Temporal complex networks modeling applied to vehicular ad-hoc networks

Santos, Fillipe; Aquino, André L. L.; Madeira, Edmundo Roberto Mauro; Cabral, Raquel S.

doi:10.1016/j.jnca.2021.103168

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…Gao et al [13] explored the spatio-temporal correlation of security monitoring data based on undirected unweighted complex networks as a risk assessment tool. Santos et al [14] presented the use of temporal graphs and temporal attitude volumes to model VANETs applications to optimize connectivity and expand vehicle coverage during vehicle travel. Some other studies introduced graph convolution theory into temporal networks and made some progress in deep learning and prediction of network evolution [15,16].…”

Section: Introductionmentioning

confidence: 99%

Identification of node rankings in temporal networks based on multilayer topological overlap coefficients

Niu

Xu³

et al. 2023

Phys. Scr.

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Identifying node ranking in complex networks over time is a crucial research topic. The topology relationship of general network nodes reflects their importance in the network. The node ranking evolution within the temporal layers depends not only on the current layer's topology relationship but also on the nodes' interaction relationships as they evolve. In this study, we propose a method called the multilayer topological overlap coefficient-based supra-adjacency matrix to identify node rankings. To account for the node evolution process, we analyze and establish the node ranking matrix structure of unweighted and weighted temporal networks in the temporal network. We also analyze the sequence multilayer node topological overlap structure throughout the whole-time layer. The experimental results demonstrate that the topological overlap coefficient unweighted supra-adjacency matrix of multilayer nodes performs up to 15.00% and 25.80% better than the two supra-adjacency matrix metrics under three different metrics. Moreover, the topological overlap coefficient weighted supra-adjacency matrix of multilayer nodes outperforms the SAM metrics by up to 70.20%.

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Section: Introductionmentioning

confidence: 99%

Identification of node rankings in temporal networks based on multilayer topological overlap coefficients

Niu

Xu³

et al. 2023

Phys. Scr.

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“…This entails the meticulous detection, prediction, tracking, and planning of paths, particularly in the presence of other autonomous vehicles operating in close proximity, as discussed in [4,5]. Such coordination requires extensive information exchange among AMHAs, necessitating the presence of robust wireless connectivity, as emphasized in [6].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of 60 GHz Wireless Connectivity for an Automated Warehouse Suitable for Industry 4.0

Gulia,

Vashist,

Ganguly

et al. 2023

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The fourth industrial revolution focuses on the digitization and automation of supply chains resulting in a significant transformation of methods for goods production and delivery systems. To enable this, automated warehousing is demanding unprecedented vehicle-to-vehicle and vehicle-to-infrastructure communication rates and reliability. The 60 GHz frequency band can deliver multi-gigabit/second data rates to satisfy the increasing demands of network connectivity by smart warehouses. In this paper, we aim to investigate the network connectivity in the 60 GHz millimeter-wave band inside an automated warehouse. A key hindrance to robust and high-speed network connectivity, especially, at mmWave frequencies stems from numerous non-line-of-sight (nLOS) paths in the transmission medium due to various interacting objects such as metal shelves and storage boxes. The continual change in the warehouse storage configuration significantly affects the multipath reflected components and shadow fading effects, thus adding complexity to establishing stable, yet fast, network coverage. In this study, network connectivity in an automated warehouse is analyzed at 60 GHz using Network Simulator-3 (NS-3) channel simulations. We examine a simple warehouse model with several metallic shelves and storage materials of standard proportions. Our investigation indicates that the indoor warehouse network performance relies on the line-of-sight and nLOS propagation paths, the existence of reflective materials, and the autonomous material handling agents present around the access point (AP). In addition, we discuss the network performance under varied conditions including the AP height and storage materials on the warehouse shelves. We also analyze the network performance in each aisle of the warehouse in addition to its SINR heatmap to understand the 60 GHz network connectivity.

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Optimal deployment of vehicular cloud computing systems with remote microclouds

et al. 2023

Wireless Netw

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Temporal complex networks modeling applied to vehicular ad-hoc networks

Cited by 7 publications

References 19 publications

Identification of node rankings in temporal networks based on multilayer topological overlap coefficients

Identification of node rankings in temporal networks based on multilayer topological overlap coefficients

Evaluation of 60 GHz Wireless Connectivity for an Automated Warehouse Suitable for Industry 4.0

Optimal deployment of vehicular cloud computing systems with remote microclouds

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