Joint Social-Aware and Mobility-Aware Computation Offloading in Heterogeneous Mobile Edge Computing

• Published in: IEEE access Vol. 10; pp. 28600 - 28613
• Main Authors: Xu, Chenglin, Xu, Cheng, Li, Bo, Li, Siqi, Li, Tao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cloud computing; Computation offloading; Device-to-device communication; Edge computing; Electronic devices; Energy consumption; Mobile computing; mobile edge computing; Mobile handsets; mobility; Resource allocation; Resource management; Servers; social networks; social-aware; Task analysis; User requirements
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3158319

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.