Real-Time Data Analytics for Large Scale Sensor Data

Himansu Das, Nilanjan Dey, Valentina Emilia Balas

• Publisher: Academic Press
• Year: 2019
• Print ISBN: 9780128180143
• eBook ISBN: 9780128182420
• Topic: Biological Sciences
• Subtopic: Biotechnology
• Index: Biological Sciences

Real-Time Data Analytics for Large-Scale Sensor Data covers the theory and applications of hardware platforms and architectures, the development of software methods, techniques and tools, applications, governance and adoption strategies for the use of massive sensor data in real-time data analytics. It presents the leading-edge research in the field and identifies future challenges in this fledging research area. The book captures the essence of real-time IoT based solutions that require a multidisciplinary approach for catering to on-the-fly processing, including methods for high performance stream processing, adaptively streaming adjustment, uncertainty handling, latency handling, and more.- Examines IoT applications, the design of real-time intelligent systems, and how to manage the rapid growth of the large volume of sensor data- Discusses intelligent management systems for applications such as healthcare, robotics and environment modeling- Provides a focused approach towards the design and implementation of real-time intelligent systems for the management of sensor data in large-scale environments

Chapter 1

Internet of Things in healthcare: Smart devices, sensors, and systems related to diseases and health conditions

Irosha Jayatilleka*; Malka N. Halgamuge^† * School of Computing and Mathematics, Charles Sturt University, Melbourne, VIC, Australia
^† Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC, Australia

Abstract

This chapter discusses recent IoT devices and wearables for various diseases in the human body and examines the benefits, algorithms used for data analysis, identified challenges, and countermeasures for each disease. In order to do this, we sourced scientific articles on the topics of innovation of wireless wearables and IoT healthcare devices published between 2016 and 2018 from the Institute of Electrical and Electronics Engineers (IEEE) database.

Graphical Abstract

Figure represents the pictorial demonstration of the abstract of this chapter.

Graphical abstract of the research: The data gathered from 35 peer-reviewed articles from 2016 to 2018. The authors studied medical conditions, IoT sensors, devices, measured parameters, methods followed, data classifiers, benefits and issues of using the devices, and proposed solutions. The central nervous system was identified as the most frequently used body system for IoT devices. The majority of IoT devices measured body parameters. The most significant benefit of using IoT devices is efficiency. The biggest challenge in using IoT devices is security and privacy of sensitive data.

Keywords

IoT; Healthcare; Sensors; Security and privacy; Body parameters; Central nervous system

1.1 Introduction

The Internet of things (IoT) is an emerging technology in the modern information age. In the healthcare sector, a considerable number of wearables have been innovated using various types of sensors. Scientific researchers and innovators have been experimenting with wearable sensors to promote remote monitoring and early diagnosis of chronic diseases. The primary use of these sensors is to record useful parameters of the body such as temperature, heart rate (BPM), and breath acetone. Sensors are also used for storing obtained data in the cloud and local data repositories for future decision-making processes. An IoT-based healthcare system primari...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Contributors
• Preface
• Chapter 1: Internet of Things in healthcare: Smart devices, sensors, and systems related to diseases and health conditions
• Chapter 2: Real-time data analytics in healthcare using the Internet of Things
• Chapter 3: Lightweight code self-verification using return-oriented programming in resilient IoT
• Chapter 4: Monte-Carlo Simulation models for reliability analysis of low-cost IoT communication networks in smart grid
• Chapter 5: Lightweight ciphertext-policy attribute-based encryption scheme for data privacy and security in cloud-assisted IoT
• Chapter 6: Soft sensor with shape descriptors for flame quality prediction based on LSTM regression
• Chapter 7: Communication-aware edge-centric knowledge dissemination in edge computing environments
• Chapter 8: An effective blockchain-based, decentralized application for smart building system management
• Chapter 9: Privacy and security of Internet of Things devices
• Chapter 10: Software-Defined Networking for the Internet of Things: Securing home networks using SDN
• Index