Edge computing has emerged as a promising technology to satisfy the demand for data computational resources in Internet of Things (IoT) networks. With edge computing, processing of the massive data-intensive tasks can...
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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 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.
Federated Learning (FL) is vulnerable to backdoor attacks - especially distributed backdoor attacks (DBA) that are more persistent and stealthy than centralized backdoor attacks. However, we observe that the attack ef...
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Social-Media-based Lifelong Learning (SMLL) have come under the spotlight nowadays because they allow people to realise lifelong learning in their quotidian existence. However, the factors that determine SMLL have rec...
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Dear Editor,In this letter, a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated (HOFA) systems with noises. The method can effe...
Dear Editor,In this letter, a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated (HOFA) systems with noises. The method can effectively deal with nonlinearities, constraints, and noises in the system, optimize the performance metric, and present an upper bound on the stable output of the system.
Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems (IDS). Data labeling difficulties, incorrect conclusions, and vulnerability to malicious data i...
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In recent years, IoT has transformed personal environments by integrating diverse smart devices. This paper presents an advanced IoT architecture that optimizes network infrastructure, focusing on the adoption of MQTT...
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Cloud computing technology provides various computing resources on demand to users on pay per use basis. The technology fails in terms of its usage due to confidentiality and privacy issues. Access control mechanisms ...
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In the realm of medical datasets, particularly when considering diabetes, the occurrence of data incompleteness is a prevalent issue. Unveiling valuable patterns through medical data analysis is crucial for early and ...
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Breast cancer poses a significant global threat, highlighting the urgent need for early detection to reduce mortality rates. Researchers are working to minimize the occurrence of false positives and false negatives, t...
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