Using Proximity Technology for Localization in Wireless Sensor Networks

Brída, Peter; Matula, Martin; Duha, Jan

doi:10.26552/com.c.2007.4.50-54

Cited by 6 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A previous approach for positioning [34] primarily identifies which AP is closest to the receiver to estimate the approximate region. However, this technique has proven difficult for indoor localization due to significant errors in the physical location of the actual receiver.…”

Section: Adaptive Location Estimationmentioning

confidence: 99%

Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator

Yoon,

Kim,

Kwon

2024

Applied Sciences

View full text Add to dashboard Cite

In this paper, we propose a proximity-based adaptive positioning algorithm to address the challenge of positioning errors in indoor localization based on RSSI (received signal strength indicator). When positioning by trilateration, if a receiver is close to one AP, the signals of other APs become rapidly unstable, so positioning accuracy is reduced. Therefore, this paper proposes an algorithm to identify the proximity state with AP and adaptively determine the positioning technique based on this state. The proposed algorithm consists of three steps: RSSI error correction, adaptive location estimation, and post-processing. The RSSI error correction step corrects unstable RSSI. The adaptive location estimation step utilizes a modified proximity technique when identified as close to an AP, employing trilateration otherwise. Finally, in the post-processing step, an efficient filtering algorithm is applied. For the static state experiment, the accuracy of the proposed algorithm is found to be improved by about 28% compared to the method measured using only the trilateration technique applying the RSSI error correction step and post-processing step. The proposed algorithm improved the positioning accuracy of the entire area by improving accuracy in regions with weak signals without additional devices.

show abstract

Section: Adaptive Location Estimationmentioning

confidence: 99%

Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator

Yoon,

Kim,

Kwon

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…However, GNSS does not work well indoors due to the high attenuation and complex reflections of electromagnetic waves in buildings. To determine the location of mobile devices in an indoor environment, it is possible to use data from various supporting technologies such as WiFi [ 1 , 2 , 3 , 4 ], Bluetooth [ 5 ], RFID (Radio Frequency IDentification) [ 6 ], ZigBee [ 7 ], MEMS (Micro-Electro-Mechanical Systems) sensors [ 8 , 9 ], UWB (Ultra-Wide Band) [ 10 , 11 ], geomagnetic field [ 12 , 13 ], LiFi (Light Fidelity) [ 14 ], and acoustic signals [ 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

“…The position estimation is performed based on the measured parameters that were mentioned above. In Table 1 , there is an overview of the basic methods for determining the location [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Design of Acoustic Signal for Positioning of Smart Devices

Hromadova,

Brida,

Machaj

2023

Sensors

Self Cite

View full text Add to dashboard Cite

This paper addresses the limitations of using smartphones in innovative localization systems based on audio signal processing, particularly in the frequency range of 18–22 kHz, due to the lack of technical specifications and noise characterization. We present a comprehensive study on signal design and performance analysis for acoustic communication in air ducts, focusing on signal propagation in indoor environments considering room acoustics and signal behavior. The research aims to determine optimal parameters, including the frequency band, signal types, signal length, pause duration, and sampling frequency, for the efficient transmission and reception of acoustic signals for commercial off-the-shelf (COST) devices. Factors like inter-symbol interference (ISI) and multiple access interference (MAI) that affect signal detection accuracy are considered. The measurements help define the frequency spectrum for common devices like smartphones, speakers, and sound cards. We propose a custom signal with specific properties and reasons for their selection, setting the signal length at 50 ms and a pause time of 5 ms to minimize overlap and interference between consecutive signals. The sampling rate is fixed at 48 kHz to maintain the required resolution for distinguishing individual signals in correlation-based signal processing.

show abstract

“…In the recent years many localization systems for indoor positioning were proposed and developed [1]. The main reason is that satellite navigation systems, which are widely used, are not feasible for use in the indoor environment [2]. This is caused by the fact that signals from the satellites are highly attenuated by the building walls and ceilings, so the receiver is not able to accurately estimate its position [3].…”

Section: Introductionmentioning

confidence: 99%

Survey of Device Calibration Techniques for Fingerprinting Localization Algorithms

Machaj¹,

Brída²

2013

Komunikácie

Self Cite

View full text Add to dashboard Cite

In the paper device calibration algorithms for indoor positioning systems based on fingerprinting localization framework are discussed and compared. Device calibration should play important role in development of localization systems based on Wi-Fi since heterogeneous devices have different receiver parameters. This fact causes that the positioning system cannot provide position estimates with sufficient accuracy. In this paper different calibration approaches will be described and two state of art automatic calibration techniques will be compared in the simulations.

show abstract

Using Proximity Technology for Localization in Wireless Sensor Networks

Cited by 6 publications

References 8 publications

Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator

Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator

Design of Acoustic Signal for Positioning of Smart Devices

Survey of Device Calibration Techniques for Fingerprinting Localization Algorithms

Contact Info

Product

Resources

About