Data compression techniques in Wireless Sensor Networks |
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| Affiliation: | 1. Department of Computer Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia;2. Jodrey School of Computer Science, Acadia University, Canada;3. American University of Ras Al Khaimah, Ras Al Khaimah, United Arab Emirates;1. Shanghai University, Shanghai, China;2. University of Macau, Macau, China;3. The Third Research Institute of Ministry of Public Security, Shanghai, China;1. Department of Mechanical Engineering, College of Engineering, University of Basrah, Basrah, Iraq;2. Department of Computer Engineering, College of Engineering, University of Basrah, Basrah, Iraq;1. Jiangsu Engineering Research Center of Communication and Network Technology, Nanjing University of Posts and Telecommunications, Nanjing, China;2. National-local Joint Engineering Laboratory for Digitalized Electrical Design Technology, Wenzhou University, Wenzhou, China;3. School of Computer and Information, Anhui Normal University, Wuhu, China;4. Key Laboratory of Mobile Network Application Technology of Zhejiang Province, Ningbo University, Ningbo, China |
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| Abstract: | The advancement in the wireless technologies and digital integrated circuits led to the development of Wireless Sensor Networks (WSN). WSN consists of various sensor nodes and relays capable of computing, sensing, and communicating wirelessly. Nodes in WSNs have very limited resources such as memory, energy and processing capabilities. Many image compression techniques have been proposed to address these limitations; however, most of them are not applicable on sensor nodes due to memory limitation, energy consumption and processing speed. To overcome this problem, we have selected Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) image compression techniques as they can be implemented on sensor nodes. Both DCT and DWT allow an efficient trade-off between compression ratio and energy consumption. In this paper, both DCT and DWT are analyzed and implemented using TinyOS on TelosB hardware platform. The metrics used for performance evaluation are peak signal-to-noise ratio (PSNR), compression ratio (CR), throughput, end-to-end (ETE) delay and battery lifetime. Moreover, we also evaluated DCT and DWT in a single-hop and in multi-hop networks. Experimental results show that DWT outperforms DCT in terms of PSNR, throughput, ETE delay and battery lifetime. However, DCT provides better compression ratio than DWT. The average media access control layer (MAC) delay for both DCT and DWT is also calculated and experimentally demonstrated. |
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| Keywords: | DCT DWT PSNR ETE |
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