Energy-Optimal and Delay-Bounded Computation Offloading in Mobile Edge Computing with Heterogeneous Clouds

By Mobile Edge Computing(MEC), computation-intensive tasks are offloaded from mobile devices to cloud servers, and thus the energy consumption of mobile devices can be notably reduced. In this paper, we study task offloading in multi-user MEC systems with heterogeneous clouds, including edge clouds and remote clouds. Tasks are forwarded from mobile devices to edge clouds via wireless channels, and they can be further forwarded to remote clouds via the Internet. Our objective is to minimize the total energy consumption of multiple mobile devices, subject to bounded-delay requirements of tasks. Based on dynamic programming, we propose an algorithm that minimizes the energy consumption, by jointly allocating bandwidth and computational resources to mobile devices. The algorithm is of pseudo-polynomial complexity. To further reduce the complexity, we propose an approximation algorithm with energy discretization, and its total energy consumption is proved to be within a bounded gap from the optimum. Simulation results show that, nearly 82.7% energy of mobile devices can be saved by task offloading compared with mobile device execution.

Wireless recommendations for internet of vehicles:Recent advances,challenges,and opportunities

Internet of Vehicles(IoV)is a distributed network of connected cars,roadside infrastructure,wireless communication networks,and central cloud platforms.Wireless recommendations play an important role in the IoV network,for example,recommending appropriate routes,recommending driving strategies,and recommending content.In this paper,we review some of the key techniques in recommendations and discuss what are the opportunities and challenges to deploy these wireless recommendations in the IoV.

A pruning-then-quantization model compression framework for facial emotion recognition

Facial emotion recognition achieves great success with the help of large neural models but also fails to be applied in practical situations due to the large model size of neural methods.To bridge this gap,in this paper,we combine two mainstream model compression methods(pruning and quantization)together,and propose a pruningthen-quantization framework to compress the neural models for facial emotion recognition tasks.Experiments on three datasets show that our model could achieve a high model compression ratio and maintain the model’s high performance well.Besides,We analyze the layer-wise compression performance of our proposed framework to explore its effect and adaptability in fine-grained modules.

Emma:An accurate,efficient,and multi-modality strategy for autonomous vehicle angle prediction

Autonomous driving and self-driving vehicles have become the most popular selection for customers for their convenience.Vehicle angle prediction is one of the most prevalent topics in the autonomous driving industry,that is,realizing real-time vehicle angle prediction.However,existing methods of vehicle angle prediction utilize only single-modal data to achieve model prediction,such as images captured by the camera,which limits the performance and efficiency of the prediction system.In this paper,we present Emma,a novel vehicle angle prediction strategy that achieves multi-modal prediction and is more efficient.Specifically,Emma exploits both images and inertial measurement unit(IMU)signals with a fusion network for multi-modal data fusion and vehicle angle prediction.Moreover,we design and implement a few-shot learning module in Emma for fast domain adaptation to varied scenarios(e.g.,different vehicle models).Evaluation results demonstrate that Emma achieves overall 97.5%accuracy in predicting three vehicle angle parameters(yaw,pitch,and roll),which outperforms traditional single-modalities by approximately 16.7%-36.8%.Additionally,the few-shot learning module presents promising adaptive ability and shows overall 79.8%and 88.3%accuracy in 5-shot and 10-shot settings,respectively.Finally,empirical results show that Emma reduces energy consumption by 39.7%when running on the Arduino UNO board.