IoT technologies for embedded computing

Samie, Farzad; Bauer, Lars; Henkel, Jörg

doi:10.1145/2968456.2974004

Cited by 170 publications

(112 citation statements)

References 33 publications

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“…To enumerate all the feature pairs in the same feature set is also inefficient (line 3-8 in Algorithm 1). Considering a feature set a (1) , a (2) , . .…”

Section: Problem Statementmentioning

confidence: 99%

“…In the era of the fourth industrial revolution, there is a growing trend to deploy sensors on industrial equipment, and analyze the industrial equipment's running status according to the sensor data. Thanks to the rapid development of IoT technologies [1], sensor data could be easily fetched from industrial equipment, and analyzed to produce further value for industrial control at the edge of the network or at data centers. Due to the considerable development of deep learning in recent years, a common practice of such analysis is to conduct deep learning [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

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A Correlation-Change Based Feature Selection Method for IoT Equipment Anomaly Detection

Sun

Gao

et al. 2019

Applied Sciences

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Selecting the right features for further data analysis is important in the process of equipment anomaly detection, especially when the origin data source involves high dimensional data with a low value density. However, existing researches failed to capture the fact that the sensor data are usually correlated (e.g., duplicated deployed sensors), and the correlations would be broken when anomalies occur with happen to the monitored equipment. In this paper, we propose to capture such sensor data correlation changes to improve the performance of IoT (Internet of Things) equipment anomaly detection. In our feature selection method, we first cluster correlated sensors together to recognize the duplicated deployed sensors according to sensor data correlations, and we monitor the data correlation changes in real time to select the sensors with correlation changes as the representative features for anomaly detection. To that end, (1) we conducted curve alignment for the sensor clustering; (2) we discuss the appropriate window size for data correlation calculation;(3) and adopted MCFS (Multi-Cluster Feature Selection) into our method to adapt to the online feature selection scenario. According to the experiment evaluation derived from real IoT equipment, we prove that our method manages to reduce the false negative of IoT equipment anomaly detection of 30% with almost the same level of false positive.

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“…To enumerate all the feature pairs in the same feature set is also inefficient (line 3-8 in Algorithm 1). Considering a feature set a (1) , a (2) , . .…”

Section: Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Correlation-Change Based Feature Selection Method for IoT Equipment Anomaly Detection

Sun

Gao

et al. 2019

Applied Sciences

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“…For instance, IoT devices need the ability to interact either node-to-node or node-to-Web based on whether the target node is in their own network [21]. In addition, the same philosophy as the IoT platform is also applied to IoT devices.…”

Section: Iot Devicesmentioning

confidence: 99%

Future of IoT Networks: A Survey

2017

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Abstract:The introduction of mobile devices has changed our daily lives. They enable users to obtain information even in a nomadic environment and provide information without limitations. A decade after the introduction of this technology, we are now facing the next innovation that will change our daily lives. With the introduction of the Internet of Things (IoT), our communication ability will not be restricted to only mobile devices. Rather, it will expand to all things with which we coexist. Many studies have discussed IoT-related services and platforms. However, there are only limited discussions about the IoT network. In this paper, we will thoroughly analyze the technical details about the IoT network. Based on our survey of papers, we will provide insight about the future IoT network and the crucial components that will enable it.

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“…With the advent of internet of things, smaller sensors and smarter environments are on the rise [1]. This increases the demand for low power hardware that can preform machine learning tasks [2], [3]. Neuromorphic computing architectures are promising hardware architectures that fulfill these requests [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization for Size and Resilience of Spiking Neural Networks

Dimovska

Johnston

Schuman

et al. 2019

2019 IEEE 10th Annual Ubiquitous Computing, Electronics &Amp; Mobile Communication Conference (UEMCON)

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Inspired by the connectivity mechanisms in the brain, neuromorphic computing architectures model Spiking Neural Networks (SNNs) in silicon. As such, neuromorphic architectures are designed and developed with the goal of having small, low power chips that can perform control and machine learning tasks. However, the power consumption of the developed hardware can greatly depend on the size of the network that is being evaluated on the chip. Furthermore, the accuracy of a trained SNN that is evaluated on chip can change due to voltage and current variations in the hardware that perturb the learned weights of the network. While efforts are made on the hardware side to minimize those perturbations, a software based strategy to make the deployed networks more resilient can help further alleviate that issue. In this work, we study Spiking Neural Networks in two neuromorphic architecture implementations with the goal of decreasing their size, while at the same time increasing their resiliency to hardware faults. We leverage an evolutionary algorithm to train the SNNs and propose a multiobjective fitness function to optimize the size and resiliency of the SNN. We demonstrate that this strategy leads to well-performing, small-sized networks that are more resilient to hardware faults.

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IoT technologies for embedded computing

Cited by 170 publications

References 33 publications

A Correlation-Change Based Feature Selection Method for IoT Equipment Anomaly Detection

A Correlation-Change Based Feature Selection Method for IoT Equipment Anomaly Detection

Future of IoT Networks: A Survey

Multi-Objective Optimization for Size and Resilience of Spiking Neural Networks

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