Data Tracking and Effect of Frequency Offset to Simultaneous Collecting Method for Wireless Sensor Networks

Myoenzono, Ryo; Takyu, Osamu; Shirai, Kohji; Fujii, Takeo; Ohta, Mitsuo; Sasamori, Fumihito; Handa, Shiro

doi:10.1155/2015/946760

Cited by 17 publications

(17 citation statements)

References 7 publications

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“…For simple explanation, the spectrum of subcarrier in v=nk and v≠nk are the main lobe and side-lobe, respectively. The ICI components mask the subcarrier components sent by the other sensor and generate the image subcarrier that causes the false subcarrier detection by FC [6].…”

Section: Overview Of Phyc-snmentioning

confidence: 99%

“…The insertion scheme of ID for specifying the information source, i.e., sensor, has been considered. For example, the particular time hopping sequence for each sensor [24] and the data tracking with the feature amount of sensing result such as time continuity are available for specifying each sensor from the received sensing results [6]. However, the former scheme requires more time with the resources for informing ID and thus the signaling overhead of inserting ID is significant.…”

Section: Overview Of Phyc-snmentioning

confidence: 99%

“…ICI makes the frequency spectrum sent by the other sensor masked and thus the FC fails to detect the sensing result. In addition, the false alarm indicating FC wrongly recognizes the ICI or the noise component as the sensing data sent by sensor occurs [6].…”

Section: Introductionmentioning

confidence: 99%

“…For separating the sensing results into the sequence of sensing result with common sensor node, a data-tracking technique applied to PhyC-SN is considered. The time tracking technique based on Kalman filter is considered for the separation of the sensing results [6]. After the sensing results are received for a certain duration, a maximum likelihood sequence detection separates the sequence of the sensing result in terms of time continuity [7].…”

Section: Introductionmentioning

confidence: 99%

“…As Ref. [6] uses data tracking for the separation of sensing results, this paper considers the data tracking based on not only sensing results but also CSI and CFO. If the sensor node and the wireless environment are static, the CSI demonstrates the time continuity [8].…”

Section: Introductionmentioning

confidence: 99%

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ID Insertion and Data Tracking with Frequency Offset for Physical Wireless Parameter Conversion Sensor Networks

Takyu

Shirai

Ohta

et al. 2019

Sensors

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As the applications of the internet of things are becoming widely diversified, wireless sensor networks require real-time data reception, accommodation of access from several sensors, and low power consumption. In physical wireless parameter conversion sensor networks (PhyC-SN), all the sensors use frequency shift keying as the modulation scheme and then access the channel to the fusion center, simultaneously. As a result, the fusion center can recognize the statistical tendency of all the sensing results at a time from the frequency spectrum of the received signal. However, the information source, i.e., the sensor, cannot be specified from the received signal because no ID-indicating sensor is inserted to the signal. The data-tracking technique for tracing the time continuity of the sensing results is available for decomposing the sequence of the sensing results per sensor but the error tracking, which is a wrong recognition between the sensing results and the sensor, occurs owing to the similarity of the sensing results. This paper proposes the sensing result separation technique using a fractional carrier frequency offset (CFO) for PhyC-SN. In the proposed scheme, the particular fractional CFO is assigned to each user and it is useful for the ID specifying sensor. The fractional CFO causes inter-carrier interference (ICI). The ICI cancellation of the narrowband wireless communications is proposed. The two types of data-tracking techniques are proposed and are selectively used by the fusion center. Since the proposed data-tracking technique is multi-dimensional, high accuracy of data tracking is achieved even under the similar tendency of the sensing results. Based on computer simulation, we elucidate the advantage of the proposed sensing results separation.

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Section: Overview Of Phyc-snmentioning

confidence: 99%

Section: Overview Of Phyc-snmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

ID Insertion and Data Tracking with Frequency Offset for Physical Wireless Parameter Conversion Sensor Networks

Takyu

Shirai

Ohta

et al. 2019

Sensors

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

et al. 2019

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Applications related to the Internet of Things are widely diversified and require both low latency and the access of massive sensors to wireless sensor networks. In physical wireless parameter conversion sensor networks (PhyC-SN), a fusion center (FC) can recognize information from all sensors on the frequency spectrum of the received signals via conversion from sensor information to signal frequency. The higher resolution of sensor information of PhyC-SNs requires securing more frequency bandwidth. Therefore, spectrum sharing between PhyC-SNs and other systems is essential. For this study, we assume that primary systems (PSs) refer to other wireless systems and that secondary systems (SSs) refer to the PhyC-SN. The SS detects any access from the PS and immediately stops access to an FC. This results in a loss of sensor information. Thus, the accuracy of the gathered sensor information by an FC is degraded. This paper proposes an adaptive channel assignment based on two predictions; sensor information and channel occupancy rate. In the proposed method, the predicted error caused by the cessation of channel access is calculated and the assignment is constructed by minimizing this predicted error. Since the sensor can select channels in accordance with the error of an instantaneous sensor result, the proposed channel assignment can utilize awareness of the instantaneous sensor result. The proposed method achieves high accuracy of gathered sensor information while delivering less frequent sensor information to the FC, thereby improving the utilization efficiency of frequency channels.

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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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Data Tracking and Effect of Frequency Offset to Simultaneous Collecting Method for Wireless Sensor Networks

Cited by 17 publications

References 7 publications

ID Insertion and Data Tracking with Frequency Offset for Physical Wireless Parameter Conversion Sensor Networks

ID Insertion and Data Tracking with Frequency Offset for Physical Wireless Parameter Conversion Sensor Networks

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

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

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