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
Pengesanan spektrum berasaskan Crowdsensing (CSD) menjanjikan untuk membolehkan ketersediaan sumber radio liputan penuh untuk mesin yang semakin bersambung dalam rangkaian Internet Perkara (IoT). Skim CSD semasa menggunakan banyak tenaga dan sumber rangkaian untuk penderiaan tempatan, pemprosesan dan pelaporan data teragih untuk setiap peranti crowdsensing. Tambahan pula, apabila jumlah data yang dilaporkan adalah besar, gabungan data yang dilaksanakan pada peminta boleh menyebabkan kependaman tinggi dengan mudah. Untuk meningkatkan kecekapan dalam kedua-dua sumber tenaga dan rangkaian, kertas kerja ini mencadangkan paradigma CSD (GCSD) hijau. Ambien backscatter (AmB) digunakan untuk mendayakan mod operasi tanpa bateri di mana data spektrum yang diterima dilaporkan secara langsung melalui backscattering tanpa pemprosesan setempat. Tenaga untuk penyerakan belakang boleh disediakan oleh sumber frekuensi radio (RF) ambien. Kemudian, bergantung pada pengiraan udara (AirComp), gabungan data boleh dilaksanakan semasa proses penyerakan belakang dan melalui udara dengan menggunakan sifat penjumlahan saluran wayarles. Kertas kerja ini menggambarkan model dan proses pelaksanaan paradigma GCSD. Ungkapan bentuk tertutup metrik pengesanan diperolehi untuk GCSD yang dicadangkan. Keputusan simulasi mengesahkan ketepatan terbitan teori dan menunjukkan sifat hijau paradigma GCSD.
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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Salinan
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
Salinan
@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},}
Salinan
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 -