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Fast Transmission of Image, Reduction of Energy Consumption and Lifetime Extension in Wireless Cameras Networks

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Fast Transmission of Image, Reduction of Energy Consumption and Lifetime Extension in Wireless Cameras Networks Vol. 11(1), pp. 1-11, April 2019 DOI: 10.5897/JETR2018.0656 Article Number: 0336C9A60770 ISSN: 2006-9790 Copyright ©2019 Journal of Engineering and Technology Author(s) retain the copyright of this article http://www.academicjournals.org/JETR Research Full Length Research Fast transmission of image, reduction of energy consumption and lifetime extension in wireless cameras networks Nirmi Hajraoui* and N. Raissouni Laboratory of Remote Sensing and Geographic Information System (LRSGIS), Department of Telecommunication, University of Abdelmalek Essaâdi, ENSA-Tetouan, Mhanech2, Tetouan, 93002, Morocco. Received 8 December, 2018; Accepted 7 March, 2019 In this paper, a fast image processing was proposed. It ensures energy efficiency and the extension of both the lifetime and the proper functioning of the network. It is a filtered zonal discrete cosine transform that allows and optimizes an effective adjustment of the trade-off between power consumption and image distortion. This is a remarkable energy saving method, in this kind of networks. It is applied throughout the chain of transmission and decompression of the image. It makes it possible to integrate a fast and a filtered zonal discrete cosine transform. This proposal dramatically improves the indicated method. The insertion of the frequency filters in this chain has greatly reduced the coefficients to be calculated and to be coded in each block. This new method ensures the fast transfer of images, decreases more the energy consumption of sensors and maintains a long network lifetime. This proposal seems to us very satisfactory as shown by the experimental results provided here. Key words: Energy saving, fast zonal discrete cosine transform, filtered fast zonal discrete cosine transform, image compression, wireless vision sensors network, zonal coding. INTRODUCTION The development in sensor technology has allowed the But, the limitation of energy reserve and memory capacity design of sensor networks. These sensors are not as in each node of any wireless network, generate several reliable or as accurate as their expensive macro sensor problems such as the reduction of the lifetime (Yong et counterparts, but their size and cost enable applications al., 2016; Cristian, 2009), of the efficiency in network to network hundreds or thousands of these sensors in operations (Angelakis et al., 2014; He et al., 2017) and of order to achieve high quality, fault tolerant sensing energy saving (Eriksson et al., 2016; Huaying et al., networks. Reliable environment monitoring is important in 2018). In addition, this makes most of the algorithms of a variety of commercial and military applications. The the embedded treatment unusable after a short time. uses of these sensors are multiplying more and more, Energy efficiency in image processing imposes to because they are inexpensive and their sizes are small. minimize any kind of operation on the mass of data used. *Corresponding author. [email protected]. Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution License 4.0 International License 2 J. Eng. Technol. Res. Indeed, if a black and white image, of standard size 512 × 512 pixels, is coded on 8 bits per pixel, it generates 256 kb in uncompressed form. This becomes three times more important for a color image. The advantage of compression is obvious. Because of their low mass of data, compressed images take less time to be PROPOSED METHOD transmitted on the same channel (Shoaib and Alshebeili, Principle 2012; Makkaoui et al., 2010; Loeffler et al., 1989; Feig and Winograd, 1992). Therefore, they require less The principle of FFZ_DCT a Filtered Fast Zonal Discrete Cosine energy. They only need a very small bandwidth to reach Transform (Figure 1) is very exciting by its easy computation and its their destination. In general, there are two main remarkable importance. The fast LLM-DCT (Loeffler et al., 1989) compression algorithms. The first is lossless or and the zonal-DCT (Makkaoui et al., 2010) were taken as the starting points for our proposal. The corresponding algorithms reversible. It guarantees the perfect reconstruction of the (Makkaoui et al., 2010) were used with their complexity. Therefore, images. But, the second algorithm is with loss or we have developed the FZ_DCT based image compression by irreversible. It modifies more or less the value of the computing the following coefficients: pixels (Shoaib and Alshebeili, 2012; Makkaoui et al., 2010; Loeffler et al., 1989; Feig and Winograd, 1992; Bracamonte et al., 1996; Liang and Tran, 2001; Taylor and Dey, 2001; Akyildiz et al., 2007; Jeong et al., 2004; Petracca et al., 2009; Ferrigno et al., 2005; Hsieh, 2005; Heyne et al., 2006; Puri et al., 2006; Sikka et al., 2004; where and Wark et al., 2007; Han et al., 2012; Ehsan and Hamdaoui, 2012; Heizelman et al., 2000; Jin et al., 2007; Wheeler and Pearlman, 2000; Wang et al., 2008). The lossless compression makes it possible to exploit the redundancies of the signal. But, it does not allow optimal Whatever the size of the image, there are only 32 cosines involved in the coefficients calculation of the FZ_DCT. The previous double compression rates. Since the image end user uses only sum can be expressed in the matrix form (CPCT) where P is the the relevant information, lossy compression is often 8×8 matrix of the pixels and the elements of C are expressed in the preferred. This algorithm makes it possible to achieve form as follows: very high compression ratios. They depend on the quality level of the accepted information relative to the initial image and on the type of the adopted compression. The aim of image compression in the context of wireless This proposal consists to filter these coefficients. Only, the relevant cameras networks (WCN), is therefore to reduce the coefficients of the upper left part of the image block will be coded initial size of data in each node. This allows fewer according to their importance. In fact, the weakest coefficients can packages to be sent via any WCN. This operation be reduced to zero. This reduces considerably the number of increases the lifetime of any vision sensor. This is coefficients to be computed and to be coded in each block. This justified by the estimated energy efficiency in it. This new reduction may be sometimes more than 50%. An important energy saving is thus achieved throughout the chain of transmission and representation will be decoded by a decompression decompression. procedure to reconstruct the image in the analysis In Figure 2, we show where each of the two approaches Fast station. This paper improves this operation. But, we do Zonal DCT and Filtered Fast Zonal DCT, react in the compression not work on communication protocol like that done by chain. They are indicated first by FZ_DCT (1) and second by Heizelman model (Heizelman et al., 2000), where they FFZ_DCT (2). used a LEACH, a clustering-based routing protocol that The image is considered to be spread over a rectangle. Filters have been inserted into the chosen area of the image as shown in minimizes global energy usage by distributing the Figure 3. They depend on the distance and on the size of the upper network load to all the nodes at different points in time. In left corner of this image. This was by adding a frequency filters to each compressed image, we reduce the information this upper left area, each one according to a distance x which redundancies. We desire to optimize as possible the corresponds to the position of the coefficient DC(i,j) with respect to relevant information in each compressed image to obtain DC(0,0). These filters are thresholds Fx. Each one is used in each high quality results. Large energy gains and a greater line vector (v) of a compressed image. If there is at least one component of this vector which is in the interval [-Fx, Fx] then the network lifetime can be achieved by this process, thereby components of (v) remain unchanged, otherwise all these requiring much less data to be transmitted to the base components will be reset. The indicated filter Fx is defined by the station. There is no work, to the best of our knowledge indicated distance x and the cycle y chosen by the experiment as it that uses this new method especially in WCN. In this appears in the following formula: operation, the FZ_DCT algorithm (Makkaoui et al., 2010), Filter x or Fx = 2 x+y-1 some image filters and the mean square error (MSE) were used. This is calculated by the following Formula 1. where x ∈ {1,2,…..,X-1} and y ∈ N Hajraoui and Raissouni 3 Figure 1. Principle of Filtered Fast Zonal DCT. Figure 2. Activity phases of the both approaches. Figure 3. Filter effect on the Fast Zonal DCT. With our new approach, we proceed as follows: This process is different to the algorithm of FZ_DCT. The FZ_DCT (1) In the quantization phase, we focus on the area chosen by the only affects the decorrelation phase. However, the new approach FZ_DCT algorithm (Makkaoui et al., 2010). also affects the quantization phase. This is where we can see the (2) We decrease with the chosen frequency filters, the number of effect of low-influence coefficients on image corruption. It is related coefficients. This is done by adding the indicted filters to the upper to the large reduction in the number of bits transmitted by any WSN. left area of the image. It depends on the coefficient position DC(i, j) Our principle is to minimize the cost of transmission and to increase defined with respect to DC(0,0). the quality of the image. The cost here represents the energy (3) We evaluate and compare the results provided by each Classic consumption related first to the number of transmitted bits of a DCT, FZ_DCT and FFZ_DCT methods.
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