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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.

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.

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]

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]

Typical fully distributed optical fiber sensors (DOFS) with dozens of kilometers are equivalent to tens of thousands of point sensors along the whole monitoring line, which means tens of thousands of data will be generated for one pulse launching period. Therefore, in an all-day nonstop monitoring, large volumes of data are created thereby triggering the demand for large storage space and high speed for data transmission. In addition, when the monitoring length and channel numbers increase, the data also increase extensively. The task of mitigating large volumes of data accumulation, large storage capacity, and high-speed data transmission is, therefore, the aim of this paper. To demonstrate our idea, we carried out a comparative study of two lossless methods, Huffman and Lempel Ziv Welch (LZW), with a lossy data compression algorithm, fast wavelet transform (FWT) based on three distinctive DOFS sensing data, such as Φ-OTDR, P-OTDR, and B-OTDA. Our results demonstrated that FWT yielded the best compression ratio with good consumption time, irrespective of errors in signal construction of the three DOFS data. Our outcomes indicate the promising potentials of FWT which makes it more suitable, reliable, and convenient for real-time compression of the DOFS data. Finally, it was observed that differences in the DOFS data structure have some influence on both the compression ratio and computational cost. [ABSTRACT FROM AUTHOR]

The purpose of this article is to describe the concept of point clouds and to present the main idea and the important steps made in this research domain. This article also presents methods that aim to solve the point clouds compression problem. [ABSTRACT FROM AUTHOR]

This paper presents an introduction about Image compression techniques. Image compression is an application of data compression that encodes the original image with few bits. The objective of image compression is to reduce the redundancy of the image and to store or transmit data in an efficient form. In recent years, the development and demand of multimedia product grows increasingly fast, contributing to insufficient bandwidth of network and storage of memory device. Therefore, the theory of data compression becomes more and more significant for reducing the data redundancy to save more hardware space and transmission bandwidth. In computer science and information theory, data compression or source coding is the process of encoding information using fewer bits or other information-bearing units than an unencoded representation. Compression is useful because it helps reduce the consumption of expensive resources such as hard disk space or transmission bandwidth. [ABSTRACT FROM AUTHOR]

Text compression is one of the most significant research fields, and various algorithms for text compression have already been developed. This is a significant issue, as the use of internet bandwidth is considerably increasing. This article proposes a Burrows–Wheeler transform and pattern matching-based lossless text compression algorithm that uses Huffman coding in order to achieve an excellent compression ratio. In this article, we introduce an algorithm with two keys that are used in order to reduce more frequently repeated characters after the Burrows–Wheeler transform. We then find patterns of a certain length from the reduced text and apply Huffman encoding. We compare our proposed technique with state-of-the-art text compression algorithms. Finally, we conclude that the proposed technique demonstrates a gain in compression ratio when compared to other compression techniques. A small problem with our proposed method is that it does not work very well for symmetric communications like Brotli. [ABSTRACT FROM AUTHOR]

Modern daily life activities result in a huge amount of data, which creates a big challenge for storing and communicating them. As an example, hospitals produce a huge amount of data on a daily basis, which makes a big challenge to store it in a limited storage or to communicate them through the restricted bandwidth over the Internet. Therefore, there is an increasing demand for more research in data compression and communication theory to deal with such challenges. Such research responds to the requirements of data transmission at high speed over networks. In this paper, we focus on deep analysis of the most common techniques in image compression. We present a detailed analysis of run-length, entropy and dictionary based lossless image compression algorithms with a common numeric example for a clear comparison. Following that, the state-of-the-art techniques are discussed based on some bench-marked images. Finally, we use standard metrics such as average code length (ACL), compression ratio (CR), pick signal-to-noise ratio (PSNR), efficiency, encoding time (ET) and decoding time (DT) in order to measure the performance of the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]