Routing Algorithm Based on User Adaptive Data Transmission Scheme in Opportunistic Social Networks

Abstract

With the rapid popularization of 5G communication and internet of things technologies, the amount of information has increased significantly in opportunistic social networks, and the types of messages have become more and more complex. More and more mobile devices join the network as nodes, making the network scale increase sharply, and the tremendous amount of datatransmission brings a more significant burden to the network. Traditional opportunistic social network routing algorithms lack effective message copy management and relay node selection methods, which will cause problems such as high network delay and insufficient cache space. Thus, we propose an opportunistic social network routing algorithm based on user-adaptive data transmission. The algorithm will combine the similarity factor, communication factor, and transmission factor of the nodes in the opportunistic social network and use information entropy theory to adaptively assign the weights of decision feature attributes in response to network changes. Also, edge nodes are effectively used, and the nodes are divided into multiple communities to reconstruct the community structure. The simulation results show that the algorithm demonstrates good performance in improving the information transmission’s success rate, reducing network delay, and caching overhead.

A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer

“…The determination of the histopathological stage of lung tumors is of great importance to physicians in both the diagnosis and treatment of patients with NSCLC. In order to report a classification prediction of the pathological stage on CT images of patients with NSCLC, Yu et al [ 20 ] used a random forest machine-learning algorithm, while Choi et al [ 21 ] used U-Net and CNN to construct a cascaded neural network. In addition, Gou et al [ 22 ] performed staging prediction on the text data of medical records of patients with NSCLC.…”

Effective path prediction and data transmission in opportunistic social networks

“…Therefore, the Spray and Wait algorithm cannot avoid the rapid consumption of resources, especially when the parameter N is manually set. In summary, the Spray and Wait algorithm may cause nodes in the network to die quickly in some cases [22][23][24].…”

“…Based on user density [24] This paper designs an adaptive routing based on user density High message delivery rate Only for sparse networks [25] This paper studies an adaptive spray and focus routing protocol based on user density…”

Effective Data Optimization and Evaluation Based on Social Communication with AI-Assisted in Opportunistic Social Networks

“…However, in algorithms commonly used to solve the problem of message transmission in opportunistic social networks, the transmission and screening methods of messages have been difficult to adapt to the complex big data environment and the variability of data-carrying mobile devices [14]. For flood-based methods or social perception methods, they may cause a massive amount of information stored in the network and may not be effectively dealt with in time [15]. In the flood-based routing algorithm, each node forwards the received data packets to all nodes that can meet by broadcasting.…”