Developing an Efficient Deep Learning-Based Trusted Model for Pervasive Computing Using an LSTM-Based Classification Model

Developing an Efficient Deep Learning-Based Trusted Model for Pervasive Computing Using an LSTM-Based Classification Model

Mobile and pervasive computing is one of the recent paradigms available in the area of information technology. The role of pervasive computing is foremost in the field where it provides the ability to distribute computational services to the surroundings where people work and leads to issues such as...

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Main Authors: Yang He, Shah Nazir, Baisheng Nie, Sulaiman Khan, Jianhui Zhang
Format: Article
Language:English
Published: Hindawi-Wiley 2020-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2020/4579495
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spelling doaj-5008342e68d34442b9841e728874e21c2020-11-25T03:54:58ZengHindawi-WileyComplexity1076-27871099-05262020-01-01202010.1155/2020/45794954579495Developing an Efficient Deep Learning-Based Trusted Model for Pervasive Computing Using an LSTM-Based Classification ModelYang He0Shah Nazir1Baisheng Nie2Sulaiman Khan3Jianhui Zhang4School of Emergency Management and Safety Engineering, China University of Mining &Technology (Beijing), Beijing 100083, ChinaDepartment of Computer Science, University of Swabi, Ambar, PakistanSchool of Emergency Management and Safety Engineering, China University of Mining &Technology (Beijing), Beijing 100083, ChinaDepartment of Computer Science, University of Swabi, Ambar, PakistanPostal Savings Bank of China, Beijing 100000, ChinaMobile and pervasive computing is one of the recent paradigms available in the area of information technology. The role of pervasive computing is foremost in the field where it provides the ability to distribute computational services to the surroundings where people work and leads to issues such as trust, privacy, and identity. To provide an optimal solution to these generic problems, the proposed research work aims to implement a deep learning-based pervasive computing architecture to address these problems. Long short-term memory architecture is used during the development of the proposed trusted model. The applicability of the proposed model is validated by comparing its performance with the generic back-propagation neural network. This model results with an accuracy rate of 93.87% for the LSTM-based model much better than 85.88% for the back-propagation-based deep model. The obtained results reflect the usefulness and applicability of such an approach and the competitiveness against other existing ones.http://dx.doi.org/10.1155/2020/4579495
collection DOAJ
language English
format Article
sources DOAJ
author Yang He
Shah Nazir
Baisheng Nie
Sulaiman Khan
Jianhui Zhang
spellingShingle Yang He
Shah Nazir
Baisheng Nie
Sulaiman Khan
Jianhui Zhang
Developing an Efficient Deep Learning-Based Trusted Model for Pervasive Computing Using an LSTM-Based Classification Model
Complexity
author_facet Yang He
Shah Nazir
Baisheng Nie
Sulaiman Khan
Jianhui Zhang
author_sort Yang He
title Developing an Efficient Deep Learning-Based Trusted Model for Pervasive Computing Using an LSTM-Based Classification Model
title_short Developing an Efficient Deep Learning-Based Trusted Model for Pervasive Computing Using an LSTM-Based Classification Model
title_full Developing an Efficient Deep Learning-Based Trusted Model for Pervasive Computing Using an LSTM-Based Classification Model
title_fullStr Developing an Efficient Deep Learning-Based Trusted Model for Pervasive Computing Using an LSTM-Based Classification Model
title_full_unstemmed Developing an Efficient Deep Learning-Based Trusted Model for Pervasive Computing Using an LSTM-Based Classification Model
title_sort developing an efficient deep learning-based trusted model for pervasive computing using an lstm-based classification model
publisher Hindawi-Wiley
series Complexity
issn 1076-2787
1099-0526
publishDate 2020-01-01
description Mobile and pervasive computing is one of the recent paradigms available in the area of information technology. The role of pervasive computing is foremost in the field where it provides the ability to distribute computational services to the surroundings where people work and leads to issues such as trust, privacy, and identity. To provide an optimal solution to these generic problems, the proposed research work aims to implement a deep learning-based pervasive computing architecture to address these problems. Long short-term memory architecture is used during the development of the proposed trusted model. The applicability of the proposed model is validated by comparing its performance with the generic back-propagation neural network. This model results with an accuracy rate of 93.87% for the LSTM-based model much better than 85.88% for the back-propagation-based deep model. The obtained results reflect the usefulness and applicability of such an approach and the competitiveness against other existing ones.
url http://dx.doi.org/10.1155/2020/4579495
work_keys_str_mv AT yanghe developinganefficientdeeplearningbasedtrustedmodelforpervasivecomputingusinganlstmbasedclassificationmodel
AT shahnazir developinganefficientdeeplearningbasedtrustedmodelforpervasivecomputingusinganlstmbasedclassificationmodel
AT baishengnie developinganefficientdeeplearningbasedtrustedmodelforpervasivecomputingusinganlstmbasedclassificationmodel
AT sulaimankhan developinganefficientdeeplearningbasedtrustedmodelforpervasivecomputingusinganlstmbasedclassificationmodel
AT jianhuizhang developinganefficientdeeplearningbasedtrustedmodelforpervasivecomputingusinganlstmbasedclassificationmodel
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