Joint Social-Aware and Mobility-Aware Computation Offloading in Heterogeneous Mobile Edge Computing
With increasing computation-intensive tasks of various applications running on mobile devices, the limitation of computing resources and battery capacity on mobile devices makes it impossible to meet the users’ Quality of Service (QoS). Fortunately, with the emergence of mobile edge compu...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9733405/ |
| _version_ | 1818306387959087104 |
|---|---|
| author | Chenglin Xu Cheng Xu Bo Li Siqi Li Tao Li |
| author_facet | Chenglin Xu Cheng Xu Bo Li Siqi Li Tao Li |
| author_sort | Chenglin Xu |
| collection | DOAJ |
| description | With increasing computation-intensive tasks of various applications running on mobile devices, the limitation of computing resources and battery capacity on mobile devices makes it impossible to meet the users’ Quality of Service (QoS). Fortunately, with the emergence of mobile edge computing (MEC), mobile devices can offload tasks to edge servers to efficiently solve the above problems. However, meeting the users’ QoS requirements with the help of deployed MEC facilities is still challenging since mobile users’ service demands vary depending on their dynamic location. In addition, increasing the number of edge servers to meet the requirements of applications would burden the initial investment and maintenance fee accordingly. In this case, using idle resources from nearby mobiles may become an effective solution. Most of the existing works do not consider the mobility of devices and users’ willingness to share. Therefore, in this paper, we propose the mobile device selection algorithms (MDSA), in which the social relationship, location correlation, and mobile activity of mobile devices were considered in the selection of target mobile devices, providing device-to-device offloading. In addition, we propose the joint social-aware and mobility-aware computation offloading algorithm (JSMCO) based on the improved Kuhn–Munkres (KM) algorithm to obtain a resource allocation strategy that minimizes the energy consumption while satisfying the minimum latency condition. The proposed algorithms have been verified to reduce the offloading success rate and decrease the users’ time and energy consumption in extended real datasets. |
| first_indexed | 2024-12-13T06:41:41Z |
| format | Article |
| id | doaj.art-c473e12f0664464e9ee2934d10108361 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T06:41:41Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c473e12f0664464e9ee2934d101083612022-12-21T23:56:24ZengIEEEIEEE Access2169-35362022-01-0110286002861310.1109/ACCESS.2022.31583199733405Joint Social-Aware and Mobility-Aware Computation Offloading in Heterogeneous Mobile Edge ComputingChenglin Xu0https://orcid.org/0000-0001-9133-613XCheng Xu1https://orcid.org/0000-0002-1323-3175Bo Li2https://orcid.org/0000-0003-4326-4519Siqi Li3https://orcid.org/0000-0003-4807-3703Tao Li4College of Computer Science and Electronic Engineering, Hunan University, Changsha, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, ChinaSchool of Computer Science and Engineering, Central South University, Changsha, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, ChinaWith increasing computation-intensive tasks of various applications running on mobile devices, the limitation of computing resources and battery capacity on mobile devices makes it impossible to meet the users’ Quality of Service (QoS). Fortunately, with the emergence of mobile edge computing (MEC), mobile devices can offload tasks to edge servers to efficiently solve the above problems. However, meeting the users’ QoS requirements with the help of deployed MEC facilities is still challenging since mobile users’ service demands vary depending on their dynamic location. In addition, increasing the number of edge servers to meet the requirements of applications would burden the initial investment and maintenance fee accordingly. In this case, using idle resources from nearby mobiles may become an effective solution. Most of the existing works do not consider the mobility of devices and users’ willingness to share. Therefore, in this paper, we propose the mobile device selection algorithms (MDSA), in which the social relationship, location correlation, and mobile activity of mobile devices were considered in the selection of target mobile devices, providing device-to-device offloading. In addition, we propose the joint social-aware and mobility-aware computation offloading algorithm (JSMCO) based on the improved Kuhn–Munkres (KM) algorithm to obtain a resource allocation strategy that minimizes the energy consumption while satisfying the minimum latency condition. The proposed algorithms have been verified to reduce the offloading success rate and decrease the users’ time and energy consumption in extended real datasets.https://ieeexplore.ieee.org/document/9733405/Computation offloadingsocial-awaremobile edge computingsocial networksmobility |
| spellingShingle | Chenglin Xu Cheng Xu Bo Li Siqi Li Tao Li Joint Social-Aware and Mobility-Aware Computation Offloading in Heterogeneous Mobile Edge Computing IEEE Access Computation offloading social-aware mobile edge computing social networks mobility |
| title | Joint Social-Aware and Mobility-Aware Computation Offloading in Heterogeneous Mobile Edge Computing |
| title_full | Joint Social-Aware and Mobility-Aware Computation Offloading in Heterogeneous Mobile Edge Computing |
| title_fullStr | Joint Social-Aware and Mobility-Aware Computation Offloading in Heterogeneous Mobile Edge Computing |
| title_full_unstemmed | Joint Social-Aware and Mobility-Aware Computation Offloading in Heterogeneous Mobile Edge Computing |
| title_short | Joint Social-Aware and Mobility-Aware Computation Offloading in Heterogeneous Mobile Edge Computing |
| title_sort | joint social aware and mobility aware computation offloading in heterogeneous mobile edge computing |
| topic | Computation offloading social-aware mobile edge computing social networks mobility |
| url | https://ieeexplore.ieee.org/document/9733405/ |
| work_keys_str_mv | AT chenglinxu jointsocialawareandmobilityawarecomputationoffloadinginheterogeneousmobileedgecomputing AT chengxu jointsocialawareandmobilityawarecomputationoffloadinginheterogeneousmobileedgecomputing AT boli jointsocialawareandmobilityawarecomputationoffloadinginheterogeneousmobileedgecomputing AT siqili jointsocialawareandmobilityawarecomputationoffloadinginheterogeneousmobileedgecomputing AT taoli jointsocialawareandmobilityawarecomputationoffloadinginheterogeneousmobileedgecomputing |