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A task offloading strategy based on sequential waiting model in MEC
In the field of mobile edge computing (MEC) research, many studies under common research scenarios focus on the optimization of energy consumption...
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Task scheduling using fuzzy logic with best-fit-decreasing for cloud computing environment
An efficient task scheduling is mandatory in cloud computing for providing virtual resources used to carry out the tasks. An effective allocation of...
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Corwdsourced Task Recommendation via Link Prediction
Mobile crowdsourcing (MCS) can solve problems that are difficult for computers to solve accurately or efficiently. Current crowdsourcing workers face...
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Optimization of uncertain dependent task map** on heterogeneous computing platforms
Dependent tasks are typically modeled using directed acyclic graphs (DAGs), and scheduling algorithms based on DAGs have been extensively researched....
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Autonomy Evaluation of Unmanned Systems Based on Task Models
In this study, relevant work on autonomy evaluation (AE) in recent years was comprehensively reviewed and classified from the perspective of task...
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Enhancing stance detection through sequential weighted multi-task learning
The exponential growth of user-generated content on social media platforms, online news outlets, and digital communication has necessitated the...
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Energy-Efficient Partial-Duplication Task Map** Under Multiple DVFS Schemes
On multicore platforms, reliable task execution, as well as low energy consumption, are essential. Dynamic Voltage/Frequency Scaling (DVFS) is...
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Group Role Assignment with Trust Between Agents
As a vital methodology for collaboration problems, Role-Based Collaboration (RBC) consists of three essential stages: agent evaluation, Group Role...
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A Cloud-Edge-Based Multi-Objective Task Scheduling Approach for Smart Manufacturing Lines
The number of task demands created by smart terminals is rising dramatically because of the increasing usage of industrial Internet technologies in...
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Adaptive task recommendation based on reinforcement learning in mobile crowd sensing
Adaptive task recommendation in Mobile crowd sensing (MCS) is a challenging problem, mainly because perceptual tasks are spatio-temporal in nature...
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Task Location Distribution Based Genetic Algorithm for UAV Mobile Crowd Sensing
The UAV mobile crowd sensing problem is a new research area due to the flexibility and low-cost advantage of UAVs. Current research rarely considers...
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Multi-agent mobile crowdsensing by pervasive machines: a robust task allocation approach
Mobile crowd sensing (MCS) is an attractive and innovation paradigm in which a crowd of users equipped with smart mobile devices conduct sensing...
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SORA: Improving Multi-agent Cooperation with a Soft Role Assignment Mechanism
Role-based multi-agent reinforcement learning (MARL) holds the promise of achieving scalable multi-agent cooperation by decomposing complex tasks...
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Mitigate Gender Bias Using Negative Multi-task Learning
Deep learning models have showcased remarkable performances in natural language processing tasks. While much attention has been paid to improvements...
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Task ordering in multiprocessor embedded system using a novel hybrid optimization model
In a multiprocessor system, the task scheduling function is a vital performance to minimize many issues. A multiprocessor system is applicable for...
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Meta-heuristic Algorithms to Optimize Two-Stage Task Scheduling in the Cloud
The development of cloud technology has led to more resources being made available on demand. The recent spike in the cloud service demand requires...
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SMT-Based Dynamic Multi-Robot Task Allocation
Multi-Robot Task Allocation (MRTA) is a problem that arises in many application domains including package delivery, warehouse robotics, and...
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Tournament based equilibrium optimization for minimizing energy consumption on dynamic task scheduling in cloud-edge computing
With the increasing advancements in the Internet of Things (IoT) and the growing production of tasks by IoT devices, the demand for cloud computing...
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Mcti: mixed-criticality task-based isolation
The ever-increasing demand for high performance in the time-critical, low-power embedded domain drives the adoption of powerful but unpredictable,...
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Leveraging Task Variability in Meta-learning
Meta-learning (ML) utilizes extracted meta-knowledge from data to enable models to perform well on unseen data that they have not encountered before....
