Tracking Performance in LoRaWAN-Like Systems and Equivalence of a Class of Distributed Learning Algorithms

Maurya, Poonam; Kherani, Arzad A.

doi:10.1109/lcomm.2020.3012569

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Cited by 4 publications

(1 citation statement)

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“…Localization and positioning have emerged as important applications of LoRaWAN technology [127]. The advanced integration of ML and DL has taken these applications to another level of development.…”

Section: F Localizationmentioning

confidence: 99%

A Comprehensive Survey of Data-Driven Solutions for LoRaWAN: Challenges & Future Directions

Maurya,

Hazra,

Kumari

et al. 2024

Preprint

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Section: F Localizationmentioning

confidence: 99%

A Comprehensive Survey of Data-Driven Solutions for LoRaWAN: Challenges & Future Directions

Maurya,

Hazra,

Kumari

et al. 2024

Preprint

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Exploring IoT Communication Technologies and Data-Driven Solutions

Maurya,

Hazra,

Awasthi

2023

Learning Techniques for the Internet of Things

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Heterogeneous sensing for target tracking: architecture, techniques, applications and challenges

Li²,

Chen³

et al. 2023

Meas. Sci. Technol.

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Target tracking applications are promising, it has great theoretical and practical significance, but the research faces great challenges. With the development of multi-modal depth sensing technology, a large number of scholars have proposed various target tracking methods based on heterogeneous sensing and got great results. This paper provides an overview of the techniques involved in target tracking in the different layers of the network and a comprehensive analysis of the research progress in heterogeneous sensing techniques in each layer. First, this paper introduced the single sensing scheme and heterogeneous sensing scheme in the physical layer. Second, we presented heterogeneous communication technologies and heterogeneous optimization methods for communication protocols in the network layer. Third, we combined several typical heterogeneous sensory target tracking applications and analyzed the application of cloud computing, edge computing, big data, and blockchain technologies. Finally, we discussed the challenges and future direction of heterogeneous sensory target tracking methods.

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Tracking Performance in LoRaWAN-Like Systems and Equivalence of a Class of Distributed Learning Algorithms

Cited by 4 publications

References 11 publications

A Comprehensive Survey of Data-Driven Solutions for LoRaWAN: Challenges & Future Directions

A Comprehensive Survey of Data-Driven Solutions for LoRaWAN: Challenges & Future Directions

Exploring IoT Communication Technologies and Data-Driven Solutions

Heterogeneous sensing for target tracking: architecture, techniques, applications and challenges

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