Vehicular edge computing (VEC) enables vehicles to offload their tasks to idle vehicles for processing. To process tasks, the needed service model should be stored ahead. Considering the storage of vehicles is limited...
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ISBN:
(数字)9798350390643
ISBN:
(纸本)9798350390650
Vehicular edge computing (VEC) enables vehicles to offload their tasks to idle vehicles for processing. To process tasks, the needed service model should be stored ahead. Considering the storage of vehicles is limited and downloading the service model upon tasks causes repeated data transmission, it is necessary to determine which service models should be cached and which tasks should be offloaded. For vehicles, the task completion time is crucial due to their mobility nature. Therefore, balancing the completion time and energy consumption for vehicles with limited battery capacity in highly dynamic system remains a significant challenge. To address this, we jointly designed task offloading and service caching scheme to minimize the tasks completion time in a cache-assisted VEC scenario while satisfying the long-term average energy constraints. We adopt Lyapunov optimization and Deep reinforcement learning (D RL) based Task offloading and Service caching (LDTS) to solve the proposed problem. Specifically, the Lyapunov optimization is used to tackle the issues of long-term energy consumption constraints by transforming the original problem into per-slot optimization problems that can be resolved using a model-free DRL method. Simulation results demonstrate that the LDTS method can minimize the completion time while satisfying the constraints of the long-term energy consumption budget. The results indicate that the LDTS method outperforms other benchmark methods.
Semantic communication, a novel paradigm in communication, could significantly enhance communication efficiency by extracting and transmitting semantic information relevant to artificial intelligence (AI) tasks. In pr...
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ISBN:
(数字)9798350377675
ISBN:
(纸本)9798350377682
Semantic communication, a novel paradigm in communication, could significantly enhance communication efficiency by extracting and transmitting semantic information relevant to artificial intelligence (AI) tasks. In practice, a device may need to perform multiple AI tasks, and the semantic features required for each task are usually different. If multiple tasks share a single encoder to extract generalized semantic features, there is unnec-essary communication overhead. To solve the above issue, this paper introduces a mask learning enabled multi-task semantic communication system with adaptive rate control (ARC-MTSC) to extract and transmit customized semantic features according to different tasks. First, a lightweight feature selection network (FSN) is proposed, which can change the number of features depending on variable channel conditions and choose desired features for each task. Then, to achieve adaptive rate control, the FSN generates a mask vector to dynamically adjust the number of transmitted features, allowing the system to adaptively balance transmission rate and task performance. The results of the simulation demonstrate that the ARC-MTSC greatly decreases transmission overhead while maintaining negligible performance loss compared to the single-task baseline.
A dynamic frequency-based parameter identification approach is applied for the nonlinear system with periodic *** from the energy equation,the presented method uses a dynamic frequency to precisely obtain the analytic...
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A dynamic frequency-based parameter identification approach is applied for the nonlinear system with periodic *** from the energy equation,the presented method uses a dynamic frequency to precisely obtain the analytical limit cycle expression of nonlinear system and utilizes it as the mathematic foundation for parameter *** from the time-domain approaches,the strategy of using limit cycle to describe the system response is unaffected by the influence of phase *** analytical expression is fitted with the value sets from phase coordinates measured in periodic oscillation of the nonlinear systems,and the unknown parameters are identified with the interior-reflective Newton *** the performance of this identification methodology is verified by an oscillator with nonlinear stiffness and ***,numerical simulations under noisy environment also verify the efficiency and robustness of the identification ***,we apply this parameter identification method to the modeling of a large-amplitude energy harvester,to improve the accuracy of mechanical *** surprisingly,good agreement is achieved between the experimental data and identified *** also verifies that the proposed approach is less time-consuming and more accuracy in identification procedure.
In the remanufacturing process, the efficient disassembly of end-of-life (EOL) products assumes paramount significance for recycling endeavors. This work introduces a novel disassembly model aiming at enhancing disass...
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This paper considers the distributed leader-follower stress-matrix-based affine formation control problem of discrete-time linear multi-agent systems with static and dynamic leaders. In leader-follower multi-agent for...
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This article proposes a novel trust-based entropy filter in distributed form for state-saturated nonlinear systems with hybrid cyber-attacks, including denial-of-service and deception attacks. A simple clustering meth...
This article proposes a novel trust-based entropy filter in distributed form for state-saturated nonlinear systems with hybrid cyber-attacks, including denial-of-service and deception attacks. A simple clustering method is employed to categorize the data received from neighboring nodes into two clusters: the trusted cluster and the untrusted cluster. By optimizing a joint cost function involving weighted least squares and a generalized maximum correntropy criterion, a two-step filter is designed in a distributed form, wherein the untrusted data from the neighbors is compensated to relieve the impact from malicious attacks. The significance of the designed algorithm is verified by conducting a target-tracking experiment at the end.
The electrical power transmission lines play a crucial role in maintaining a continuous electricity supply. However, the exposed environment of these lines increases the risks of faults occurrence, necessitating promp...
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In order to fully explore the time-series correlation of power load data and improve the prediction accuracy of power load, this paper proposes a neural network-based deep learning approach for power load prediction. ...
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Disassembling and recycling scrapped products play important roles in effectively reducing environmental pollution and improving resource sustainability. A multi-product human-robot collaborative disassembly-line-bala...
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Medical Body Area Networks (MBANs) increasingly utilize wireless communication technologies to enhance the freedom and convenience for patients and caregivers. Currently, radio frequency (RF) is the prevalent medium i...
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