FTrack

Xia, Xianjin; Zheng, Yuanqing; Gu, Tao

doi:10.1145/3356250.3360024

Cited by 92 publications

(4 citation statements)

References 36 publications

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“…In [96], they utilized the continuity of signal frequency concept to analyze interference-removing methods performed together efficiently. Emulation consequences demonstrate that the FTrack, a parallel decoding method, improves reliability performance in LP-WANs.…”

Section: Energy Efficiencybandwidthmentioning

confidence: 99%

“…To achieve a high EE and high-reliability communication network, numerous effective optimization strategies for optimizing EE and reliability simultaneously for 5G and 6G IoT communication systems has been given in this section [95], [96], [97]. The three papers investigate methods to enhance reliability in IoT networks, addressing different aspects of the challenge.…”

Section: Energy Efficiencybandwidthmentioning

confidence: 99%

“…The three papers investigate methods to enhance reliability in IoT networks, addressing different aspects of the challenge. Paper [95] focuses on interference mitigation and reliability optimization in dense IoT scenarios using the Choir approach, while [96] analyzes interference-removing methods, particularly the FTrack parallel decoding method. On the other hand, [97] employs mixed integer linear programming to improve reliability with energy efficiency constraints, incorporating schemes such as standby routes selection and reliability-based data compression.…”

Section: Energy Efficiencybandwidthmentioning

confidence: 99%

See 2 more Smart Citations

Toward Efficient 6G IoT Networks: A Perspective on Resource Optimization Strategies, Challenges, and Future Directions

Liwen,

Qamar,

Liaqat

et al. 2024

IEEE Access

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The next generation (6G) wireless communication technology has super advantages in high transmission rates scenarios. Internet of Things (IoT) has been applied in recent years due to its wide connection. However, effective resource optimization methods must be analyzed to meet the high requirements of key performance indicators in 6G wireless IoT networks. This paper discusses a general investigation of the resource optimization strategies in the 6G IoT system. The study aims to find the main solutions to optimize 6G IoT network performance. First, an overall summary of current research is preferred resource optimization in latency, reliability, Energy Efficiency (EE), Spectrum Efficiency (SE), bandwidth utilization efficiency, rate, and power efficiency. Second, we propose the multi-indicator tradeoff strategies associated with the latest resource optimization approaches and investigate optimal strategies. Furthermore, we show the limitations of the current resource optimization methods and discuss the future works of resource optimization for IoT devices in 6G communication. Our survey aims to help researchers optimize 6G IoT network performance using advanced techniques.

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Section: Energy Efficiencybandwidthmentioning

confidence: 99%

Section: Energy Efficiencybandwidthmentioning

confidence: 99%

Section: Energy Efficiencybandwidthmentioning

confidence: 99%

See 1 more Smart Citation

Toward Efficient 6G IoT Networks: A Perspective on Resource Optimization Strategies, Challenges, and Future Directions

Liwen,

Qamar,

Liaqat

et al. 2024

IEEE Access

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“…LoRa uses the chirp spread spectrum (CSS) to achieve long-range wireless communications. It was initially developed for radar and has received much attention from the research community [ 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ].…”

Section: Related Workmentioning

confidence: 99%

Leveraging Deep Learning for IoT Transceiver Identification

Gao

Fan

Zhao³

et al. 2023

Entropy

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With the increasing demand for Internet of Things (IoT) network applications, the lack of adequate identification and authentication has become a significant security concern. Radio frequency fingerprinting techniques, which utilize regular radio traffic as the identification source, were then proposed to provide a more secured identification approach compared to traditional security methods. Such solutions take hardware-level characteristics as device fingerprints to mitigate the risk of pre-shared key leakage and lower computational complexity. However, the existing studies suffer from problems such as location dependence. In this study, we have proposed a novel scheme for further exploiting the spectrogram and the carrier frequency offset (CFO) as identification sources. A convolutional neural network (CNN) is chosen as the classifier. The scheme addressed the location-dependence problem in the existing identification schemes. Experimental evaluations with data collected in the real world have indicated that the proposed approach can achieve 80% accuracy even if the training and testing data are collected on different days and at different locations, which is 13% higher than state-of-the-art approaches.

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