Remote Monitoring Systems of Unsafe Software Execution using QR Code-based Power Consumption Profile for IoT Edge Devices

Kang, Myeongjin; Park, Daejin

doi:10.1109/iceic51217.2021.9369725

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…The root cause of simulations being far from realism is the improper tuning of the simulation parameters for diverse scenarios that need to be simulated. For instance, in an edge simulator that simulates the energy consumption of edge devices for a given set of workloads, the power profile is often set by human experts using existing profiling data 33 . However, this profile, i.e., power consumption for varying utilization of the CPU may change based on the temperature of the ambient environment, cooling solutions as well as device characteristics.…”

mentioning

confidence: 99%

SimTune: bridging the simulator reality gap for resource management in edge-cloud computing

Tuli

Casale

Jennings

2022

Sci Rep

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Industries and services are undergoing an Internet of Things centric transformation globally, giving rise to an explosion of multi-modal data generated each second. This, with the requirement of low-latency result delivery, has led to the ubiquitous adoption of edge and cloud computing paradigms. Edge computing follows the data gravity principle, wherein the computational devices move closer to the end-users to minimize data transfer and communication times. However, large-scale computation has exacerbated the problem of efficient resource management in hybrid edge-cloud platforms. In this regard, data-driven models such as deep neural networks (DNNs) have gained popularity to give rise to the notion of edge intelligence. However, DNNs face significant problems of data saturation when fed volatile data. Data saturation is when providing more data does not translate to improvements in performance. To address this issue, prior work has leveraged coupled simulators that, akin to digital twins, generate out-of-distribution training data alleviating the data-saturation problem. However, simulators face the reality-gap problem, which is the inaccuracy in the emulation of real computational infrastructure due to the abstractions in such simulators. To combat this, we develop a framework, SimTune, that tackles this challenge by leveraging a low-fidelity surrogate model of the high-fidelity simulator to update the parameters of the latter, so to increase the simulation accuracy. This further helps co-simulated methods to generalize to edge-cloud configurations for which human encoded parameters are not known apriori. Experiments comparing SimTune against state-of-the-art data-driven resource management solutions on a real edge-cloud platform demonstrate that simulator tuning can improve quality of service metrics such as energy consumption and response time by up to 14.7% and 7.6% respectively.

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mentioning

confidence: 99%

SimTune: bridging the simulator reality gap for resource management in edge-cloud computing

Tuli

Casale

Jennings

2022

Sci Rep

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“…We then selected the label of minimum error as shown in Equation ( 6). ĵ = argmin j (error(RP j, k , AD)) (6) where: error(RP j, k , AD) = popcount(RP j, k ⊕ AD) AD = Input activity data…”

Section: Activity Data Classification At Edgementioning

confidence: 99%

“…For instance, to determine the state of numerous edge devices, a study used a QR code generated from power consumption data of edge devices. By handling complex data as efficient image data, they classified error states with reduced load on edge devices [6].…”

Section: Introductionmentioning

confidence: 99%

Low-Power On-Chip Implementation of Enhanced SVM Algorithm for Sensors Fusion-Based Activity Classification in Lightweighted Edge Devices

2022

Self Cite

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Smart homes assist users by providing convenient services from activity classification with the help of machine learning (ML) technology. However, most of the conventional high-performance ML algorithms require relatively high power consumption and memory usage due to their complex structure. Moreover, previous studies on lightweight ML/DL models for human activity classification still require relatively high resources for extremely resource-limited embedded systems; thus, they are inapplicable for smart homes’ embedded system environments. Therefore, in this study, we propose a low-power, memory-efficient, high-speed ML algorithm for smart home activity data classification suitable for an extremely resource-constrained environment. We propose a method for comprehending smart home activity data as image data, hence using the MNIST dataset as a substitute for real-world activity data. The proposed ML algorithm consists of three parts: data preprocessing, training, and classification. In data preprocessing, training data of the same label are grouped into further detailed clusters. The training process generates hyperplanes by accumulating and thresholding from each cluster of preprocessed data. Finally, the classification process classifies input data by calculating the similarity between the input data and each hyperplane using the bitwise-operation-based error function. We verified our algorithm on `Raspberry Pi 3’ and `STM32 Discovery board’ embedded systems by loading trained hyperplanes and performing classification on 1000 training data. Compared to a linear support vector machine implemented from Tensorflow Lite, the proposed algorithm improved memory usage to 15.41%, power consumption to 41.7%, performance up to 50.4%, and power per accuracy to 39.2%. Moreover, compared to a convolutional neural network model, the proposed model improved memory usage to 15.41%, power consumption to 61.17%, performance to 57.6%, and power per accuracy to 55.4%.

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Fast Bit Inversion Vulnerability Pre-estimation using Tcl and UPF in RTL Simulation Runtime

Kang,

Kwon,

Lee

et al. 2023

2023 14th International Conference on Information and Communication Technology Convergence (ICTC)

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Remote Monitoring Systems of Unsafe Software Execution using QR Code-based Power Consumption Profile for IoT Edge Devices

Cited by 3 publications

References 12 publications

SimTune: bridging the simulator reality gap for resource management in edge-cloud computing

SimTune: bridging the simulator reality gap for resource management in edge-cloud computing

Low-Power On-Chip Implementation of Enhanced SVM Algorithm for Sensors Fusion-Based Activity Classification in Lightweighted Edge Devices

Fast Bit Inversion Vulnerability Pre-estimation using Tcl and UPF in RTL Simulation Runtime

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