2007 16th International Conference on Computer Communications and Networks最新文献_第2页

Packet Buffer Management for a High-Speed Network Interface Card 高速网络接口卡的数据包缓冲管理

2007 16th International Conference on Computer Communications and Networks Pub Date : 2007-09-24 DOI: 10.1109/ICCCN.2007.4317818

Shalini Batra, Yul Chu, Yan Bai

引用次数: 7

A Voronoi Approach for Scalable and Robust DV-Hop Localization System for Sensor Networks 传感器网络中可扩展鲁棒DV-Hop定位系统的Voronoi方法

2007 16th International Conference on Computer Communications and Networks Pub Date : 2007-09-24 DOI: 10.1109/ICCCN.2007.4317868

A. Boukerche, Horácio Oliveira, E. Nakamura, A. Loureiro

{"title":"A Voronoi Approach for Scalable and Robust DV-Hop Localization System for Sensor Networks","authors":"A. Boukerche, Horácio Oliveira, E. Nakamura, A. Loureiro","doi":"10.1109/ICCCN.2007.4317868","DOIUrl":"https://doi.org/10.1109/ICCCN.2007.4317868","url":null,"abstract":"Localization systems have been identified as a key issue to the development and operation of the Wireless Sensor Networks (WSN). A DV-Hop localization system works by transforming the distance to all beacon nodes from hops to units of length measurement (e.g., meters, feet) using the average size of a hop as a correction factor. Despite its advantages, a DV-Hop algorithm has some disadvantages, such as its large communication cost that limits its scalability, and its mapping from hops to distance units that introduces errors that are propagated to the computation of a node location. This last issue has been solved by some recent works, but the scalability problem still is an open problem that limits this technique to small or medium sized networks. In this work, we propose a novel approach that uses Voronoi diagrams in order to scale a DV-Hop localization algorithm while mantaining or even reducing its localization error. Two types of localization can result from the proposed algorithm: the physical location of the node (e.g., latitude, longitude), or a region limited by the node's Voronoi cell. The algorithm evaluation is performed by comparison with similar algorithms. We show how the proposed algorithm can scale in different aspects such as communication and processing costs when increasing the number of nodes and beacons.","PeriodicalId":388763,"journal":{"name":"2007 16th International Conference on Computer Communications and Networks","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128695881","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: 33

Facts of LIFE 生活的真相

2007 16th International Conference on Computer Communications and Networks Pub Date : 2007-09-24 DOI: 10.1109/ICCCN.2007.4317921

A. Barbero, Øyvind Ytrehus

引用次数: 0

Broadcast Collision Avoidance in Wireless Packet Networks 无线分组网络中的广播冲突避免

2007 16th International Conference on Computer Communications and Networks Pub Date : 2007-09-24 DOI: 10.1109/ICCCN.2007.4317925

Jun Peng

引用次数: 1

Opportunistic Scheduling for Delay Sensitive Flows in Wireless Networks 无线网络中延迟敏感流的机会调度

2007 16th International Conference on Computer Communications and Networks Pub Date : 2007-09-24 DOI: 10.1109/ICCCN.2007.4317789

Yuehua Wu, Jibo Wei, Byung-Seo Kim, Y. Xi, Dongtang Ma

引用次数: 1

Bayesian Learning of Neural Networks for Mobile User Position Prediction 基于贝叶斯学习的神经网络移动用户位置预测

2007 16th International Conference on Computer Communications and Networks Pub Date : 2007-09-24 DOI: 10.1109/ICCCN.2007.4317989

Sherif Akoush, A. Sameh

{"title":"Bayesian Learning of Neural Networks for Mobile User Position Prediction","authors":"Sherif Akoush, A. Sameh","doi":"10.1109/ICCCN.2007.4317989","DOIUrl":"https://doi.org/10.1109/ICCCN.2007.4317989","url":null,"abstract":"In this paper, a novel technique for location prediction of mobile users has been proposed, and a paging technique based on it is developed. Mobile users are creatures of habits. They tend to repeat their behaviors. Hence, neural networks with its learning and generalization ability may act as a suitable tool to predict the location of a mobile user provided it is trained appropriately by the personal mobility profile. For prediction, a novel hybrid Bayesian neural network model for predicting locations on Cellular Networks (can also be extended to other wireless networks such as Wi-Fi and WiMAX) is suggested. We investigate its different parallel implementation techniques on mobile devices, and compare its performance to many standard neural network techniques such as: Back-propagation, Elman, Resilient, Levenberg-Marqudat, and One-Step Secant models. This approach is free from all unrealistic assumptions about the movement of the users. It is applicable to any arbitrary cell architecture. It attempts to reduce the total location management cost and paging delay. In general, it enhances mobility management in wireless networks (in location management and hand-off management). In our experiments, we compare results of the proposed Bayesian Neural Network with 5 standard neural network techniques in predicting next location. Bayesian learning for Neural Networks predicts location better than standard neural network techniques since it uses well founded probability model to represent uncertainty about the relationship being learned. The result of Bayesian training is a posterior distribution over network weights.","PeriodicalId":388763,"journal":{"name":"2007 16th International Conference on Computer Communications and Networks","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134011271","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: 19

Functionally Layered Video Coding for River Surveillance 河流监控的功能分层视频编码

2007 16th International Conference on Computer Communications and Networks Pub Date : 2007-09-24 DOI: 10.1109/ICCCN.2007.4317970

M. Iwahashi, S. Udomsiri

引用次数: 2

On the Suitability of Framed Slotted Aloha based RFID Anti-collision Protocols for Use in RFID-Enhanced WSNs 基于框架开槽Aloha的RFID防碰撞协议在RFID增强型无线传感器网络中的适用性研究

2007 16th International Conference on Computer Communications and Networks Pub Date : 2007-09-24 DOI: 10.1109/ICCCN.2007.4317881

Dheeraj K. Klair, Kwan-Wu Chin, R. Raad

引用次数: 40

Network-Aware State Update For Large Scale Mobile Games 面向大型手机游戏的网络感知状态更新

2007 16th International Conference on Computer Communications and Networks Pub Date : 2007-09-24 DOI: 10.1109/ICCCN.2007.4317878

Yang Yu, Zhuo Li, Larry Shi, Yi-Chiun Chen, Hua Xu

引用次数: 23

Applying Design Patterns to Wireless Sensor Network Programming 设计模式在无线传感器网络编程中的应用

2007 16th International Conference on Computer Communications and Networks Pub Date : 2007-09-24 DOI: 10.1109/ICCCN.2007.4317965

K. Tei, Y. Fukazawa, S. Honiden

引用次数: 11