Smart probabilistic approach with RSSI fingerprinting for indoor localization

Njima, Wafa; Ahriz, Iness; Zayani, Rafik; Terré, Michel; Bouallègue, Ridha

doi:10.23919/softcom.2017.8115509

Cited by 9 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many localization techniques and algorithms have been proposed in the literature to find the real-time location of a sensor node in an IoT system. Most of the existing works for indoor localization are based on deterministic algorithms [2][3][4][5]. The main drawback of using deterministic or probabilistic algorithms is less efficiency, low prediction accuracy, and difficulty implementing on real IoT devices.…”

Section: Introductionmentioning

confidence: 99%

TreeLoc: An Ensemble Learning-based Approach for Range Based Indoor Localization

Maduranga¹,

Abeysekera²

2021

IJWMT

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

TreeLoc: An Ensemble Learning-based Approach for Range Based Indoor Localization

Maduranga¹,

Abeysekera²

2021

IJWMT

View full text Add to dashboard Cite

“…Since the early 2000s, fingerprinting [7][8][9][10][11], especially that based on Wi-Fi (IEEE 802.11) and received signal strength (RSS), has become a mainstream approach for scene analysis-based localization due to Wi-Fi's pervasiveness. Various fingerprinting localization approaches, such as k-nearest neighbors [7,10], weighted k-nearest neighbors (WKNN) [9], and variants with heuristic filters [8], assisted localization [12], and model enhancements of probabilistic analyses [13][14][15], have therefore evolved.…”

Section: Introductionmentioning

confidence: 99%

“…Information theory-oriented enhancements also go one step further by processing the entropy/ information gain of digitized RSS readings [15][16][17][18][19] primarily for better Wi-Fi access point (AP) selection. Another option is to use characteristics such as channel state information (CSI) for fingerprint ingredients.…”

Section: Introductionmentioning

confidence: 99%

A Mutual Information–Based Infrastructureless Radio Frequency Positioning System via Deep Learning

2021

RSL

View full text Add to dashboard Cite

We proposed a novel, scalable, spatial positioning solution based on existing radio frequency (RF) signals and available infrastructure by harvesting information from the signals, distilling the readings to get the mutual information gain of each perceived RF characteristic at the corresponding location of interest, and processing such information with a deep neural network for localization. The solution leveraged all available characteristics of perceived RF signal sources instead of filtering them out. We built and tested a scalable positioning prototype on the Google Kubernetes containerization framework accordingly. The preliminary results achieved at least 1.8 m (6 ft) of accuracy within 90% cumulative distribution and 1.08 m (3.3 ft) mean accuracy by solely using 802.11ac received signal strength fingerprints. The prototype performed approximately three times as good as the referenced baseline of weighted k-nearest neighbors.

show abstract

“…general, the most recent solutions are based on geometrical, as trilateration and multilateration techniques, or probabilistic approaches. They all have in common the search for improvement over existing methods Njima et al (2017). proposed an enhanced probabilistic algorithm with RSSI fingerprinting, which benefited from an AP selection strategy based on information theory.…”

mentioning

confidence: 99%

Robust RSSI-Based Indoor Positioning System Using K-Means Clustering and Bayesian Estimation

et al. 2021

View full text Add to dashboard Cite

This work proposes a new indoor positioning system, named KLIP, that uses the Kmeans clustering algorithm to split the environment into different sets of log-distance propagation models in order to better characterize the indoor environment and further improve the position estimation using Bayesian inference. The proposed method is validated in a large-scale, real-world scenario composed of Bluetooth Low Energy (BLE)-based devices. It is demonstrated, throughout the work, that the addition of location information of training points to the received signal strength indicator (RSSI) as an attribute for the clustering step improves the positioning accuracy. Moreover, the obtained results show that the solution outperforms the naive Bayesian estimation up to 12% -regarding the positioning accuracy -and the broadly deployed kNN for reduced training dataset size -regarding both accuracy and online processing time. In this sense, KLIP proves to be an efficient and scalable alternative when both site-survey effort and energy consumption constraints must be taken into account.

show abstract

Smart probabilistic approach with RSSI fingerprinting for indoor localization

Cited by 9 publications

References 12 publications

TreeLoc: An Ensemble Learning-based Approach for Range Based Indoor Localization

TreeLoc: An Ensemble Learning-based Approach for Range Based Indoor Localization

A Mutual Information–Based Infrastructureless Radio Frequency Positioning System via Deep Learning

Robust RSSI-Based Indoor Positioning System Using K-Means Clustering and Bayesian Estimation

Contact Info

Product

Resources

About