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
No.5 May 2018
No.5 May 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

Temporal Multiscale Consumption Strategies of Intermittent Energy based on Parallel Computing

Huifen Chen, Yiming Zhang, Feng Yao, Zhice Yang, Fang Liu, Yi Liu, Zhiheng Li, and Jinggang Wang

Read more

Decision Tree Incremental Learning Algorithm Oriented Intelligence Data

Hongbin Wang, Ci Chu, Xiaodong Xie, Nianbin Wang, and Jing Sun

Read more

Spark-based Ensemble Learning for Imbalanced Data Classification

Jiaman Ding, Sichen Wang, Lianyin Jia, Jinguo You, and Ying Jiang

Read more

Classification Decision based on a Hybrid Method of Weighted kNN and Hyper-Sphere SVM

Peng Chen, Guoyou Shi, Shuang Liu, Yuanqiang Zhang, and Denis Špelič

Read more

An Improved Algorithm based on Time Domain Network Evolution

Guanghui Yan, Qingqing Ma, Yafei Wang, Yu Wu, and Dan Jin

Read more

Auto-Tuning for Solving Multi-Conditional MAD Model

Feng Yao, Yi Liu, Huifen Chen, Chen Li, Zhonghua Lu, Jinggang Wang, Zhiheng Li, and Ningming Nie

Read more

Smart Mine Construction based on Knowledge Engineering and Internet of Things

Xiaosan Ge, Shuai Su, Haiyang Yu, Gang Chen, and Xiaoping Lu

Read more

A Mining Model of Network Log Data based on Hadoop

Yun Wu, Xin Ma, Guangqian Kong, Bin Wang, and Xinwei Niu

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