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Future of Big Data and Deep Learning for Wireless Body Area Networks

  • Fasee UllahEmail author
  • Ihtesham Ul Islam
  • Abdul Hanan Abdullah
  • Atif Khan
Chapter
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

Deep learning is an innovative set of algorithms in machine learning and requires minimum efforts of human engineering in extraction of features from data. It has the ability to find the optimum set of parameters for the network layers using a back-propagation algorithm, thereby modeling intricate structures in the data distribution. Further, deep learning architectures have resulted in tremendous performance on most recent machine learning challenges included working with sequential data such as text and time series data. In this connection, big data technology is an asset for modern businesses and is useful if powered by intelligent automation. Big data involves huge datasets that can be analyzed by machine learning such as deep learning algorithms to find insightful patterns and trends. With modern-day machine learning and big data technology, organizations can drive its long-term business value far more successful than ever before. Potential real-world applications of big data are not limited to healthcare, retail, financial services, and the automotive industry. In this way, the deep learning can have a great impact on analyzing the patient’s data generated from wireless body area networks (WBANs). WBAN is the emerging technology in healthcare to assist in monitoring of vital signs of patients using biomedical sensors. The monitored data is transmitted to the medical doctor for an optimal treatment in a life-threatening situation. At the end of this book, open research issues in WBAN and big data have discussed.

List of Acronyms

BMS

Biomedical sensor

CAP

Contention-access period

CNN

Convolutional neural networks

CEP

Complex event processing

CGOC

Compliance, Governance and Oversight Council

CFP

Contention-free period

CS

Conventional server

CSMA/CA

Carrier-sense multiple access with collision avoidance

DNN

Deep neural network

EAP

Exclusive access phase

ECG

Electrocardiogram

EEG

Electroencephalogram

EMG

Electromyography

IEEE

Institute of Electrical and Electronics Engineers

IP

Inactive period

GPU

Graphics processing unit

GSM

Global system for mobile

GST

Guaranteed time slot

HDFS

Hadoop Distributed File Systems

LOS

Line-of-sight

LSTM

Long short-term memory

MLP

Multilayer perceptron

MAC

Medium access control

NLOS

Non-line-of-sight

PHY

Physical layer

QoS

Quality of service

RAP

Random-access phase

RNN

Recurrent neural network

SPO2

Peripheral capillary oxygen saturation

TDMA

Time-division medium access

VC

Virtualized cloudlet

WBAN

Wireless body area networks

WHO

World Health Organization

WSN

Wireless sensor network

TG6

Task Group 6

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Copyright information

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Fasee Ullah
    • 1
    Email author
  • Ihtesham Ul Islam
    • 1
  • Abdul Hanan Abdullah
    • 2
  • Atif Khan
    • 3
  1. 1.Department of Computer Science & ITSarhad University of Science & ITPeshawarPakistan
  2. 2.Pervasive Computing Research Group, Faculty of ComputingUniversiti Teknologi MalaysiaJohor BahruMalaysia
  3. 3.Department of Computer ScienceIslamia College PeshawarPeshawarPakistan

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