Backscatter-based UAV-enabled mobile edge computing IoT network: design and analysis

In the 6G mobile networks, ensuring low latency and low energy consumption is paramount. This study explores a novel approach for addressing these issues in a backscatter communication (BC) - based multiple user unmanned aerial vehicle (UAV) - enabled mobile edge computing (MEC) Internet of Things (IoT) network. Our proposed framework incorporates a partial offloading strategy, a time division multiple access (TDMA) scheme, and a radio frequency energy harvesting mechanism. We use the channel gains statistical characteristics to derive approximate closed-form expressions for the successful computation and energy outage probabilities. Using these benchmarks, we investigate the impact of critical parameters such as transmit power, number of sensor nodes, task division ratio, the altitude of the UAV, and threshold tolerance. We validate our analysis through computer simulations and provide results to support our findings. The study reveals that selecting an optimal UAV altitude can significantly improve latency and energy consumption performance.