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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張瑞益(Ray-I Chang) | |
| dc.contributor.author | Meng-Han Li | en |
| dc.contributor.author | 李孟翰 | zh_TW |
| dc.date.accessioned | 2021-06-13T16:27:25Z | - |
| dc.date.available | 2016-07-27 | |
| dc.date.copyright | 2011-07-27 | |
| dc.date.issued | 2011 | |
| dc.date.submitted | 2011-07-19 | |
| dc.identifier.citation | [1] K. Dasgupta, K. Kalpakis, and P. Namjoshi, 'An Efficient Clustering-based Heuristic for Data Gathering and Aggregation in Sensor Networks,' in Proceedings of Wireless Communications and Networking Conference, 2003.
[2] A. Biligin, M. W. Marcellin, and I. Altbach, ' Low energy compression of EEG signals using JPEG2000,' in IEEE Transactions on Consumer Electronics, 2003. [3] A. Bendifallah, R. Benzid, and M. Boulemden, 'Improved ECG compression method using discrete cosine transform,' in Electronics Letters, 2011. [4] J. Chen, J. Ma, Y. Zhang, and X. Shi, 'A Wavelet-Based ECG Compression Algorithm Using Golomb Codes,' in Proceedings of Communications, Circuits, and Systems Conference, 2006. [5] J. Fang and H. B. Li, 'Hyperplane-Based Vector Quantization for Distributed Estimation in Wireless Sensor Networks,' in IEEE Transactions on Information Theory, 2009. [6] Y. C. Wang, Y. Y. Hsieh, and Y. C. Tseng, 'Multiresolution Spatial and Temporal Coding in a Wireless Sensor Network for Long-Term Monitoring Applications,' in IEEE Transactions on Computers, 2009. [7] M. Kawahara, Y. Chiu, and T. Berger, 'High-speed software implementation of Huffman coding,' in IEEE Transactions on Data Compression Conference, 1998. [8] F. Marcelloni and M. Vecchio, 'A Simple Algorithm for Data Compression in Wireless Sensor Networks,' in IEEE Communication Letters, 2008. [9] M. Y. Javed and A. Nadeem, 'Data compression through adaptive Huffman coding schemes,' in Proceedings of TENCON, 2000. [10] C. Tharini and P. V.Ranjan, 'Design of Modified Adaptive Huffman Data Compression Algorithm for Wireless Sensor Network,' in Journal of Computer Science, 2009. [11] N. Y. Huang, C. C. Lin, C. C. Chuang, and R. I. Chang, 'Lossless Compression for Low Correlation Data in Wireless Sensor Networks,' in Workshop on Wireless Ad Hoc and Sensor Network, 2010. [12] H. Luo, Y. Liu, and S. K. Das, 'Routing Correlation Data with Fusion Cost in Wireless Sensor Networks,' in IEEE Transaction on Mobile Computing, 2006. [13] H. Luo, Y. Liu, S. K. Das, and J. Luo, 'Adaptive Data Fusion for Energy Efficient Routing in Wireless Sensor Networks,' in IEEE Transaction on Mobile Computing, 2006. [14] R. Kumar, M. Wolenetz, B. Agarwalla, 'DFuse: A Framework for Distributed Data Fusion,' in ACM SenSys, 2003. [15] G. Jin and M. Park, 'CAC: Context Adaptive Clustering for Efficient Data Aggregation in Wireless Sensor Networks,' in NETWORKING LNCS, 2006. [16] S. Lee, J. Yoo, and T. Chung, 'Distance-based Energy Efficient Clustering for Wireless Sensor Networks,' in IEEE International Conference on Local Computer Networks LCN, 2004. [17] A. Makarenko and H. D. Whyte, 'Decentralized Data Fusion and Control in Active Sensor Networks,' in International Conference on Information Fusion, 2004. [18] W. Yuan, S. V. Krishnamurthy, and S. K. Tripathi, 'Synchronization of Multiple Levels of Data Fusion in Wireless Sensor Networks,' in Global Telecommunications Conference, 2003. [19] M. A. Sharaf, J. Beaver, A. Labrinidis, and P. K. Chrysanthis, 'TiNA: A Scheme for Temporal Coherency-Aware in-Network Aggregation,' in ACM MobiDE, 2003. [20] S. Yoon, C. Shahabi, 'The Clustered AGgregation (CAG) Technique Leveraging Spatial and Temporal Correlations in Wireless Sensor Networks,' in ACM Transactions on Sensor Network, 2007. [21] S. Goel and T. Imielinski, 'Prediction-based Monitoring in Sensor Network: Taking Lessons from MPEG,' in ACM Computer Comm. Review, 2001. [22] K. Choi, M. H. Kim, K. J. Chae, J. J. Park, and S. S. Joo, 'An Efficient Data Fusion and Assurance Mechanism using Temporal and Spatial Correlations for Home Automation Networks,' in IEEE Transactions on Consumer Electronics, 2009. [23] R. Starosolski, “Simple Fast and Adaptive Lossless Image Compression Algorithm,” in Journal of Software - Practice and Experience, 2007. [24] Y. Liang and W. Peng, “Minimizing energy consumptions in wireless sensor networks via two-model transmission,” in ACM SIGCOMM, 2010. [25] M. Mastriani, 'Single Frame Supercompression of Still Images, Video, High Definition TV and Digital Cinema,' in International Journal of Information and Mathematical Sciences, 2010. [26] ChEAS: Chequamegon Ecosystem Atmosphere Study. Homepage: http://cheas.psu.edu/data. [27] U.S. Geological Survey Earthquake Hazards Program. Hompage: http://earthquake.usgs.gov/. [28] Physionet Homepage: http://www.physionet.org/cgi-bin/ATM. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/38173 | - |
| dc.description.abstract | 感測節點在實際應用中常常布置於無法供給電力甚至無法進行更換電池的環境，因此如何節省能量消耗以延長感測網路時間成為重要的研究議題。因此，不少失真資料壓縮演算法被提出來，以減少資料傳輸的能量消耗，但這些失真資料壓縮演算法並無法控制誤差大小，難以用在有限誤差的實用問題上，因此本論文將提出有限誤差的資料壓縮演算法，以同時維持良好的資料壓縮率以及有限誤差控制。本研究針對無線感測網路的省電需求，利用資料相關性、時間相關性以及空間相關性等資料特性進行編碼，提出BEDCA (Bounded Error Data Compression and Aggregation in Wireless Sensor Network)。傳統的失真資料壓縮VQ (Vector Quantization)演算法跟DCT (Discrete Cosine Transform)演算法無法控制誤差大小，當容許誤差設定為0.5%，我們的方法可以較其節省至少70%的能量消耗；當容許誤差設定為1.0%，則可以較其節省超過90%的能量消耗。此外，跟 VQ相比，我們方法可以改進20% 壓縮率跟節省62%能量；與DCT相比則可以改進38%壓縮率跟52%能量。經實驗發現，若在傳送過程中就先進行壓縮以及聚合，可比在目的端節點才進行處理節省超過80%以上的能量消耗。本論文針對四種不同屬性的資料進行查詢評估，發現我們的方法之執行結果都較傳統方法為佳。然而時間相關性高的資料，其壓縮率可能會因為誤差的增加反而變差，原因可能歸因於我們所採取的codebook是靜態的，採取動態codebook應可解決此問題。 | zh_TW |
| dc.description.abstract | In this paper, an efficient data compression and aggregation method, called BEDCA, is proposed to reduce the size of transmission data under the given bounded error. We first apply the observed transmission data to construct a codebook which is related to the data correlation of the monitoring environment. Given a bounded error, the proposed method determines whether the new sensed data should be compressed or not by comparing it with the reference data such as the previous sensed data (for temporal correlation), the neighboring sensed data (for spatial correlation), and the codebook encoded data (for data correlation). Thus, the total size of transmission data can be minimized for energy saving. We use a real dataset to evaluate the performance of our mechanism. Even if the bounded error is set as a small value (under 0.5%), the proposed method can reduce a lot of the transmission data (over 70%) to cut down the total energy consumption. Our improvement exceeds 90% in the total energy consumed when bounded error is more than 1%. Compared to VQ, our proposed methods can enhance 20% better compression ratio and save 62% energy at least. Our method improve 38% compression ratio and retain 52% energy at least than DCT. Moreover, in our simplified hierarchy model of architecture, more than 20% energy can be saved in any aggregation function in SN than aggregated in sink. Experiment results show that the proposed method can make WSNs more efficient in energy consumption. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T16:27:25Z (GMT). No. of bitstreams: 1 ntu-100-R98525087-1.pdf: 2538690 bytes, checksum: 882c216205d12f0bb6d5cd02d2564079 (MD5) Previous issue date: 2011 | en |
| dc.description.tableofcontents | Chapter 1 Introduction 1
1.1 Motivation 1 1.2 Contribution 2 1.3 Thesis Organization 3 Chapter 2 Related Works 4 2.1 Data compression mechanisms 4 2.2 Correlations considered in compression mechanisms 6 Chapter 3 BEDCA 7 3.1 Codebook Generation 8 3.2 Bounded Error Assignment 9 3.3 Bounded Error Data Compression 10 3.3.1 Temporal Correlated Compression 11 3.3.2 Spatial Correlated Compression 13 3.3.3 Data Correlated Compression 15 3.3.4 Compression Algorithm of BEDCA 17 3.4 Bounded Error Query Operations 19 3.4.1 Aggregation Functions 20 3.4.2 Aggregation Algorithm of EBDCA 21 3.4.3 Correctness of Aggregation Algorithm 22 Chapter 4 Performance Evaluation 24 4.1 Experimental Setting 24 4.2 Compression Ratio 27 4.2.1 Case 1: High Temporal Correlation and High Spatial Correlation 29 4.2.2 Case 2: Low Temporal Correlation and High Spatial Correlation 33 4.2.2 Case 3: High Temporal Correlation and Low Spatial Correlation 37 4.2.3 Case 4: Low Temporal Correlation and Low Spatial Correlation 41 4.3 Query Performance 45 4.3.1 Selection Query Overhead 45 4.3.2 Selection Query Time 48 Chapter 5 Conclusions and Future Works 49 REFERENCES 51 | |
| dc.language.iso | en | |
| dc.subject | 資料相關性 | zh_TW |
| dc.subject | 能源效率 | zh_TW |
| dc.subject | 空間相關性 | zh_TW |
| dc.subject | 查詢指令 | zh_TW |
| dc.subject | 有限誤差 | zh_TW |
| dc.subject | 時間相關性 | zh_TW |
| dc.subject | 無線感測網路 | zh_TW |
| dc.subject | 資料壓縮 | zh_TW |
| dc.subject | energy efficiency | en |
| dc.subject | wireless sensor networks | en |
| dc.subject | bounded error | en |
| dc.subject | data compression | en |
| dc.subject | query operations | en |
| dc.subject | temporal correlation | en |
| dc.subject | spatial correlation | en |
| dc.subject | data correlation | en |
| dc.title | 考慮有限誤差的無線感測網路資料壓縮與聚合 | zh_TW |
| dc.title | Bounded Error Data Compression and Aggregation in Wireless Sensor Networks | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 99-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 丁肇隆(Chao-Lung Ting),黃乾綱(Chien-Kang Huang),王家輝(Chia-Hui Wang),林正偉(Jeng-Wei Lin) | |
| dc.subject.keyword | 無線感測網路,有限誤差,資料壓縮,查詢指令,時間相關性,空間相關性,資料相關性,能源效率, | zh_TW |
| dc.subject.keyword | wireless sensor networks,bounded error,data compression,query operations,temporal correlation,spatial correlation,data correlation,energy efficiency, | en |
| dc.relation.page | 53 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2011-07-19 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
| 顯示於系所單位： | 工程科學及海洋工程學系 | |
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| ntu-100-1.pdf 未授權公開取用 | 2.48 MB | Adobe PDF |
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