The dynamic nature of many IoT ecosystems poses unique challenges to the efficacy of IoT ML-based applications. One such challenge is data incompleteness. Furthermore, most IoT systems are severely power-constrained. ...
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ISBN:
(纸本)9798350369458;9798350369441
The dynamic nature of many IoT ecosystems poses unique challenges to the efficacy of IoT ML-based applications. One such challenge is data incompleteness. Furthermore, most IoT systems are severely power-constrained. It is important that we build IoT-based ML systems that are robust against data incompleteness while simultaneously being energy efficient. This paper presents an empirical study of SECOE a recent technique for alleviating data incompleteness in IoT with respect to its energy bottlenecks. Towards addressing the energy bottlenecks of SECOE, we propose ENAMLE a proactive, energy-aware technique for mitigating the impact of concurrent missing data. ENAMLE is unique in the sense that it builds an energy-aware ensemble of sub-models, each trained with a subset of sensors chosen carefully based on their correlations. Furthermore, at inference time, ENAMLE adaptively alters the number of the ensemble of models based on the amount of missing data rate and the energy-accuracy trade-off. ENAMLE's design includes several novel mechanisms for minimizing energy consumption while maintaining accuracy. Through experimental studies, we demonstrate the energy efficiency of ENAMLE and its ability to alleviate sensor failures.
Smart city, health care, agriculture etc applications under the umbrella of the Internet of Things (loT) which they present a new technology with a millions of devices connected together. In the End, all loT devices a...
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This research study proposes a CNN-based method for face emotion recognition. The model is created using many convolutional layers, then pooling and dense layers, so effectively extracting and analyzing both linear an...
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This research presents a real-time automatic control system for ensuring reliable power supply in distributed generation systems. Leveraging Programmable Logic Controllers (PLCs) and Supervisory Control and Data Acqui...
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The integration of Internet of Things (IoT) applications in our daily lives has led to a surge in data traffic, posing significant security challenges. IoT applications using cloud and edge computing are at higher ris...
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ISBN:
(纸本)9798350369458;9798350369441
The integration of Internet of Things (IoT) applications in our daily lives has led to a surge in data traffic, posing significant security challenges. IoT applications using cloud and edge computing are at higher risk of cyberattacks because of the expanded attack surface from distributed edge and cloud services, the vulnerability of IoT devices, and challenges in managing security across interconnected systems leading to oversights. This led to the rise of ML-based solutions for intrusion detection systems (IDSs), which have proven effective in enhancing network security and defending against diverse threats. However, ML-based IDS in IoT systems encounters challenges, particularly from noisy, redundant, and irrelevant features in varied IoT datasets, potentially impacting its performance. Therefore, reducing such features becomes crucial to enhance system performance and minimize computational costs. This paper focuses on improving the effectiveness of ML-based IDS at the edge level by introducing a novel method to find a balanced trade-off between cost and accuracy through the creation of informative features in a two-tier edge-user IoT environment. A hybrid Binary Quantum-inspired Artificial Bee Colony and Genetic Programming algorithm is utilized for this purpose. Three IoT intrusion detection datasets, namely NSL-KDD, UNSW-NB15, and BoT-IoT, are used for the evaluation of the proposed approach. Performance analysis is conducted using various evaluation metrics such as accuracy, sensitivity, specificity, and False Positive Rate (FPR) are employed, while the cost of the IDS system is assessed based on computational time. The results are compared with existing methods in the literature, revealing that the IDS performance can be enhanced with fewer features, consequently reducing computational time, through the proposed method. This offers a better performance-cost trade-off for the IDS system.
In recent years, there has been a shift in computing architectures, moving away from centralized cloud computing towards decentralized edge and fog computing. This shift is driven by factors such as the increasing vol...
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ISBN:
(纸本)9798350319569
In recent years, there has been a shift in computing architectures, moving away from centralized cloud computing towards decentralized edge and fog computing. This shift is driven by factors such as the increasing volume of data generated at the edge, the growing demand for real-time processing and low-latency applications, and the need for improved privacy and data locality. Although this new paradigm offers numerous advantages, it also introduces significant security and reliability challenges. This paper aims to review the architectures and technologies employed in fog computing and identify opportunities for developing novel security assessment and security hardening techniques. These techniques include secure configuration and debloating to enhance the security of middleware, testing techniques to assess secure communication mechanisms, and automated rehosting to speed up the security testing of embedded firmware.
5G technology is a noteworthy achievement in developing wireless communication, offering unparalleled speed, little delay, and extensive connection. This article examines the profound influence of 5G on the (IoT), emp...
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ISBN:
(纸本)9798331540661;9798331540678
5G technology is a noteworthy achievement in developing wireless communication, offering unparalleled speed, little delay, and extensive connection. This article examines the profound influence of 5G on the (IoT), emphasizing how 5G's advanced features enable the smooth incorporation of IoT devices in several industries. 5G facilitates real-time data processing and transmission, which is essential for applications in smart cities, healthcare, industrial automation, and autonomous cars, due to its improved data transfer speeds and dependability. The study explores the technological connections between 5G and IoT, analyzing how network slicing, edge computing, and massive MIMO enhance the effectiveness and scalability of IoT systems. Moreover, it examines the difficulties and possibilities brought about by this merging, including issues of security, distribution of spectrum, and the need for novel regulatory structures. In conclusion, this article highlights the capacity of 5G to completely transform the (IoT), by stimulating progress and promoting a more interconnected and smarter global society.
time-series analysis plays a crucial role in numerous real-world applications, ranging from financial forecasting to environmental monitoring and beyond. Traditional classification techniques often struggle to effecti...
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ISBN:
(纸本)9798350319552;9798350319545
time-series analysis plays a crucial role in numerous real-world applications, ranging from financial forecasting to environmental monitoring and beyond. Traditional classification techniques often struggle to effectively handle the uncertainties and imprecision inherent in time series data. To address this challenge, fuzzy time series models have emerged as a promising alternative, offering a flexible framework capable of capturing the uncertainties and vagueness intrinsic to temporal patterns. In this paper, we propose an Optimized time Series Interval Valued Fuzzy System (OTS-IVFS) that leverages the power of type-2 fuzzy logic to handle temporal data effectively and provide fully explainable models. We have performed experiments on five data sets from a diverse set of use cases. Our system significantly outperforms the best-in-class algorithms for the Earthquake dataset by 16.67% increase in accuracy while giving comparable results in 3 other datasets, whilst maintaining full interpretability.
This paper presents an improvement of the Polar Lights Optimizer (PLO) algorithm by integrating three chaotic maps. A dynamic diversification method based on chaotic systems enables the enhanced algorithm, known as Ch...
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The proceedings contain 58 papers. The topics discussed include: policy transfer with maximum entropy deep reinforcement learning for real cart-pole control;COR-MP: conservation of resources model for maneuver plannin...
ISBN:
(纸本)9798331539979
The proceedings contain 58 papers. The topics discussed include: policy transfer with maximum entropy deep reinforcement learning for real cart-pole control;COR-MP: conservation of resources model for maneuver planning;improved grapes detection and tracking in drones imagery by integrating the coordinate attention mechanism;optimization methods of deep neural networks on automotive embeddedcomputingsystems;6D pose estimation of unseen objects for industrial augmented reality;self-sovereign identity system using blockchain and smart contracts;a novelty real-time gesture recognition model for air-hand piano playing using MediaPipe;classifying emotional states using data collected from wearable devices;and navigating the labyrinth: the mixed Chinese postman problem and its SLAM algorithm application.
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