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Wireless body area network mobility-aware task offloading scheme
- Source :
- IEEE Access, Vol 6, Pp 61366-61376 (2018)
- Publication Year :
- 2018
- Publisher :
- IEEE, 2018.
-
Abstract
- The increasing amount of user equipment (UE) and the rapid advances in wireless body area networks bring revolutionary changes in healthcare systems. However, due to the strict requirements on size, reliability and battery lifetime of UE devices, it is difficult for them to execute latency sensitive or computation intensive tasks effectively. In this paper, we aim to enhance the UE computation capacity by utilizing small size coordinator-based mobile edge computing (C-MEC) servers. In this way, the system complexity, computation resources, and energy consumption are considerably transferred from the UE to the C-MEC, which is a practical approach since C-MEC is power charged, in contrast to the UE. First, the system architecture and the mobility model are presented. Second, several transmission mechanisms are analyzed along with the proposed mobility-aware cooperative task offloading scheme. Numerous selected performance metrics are investigated regarding the number of executed tasks, the percentage of failed tasks, average service time, and the energy consumption of each MEC. The results validate the advantage of task offloading schemes compared with the traditional relay-based technique regarding the number of executed tasks. Moreover, one can obtain that the proposed scheme archives noteworthy benefits, such as low latency and efficiently balance the energy consumption of C-MECs.
- Subjects :
- Mobility model
task offloading
General Computer Science
Computer science
WBANs
TK
Cloud computing
02 engineering and technology
C-MEC
QA76
Server
Body area network
0202 electrical engineering, electronic engineering, information engineering
Wireless
General Materials Science
mobility-aware
Mobile edge computing
business.industry
General Engineering
020206 networking & telecommunications
Energy consumption
User equipment
Systems architecture
020201 artificial intelligence & image processing
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
lcsh:TK1-9971
Computer network
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Database :
- OpenAIRE
- Journal :
- IEEE Access, Vol 6, Pp 61366-61376 (2018)
- Accession number :
- edsair.doi.dedup.....583fb7fbc5721638b23c3b08c50949ab