Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices

Liu, Wei; Kulin, Merima; Kazaz, Tarik; Shahid, Adnan; Moerman, Ingrid; Poorter, Eli De

doi:10.3390/s17092081

Cited by 31 publications

(8 citation statements)

References 33 publications

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“…For instance, the authors of [29] engineered features from the RSSI (Received Signal Strength Indication) distribution to identify wireless signals. The importance of feature engineering highlights the bottleneck of ML algorithms: their inability to automatically extract the discriminative information from data.…”

Section: Learning the Modelmentioning

confidence: 99%

A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

et al. 2021

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This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY, MAC and network. First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.

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Section: Learning the Modelmentioning

confidence: 99%

A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

et al. 2021

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“…Although our research efforts focus on difficulty of adaptive frequency hopping map prediction in Bluetooth traffic sniffing, there have been various studies [18][19][20][21][22][23] to resolve spectrum issues (e.g., Wi-Fi and Bluetooth coexistence, non-RF device interference, etc.) in the 2.4 GHz band.…”

Section: Related Workmentioning

confidence: 99%

Revisiting Adaptive Frequency Hopping Map Prediction in Bluetooth with Machine Learning Classifiers

2021

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Thanks to the frequency hopping nature of Bluetooth, sniffing Bluetooth traffic with low-cost devices has been considered as a challenging problem. To this end, BlueEar, a state-of-the-art low-cost sniffing system with two Bluetooth radios proposes a set of novel machine learning-based subchannel classification techniques for adaptive frequency hopping (AFH) map prediction by collecting packet statistics and spectrum sensing. However, there is no explicit evaluation results on the accuracy of BlueEar’s AFH map prediction. To this end, in this paper, we revisit the spectrum sensing-based classifier, one of the subchannel classification techniques in BlueEar. At first, we build an independent implementation of the spectrum sensing-based classifier with one Ubertooth sniffing radio. Using the implementation, we conduct a subchannel classification experiment with several machine learning classifiers where spectrum features are utilized. Our results show that higher accuracy can be achieved by choosing an appropriate machine learning classifier and training the classifier with actual AFH maps.Our results show that higher accuracy can be achieved by choosing an appropriate machine learning classifier and training the classifier with actual AFH maps.

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“…There are two possible regression functions that may be fitted into the calibration data. They are the power regression function and logarithmic regression function [23,24]. For the power regression function:…”

Section: Calibration For Beaconsmentioning

confidence: 99%

Low-cost System with Handheld Analyzer for Optimizing the Position of Indoor Base Stations

2021

KSII TIIS

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In this paper, an automatic system of locating the indoor area with weak or no mobile signal was proposed and demonstrated experimentally by using the Internet of Things (IoT) technology. Nowadays, the technicians of mobile services providers need to go along with numerous heavy equipment to measure and record the mobile signal strength at outside environment. Recently, there are systems proposed to do such measurement at outdoor area by using the IoT technology automatically. However, these works could not be applied in the indoor area since there are difficulties to do the indoor mapping and positioning. In this work, the Bluetooth Low Energy (BLE) was used to tackle these two difficulties. After a proper placement of BLE in the testing site, while the technician walk around with a handheld analyzer, the data can be obtained accordingly for further analysis in the proposed system which includes the construction of floor plan, detection of mobile signal strength and suggestion of indoor base stations. The gift wrapping and centroid algorithms were used during the analysis. The experimental results showed that the proposed system successfully demonstrated the indoor mapping, positioning of weak mobile signal area and suggestion of indoor base stations for the normal rectangular rooms with an area of 100 m 2 on single floor.

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Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices

Cited by 31 publications

References 33 publications

A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

Revisiting Adaptive Frequency Hopping Map Prediction in Bluetooth with Machine Learning Classifiers

Low-cost System with Handheld Analyzer for Optimizing the Position of Indoor Base Stations

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