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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yuan, Haitao Wang, Shen Ma, Yaofei Bi, Jing Yang, Jinhong Zhang, Jia Zhou, MengChu School of Automation Science and Electrical Engineering Beihang University Beijing100191 China College of Computer Beijing University of Technology Beijing100124 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

Currently, a cloud-edge collaborative system combines almost unlimited storage and computing resources where tasks can be migrated to high-performance servers in edge servers or the cloud. However, resource allocation and task offloading present big challenges due to the competition among mobile devices (MDs) for communication and computing resources of edge servers. Therefore, it is significant to properly offload MDs' tasks to edge servers or the cloud. This work proposes a collaborative edge-cloud architecture, including a centralized cloud, edge servers, and MDs. Then, this work jointly considers computing power, task sizes, computing resources, transmission power of MDs, transmission rates, computing power, transmission power, computing resource of edge servers, and computing resource of the cloud. Considering the abovementioned factors, this work designs a mixed-integer non-linear programming problem. To solve it, a Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO) algorithm is proposed to obtain the best solution. Building upon it, this work proposes an energy-minimized task offloading and resource allocation strategy, thereby minimizing the system's energy consumption while ensuring strict task response time limits. Experimental results show that GSPSO reduces the system's energy by 66.34%, 34.65%, and 4.95% more than particle swarm optimization (PSO), self-adaptive PSO, and Tyrannosaurus optimization. © 2024 IEEE.

关键词： Mobile edge computing

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Multi-Indicator Water Quality Prediction Using Multimodal Bottleneck Fusion and ITransformer with Attention

Multi-Indicator Water Quality Prediction Using Multimodal Bo...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Li, Yibo Zhang, Xuan Yuan, Haitao Wang, Ziqi Zhang, Jia Zhou, MengChu College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science in Lyle School of Engineering DallasTX United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ United States

ISBN: (纸本)9781665410205

Water quality prediction methods forecast the future short or long-term trends of its changes, providing proactive advice for water pollution prevention and control. Existing water quality prediction methods only consider the historical data of single-type or multi-type water quality. However, meteorology and other factors also have a significant impact on water quality indicators. Therefore, only considering the historical data of water quality is not feasible. Unlike existing studies, this work proposes a hybrid water quality prediction model called CMI to solve the above problem. Before prediction, CMI incorporates a multimodal fusion mechanism of water quality time series and remote sensing images of meteorological rainfall. Moreover, CMI integrates the model of ConvNeXt V2 and a multimodal bottleneck transformer to extract image features for fusing the time series and images. Furthermore, it utilizes an emerging model of iTransformer to realize prediction with the fused features. Experimental results with real-life water quality time series and remotely sensed rainfall images demonstrate that CMI outperforms other state-of-the-art fusion algorithms, and the water quality prediction accuracy with fused meteorological data is 13% higher on average than that with only water quality time series. © 2024 IEEE.

关键词： Rain

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Energy and Time-Optimized Task Scheduling with Simulated-Annealing-Based Firefly Algorithm in Hybrid Cloud Edge Computing

Energy and Time-Optimized Task Scheduling with Simulated-Ann...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Zhou, Xinmin Yuan, Haitao Zhang, Jia Zhou, MengChu College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

In a cloud-edge system, data analysis, processing, and storage can be performed in edge servers, avoiding transferring data to more distant cloud servers. This greatly improves the efficiency of data processing, saves network bandwidth and cloud resources, and reduces operating and maintenance costs. However, it is a challenge of how to perform task scheduling. It is difficult to schedule tasks for joint optimization of the total energy consumption and completion time of a task sequence within a limited time in a resource-constrained cloud-edge system. The work proposes an improved Simulated-Annealing-based Firefly Algorithm with Linear position update, called SAFAL for short. SAFAL incorporates a simulated annealing mechanism and an efficient position update strategy into the firefly algorithm, enabling fireflies to find the optimal solution more quickly and avoid getting trapped in local optima. SAFAL adopts a probabilistic mapping operator to map the position of each firefly to a task scheduling sequence, thus linking the firefly space and the task space. Several test instances in cloud-edge systems are designed to validate the superiority of SAFAL over the firefly algorithm, simulated annealing, and firefly algorithm with a self-adaptive strategy. Results show that the weighted cost of total energy consumption and completion time of SAFAL is reduced by 16.32%, 17.62%, and 14.21%, respectively, with 20 tasks. © 2024 IEEE.

关键词： Simulated annealing

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Fixed-Time Trajectory Tracking Control of Nonholonomic WMRs Subject to Spatial Constraint

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2023年第3期50卷 954页

作者： Chen, Tianjiao Zhao, Zixing Zhao, Qu Yao, Yi Zhou, Runzhi Shang, Yanling School of Automation Nanjing Institute of Technology Nanjing211167 China School of Computer engineering Nanjing Institute of Technology Nanjing211167 China School of Automation Nanjing Institute of Technology Nanjing211167 China

The design of fixed-time trajectory tracking controller for nonholonomic wheeled mobile robots (WMRs) subject to spatial constraint is addressed in this paper. Firstly, a tan-type Barrier Lyapunov Function (BLF) that equates to the classical one for unconstrained systems is exploited. Then, by employing the adding a power integrator technique and switching control strategy, a state feedback controller is successfully constructed to forces the error dynamic states of the closed-loop system (CLS) to zero in a given fixed time without violation of the constraint. Finally, simulation results are given to confirm the efficacy of the presented control scheme. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： Lyapunov functions

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Energy-Optimized Computation Offloading with Improved Differential Evolution in UAV-Enabled Edge and Cloud Computing

Energy-Optimized Computation Offloading with Improved Differ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yuan, Haitao Wang, Meijia Bi, Jing Zhang, Jia Zhou, MengChu School of Automation Science and Electrical Engineering Beihang University Beijing100191 China College of Computer Beijing University of Technology Beijing100124 China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

Mobile edge computing (MEC) emerges as a vital paradigm to support the increasing use of mobile users (MUs) with capabilities similar to cloud computing. While most research concentrates on MEC facilitated by terrestrial base stations (BSs), its applicability in scenarios such as disaster rescue and field operations is limited. Efforts have been made to explore MEC assisted by unmanned aerial vehicles (UAVs) with efficient scheduling algorithms. However, relying solely on UAVs for MEC has limitations, particularly for computation-intensive applications. This work proposes a hybrid MEC system lever-aging UAVs and BS. Multiple UAVs and a BS are deployed to provide MEC services directly from UAVs or indirectly from the BS. We formulate an energy-efficient scheduling problem to minimize energy consumption by jointly optimizing UAV trajectories, task associations, and allocation of computing and transmitting resources. To solve it, this work designs a hybrid algorithm named _S_uccess History-based parameter Adaptation for Differential volution with a Niching-based population size reduction strategy and an efficient nsemble sinusoidal scheme (SHADE-NE). Experimental results validate the superiority of SHADE-NE over its benchmark peers, thus proving that SHADE-NE greatly enhances the performance of the system. © 2024 IEEE.

关键词： Computation offloading

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Multi-User Computation Offloading in Mobile Edge Computing with Hybrid Whale Optimization

Multi-User Computation Offloading in Mobile Edge Computing w...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Bil, Jing Li, Ning Yuan, Haitao Zhang, Jia Zhou, MengChu College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

With the increasing amount of data and the need for real-time processing, Mobile Edge Computing (MEC) is growing rapidly, driving the shift from traditional cloud computing to distributed edge architectures. When offloading these applications with large amounts of data on mobile devices, a lot of computing and storage resources and high energy consumption are required. Yet, mobile devices' computing power, resource storage, and battery power are often limited and cannot meet these needs. To solve a computation offloading problem for joint optimization of time, cost, and energy, this work proposes an improved hybrid algorithm called Chaos and Lévy flights-based Whale Optimization Algorithm (CLWOA) to solve the multi-user offloading problem in an MEC-Cloud system. Each task is offloaded to local processors of mobile devices, edge servers, and cloud servers in proportion to jointly minimize the completion time, energy consumption, and total cost. Finally, compared with the whale optimization algorithm, lévy flight whale optimization algorithm, refined whale optimization algorithm, and chaos-based whale optimization algorithm, CLWOA reduces the weighted cost by 1.89%, 0.31%, 0.19%, and 0.42%, respectively. © 2024 IEEE.

关键词： Mobile edge computing

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Energy-Efficient and Latency-Optimized Computation Offloading with Improved MOEA for Industrial Internet of Things

Energy-Efficient and Latency-Optimized Computation Offloadin...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Yang, Jinhong Zhang, Jia Zhou, MengChu College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

The unprecedented prosperity of the industrial Internet of Things has thoroughly facilitated the transition from traditional manufacturing towards intelligent manufacturing. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm for lower latency and energy consumption for IEs. However, computational offloading and coordinating of multiple IEs with diverse task types and multiple edge nodes in industrial environments poses challenges. To address this challenge, we propose a multi-task approach encompassing scientific and concurrent workflow tasks to achieve energy-efficient and latency-optimized computation offloading. Furthermore, this work designs an improved Quantum Multi-objective Grey wolf optimizer with Manta ray foraging and Associative learning (QMGMA) to optimize multi-task computation offloading. Comprehensive experiments demonstrate the superior efficiency and stability of QMAGA compared to state-of-the-art algorithms in balancing latency and energy consumption. QMAGA improves average inverse generation distance and average spacing by 37% and 31% on average than multi-objective grey wolf optimizer, non-dominated sorting genetic algorithm II, and multi-objective multi-verse optimization, proving the convergence and diversity of its non-dominated solutions. © 2024 IEEE.

关键词： Smart manufacturing

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SCON:Semantic Cross-Modal Data Association Offset Estimation Network for Radar-Vision Feature Fusion

SCON:Semantic Cross-Modal Data Association Offset Estimation...

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2024 IEEE International Conference on Unmanned Systems, ICUS 2024

作者： Zheng, Wenbo Li, Zhenglin Xie, Wenbo Zhong, Songyi Yuan, Tianxin Peng, Yan School of Future Technology Institute of Artificial Intelligence Shanghai University Shanghai China School of Mechatronic Engineering and Automation Shanghai University Shanghai China School of Computer Engineering and Science Shanghai University Shanghai China

ISBN: (纸本)9798350384185

Unmanned surface vehicles (USVs) play significant roles among various domains. Accurate and robust perception relies on effective information fusion of multiple sensors. The techniques based on fusion of Millimeter-Wave(MMW) radar and cameras performs well in complex environments of water surface, especially in adverse weather conditions. However, the inaccurate height information of MMW measurements, together with the influence of water surface disturbance, results in difficulties in accurately associating radar points with corresponding targets in images. To address these challenges, we propose a network for cross-modal data association offset estimation to improve the fusion of MMW and vision information. It initially conducts column sampling and a category prediction to determine whether radar measurements are associated with target, and subsequently performs offset prediction to align radar points with targets. Additionally, a novel metric named as PTAR (Point Cloud-Target Association Rate) is introduced to quantitatively assess the spatial correlation between radar points and image targets. We validate the effectiveness of the proposed method using a real-world dataset collected on water surface by us. Experimental results demonstrate that the proposed method achieves correct spatial alignment for up to 16% of potential radar points-target pairs. © 2024 IEEE.

关键词： Unmanned surface vehicles

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Energy-Optimized Task Offloading with Genetic Simulated-Annealing-Based PSO for Heterogeneous Edge and Cloud Computing

Energy-Optimized Task Offloading with Genetic Simulated-Anne...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yuan, Haitao Zheng, Ziyue Bi, Jing Zhang, Jia Zhou, MengChu School of Automation Science and Electrical Engineering Beihang University Beijing100191 China College of Computer Beijing University of Technology Beijing100124 China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

Recent years have seen a surge in Internet of Things (IoT) technologies, with billions of mobile devices (MDs) straining limited computing and networking resources. Mobile edge computing offloads tasks from MDs to edge servers, saving energy and reducing network pressure. Edge servers provide closer services yet have fewer resources than cloud servers. A new heterogeneous edge and cloud computing paradigm combines the benefits of both. Edge servers provide close proximity services to MDs, while the cloud owns enough resources. The existence of mobile IoT devices makes it more practical to consider mobility when allocating resources of edge servers to decrease the energy consumption of the heterogeneous edge and cloud while meeting the latency needs of tasks. This work formulate a constrained energy consumption optimization problem and design a hybrid algorithm named Genetic Simulated-annealing-based particle swarm optimization (PSO) to yield a near-optimal solution. Simulation results prove that compared to genetic algorithm, PSO, simulated-annealing-based PSO, and Trex, GSPSO reduces the total energy consumption by 38.64%, 54.63%, 45.94%, and 36.21%, respectively. © 2024 IEEE.

关键词： Mobile edge computing

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Mobility and Privacy-aware Computation Offloading with Energy Harvesting in MEC-enabled Networks

Mobility and Privacy-aware Computation Offloading with Energ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Niu, Siyu Yuan, Haitao Zhai, Jiahui Zhang, Jia Zhou, MengChu College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

Many new IoT applications have emerged with the fast evolution of 5G and the Internet of Things (IoT). These applications place higher demands on network energy consumption and processing capabilities. Mobile edge computing (MEC) significantly enhances execution efficiency, while energy harvesting (EH) modules further augment the operational features of IoT devices. However, existing studies mainly concentrate on energy consumption and latency problems, often neglecting issues about user mobility and potential privacy leakage within the MEC environment. Therefore, optimizing computation offloading and resource allocation for MEC-enabled IoT networks is essential. This work proposes an innovative architecture with EH for collaborative computing between multiple mobile devices (MDs) and MEC servers. To tackle the problem, this work also proposes an advanced hybrid algorithm named Self-adaptive Bat Optimizer with Genetic operations and individual update of Grey wolf optimizer (SBG2). With SBG2, this work aims to minimize the energy consumption of MDs while providing user mobility and privacy protection. Simulation experiments show that SBG2 reduces energy consumption by 79.15%, 93.20%, and 89.58%, respectively, compared to the other three typical algorithms. © 2024 IEEE.

关键词： Differential privacy

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