scholarly journals Surrogate Model-Based Energy-Efficient Scheduling for LPWA-Based Environmental Monitoring Systems

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 59940-59948 ◽  
Author(s):  
Jin Tong ◽  
Mingjian Cui ◽  
Meng Tian ◽  
Yigang He
Keyword(s):  
Environmental Monitoring ◽  
Surrogate Model ◽  
Energy Efficient ◽  
Monitoring Systems ◽  
Model Based ◽  
Energy Efficient Scheduling

Energy Efficient Scheduling and Optimization for Parallel Tasks on Homogeneous Clusters

2012 ◽  
Vol 35 (3) ◽  
pp. 591-602 ◽  
Author(s):  
Xin LI ◽  
Zhi-Ping JIA ◽  
Lei JU ◽  
Yan-Heng ZHAO ◽  
Zi-Liang ZONG
Keyword(s):  
Energy Efficient ◽  
Parallel Tasks ◽  
Energy Efficient Scheduling

Using Machine Learning for Dependable Outlier Detection in Environmental Monitoring Systems

2021 ◽  
Vol 5 (3) ◽  
pp. 1-30
Author(s):  
Gonçalo Jesus ◽  
António Casimiro ◽  
Anabela Oliveira
Keyword(s):  
Machine Learning ◽  
Environmental Monitoring ◽  
Data Quality ◽  
Outlier Detection ◽  
Prediction Models ◽  
Sensor Data ◽  
Natural Phenomenon ◽  
Monitoring Systems ◽  
Data Errors ◽  
Redundant Data

Sensor platforms used in environmental monitoring applications are often subject to harsh environmental conditions while monitoring complex phenomena. Therefore, designing dependable monitoring systems is challenging given the external disturbances affecting sensor measurements. Even the apparently simple task of outlier detection in sensor data becomes a hard problem, amplified by the difficulty in distinguishing true data errors due to sensor faults from deviations due to natural phenomenon, which look like data errors. Existing solutions for runtime outlier detection typically assume that the physical processes can be accurately modeled, or that outliers consist in large deviations that are easily detected and filtered by appropriate thresholds. Other solutions assume that it is possible to deploy multiple sensors providing redundant data to support voting-based techniques. In this article, we propose a new methodology for dependable runtime detection of outliers in environmental monitoring systems, aiming to increase data quality by treating them. We propose the use of machine learning techniques to model each sensor behavior, exploiting the existence of correlated data provided by other related sensors. Using these models, along with knowledge of processed past measurements, it is possible to obtain accurate estimations of the observed environment parameters and build failure detectors that use these estimations. When a failure is detected, these estimations also allow one to correct the erroneous measurements and hence improve the overall data quality. Our methodology not only allows one to distinguish truly abnormal measurements from deviations due to complex natural phenomena, but also allows the quantification of each measurement quality, which is relevant from a dependability perspective. We apply the methodology to real datasets from a complex aquatic monitoring system, measuring temperature and salinity parameters, through which we illustrate the process for building the machine learning prediction models using a technique based on Artificial Neural Networks, denoted ANNODE ( ANN Outlier Detection ). From this application, we also observe the effectiveness of our ANNODE approach for accurate outlier detection in harsh environments. Then we validate these positive results by comparing ANNODE with state-of-the-art solutions for outlier detection. The results show that ANNODE improves existing solutions regarding accuracy of outlier detection.

scholarly journals The ensemble of surrogate model based on local and global errors

2021 ◽  
Vol 1043 (5) ◽  
pp. 052049
Author(s):  
X Zhang ◽  
H Li ◽  
G Xiang ◽  
H W Xu
Keyword(s):  
Surrogate Model ◽  
Model Based
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