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Structure-Free General Data Aggregation scheduling for Multihop Battery-Free Wireless Networks

IEEE TRANSACTIONS ON MOBILE COMPUTING 2022年第9期21卷 3342-3359页

作者： Chen, Quan Cai, Zhipeng Cheng, Lianglun Gao, Hong Guangdong Univ Technol Sch Comp Guangzhou 510006 Peoples R China Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China

With advances in wireless power transfer techniques, battery-free wireless sensor networks (BF-WSNs) which can support long-term applications, has been attracting increasing interests in recent years. Unfortunately, the problem of minimum latency aggregation scheduling (MLAS) is not well studied in BF-WSNs. Existing works always have a rigid assumption that there is only one single query which is targeted at the whole network. Aiming at making the work more practical and general, we investigate the general MLAS problem in BF-WSNs, which is targeted at any subset of nodes in the network and aimed for an arbitrary number of aggregation queries. First, the general MLAS problem when there is one single query is studied. To control the number of nodes participating in the aggregation process, a node selection algorithm is proposed to cover and connect the whole target nodes. Then, a latency and energy aware scheduling algorithm is proposed to integrate the construction of aggregation tree with the chosen nodes, and the computation of a conflict-free schedule simultaneously, relying on non-predetermined structures. Second, the general MLAS problem when there is a group of aggregation queries is studied. Through designing some special structures to avoid collisions between both current and existing aggregation schedules, an algorithm without any waiting time is proposed. Additionally, the algorithm under physical interference model and dynamic energy arrival model are also presented. The theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency and energy efficiency.

关键词： Wireless sensor networks Schedules Heuristic algorithms scheduling algorithms scheduling Data aggregation Sensors Data aggregation multiple queries low latency battery-free wireless sensor networks (WSNs)

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Flexible Task scheduling in Data Relay Satellite Networks

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2022年第2期58卷 1055-1068页

作者： Wu, Guohua Luo, Qizhang Zhu, Yanqi Chen, Xinjiang Feng, Yanghe Pedrycz, Witold Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Peoples R China Beijing Inst Remote Sensing Informat Beijing 100000 Peoples R China Peking Univ Coll Engn Beijing 100091 Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2V4 Canada King Abdulaziz Univ Fac Engn Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland

The task-schedulingalgorithm is a key module to satisfy various complex user requirements, and improve the usage flexibility and efficiency of data relay satellites networks (DRSN). In this context, we first propose a novel application mode for DRSN, in which users are allowed to submit multiple optional service time windows and specify a preferred antenna as well as an expected execution duration for each task. Meanwhile, the start time of a service time window can be adjusted within a specified range. A mathematical programming model that maximizes the completion ratio of tasks and the expectation satisfaction of users is established. Moreover, a conflict resolution-assisted iterative task-scheduling algorithm (CRITS) is designed, composing of five closely dependent operators: resource matching, service durations generation, conflict evaluation, conflict resolution, and solution update. To verify the effectiveness of the proposed CRITS, extensive experiments are carried out. The experimental results demonstrate the competitive performance of CRITS in addressing the DRSN scheduling problem. In comparison with two heuristic algorithms (heuristic algorithm based on time-freedom degree and a heuristic algorithm based on task priority) and a meta-heuristic algorithm (adaptive variable neighborhood descent combined with a tabu list), the proposed CRITS increases the overall completion ratio of tasks by 6.65, 10.26, and 10.96%, respectively.

关键词： Task analysis Satellites Relays scheduling Space vehicles Antennas scheduling algorithms Conflict resolution constrained optimization data relay satellite networks iterative search task scheduling

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CHAOS-ADAPTIVE MULTI-OBJECTIVE PSO ALGORITHM FOR MICROGRID OPTIMIZATION scheduling 20

CHAOS-ADAPTIVE MULTI-OBJECTIVE PSO ALGORITHM FOR MICROGRID O...

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20th International Conference on AC and DC Power Transmission 2024, ACDC 2024

作者： Luo, Ziyi Cheng, Caixue Li, Sihong Liu, Yikun Liu, Fanming School of Automation and Electronic Information Xiangtan University Hunan Province Xiangtan411100 China

ISBN: (纸本)9781837242672

To enhance the economic and environmental benefits of daily microgrid operations and address the optimization accuracy challenges in particle swarm optimization (PSO) algorithms, a chaotic adaptive multi-objective particle swarm algorithm is proposed. Firstly, the Logistical chaotic mapping is introduced to generate the initial population, enhancing population diversity. Secondly, based on chaotic theory, the control factor y adaptively adjusts the chaotic search range, and the algorithm's adaptability is further improved through the adaptive adjustment of the inertia weight parameter ω and acceleration coefficients c1, c2. The proposed algorithm is simulated using six typical test functions, demonstrating superior performance in terms of optimization accuracy, solution stability, and convergence speed compared to heuristic algorithms such as the grey wolf algorithm. Finally, the enhanced PSO algorithm is applied to solve multi-objective optimization and scheduling problems in microgrid integration. Simulation results indicate that the algorithm effectively enhances the overall benefits of daily microgrid operations. © The Institution of Engineering & Technology 2024.

关键词： scheduling algorithms

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Human-Computer Interaction Data scheduling Algorithm Based on Artificial Intelligence 4

Human-Computer Interaction Data Scheduling Algorithm Based o...

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4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： An, Ning Ibrahim, Huda Ahmad, Mazida Northern University Iasdo Institute for Advanced and Intelligent Digital Opportunities School of Computer Science Malaysia

ISBN: (纸本)9798350394375

Traditional scheduling algorithms have certain limitations when faced with complex tasks and dynamically changing resource environments, making it difficult to achieve efficient task allocation and resource scheduling. This paper proposes a human-computer interaction data scheduling algorithm based on artificial intelligence, and elaborates its research methods. The algorithm uses machine learning and intelligent optimization technology to learn and optimize resource allocation strategies through the analysis of historical data and real-time data, improving resource utilization and scheduling accuracy. The algorithm also has the characteristics of adaptive adjustment and decision-making optimization, and can dynamically adjust according to task characteristics and resource status to find the optimal scheduling strategy. Through the comparison and experimental evaluation of the human-computer interaction data scheduling algorithm based on artificial intelligence and the traditional scheduling algorithm, this paper obtains the research results, and the mean square error of the algorithm in this paper is kept between 0.06-0.08. In terms of resource utilization, the algorithm in this paper shows obvious advantages, which can accurately predict the resource requirements of tasks and avoid resource waste and idleness. In terms of scheduling accuracy, this algorithm can better match tasks and resources and improve task execution efficiency and response time. In terms of mean square error, the algorithm in this paper reduces prediction errors and improves system stability and performance by establishing task requirements and resource allocation models. © 2024 IEEE.

关键词： scheduling algorithms

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An Intelligent scheduling System for Large-Scale Online Judging 18th

An Intelligent Scheduling System for Large-Scale Online Jud...

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18th International Conference on Computer Science and Education, ICCSE 2023

作者： Zhang, En Wu, Fan Lu, Xuesong School of Data Science and Engineering East China Normal University Shanghai China

ISBN: (纸本)9789819707294

Online judge (OJ) systems have been widely used for programming skill evaluation in various fields, including programming education, programming competition and talent recruitment. Existing OJ systems put the codes into a judge queue according to the order of user submission, and use the judge server to evaluate the correctness of the codes in turn. With the surge in the number of code submissions, this scheduling method causes the rapid increase of average response time for judge requests, resulting in a decline in user experience. To alleviate the problem, we develop an intelligent scheduling system, which consists of two modules. In the first module, we employ a deep representation learning model to predict the running time of the codes in the judge queue;in the second module, the judge queue is divided into fixed-size windows. The codes in each window are sorted according to their predicted running time in ascending order, and are scheduled to the judge server using the shortest job first algorithm. The experimental results show that, 1) the constructed prediction model predicts the running time of the codes accurately;2) compared with the scheduling algorithm of existing OJ systems, the proposed scheduling algorithm can effectively reduce the average response time for large-scale online judging. Furthermore, by varying the code running time distribution and window size in the judge queue, we demonstrate the performance improvements of the proposed intelligent scheduling system under different settings, compared with the existing systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： scheduling algorithms

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Emergency scheduling Optimization Method of Natural Gas Supply Chain based on Differential Evolution 24

Emergency Scheduling Optimization Method of Natural Gas Supp...

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2024 International Conference on Cloud Computing and Big Data, ICCBD 2024

作者： Sun, Hailu Yang, Chao Ning, Xifeng Fang, Ruonan Xu, Jun Nan, Yufeng PetroChina Planning & Engineering Institute China National Petroleum Corporation China Production & Operation Management Department China National Petroleum Corporation China

ISBN: (纸本)9798400710223

In this paper, an emergency scheduling optimization method of natural gas supply chain based on Differential Evolution (DE) is proposed to guarantee the natural gas supply under extreme conditions. In order to verify the effectiveness of the proposed method, this paper selects a local natural gas pipeline network in Europe, constructs its pipeline network topology model based on the graph theory, and conducts experiments under four disturbance scenarios. The result shows that the proposed scheduling optimization method based on DE can not only completely avoid the occurrence of extreme situations such as supply interruption, but also effectively improve the overall user satisfaction, which is an effective emergency scheduling method for natural gas supply chain. © 2024 ACM.

关键词： scheduling algorithms

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Energy-Aware Priority-Based Task scheduling for Edge Devices in Internet of Things 5

Energy-Aware Priority-Based Task Scheduling for Edge Devices...

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5th IEEE India Council International Subsections Conference, INDISCON 2024

作者： Pawar, Vijaykumar Naik Mini, S. National Institute of Technology Department of Computer Science and Engineering Goa India

ISBN: (纸本)9798350376753

Billions of networked devices interact and collaborate with one another via the Internet as part of the Internet of Things (IoT). The performance and energy efficiency of IoT environments are greatly enhanced by effective task scheduling, especially for edge devices. In this paper, we present an energyaware, priority-based task scheduling algorithm specifically for edge devices in IoT networks, which prioritizes tasks according to their urgency and resource requirements while optimizing energy consumption to prolong device lifetime and lower operating costs. The model takes into consideration the dynamic nature of task demands as well as the varying energy profiles of edge devices. Simulations validate the efficacy of our approach, revealing that it achieves a substantial reduction in energy consumption while maintaining competitive task completion rates. © 2024 IEEE.

关键词： scheduling algorithms

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CQF-Based Joint Routing And scheduling Algorithm In Time-Sensitive Networks 7

CQF-Based Joint Routing And Scheduling Algorithm In Time-Sen...

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7th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2024

作者： Yang, Shuang Zhang, Ying Lu, Tao Chen, Ze Nari Information & Communication Technology Co.Ltd Nanjing China

ISBN: (纸本)9798350339161

Time-sensitive networks provide deterministic transport for time-sensitive flows in industrial control. IEEE802.1Qch proposes a circular queuing and forwarding mechanism based on ping-pong queues. Many of the scheduling algorithms proposed for time-sensitive networks do not take into account the network topology. For the IEEE 802.1Qch standard in time-sensitive networks, a joint routing and scheduling model that allocates resources based on starting time slots is proposed, taking into account the traffic, network model and traffic characteristics. And by reasonably allocating the network resources for each scheduling time slot, the network load is more balanced. Simulation results show that the number of successfully scheduled flows can be increased by 17% compared with other algorithms. On this basis, the effect of the sorting method and the length of the queue on the results with is explored. © 2024 IEEE.

关键词： scheduling algorithms

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Day-Ahead Optimal scheduling of Time-Series Multi-Scenario Considering Source-Load Uncertainty 16

Day-Ahead Optimal Scheduling of Time-Series Multi-Scenario C...

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16th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2024

作者： Sun, Junjie Hu, Shubo Ge, Yangyang Zhang, Qiang Li, Xinwei Wang, Chao Electric Power Research Institute State Grid Liaoning Electric Power Co. Ltd. Shenyang China

ISBN: (纸本)9798350386127

In order to solve the problems of high-risk scenario leakage, poor scheduling plan economy and optimal scheduling plan determination in the scenario analysis method in the day-ahead scheduling optimization considering source and load uncertainty, the time series multi-scenario considering spatiotemporal correlation is divided into normal and extreme time series scenario through confidence interval. The typical time series scenario is obtained by the improved K-means method. A two-stage optimization scheduling model based on time series scenarios including a scheduling plan optimization layer and a dispatching plan optimization layer was established. When solving the model, the typical normal time series scenario is first optimized and solved, and the optimal scheduling plan for the day ahead is obtained with the goal of maximizing the sum of the positive reserve and negative reserve of the system in different time periods. On this basis, the typical extreme time series scenarios are optimized to obtain dispatching plans. The example results verify the feasibility and economy of the proposed day-ahead scheduling optimization method. © 2024 IEEE.

关键词： scheduling algorithms

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Research on Adaptive Control and Optimal scheduling Algorithm under Mixed Energy Architecture of Data Center 7

Research on Adaptive Control and Optimal Scheduling Algorith...

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7th IEEE International Conference on Information Systems and Computer Aided Education, ICISCAE 2024

作者： Zhang, Wancai Xia, Xuwei Zhang, Nan Yang, Wenqing Mu, Jun Wang, Tao State Grid Electric Power Research Institute Co. Ltd. Nanjing China Electric Power Research Institute of State Grid Ningxia Electric Power Co. Ltd. Yinchuan China

ISBN: (纸本)9798350350760

An approximate dynamic programming optimization scheduling strategy combining independent microgrid day-ahead scheduling system and adaptive weighted sum algorithm is proposed. The strategy aims to minimize energy consumption and maximize energy utilization efficiency of data centers through intelligent control means. Firstly, this paper designs a day-ahead scheduling system of an independent micro-grid, which can integrate a variety of energy inputs, including solar energy, wind energy, traditional grid power supply and energy storage equipment, to ensure the stability and reliability of the energy supply of data centers. Secondly, the adaptive weighted sum algorithm is introduced, which can dynamically adjust the proportion of each energy input according to the changes of real-time energy market price and environmental conditions, so as to achieve the optimization of energy cost. Finally, through the approximate dynamic programming algorithm, the adaptive ability of the system is further improved, so that it can automatically learn and optimize the scheduling decision in the complex and changeable energy environment. In order to verify the effectiveness of the proposed strategy, a system simulation experiment is carried out. The experimental results show that compared with the traditional fixed scheduling strategy, the proposed adaptive control and optimal scheduling algorithm can significantly reduce the energy cost of data centers, and improve the flexibility and response speed of energy use. ©2024 IEEE.

关键词： scheduling algorithms

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