A Study on the Machine Learning Model for Product Faulty Prediction in Internet of Things Environment

2017 ◽  
Vol 7 (1) ◽  
pp. 55-60
Author(s):  
Jin-Hee Ku ◽  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Learning Model ◽  
Internet Of Things Environment ◽  
Machine Learning Model

scholarly journals IFed: A novel federated learning framework for local differential privacy in Power Internet of Things

2020 ◽  
Vol 16 (5) ◽  
pp. 155014772091969
Author(s):  
Hui Cao ◽  
Shubo Liu ◽  
Renfang Zhao ◽  
Xingxing Xiong
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Differential Privacy ◽  
Learning Model ◽  
Wireless Sensor ◽  
Trade Off ◽  
Privacy Requirements ◽  
Learning Framework ◽  
Machine Learning Model ◽  
Internet Of Thing

Nowadays, wireless sensor network technology is being increasingly popular which is applied to a wide range of Internet of Things. Especially, Power Internet of Things is an important and rapidly growing section in Internet of Thing systems, which benefited from the application of wireless sensor networks to achieve fine-grained information collection. Meanwhile, the privacy risk is gradually exposed, which is the widespread concern for electricity power consumers. Non-intrusive load monitoring, in particular, is a technique to recover state of appliances from only the energy consumption data, which enables adversary inferring the behavior privacy of residents. There can be no doubt that applying local differential privacy to achieve privacy preserving in the local setting is more trustworthy than centralized approach for electricity customers. Although it is hard to control the risk and achieve the trade-off between privacy and utility by traditional local differential privacy obfuscation mechanisms, some existing obfuscation mechanisms based on artificial intelligence, called advanced obfuscation mechanisms, can achieve it. However, the large computing resource consumption to train the machine learning model is not affordable for most Power Internet of Thing terminal. In this article, to solve this problem, IFed was proposed—a novel federated learning framework that let electric provider who normally is adequate in computing resources to help Power Internet of Thing users. First, the optimized framework was proposed in which the trade-off between local differential privacy, data utility, and resource consumption was incorporated. Concurrently, the following problem of privacy preserving on the machine learning model transport between electricity provider and customers was noted and resolved. Last, users were categorized based on different levels of privacy requirements, and stronger privacy guarantee was provided for sensitive users. The formal local differential privacy analysis and the experiments demonstrated that IFed can fulfill the privacy requirements for Power Internet of Thing users.

A Hybrid Machine Learning Model for Demand Prediction of Edge-Computing-Based Bike-Sharing System Using Internet of Things

2020 ◽  
Vol 7 (8) ◽  
pp. 7345-7356 ◽  
Author(s):  
Tiantian Xu ◽  
Guangjie Han ◽  
Xingyue Qi ◽  
Jiaxin Du ◽  
Chuan Lin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Learning Model ◽  
Edge Computing ◽  
Demand Prediction ◽  
Machine Learning Model ◽  
Bike Sharing ◽  
Hybrid Machine

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

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