Research on Resource Reservation Strategy for Edge Federation by Hengzhou Ye, Huangran Li, Jiaming Li, Qiu Lu, Gong Chen

“…Subsequently, we introduce a multi-agent deep deterministic policy gradient (RRP-MADDPG) approach based on a multi-agent deep reinforcement learning algorithm aimed at reducing average task delay. Simulation results demonstrate that both proposed resource reservation strategies can significantly reduce average task delays. …”
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Dynamic Split Computing Framework for Multi-Task Learning Models: A Deep Reinforcement Learning Approach by Haneul Ko, Sangwon Seo, Sangheon Pack

“…Evaluation results demonstrate that D2SCF reduces the average task completion time by more than 50% compared to conventional split computing schemes, while maintaining low energy consumption on the IoT device. …”
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Power inspection UAV task assignment matrix reversal genetic algorithm by Kai Liu, Meizhao Liu, Ming Tang, Chen Zhang

“…The results show that TMGA outperforms these algorithms in terms of average task time, task completion rate, and unit utility. …”
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Task distribution offloading algorithm of vehicle edge network based on DQN by Haitao ZHAO, Tangwei ZHANG, Yue CHEN, Houlin ZHAO, Hongbo ZHU

“…In order to achieve the best balance between latency,computational rate and energy consumption,for a edge access network of IoV,a distribution offloading algorithm based on deep Q network (DQN) was considered.Firstly,these tasks of different vehicles were prioritized according to the analytic hierarchy process (AHP),so as to give different weights to the task processing rate to establish a relationship model.Secondly,by introducing edge computing based on DQN,the task offloading model was established by making weighted sum of task processing rate as optimization goal,which realized the long-term utility of strategies for offloading decisions.The performance evaluation results show that,compared with the Q-learning algorithm,the average task processing delay of the proposed method can effectively improve the task offload efficiency.…”
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Modeling and Performance Analysis of Task Offloading of Heterogeneous Mobile Edge Computing Networks by Wenwang Li, Haohao Zhou

“…Since it is necessary to evaluate and analyze the service performance of MEC to guarantee Quality of Service (QoS), we design some indicators by solving the probability distribution function of response time, such as average task offloading delay, immediate service probability and blocking probability. …”
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Multi-objective multi-workflow task offloading based on evolutionary optimization by Teliekebieke Misha, Lisheng Sun, Zheng-yi Chai

“…The aforementioned problem is formulated as a multi-objective optimization problem with two objectives: average task completion latency and device energy consumption. …”
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VR environment of digital design laboratory: a usability study by Mariam Ali Alnuaimi, Mamoun Awad

“…With confidence level of 95%, the VR application had significant improvement on students’ learnability, memorability, and the average task completion time.DiscussionAdditionally, in the study, we found that careful design and technology must be used selectively due to population variation in terms of technology knowledge level, health factor, and readiness.…”
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An Efficient Resource Allocation Algorithm for Task Offloading in the Internet of Vehicles by Ahmad Salehi, Sadoon Azizi

“…The algorithm optimizes CPU resource allocation based on task generation rates, average task sizes, and a calculated weight coefficient for each task type. …”
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GAPO: A Graph Attention-Based Reinforcement Learning Algorithm for Congestion-Aware Task Offloading in Multi-Hop Vehicular Edge Computing by Hongwei Zhao, Xuyan Li, Chengrui Li, Lu Yao

“…Comprehensive simulation experiments and ablation studies show that, compared to traditional heuristic algorithms and standard deep reinforcement learning methods, GAPO significantly reduces average task completion latency and substantially decreases backbone link congestion. …”
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A dual scheduling framework for task and resource allocation in clouds using deep reinforcement learning by Jiahui Pan, Yi Wei, Lei Meng, Xiangxu Meng

“…Experimental results show that our approach is effective in ensuring higher task success rates, improving resource utilization, shortening average task response time, and significantly reducing the cost of renting VM instances compared to other baseline solutions.…”
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Task offloading delay minimization in vehicular edge computing based on vehicle trajectory prediction by Feng Zeng, Zheng Zhang, Jinsong Wu

“…Simulation results show that, compared with other classical schemes, the proposed strategy has lower average task offloading delays.…”
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HOODIE: Hybrid Computation Offloading via Distributed Deep Reinforcement Learning in Delay-Aware Cloud-Edge Continuum by Anastasios E. Giannopoulos, Ilias Paralikas, Sotirios T. Spantideas, Panagiotis Trakadas

“…Extensive simulation results demonstrate that HOODIE effectively reduces task drop rates and average task processing delays, outperforming several baseline methods under changing CEC settings and dynamic conditions.…”
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A Human-Centered Design Approach in Developing the “Jejak Cilik” Parenting App Prototype by M. Azman Maricar, Affan Irfan Fauziawan, Ni Luh Made Vinaya Medhiatika, Ni Luh Putu Shinta Juliantari, I Made Dwipa Aditya Putra, Komang Lazuardi Edo Karang

“…Meanwhile, the completion and duration metrics, tested by 20 participants, showed promising results: all tasks were successfully completed by all users, with an average task completion time of less than one minute. …”
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Computation Offloading Strategy Based on Improved Polar Lights Optimization Algorithm and Blockchain in Internet of Vehicles by Yubao Liu, Bocheng Yan, Benrui Wang, Quanchao Sun, Yinfei Dai

“…Simulation results show that the strategy significantly reduces the average task processing latency (64.4%), the average system energy consumption (71.1%), and the average system overhead (75.2%), and at the same time effectively extends the vehicle’s power range, improves the real-time performance of the emergency accident warning and dynamic path planning, and significantly reduces the cost of edge computing usage for small and medium-sized fleets, providing an efficient, secure, and stable collaborative computing solution for IoV.…”
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A digital twin-driven enhanced visualization method for high-steep slope scene by Jun Zhu, Ying Ren, Yukun Guo, Jingyi Lu, Jianlin Wu, Zhihao Guo, Hengchao Gu, Hongyue Zhao

“…Enhanced visualization reduced users’ average task completion time by 87.5% and increased accuracy to 91.5%. …”
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Adaptive Multi-Objective Firefly Optimization for Energy-Efficient and QoS-Aware Scheduling in Distributed Green Data Centers by Ahmed Chiheb Ammari, Wael Labidi, Rami Al-Hmouz

“…We formulate a multi-objective optimization problem that minimizes total operational costs while reducing the Average Task Loss Probability (ATLP), a key Quality of Service (QoS) metric. …”
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Integration of Multi-Source Landslide Disaster Data Based on Flink Framework and APSO Load Balancing Task Scheduling by Zongmin Wang, Huangtaojun Liang, Haibo Yang, Mengyu Li, Yingchun Cai

“…This implementation achieves efficient integration of multi-source landslide data. (3) Compared to Flink’s default task scheduling strategy, the cluster load balancing strategy based on APSO demonstrated a reduction of approximately 4.7% in average task execution time and an improvement of approximately 5.4% in average system throughput during actual tests using landslide data sets. …”
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Securing fog computing in healthcare with a zero-trust approach and blockchain by Navjeet Kaur, Ayush Mittal, Umesh Kumar Lilhore, Sarita Simaiya, Surjeet Dalal, Kashif Saleem, Ehab Seif Ghith

“…The key evaluation performance metrics include intrusion detection rate (IDR), data integrity (DI), task completion rate (TCR), average task response time (ART), and average block time. …”
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Educational and Clinical Applications of a Web- and Android-Based Telemedicine Platform to Expand Rural Health Care in Ecuador by Leonel Vasquez-Cevallos, Andrew Mitchell, Susana Muñoz-Hernández, Ángel Herranz-Nieva, Ana Garcia-Mingo, Paula De Corral-San Martin, Mijail Castro, Paul E.D. Soto-Rodriguez, Franklin Parrales-Bravo, Rosangela Caicedo-Quiroz

“…Results: Technical validation demonstrated low error rates, high user satisfaction, and average task completion times of 5 min for general practitioners and 3 min for specialists. …”
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