A Model for Using Machine Learning in Smart Environments

2012 ◽  
pp. 24-33 ◽  
Author(s):  
Sakari Stenudd
Keyword(s):  
Machine Learning ◽  
Smart Environments

scholarly journals A Non-Intrusive Approach for Indoor Occupancy Detection in Smart Environments

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3953 ◽  
Author(s):  
Bruno Abade ◽  
David Perez Abreu ◽  
Marilia Curado
Keyword(s):  
Machine Learning ◽  
Environmental Data ◽  
Machine Learning Techniques ◽  
Smart Environments ◽  
Prototype System ◽  
Indoor Environments ◽  
Process Data ◽  
Indoor Space ◽  
Occupancy Detection ◽  
Real Scenario

Smart Environments try to adapt their conditions focusing on the detection, localisation, and identification of people to improve their comfort. It is common to use different sensors, actuators, and analytic techniques in this kind of environments to process data from the surroundings and actuate accordingly. In this research, a solution to improve the user’s experience in Smart Environments based on information obtained from indoor areas, following a non-intrusive approach, is proposed. We used Machine Learning techniques to determine occupants and estimate the number of persons in a specific indoor space. The solution proposed was tested in a real scenario using a prototype system, integrated by nodes and sensors, specifically designed and developed to gather the environmental data of interest. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. Additionally, the analysis performed over the gathered data using Machine Learning and pattern recognition mechanisms shows that it is possible to determine the occupancy of indoor environments.

scholarly journals Resource Usage and Performance Trade-offs for Machine Learning Models in Smart Environments

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1176 ◽  
Author(s):  
Davy Preuveneers ◽  
Ilias Tsingenopoulos ◽  
Wouter Joosen
Keyword(s):  
Machine Learning ◽  
Data Storage ◽  
Intelligent Systems ◽  
Smart Cities ◽  
Smart Environments ◽  
Learning Models ◽  
Resource Constrained ◽  
Network Bandwidth ◽  
Trade Offs ◽  
Machine Learning Models

The application of artificial intelligence enhances the ability of sensor and networking technologies to realize smart systems that sense, monitor and automatically control our everyday environments. Intelligent systems and applications often automate decisions based on the outcome of certain machine learning models. They collaborate at an ever increasing scale, ranging from smart homes and smart factories to smart cities. The best performing machine learning model, its architecture and parameters for a given task are ideally automatically determined through a hyperparameter tuning process. At the same time, edge computing is an emerging distributed computing paradigm that aims to bring computation and data storage closer to the location where they are needed to save network bandwidth or reduce the latency of requests. The challenge we address in this work is that hyperparameter tuning does not take into consideration resource trade-offs when selecting the best model for deployment in smart environments. The most accurate model might be prohibitively expensive to computationally evaluate on a resource constrained node at the edge of the network. We propose a multi-objective optimization solution to find acceptable trade-offs between model accuracy and resource consumption to enable the deployment of machine learning models in resource constrained smart environments. We demonstrate the feasibility of our approach by means of an anomaly detection use case. Additionally, we evaluate the extent that transfer learning techniques can be applied to reduce the amount of training required by reusing previous models, parameters and trade-off points from similar settings.

scholarly journals A MACHINE LEARNING FRAMEWORK BASED ON VARIOUS NETWORK TRAFFIC CHARACTERISTICS TO IDENTIFY AND CLASSIFY THE DEFAULT BEHAVIOR OF IOT DEVICES ON A NETWORK

2021 ◽  
Vol 6 (5) ◽  
Author(s):  
Pagalla Bhavani Shankar ◽  
Yogi Reddy Maramreddy ◽  
Padala S Venkata Durga Gayatri
Keyword(s):  
Machine Learning ◽  
Network Traffic ◽  
Network Flow ◽  
New Technologies ◽  
Smart Cities ◽  
Smart Environments ◽  
Traffic Characteristics ◽  
Trade Offs ◽  
Iot Devices ◽  
Robust Framework

The Internet of Things (IoT) is being well acquire to the next era of revolutionary generations amongst the new technologies. IoT technology being hailed so hard we had to stop in our society, smart homes, enterprises, and smart cities. Dynamics of smart one’s are increasingly being equipped with a profusion of IoT devices. Due to the tremendous upgradation of knowledge in various aspects impresarios of such smart environments may not even be fully aware of their working nature or principles of IoT devices, assets and functioning properly safe from cyberattacks. In this paper, we addressing this challenge by developing a robust framework for IoT device classification using traffic characteristics obtained at the level of network level. As a part of robust framework, firstly, we have a tendency to instrument a smart environment with 28 completely different IoT devices, spanning cameras, lights, plugs, motion sensors, appliances and health-monitors. We have a tendency to collect and synthesize traffic traces from this framework infrastructure for a period of 6 months, a type of subset of which we release as open data for the community to use. Second, we have to present or gifts the insights into the underlying network traffic characteristics using statistical and applied mathematical attributes such as activity cycles, port numbers, signaling patterns and cipher suites. Third, we have a tendency to develop a multi-stage machine learning based classification algorithm and demonstrate its ability to identify specific IoT devices with over 99% accuracy based on their network flow of activity. Finally, we have a tendency to discuss the trade-offs between cost, speed, and performance involved in deploying the classification network framework in real-time. Our study paves the way for impresarios of smart environments to monitor their IoT devices and assets for presence, functionality, and cyber-security without requiring any specialized devices or protocols.

scholarly journals Transfer Learning in Smart Environments

2021 ◽  
Vol 3 (2) ◽  
pp. 318-332
Author(s):  
Amin Anjomshoaa ◽  
Edward Curry
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
Intelligent Systems ◽  
Large Scale ◽  
Fine Tuning ◽  
Smart Environments ◽  
Learning Models ◽  
Network Training ◽  
Collection Data ◽  
Machine Learning Models

The knowledge embodied in cognitive models of smart environments, such as machine learning models, is commonly associated with time-consuming and costly processes such as large-scale data collection, data labeling, network training, and fine-tuning of models. Sharing and reuse of these elaborated resources between intelligent systems of different environments, which is known as transfer learning, would facilitate the adoption of cognitive services for the users and accelerate the uptake of intelligent systems in smart building and smart city applications. Currently, machine learning processes are commonly built for intra-organization purposes and tailored towards specific use cases with the assumption of integrated model repositories and feature pools. Transferring such services and models beyond organization boundaries is a challenging task that requires human intervention to find the matching models and evaluate them. This paper investigates the potential of communication and transfer learning between smart environments in order to empower a decentralized and peer-to-peer ecosystem for seamless and automatic transfer of services and machine learning models. To this end, we explore different knowledge types in the context of smart built environments and propose a collaboration framework based on knowledge graph principles for describing the machine learning models and their corresponding dependencies.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Machine Learning for Speaker Recognition

2020 ◽  
Author(s):  
Man-Wai Mak ◽  
Jen-Tzung Chien
Keyword(s):  
Machine Learning ◽  
Speaker Recognition

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining
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