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 effe...
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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.
The Internet of Things (IoT) is transforming how devices interact and share data, especially in areas like vehicle monitoring. However, transmitting large volumes of real-time data can result in high latency and subst...
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The Internet of Things (IoT) is transforming how devices interact and share data, especially in areas like vehicle monitoring. However, transmitting large volumes of real-time data can result in high latency and substantial energy consumption. In this context, Tiny Machine Learning (TinyML) emerges as a promising solution, enabling the execution of machine-learning models on resource-constrained embedded devices. This paper aims to develop two online multivariate compression approaches specifically designed for TinyML, utilizing the Typicality and Eccentricity Data Analytics (TEDA) framework. The proposed approaches are based on data eccentricity and do not require predefined mathematical models or assumptions about data distribution, thereby optimizing compression performance. The methodology involves applying the approaches to a case study using the OBD-II Freematics ONE+ dataset, which is focused on vehicle monitoring. Results indicate that both proposed approaches, whether parallel or sequential compression, show significant improvements in execution time and compression errors. These findings highlight the approach's potential to enhance the performance of embedded IoT systems, thereby improving the efficiency and sustainability of vehicular applications.
Cluster analysis is an important data mining issue, where objects under investi-gation are grouped into subsets of the original set of objects. In recent several years, a few clustering algorithms have been developed ...
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ISBN:
(纸本)9780769547923
Cluster analysis is an important data mining issue, where objects under investi-gation are grouped into subsets of the original set of objects. In recent several years, a few clustering algorithms have been developed for the data stream problem. However these algorithms lack of extensibility or efficiency. In this paper we propose a new evolving data streams system with data fusion. We discuss a fundamentally different philosophy for data stream clustering which is guided by application centered requirements. The system is highly suitable for real-time implementation and is demonstrated through a series of experiments. The experiments over a number of real and synthetic data sets illustrate the effectiveness and efficiency.
The technological revolution of past decades has led teaching and learning of evolutionary biology to move away from its naturalist origins. As a result, students’ learning experiences and training on the science of ...
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To overcome the decrease of diversity of solutions in NSGA II, a multi-objective evolutionary algorithm based on the elitist strategy, a distribution function is proposed here to improve the elitist strategy. By adjus...
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ISBN:
(纸本)9780769536996
To overcome the decrease of diversity of solutions in NSGA II, a multi-objective evolutionary algorithm based on the elitist strategy, a distribution function is proposed here to improve the elitist strategy. By adjusting the parameters of the distribution function and limiting the elitist solutions, some of the non-elitist solutions will be involved in the genetic computation process. The experimental results show that the improved multi-purpose genetic algorithm has a better diversity and faster convergence of solutions than NSGA II.
The paper contains a new concept and some research results as regards creating initial population for Systematical Evolutionary algorithms (SEA). First, different models of development of Electrical Power System (EPS)...
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ISBN:
(纸本)9781424439676
The paper contains a new concept and some research results as regards creating initial population for Systematical Evolutionary algorithms (SEA). First, different models of development of Electrical Power System (EPS) obtained a result of identification such as matrix th and model in state space ss, and model in the form artificial genetic code such as a specific information development model of EPS system. A method of interconnecting the EPS system development with the movement of roots on the complex variable plane s is presented also.
To overcome the decrease of diversity of solutions in NSGA,a multi-objective evolutionary algorithm based on the elitist strategy,a distribution function is proposed here to improve the elitist *** adjusting the param...
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To overcome the decrease of diversity of solutions in NSGA,a multi-objective evolutionary algorithm based on the elitist strategy,a distribution function is proposed here to improve the elitist *** adjusting the parameters of the distribution function and limiting the elitist solutions,some of the non-elitist solutions will be involved in the genetic computation *** experimental results show that the improved multi-purpose genetic algorithm has a better diversity and faster convergence of solutions than NSGA.
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