Comput. Phys. Commun.最新文献_第8页

Channel hopping for blind rendezvous in cognitive radio networks: A review 认知无线网络中盲交会的信道跳变研究进展

Comput. Phys. Commun. Pub Date : 2022-08-01 DOI: 10.1016/j.comcom.2022.08.011

Erik Ortiz Guerra, V. A. Reguera, C. Duran-Faundez, T. Nguyen

引用次数: 2

An automated approach to Web Offensive Security 一种自动化的网络攻击安全方法

Comput. Phys. Commun. Pub Date : 2022-08-01 DOI: 10.2139/ssrn.4057341

N. Auricchio, A. Cappuccio, Francesco Caturano, G. Perrone, S. Romano

引用次数: 4

DRL-M4MR: An Intelligent Multicast Routing Approach Based on DQN Deep Reinforcement Learning in SDN DRL-M4MR: SDN中基于DQN深度强化学习的智能组播路由方法

Comput. Phys. Commun. Pub Date : 2022-07-31 DOI: 10.48550/arXiv.2208.00383

Chenwei Zhao, Miao Ye, Xingsi Xue, Jianhui Lv, Qiuxiang Jiang, Yong Wang

{"title":"DRL-M4MR: An Intelligent Multicast Routing Approach Based on DQN Deep Reinforcement Learning in SDN","authors":"Chenwei Zhao, Miao Ye, Xingsi Xue, Jianhui Lv, Qiuxiang Jiang, Yong Wang","doi":"10.48550/arXiv.2208.00383","DOIUrl":"https://doi.org/10.48550/arXiv.2208.00383","url":null,"abstract":"Traditional multicast routing methods have some problems in constructing a multicast tree, such as limited access to network state information, poor adaptability to dynamic and complex changes in the network, and inﬂexible data forwarding. To address these defects, the optimal multicast routing problem in software-deﬁned networking (SDN) is tailored as a multi-objective optimization problem, and an intelligent multicast routing algorithm DRL-M4MR based on the deep Q network (DQN) deep reinforcement learning (DRL) method is designed to construct a multicast tree in SDN. First, the multicast tree state matrix, link bandwidth matrix, link delay matrix, and link packet loss rate matrix are designed as the state space of the DRL agent by combining the global view and control of the SDN. Second, the action space of the agent is all the links in the network, and the action selection strategy is designed to add the links to the current multicast tree under four cases. Third, single-step and ﬁnal reward function forms are designed to guide the intelligence to make decisions to construct the optimal multicast tree. The experimental results show that, compared with existing algorithms, the multicast tree construct by DRL-M4MR can obtain better bandwidth, delay, and packet loss rate performance after training, and it can make more intelligent multicast routing decisions in a dynamic network environment.","PeriodicalId":10679,"journal":{"name":"Comput. Phys. Commun.","volume":"70 1","pages":"101919"},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83814982","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: 7

Wireless ultraviolet light MIMO assisted UAV direction perception and collision avoidance method 无线紫外光MIMO辅助无人机方向感知与避碰方法

Comput. Phys. Commun. Pub Date : 2022-07-01 DOI: 10.2139/ssrn.4097297

Taifei Zhao, Jiatong Yao, Chunjie Gong, Yiqiong Wang

引用次数: 3

Sub-messages extraction for industrial control protocol reverse engineering 面向工业控制协议逆向工程的子消息提取

Comput. Phys. Commun. Pub Date : 2022-07-01 DOI: 10.2139/ssrn.4017050

Yuhuan Liu, Fengyun Zhang, Yulong Ding, Jie Jiang, Shuanghua Yang

引用次数: 0

EC-MASS: Towards an efficient edge computing-based multi-video scheduling system 基于边缘计算的高效多视频调度系统

Comput. Phys. Commun. Pub Date : 2022-07-01 DOI: 10.1016/j.comcom.2022.07.002

Shu Yang, Qingzhen Dong, Laizhong Cui, Xun Chen, Siyu Lei, Yulei Wu, Chengwen Luo

引用次数: 0

Content Privacy Enforcement Models in Decentralized Online Social Networks: State of Play, Solutions, Limitations, and Future Directions 分散在线社交网络中的内容隐私执行模型:游戏状态、解决方案、限制和未来方向

Comput. Phys. Commun. Pub Date : 2022-06-07 DOI: 10.48550/arXiv.2206.03084

Andrea De Salve, P. Mori, L. Ricci, R. D. Pietro

{"title":"Content Privacy Enforcement Models in Decentralized Online Social Networks: State of Play, Solutions, Limitations, and Future Directions","authors":"Andrea De Salve, P. Mori, L. Ricci, R. D. Pietro","doi":"10.48550/arXiv.2206.03084","DOIUrl":"https://doi.org/10.48550/arXiv.2206.03084","url":null,"abstract":"In recent years, Decentralized Online Social Networks (DOSNs) have been attracting the attention of many users because they reduce the risk of censorship, surveillance, and information leakage from the service provider. In contrast to the most popular Online Social Networks, which are based on centralized architectures (e.g., Facebook, Twitter, or Instagram), DOSNs are not based on a single service provider acting as a central authority. Indeed, the contents that are published on DOSNs are stored on the devices made available by their users, which cooperate to execute the tasks needed to provide the service. To continuously guarantee their availability, the contents published by a user could be stored on the devices of other users, simply because they are online when required. Consequently, such contents must be properly protected by the DOSN infrastructure, in order to ensure that they can be really accessed only by users who have the permission of the publishers. As a consequence, DOSNs require efficient solutions for protecting the privacy of the contents published by each user with respect to the other users of the social network. In this paper, we investigate and compare the principal content privacy enforcement models adopted by current DOSNs evaluating their suitability to support different types of privacy policies based on user groups. Such evaluation is carried out by implementing several models and comparing their performance for the typical operations performed on groups, i.e., content publish, user join and leave. Further, we also highlight the limitations of current approaches and show future research directions. This contribution, other than being interesting on its own, provides a blueprint for researchers and practitioners interested in implementing DOSNs, and also highlights a few open research directions.","PeriodicalId":10679,"journal":{"name":"Comput. Phys. Commun.","volume":"146 1","pages":"199-225"},"PeriodicalIF":0.0,"publicationDate":"2022-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86024581","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 collaborative computation for double-RIS assisted mobile edge networks 双ris辅助移动边缘网络的节能协同计算

Comput. Phys. Commun. Pub Date : 2022-06-01 DOI: 10.1016/j.phycom.2022.101774

Wancheng Xie, Bin Li, Yiwen Xiong, Wenshuai Liu, Jianghong Ou, Dahua Fan

引用次数: 2

Relay selection scheme based on deep reinforcement learning in wireless sensor networks 基于深度强化学习的无线传感器网络中继选择方案

Comput. Phys. Commun. Pub Date : 2022-06-01 DOI: 10.2139/ssrn.4040127

Dongmei Zhou, Baowan Yan, Cuiran Li, A. Wang, Haixia Wei

引用次数: 4

Low complexity PPN-FBMC Receivers with improved sliding window equalizers 具有改进滑动窗口均衡器的低复杂度PPN-FBMC接收机

Comput. Phys. Commun. Pub Date : 2022-06-01 DOI: 10.2139/ssrn.4047891

Al-amaireh Husam, Z. Kollár

引用次数: 2