Username   Password       Forgot your password?  Forgot your username? 

ISSUES BY YEAR

Volume 14 - 2018

No.1 January 2018
No.1 January 2018
No.3 March 2018
No.3 March 2018
No.4 April 2018
No.4 April 2018

Volume 13 - 2017

No.4 July 2017
No.4 July 2017
No.5 September 2017
No.5 September 2017
No.7 November 2017
No.7 November 2017
No.8 December 2017
No.8 December 2017

Volume 12 - 2016

Volume 11 - 2015

Volume 10 - 2014

Volume 9 - 2013

Volume 8 - 2012

Volume 7 - 2011

Volume 6 - 2010

Volume 5 - 2009

Volume 4 - 2008

Volume 3 - 2007

Volume 2 - 2006

Survivable Data Transmission via Selective Hybrid Cipher in Sensor Networks

Volume 7, Number 4, July 2011 - Paper 1 - pp. 303-312

RUIPING MA, LIUDONG XING, HOWARD E. MICHEL and HONGGANG WANG

Electrical and Computer Engineering Department, University of Massachusetts
Dartmouth, 285 Old Westport Rd., Dartmouth, MA, 02747, USA

(Received on April 29, 2010, revised on April 5, 2011)


Abstract:

In wireless sensor networks (WSN), data packets being sent over wireless environments could get corrupted or compromised due to channel noises or malicious attacks. Using traditional full encryption to secure the transmitted data is costly and even not practical for WSN due to the inherent resource-constrained nature of sensor nodes. Selective encryption (SE) that encrypts part of the data can greatly reduce the computational overhead for huge volumes of data in low-power networks. Encrypted data is more sensitive to transmission errors; therefore, additional error correction capability is required to efficiently recover the lost/erroneous encrypted information. In this paper, we propose a new Selective Hybrid Cipher-based mechanism, which integrates AES-based SE and Forward Error Correction codes to achieve both secure and reliable data transmission in WSN. Performance of the proposed mechanism is evaluated using simulations, and is compared with that of the traditional SE-based and full encryption-based mechanisms.

 

References: 23

Click here to download the paper.

Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

 

CURRENT ISSUE

Prev Next

Semi-Supervised Extreme Learning Machine using L1-Graph

Hongwei Zhao, Yang Liu, Shenglan Liu, and Lin Feng

Read more

Collision Analysis and an Efficient Double Array Construction Method

Lianyin Jia, Wenyan Chen, Jiaman Ding, Xiaohui Yuan, Binglin Shen, and Mengjuan Li

Read more

A Measuring Method for User Similarity based on Interest Topic

Yang Bai, Guishi Deng, Liying Zhang, and Yi Wang

Read more

Performance Analysis of Information Fusion Method based on Bell Function

Meiyu Wang, Zhigang Li, Dongmei Huang, and Xinghao Guo

Read more

Two-Stage Semantic Matching for Cross-Media Retrieval

Gongwen Xu, Lina Xu, Meijia Zhang, and Xiaomei Li

Read more
This site uses encryption for transmitting your passwords. ratmilwebsolutions.com