scholarly journals Data-Driven Self-Learning Controller for Power-Aware Mobile Monitoring IoT Devices

2022 ◽  
Vol 70 (2) ◽  
pp. 2601-2618
Author(s):  
Michal Prauzek ◽  
Tereza Paterova ◽  
Jaromir Konecny ◽  
Radek Martinek
Keyword(s):  
Data Driven ◽  
Mobile Monitoring ◽  
Iot Devices ◽  
Self Learning

scholarly journals Evaluation of the COVID-19 Era by Using Machine Learning and Interpretation of Confidential Dataset

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2910
Author(s):  
Andreas Andreou ◽  
Constandinos X. Mavromoustakis ◽  
George Mastorakis ◽  
Jordi Mongay Batalla ◽  
Evangelos Pallis
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Research Study ◽  
Nonlinear Least Squares ◽  
Data Driven ◽  
Machine Learning Technique ◽  
Marquardt Algorithm ◽  
Learning Technique ◽  
Iot Devices ◽  
Algorithmic Techniques

Various research approaches to COVID-19 are currently being developed by machine learning (ML) techniques and edge computing, either in the sense of identifying virus molecules or in anticipating the risk analysis of the spread of COVID-19. Consequently, these orientations are elaborating datasets that derive either from WHO, through the respective website and research portals, or from data generated in real-time from the healthcare system. The implementation of data analysis, modelling and prediction processing is performed through multiple algorithmic techniques. The lack of these techniques to generate predictions with accuracy motivates us to proceed with this research study, which elaborates an existing machine learning technique and achieves valuable forecasts by modification. More specifically, this study modifies the Levenberg–Marquardt algorithm, which is commonly beneficial for approaching solutions to nonlinear least squares problems, endorses the acquisition of data driven from IoT devices and analyses these data via cloud computing to generate foresight about the progress of the outbreak in real-time environments. Hence, we enhance the optimization of the trend line that interprets these data. Therefore, we introduce this framework in conjunction with a novel encryption process that we are proposing for the datasets and the implementation of mortality predictions.

scholarly journals Self-Learning Pipeline for Low-Energy Resource-Constrained Devices

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6636
Author(s):  
Fouad Sakr ◽  
Riccardo Berta ◽  
Joseph Doyle ◽  
Alessandro De De Gloria ◽  
Francesco Bellotti
Keyword(s):  
Embedded System ◽  
Nearest Neighbor ◽  
Energy Resource ◽  
K Nearest Neighbor ◽  
Resource Constrained ◽  
Confidence Measure ◽  
Embedded Devices ◽  
Remote Training ◽  
Iot Devices ◽  
Self Learning

The trend of bringing machine learning (ML) to the Internet of Things (IoT) field devices is becoming ever more relevant, also reducing the overall energy need of the applications. ML models are usually trained in the cloud and then deployed on edge devices. Most IoT devices generate large amounts of unlabeled data, which are expensive and challenging to annotate. This paper introduces the self-learning autonomous edge learning and inferencing pipeline (AEP), deployable in a resource-constrained embedded system, which can be used for unsupervised local training and classification. AEP uses two complementary approaches: pseudo-label generation with a confidence measure using k-means clustering and periodic training of one of the supported classifiers, namely decision tree (DT) and k-nearest neighbor (k-NN), exploiting the pseudo-labels. We tested the proposed system on two IoT datasets. The AEP, running on the STM NUCLEO-H743ZI2 microcontroller, achieves comparable accuracy levels as same-type models trained on actual labels. The paper makes an in-depth performance analysis of the system, particularly addressing the limited memory footprint of embedded devices and the need to support remote training robustness.

Train++: An Incremental ML Model Training Algorithm to Create Self-Learning IoT Devices

2021 ◽  
Author(s):  
Bharath Sudharsan ◽  
Piyush Yadav ◽  
John G. Breslin ◽  
Muhammad Intizar Ali
Keyword(s):  
Training Algorithm ◽  
Model Training ◽  
Iot Devices ◽  
Self Learning

scholarly journals Here Comes the Hyper-Connected Augmented Consumer

2017 ◽  
Vol 9 (2) ◽  
pp. 10-17 ◽  
Author(s):  
Andrew T. Stephen
Keyword(s):  
Artificial Intelligence ◽  
New Technologies ◽  
Search Costs ◽  
Data Driven ◽  
Smart Sensors ◽  
Market Intelligence ◽  
Consumer Data ◽  
New Type ◽  
To Come ◽  
Iot Devices

Abstract Consumers have become always on and constantly connected. Search costs have plummeted, individuals’ abilities to digitally express themselves and their opinions increased, and the opportunities for superior business and market intelligence for companies have skyrocketed. This has given rise to more, richer, and new sources of consumer data that marketers can leverage, and has fueled the data-driven insights revolution in marketing. But there is more to come very soon. In marketing, we are quickly moving from the age of the connected consumer to the age of the augmented consumer. New technologies like wearable devices, smart sensors, consumer IoT devices, smart homes, and, critically, artificial intelligence ecosystems will not only connect, but will substantially and meaningfully augment the consumer in terms of their thoughts and behaviors. The biggest challenge for marketers will lie in how they approach marketing to this new type of consumer, particularly personal artificial intelligence ecosystems. This means marketing to algorithms, instead of people, and that is very different to how most marketing work is currently done.

scholarly journals A Review of Data-Driven Decision-Making Methods for Industry 4.0 Maintenance Applications

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 828
Author(s):  
Alexandros Bousdekis ◽  
Katerina Lepenioti ◽  
Dimitris Apostolou ◽  
Gregoris Mentzas
Keyword(s):  
Decision Making ◽  
Virtual Worlds ◽  
Industry 4.0 ◽  
Autonomous Robots ◽  
Simulation Software ◽  
Data Driven ◽  
Future Research ◽  
Data Driven Decision Making ◽  
Main Research ◽  
Iot Devices

Decision-making for manufacturing and maintenance operations is benefiting from the advanced sensor infrastructure of Industry 4.0, enabling the use of algorithms that analyze data, predict emerging situations, and recommend mitigating actions. The current paper reviews the literature on data-driven decision-making in maintenance and outlines directions for future research towards data-driven decision-making for Industry 4.0 maintenance applications. The main research directions include the coupling of decision-making with augmented reality for seamless interfacing that combines the real and virtual worlds of manufacturing operators; methods and techniques for addressing uncertainty of data, in lieu of emerging Internet of Things (IoT) devices; integration of maintenance decision-making with other operations such as scheduling and planning; utilization of the cloud continuum for optimal deployment of decision-making services; capability of decision-making methods to cope with big data; incorporation of advanced security mechanisms; and coupling decision-making with simulation software, autonomous robots, and other additive manufacturing initiatives.

scholarly journals Hull Shape Design Optimization with Parameter Space and Model Reductions, and Self-Learning Mesh Morphing

2021 ◽  
Vol 9 (2) ◽  
pp. 185
Author(s):  
Nicola Demo ◽  
Marco Tezzele ◽  
Andrea Mola ◽  
Gianluigi Rozza
Keyword(s):  
Gaussian Process Regression ◽  
Order Reduction ◽  
Data Driven ◽  
Reduction Techniques ◽  
Computational Mesh ◽  
Computational Resources ◽  
Proper Orthogonal ◽  
Active Subspace ◽  
Self Learning

In the field of parametric partial differential equations, shape optimization represents a challenging problem due to the required computational resources. In this contribution, a data-driven framework involving multiple reduction techniques is proposed to reduce such computational burden. Proper orthogonal decomposition (POD) and active subspace genetic algorithm (ASGA) are applied for a dimensional reduction of the original (high fidelity) model and for an efficient genetic optimization based on active subspace property. The parameterization of the shape is applied directly to the computational mesh, propagating the generic deformation map applied to the surface (of the object to optimize) to the mesh nodes using a radial basis function (RBF) interpolation. Thus, topology and quality of the original mesh are preserved, enabling application of POD-based reduced order modeling techniques, and avoiding the necessity of additional meshing steps. Model order reduction is performed coupling POD and Gaussian process regression (GPR) in a data-driven fashion. The framework is validated on a benchmark ship.

scholarly journals Cybersecurity Model Based on Hardening for Secure Internet of Things Implementation

2021 ◽  
Vol 11 (7) ◽  
pp. 3260
Author(s):  
Aarón Echeverría ◽  
Cristhian Cevallos ◽  
Ivan Ortiz-Garces ◽  
Roberto O. Andrade
Keyword(s):  
Internet Of Things ◽  
Smart Grids ◽  
Smart Cities ◽  
Data Driven ◽  
Security Level ◽  
Risk Matrix ◽  
Security Risks ◽  
Attack Surface ◽  
Iot Devices ◽  
Meta Analyses

The inclusion of Internet of Things (IoT) for building smart cities, smart health, smart grids, and other smart concepts has driven data-driven decision making by managers and automation in each domain. However, the hyper-connectivity generated by IoT networks coupled with limited default security in IoT devices increases security risks that can jeopardize the operations of cities, hospitals, and organizations. Strengthening the security aspects of IoT devices prior to their use in different systems can contribute to minimize the attack surface. This study aimed to model a sequence of seven steps to minimize the attack surface by executing hardening processes. Conducted a systematic literature review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) techniques. In this way, we were able to define a proposed methodology to evaluate the security level of an IoT solution by means of a checklist that considers the security aspects in the three layers of the IoT architecture. A risk matrix adapted to IoT is established to evaluate the attack surface. Finally, a process of hardening and vulnerability analysis is proposed to reduce the attack surface and improve the security level of the IoT solution.

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