Self-adapting cloud services orchestration for fulfilling intensive sensory data-driven IoT workflows

2020 ◽  
Vol 108 ◽  
pp. 583-597
Author(s):  
M. Adel Serhani ◽  
Hadeel T. El-Kassabi ◽  
Khaled Shuaib ◽  
Alramzana N. Navaz ◽  
Boualem Benatallah ◽  
...  
Keyword(s):  
Cloud Services ◽  
Data Driven ◽  
Sensory Data ◽  
Services Orchestration

scholarly journals Novel holistic architecture for analytical operation on sensory data relayed as cloud services

2020 ◽  
Vol 10 (4) ◽  
pp. 4322
Author(s):  
Manujakshi B. C ◽  
K. B. Ramesh
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
Cost Effective ◽  
Cloud Services ◽  
Sensor Nodes ◽  
Sensory Data ◽  
Energy Constrained ◽  
Analytical Operation

With increasing adoption of the sensor-based application, there is an exponential rise of the sensory data that eventually take the shape of the big data. However, the practicality of executing high end analytical operation over the resource-constrained big data has never being studied closely. After reviewing existing approaches, it is explored that there is no cost effective schemes of big data analytics over large scale sensory data processiing that can be directly used as a service. Therefore, the propsoed system introduces a holistic architecture where streamed data after performing extraction of knowedge can be offered in the form of services. Implemented in MATLAB, the proposed study uses a very simplistic approach considering energy constrained of the sensor nodes to find that proposed system offers better accuracy, reduced mining duration (i.e. faster response time), and reduced memory dependencies to prove that it offers cost effective analytical solution in contrast to existing system.

scholarly journals The Curious Case of the PDF Converter that Likes Mozart: Dissecting and Mitigating the Privacy Risk of Personal Cloud Apps

2016 ◽  
Vol 2016 (4) ◽  
pp. 123-143 ◽  
Author(s):  
Hamza Harkous ◽  
Rameez Rahman ◽  
Bojan Karlas ◽  
Karl Aberer
Keyword(s):  
Detailed Analysis ◽  
Current Model ◽  
Activity Patterns ◽  
Third Parties ◽  
Cloud Services ◽  
Third Party ◽  
Data Driven ◽  
Privacy Risk ◽  
Privacy Leakage ◽  
Google Drive

AbstractThird party apps that work on top of personal cloud services, such as Google Drive and Drop-box, require access to the user’s data in order to provide some functionality. Through detailed analysis of a hundred popular Google Drive apps from Google’s Chrome store, we discover that the existing permission model is quite often misused: around two-thirds of analyzed apps are over-privileged, i.e., they access more data than is needed for them to function. In this work, we analyze three different permission models that aim to discourage users from installing over-privileged apps. In experiments with 210 real users, we discover that the most successful permission model is our novel ensemble method that we call Far-reaching Insights. Far-reaching Insights inform the users about the data-driven insights that apps can make about them (e.g., their topics of interest, collaboration and activity patterns etc.) Thus, they seek to bridge the gap between what third parties can actually know about users and users’ perception of their privacy leakage. The efficacy of Far-reaching Insights in bridging this gap is demonstrated by our results, as Far-reaching Insights prove to be, on average, twice as effective as the current model in discouraging users from installing over-privileged apps. In an effort to promote general privacy awareness, we deployed PrivySeal, a publicly available privacy-focused app store that uses Far-reaching Insights. Based on the knowledge extracted from data of the store’s users (over 115 gigabytes of Google Drive data from 1440 users with 662 installed apps), we also delineate the ecosystem for 3rd party cloud apps from the standpoint of developers and cloud providers. Finally, we present several general recommendations that can guide other future works in the area of privacy for the cloud. To the best of our knowledge, ours is the first work that tackles the privacy risk posed by 3rd party apps on cloud platforms in such depth.

scholarly journals Predicting the Trend of Dissolved Oxygen based on kPCA-RNN Model

2019 ◽  
Author(s):  
Yi-Fan Zhang ◽  
Peter Fitch ◽  
Peter J. Thorburn
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Time Series Data ◽  
Predictive Accuracy ◽  
Data Driven ◽  
Quality Data ◽  
Series Data ◽  
Water Quality Data ◽  
Sensory Data ◽  
Water Quality Variables

Water quality forecasting is increasingly significant for agricultural management and environmental protection. Enormous amounts of water quality data are increasingly being collected by advanced sensors, which leads to an interest in using data-driven models for predicting trends in water quality. However, the unpredictable background noises introduced during water quality monitoring seriously degrade the performance of those models. Meanwhile, artificial neural networks (ANN) with feed-forward architecture lack the capability of maintaining and utilizing the accumulated temporal information, which leads to biased predictions in processing time series data. Hence, we propose a water quality predictive model based on a combination of Kernal Principal Component Analysis (kPCA) and Recurrent Neural Network (RNN) to forecast the trend of dissolved oxygen. Water quality variables are reconstructed based on kPCA method, which aims to reduce the noise from the raw sensory data and preserve actionable information. With the RNN's recurrent connections, our model can make use of the previous information in predicting the trend in the future. Data collected from Burnett River, Australia was applied to evaluate our kPCA-RNN model. The kPCA-RNN model achieved R2 scores up to 0.908, 0.823 and 0.671 for predicting the concentration of dissolved oxygen in the upcoming 1, 2 and 3 hours, respectively. Compared to current data-driven methods like ANN and SVR, the predictive accuracy of the kPCA-RNN model was at least 8 %, 17 % and 21 % better than the comparative models in these 3 cases. The study demonstrates the effectiveness of the kPAC-RNN modeling technique in predicting water quality variables with noisy sensory data.

scholarly journals Predicting the Trend of Dissolved Oxygen Based on the kPCA-RNN Model

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 585 ◽  
Author(s):  
Yi-Fan Zhang ◽  
Peter Fitch ◽  
Peter J. Thorburn
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Data Driven ◽  
Quality Data ◽  
Series Data ◽  
Support Vector ◽  
Feed Forward ◽  
Sensory Data ◽  
Water Quality Variables

Water quality forecasting is increasingly significant for agricultural management and environmental protection. Enormous amounts of water quality data are collected by advanced sensors, which leads to an interest in using data-driven models for predicting trends in water quality. However, the unpredictable background noises introduced during water quality monitoring seriously degrade the performance of those models. Meanwhile, artificial neural networks (ANN) with feed-forward architecture lack the capability of maintaining and utilizing the accumulated temporal information, which leads to biased predictions in processing time series data. Hence, we propose a water quality predictive model based on a combination of Kernal Principal Component Analysis (kPCA) and Recurrent Neural Network (RNN) to forecast the trend of dissolved oxygen. Water quality variables are reconstructed based on the kPCA method, which aims to reduce the noise from the raw sensory data and preserve actionable information. With the RNN’s recurrent connections, our model can make use of the previous information in predicting the trend in the future. Data collected from Burnett River, Australia was applied to evaluate our kPCA-RNN model. The kPCA-RNN model achieved R 2 scores up to 0.908, 0.823, and 0.671 for predicting the concentration of dissolved oxygen in the upcoming 1, 2 and 3 hours, respectively. Compared to current data-driven methods like Feed-forward neural network (FFNN), support vector regression (SVR) and general regression neural network (GRNN), the predictive accuracy of the kPCA-RNN model was at least 8%, 17% and 12% better than the comparative models in these three cases. The study demonstrates the effectiveness of the kPAC-RNN modeling technique in predicting water quality variables with noisy sensory data.

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