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Average AoI Minimization With Directional Charging for Wireless-Powered Network Edge

IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第6期24卷 4889-4906页

作者： Chen, Quan Guo, Song Xu, Wenchao Li, Jing Shi, Tuo Gao, Hong Cai, Zhipeng Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou 510006 Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong 999077 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong 999077 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321017 Peoples R China Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA

Age of Information (AoI) has been proposed as a new performance metric to capture the freshness of data. At wireless-powered network edge, the source nodes first need to be charged ready for update transmissions, which means the system AoI is not only decided by the scheduling of update transmissions but also by the designing of charging plan. However, the existing works either only focused on the point of scheduling update transmissions or have a rigid assumption that only one source node can be charged per time. Aiming at making the work more practical and general, we investigate the average AoI optimization problem at wireless-powered network edge with directional charging. First, the theoretical bound of the weighted sum of average AoI of the entire network with a directional charger is analyzed, which is proved to be related to nodes' maximum transmitting interval and the charging strategy. An optimal charging time computation algorithm is proposed to obtain the maximum transmitting interval of each source node by considering the overlapped areas of different charging orientations. After then, an AoI-aware periodical charging scheduling algorithm is proposed to compute a periodical charging schedule while the average AoI is bounded, including a charging period T and the charging orientations assigned to each time slot within T. The proposed algorithm is proved to have an approximation ratio of up to 1.5625. Furthermore, several approximate algorithms are also proposed for average AoI optimization with multiple chargers and network bandwidth constraint. Finally, the extensive simulations demonstrate the high performance of the proposed algorithm in terms of AoI.

关键词： Optimization Approximation algorithms Bandwidth scheduling scheduling algorithms Schedules Computer science Mobile computing Minimization Lower bound Age of Information (AoI) average AoI directional charging wireless-powered network

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Demand response and energy dispatch system for intelligent buildings based on improved MOALO algorithm

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Energy Informatics 2025年第1期8卷 1-16页

作者： Han, Weiwei Puyang Vocational and Technical College Puyang457000 China

As the rate of energy consumption in intelligent buildings increases, the uneven distribution of energy among different devices in intelligent buildings leads to further acceleration of energy consumption. The study suggested designing an energy dispatch system for intelligent buildings based on the enhanced multi-objective ant-lion optimizer algorithm in an attempt to address the issue that the conventional energy dispatch system for intelligent buildings is unable to carry out energy dispatch in accordance with the electricity price and incentives. The initialization of different energy data parameters was carried out by the multi-objective ant-lion optimizer algorithm, and the variance crossover operation of the data parameters was carried out by the differential evolution algorithm. Based on the improved multi-objective ant-lion optimizer algorithm, a demand response model was constructed, and the energy dispatch system of intelligent buildings was constructed accordingly. The results revealed that the area under the PR curve of the improved multi-objective ant-lion optimizer algorithm was 0.9653, which was significantly higher than the other three algorithms. The root mean square error and the mean absolute error of the algorithm were 0.839 and 0.648, respectively. In the experiments on the practical application of the dispatch system, it was found that the average power of the dispatched energy sources was significantly lower than that of the non-dispatched energy power distribution. The aforementioned findings indicate the suggested approach can more effectively schedule various energy sources in intelligent buildings, offering technical assistance in the area of energy dispatch. © The Author(s) 2025.

关键词： scheduling algorithms

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A STUDY ON OPTIMAL PRODUCTION scheduling UNDER ASYMMETRICAL SET-UP TIME AND MULTI-ROUTING FOR DUAL-TYPE CUSTOMERS

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Journal of Technology 2025年第1期40卷 37-57页

作者： Huang, Yun-Cheng Hou, Yi-Xuan Huang, Bo-Ru Department of Industrial Management National Pingtung University of Science and Technology Pingtung912 Taiwan

This study considers that the production routes of different products are different, and the conversion between products requires set-up time. However, the set-up time is different due to the difficulty of disassembly and assembly, so the asymmetric set-up time and production batch are included in the multi-routing production scheduling system. Due to the practical needs of urgent orders insertion and dual-type customers, this study proposes a Random Optimization Algorithm (ROA) to search for the optimal or near optimal scheduling under three performance indicators on demand and compares the results under two initial scheduling methods. The results show that the scheduling results obtained using the algorithm are better than the initial scheduling results selected by the user, and the integrated scheduling of different customer types is better than the separate scheduling (that is, VIP Priority scheduling Method). The random optimization algorithm proposed by this research can effectively improve the performance of scheduling. © 2025, National Taiwan University of Science and Technology. All rights reserved.

关键词： scheduling algorithms

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Klotski v2: Improved DNN Model Orchestration Framework for Dataflow Architecture Accelerators

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 1045-1058页

作者： Bai, Chen Wei, Xuechao Zhuo, Youwei Cai, Yi Zheng, Hongzhong Yu, Bei Xie, Yuan The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong Alibaba DAMO Academy Computing Technology Laboratory Hangzhou311121 China Peking University School of Integrated Circuits Beijing100871 China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong

Dataflow architecture accelerators are a new kind of scalable DNN accelerators. For an instruction, the availability of input operands solely determines the beginning of executions. DNN model orchestration determines how to partition, schedule, and map the computation to the underlying hardware. In this article, we propose the Klotski v2 framework to solve DNN model orchestration for dataflow architecture accelerators. First, a Bayesian optimization-based entropy-directed partition algorithm is proposed to transform a DNN model into μ ops. Second, a unified formal formulation for μ ops scheduling and mapping is presented. Third, a two-stage methodology is proposed to decouple the scheduling and mapping. Fourth, a Hilbert curve-based mapping heuristic is proposed to enhance problem-solving efficiency, improving the tradeoff between solution quality and algorithm runtime. Extensive results show that Klotski v2 can achieve an average of 21.57% higher execution performance improvement than the previous methodologies. With the Hilbert curve-based mapping heuristic, we improve the algorithm efficiency by an average of 63.50% across different DNN workloads. © 1982-2012 IEEE.

关键词： scheduling algorithms

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Energy-Aware scheduling of Parallel Application in Hybrid Computing System

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Chinese Journal of Electronics 2025年第4期23卷 688-694页

作者： Yan Ma Bin Gong Zhihong Guo Yufeng Chen Lida Zou State Grid Shandong Electric Power Research Institute Jinan China School of Computer Science and Technology Shandong University Jinan China

Energy management is emerging as an important issue for High performance computing (HPC) owning to high operational cost and low reliability. Compared with low-power architectural approach, energy-aware scheduling based on Dynamic voltage scaling (DVS) and Dynamic power management (DPM) is regarded as a promising way since it is practical and low-cost. At present, most studies focus on pure DVS or non-DVS environment, while most high performance computing systems are hybrid non-DVS/DVS platforms. We propose an energy-aware scheduling algorithm for parallel application to consider both DVS and non-DVS characteristics of hybrid system. We present the rule of task assignment, make analysis on DVS and DPM technique and give their mathematical formulation, which maintains makespan optimization and energy conservation. The clustering and merging algorithm, and priority computation method consider the situation of resource constraints. The extensive simulations demonstrate that the proposed algorithm has stronger ability of energy saving and time optimization than Heterogeneous earliest finish time (HEFT), Energy-efficient task duplication scheduling (EETDS) and Heterogeneous energy-aware duplication scheduling (HEADUS) algorithm no matter for synthetic workload or realistic workload.

关键词： scheduling algorithms High performance computing Power system management Energy conservation Clustering algorithms Dynamic scheduling Energy efficiency Reliability Voltage control Optimization

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Real time broadcast scheduling in TSCH for smart healthcare using cuckoo search algorithm

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Results in Engineering 2025年 26卷

作者： Pavithra, L. Rekha, D. School of Computer Science and Engineering VIT-AP University AP India Centre for e-Automation Technologies VIT University Tamil Nadu Chennai India

Smart Healthcare Sensor Networks (SHSNs) have garnered increasing attention due to their potential to support real-time health monitoring. However, SHSNs often suffer from low bandwidth, leading to reduced successful packet transmission. To overcome this limitation and achieve efficient broadcast scheduling without collisions, Time Slotted Channel Hopping (TSCH) is utilized. The broadcast scheduling challenge in SHSNs arises from limited bandwidth and the need for simultaneous data transmissions that avoid interference. This work addresses the problem by enabling concurrent transmissions in the same time slot, provided that they are interference-free, thereby reducing the overall network turnaround time and enhancing time slot utilization within the TSCH frame. The primary goal is to minimize the TSCH cycle duration and maximize node communication efficiency with reduced computational overhead. To this end, the proposed method employs Genetic Algorithm (GA), Immune Genetic Algorithm (IGA), and Cuckoo Search Algorithm (CSA) to optimize TSCH scheduling. Unlike GA and IGA, CSA emphasizes intelligent exploration of the search space by leveraging Lévy flights and adaptive strategies, which helps it exploit known information more effectively. The core philosophy behind using CSA lies in its bio-inspired, adaptive mechanism that balances global search and local refinement, making it highly suitable for dynamic and constrained environments like SHSNs. Simulation results demonstrate that CSA outperforms GA and IGA in terms of throughput, delay, and computational time. Specifically, CSA achieves up to a 12.4 % increase in throughput, a 17.8 % reduction in transmission delay, and a 15.6 % improvement in computational efficiency, validating its superiority and practical applicability in real-world smart healthcare systems. © 2025

关键词： scheduling algorithms

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Adaptive-Strategies based Quantum Genetic Algorithm for Agile Earth Observation Satellite scheduling Problem

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IEEE Aerospace and Electronic Systems Magazine 2025年

作者： Sun, Xiaohan Ren, Yuan Yu, Linghui Space Engineering University Department of Aerospace Engineering and Technology Beijing101416 China DFH Satellite Co. Ltd Beijing100094 China

The agile Earth observation satellite scheduling problem (AEOSSP) has been the subject of more research in recent years. Still, numerous researchers ignore the complex constraints of the scheduling model and the enormous complexity of solving the scheduling model. This paper is grounded in practical engineering applications and utilizes an adaptive-strategies based quantum genetic algorithm (ASQGA) to settle complex constraints and solution space. This research includes the development of a refined satellite scheduling model that integrates a variety of constraints, considering the attitude maneuvering capability of an agile satellite, the continuous working capability of the payload, the energy balance, and the storage space balance of the entire satellite system. Inspired by the genetic algorithm (GA) and quantum genetic algorithm (QGA), an improved QGA based on an adaptive evolutionary strategy and a multi-constraints conflict resolution based adaptive mutation strategy is proposed. Numerical simulation results demonstrate that the method solves the AEOSSP for large-scale tasks with notable advancements in computational speed and scheduling solution revenue. This paper also analyzes the impact of dynamic adaptive evolutionary parameters on the efficiency of scheduling solutions. For satellite engineering projects with large-scale tasks, the method studied in this paper can offer a viable solution to the AEOSSP. © 2025 IEEE. All rights reserved.

关键词： scheduling algorithms

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THEMIS: Time, Heterogeneity, and Energy Minded scheduling for Fair Multi-Tenant Use in FPGAs

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IEEE Transactions on Computers 2025年

作者： Karabulut, Emre Malik, Arsalan Ali Awad, Amro Aysu, Aydin University of Oxford Department of Electrical Engineering United Kingdom North Carolina State University Department of Electrical and Computer Engineering RaleighNC27606 United States

Using correct design metrics and understanding the limitations of the underlying technology is critical to developing effective scheduling algorithms. Unfortunately, existing scheduling techniques used incorrect metrics and had un-realistic assumptions for fair scheduling of multi-tenant FPGAs where each tenant is aimed to share approximately the same number of resources both spatially (in space) and temporally (in time). This paper proposes an improved fair scheduling algorithm that fixes earlier issues with metrics and assumptions for ‘fair’ multi-tenant FPGA use. Specifically, we claim three improvements. First, we consider both spatial and temporal aspects to provide spatiotemporal fairness—this improves a recent prior work that assumed all tasks would have the same latency. Second, we add the energy dimension to fairness: by calibrating the scheduling decision intervals and including their energy overhead, our algorithm offers trading off energy efficiency for fairness. Third, we consider previously ignored facts about FPGA multi-tenancy, such as the existence of heterogeneous regions and the inflexibility of run-time merging/splitting of partially reconfigurable *** develop and evaluate our improved fair scheduling algorithm with these three enhancements. Inspired by the Greek goddess of law and personification of justice, we name our fair scheduling solution THEMIS: Time, Heterogeneity, and Energy Minded scheduling. We implemented our solution on Xilinx Zedboard XC7Z020 with real hardware workloads and used real measurements on the FPGA to quantify the savings of our approach. Compared to previous algorithms, our improved scheduling algorithm enhances fairness between 24.2–98.4% and allows a trade-off between 55.3× in energy vs. 69.3× in fairness. The paper thus informs cloud providers about future scheduling optimizations for fairness with related challenges and opportunities. © 1968-2012 IEEE.

关键词： scheduling algorithms

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Adaptive Resource scheduling Algorithm for Multi-Feature Optimization in Personalized Wireless Body Area Networks

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhang, Zheng Ren, Xinxing Lai, Chun Sing Wu, Ruiheng Lin, Jinzhao Li, Zhangyong Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Brunel University of London College of Engineering Design and Physical Sciences UB8 3PH United Kingdom Chongqing University of Posts and Telecommunications Chongqing Engineering Research Center of Medical Electronics and Information Technology Chongqing400065 China

Wireless Body Area Network (WBANs), as a crucial technology in the field of healthcare monitoring, also plays a significant role in personal consumer electronics (CE). However, existing solutions have yet to effectively address challenges such as personalized demands, data heterogeneity, and dynamic link variations caused by diverse environmental factors. To address these issues, this study proposes a novel approach based on the IEEE 802.15.6 standard, which incorporates personalized node characteristics to cater to individual needs and adapt to node heterogeneity. Furthermore, a dynamic scheduling mechanism for node data is introduced, leveraging multi-feature environmental factors to enhance the practicality of WBAN systems. Finally, for real-time scheduling of emotional anomaly data, this study incorporates skin conductance nodes for the first time to evaluate emotional factors, ensuring the efficient transmission of urgent data. Theoretical analysis and simulation results demonstrate that the proposed approach significantly outperforms traditional methods in terms of energy efficiency, latency, throughput, and resource balance. © 1975-2011 IEEE.

关键词： scheduling algorithms

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PTPS: Precision-Aware Task Partitioning and scheduling for SpMV on CPU-FPGA Heterogeneous Platforms

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Gao, Jianhua Zhou, Zhi Huang, Xingze Wang, Juan Wang, Yizhuo Ji, Weixing Beijing Normal University School of Artificial Intelligence Beijing100875 China Beijing Institute of Technology School of Computer Science and Technology Beijing100081 China

The CPU-FPGA heterogeneous computing architecture is extensively employed in the embedded domain due to its low cost and power efficiency, with numerous sparse matrix-vector multiplication (SpMV) acceleration efforts already targeting this architecture. However, existing work rarely includes collaborative SpMV computations between CPU and FPGA, which limits the exploration of hybrid architectures that could potentially offer enhanced performance and flexibility. This paper introduces an FPGA architecture design that supports multi-precision SpMV computations, including FP16, FP32, and FP64. Building on this, PTPS, a precision-aware SpMV task partitioning and dynamic scheduling algorithm tailored for the CPU-FPGA heterogeneous architecture, is proposed. The core idea of PTPS is lossless partitioning of sparse matrices across multiple precisions, prioritizing low-precision SpMV computations on the FPGA and high-precision computations on the CPU. PTPS not only leverages the strengths of CPU and FPGA for collaborative SpMV computations but also reduces data transmission overhead between them, thereby improving the overall computational efficiency. Experimental evaluation demonstrates that the proposed approach offers an average speedup of 1.57x over the CPU-only approach and 2.58x over the FPGA-only approach. © 1982-2012 IEEE.

关键词： scheduling algorithms

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