Wireless body area networks task offloading method combined with multiple communication and computing resources supported by MEC
Abstract In recent years, mobile edge computing (MEC) has become a promising solution to solve the shortage of technical resources in wireless body area networks (WBANs). However, the existing research work has not fully utilized the communication and computing resources in WBANs scenarios. To solve...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-06-01
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| Series: | IET Communications |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cmu2.12606 |
| _version_ | 1797808469289467904 |
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| author | Changhong Zhu Junyu Ren Haibin Wan Tuanfa Qin |
| author_facet | Changhong Zhu Junyu Ren Haibin Wan Tuanfa Qin |
| author_sort | Changhong Zhu |
| collection | DOAJ |
| description | Abstract In recent years, mobile edge computing (MEC) has become a promising solution to solve the shortage of technical resources in wireless body area networks (WBANs). However, the existing research work has not fully utilized the communication and computing resources in WBANs scenarios. To solve this problem, a task offloading framework that combined with cellular, WiFi networks and device‐to‐device communications is proposed, that makes full use of resources to improve system reliability. Considering that a single MEC server may be overloaded by a large number of patients, the total task offloading cost and load variance is formulated into a multi‐objective optimization problem (MOOP). A non‐dominated sorting genetic algorithm with smart mobile device ‐ patient connection matrix (NSGA ‐SPCM) to solve the MOOP. In view that an SDM may connect multiple patients at the same time during chromosome crossing, the SPCM can quickly detect the unfeasible gene location and mutate it into viable. Simulation results show that the proposed framework and algorithm have good performance. |
| first_indexed | 2024-03-13T06:37:58Z |
| format | Article |
| id | doaj.art-4ba14dccb1af45818838da44507a1566 |
| institution | Directory Open Access Journal |
| issn | 1751-8628 1751-8636 |
| language | English |
| last_indexed | 2024-03-13T06:37:58Z |
| publishDate | 2023-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Communications |
| spelling | doaj.art-4ba14dccb1af45818838da44507a15662023-06-09T03:35:55ZengWileyIET Communications1751-86281751-86362023-06-0117101188119810.1049/cmu2.12606Wireless body area networks task offloading method combined with multiple communication and computing resources supported by MECChanghong Zhu0Junyu Ren1Haibin Wan2Tuanfa Qin3School of Computer and Electronic Information Guangxi University Nanning ChinaSchool of Computer and Electronic Information Guangxi University Nanning ChinaSchool of Computer and Electronic Information Guangxi University Nanning ChinaSchool of Computer and Electronic Information Guangxi University Nanning ChinaAbstract In recent years, mobile edge computing (MEC) has become a promising solution to solve the shortage of technical resources in wireless body area networks (WBANs). However, the existing research work has not fully utilized the communication and computing resources in WBANs scenarios. To solve this problem, a task offloading framework that combined with cellular, WiFi networks and device‐to‐device communications is proposed, that makes full use of resources to improve system reliability. Considering that a single MEC server may be overloaded by a large number of patients, the total task offloading cost and load variance is formulated into a multi‐objective optimization problem (MOOP). A non‐dominated sorting genetic algorithm with smart mobile device ‐ patient connection matrix (NSGA ‐SPCM) to solve the MOOP. In view that an SDM may connect multiple patients at the same time during chromosome crossing, the SPCM can quickly detect the unfeasible gene location and mutate it into viable. Simulation results show that the proposed framework and algorithm have good performance.https://doi.org/10.1049/cmu2.126065G mobile communicationbody area networksgenetic algorithms |
| spellingShingle | Changhong Zhu Junyu Ren Haibin Wan Tuanfa Qin Wireless body area networks task offloading method combined with multiple communication and computing resources supported by MEC IET Communications 5G mobile communication body area networks genetic algorithms |
| title | Wireless body area networks task offloading method combined with multiple communication and computing resources supported by MEC |
| title_full | Wireless body area networks task offloading method combined with multiple communication and computing resources supported by MEC |
| title_fullStr | Wireless body area networks task offloading method combined with multiple communication and computing resources supported by MEC |
| title_full_unstemmed | Wireless body area networks task offloading method combined with multiple communication and computing resources supported by MEC |
| title_short | Wireless body area networks task offloading method combined with multiple communication and computing resources supported by MEC |
| title_sort | wireless body area networks task offloading method combined with multiple communication and computing resources supported by mec |
| topic | 5G mobile communication body area networks genetic algorithms |
| url | https://doi.org/10.1049/cmu2.12606 |
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