An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity

Abstract

Currently, the applications of the Internet of Things (IoT) generate a large amount of sensor data at a very high pace, making it a challenge to collect and store the data. This scenario brings about the need for effective data compression algorithms to make the data manageable among tiny and battery-powered devices and, more importantly, shareable across the network. Additionally, considering that, very often, wireless communications (e.g., low-power wide-area networks) are adopted to connect field devices, user payload compression can also provide benefits derived from better spectrum usage, which in turn can result in advantages for high-density application scenarios. As a result of this increase in the number of connected devices, a new concept has emerged, called TinyML. It enables the use of machine learning on tiny, computationally restrained devices. This allows intelligent devices to analyze and interpret data locally and in real time. Therefore, this work presents a new data compression solution (algorithm) for the IoT that leverages the TinyML perspective. The new approach is called the Tiny Anomaly Compressor (TAC) and is based on data eccentricity. TAC does not require previously established mathematical models or any assumptions about the underlying data distribution. In order to test the effectiveness of the proposed solution and validate it, a comparative analysis was performed on two real-world datasets with two other algorithms from the literature (namely Swing Door Trending (SDT) and the Discrete Cosine Transform (DCT)). It was found that the TAC algorithm showed promising results, achieving a maximum compression rate of 98.33%. Additionally, it also surpassed the two other models regarding the compression error and peak signal-to-noise ratio in all cases.

A TinyML Soft-Sensor Approach for Low-Cost Detection and Monitoring of Vehicular Emissions

“…Since the employed devices may have low processing power and restricted memory, more complex machine learning techniques may be prohibitive [ 18 , 19 ]. Therefore, the concept of TinyML arises as a method to fit ML models into resource-constrained devices [ 20 , 21 , 22 ], which tries to establish the best trade-off between the restricted hardware and a ML algorithm [ 23 ].…”

Demand-Driven Data Acquisition for Large Scale Fleets

“…In [23], the authors utilize downsampling and histograms to reduce data transmission size, and a trajectory compression algorithm is used by [18]. In contrast, the authors of [54] propose to detect anomalies locally on the vehicle and restrict data transmission to them.…”

“…Another work is utilizing downsampling and histograms to reduce transmission size [23]. The authors of [54] also propose a lossy compression that restricts data transmission to anomalies detected locally on the vehicle. Finally, the authors of [22] mention using a proprietary message format to minimize the volume of transmitted data but do not elaborate on this issue.…”

TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications

“…The IoT concept has been widely used in different aspects of our lives with applications and technologies [1,2] including smart cities, smart environments, smart homes, etc. Billions of IoT devices are connected to the internet; with extensive numbers of IoT devices comes the mass production of IoT platforms [1,3]. The role of these devices is to sense the physical features of their deployment environments-twenty-four hours a day, seven days a week.…”