In this paper, we propose a self-adaptive image transmission scheme driven by energy efficiency considerations in order to be suitable for wireless sensor networks. It is based on wavelet image transform and semi-reliable transmission to achieve energy conservation. Wavelet image transform provides data decomposition in multiple levels of resolution, so the image can be divided into packets with different priorities. Semireliable transmission enables priority-based packet discarding by intermediate nodes according to their battery's state-of-charge. Such an image transmission approach provides a graceful trade-off between the reconstructed images quality and the sensor nodes' lifetime.An analytical study in terms of dissipated energy is performed to compare the selfadaptive image transmission scheme to a fully reliable scheme. Since image processing is computationally intensive and operates on a large data set, the cost of the wavelet image transform is considered in the energy consumption analysis. Results show up to 80% reduction in the energy consumption acheived by our proposal compared to a non energy-aware one, with the guarantee for the image quality to be lower-bounded.

In this paper, we propose two image transmission schemes driven by energy efficiency considerations in order to be suitable for wireless sensor networks. The first one is an open-loop image transmission scheme while the second one is closed-loop. Both schemes are based on wavelet image transform and semi-reliable transmission to achieve energy conservation. Wavelet image transform provides data decomposition in multiple levels of resolution, so the image can be divided into packets with different priorities. Semi-reliable transmission enables priority-based packet discarding by intermediate nodes according to their battery's state-of-charge. Such an image transmission approach provides a graceful trade-off between the image quality played out and the sensor nodes' lifetime.An analytical study in terms of dissipated energy is performed to compare our two schemes to a fully reliable image transmission scheme. Since image processing is computationally intensive and operates on a large data set, the cost of the wavelet image transform is considered in the energy consumption analysis. Results show up to 70% and 90% reductions in energy consumption with the open-loop and closed-loop schemes respectively compared to a non energy-aware one, with a guarantee for the image quality to be lower-bounded.

This paper deals with image compression over Wireless Camera Sensor Networks (WCSNs) in order to decrease the energy consumption of sensors and thus to maintain a long network lifetime. As the radio tranceiver is the most power greedy components of sensor nodes, it seems natural to consider lossy compression before transmission as the appropriate answer to the problem of energy consumption. However, the limitation of sensor nodes in terms of memory as well as processor speed makes most of the compression algorithms inapplicable. Indeed, the most popular methods such as JPEG or JPEG2000 can yield a higher energy consumption than when transmitting uncompressed images. Here we propose to solve this problem by the design of a fast zonal DCT-based image compression algorithm which allows an efficient tuning of the trade-off between energy consumption and image distortion, as shown by experimental results provided in the paper.

The problem of energy conservation in wireless image sensor networks is addressed, and a fast zonal discrete consine transform (DCT) design which aims to decrease the complexity of JPEG baseline compression is presented. Energy consumption measurements have been made on a real wireless camera node in order to evaluate the amount of energy which can be saved during the image compression process without loss of visual quality. Although the gain depends on the compression rate, such a DCT design is a simple and effective way to prolong the lifetime of the camera nodes, and thereby the network lifetime.Introduction: Wireless sensor networks refer to the large-scale deployment of small, inexpensive, and battery-powered sensing devices with on-board processing and wireless communication capabilities which achieve a self-organised, infrastructure-less, and fault-tolerant sensor network in a cooperative way. Such networks make possible dense monitoring and analysis of complex physical or biological phenomena over large regions of space and over long periods of time (e.g. for months or even years) [1]. Of course, energy conservation is a fundamental concern for achieving the required network lifetime since battery replacement of sensor nodes is often undesirable or impossible. Recently, there is a growing interest in the applications which require image sensing in order to achieve object detection, localisation, tracking, and counting [2]. In this case, all or some of the sensor nodes are equipped with a small CMOS camera, referred to as Wireless Image Sensor Network (WISN). However, WISNs make the energy consumption problem worse owing to the large volumes of data to be transmitted compared to applications dealing with scalar data such as temperature and soil moisture. As the radio transceiver is one of the most power hungry of all electronic components of sensor nodes, lossy compression of image data before transmission seems an appropriate answer to this problem, for the source camera node as well as the nodes forwarding data towards the sink. Unfortunately, most of the compression algorithms, for example JPEG, may perform poorly owing to the resource limitations of sensor nodes in terms of memory size or processor speed, and even sometimes lead to greater energy consumption than in the uncompressed case [3]! For JPEG baseline compression, the energy conservation problem can be dealt with by a zonal DCT approach (also referred to as pruned DCT [4]). In a previous work presented in [5], we showed that the zonal DCT using a square shape provides a better energy-distortion trade-off than the one using a triangle shape.In this Letter, we present an energy-efficient design for JPEG baseline compression. The novelty of our contribution is threefold. First, we propose a low-complexity DCT scheme combining the best lifting DCT algorithm of the literature and the zonal DCT using a square shape. The former aims to reduce the number of operations per coefficient while the latter aims to reduce the number of coefficients to be pro...