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Summary: Wireless Multimedia Sensor Networks (WMSNs) are composed of a large number of sensor nodes that are distributed in a region to collect and transmit data. Video transmission is one of the most important applications of WMSNs because it can provide critical information about monitored areas. WMSNs face challenges related to energy consumption, bandwidth usage, and network congestion related to huge amounts of data collected by sensors. To tackle this problem, this paper proposes the Distributed Video Transmission Reduction Approach for Energy Saving in WMSN (DiViTRA). The method involves two phases: sensing and transmission phases. DiViTRA achieves frame rate adaptation to reduce the number of captured video frames and save energy during the sensing phase. In the transmission phase, three effective techniques, ORB (Oriented FAST and Rotated BRIEF), Brute‐Force (BF) Matcher, and Grid‐based Motion Statistics (GMS) are applied to decide whether to transmit the current captured frame or remove it and adjust the frame capturing rate of the video sensor accordingly. In the case of frame transmission, the DiViTRA approach compresses the frame using two data reduction approaches: PCA (Principal Component Analysis) and Huffman encoding. Through simulations, DiViTRA demonstrates a 12% reduction in energy consumption, and 71% is a ratio of reduction in sent frames while preserving stream quality. The approach has been validated in scenarios involving critical events, showcasing its efficacy in maintaining data integrity during transmission. [ABSTRACT FROM AUTHOR]

In the last decade, data hiding in encrypted image has got a lot of attention within the security and privacy researcher's communities. This attention has led to the development of various reversible data hiding techniques for encrypted images (RDHEI) which enables separable lossless recovery of cover media and extraction of the secret message, at the receiving end. In this paper, a link chain driven (LCD) RDHEI algorithm for providing high embedding capacity, is proposed. The LCD-RDHEI exploits neighbourhood similarity of image pixels and clubs them in variable size link chains. The pixels in each link chain are estimated either by their average or using two quantization values based on the size of link chains. This helps in reserving a big room inside the image which can be used to fill in the secret information. Additionally, the pixels which are not part of any of the link chains are also utilized for embedding the secret information by adaptive multi MSB prediction and Huffman Coding method. Experimental results show that the LCD-RDHEI provides significantly greater embedding capacity than the existing and related modern data hiding method for encrypted images. [ABSTRACT FROM AUTHOR]

In this era of information security and communication, a major priority is the achievement of a robust and secure steganography system when thinking about information concealment. The development of such an information-hiding scheme demands that the scheme be able to hide a secret message within the cover media. The most vexing issues in existing steganography protocols are imperceptibility, security, and capacity, and researchers have frequently emphasized a trade-off between these issues. Scholars have consistently ignored the balance between security and payload because resolving one problem has been shown to have an impact on the other, and vice versa. To overcome these problems, an effective method known as the Conventional Neural Network based Edge Detection Method (CNN-EDM) has been presented for image steganography in this study. The CNN-EDM is used to improve the contributions of the proposed scheme. Four main stages were used to achieve the objectives in this research, beginning with the cover image and secret image preparation, followed by embedding, and culminating in extraction. The last stage is the evaluation stage, which employs several evaluations to benchmark the obtained results. A standard database from the Signal and Image Processing Institute (SIPI) containing color and grayscale images with 512 × 512 pixels was utilized in this study. Different parameters were used to test the performance of the suggested scheme based on security and imperceptibility (image quality). The image quality was evaluated using three important metrics: histogram analysis, peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Furthermore, two metrics were used to evaluate the security properties of the proposed system: the Human Visual System (HVS) and Chi-square (X2) attacks. The evaluations showed that the proposed scheme can enhance the capacity, invisibility, and security features and address the already existing problems in this domain. [ABSTRACT FROM AUTHOR]

Protecting information from manipulation and theft is a top priority as a result of progress in technology and the infrastructure of the multimedia network in addition to the development of illegal methods of obtaining information. One of the means of protecting and preserving information is to hide it in a digital medium. The motivation for introducing such a system is to enhance the security of confidential data by providing ways to protect the data and reduce attempts to attack it. The proposed system contains several steps summarized as follows: The sender side includes firstly the stage of generating hiding locations randomly depending on the genetic algorithm (GA) to generate rows and seed to generate columns. Secondly, the stage of including data after compressing it by the Huffman method. Data embedding depends on the pixel index as an indicator to choose one of the three bands to hide using LSB. The recipient side extracts the important information hiding in the two last rows which helps to extract the data and convert it into the original text. The proposed system gained efficiency and robustness with the help of genetic and Huffman where genetic chooses the best way for hidding among a set of suggested solution in addition to the randomness it possesses. The role of Huffman reduce data size and thus increase the cover capacity. System efficiency has been measured by PSNR through conducting a number of experiments that included using set of texts with different sizes and two types of standard colored cover images. [ABSTRACT FROM AUTHOR]

Goal: Purpose of this paper is Study of the efficiency of using RLE and Hoffman compression methods for image compression, taking into account the limited resources of microcontrollers and, accordingly, the need to reduce image transmission time and reduce energy consumption in the wireless sensor networks. Method: An experimental study of the degree of image compression was conducted using the Hoffman and RLE methods. The algorithms are implemented in software for the ESP32-CAM microcontroller module with a built-in camera. The degree of image compression performed using the Hoffman and RLE algorithms was evaluated and compared. Results: A software module was created to implement the Huffman and RLE compression algorithms on the ESP32-CAM microcontroller. The effectiveness of using Huffman and RLE algorithms for image compression with limited microcontroller resources is investigated, compression efficiency and compressed file sizes are compared. As a result of the research, it was found that the RLE method showed a low compression efficiency (less than 1%) compared to the Huffman method, which provided a compression ratio of 11% for the image under test. The previous use of the Haar wavelet transform has a positive effect on the compression results for both investigated methods: for RLE, the degree of compression increased to 16%; for Huffman to 31% without loss of image quality. The JPEG method was used for efficiency comparison, which provides a degree of compression of the same image up to 70%, but with a loss of compressed data quality. Scientific innovation: An analysis of the data compression efficiency using the Huffman and RLE methods, implemented taking into account the limited computing capabilities of 32-bit microcontrollers with the use of additional external memory, was performed. The influence of the choice of method on the time of image processing and the amount of memory used was studied. Practical significance: Software module has been created that implements Huffman and RLE data compression algorithms. The expediency of using the above methods in systems on 32-bit microcontrollers to reduce energy consumption during image transformations and transmission, which makes it possible to extend the battery life of the sensor node, has been confirmed. At the same time, the investigated algorithms for image compression can ensure the quality of the original image with the least possible data loss. [ABSTRACT FROM AUTHOR]

Data storage on the device can affect the access speed of the device used; for example, files, images, and data will affect the performance of the device, become slow to access, difficult to open, download, and save images, files, because the available storage capacity is limited, with the problems that arise, an image compression technique is needed to minimize storage space and speed up the access. The compression technique can reduce a file, image, and data size but does not reduce the existing image's quality or lower the threshold during the sending or receiving. This research aims to reduce the size, speed up the process of accessing data on devices, and, more importantly, minimize memory space. It can also affect the bandwidth used when sending and receiving files and can speed up the process of sending from source to destination. The method used in this study is Lossy Compression, lose less Compression by comparing RLE, Huffman, and LZW using different image file types. For the Lossless Technique, the best quality reduction ratio is in binary image types, whether using a background or not using a background. The best results obtained are 99.10% (PNG Compression). Using the BMP file extension type, the recommended reduced ratio is lossy compression with format image BMP (JPG compression) for binary image using Lossless Compression has a good reduced ratio compression with an average of 99%. [ABSTRACT FROM AUTHOR]

Compression is a process that is always carried out in terms of digitizing data, which is considered very important, especially in the development and growth of the Big Data era. Lossless compression is the process of reducing the size of the data but with the condition that it can be returned to its original source during the decompression process. One of the purposes of doing Lossless compression is to archive a file, usually the file is RAW and has a large file size with a minimum 16-Bit file system (65,536 possible differences in values). Huffman's algorithm is currently still very effective in compressing 8-bit data, which can be grouped into Static, Dynamic, and Adaptive extensions, but its performance cannot be determined if it is performed on data with complex variables and probabilities such as WAV format audio data. Based on a literature review, the compression performance measurement for file archiving uses the Compression Ratio (CR) and Compression Time (CT) indicators. This research resulted in a new scheme which we named 4-ary/MQ, the architectural basis of which is based on entropy coding rooted in the static, dynamic and adaptive variants of the Huffman scheme. For the variable code length characteristics, it follows the Quad Tree dynamic branching (FGK rule), the node symbol setting adopts an adaptive method, namely adding a maximum of 2 variables with a value of '0' to maintain the root of the branch after the root always has 4 branches. Based on descriptive analysis of compression results, deviation, average, ANOVA and DMRT, 4-ary/MQ produces optimal CR with fast CT when compared to various variants of the Huffman algorithm and other lossless compression applications such as (PKZIP, WinZip, 7-Zip, and Monkeys Audio). From the results of trial analysis based on manual mathematical and statistical calculations, it is certain that 4-ary/MQ provides high compression results with a very fast process, so it has many benefits if it is used to compress data on local storage media, hosting/cloud and bandwidth. [ABSTRACT FROM AUTHOR]

Steganography is the practice of hiding data, such as images, videos, or text, within a cover image without it being detectable by the human eye. Several factors, such as the capacity, security, and robustness of the technique, are essential when transferring information using this method. In this study, we propose a new approach to image steganography that improves the Least Significant Bit (LSB) technique by utilizing images of 24 bits in each pixel. Improving the capacity and security of LSB-based steganography requires a combination of techniques, such as indirect embedding, embedding in multiple channels, and applying cryptographic and compression techniques. This approach conducts by encrypting each compressed secret message bit with the most significant bit of the red channel and then saving the output bit (hidden bit) in the least significant bit of the (green/blue) channel according to the row value (odd/ even). To further security, the suggested approach uses multi-level encryption; employs RSA to encrypt the secret message before applying the Huffman compression and encrypting the hidden bit by (XOR/XNOR) in the embedding method based on the row value of the pixel. Meanwhile, it is planned to use the Huffman coding technique to shorten the length of the encrypted message that will be inserted in the cover image. For the color photos in this work, the standard images were acquired from a standard dataset (USC-SIPI). The suggested approach performs better when measured in terms of mean square error (MSE), peak signalto- noise ratio (PSNR), and comparison to findings from related prior efforts. [ABSTRACT FROM AUTHOR]

As the number of Internet of Things (IoT) devices continues to rise dramatically each day, the data generated and transmitted by them follow similar trends. Given that a significant portion of these embedded devices operate on battery power, energy conservation becomes a crucial factor in their design. This paper aims to investigate the impact of data compression on the energy consumption required for data transmission. To achieve this goal, we conduct a comprehensive study using various transmission modules in a severely resource-limited microcontroller-based system designed for battery power. Our study evaluates the performance of several compression algorithms, conducting a detailed analysis of computational and memory complexity, along with performance metrics. The primary finding of our study is that by carefully selecting an algorithm for compressing different types of data before transmission, a significant amount of energy can be saved. Moreover, our investigation demonstrates that for a battery-powered embedded device transmitting sensor data based on the STM32F411CE microcontroller, the recommended transmission module is the nRF24L01+ board, as it requires the least amount of energy to transmit one byte of data. This module is most effective when combined with the LZ78 algorithm for optimal energy and time efficiency. In the case of image data, our findings indicate that the use of the JPEG algorithm for compression yields the best results. Overall, our research underscores the importance of selecting appropriate compression algorithms tailored to specific data types, contributing to enhanced energy efficiency in IoT devices.

Advanced therapeutic imaging innovation produces an immense amount of information, predominantly from processed tomography and other imaging modalities. This causes a significant challenge when storing them on a local personal computer or communicating them over cyberspace. Therefore, a proficient image compression system is fundamentally required. From this perspective, this paper proposes a lossless image compression procedure by reducing image dimension and using a prediction technique. In the proposed strategy, the column dimension of a grey-scale image is first reduced and then the prediction errors are encoded using Huffman coding. The decoding process is carried out in the reverse direction. The proposed method is executed and applied to several bench-marked images. The performance of this proposed algorithm is assessed and compared with the state-of-the-art techniques based on several assessment criteria, such as average code length (ACL), compression ratio (CR), encoding time, decoding time, efficiency, peak signal to noise ratio (PSNR) and normalised correlation (NC). The proposed algorithm also demonstrates an improvement in the average code length compared with the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Shannon-Fano coding (SFC) and Huffman coding (HC) are classic and well-known algorithms, but still in use today. The search for the simplest example that proves HC overperforms SFC is still of interest. The problem is not as trivial as it looks like at first view because of several decisions that must be considered. We perform a full-search of the stream data space for a maximum stream length of 100. Depending on additional requests we impose, the simplest solution we found is {1,1,1,1,3} when we accept to select a specific cutting, {2,3,3,3,7} when we accept only deterministic (unique) cuttings and {4,5,6,7,14} when we also ask for different frequencies for symbols as well. [ABSTRACT FROM AUTHOR]

In the meantime, speech coding is one of the methods to represent the digital speech signal as in possible fewer bits value and to maintain the quality and its clearness. In omnipresent situations, encryption and examination of speech maintain a crucial role in various acoustic-based coding systems. This paper, using subband and Huffman coding technique, has been used for speech signals description to reduce the occupied by the speech data memory. The amplitude values of the taken speech are segregated after pre-processing, windowing and decomposition techniques. These data are converted into the frequency domain using discrete cosine transform (DCT). Then 90 foremost coefficients have been coded by Huffman method, they contain the most valuable information of speech signals. Signals are segregated then and subband coding techniques applied. To reconstruct the input speech, the taken speech is re-transformed in the form of time-domain applying through inverse discrete cosine transform (IDCT). This experiment is carried out by speech data at 8 kHz with 16 bits/per sample. The SNR (Signal to Noise Ratio) shows the efficiency of this applied technique.

Now a day to achieve security of digital data, utilizing digital image watermarking scheme is the option. The primary purpose to upgrade image watermarking scheme is to fulfill the need of sufficient imperceptibility, capacity, and robustness of watermark against several attacks like JPEG compression parameter, salt and pepper noise, cropping factor of the image part which contains a watermark and rotating the watermark at one particular angle. During embedding stage in digital image watermarking scheme, a secret message or image introduced into a host image without destroying the quality of original cover image for security and authentication of the data. During extraction stage in the watermarking scheme applying a process to that employed in insertion step but in a reverse way. In this paper different evaluation based on the watermarking methods described. Many projects have been presented such as DCT, DWT, DFT, SVD using FWHT, QIM, BPNN, DFIS, EZW, Huffman coding to provide protection, robustness and high embedding capacity. In the digital image watermarking world, where Peak signal-to-noise ratio (PSNR) is utilized to determine the image quality factor and Normalized correlation (NC) and robustness of the watermark. Using this learning opportunity an environment friendly digital image watermarking scheme can be used with sustainable infrastructure in developing countries.

Today, the transmission of hypersensitive data through public communication, poses a real problem for an unauthorized recipient which makes information security very important. The basic idea behind the proposed research work is to exploit the robustness of the genetic material in order to improve and outperform the performance of other conventional algorithm. In this paper, Huffman coding method has been adopted to develop a new and efficient symmetric DNA encryption algorithm. Firstly, the algorithm codifies the secondary DNA key which is extracted from the main DNA key according to Huffman coding. Then an XOR is applied between the coded DNA sequence and the plain-image. And in order to strengthen our algorithm, we have performed diffusion with a permutation box. [ABSTRACT FROM AUTHOR]

The Vernam-Cipher is known as a one-time pad of algorithm that is an unbreakable algorithm because it uses a typically random key equal to the length of data to be coded, and a component of the text is encrypted with an element of the encryption key. In this paper, we propose a novel technique to overcome the obstacles that hinder the use of the Vernam algorithm. First, the Vernam and advance encryption standard AES algorithms are used to encrypt the data as well as to hide the encryption key; Second, a password is placed on the file because of the use of the AES algorithm; thus, the protection record becomes very high. The Huffman algorithm is then used for data compression to reduce the size of the output file. A set of files are encrypted and decrypted using our methodology. The experiments demonstrate the flexibility of our method, and it is successful without losing any information. [ABSTRACT FROM AUTHOR]

One must compress data prior to sending it to the internet since it helps minimize time and the cost. The main aim of the essay is to develop and design a systemic and a secure method for encrypting data, so as it can be used in executing a lossless compacting steganography method. The technique can be used to decrease the amount of every transmitted data aiding fast transmission while using slow internet or take a small space ion a disk. The paper proposes a compacting with stenography method which might be used to hide of data with significant security and also a perfect invisibility while using a combination of RSA (Rivest–Shamir–Adleman) cryptography method and also used steganography proficiency which are Huffman coding, Run-length encoding (RLE), or Discrete Wavelet Transform (DWT) with Least Significant Bits (LSB) steganography technique. The first step is grounded on the RSA skill to both decrypts and encrypt confidential messages. The next step is compression, which is Huffman coding based techniques that will be used to compress the sensitive data through lossless technology, RLE, which is a more natural way to compress data or DWT based that compacts cover image through lossy compression in order reducing the cover image's dimensions. LSB will then be used to implant the encrypted pieces of information in the compacted cover image. This system shows high ranking performance and a systematic system as compared to other states [1-5] of the structure that uses simulation outcomes. [ABSTRACT FROM AUTHOR]

Enkripsi yaitu proses merubah data asli menjadi data samaran dan dekripsi yaitu proses pengembalian data samara menjadi data asli kembali. Terlepas dari masalah keamanan data, masalah lain yang harus kita perhatikan adalah ukuran data atau informasi yang semakin besar oleh karena itu perlu adanya upaya untuk menanggulangi masalah tersebut salah satunya yaitu dengan menggunkan algoritma kombinasi. Maka dari itu penulis mengkombinasikan kedua metode tersebut yaitu metode Enkripsi dan metode Kombinasi untuk melihat manakah cara yang lebih efisien digunakan antara dienkripsi terlebih dahulu kemudian dikompresi atau sebaliknya.Dengan mengkombinasikan kedua algoritma tersebut penulis menemukan bahwa hasil yang diberikan dari kombinasi tersebut tidak maksimal karena ukuran file hasil proses menjadi lebih besar dibandingkan file asli sebelum diproses. Dari hasil analisis data dalam penelitian ini, maka untuk persentasi keberhasilan prosesnya dapat dibagi menjadi empat bagian.yaitu proses enkripsi-kompresi,kompresi-enkripsi,dekompresi-dekripsi dan dekripsi-dekompresi dengan tingkat keberhasilan 100%. Jika dilihat dari proses tingkat keberhasilan sangat memuaskan namun jika dilihat keberhasilan mengembalikan isi file itu dapat dibagi menjadi dua bagian yaitu proses dekompresi-dekripsi persentasi keberhasilannya itu sebanyak 33,34% dan pesentasi kegagalan sekitar 66,67% sedangkan untuk proses dekripsi-dekopresi persentasi keberhasilan sekitar 33,34% dan persentasi kegagalan sekitar 66,67%

互联网的飞速发展，产生了大量的图像信息.为了减少图片占用的存储空间，提高图像质量，提出了一种将主成分分析(PCA)和分层树集合划分(SPIHT)压缩算法相结合的有损图像压缩算法.首先对图像进行主成分分解，选取主要特征值进行压缩，再利用SPIHT算法将图像分解成不同子带的小波系数进行压缩，对SPIHT压缩系数进行哈夫曼编码，实现图像二级压缩.将本文提出的算法与SPIHT、SPIHT的哈夫曼编码、JEPG2000、PCA压缩算法进行了比较，结果表明本算法较其他压缩算法具有更好的性能，在压缩比相同的情况下能获得更高的PNSR 和 SSIM.

In the last few years, Low-Power Wide-Area Network (LPWAN) technologies have been proposed for Machine-Type Communications (MTC). In this paper, we evaluate wireless relay technologies that can improve LPWAN coverage for smart meter communication applications. We provide a realistic coverage analysis using a realistic correlated shadow-fading map and path-loss calculation for the environment. Our analysis shows significant reductions in the number of MTC devices in outage by deploying either small cells or Device-to-Device (D2D) communications. In addition, we analyzed the energy consumption of the MTC devices for different data packet sizes and Maximum Coupling Loss (MCL) values. Finally, we study how compression techniques can extend the battery lifetime of MTC devices.

A survey of emerging African American artists. Artists discussed are Iona Rozeal Brown, Beverly McIver, David Huffman, and Fan Warren.

Penelitian ini bertujuan untuk mengkompresi citra dengan menggabungkan metode DCT dan Algoritma Huffman untuk membuat kapasitas file gambar menjadi kecil sehingga dapat menghemat media penyimpanan dan tidak lambat jika pengiriman citra dari satu tempat ke tempat lain. Discrete Cosine Transform (DCT) adalah sebuah teknik yang mengubah sinyal ke dalam komponen frekwensi dasar dan Algoritma Huffman adalah algoritma yang digunakan untuk membuat kompresi jenis lossy compression yaitu penempatan data dimana tidak ada satu byte pun data yang hilang sehingga data tersebut utuh dan disimpan sesuai dengan aslinya. Dengan menggabungkan metode Discrete Cosine Transform (DCT) dan Algoritma Huffman dapat mengkompresi gambar dengan maksimal. Dengan teknik lossy compression pada DCT, kompresi citra yang dihasilkan sedikit mengurangi warna (pixel) namun tampak tidak terlihat perbedaannya dengancitra asli sebelum dikompresi. Hasil kompresi tergantung pada pemilihan kualitas kompresi yang diinginkan. Jika memilih kompresi dengan kualitas standar, maka citra hasil kompresi dengan citra yang asli tidak akan terlihat perbedaannya namun pengurangan ukuran bytes tidak terlalu drastis. Tetapi apabila kita memilih kualitas kompresi rendah, maka ukuran bytes pada citra akan berkurang namun kualitas gambar hasil kompresi akan terlihat perbedaannya dengan citra asli.

As the number of Internet of Things (IoT) devices continues to rise dramatically each day, the data generated and transmitted by them follow similar trends. Given that a significant portion of these embedded devices operate on battery power, energy conservation becomes a crucial factor in their design. This paper aims to investigate the impact of data compression on the energy consumption required for data transmission. To achieve this goal, we conduct a comprehensive study using various transmission modules in a severely resource-limited microcontroller-based system designed for battery power. Our study evaluates the performance of several compression algorithms, conducting a detailed analysis of computational and memory complexity, along with performance metrics. The primary finding of our study is that by carefully selecting an algorithm for compressing different types of data before transmission, a significant amount of energy can be saved. Moreover, our investigation demonstrates that for a battery-powered embedded device transmitting sensor data based on the STM32F411CE microcontroller, the recommended transmission module is the nRF24L01+ board, as it requires the least amount of energy to transmit one byte of data. This module is most effective when combined with the LZ78 algorithm for optimal energy and time efficiency. In the case of image data, our findings indicate that the use of the JPEG algorithm for compression yields the best results. Overall, our research underscores the importance of selecting appropriate compression algorithms tailored to specific data types, contributing to enhanced energy efficiency in IoT devices.

The data or image transmission plays a very important role in current days. In general image can be transmitted in terms of data. The basic image can be converted or encoded into bits or chunks. This data can be transmitted in efficient form. Transmitted images in the multimedia field is difficult task. The images will occupies huge amount of size; without compressing it will take more amount of disk space and time. In this, there is a chance of data loss due to timeout. There is a solution to overcome such difficult issue is to reduce the size of an image without losing image data by implementing compressing technique. In this paper, we discussed about image compression and different image compression techniques without any data loss. Few of it are run length arithmetic encoding, Huffman coding and LZW. Finally, we presented the performance issues and pros of compression techniques. [ABSTRACT FROM AUTHOR]