KJA: Kookaburra Jellyfish Algorithm Based Task Offloading in UAV-Enabled Mobile Edge Computing Network

Abstract

Mobile edge computing (MEC) is extensively utilized for supporting diverse mobile applications and the Internet of Things (IoT). One of MEC's prime operations is utilizing unmanned aerial vehicles (UAVs) included with the MEC servers for providing computational aids for offloaded tasks by mobile users in temporal hotspot regions or a few emerging situations like sports areas or environmental disaster regions. However, despite the various merits of UAVs executed with MEC servers, it is constrained by their insufficient sensible energy consumption and computational resources. Furthermore, owing to the complication of UAV-aided MEC systems, energy consumption optimizations and computation resource optimizations cannot be obtained better in conventional optimization schemes. In this research, the kookaburra jellyfish algorithm (KJA) is presented for task offloading in a UAV-enabled MEC network. The main objective is to enhance the efficiency of task offloading in UAV-enabled MEC networks by optimizing energy consumption, computational resources, and communication time using the KJA. Initially, the UAV-enabled MEC network model is simulated. Next, task computation is performed, and thereafter, task uploading is carried out. Then, task offloading is executed using KJA with consideration of multiobjective models, namely, energy consumption, communication time, and cost. Moreover, KJA is devised by integrating kookaburra optimization algorithm (KOA) with jellyfish search optimizer (JSO). Afterward, the task offloading process and data transmission are conducted. In addition, KJA obtained minimum energy, load, and time of 0.448 J, 0.122, and 1.036 s.