The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
크라우드센싱 기반 스펙트럼 감지(CSD)는 사물 인터넷(IoT) 네트워크에서 점점 더 연결되는 기계에 대한 전체 범위의 무선 리소스 가용성을 가능하게 할 것으로 기대됩니다. 현재 CSD 방식은 각 군중 감지 장치에 대한 로컬 감지, 처리 및 분산 데이터 보고를 위해 많은 에너지와 네트워크 리소스를 소비합니다. 또한, 보고되는 데이터의 양이 많은 경우, 요청자 측에서 구현하는 데이터 융합으로 인해 높은 지연 시간이 발생하기 쉽습니다. 본 논문에서는 에너지와 네트워크 자원의 효율성 향상을 위해 그린 CSD(GCSD) 패러다임을 제안한다. 주변 후방 산란(AmB)은 수신된 스펙트럼 데이터가 로컬 처리 없이 후방 산란을 통해 직접 보고되는 배터리 없는 작동 모드를 활성화하는 데 사용됩니다. 후방 산란을 위한 에너지는 주변 무선 주파수(RF) 소스를 통해 제공될 수 있습니다. 그런 다음 공중 계산(AirComp)에 의존하여 무선 채널의 합산 특성을 활용하여 후방 산란 과정과 공중에서 데이터 융합을 구현할 수 있습니다. 본 논문에서는 GCSD 패러다임의 모델과 구현 과정을 설명합니다. 제안된 GCSD에 대해 탐지 지표의 폐쇄형 표현이 파생됩니다. 시뮬레이션 결과는 이론적 도출의 정확성을 검증하고 GCSD 패러다임의 친환경적 특성을 보여줍니다.
Xiaohui LI
Taiyuan University of Technology
Qi ZHU
Nanjing University of Posts and Telecommunications
Wenchao XIA
Nanjing University of Posts and Telecommunications
Yunpei CHEN
Nanjing University of Posts and Telecommunications
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부
Xiaohui LI, Qi ZHU, Wenchao XIA, Yunpei CHEN, "A Resource-Efficient Green Paradigm For Crowdsensing Based Spectrum Detection In Internet of Things Networks" in IEICE TRANSACTIONS on Communications,
vol. E106-B, no. 3, pp. 275-286, March 2023, doi: 10.1587/transcom.2022EBP3025.
Abstract: Crowdsensing-based spectrum detection (CSD) is promising to enable full-coverage radio resource availability for the increasingly connected machines in the Internet of Things (IoT) networks. The current CSD scheme consumes a lot of energy and network resources for local sensing, processing, and distributed data reporting for each crowdsensing device. Furthermore, when the amount of reported data is large, the data fusion implemented at the requestor can easily cause high latency. For improving efficiencies in both energy and network resources, this paper proposes a green CSD (GCSD) paradigm. The ambient backscatter (AmB) is used to enable a battery-free mode of operation in which the received spectrum data is reported directly through backscattering without local processing. The energy for backscattering can be provided by ambient radio frequency (RF) sources. Then, relying on air computation (AirComp), the data fusion can be implemented during the backscattering process and over the air by utilizing the summation property of wireless channel. This paper illustrates the model and the implementation process of the GCSD paradigm. Closed-form expressions of detection metrics are derived for the proposed GCSD. Simulation results verify the correctness of the theoretical derivation and demonstrate the green properties of the GCSD paradigm.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2022EBP3025/_p
부
@ARTICLE{e106-b_3_275,
author={Xiaohui LI, Qi ZHU, Wenchao XIA, Yunpei CHEN, },
journal={IEICE TRANSACTIONS on Communications},
title={A Resource-Efficient Green Paradigm For Crowdsensing Based Spectrum Detection In Internet of Things Networks},
year={2023},
volume={E106-B},
number={3},
pages={275-286},
abstract={Crowdsensing-based spectrum detection (CSD) is promising to enable full-coverage radio resource availability for the increasingly connected machines in the Internet of Things (IoT) networks. The current CSD scheme consumes a lot of energy and network resources for local sensing, processing, and distributed data reporting for each crowdsensing device. Furthermore, when the amount of reported data is large, the data fusion implemented at the requestor can easily cause high latency. For improving efficiencies in both energy and network resources, this paper proposes a green CSD (GCSD) paradigm. The ambient backscatter (AmB) is used to enable a battery-free mode of operation in which the received spectrum data is reported directly through backscattering without local processing. The energy for backscattering can be provided by ambient radio frequency (RF) sources. Then, relying on air computation (AirComp), the data fusion can be implemented during the backscattering process and over the air by utilizing the summation property of wireless channel. This paper illustrates the model and the implementation process of the GCSD paradigm. Closed-form expressions of detection metrics are derived for the proposed GCSD. Simulation results verify the correctness of the theoretical derivation and demonstrate the green properties of the GCSD paradigm.},
keywords={},
doi={10.1587/transcom.2022EBP3025},
ISSN={1745-1345},
month={March},}
부
TY - JOUR
TI - A Resource-Efficient Green Paradigm For Crowdsensing Based Spectrum Detection In Internet of Things Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 275
EP - 286
AU - Xiaohui LI
AU - Qi ZHU
AU - Wenchao XIA
AU - Yunpei CHEN
PY - 2023
DO - 10.1587/transcom.2022EBP3025
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E106-B
IS - 3
JA - IEICE TRANSACTIONS on Communications
Y1 - March 2023
AB - Crowdsensing-based spectrum detection (CSD) is promising to enable full-coverage radio resource availability for the increasingly connected machines in the Internet of Things (IoT) networks. The current CSD scheme consumes a lot of energy and network resources for local sensing, processing, and distributed data reporting for each crowdsensing device. Furthermore, when the amount of reported data is large, the data fusion implemented at the requestor can easily cause high latency. For improving efficiencies in both energy and network resources, this paper proposes a green CSD (GCSD) paradigm. The ambient backscatter (AmB) is used to enable a battery-free mode of operation in which the received spectrum data is reported directly through backscattering without local processing. The energy for backscattering can be provided by ambient radio frequency (RF) sources. Then, relying on air computation (AirComp), the data fusion can be implemented during the backscattering process and over the air by utilizing the summation property of wireless channel. This paper illustrates the model and the implementation process of the GCSD paradigm. Closed-form expressions of detection metrics are derived for the proposed GCSD. Simulation results verify the correctness of the theoretical derivation and demonstrate the green properties of the GCSD paradigm.
ER -