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EP-4742543-A1 - CIRCUITS AND METHODS FOR LOSSLESS COMPRESSION OF SENSOR DATA

EP4742543A1EP 4742543 A1EP4742543 A1EP 4742543A1EP-4742543-A1

Abstract

The present disclosure relates to a method (210) of lossless compression of sensor data, the method including: receiving sensor data (216) comprising a plurality of sensor reading values, wherein the sensor data (216) are associated with a corresponding sensor; converting a format for representing the sensor reading values from a first format defined by the corresponding sensor into a second format; providing (220) the sensor reading values as sequenced sensor reading values (218) represented with the second format; selecting (230), from a plurality of predefined predetermined compression profiles (221), a compression profile (222) for carrying out a lossless compression of the sequenced sensor reading values, wherein the compression profile (222) comprises a plurality of compressed data formats (224), wherein each compressed data format (224) defines a respective size of a data slot for transmitting one or more of the sequenced sensor reading values (218); carrying out (240) a lossless compression of the sequenced sensor reading values (218) to obtain compressed sensor reading values, wherein the second format is a data format supported by the lossless compression; and preparing the compressed sensor reading values for transmission using the compressed data formats (224) defined by the compression profile.

Inventors

  • TÖMÖSKÖZI, Máté
  • TAGHOUTI, Maroua

Assignees

  • Technische Universität Dresden Körperschaft des öffentlichen Rechts

Dates

Publication Date
20260513
Application Date
20241108

Claims (15)

  1. A method (210) of lossless compression of sensor data, the method comprising: receiving sensor data (216) comprising a plurality of sensor reading values, wherein the sensor data (216) are associated with a corresponding sensor; converting a format for representing the sensor reading values from a first format defined by the corresponding sensor into a second format; providing (220) the sensor reading values as sequenced sensor reading values (218) represented with the second format; selecting (230), from a plurality of predetermined compression profiles (221), a compression profile (222) for carrying out a lossless compression of the sequenced sensor reading values, wherein the compression profile (222) comprises a plurality of compressed data formats (224), wherein each compressed data format (224) defines a respective size of a data slot for transmitting one or more of the sequenced sensor reading values (218); carrying out (240) a lossless compression of the sequenced sensor reading values (218) to obtain compressed sensor reading values, wherein the second format is a data format supported by the lossless compression; and preparing the compressed sensor reading values for transmission using the compressed data formats (224) defined by the compression profile.
  2. The method (210) according to claim 1, wherein preparing the compressed sensor reading values for transmission comprises providing compressed data packets (226) that contain the compressed sensor reading values according to the compression profile (222).
  3. The method (210) according to claim 1 or 2, further comprising: causing a transmission of the compressed data packets (226) containing the compressed sensor reading values.
  4. The method (210) according to any one of claims 1 to 3, further comprising: receiving feedback information representative of a quality of a decompression of the compressed sensor reading values; changing one or more parameters associated with the lossless compression of the sensor reading values based on the received feedback information; and carrying out a lossless compression of further sensor data using the changed one or more parameters.
  5. The method (210) according to any one of claims 1 to 4, wherein converting a format for representing the sensor reading values comprises using a trained machine learning model that receives, as input, the sensor reading values and delivers, as output, a corresponding rule for converting the representation of the sensor reading values into the second format.
  6. The method (210) according to any one of claims 1 to 5, wherein selecting the compression profile (222) comprises identifying the compressed data formats based on a target data packet size for the compressed data packets and a target robustness of a data transmission of the compressed data packets.
  7. The method (210) according to any one of claims 1 to 6, wherein selecting the compression profile comprises using a trained machine learning model that receives, as input, the sequenced sensor reading values to be compressed and delivers, as output, the compression profile to be used for the lossless compression of the sequenced sensor reading values.
  8. The method (210) according to any one of claims 1 to 7, wherein the sensor data (216) comprises a plurality of sensor data associated with a plurality of corresponding sensors; and wherein carrying out the lossless compression of the sequenced sensor reading values comprises providing at least one compressed data packet that contains compressed sensor reading values associated with different sensors.
  9. A computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of any one of claims 1 to 8.
  10. A method (260) of decompression of sensor data compressed according to the method of any one of claims 1 to 8, the method of decompression comprising: receiving (270) the plurality of compressed data packets containing the plurality of compressed sensor reading values; and carrying out (280) a decompression of the compressed data packets to reconstruct the sequenced sensor reading values (218).
  11. The method (260) according to claim 10, further comprising, after the decompression of the compressed data packets: converting the format of the sequenced sensor reading values from the second format into the first format defined by the corresponding sensor.
  12. A computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of any one of claims 10 to 11.
  13. A data compression circuit (200) comprising: • a sequencer (206) configured to: receive sensor data (216) comprising a plurality of sensor reading values, wherein the sensor data (216) are associated with a corresponding sensor; convert a format for representing the sensor reading values from a first format defined by the corresponding sensor into a second format; and deliver, as output, the sensor reading values as sequenced sensor reading values (218) represented with the second format; and • a compressor (212) configured to: select, from a plurality of predetermined compression profiles (221), a compression profile (222) for carrying out a lossless compression of the sequenced sensor reading values (218), wherein the compression profile (222) comprises a plurality of compressed data formats (224), wherein each compressed data format (224) defines a respective size of a data slot for transmitting one or more of the sequenced sensor reading values (218); carry out a lossless compression of the sequenced sensor reading values (218) to obtain compressed sensor reading values; and prepare the compressed sensor reading values for transmission using the compressed data format (224) defined by the compression profile (222), wherein the second format is a data format supported by the compressor (212).
  14. A sensor box (150) comprising: the data compression circuit (200) according to claim 13; and one or more sensors (152), wherein each sensor (152) is configured to generate respective sensor data and deliver the sensor data to the data compression circuit (200).
  15. The sensor box (150) according to claim 14, wherein the plurality of sensors (152) comprises a first sensor (152-1) of a first sensor type and a second sensor (152-2) of a second sensor type, and wherein the first sensor type is different from the second sensor type.

Description

Technical Field The present disclosure is generally related to a data compression circuit configured to carry out lossless compression of sensor data, to a corresponding circuit configured to carry out decompression of the compressed sensor data, and to methods thereof (e.g., a method of carrying out lossless compression of sensor data, a method of carrying out decompression of the compressed sensor data). Background In general, "Internet of Things (IoT)" refers to networks of interconnected devices that exchange information with one another and with a central data processing system (e.g., a cloud system), at which data from various sources are processed and managed to then provide various services to the network users. IoT devices have various applications, such as for smart household appliances (e.g., a smart lighting system, a smart thermostat, and the like), for industrial machines (e.g., robots to carry out automated tasks), for smart farming systems, for wearable devices that monitor health parameters (e.g., heartbeat, blood pressure, etc.), as examples. In this framework, IoT sensors play an important role in monitoring the environment, collecting data on a certain physical quantity, and reporting the collected data to the data processing system for further action. IoT sensors enable the system to take informed decisions, thus reacting promptly to changes in the environment, e.g., changes in temperature, pressure, illuminance, and the like. The efficiency of the transmission of sensor data representing the result of a sensing process has thus an influence on the overall performance of the IoT network. However, data compression, which is a practical technique to improve data transmission efficacy, is not well designed to be ran on sensors and handle sensor data. Existing solutions tackle the problem by equipping each type of IoT sensor box either with a dedicated compression solution or with a standard static and common compression solution. The dedicated compression solution may be developed through extensive dedicated experiments, but is not necessarily optimized. The standard compression solution, such as ZIP, may be applied on a cumulative set of values (not in real time). Moreover, the best practice typically is to convert the readings into a text-based JSON (or even XML) representation, which is very wasteful compared to transmitting in the binary format. This approach is however considered obsolete compared to what can be achieved with today's computation toolsets. Improvements in data compression strategies for transfer of sensor data may thus be of particular relevance for the further advancement of IoT technologies. Brief Description of the Drawings In the drawings, like reference characters generally refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead generally being placed upon illustrating the principles of the invention. In the following description, various aspects of the invention are described with reference to the following drawings, in which: FIG. 1A shows an IoT architecture in a schematic representation, according to various aspects;FIG.1B shows a sensor box including a plurality of sensors in a schematic representation, according to various aspects;FIG.2A shows a data compression circuit in a schematic representation, according to various aspects;FIG.2B shows a data decompression circuit in a schematic representation, according to various aspects;FIG.2C shows an exchange of data between a data compression circuit and a data decompression circuit in a schematic representation, according to various aspects;FIG.3 shows a sequencing process for the data compression in a schematic representation, according to various aspects; andFIG.4 shows a profiling and compression process for the data compression in a schematic representation, according to various aspects. Description The following detailed description refers to the accompanying drawings that show, by way of illustration, specific details and aspects in which the invention may be practiced. These aspects are described in sufficient detail to enable those skilled in the art to practice the invention. Other aspects may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the invention. The various aspects are not necessarily mutually exclusive, as some aspects may be combined with one or more other aspects to form new aspects. Various aspects are described in connection with methods and various aspects are described in connection with devices (e.g., a data compression circuit, a sensor, a sensor box, a data decompression circuit, etc.). However, it is understood that aspects described in connection with methods may similarly apply to the devices, and vice versa. In general, sensors are a key part of an IoT architecture, as they allow the network to have a constantly updated understanding of the environment, thus ena