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(a) ECG data (b) Waveform for the ECG data  

(a) ECG data (b) Waveform for the ECG data  

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Article
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Wireless Sensor Networks (WSNs) are becoming important in today’s technology in helping monitoring our surrounding environment. However, wireless sensor nodes are powered by limited energy supply. To extend the lifetime of the device, energy consumption must be reduced. Data transmission is known to consume the largest amount of energy in a sensor...

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Citations

... For wireless communications and sensor networks, the most studied lossless data compression algorithms have been the Huffman and Lempel-Ziv Welch (LZW) algorithms [3][4][5][6]. In the present study, it has been implemented the Huffman algorithm for the compression of the spectrum of underwater noise data. ...
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The monitoring of the marine environment results in large amounts of data that must be processed and transmitted effectively for efficient resource management. In particular, given its high sampling rate, underwater noise signal acquisition technologies deserve special attention. In this article, a comparative study of the efficiency of different information processing and compression techniques is carried out, depending on the characteristics that want to be transmitted from the original signal. The applications and experiments carried out are focused on responding to the Marine Strategies, a marine environment planning instrument created under the Marine Strategy Framework Directive 2008/56/EC of 17 June, 2008 (MSFD), and, more specifically, to Descriptor 1, which regards the noise levels (both continuous and impulsive), as well as part of Descriptor 11, which is focused on the detection and abundance of cetaceans.
... Por otro lado, existen dos algoritmos principales de descompresión: los que no tienen pérdidas (lossless), que se reproducen descomprimiendo bit a bit; y los que sí las tienen (lossy), que permiten obviar parte de la información.En PAAMSY se ha decidido emplear la compresión de datos sin pérdidas (lossless) dado que, además de no perder información en su descompresión, es la técnica más utilizada para la transmisión mediante redes de sensores inalámbricos (WSN).Las WSN presentan un consumo muy elevado, debido en gran parte a la comunicación entre sensores ya que necesita mucha potencia para transmitir los datos. Por ello, la compresión de datos resulta inevitable pues así se consigue disminuir el consumo de potencia y enviar una mayor cantidad de información[1]. Para comunicaciones inalámbricas y redes de sensores los ...
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The planning, management and exploitation of marine natural resources requires monitoring systems that generates a large volume of data. Among other aspects, these systems require technologies capable of transmitting and processing information quickly and efficiently. In this way, information compression techniques play a fundamental role. In this text, the CTN proposes lossless compression techniques, based on Huffman and Lempel Zip Welch (LZM) algorithms, used for the transmission of the frequency information of underwater acoustic signals to improve the performance of current underwater noise monitoring systems. The applications and experiments carried out focus on responding to the Marine Strategies, an instrument for planning the marine environment created under Directive 2008/56 / CE, of June 17, 2008, and more specifically of the D1 MSFD.
... Data transmission consumes the largest amount of energy in a sensor node. Thus, one method to reduce the energy used is by compressing the data before transmitting it [1] [6]. ...
... It is mentioned in the literature that the Huffman algorithm is able to reduce the data size by 43% on average [6]. It is noticed that minimum variance Huffman codes are not focused in ECG signal lossless compression methods [5] [8]. ...
... The work proposed by Asral B. and Nor A. [16] presents a performance comparison of data compression in WSNs. They consider Huffman coding and LZW coding in this work. ...
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In recent years, the demand and development of multimedia product expands increasingly fast, sharing out networks bandwidth and appliances memory. Therefore, data compression theory becomes the major considerable for reducing datum volume so as to economize extra transmission bandwidth and hardware sharing. Energy limitations have become fundamental challenge for designing wireless sensor networks. Network lifetime represent the most important and interested metric. This paper proposes data compression methods for maximizing lifetime of the network. Two lossless compression methods are proposed for compressing patients ECG data. The first is Huffman coding and the second is arithmetic coding. Dijkstra routing has been used as the main routing protocol. A comparison has been made for the two compression methods. Simulations demonstrate that Huffman coding has superior performance against arithmetic coding. It shows an increase in network lifetime of about 63% while arithmetic coding shows lifetime increase of about 16.76%. Results show the effectiveness of the Huffman coding method for maximizing WSNs lifetime.