A Resource and Energy Aware Image Compression Scheme for Wireless Multimedia Sensor Networks(WMSNs)
- 주제(키워드) Image Compression , Wireless Multimedia Sensor , Energy Aware , Networks
- 발행기관 아주대학교
- 발행년도 2010
- 학위수여년월 2010. 2
- 학위명 석사
- 학과 및 전공 일반대학원 컴퓨터공학과
- 파일정보 pdf
- 실제URI http://www.dcollection.net/handler/ajou/000000010304
- 본문언어 영어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
The availability of inexpensive hardware such as CMOS cameras and microphones has fostered the development of Wireless Multimedia Sensor Networks (WMSNs). In WMSNs, wirelessly interconnected devices enable to ubiquitously retrieve multimedia contents such as video and audio streams, and still images along with scalar data from surroundings for wide range of applications are constrained by processing, memory, and energy resources. Image compression via low-complexity and resource eficient transforms has been addressed by several researchers to prolong network lifetime where energy conservation is achieved through sharing computational load among sensor nodes and by adjusting the transmission ranges of camera nodes. However, those scheme are not adaptive to the presences and changes of energy-level of computational sensor nodes and to the amount of computational load. In this master degree thesis, we propose a resource and energy eficient distributed image compression algorithm that dynamically con gures according to the energy-levels of computational nodes and the forwarding strategy that is based on the entropy of the image. The simulation results show that our adaptive distributed image compression scheme signi cantly prolong the network lifetime and improve network utilization eficiency, while maintaining adequate image quality.
more목차
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Chapter
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Contribution of this Dissertation . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Dissertation Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Wireless Multimedia Sensor Networks (WMSNs) . . . . . . . . . . . . . . . . . . 4
2.1 Constraints and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1.1 Characteristic Requirements . . . . . . . . . . . . . . . . . . . . . . 6
2.1.2 Required Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2 Hardware of Sensor Nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3 Sensor Network Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.4 Single-Hop versus Multi-Hop Networks . . . . . . . . . . . . . . . . . . . . 13
2.5 Protocol Stack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.5.1 Physical Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.5.2 Data Link Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.5.3 Network Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.6 Transport Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.7 Application Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
viii
3 Lapped Bio-orthogonal Transform - Background . . . . . . . . . . . . . . . . . . 20
3.1 LBT-based compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.2 Ecient Source Encoding . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4 Distributed Image compression . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.2 Initializing Image Compression . . . . . . . . . . . . . . . . . . . . . . . . 25
4.3 Energy-Level Classi cation (ELC) . . . . . . . . . . . . . . . . . . . . . . . 25
4.4 Information Quanti cation Measure . . . . . . . . . . . . . . . . . . . . . . 27
4.5 Distributed LBT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
5 Evaluations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
5.1 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
6 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Curriculum Vitae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
