IoT Data Management System for Rapid Development of Machine Learning Models

Grueneberg, Keith; Ko, Bongjun; Wood, David; Steuer, Dean; Lim, Yong Beom

doi:10.1109/iccc.2019.00021

Cited by 5 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditionally, the redundancy, noise and error embedded in the sensory data is pre-processed [11] for effective utilization. However, considering the multiplicity, heterogeneity and resource scarcity towards the edge, the overall cost of streaming huge amount of multimodal data during continuous monitoring, [12] could be significant.…”

Section: Introductionmentioning

confidence: 99%

UniPreCIS : A data pre-processing solution for collocated services on shared IoT

Das¹,

Singh²,

Chakraborty³

2022

Preprint

View full text Add to dashboard Cite

Next-generation smart city applications, attributed by the power of Internet of Things (IoT) and Cyber-Physical Systems (CPS), significantly rely on the quality of sensing data. With an exponential increase in intelligent applications for urban development and enterprises offering sensing-as-aservice these days, it is imperative to provision for a shared sensing infrastructure for better utilization of resources. However a shared sensing infrastructure that leverages low-cost sensing devices for a cost effective solution, still remains an unexplored territory. A significant research effort is still needed to make edge based data shaping solutions, more reliable, feature-rich and costeffective while addressing the associated challenges in sharing the sensing infrastructure among multiple collocated services with diverse Quality of Service (QoS) requirements. Towards this, we propose a novel edge based data pre-processing solution, named UniPreCIS that accounts for the inherent characteristics of lowcost ambient sensors and the exhibited measurement dynamics with respect to application-specific QoS. UniPreCIS aims to identify and select quality data sources by performing sensor ranking and selection followed by multimodal data pre-processing in order to meet heterogeneous application QoS and at the same time reducing the resource consumption footprint for the resource constrained network edge. As observed, the processing time and memory utilization has been reduced in the proposed approach while achieving upto 90% accuracy which is arguably significant as compared to state-of-the-art techniques for sensing. The effectiveness of UniPreCIS has been evaluated on a testbed for a specific use case of indoor occupancy estimation that proves its effectiveness.

show abstract

Section: Introductionmentioning

confidence: 99%

UniPreCIS : A data pre-processing solution for collocated services on shared IoT

Das¹,

Singh²,

Chakraborty³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…For most IoT applications, the anomaly events with a long timeseries data are rare. Generally, the IoT generates a large volume of normal data with 24*7, and the actual anomalous sounds rarely occur and are highly diverse [6] because of the changing and complex operating conditions [7] [8]. The ratio of anomalous data files to normal ones depends on the different types of machines, maintenance conditions and external environments eg: changes of seasons etc.…”

Section: Introduction 1anomalous Sound Detection and Issues Of Implem...mentioning

confidence: 99%

Untitled

2022

IJSC

View full text Add to dashboard Cite

This paper is to introduce a novel semi-supervised methodology, the enhanced incremental principal component analysis ("IPCA") based deep convolutional neural network autoencoder ("DCNN-AE) for Anomalous Sound Detection ("ASD") with high accuracy and computing efficiency. This hybrid methodology is to adopt Enhanced IPCA to reduce the dimensionality and then to use DCNN-AE to extract the features of the sample sound and detect the anomality. In this project, 228 sets of normal sounds and 100 sets of anomaly sounds of same machine are used for the experiments. And the sound files of machines (stepper motors) for the experiments are collected from a plant site. 50 random test cases are executed to evaluate the performance of the algorithm with AUC, PAUC, F measure and Accuracy Score. IPCA Based DCNN-AE shows high accuracy with the average AUC of 0.815793282, comparing with that of Kmeans++ of 0.499545351, of Incremental PCA based DBSCAN clustering of 0.636348073, of Incremental based PCA based One-class SVM of 0.506749433 and of DCGAN of 0.716528104. From the perspective of computing efficiency, because of the dimensions-reduction by the IPCA layer, the average execution time of the new methodology is 15 minutes in the CPU computing module of 2.3 GHz quad-core processors, comparing with that of DCGAN with 90 minutes in GPU computing module of 4 to 8 kernels.

show abstract

Automatic EPS Calculation Guided Genetic Algorithm and Incremental PCA Based DBSCAN of Extracted Acoustic Features for Anomalous Sound Detection

Tan,

Yiu

2023

Communications in Computer and Information Science

View full text Add to dashboard Cite

IoT Data Management System for Rapid Development of Machine Learning Models

Cited by 5 publications

References 3 publications

UniPreCIS : A data pre-processing solution for collocated services on shared IoT

UniPreCIS : A data pre-processing solution for collocated services on shared IoT

Untitled

Automatic EPS Calculation Guided Genetic Algorithm and Incremental PCA Based DBSCAN of Extracted Acoustic Features for Anomalous Sound Detection

Contact Info

Product

Resources

About