Figure - available from: Wireless Networks
This content is subject to copyright. Terms and conditions apply.
Source publication
![An example of DBN networks (m=3\documentclass[12pt]{minimal}...](publication/368391475/figure/fig3/AS:11431281154053194@1682648898627/An-example-of-DBN-networks-m3documentclass12ptminimal-usepackageamsmath_Q320.jpg)
Typically, delay tolerant network (DTN) suffers from frequent disruption, high latency, and lack of stable connections between nodes. As a special case of DTN, vehicular delay tolerant network (VDTN) has particular spatial-temporal characteristics. Different kinds of vehicles may have different movement ranges and movement patterns and the movement...
Similar publications
A large-scale low Earth orbit (LEO) satellite network has the characteristics of a complex link environment, a large number of satellites, and the limited resources of a single satellite. Applying traditional routing algorithms has disadvantages such as high overhead, high end-to-end latency, and low message delivery rate. This paper proposes an im...
Citations
... The former regards each trajectory sample as a cluster and gradually merges into larger clusters by defining the similarity between clusters; the latter gradually divides the trajectory data set into smaller groups by using the idea of divide and conquer until it meets the requirements of clustering. Supervised trajectory clustering algorithm is represented by the K-NN algorithm [38] and statistical model method [39]. The K-NN algorithm uses the average nearest distance as the evaluation criterion to realize trajectory clustering. ...
With the emergence of intelligent transportation and smart city system, the issue of how to perform an efficient and reasonable clustering analysis of the mass vehicle trajectories on multi-camera monitoring videos through computer vision has become a significant area of research. The traditional trajectory clustering algorithm does not consider camera position and field of view and neglects the hierarchical relation of the video object motion between the camera and the scenario, leading to poor multi-camera video object trajectory clustering. To address this challenge, this paper proposed a hierarchical clustering algorithm for multi-camera vehicle trajectories based on spatio-temporal grouping. First, we supervised clustered vehicle trajectories in the camera group according to the optimal point correspondence rule for unequal-length trajectories. Then, we extracted the starting and ending points of the video object under each group, hierarchized the trajectory according to the number of cross-camera groups, and supervised clustered the subsegment sets of different hierarchies. This method takes into account the spatial relationship between the camera and video scenario, which is not considered by traditional algorithms. The effectiveness of this approach has been proved through experiments comparing silhouette coefficient and CPU time.
... GR-PDR [31] solves the local maxima issue by using the delegation replication approach for both single and multiple copy forwarding, considering a buffer delivery priority. [32] proposes an improved VDTN routing based on K-means clustering. ...
... Glowworm Swarm Optimization (GSO) GSO is a swarm-inspired metaheuristic suggested by [32] to model the collective movement of lightning flies called glowworms. This movement is controlled by the changing quantity of a luminescent substance called luciferin. ...
Vehicular Delay Tolerant Network (VDTN) routing has been improved using probabilistic routing to improve the coverage of intermittent vehicular networks. VDTNs are characterized by large transmission ranges of vehicles, rapid vehicular speeds, and restricted mobility movements, particularly in urban areas. As a result, the probabilistic Sore-Carry-and-Forward (SCF) relay vehicle selection can be inaccurate when considering remote vehicles, leading to high overheads. To address this issue, a new bio-inspired VDTN routing protocol is proposed to better estimate the SCF capabilities of remote vehicles so that the duplicated generated bundles' copies are reduced without altering the bundles' delivery ratio and average delivery delay. This solution sequentially employs the Ant Colony Optimizer (ACO) and Glowworm Swarm Optimization (GSO) to adaptively control the replication of bundle copies at each bundle forwarding stage based on predefined probabilistic forwarding parameters. Simulation results from a sparse urban mobility scenario show reduced bundle replication rates compared to several probabilistic VDTN routing protocols.
