Sensor Data Analysis and Management

The Role of Deep Learning

A. Suresh, R. Udendhran, M. S. Irfan Ahmed, A. Suresh, R. Udendhran, M. S. Irfan Ahmed

• Publisher: Wiley-IEEE Press
• Year: 2021
• Print ISBN: 9781119682424
• eBook ISBN: 9781119682486
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Electrical Engineering & Telecommunications
• Index: Technology & Engineering

Discover detailed insights into the methods, algorithms, and techniques for deep learning in sensor data analysis

Sensor Data Analysis and Management: The Role of Deep Learning delivers an insightful and practical overview of the applications of deep learning techniques to the analysis of sensor data. The book collects cutting-edge resources into a single collection designed to enlighten the reader on topics as varied as recent techniques for fault detection and classification in sensor data, the application of deep learning to Internet of Things sensors, and a case study on high-performance computer gathering and processing of sensor data.

The editors have curated a distinguished group of perceptive and concise papers that show the potential of deep learning as a powerful tool for solving complex modelling problems across a broad range of industries, including predictive maintenance, health monitoring, financial portfolio forecasting, and driver assistance.

The book contains real-time examples of analyzing sensor data using deep learning algorithms and a step-by-step approach for installing and training deep learning using the Python keras library. Readers will also benefit from the inclusion of:

• A thorough introduction to the Internet of Things for human activity recognition, based on wearable sensor data
• An exploration of the benefits of neural networks in real-time environmental sensor data analysis
• Practical discussions of supervised learning data representation, neural networks for predicting physical activity based on smartphone sensor data, and deep-learning analysis of location sensor data for human activity recognition
• An analysis of boosting with XGBoost for sensor data analysis

Perfect for industry practitioners and academics involved in deep learning and the analysis of sensor data, Sensor Data Analysis and Management: The Role of Deep Learning will also earn a place in the libraries of undergraduate and graduate students in data science and computer science programs.

1
Efficient Resource Allocation Using Multilayer Neural Network in Cloud Environment

N. Vijayaraj¹, G. Uganya ², M. Balasaraswathi ³, V. Sivasankaran ⁴, Radhika Baskar ³, A.S. Syed Fiaz ¹

¹ Assistant Professor, CSE1 Assistant Professor, CSE, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science & Technology, Chennai, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science & Technology, Chennai2 Assistant Professor, ECE, Saveetha School of Engineering, SIMATS, Chennai
² Assistant Professor, ECE3 Associate Professor, ECE, Saveetha School of Engineering, SIMATS, Chennai, Saveetha School of Engineering, SIMATS, Chennai4 Sreenivasa Institute of Technology and Management Studies, Chittoor, Andhra Pradesh
³ Associate Professor, ECE, Saveetha School of Engineering, SIMATS, Chennai
⁴ Sreenivasa Institute of Technology and Management Studies, Chittoor, Andhra Pradesh

1.1 Introduction

Nowadays, due to the development of the cloud computing environment, most information and communications technology (ICT) players have moved to new product management and application models—for example, Apple iCloud, Google App Engine, Amazon EC2, IBM Cloud, VMware Cloud, etc. Cloud computing is an important emerging field in ICT, making people’s life easier by increasing productivity and processing speed, reducing cost and time consumption, facilitating backup and storing of multiple data, and enabling automation in the distribution of products and in development. It is quite challenging to offer trustworthy, powerful, qualitative, and cost-effective cloud services. In distributed cloud environments, a large number of dynamic resources are circulated around the world. Hence, the allocation of resources between the cloud user and cloud provider is complex, and cloud providers should be able to manage their resources with QoS and maximum customer satisfaction. Inadequate resource allocation leads to poor quality, bad performance, and substandard customer satisfaction, all falling below the criteria specified in service-level agreements (SLAs). Therefore, efficient and heterogeneous resource allocation is essential to avoid these problems.

In previous studies, the resource allocation problem was solved based on two methods: (i) reactive method, and (ii) proactive method. The reactive method is a common method in ICT, but it is not considered an effective method. The proactive method was developed to improve the performance of the system by allocating resources in a predefined manner. However, proactive-based methods, including time series (TS), queuing theory (QT), and reinforcement learning (RL), have some limitations. These include numerous data in TS, reconstruction of architecture when changing resources in QT, and large time requirements in RL. To resolve these proactive method constraints, feedback-based approaches have been introduced in difficult computing systems.

These feedback-based mechanisms are of two types, based on the allocation of resources to cloud services. These are (i) single input and single output (SISO), and (ii) multiple input and multiple output (MIMO). SISO is developed only for providing a single kind of resource allocation. However, in distributed cloud environments, users and providers need a mixture of resource allocation, and this leads to the violation of SLAs. To overcome this SISO limitation, the MIMO feedback control system was developed for multiple groupings of resource allocation by combining multiple numbers of separate SISO feedback control systems. But this type of MIMO feedback control system also leads to poor QoS and SLA violations. To overcome this issue, a coordinated multiple input multiple output feedback system was developed, which enhances ...

Table of contents

• Cover
• Title page
• Copyright
• Table of Contents
• About the Editors
• List of Contributors
• Preface
• 1 Efficient Resource Allocation Using Multilayer Neural Network in Cloud Environment
• 2 Internet of Things for Human-Activity Recognition Based on Wearable Sensor Data
• 3 Evaluation of Feature Selection Techniques in Intrusion Detection Systems Using Machine Learning Models in Wireless Ad Hoc Networks
• 4 Neuro-Fuzzy-Based Bidirectional and Biobjective Reactive Routing Schema for Critical Wireless Sensor Networks
• 5 Feature Detection and Extraction Techniques for Real-Time Student Monitoring in Sensor Data Environments
• 6 Deep Learning Analysis of Location Sensor Data for Human-Activity Recognition
• 7 A Quantum-Behaved Particle-Swarm-Optimization-Based KNN Classifier for Improving WSN Lifetime
• 8 Feature Detection and Extraction Techniques for Sensor Data
• 9 Object Detection in Satellite Images Using Modified Pyramid Scene Parsing Networks
• 10 Coronary Illness Prediction Using the AdaBoost Algorithm
• 11 Geographic Information Systems and Confidence Interval with Deep Learning Techniques for Traffic Management Systems in Smart Cities
• Index
• End User License Agreement