View source for CHUNG ANG UNIVERSITY INDUSTRY ACADEMIC COOPERATION FOUNDATION (20240236693). SEMI-DISTRIBUTED SPECTRUM SENSING METHOD AND APPARATUS IN COGNITIVE INTERNET OF THINGS NETWORKS simplified abstract

=SEMI-DISTRIBUTED SPECTRUM SENSING METHOD AND APPARATUS IN COGNITIVE INTERNET OF THINGS NETWORKS=


==Organization Name==

[[:Category:CHUNG ANG UNIVERSITY INDUSTRY ACADEMIC COOPERATION FOUNDATION|CHUNG ANG UNIVERSITY INDUSTRY ACADEMIC COOPERATION FOUNDATION]]


[[Category:CHUNG ANG UNIVERSITY INDUSTRY ACADEMIC COOPERATION FOUNDATION]]

==Inventor(s)==

[[:Category:Sung Rae Cho of Seongnam-si (KR)|Sung Rae Cho of Seongnam-si (KR)]][[Category:Sung Rae Cho of Seongnam-si (KR)]]

[[:Category:Chung Hyun Lee of Seoul (KR)|Chung Hyun Lee of Seoul (KR)]][[Category:Chung Hyun Lee of Seoul (KR)]]

[[:Category:Dong Hyun Lee of Seoul (KR)|Dong Hyun Lee of Seoul (KR)]][[Category:Dong Hyun Lee of Seoul (KR)]]

[[:Category:Jun Suk Oh of Seoul (KR)|Jun Suk Oh of Seoul (KR)]][[Category:Jun Suk Oh of Seoul (KR)]]

==SEMI-DISTRIBUTED SPECTRUM SENSING METHOD AND APPARATUS IN COGNITIVE INTERNET OF THINGS NETWORKS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240236693 titled 'SEMI-DISTRIBUTED SPECTRUM SENSING METHOD AND APPARATUS IN COGNITIVE INTERNET OF THINGS NETWORKS

==Simplified Explanation==
The patent application describes a method for semi-distributed spectrum sensing in cognitive IoT networks, along with the apparatus used for this method.

* Grouping secondary terminals based on local information into local clusters.
* Generating overlapping point information to determine directional antenna beams of secondary terminals.
* Calculating and adjusting the overlapping range between local clusters.

==Key Features and Innovation==
* Semi-distributed spectrum sensing method in cognitive IoT networks.
* Grouping secondary terminals into local clusters based on local information.
* Determining directional antenna beams of secondary terminals using overlapping point information.
* Calculating and adjusting overlapping ranges between local clusters.

==Potential Applications==
This technology can be applied in various industries such as telecommunications, smart cities, industrial IoT, and healthcare for efficient spectrum sensing and utilization.

==Problems Solved==
The technology addresses the challenges of spectrum congestion, interference, and inefficient spectrum utilization in cognitive IoT networks.

==Benefits==
* Improved spectrum efficiency.
* Enhanced network performance.
* Reduced interference.
* Optimal spectrum utilization.

==Commercial Applications==
* Telecommunications industry for better spectrum management.
* Smart cities for efficient IoT connectivity.
* Industrial IoT for improved automation and connectivity.
* Healthcare for reliable wireless communication.

==Questions about Semi-Distributed Spectrum Sensing in Cognitive IoT Networks==
===How does the method of grouping secondary terminals into local clusters improve spectrum sensing efficiency?===
Grouping secondary terminals into local clusters allows for more focused and coordinated spectrum sensing, leading to better utilization of available spectrum resources.

===What are the potential implications of calculating and adjusting overlapping ranges between local clusters in cognitive IoT networks?===
Calculating and adjusting overlapping ranges between local clusters can help in reducing interference and optimizing spectrum allocation, leading to improved network performance and reliability.


==Original Abstract Submitted==

disclosed are a semi-distributed spectrum sensing method in cognitive iot networks, and an apparatus thereof. the semi-distributed spectrum sensing method in cognitive iot networks includes: (a) grouping, based on local information of pre-shared secondary terminals, each secondary terminal into each local cluster; (b) generating overlapping point information by calculating an overlapping range between directional antenna beams of the respective secondary terminals in the each local cluster, and determining a beam determination binary indicator of the each secondary terminal by using the overlapping point information; and (c) calculating and adjusting the overlapping range between the respective local clusters.



[[Category:H04W16/14]]
[[Category:H04W16/28]]
[[Category:H04W84/18]]
[[Category:CPC_H04W16/14]]