An autonomic resource provisioning framework for efficient data collection in cloudlet-enabled wireless body area networks: a fuzzy-based proactive approach

Integrating wireless body area networks (WBANs) with cloudlet introduces an edge-of-things computing environment for pervasive applications. The variation in the number of active WBANs nodes and its data transmission rate requires optimal computing resources to avoid performance degradation and data...

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Bibliographic Details
Published inSoft computing (Berlin, Germany) Vol. 23; no. 20; pp. 10361 - 10383
Main Authors Bhardwaj, Tushar, Sharma, Subhash Chander
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2019
Springer Nature B.V
Subjects
Artificial Intelligence
Body area networks
Cloud computing
Computation
Computational Intelligence
Control
Data collection
Data integrity
Data loss
Data transmission
Decision making
Engineering
Fuzzy control
Fuzzy logic
Mathematical Logic and Foundations
Mathematical models
Mechatronics
Methodologies and Application
Performance degradation
Propagation
Provisioning
Regression analysis
Regression models
Resource allocation
Response time
Robotics
Transmission rate (communications)
Wireless networks
Fuzzy logic control
Cloud computing
CloudSim
Wireless body area networks (WBANs)
Autonomic computing
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Summary:Integrating wireless body area networks (WBANs) with cloudlet introduces an edge-of-things computing environment for pervasive applications. The variation in the number of active WBANs nodes and its data transmission rate requires optimal computing resources to avoid performance degradation and data loss. We argue the research gap in terms of optimal resource provisioning that predicts and automatically adjusts the computing resources on the basis of sensory data volume and application’s type. In this paper, we propose a hybrid autonomic resource provisioning framework, which is the combination of autonomic computing, fuzzy logic control and linear regression model. The proposed framework is built over CloudSim toolkit with autonomic resource provisioning framework inspired by the cloud layer model. The effectiveness of the proposed approach is evaluated under a real workload trace. The experimental results show that the proposed approach minimizes the cost by at least 27% and SLA violations by at least 78% as compared to other approaches.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-018-3587-x