In task offloading, the movement of vehicles causes the switching of connected RSUs and servers, which may lead to task offloading failure or high service delay. In this paper, we analyze the impact of vehicle movemen...
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In task offloading, the movement of vehicles causes the switching of connected RSUs and servers, which may lead to task offloading failure or high service delay. In this paper, we analyze the impact of vehicle movements on task offloading and reveal that data preparation time for task execution can be minimized via forward-looking scheduling. Then, a Bi-LSTM-based model is proposed to predict the trajectories of vehicles. The service area is divided into several equal-sized grids. If the actual position of the vehicle and the predicted position by the model belong to the same grid, the prediction is considered correct, thereby reducing the difficulty of vehicle trajectory prediction. Moreover, we propose a scheduling strategy for delay optimization based on the vehicle trajectory prediction. Considering the inevitable prediction error, we take some edge servers around the predicted area as candidate execution servers and the data required for task execution are backed up to these candidate servers, thereby reducing the impact of prediction deviations on task offloading and converting the modest increase of resource overheads into delay reduction in task offloading. Simulation results show that, compared with other classical schemes, the proposed strategy has lower average task offloading delays.
This paper investigates the resource allocation for rate-splitting multiple access(RSMA)enabled multibeam satellite communication ***,we minimize the total unmet user rate,which denotes the difference between the user...
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This paper investigates the resource allocation for rate-splitting multiple access(RSMA)enabled multibeam satellite communication ***,we minimize the total unmet user rate,which denotes the difference between the users’rate requirement and the practical achievable rate,as well as the total transmit power of the satellite by optimizing the precoding,power allocation,and rate allocation,under the per-feed power and rate *** solve the non-convex optimization problem,a twostage scheme is *** particular,in the first stage,we present a precoding scheme by maximizing the signal-to-leakage-plus-noise ratio of each beam to eliminate the inter-beam *** the second stage,we introduce auxiliary variables to obtain an upper bound on the objective function under the given precoding matrix and transform the rate constraints of the original problem into second-order cones(SOC)and linear matrix inequations(LMI).Then,the successive convex approximation(SCA)technique is used to obtain suboptimal power and rate allocation ***,the initial feasible solution for power allocation is provided by using the standard interior point ***,numerical results verify the superiority of our proposed solution compared to the benchmark methods in terms of objective function values.
In this article the legend of Fig. 6 was presented without a reference. The legend of Fig. 6 has been changed from "The general framework for knowledge distillation involving a teacher-student relationship&q...
DeepFakes and face image manipulation methods have been widely distributed in the last few years and several techniques have been presented to check the authenticity of the face image and detect manipulation if exists...
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Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)*** identification of skin cancer enhances the likelihood of effective treatment,as delays may lead to severe tum...
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Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)*** identification of skin cancer enhances the likelihood of effective treatment,as delays may lead to severe tumor *** study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis,with an architecture that integrates a Vision Transformer,a bespoke convolutional neural network(CNN),and an Xception *** were evaluated using two benchmark datasets,HAM10000 and Skin Cancer *** the HAM10000,the model achieves a precision of 95.46%,an accuracy of 96.74%,a recall of 96.27%,specificity of 96.00%and an F1-Score of 95.86%.It obtains an accuracy of 93.19%,a precision of 93.25%,a recall of 92.80%,a specificity of 92.89%and an F1-Score of 93.19%on the Skin Cancer ISIC *** findings demonstrate that the model that was proposed is robust and trustworthy when it comes to the classification of skin *** addition,the utilization of Explainable AI techniques,such as Grad-CAM visualizations,assists in highlighting the most significant lesion areas that have an impact on the decisions that are made by the model.
Innovative mobility aids like smart wheelchairs and walkers are transforming the lives of individuals with mobility challenges. These devices use sensors and in-vehicle network technologies, such as Controller Area Ne...
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This paper develops a new distributed attention-enabled multi-agent reinforcement learning method for frequency regulation of power systems. Specifically, the controller of each generator is modelled as an agent, and ...
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Ensuring secure and accurate node localization in Underwater Wireless Sensor Networks (UWSN) is a significant challenge, as conventional methods tend to neglect the security risks associated with malicious node interf...
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With the rise of digital infrastructure and Internet of Things (IoT), a substantial amount of data is continuously generated that needs to be processed efficiently. While modern artificial intelligence (AI) approaches...
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Depression has the potential to impact death rates, particularly when it comes to death by suicide. Inadequate diagnosis may result in a delay or unsuitable therapy, which can worsen symptoms of depression. Unaddresse...
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