The Industrial Edge computing (IEC) network has recently received considerable attention, where industrial devices offload their computation-intensive and delay-sensitive tasks to servers located at the network edge. ...
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ISBN:
(纸本)9798350386066;9798350386059
The Industrial Edge computing (IEC) network has recently received considerable attention, where industrial devices offload their computation-intensive and delay-sensitive tasks to servers located at the network edge. Task offloading scheduling is a fundamental problem in IEC networks to achieve satisfactory quality of service. Many prior efforts have been devoted to scheduling task offloading for networks with complete information, while the complete information is hard or even infeasible to acquire by the scheduler. Therefore, their performance degrades in IEC networks with incomplete information. Scheduling task offloading for IEC networks with incomplete information is urgent and presents great technical challenges. This paper proposes a group-centric task offloading framework tailored for IEC networks with incomplete information, and models the minimum delay scheduling problem as a Partially Observable Markov Decision Process. Then, the SGOS algorithm integrating the Long Short-Term Memory with Soft Actor-Critic networks in reinforcement learning is proposed to devise online task offloading schedules for IEC networks with incomplete information. Extensive experimental results verify that the SGOS algorithm can achieve the best performance compared with baseline schemes in terms of major metrics, including convergence, delay, and workload balance.
Heart arrhythmias, if not detected early, can lead to severe conditions like stroke or heart failure, making timely and accurate classification critical. Traditional methods of analyzing Electrocardiogram (ECG) data a...
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SMS continues to be an indispensable component of contemporary digital communication. Because it is so widely available, cost-effective, reliable, and delivers information instantly. Its versatility enables its utiliz...
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Breast cancer poses a significant threat to women's health, being a leading cause of cancer-related mortality among female population. In recent years, machine learning has emerged as a promising approach in medic...
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Several transient execution and micro-architectural data sampling attacks exploit race conditions and memory inconsistencies to leak security-critical information from microprocessors. The sfence instruction is common...
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ISBN:
(纸本)9798350330656;9798350330649
Several transient execution and micro-architectural data sampling attacks exploit race conditions and memory inconsistencies to leak security-critical information from microprocessors. The sfence instruction is commonly employed to create a memory barrier, preventing the reordering of store instructions by the compiler or the processor, and enhancing security. However, potential vulnerabilities, such as bugs and trojans in the hardware implementation of sfence could be exploited by attackers, undermining its defense mechanism. In this paper, a formal method based on invariants is provided to address the verification of sfence hardware implementation. Potential areas where hardware trojans can be designed to circumvent sfence are identified and, demonstrate that the proposed verification methodology will flag such trojans. The effectiveness of the approach is validated on the RSD, an open source RISC-V based superscalar out-of-order processor.
The Internet of Things (loT) is a new technology trend that is being used in almost every area of human life. IoT is used almost every aspect of people's lives. Significantly, with a projected increase in the worl...
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This paper discuss about the scheduling problem for flexible manufacturing systems (FMSs) using the genetic algorithm. In FMSs, the operation of a job is performed on more than one machine. Therefore, FMS scheduling i...
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Since the latest deep neural network (DNN) models are complex and have many layers, processing an entire DNN model on mobile devices is challenging. To cope with this challenge, a split computing (SC) approach has bee...
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ISBN:
(纸本)9798350330946;9798350330953
Since the latest deep neural network (DNN) models are complex and have many layers, processing an entire DNN model on mobile devices is challenging. To cope with this challenge, a split computing (SC) approach has been proposed, which divides a DNN model into multiple layers and distributes them to mobile devices and edge servers. On the other hand, in-network computing (INC) is a promising technology that offloads computational tasks to network devices (e.g., programmable switches) and thus provides low latency and line-rate packet processing. Although the switch cannot directly process complex DNN models due to its limited computing and memory resources, it has the potential to process specific layers that require simple arithmetic operations. For example, processing the max-pooling layer of convolutional neural network (CNN) models can be offloaded to the switch. In this paper, we consider a network where there are three types of computing nodes: mobile device, edge servers, and switches, and formulate the problem of placing the layers of the CNN model on the computing nodes to minimize the inference latency considering the resource constraints of computing nodes. Then, we derive the optimal results by solving the formulated optimization problem. Evaluation results demonstrate that the optimal results show lower inference latency than a random layer placement scheme and a server-only placement scheme.
In this paper, an interface circuit based on aircraft consumption sensor is proposed. Sinusoidal AC excitation signal with a peak value of 10V± 20% to the ground and a frequency of 4000HZ±10HZ is applied to ...
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This paper provides a smart grid power information security communication system, which belongs to the power information communication technology field of the power grid, including power distribution devices, multiple...
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