Adaptive Channel Assignment With Predictions of Sensor Results and Channel Occupancy Ratio in PhyC-SN

Takyu, Osamu; Shirai, Kohji; Fujii, Takeo; Ohta, Mitsuo

doi:10.1109/access.2019.2909525

Cited by 8 publications

(4 citation statements)

References 46 publications

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“…PhyC-SN is used [15] as a communication method for transmitting the RSSI of each sensor to the CS. Figure 2 shows an image of the data-gathering flow by PhyC-SN.…”

Section: B Data Gathering By Phyc-snmentioning

confidence: 99%

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Position Estimation of Radio Source Based on Fingerprinting With Physical Wireless Parameter Conversion Sensor Networks

et al. 2023

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Establishing a highly accurate positioning of radio sources for radio wave monitoring and frequency spectrum sharing is attracting considerable attention. Because the positioning method generally requires specific processing of the position target, it is not applicable when the position target cannot perform any processing. The location fingerprinting method uses multiple sensors to observe the received signal strength indication (RSSI) emitted by a radio source and estimates the position from the radio wave propagation characteristics. This does not require a certain process for the target position. However, it takes time to gather RSSI from many sensors by wireless communication. In this study, we propose an RSSI gathering method using physical wireless parameter conversion sensor networks (PhyC-SN) for the highspeed positioning of radio sources. In the proposed method, each sensor selects the radio carrier frequency corresponding to its measured RSSI and transmits the signal. Projecting the RSSI distribution of each sensor onto the frequency distribution of the received signal enables the center to detect the RSSI of multiple sensors simultaneously. Furthermore, to improve the gathering accuracy, we established a sensor group method by considering the regional characteristics and access timing control based on each sensor group. Computer simulations and experimental evaluations show that the proposed method significantly reduces the data-gathering time compared with conventional packet communications and achieves a high positioning accuracy.

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“…PhyC-SN is used [15] as a communication method for transmitting the RSSI of each sensor to the CS. Figure 2 shows an image of the data-gathering flow by PhyC-SN.…”

Section: B Data Gathering By Phyc-snmentioning

confidence: 99%

“…Physical wireless parameter conversion sensor networks (PhyC-SNs) have been proposed as a communication method that enables the simultaneous access of multiple sensors [15]. Each sensor transmits a carrier by switching the carrier frequency according to the sensing data.…”

Section: Introductionmentioning

confidence: 99%

Position Estimation of Radio Source Based on Fingerprinting With Physical Wireless Parameter Conversion Sensor Networks

et al. 2023

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“…This study focuses on physical wireless parameter conversion sensor networks (Phyc-SNs) [ 13 ]. In this access control technique, a receiver analyzes the frequency spectrum of the received signal by using a fast Fourier transform (FFT).…”

Section: Introductionmentioning

confidence: 99%

Three Level Recognition Based on the Average of the Phase Differences in Physical Wireless Parameter Conversion Sensor Networks and Its Effect to Localization with RSSI

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Oda

Takyu

et al. 2023

Sensors

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In recent years, there have been increased demands for aggregating sensor information from several sensors owing to the spread of the Internet of Things (IoT). However, packet communication, which is a conventional multiple-access technology, is hindered by packet collisions owing to simultaneous access by sensors and waiting time to avoid packet collisions; this increases the aggregation time. The physical wireless parameter conversion sensor network (PhyC-SN) method, which transmits sensor information corresponding to the carrier wave frequency, facilitates the bulk collection of sensor information, thereby reducing the communication time and achieving a high aggregation success rate. However, when more than one sensor transmits the same frequency simultaneously, the estimation accuracy of the number of accessed sensors deteriorates significantly because of multipath fading. Thus, this study focuses on the phase fluctuation of the received signal caused by the frequency offset inherent to the sensor terminals. Consequently, a new feature for detecting collisions is proposed, which is a case in which two or more sensors transmit simultaneously. Furthermore, a method to identify the existence of 0, 1, 2, or more sensors is established. In addition, we demonstrate the effectiveness of PhyC-SNs in estimating the location of radio transmission sources by utilizing three patterns of 0, 1, and 2 or more transmitting sensors.

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“…Physical wireless parameter conversion sensor networks (PhyC-SN) can recognize the information from numerous sensors by a single access. Essentially, they send signals with carrier frequencies switched for sensing data and detect the sensor information reported by the sensors that are accessing simultaneously with the frequency power distribution of the received signal [1]. A previous study reported a method for detecting collisions that occur when multiple sensors transmit the same sensing data in a PhyC-SN; the method employs the average phase variance between the receiving antennas as an identifier [2].…”

Section: Introductionmentioning

confidence: 99%

Enhanced sensitivity of collision detection by carrier frequency offset switching in physical wireless parameter conversion sensor networks

et al. 2023

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This study proposes a novel method for determining the number of simultaneously accessing sensors in physical wireless parameter conversion sensor networks (PhyC-SN), which can collect sensor information from numerous sensors simultaneously, using phase variation due to frequency offset as an identifier. However, the identification accuracy deteriorates when the frequency offsets of the simultaneously accessed sensors are close to each other. In the proposed method, the transmitted signal is divided by time and the frequency offset is switched for each signal transmission. Computer simulations reveal that the identification accuracy of collision detection improves and an appropriate number of frequency switches exist.

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Adaptive Channel Assignment With Predictions of Sensor Results and Channel Occupancy Ratio in PhyC-SN

Cited by 8 publications

References 46 publications

Position Estimation of Radio Source Based on Fingerprinting With Physical Wireless Parameter Conversion Sensor Networks

Position Estimation of Radio Source Based on Fingerprinting With Physical Wireless Parameter Conversion Sensor Networks

Three Level Recognition Based on the Average of the Phase Differences in Physical Wireless Parameter Conversion Sensor Networks and Its Effect to Localization with RSSI

Enhanced sensitivity of collision detection by carrier frequency offset switching in physical wireless parameter conversion sensor networks

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