Journal of High Speed Networks - Volume 29, issue 4

Authors: Liao, Daqiang | Zou, Du | Srivastava, Gautam

Article Type: Research Article

Abstract: In this paper, an improved routing protocol for multipath network load balancing is proposed for defects in the traditional AOMDV (Ad hoc On-demand Multipath Distance Vector) protocol. This research work analyzes problems in traditional routing protocols and estimates the available path load according to network transmission in Wireless Mesh Networks (WMN). Moreover, we design a load distribution scheme according to a given load and improve multi-path load balancing by using the MCMR method. We also control path discovery and the number of paths while also establishing routing paths and probability balancing. Lastly, improvements are made to the AOMDV protocol and …efficient data transmission is acheived. The performance results of the modified routing protocol show that the designed protocol can improve successful delivery rate and prolong network survival time. Show more

Keywords: Probabilistic diversion, multipath, load balancing, routing protocols, congestion control, successful delivery rate, defect improvement

DOI: 10.3233/JHS-222063

Citation: Journal of High Speed Networks, vol. 29, no. 4, pp. 265-278, 2023

Authors: Krishna, Boddu L.V. Siva Rama | Mahalakshmi, V. | Nookala, Gopala Krishna Murthy

Article Type: Research Article

Abstract: Recently, deep learning has been used in enormous successful applications, specifically considering medical applications. Especially, a huge number of data is captured through the Internet of Things (IoT) based devices related to healthcare systems. Moreover, the given captured data are real-time and unstructured. However, the existing approaches failed to reach a better accuracy rate, and the processing time needed to be lower. This work considers the medical database for accessing the patient’s record to determine the outliers over the dataset. Based on this successful analysis, a novel approach is proposed where some feasible and robust features are extracted to acquire …the emotional variations for various ways of expression. Here, a novel dense-Convolutional Neural Network (CNN) with ResNet (CNN-RN) extracts features from patients’, while for establishing visual modality, deep residual network layers are used. The significance of feature extraction is less sensitive during outlier prediction while modeling the context. To handle these issues, this dense network model is used for training the network in an end-to-end manner by correlating the significance of CNN and RN of every stream and outperforming the overall approach. Here, MATLAB 2020b is used for simulation purposes, and the model outperforms various prevailing methods for consistent prediction. Some performance metrics include detection accuracy, F1-score, recall, MCC, p-value, etc. Based on this evaluation, the experimental results attained are superior to other approaches. Show more

Keywords: Outlier prediction, heart disease recognition, deep learning, convolutional neural networks, ResNet

DOI: 10.3233/JHS-222079

Citation: Journal of High Speed Networks, vol. 29, no. 4, pp. 279-294, 2023

Authors: Chen, Sichao | Huang, Liejiang | Pan, Yuanjun | Hu, Yuanchao | Shen, Dilong | Dai, Jiangang

Article Type: Research Article

Abstract: Today, the Internet of Things (IoT) has an important role for deploying power and energy management in the smart grids as emerging trend for managing power stability and consumption. In the IoT, smart grids has important role for managing power communication systems with safe data transformation using artificial intelligent approaches such as Machine Learning (ML), evolutionary computation and meta-heuristic algorithms. One of important issues to manage renewable energy consumption is intelligent aggregation of information based on smart metering and detecting the user behaviors for power and electricity consumption in the IoT. To achieve optimal performance for detecting this information, a …context-aware prediction system is needed that can apply a resource management effectively for the renewable energy consumption for smart grids in the IoT. Also, prediction results from machine learning methods can be useful to manage optimal solutions for power generation activities, power transformation, smart metering at home and load balancing in smart grid networks. This paper aims to design a new periodical detecting, managing, allocating and analyzing useful information regarding potential renewable power and energy consumptions using a context-aware prediction approach and optimization-based machine learning method to overcome the problem. In the proposed architecture, a decision tree algorithm is provided to predict the grouped information based on important and high-ranked existing features. For evaluating the proposed architecture, some other well-known machine learning methods are compared to the evaluation results. Consequently, after analyzing various components by solving different smart grids datasets, the proposed architecture’s capacity and supremacy are well determined among its traditional approaches. Show more

Keywords: Smart grids, IoT, energy management, machine learning, decision tree, accuracy

DOI: 10.3233/JHS-230002

Citation: Journal of High Speed Networks, vol. 29, no. 4, pp. 295-305, 2023

Authors: Song, Limin | Rho, Seungmin

Article Type: Research Article

Abstract: Instantaneous traffic changes in high-speed networks will interfere with abnormal traffic characteristics, making it difficult to accurately identify hidden targets of security threats. This paper designs a high-speed network security threat hidden target recognition method based on attack graph theory. Using the high-speed network traffic reduction method, under the condition that the network topology remains unchanged, the instantaneous input traffic is reduced according to a certain proportion, and after compressing the flow data scale, the abnormal traffic of the high-speed network is identified through the convolutional recurrent neural network, and the information entropy is used to describe the high-speed network. …The abnormal traffic characteristics of the network are used as constraints to design an attack graph of hidden targets of high-speed network security threats, and an attack path discovery method based on multi-heuristic information fusion is designed to extract attack paths of high-speed networks, locate attacking hosts, and identify hidden threat targets. In the experiment, the method can accurately identify the hidden targets of high-speed network security threats, and has better identification ability. Show more

Keywords: Attack graph theory, high-speed network, security threat, hidden target, target identification, abnormal traffic

DOI: 10.3233/JHS-222048

Citation: Journal of High Speed Networks, vol. 29, no. 4, pp. 307-320, 2023

Authors: Sun, Weiqing | Wang, Shengxin | Wei, Shen | Cao, Pengfei | Zhao, Yan | Xi, Lechao | Liu, Xinlu | Wang, Lihua

Article Type: Research Article

Abstract: Blockchain has the advantages of tamper proof, high reliability and traceability, so it can better support the agricultural product traceability system. However, the agricultural product supply chain has the characteristics of long chain and decentralized production, which makes the application of the combination of blockchain technology and agricultural product traceability have many nodes, large system overhead and other problems. In order to solve the above problems, this paper proposes a Practical Byzantine Fault Tolerance (PBFT) consensus mechanism based on trust value evaluation, and uses this optimization algorithm to propose an architecture model to meet the demand for trusted traceability of …agricultural product supply chain, so as to realize the distributed storage and security protection of supply chain data. In order to ensure the effective operation of the blockchain system, this paper studies the consensus mechanism in the blockchain network, designs a trust value evaluation model to calculate the trust value of nodes, and selects trusted nodes to join the consensus group based on the trust value, which ensures the credibility of the consensus nodes. At the same time, the consistency protocol is optimized to reduce the communication overhead in the consensus process. The experimental results show that the consensus mechanism improved in this paper has certain advantages in communication overhead, transaction delay and throughput. Compared with the existing storage model, the model described in this paper has higher security and throughput efficiency, effectively ensures the reliable traceability of data, and can be better applied to the traceability scenario of agricultural product supply chain. Show more

Keywords: Blockchain, consensus mechanism, PBFT consensus, trust value evaluation, traceability of agricultural products

DOI: 10.3233/JHS-222077

Citation: Journal of High Speed Networks, vol. 29, no. 4, pp. 321-336, 2023

Authors: Yang, Mengjiao | Ke, Dongmei | Li, Fengyin

Article Type: Research Article

Abstract: The methods of network attacks are becoming more and more diverse In high-speed networks, and protecting private data is a key issue for users’ security. Proxy blind signature technology combines proxy and blind signature technology to achieve signature transitivity and message confidentiality. However, current signature schemes will not be secure in quantum environments. Meanwhile, the certificate storage of the existing scheme is relatively complicated. Therefore, to avoid the need for certificates and resist quantum attacks, this paper proposes an anti-quantum identity-based proxy blind signature (ID-Proxy-BS) scheme on lattice. This ID-Proxy-BS scheme improves the efficiency of signature through Gaussian distribution and …sampling techniques. Security analysis shows that the scheme satisfies correctness, efficiency, blindness, and unforgeability. Show more

Keywords: High-speed network, proxy blind signatures, identity-based, lattice, Gaussian distribution

DOI: 10.3233/JHS-222021

Citation: Journal of High Speed Networks, vol. 29, no. 4, pp. 337-341, 2023