi
Keyword(s):  
Quality Of Service ◽  
Indoor Localization ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Storage Space ◽  
Data Traffic ◽  
Positioning Accuracy ◽  
Hardware Configuration ◽  
Fine Resolution

In recent times, Received Signal Strength (RSS)-based Wi-Fi fingerprinting localization has become one of the most promising techniques for indoor localization. The primary aim of RSS is to check the quality of the signal to determine the coverage and the quality of service. Therefore, fine-resolution RSS is needed, which is generally expressed by 1-dBm granularity. However, we found that, for fingerprinting localization, fine-granular RSS is unnecessary. A coarse-granular RSS can yield the same positioning accuracy. In this paper, we propose quantization for only the effective portion of the signal strength for fingerprinting localization. We found that, if a quantized RSS fingerprint can carry the major characteristics of a radio environment, it is sufficient for localization. Five publicly open fingerprinting databases with four different quantization strategies were used to evaluate the study. The proposed method can help to simplify the hardware configuration, enhance security, and save approximately 40–60% storage space and data traffic.

scholarly journals Automatic Detection of Changes in Signal Strength Characteristics in a Wi-Fi Network for an Indoor Localisation System

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1828
Author(s):  
Marcin Luckner ◽  
Rafał Górak
Keyword(s):  
Quality Of Service ◽  
Dynamic System ◽  
Automatic Detection ◽  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Strength Characteristics ◽  
The Mean ◽  
Indoor Localisation

This paper faces the issue of changing the received signal strength (RSS) from an observed access point (AP). Such a change can reduce the Quality of Service (QoS) of a Wi-Fi-based Indoor Localisation System. We have proposed a dynamic system based on an estimator of RSS using the readings from other APs. Using an optimal threshold, the algorithm recognises an AP that has changed its characteristics. Next, the system rebuilds the localisation model excluding the changed AP to keep QoS. For the tests, we simulated a change in the analysed Wi-Fi network by replacing the measured RSS by an RSS obtained from the same AP model that lies in another place inside the same multi-floor building. The algorithm was evaluated in simulations of an isolated single-floor building, a single-floor building and a multi-floor building. The mean increase of the localisation error obtained by the system varies from 0.25 to 0.61 m after the RSS changes, whereas the error increase without using the system is between 1.21 and 1.98 m. The system can be applied to any service based on a Wi-Fi network for various kinds of changes like a reconfiguration of the network, a local malfunction or ageing of the infrastructure.

scholarly journals A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2392
Author(s):  
Óscar Belmonte-Fernández ◽  
Emilio Sansano-Sansano ◽  
Antonio Caballer-Miedes ◽  
Raúl Montoliu ◽  
Rubén García-Vidal ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Indoor Localization ◽  
Hidden Markov ◽  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Machine Learning Algorithms ◽  
Forward Algorithm ◽  
Generative Method

Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most Wi-Fi fingerprinting localization methods presented in the literature are discriminative methods. We present a generative method for indoor localization based on Wi-Fi fingerprinting. The Received Signal Strength Indicator received from a Wireless Access Point is modeled by a hidden Markov model. Unlike other algorithms, the use of a hidden Markov model allows ours to take advantage of the temporal autocorrelation present in the Wi-Fi signal. The algorithm estimates the user’s location based on the hidden Markov model, which models the signal and the forward algorithm to determine the likelihood of a given time series of Received Signal Strength Indicators. The proposed method was compared with four other well-known Machine Learning algorithms through extensive experimentation with data collected in real scenarios. The proposed method obtained competitive results in most scenarios tested and was the best method in 17 of 60 experiments performed.

Quality of Service in UMTS Mobile Systems

2010 ◽  
pp. 14-51
Author(s):  
Jahangir Dadkhah Chimeh
Keyword(s):  
Quality Of Service ◽  
Mobile Systems ◽  
System Capacity ◽  
Data Traffic ◽  
Air Interface ◽  
Layer 2 ◽  
Qos Parameters ◽  
Erlang Capacity ◽  
Transport Channels

Mobile systems and particularly UMTS are growing fast. These systems convey data based services in addition to customary voice services. Quality of service is a function of data rate, delay and signal to noise plus interference ratio in these systems. In this Chapter first the author pays attention to UMTS and its QoS architecture, then to service categorization due to QoS. Afterwards he reviews some QoS parameters. Then he studies Layer 2 QoS parameters and general concepts about Transport channels. Then he review TCP effects on the throughput in the air interface. he introduces HSDPA in the next section. Finally he pays attention to data traffic models and their effects on the system capacity and Erlang capacity and delay in the system.

scholarly journals Indoor Localization Based on Wi-Fi Received Signal Strength Indicators: Feature Extraction, Mobile Fingerprinting, and Trajectory Learning

2019 ◽  
Vol 9 (18) ◽  
pp. 3930 ◽  
Author(s):  
Jaehyun Yoo ◽  
Jongho Park
Keyword(s):  
Feature Extraction ◽  
Indoor Localization ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Position Estimation ◽  
Training Data ◽  
Data Sets ◽  
Feature Extraction Method ◽  
Fingerprinting Method ◽  
Trajectory Learning

This paper studies the indoor localization based on Wi-Fi received signal strength indicator (RSSI). In addition to position estimation, this study examines the expansion of applications using Wi-Fi RSSI data sets in three areas: (i) feature extraction, (ii) mobile fingerprinting, and (iii) mapless localization. First, the features of Wi-Fi RSSI observations are extracted with respect to different floor levels and designated landmarks. Second, the mobile fingerprinting method is proposed to allow a trainer to collect training data efficiently, which is faster and more efficient than the conventional static fingerprinting method. Third, in the case of the unknown-map situation, the trajectory learning method is suggested to learn map information using crowdsourced data. All of these parts are interconnected from the feature extraction and mobile fingerprinting to the map learning and the estimation. Based on the experimental results, we observed (i) clearly classified data points by the feature extraction method as regards the floors and landmarks, (ii) efficient mobile fingerprinting compared to conventional static fingerprinting, and (iii) improvement of the positioning accuracy owing to the trajectory learning.

scholarly journals A Novel Enhanced Positioning Trilateration Algorithm Implemented for Medical Implant In-Body Localization

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Peter Brida ◽  
Juraj Machaj
Keyword(s):  
Signal Strength ◽  
Received Signal Strength ◽  
Position Estimation ◽  
Time Of Arrival ◽  
Medical Implant ◽  
Exact Position ◽  
Two Phases ◽  
Positioning Algorithm ◽  
Selection Of

Medical implants based on wireless communication will play crucial role in healthcare systems. Some applications need to know the exact position of each implant. RF positioning seems to be an effective approach for implant localization. The two most common positioning data typically used for RF positioning are received signal strength and time of flight of a radio signal between transmitter and receivers (medical implant and network of reference devices with known position). This leads to positioning methods: received signal strength (RSS) and time of arrival (ToA). Both methods are based on trilateration. Used positioning data are very important, but the positioning algorithm which estimates the implant position is important as well. In this paper, the proposal of novel algorithm for trilateration is presented. The proposed algorithm improves the quality of basic trilateration algorithms with the same quality of measured positioning data. It is called Enhanced Positioning Trilateration Algorithm (EPTA). The proposed algorithm can be divided into two phases. The first phase is focused on the selection of the most suitable sensors for position estimation. The goal of the second one lies in the positioning accuracy improving by adaptive algorithm. Finally, we provide performance analysis of the proposed algorithm by computer simulations.

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

Data ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 67 ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document