Yiming Ge

Due to the adaptable deployment and extensive coverage, autonomous aerial vehicles (AAVs) have gained considerable attention in the field of mobile edge computing (MEC). Nevertheless, meeting the requirements of task offloading in AAV-assisted MEC remains challenging due to limitations in computing power and battery life. To address this challenge, we investigate the problem of distributed joint task partial offloading and energy harvesting (EH) in AAV-assisted MEC to minimize both task execution latency and energy cost of the AAV system. We formulate this problem as a mixed-integer nonlinear programming problem and decompose this problem into two subproblems: 1) task offloading strategy problem and 2) task offloading ratio and EH problem. We propose a distributed algorithm to determine task offloading strategies of AAVs iteratively. Given the task offloading strategies of AAVs, the subproblem of task offloading ratio and EH duration is solved by the sine cosine algorithm. Experimental results demonstrate that our proposed algorithm can effectively reduce the task execution latency and energy cost of the AAV-assisted MEC system, and outperforms other baselines significantly.

Mobile edge computing introduces a novel computing paradigm for mobile devices, reducing execution latency and energy consumption by offloading tasks to edge servers or other idle mobile devices. In this paper, we consider the utility optimization problem of two typical computing tasks, latency-sensitive tasks and latency-tolerant tasks, among multiple mobile devices and base stations. Mobile devices can choose three computing modes to optimize utility: local computing, task allocation to base stations, and task allocation to other mobile devices through device-to-device communication. To address this problem, we formalize it as a potential game for multi-mobile device multi-base station task offloading. Furthermore, we prove the existence of a Nash equilibrium for the modeled potential game and propose a task allocation scheme for hybrid tasks. This scheme maximizes both energy consumption utility and task execution utility by optimizing task offloading mode selection and task execution order scheduling. Simulation results show that our proposed scheme can substantially enhance user utility and has good scalability with the increase of mobile devices.

Distracted driving behavior is known as a leading factor in road traffic injuries and deaths. Fortunately, rapidly developing deep learning technology has shown its potential in distracted driving detection. Nevertheless, deep learning-based solutions need to collect large amounts of driving data captured by camera sensors in the vehicle, which will cause serious privacy concerns. As a privacy-preserving distributed learning paradigm, federated learning (FL) has achieved competitive performance in many applications recently. Inspired by this, we introduce FL into distracted driving detection tasks. However, we observe that the heterogeneous data distribution across drivers leads to significant performance degradation of the model learned in FL. To address this challenge, we propose a simple and effective federated bidirectional knowledge distillation framework, FedBiKD. Specifically, FedBiKD utilizes the knowledge from the global model in guiding local training to mitigate the issue of local deviation. Meanwhile, the consensus from the ensemble of local models is also employed to fine-tune the aggregated global model for less volatility in training. Our extensive experiments demonstrate the effectiveness of FedBiKD in distracted driving detection. The results show that FedBiKD significantly outperforms other FL algorithms in terms of accuracy, communication efficiency, convergence rate, and stability.