SecAODV: A Secure Healthcare Routing Scheme Based on Hybrid Cryptography in Wireless Body Sensor Networks.

Internet of things (IoT) healthcare secure routing security wireless body sensor networks (WBSNs)

Journal

Frontiers in medicine
ISSN: 2296-858X
Titre abrégé: Front Med (Lausanne)
Pays: Switzerland
ID NLM: 101648047

Informations de publication

Date de publication:
2022
Historique:
received: 04 12 2021
accepted: 08 04 2022
entrez: 8 8 2022
pubmed: 9 8 2022
medline: 9 8 2022
Statut: epublish

Résumé

In recent decades, the use of sensors has dramatically grown to monitor human body activities and maintain the health status. In this application, routing and secure data transmission are very important to prevent the unauthorized access by attackers to health data. In this article, we propose a secure routing scheme called SecAODV for heterogeneous wireless body sensor networks. SecAODV has three phases: bootstrapping, routing between cluster head nodes, and communication security. In the bootstrapping phase, the base station loads system parameters and encryption functions in the memory of sensor nodes. In the routing phase, each cluster head node calculates its degree based on several parameters, including, distance, residual energy, link quality, and the number of hops, to decide for rebroadcasting the route request (RREQ) message. In the communication security phase, a symmetric cryptography method is used to protect intra-cluster communications. Also, an asymmetric cryptography method is used to secure communication links between cluster head nodes. The proposed secure routing scheme is simulated in the network simulator version 2 (NS2) simulator. The simulation results are compared with the secure multi tier energy-efficient routing scheme (SMEER) and the centralized low-energy adaptive clustering hierarchy (LEACH-C). The results show that SecAODV improves end-to-end delay, throughput, energy consumption, packet delivery rate (PDR), and packet loss rate (PLR).

Identifiants

pubmed: 35935783
doi: 10.3389/fmed.2022.829055
pmc: PMC9351592
doi:

Types de publication

Journal Article

Langues

eng

Pagination

829055

Informations de copyright

Copyright © 2022 Jeong, Lee, Hussain Malik, Yousefpoor, Yousefpoor, Ahmed, Hosseinzadeh and Mosavi.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Références

PLoS One. 2016 Jan 15;11(1):e0146464
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pubmed: 30823560
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pubmed: 31888095
J Clean Prod. 2020 Nov 20;274:122877
pubmed: 32834567

Auteurs

Heon Jeong (H)

Department of Fire Service Administration, Chodang University, Muan-gun, South Korea.

Sang-Woong Lee (SW)

Pattern Recognition and Machine Learning Lab, Gachon University, Seongnam, South Korea.

Mazhar Hussain Malik (M)

HoD Computing and IT (CIT) Global College of Engineering and Technology, Muscat, Oman.

Efat Yousefpoor (E)

Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran.

Mohammad Sadegh Yousefpoor (MS)

Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran.

Omed Hassan Ahmed (OH)

Department of Information Technology, University of Human Development, Sulaymaniyah, Iraq.

Mehdi Hosseinzadeh (M)

Pattern Recognition and Machine Learning Lab, Gachon University, Seongnam, South Korea.

Amir Mosavi (A)

Faculty of Civil Engineering, Technische Universität Dresden, Dresden, Germany.
John von Neumann Faculty of Informatics, Óbuda University, Budapest, Hungary.
Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, Bratislava, Slovakia.

Classifications MeSH