Research on network cognition model and mechanism of intelligent information network

Dong, Nannan; Yin, Hao; Ren, Baoquan; Li, Hongjun; Gong, Xiangwu; Zhong, Xudong; Han, Junmei; Lyu, Jiazheng

doi:10.23919/jcc.2023.02.017

Cited by 1 publication

(1 citation statement)

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“…Yan et al [13] proposed a simulated annealing algorithm that considers multiple time slices as states under multiple constraints. Additionally, greedy algorithms [14], other simulated annealing algorithms [15], scheduling algorithms [16], load balancing algorithms [17], and immune algorithms [18] based on single time slices were proposed one after another.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Topology in Satellite–UAV Collaborative IoT: A Graph Partitioning Simulated Annealing Approach

Zhuo,

Feng,

Yang

et al. 2024

Drones

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Currently, space-based information networks, represented by satellite Internet, are rapidly developing. UAVs can serve as airborne mobile terminals, representing a novel node in satellite IoT, offering more accurate and robust data streaming for connecting global satellite–UAV collaborative IoT systems. It is characterized by high-speed dynamics, with node distances and visibility constantly changing over time. Therefore, there is a need for faster and higher-quality topology optimization research. A reliable, secure, and adaptable network topology optimization algorithm has been proposed to handle various complex scenarios. Additionally, considering the dynamic and time-varying nature of these types of networks, the concept of time slices has been introduced to accelerate the iterative efficiency of problem-solving. Experimental results demonstrate that the proposed algorithm is expected to exhibit better convergence and performance in subsequent iterations compared with traditional solutions. Besides being a solution for topology optimization, the proposed algorithm offers a new way of thinking, enabling the handling of larger satellite–UAV collaborative IoT systems.

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Section: Related Workmentioning

confidence: 99%