JOURNAL OF INTERCONNECTION NETWORKS最新文献_第4页

Multi-Scale Segmentation Method of Remote Sensing Big Data Image Using Deep Learning 基于深度学习的遥感大数据图像多尺度分割方法

IF 0.7

JOURNAL OF INTERCONNECTION NETWORKS Pub Date : 2022-12-31 DOI: 10.1142/s021926592242004x

Huiping Li

{"title":"Multi-Scale Segmentation Method of Remote Sensing Big Data Image Using Deep Learning","authors":"Huiping Li","doi":"10.1142/s021926592242004x","DOIUrl":"https://doi.org/10.1142/s021926592242004x","url":null,"abstract":"Remote sensing image (RSI) segmentation is an effective method to interpret remote sensing information and an important means of remote sensing data information processing. Traditional RSI segmentation methods have some problems such as poor segmentation accuracy and low similarity difference measurement. Therefore, we propose a multi-scale segmentation (MSS) method for remote sensing big data image. First, the segmentation scale of RSI is divided, and the quantitative value of histogram band is used to calculate the similarity index between different objects; Second, the parameters in the same spot are improved based on the maximum area method to determine the shape factor of RSI; Finally, the object closure model is established to clarify the region conversion cost, and the RSI is dynamically segmented based on Multi-scale convolutional neural networks; The MSS algorithm of RSI is designed, and the MSS method of RSI is obtained. The results show that the maximum similarity difference measure of the proposed method is 0.648, and the similarity difference measure always remains the largest. The maximum recall of RSI is 0.954, and the highest recall is 0.988, indicating that the RSI segmentation accuracy of the proposed method is good.","PeriodicalId":53990,"journal":{"name":"JOURNAL OF INTERCONNECTION NETWORKS","volume":"18 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79319980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy-Efficient Model for Intruder Detection Using Wireless Sensor Network 基于无线传感器网络的入侵者检测节能模型

IF 0.7

JOURNAL OF INTERCONNECTION NETWORKS Pub Date : 2022-12-19 DOI: 10.1142/s0219265921490025

Ashok Kumar Rai, A. K. Daniel

{"title":"Energy-Efficient Model for Intruder Detection Using Wireless Sensor Network","authors":"Ashok Kumar Rai, A. K. Daniel","doi":"10.1142/s0219265921490025","DOIUrl":"https://doi.org/10.1142/s0219265921490025","url":null,"abstract":"A wireless sensor network (WSN) can be used for various purposes, including area monitoring, health care, smart cities, and defence. Numerous complex issues arise in these applications, including energy efficiency, coverage, and intruder detection. Intruder detection is a significant obstacle in various wireless sensor network applications. It causes data fusion that jeopardizes the network’s confidentiality, lifespan, and coverage. Various algorithm has been proposed for intruder detection where each node act as an agent, or some monitoring nodes are deployed for intruder detection. The proposed protocol detects intruders by transmitting a known bit from the Cluster Head (CH) to all nodes. The legal nodes must acknowledge their identification to the CH in order to be valid; otherwise, if the CH receives an incorrect acknowledgement from a node or receives no acknowledgement at all, it is an intruder. The proposed protocol assists in protecting sensor data from unauthorized access and detecting the intruder with its location through the identity of other legal nodes. The simulation results show that the proposed protocol delivers better results for identifying intruders for various parameters.","PeriodicalId":53990,"journal":{"name":"JOURNAL OF INTERCONNECTION NETWORKS","volume":"54 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80413299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-Driven Information Management Method of Power Supply Chains Using Mobile Cloud Computing 基于移动云计算的电力供应链数据驱动信息管理方法

IF 0.7

JOURNAL OF INTERCONNECTION NETWORKS Pub Date : 2022-11-21 DOI: 10.1142/s0219265922420026

Ma Jingze, Zhan Guoye, Yang Fan, Chen Xingpei

引用次数: 0

Trust-Based Permissioned Blockchain Network for Identification and Authentication of Internet of Smart Devices: An E-Commerce Prospective 基于信任的智能设备互联网识别与认证许可区块链网络:电子商务展望

IF 0.7

JOURNAL OF INTERCONNECTION NETWORKS Pub Date : 2022-10-13 DOI: 10.1142/s0219265922430010

E. Babu, Ilaiah Kavati, Ramalingaswamy Cheruku, Soumyabrata Nayak, Uttam Ghosh

{"title":"Trust-Based Permissioned Blockchain Network for Identification and Authentication of Internet of Smart Devices: An E-Commerce Prospective","authors":"E. Babu, Ilaiah Kavati, Ramalingaswamy Cheruku, Soumyabrata Nayak, Uttam Ghosh","doi":"10.1142/s0219265922430010","DOIUrl":"https://doi.org/10.1142/s0219265922430010","url":null,"abstract":"The Internet of Things refers to billions of devices around us connected to the wireless internet. These IoT devices are memory-constrained devices that can collect and transfer data over the network without human assistance. Recently, IoT is materialized in retail commerce, transforming from recognition service to post-purchase engagement service. IoT examples in retail commerce are smart refrigerators, smart speakers, smart washing machines, smart automobiles, and automatic re-purchase of groceries using RFID tags. Despite the rise, one of the significant inconveniences slowing rapid adaption is the “security” of these devices, which are vulnerable to various attacks. One such attack is Distributed Denial-of-Service (DDoS) attacks targeting offline or online sensitive data. Hence, a lightweight cryptographic mechanism needs to establish secure communication among IoT devices. This paper presents the solution to secure communication among IoT devices using a permissioned blockchain network. Specifically, in this work, we proposed a mechanism for identifying and authenticating the smart devices using the Elliptic-curve cryptography (ECC) protocol. This proposed work uses permissioned blockchain infrastructure, which acts as a source of trust that aids the authentication process using ECC cryptosystem. In addition, lightweight Physical Unclonable Function (PUF) technology is also used to securely store the device’s keys. Using this technology, the private keys need not be stored anywhere, but it is generated on the fly from the trusted zone whenever the private key is required.","PeriodicalId":53990,"journal":{"name":"JOURNAL OF INTERCONNECTION NETWORKS","volume":"108 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73697485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Author Index Volume 22 (2022) 作者索引第22卷(2022)

IF 0.7

JOURNAL OF INTERCONNECTION NETWORKS Pub Date : 2022-09-28 DOI: 10.1142/s0219265922990018

引用次数: 0

Remote Sensing Image Registration Via Cyclic Parameter Synthesis and Spatial Transformation Network 基于循环参数综合和空间变换网络的遥感图像配准

IF 0.7

JOURNAL OF INTERCONNECTION NETWORKS Pub Date : 2022-08-30 DOI: 10.1142/s0219265922420014

Chen Ying, Liao Xianjing, Wang Wei, Wang Jiahao, Zhang Wencheng, Shi Yanjiao, Zhang Qi

引用次数: 0

DTAR: A Dynamic Threshold Adaptive Ranking-Based Energy-Efficient Routing Algorithm for WSNs 基于动态阈值自适应排序的无线传感器网络节能路由算法

IF 0.7

JOURNAL OF INTERCONNECTION NETWORKS Pub Date : 2022-05-31 DOI: 10.1142/s0219265921490013

R. Amutha, G. Sivasankari, K. Venugopal, Thompson Stephan

{"title":"DTAR: A Dynamic Threshold Adaptive Ranking-Based Energy-Efficient Routing Algorithm for WSNs","authors":"R. Amutha, G. Sivasankari, K. Venugopal, Thompson Stephan","doi":"10.1142/s0219265921490013","DOIUrl":"https://doi.org/10.1142/s0219265921490013","url":null,"abstract":"Owing to uncertainties associated with energy and maintenance of large Wireless Sensor Networks (WSN) during transmission, energy-efficient routing strategies are gaining popularity. A Dynamic Threshold Adaptive Routing Algorithm (DTAR) is proposed for determining the most appropriate node to become a Cluster Head (CH) using adaptive participation criteria. For determining the next Forwarder Node (FN), an adaptive ranking scheme depends on distance ([Formula: see text]) and Residual Energy ([Formula: see text]). However, additional parameters such as Delivery Ratio (DR), End-to-End delay ([Formula: see text] delay), and Message Success Rate (MSR) should be considered to achieve the most optimal approach to achieve energy efficiency. The proposed DTAR algorithm is validated on variable clustered networks in order to investigate the effect of opportunistic routing with increasing network size and energy resources. The proposed algorithm shows a substantial decrease in energy consumption during transmission. Energy Consumption (EC), Packet Delivery Ratio (PDR), End-to-End delay ([Formula: see text] delay), and Message Success Rate (MSR) are used to illustrate the effectiveness of the proposed algorithm for energy efficiency.","PeriodicalId":53990,"journal":{"name":"JOURNAL OF INTERCONNECTION NETWORKS","volume":"71 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85894656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Health Ratio Optimization of Group Detection-Based Data Network Using Genetic Algorithm 基于遗传算法的群检测数据网络健康率优化

IF 0.7

JOURNAL OF INTERCONNECTION NETWORKS Pub Date : 2022-05-12 DOI: 10.1142/s0219265922410018

A. R. Suhas, M. Manoj Priyatham

引用次数: 0

A New Multi-Level Semi-Supervised Learning Approach for Network Intrusion Detection System Based on the ‘GOA’ 一种基于GOA的网络入侵检测系统多级半监督学习新方法

IF 0.7

JOURNAL OF INTERCONNECTION NETWORKS Pub Date : 2022-01-31 DOI: 10.1142/s0219265921430477

A. Madhuri, V. E. Jyothi, S. Praveen, S. Sindhura, V. S. Srinivas, D. L. S. Kumar

{"title":"A New Multi-Level Semi-Supervised Learning Approach for Network Intrusion Detection System Based on the ‘GOA’","authors":"A. Madhuri, V. E. Jyothi, S. Praveen, S. Sindhura, V. S. Srinivas, D. L. S. Kumar","doi":"10.1142/s0219265921430477","DOIUrl":"https://doi.org/10.1142/s0219265921430477","url":null,"abstract":"One of the important technologies in present days is Intrusion detection technology. By using the machine learning techniques, researchers were developed different intrusion systems. But, the designed models toughness is affected by the two parameters, in that first one is, high network traffic imbalance in several categories, and another is, non-identical distribution is present in between the test set and training set in feature space. An artificial neural network (ANN) multi-level intrusion detection model with semi-supervised hierarchical [Formula: see text]-means method (HSK-means) is presented in this paper. Error rate of intrusion detection is reduced by the ANN’s accurate learning so it uses the Grasshopper Optimization Algorithm (GOA) which is analysed in this paper. Based on selection of important and useful parameters as bias and weight, error rate of intrusion detection system is reduced in the GOA algorithm and this is the main objective of the proposed system. Cluster based method is used in the pattern discovery module in order to find the unknown patterns. Here the test sample is treated as unlabelled unknown pattern or the known pattern. Proposed approach performance is evaluated by using the dataset as KDDCUP99. It is evident from the experimental findings that the projected model of GOA based semi supervised learning approach is better in terms of sensitivity, specificity and overall accuracy than the intrusion systems which are existed previously.","PeriodicalId":53990,"journal":{"name":"JOURNAL OF INTERCONNECTION NETWORKS","volume":"29 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76894161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Author Index Volume 21 (2021) 作者索引第21卷(2021)

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JOURNAL OF INTERCONNECTION NETWORKS Pub Date : 2021-12-01 DOI: 10.1142/s0219265921990012

引用次数: 0