L2C: Combining Lossy and Lossless Compression on Memory and I/O

In this paper we introduce L2C, a hybrid lossy/lossless compression scheme applicable both to the memory subsystem and I/O traffic of a processor chip. L2C employs general-purpose lossless compression and combines it with state of the art lossy compression to achieve compression ratios up to 16:1 and improve the utilization of chip\u27s bandwidth resources. Compressing memory traffic yields lower memory access time, improving system performance and energy efficiency. Compressing I/O traffic offers several benefits for resource-constrained systems, including more efficient storage and networking.We evaluate L2C as a memory compressor in simulation with a set of approximation-tolerant applications. L2C improves baseline execution time by an average of 50\%, and total system energy consumption by 16%. Compared to the lossy and lossless current state of the art memory compression approaches, L2C improves execution time by 9% and 26% respectively, and reduces system energy costs by 3% and 5%, respectively.I/O compression efficacy is evaluated using a set of real-life datasets. L2C achieves compression ratios of up to 10.4:1 for a single dataset and on average about 4:1, while introducing no more than 0.4% error

Multi-dimensional data stream compression for embedded systems

The rise of embedded systems and wireless technologies led to the emergence of the Internet of Things (IoT). Connected objects in IoT communicate with each other by transferring data streams over the network. For instance, in Wireless Sensor Networks (WSNs), sensor-equipped devices use sensors to capture properties, such as temperature or accelerometer, and send 1D or nD data streams to a host system. Power consumption is a critical problem for connected objects that have to work for a long time without being recharged, as it greatly affects their lifetime and usability. Data summarization is key for energy-constrained connected devices, as transmitting fewer data can reduce energy usage during transmission. Data compression, in particular, can compress the data stream while preserving information to a great extent. Many compression methods have been proposed in previous research. However, most of them are either not applicable to connected objects, due to resource limitation, or only handle one-dimensional streams while data acquired in connected objects are often multi-dimensional. Lightweight Temporal Compression (LTC) is among the lossy stream compression methods that provide the highest compression rate for the lowest CPU and memory consumption. In this thesis, we investigate the extension of LTC to multi-dimensional streams. First, we provide a formulation of the algorithm in an arbitrary vectorial space of dimension n. Then, we implement the algorithm for the infinity and Euclidean norms, in spaces of dimension 2D+t and 3D+t. We evaluate our implementation on 3D acceleration streams of human activities, on Neblina, a module integrating multiple sensors developed by our partner Motsai. Results show that the 3D implementation of LTC can save up to 20% in energy consumption for slow-paced activities, with a memory usage of about 100 B. Finally, we compare our method with polynomial regression compression methods in different dimensions. Our results show that our extension of LTC gives a higher compression ratio than the polynomial regression method, while using less memory and CPU

Dimensionality reduction for smart IoT sensors

Smart IoT sensors are characterized by their ability to sense and process signals, producing high-level information that is usually sent wirelessly while minimising energy consumption and maximising communication efficiency. Systems are getting smarter, meaning that they are providing ever richer information from the same raw data. This increasing intelligence can occur at various levels, including in the sensor itself, at the edge, and in the cloud. As sending one byte of data is several orders of magnitude more energy-expensive than processing it, data must be handled as near as possible to its generation. Thus, the intelligence should be located in the sensor; nevertheless, it is not always possible to do so because real data is not always available for designing the algorithms or the hardware capacity is limited. Smart devices detecting data coming from inertial sensors are a good example of this. They generate hundreds of bytes per second (100 Hz, 12-bit sampling of a triaxial accelerometer) but useful information comes out in just a few bytes per minute (number of steps, type of activity, and so forth). We propose a lossy compression method to reduce the dimensionality of raw data from accelerometers, gyroscopes, and magnetometers, while maintaining a high quality of information in the reconstructed signal coming from an embedded device. The implemented method uses an adaptive vector-quantisation algorithm that represents the input data with a limited set of codewords. The adaptive process generates a codebook that evolves to become highly specific for the input data, while providing high compression rates. The codebook’s reconstruction quality is measured with a peak signal-to-noise ratio (PSNR) above 40 dB for a 12-bit representation

The Compression of IoT Operational Data Time Series in Automatic Weather Station (AWS)

This project examines compression algorithms for time series data from Automatic Weather Station (AWS). The main goal of the project is to provide a solution using data compression algorithm to reduce the amount of data that needs to be transmitted from AWS to the server. Also to provide information about the comparison of algorithms that are suitable for application in AWS data. By reducing the size of the data, we can decrease the cost of transmission. Four compression algorithms are compared, those are Huffman Code, Arithmetic Coding, Lempel Ziv 7 (LZ77), and Lempel Ziv 4 (LZ4). At the end, the simulation and analysis have been carried out based on the results of experiments that have been done in the laboratory

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe

Review and classification of trajectory summarisation algorithms: From compression to segmentation

With the continuous development and cost reduction of positioning and tracking technologies, a large amount of trajectories are being exploited in multiple domains for knowledge extraction. A trajectory is formed by a large number of measurements, where many of them are unnecessary to describe the actual trajectory of the vehicle, or even harmful due to sensor noise. This not only consumes large amounts of memory, but also makes the extracting knowledge process more difficult. Trajectory summarisation techniques can solve this problem, generating a smaller and more manageable representation and even semantic segments. In this comprehensive review, we explain and classify techniques for the summarisation of trajectories according to their search strategy and point evaluation criteria, describing connections with the line simplification problem. We also explain several special concepts in trajectory summarisation problem. Finally, we outline the recent trends and best practices to continue the research in next summarisation algorithms.The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was funded by public research projects of Spanish Ministry of Economy and Competitivity (MINECO), reference TEC2017-88048-C2-2-

Biometric signals compression with time- and subject-adaptive dictionary for wearable devices

This thesis work is dedicated to the design of a lightweight compression technique for the real-time processing of biomedical signals in wearable devices. The proposed approach exploits the unsupervised learning algorithm of the time-adaptive self-organizing map (TASOM) to create a subject-adaptive codebook applied to the vector quantization of a signal. The codebook is obtained and then dynamically refined in an online fashion, without requiring any prior information on the signal itsel