With the increasing popularity of image and video analysis on mobile devices, high-throughput image inference has become essential. However, current mobile deep learning frameworks face key bottlenecks: high computational load in JPEG image recognition and low processor efficiency, which limit overall image processing throughput. To address these issues, this paper proposes the FCG framework (Frequency Domain model for CPU and GPU), a mobile JPEG inference framework based on frequency domain data and a hybrid parallel architecture that enables high-throughput inference for JPEG-encoded images on mobile devices. FCG decouples JPEG decoding from model inference by discarding the traditional RGB decoding process and retaining only the Huffman decoding. This decoding step is further accelerated through multi-core processing, significantly reducing the computational burden and latency during preprocessing. In light of the characteristics of frequency domain data and the heterogeneous CPU/GPU processors on mobile devices, FCG reconstructs the deep learning model to ensure recognition accuracy while optimizing resource utilization. By effectively allocating tasks and combining parallel and sequential execution, FCG optimizes processor resource utilization to achieve high throughput and low latency. FCG outperforms the state-of-the-art NN-Stretch by reducing latency by 36%. It also achieves significant throughput improvements—3.6x, 3.3x, and 2.8x—on CPU, GPU, and CPU+GPU configurations, respectively, compared to sequential inference systems. Additionally, FCG reduces power consumption by 56%, 35%, and 43% in these configurations.

Mission-oriented drone networks have been widely used for structural inspection, disaster monitoring, border surveillance, etc. Due to the limited battery capacity of drones, mission execution strategy impacts network performance and mission completion. However, collaborative execution is a challenging problem for drones in such a dynamic environment as it also involves efficient trajectory design. We leverage multi-agent reinforcement learning (MARL) to manage the challenge in this study, letting each drone learn to collaboratively execute tasks and plan trajectories based on its current status and environment. Simulation results show that the proposed collaborative execution model can successfully complete the mission at least 80% of the time, regardless of task locations and lengths, and can even achieve a 100% success rate when the task density is not way too sparse. To the best of our knowledge, our work is one of the pioneer studies on leveraging MARL on collaborative execution for mission-oriented drone networks; the unique value of this work lies in drone battery level driving our model design.