Optimizing Task Offloading Energy in Multi-User Multi-UAV-Enabled Mobile Edge-Cloud Computing Systems

With the emergence of various new Internet Things (IoT) devices and rapid increase in number users, enormous services complex applications are growing rapidly. However, these resource-intensive data-hungry, requiring satisfactory quality-of-service (QoS) network coverage density guarantees sparsely populated areas, whereas limited battery life computing resources IoT will inevitably become insufficient. Unmanned aerial vehicle (UAV)-enabled mobile edge (MEC) is one most promising solutions that ensures stability expansion area for provides them with computational capabilities. In this paper, computation offloading resource allocation jointly considered multi-user multi-UAV-enabled edge-cloud systems. First, we propose an efficient model a system. Our proposed system scalable can support increases traffic without performance degradation. addition, deploys multi-level technology to provide capabilities at radio access (RAN). The core based on software-defined networking (SDN) manage traffic. Experimental results demonstrate dramatically boost terms time energy.