Intelligent Image compression in Multi-agent system

International Journal of Electrical and Computer Engineering (IJECE), 2023

Transferring images in a wireless multimedia sensor network (WMSN) knows a fast development in both research and fields of application. Nevertheless, this area of research faces many problems such as the low quality of the received images after their decompression, the limited number of reconstructed images at the base station, and the high-energy consumption used in the process of compression and decompression. In order to fix these problems, we proposed a compression method based on the classic discrete wavelet transform (DWT). Our method applies the wavelet compression technique multiple times on the same image. As a result, we found that the number of received images is higher than using the classic DWT. In addition, the quality of the received images is much higher compared to the standard DWT. Finally, the energy consumption is lower when we use our technique. Therefore, we can say that our proposed compression technique is more adapted to the WMSN environment.

Wireless Personal Communications, 2014

Multidimensional sensors, such as digital camera sensors in the visual sensor networks VSNs generate a huge amount of information compared with the scalar sensors in the wireless sensor networks WSNs. Processing and transmitting such data from low power sensor nodes is a challenging issue through their limited computational and restricted bandwidth requirements in a hardware constrained environment. Source coding can be used to reduce the size of vision data collected by the sensor nodes before sending it to its destination. With image compression, a more efficient method of processing and transmission can be obtained by removing the redundant information from the captured image raw data. In this paper, a survey of the main types of the conventional state of the art image compression standards such as JPEG and JPEG2000 is provided. A literature review of their advantages and shortcomings of the application of these algorithms in the VSN hardware environment is specified. Moreover, the main factors influencing the design of compression algorithms in the context of VSN are presented. The selected compression algorithm may have some hardwareoriented properties such as; simplicity in coding, low memory need, low computational load, and high-compression rate. In this survey paper, an energy efficient hardware based image compression is highly requested to counter the severe hardware constraints in the WSNs.

The problem related to energy conservation in a resource-constrained wireless visual sensor network applied for habitat monitoring, border patrol, battlefield surveillance and so on, where images have to be transmitted over a wireless medium with limited bandwidth, is addressed. The proposed approximation band transform algorithm is unique and novel since it extracts and encodes only approximations of the image using fixed-point arithmetic without applying highpass analysis filters. This low bit rate image compression algorithm is specifically designed for resource-constrained low-power sensors. Its performance is analysed in terms of bit rate (bits per pixel), image quality, processing time and its energy consumption in an Atmel Atmega128 processor. It is shown that this algorithm consumes only 12% of energy needed by joint photographic experts group (JPEG) [independent JPEG group (IJG)] version and gives better results than JPEG at very low bit rates and thus the proposed scheme can significantly enhance the lifetime of low-power sensors.

2010

Wireless Sensor Network (WSN) consists of a group of energy constrained sensor nodes. Since the sensor nodes are often equipped with the battery that usually cannot be replaced or recharged, the way to prolong the lifetime of WSN becomes a critical issue. The utilization of radio communication is the main cause of power consumption. If the data size can be

2012

In Wireless Multimedia Sensor Networks, the amount of multimedia traffic generated by the source nodes and relayed throughout the network towards the sink nodes is relatively vary large. As a result, the overall network performance and operational lifetime rely heavily on the efficiency and the complexity of the compression techniques used. However, known compression techniques have either high efficiency or low complexity but not both, which causes an imbalance in the distribution of the available network resources and hence shortens the network lifetime. In this work, a testbed to experimentally evaluate the performance and energy consumption of image compression and transmission was developed. Based on the testbed, the JPEG and JPEG2000 image compression algorithms were simultaneously embedded and evaluated with the purpose of jointly utilizing the features of both algorithms in order to facilitate a balanced tradeoff between the application performance requirements and the current network status. The analysis of the results verified that, in practical implementations of Wireless Multimedia Sensor Networks, using more than one compression technique by the same source node is feasible and has the potential of utilizing the capabilities of each technique based on the given situation.

2009

Wireless Sensor networks are battery powered due to which their lifetime is very limited. In camera an equipped wireless sensor node, life time of sensor network is decreased quickly due to battery and processing power constraints when image is processed and is transferred to the destination. The image compression not only helps to reduce the communication latency but also gives energy efficiency in wireless sensor networks. In this paper, we present a novel technique, Image Subtraction with Quantization of image (ISQ). We have shown that ISQ improves the energy efficiency of each node of the sensor networks consequently the lifetime of the sensor network is increased.

International journal of …, 2010

Abstract— Life time of wireless sensor networks is limited due to battery constraint. In camera equipped sensor nodes, due to battery and processing power constraints, the life time of sensor network is decreased quickly when image is processed and is transferred to the ...

Wireless Communications and Mobile Computing, 2017

In a wireless multimedia sensor network (WMSN), the minimization of network energy consumption is a crucial task not just for scalar data but also for multimedia. In this network, a camera node (CN) captures images and transmits them to a base station (BS). Several sensor nodes (SNs) are also placed throughout the network to facilitate the proper functioning of the network. Transmitting an image requires a large amount of energy due to the image size and distance; however, SNs are resource constrained. Image compression is used to scale down image size; however, it is accompanied by a computational complexity trade-off. Moreover, direct image transmission to a BS requires more energy. Thus, in this paper, we present a distributed image compression architecture over WMSN for prolonging the overall network lifetime (at high throughput). Our scheme consists of three subtasks: determining the optimal camera radius for prolonging the CN lifetime, distributing image compression tasks amon...

2013

The availability of low cost hardware like microphone and CMOS cameras has led to spread in multimedia wireless sensor network. Data transfer is considered one of the high cost tasks in wireless sensor network consuming more than 80 percent of dedicated energy. The most appropriate way to reduce energy consumption is to use an appropriate compression image technique. Designing a compression method requires balance and careful estimation among compression ratio factors, quality, complexity, and time. The present article presents a new method, unlike previous ones, focusing only on the structure of the network. In addition, it tries to deal with low compression rate along with high quality of recovered images and reduction in energy consumption. Hence, the method used here is Distributed Vector Quantization (DVQ) whose codebook, being real-time and dynamic, was created by applying neural network of adaptive resonance theory. The results from tests on primary images demonstrate the acc...

IEEE Access

Traditionally, image compression algorithms have primarily focused on optimizing storage without considering resource-constrained applications, such as wireless sensor networks (WSNs). However, for practical application in WSNs, a balanced trade-off between compression ratio, distortion, and energy consumption is crucial to improve the network lifetime while maintaining acceptable image reconstruction quality at lower bit and error rates. Previous studies have focused on higher image compression rates to address storage limitations rather than on the optimization of WSNs. In addition, most previous research on image compression for WSNs either requires an error-bound mechanism or compromises the trade-off between compression ratios and reconstructed image quality measured using image quality assessment metrics, such as root mean square error (RMSE), and coefficient of determination (R 2), leading to a reduced network lifetime and uncontrolled reconstructed image quality that is not application-specific. Therefore, we present an optimized block-based image compression algorithm for WSNs with a relative error-bound mechanism that adapts to a given dataset to improve reconstruction fidelity and energy consumption at higher compression ratios. A comparison of our proposed algorithm with existing algorithms demonstrated that using a convolutional variational autoencoder and relative error-bound mechanism leads to a significant trade-off in distortion, compression ratio, and energy consumption in WSNs. Our results demonstrated that an average reconstruction fidelity of more than 90% was achieved using image quality evaluation metrics at compression ratios of 60% or more. Furthermore, more than 50% energy conservation at compression ratios greater than 60% from image compression was achieved compared with the transmission of raw data within a WSN. INDEX TERMS Image Compression, Neural Networks Compression, Reconstruction Fidelity, Wireless Sensor Networks. I. INTRODUCTION The research focus areas in sensor networks include compression, bandwidth management, storage, data transmission, energy conservation, and network interference. The lifetime of a wireless sensor network (WSNs) depends on the battery lifetime of the sensor nodes. Therefore, minimizing the power consumption of sensor nodes has become a research focus to ensure a continuous communication lifetime between the transmitter and receiver nodes. This is achieved via image compression, which involves quantizing the image data. However, quantization leads to lossy compression, which affects the quality of the reconstructed images. Therefore, the level of compression for energy conservation and distortion of the reconstructed image forms the basis for optimization in image compression. Several optimized compression algorithms have been developed based on their achievements and defects[1]. Traditionally, the pipeline for image-compression transformation consists of quantization, transformation, and entropy coding. Image compression transformation involves the transformation of an image signal into decorrelated coefficients. The Discrete Wavelet Transform (DWT) [2] This article has been accepted for publication in IEEE Access.