2020
DOI: 10.1109/access.2020.3028686
|View full text |Cite
|
Sign up to set email alerts
|

A Lightweight Genetic Based Algorithm for Data Security in Wireless Body Area Networks

Abstract: The new generation of the wireless body area networks (WBAN) for internet of things (IoT) is emerging in a fast-paced. Today patients can be tested using remote clinical nanosensors. WBAN involves interconnected small sensors for the collection of ongoing medical data and transmitted through the networks for further processing. However, the protection of healthcare data is very important and difficult because of the various active and passive numbers of attacks. Although there exists several literature on data… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
13
0

Year Published

2021
2021
2023
2023

Publication Types

Select...
5
4

Relationship

1
8

Authors

Journals

citations
Cited by 33 publications
(13 citation statements)
references
References 30 publications
0
13
0
Order By: Relevance
“…ough the privacy of the user is preserved in this work, it lacks the confidentiality of the information transferred. Jabeen et al [21] proposed a scheme for the protection of data based on a genetic algorithm. But the complexity of the algorithm leads to an increase in the computational cost analysis.…”
Section: Related Workmentioning
confidence: 99%
“…ough the privacy of the user is preserved in this work, it lacks the confidentiality of the information transferred. Jabeen et al [21] proposed a scheme for the protection of data based on a genetic algorithm. But the complexity of the algorithm leads to an increase in the computational cost analysis.…”
Section: Related Workmentioning
confidence: 99%
“…The concept of water rendering ECG-based signal tempering was proposed by Kaur et al [17]. Jabeen et al [18] proposed a genetic-based encryption algorithm for data security with an efficient and lightweight MQTT protocol in WBAN. Reference [19] provides better survey for multiple safety schemes to present comparison analysis and security parameters.…”
Section: Literature Surveymentioning
confidence: 99%
“…(11) Ensure that only one picking stations is responsible for each task; (12) represents the constraint of the starting time of continuous tasks before and after a single picking platform; (13) and (14) indicate that the election platform can only execute one task at most before and after each task, to ensure the relationship between tasks before and after the election platform. (15) represents the constraint between the time when AGV starts to execute the task and the time when the picking station starts to execute the task under the same task; (16) represents the relationship between the time when the picking stage begins to execute the task under the same task and the time when the AGVs end the task; (17) shows the value range of the variable.…”
Section: B Equipment-task Scheduling Modelmentioning
confidence: 99%
“…Scheduling problems are considered as NP-hard problems [16]. Scheduling problems are often solved by intelligent algorithms, a genetic algorithm is widely used in NP-hard problems [17][18][19]. Ronghua Chen [20] proposed a self-learning genetic algorithm based on GA is proposed to solve the jobshop scheduling problem.…”
Section: Introductionmentioning
confidence: 99%