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Overview of Energy Consumption Optimization in Mobile Edge Computing

Overview of Energy Consumption Optimization in Mobile Edge C...

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2021 International Conference on Electronic Communication, computer science and Technology, ECCST 2021

作者： Hu, Bingyi Gao, Jixun Hu, Yanxin Wang, Huaichen Liu, Jialei School of Computer Science and Technology Henan Polytechnic University Jiaozuo454000 China School of Computer Henan University of Engineering Zhengzhou451191 China School of Software Engineering Anyang Normal University Anyang455000 China

The energy consumption of traditional mobile network is mainly reflected by task calculation and data transmission. The edge server in mobile edge computing is placed at the edge of the network closer to the mobile terminal device. When the computing resources of the mobile terminal device are insufficient, the task can be offloaded to the edge server for processing. This paper discusses the energy consumption optimization in the Mobile Edge Computing (MEC) system, which is composed of the edge server and mobile devices at the edge of the network. Firstly, the system model of MEC is elaborated. Secondly, the energy consumption optimization for mobile devices and base stations is studied. Which oriented to the mobile device energy consumption optimization is divided into multiple users - single MEC server scenarios of energy consumption optimization and multiple users - the energy optimization of MEC server scenario, and enumerate the oriented to the base station and a new method for the optimization of energy consumption for mobile equipment energy consumption optimization method, in addition to the insufficiencies of these methods and the prospect of the future are also explained. © Published under licence by IOP Publishing Ltd.

关键词： Mobile edge computing

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Cur-CoEdge: Curiosity-Driven Collaborative Request Scheduling in Edge-Cloud Systems

Cur-CoEdge: Curiosity-Driven Collaborative Request Schedulin...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Yunfeng Zhao Chao Qiu Xiaoyun Shi Xiaofei Wang Dusit Niyato Victor C. M. Leung College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

The collaboration between clouds and edges unlocks the full potential of edge-cloud systems. Edge-cloud platform has brought about significant decentralization, heterogeneity, complexity, and instability. These characteristics have posed unprecedented challenges to the optimal scheduling problem in the edge-cloud system, including inaccurate decision-making and slow convergence. In this paper, we propose a curiosity-driven collaborative request scheduling scheme in edge-cloud systems, namely Cur-CoEdge. To tackle the challenge of inaccurate decision-making, we introduce a time-scale and decision-level interaction mechanism. This mechanism employs a small-large-time-scale scheduling learning framework, facilitating mutual learning between different decision levels. To address the challenge of slow convergence, we investigate the underlying reasons, such as the sparse reward-setting in reinforcement learning. In response, we develop a curiosity-driven collaborative exploration approach that fosters intrinsic curiosity in the cloud and simultaneously motivates dispatchers to explore the environment both individually and collectively. The effectiveness of this collaborative exploration is also supported by theoretical proof of convergence. Finally, we implement a prototype system on a network hardware system along with two real-world traces. Evaluations demonstrate significant improvements, with up to a 26% increase in time efficiency, a 40% rise in system throughput, and a 71% enhancement in convergence speed.

关键词： Decision making Collaboration Prototypes Optimal scheduling Reinforcement learning Throughput Hardware

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A Novel Logic-Based Adaptive Monitoring for Composite Edge Services

A Novel Logic-Based Adaptive Monitoring for Composite Edge S...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Zhao, Deng Zhou, Zhangbing Xue, Xiao Diao, Jin Yangui, Sami Liu, Bo Gaaloul, Walid School of Information Engineering Beijing100083 China Telecom SudParis Department of Computer Science Evry91011 France Tianjin University College of Intelligence and Computing Tianjin300072 China LAAS-CNRS University of Toulouse Toulouse31013 France Beijing University of Technology School of Software Engineering Beijing100124 China

ISBN: (纸本)9798350304855

With the wide-adoption of edge computing, the functionalities of Internet of Things (IoT) devices can be encapsulated as edge services, to facilitate domain applications through edge service compositions. Considering the capacity-fluctuating and resource-varying of IoT devices, edge service monitoring is essential to guarantee the healthy of their compositions at runtime. Current techniques focus mostly on the monitoring of atomic edge services, which, however, are inadequate for that of inter-and composite services. Besides, constraints to be monitored are usually pre-specified, although certain parameters may have to be adapted online according to execution context. To address these challenges, this paper proposes a novel logic-based adaptive monitoring mechanism, to achieve the interpretation of temporal constraints and time-dependent QoS constraints upon intra-, inter-, and composite services. Leveraging our proposed Compositional Signal Temporal Logic (CSTL) with extended compositional modalities and online parameter settings, constraints can be converted to CSTL formulae, and QoS variations and temporal violations are interpreted qualitatively and quantitatively at runtime. Extensive experiments are conducted upon publicly-available datasets, and evaluation results demonstrate that our CSTL performs better than baseline techniques in terms of expressiveness, applicability, and robustness. © 2023 IEEE.

关键词： Internet of things

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SDCM: An Efficient Camellia Oleifera Fruits Detection Algorithm in Dense Occlusion Scenes 6

SDCM: An Efficient Camellia Oleifera Fruits Detection Algori...

引用

6th International Conference on Computing and Big Data, ICCBD 2023

作者： Zhang, Youyang Chen, Qiuxia Cui, Wenhua Yang, Jinfeng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China Institute of Applied Artificial Intelligence The Guang-dong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic Shenzhen China

ISBN: (纸本)9798350317008

To realize intelligent management in the Camellia oleifera fruit industry and foster the efficient growth of its supply chain, the accurate identification and detection of Camellia oleifera fruits are essential. However, in natural environments, these fruits exhibit challenges such as clustering, mutual occlusion, and obstruction by leaves. This paper proposes a Camellia oleifera fruits detection algorithm based on an improved YOLOv7 deep convolutional neural network, which is referred to as the SDCM network model, and add the function of counting the number of fruits. We introduce an enhancement to the backbone layer of the YOLOv7 model by incorporating a module called SDCM (Shortcut and Dilation Convolution based Module). This module enriches multiscale edge information, enabling the network to extract features more effectively, thus improving detection accuracy. The experimental results show that the detection accuracy of the SDCM network model is significantly improved compared with the original YOLOv7 model, and the detection performance of the model in this paper is superior compared with other classical detection models. By enhancing the detection accuracy of the model and adding the function of fruit counting, the model in this paper can provide more reliable and accurate detection performance and the function of predicting fruit yield for the development of the Camellia oleifera fruit industry, improve the production efficiency, and reduce the artificial resources. © 2023 IEEE.

关键词： Supply chains

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A Heuristic-based Dynamic Scheduling and Routing Method for Industrial TSN Networks 10

A Heuristic-based Dynamic Scheduling and Routing Method for ...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Chen, Honglong Liu, Mindong Huang, Jing Zheng, Zhiling Huang, Weihong Xiao, Yufeng Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China Hunan Industry Polytechnic Information Center Changsha410125 China

ISBN: (纸本)9798350312461

In the industrial environment, machines often need to reflect the anomaly detection results to the total control center in time, and the general industrial network can not achieve high real-time. In order to solve such challenges, a set of protocol standards developed by IEEE802.1 working group, namely Time-sensitive Networking (TSN), has been introduced into industrial networks. TSN can provide high real-time and reliability for data transmission, where the reliability is achieved by Frame duplication and Frame Elimination (FRER). In the realization process of FRER, it is necessary to determine the source node, destination node, and multiple disjoint paths to transmit redundant data. However, the transmission of these redundant traffic may result in the delay of other flows, and then affects the user experience. Therefore, it is very important to choose excellent redundant traffic paths to ensure reliability and reduce the impact on other flows. In the existing research, there are many dynamic scheduling and routing heuristics to determine the path, but they do not consider the influence of the location of the source node on the whole route scheduling. This paper proposes an improved dynamic scheduling and routing heuristic method, which takes the source node into account in the routing selection. In the flow test experiments of different magnitudes, it is found that the total delay of all flows is reduced by 1.4%-4.5% under the same magnitude of schedulability compared with Ant Colony Optimization. © 2023 IEEE.

关键词： Heuristic algorithms

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Efficient Privacy-Preserving Multi-Functional Data Aggregation Scheme for Multi-Tier IoT System 28

Efficient Privacy-Preserving Multi-Functional Data Aggregati...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Tao, Yunting Kong, Fanyu Shi, Yuliang Yu, Jia Zhang, Hanlin Liu, Huiyi School of Software Shandong University China College of Computer Science and Technology Qingdao University China College of Information Engineering Binzhou Polytechnic China Shandong Sansec Information Technology Co. Ltd China

ISBN: (纸本)9798350300482

The proliferation of Internet of Things (IoT) devices has led to the generation of massive amounts of data that require efficient aggregation for analysis and decision-making. However, multi-tier IoT systems, which involve multiple layers of devices and gateways, face more complex security challenges in data aggregation compared to ordinary IoT systems. In this paper, we propose an efficient privacy-preserving multi-functional data aggregation scheme for multi-tier IoT architecture. The scheme supports privacy-preserving calculation of mean, variance, and anomaly proportion. The scheme uses the Paillier cryptosystem and the BLS algorithm for encryption and signature, and uses blinding techniques to keep the size of the IoT system secret. In order to make the Paillier algorithm more suitable for the IoT scenario, we also improve its efficiency of encryption and decryption. The performance evaluation shows that the scheme improves encryption efficiency by 43.7% and decryption efficiency by 45% compared to the existing scheme. © 2023 IEEE.

关键词： Internet of things

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Gated Convolutional Neural Networks for Text Classification 11th

Gated Convolutional Neural Networks for Text Classification

引用

11th International Conference on computer science and its Applications, CSA 2019 and 14th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2019

作者： Sun, Jin Jin, Rize Ma, Xiaohan Park, Joon-young Sohn, Kyung-ah Chung, Tae-sun Computer Engineering Ajou University Suwon-si Gyeonggi-do16499 Korea Republic of School of Computer Science and Software Engineering Tianjin Polytechnic University Tianjin300160 China

ISBN: (纸本)9789811593420

The popular approach for several natural language processing tasks involves deep neural networks, and in particular, recurrent neural networks (RNNs) and convolutional neural networks (CNNs). While RNNs can capture the dependency in a sequence of arbitrary length, CNNs are suitable for extracting position-invariant features. In this study, a state-of-the-art CNN model incorporating a gate mechanism that is typically used in RNNs, is adapted to text classification tasks. The incorporated gate mechanism allows the CNNs to better select which features or words are relevant for predicting the corresponding class. Through experiments on various large datasets, it was found that the introduction of a gate mechanism into CNNs can improve the accuracy of text classification tasks such as sentiment classification, topic classification, and news categorization. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Recurrent neural networks

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A Performance Investigation of Genetic Algorithm with Different Evolutionary Operators 11

A Performance Investigation of Genetic Algorithm with Differ...

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11th International Conference on Machine Intelligence Theory and Applications, MiTA 2024

作者： Li, Jian-Yu Shao, Zhao-Kun Wang, Zi-Jia Zhang, Yuhui Zhu, Ruoyu Chen, Zong-Gan Li, Tao Nankai University College of Artificial Intelligence Tianjin300350 China Hanyang University College of Electrical and Electronic Engineering Ansan15588 Korea Republic of Guangzhou University College of Software Engineering Guanzhou510006 China Dongguan University of Technology College of Computer Science and Technology Dongguan523808 China South China Normal University School of Computer Science Guangzhou510631 China Henan Normal University College of Computer and Information Engineering Xinxiang453007 China

ISBN: (纸本)9798350385670

Genetic Algorithm (GA) as a bio-inspired optimization technique, has shown significant potential for applications in many fields. This paper aims to provide a comprehensive overview of the main types of evolutionary operators in GA - selection, crossover, and mutation, which have a direct and significant influence on optimization performance. First, this study systematically summarizes the various evolutionary operators currently used in the field of GA. Subsequently, these different operators are embedded into the standard GA framework to assess their influence on algorithm performance. Last, this paper also explores the effect of setting crossover and mutation rates on GA performance, determining the optimal values of these parameters through a series of experiments. This research not only offers a comprehensive overview of the evolutionary operators in GA but also demonstrates the specific performance of different operator combinations through empirical evidence, providing valuable reference for further research and application of GA. © 2024 IEEE.

关键词： Genetic algorithms

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EPCP: An Efficient Point Cloud Classification Network with Position Fusion 19

EPCP: An Efficient Point Cloud Classification Network with P...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Liu, Yunming Wang, Jianming Ma, Haoran Sun, Yukuan Tiangong University School of Computer Science and Technology Tianjin China Tiangong University Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems Tianjin China Tiangong University School of Software Tianjin China Tiangong University Center for Engineering Intership and Training Tianjin China

ISBN: (纸本)9798350346558

Recently, deep learning on 3D point cloud data has driven rapid development in the field of autonomous driving. However, processing 3D point clouds is a challenging task. Transformers are widely used in various fields of deep learning. In the community of 3D point clouds, Point Cloud Transformer(PCT, a transformer-based framework) also achieves excellent performance on 3D point cloud tasks. However, the well-known attention mechanism benefits performance improvement while it also leads to expensive computational costs. To alleviate the problem, we propose a novel structure named efficient Point Cloud Classification Network with Position Fusion(EPCP), which uses the strategy of fusing absolute coordinates-based features and relative position information instead of the attention mechanism. By fusing relative position information, our method ensures that the output features maintain different sensitivity to specific areas, which plays a similar role as the attention mechanism. To verify the effectiveness of our method, we conduct experiments on ModelNet40 and real-world ScanObjectNN datasets. Compared with PCT, our method maintains the same accuracy on ModelNet40 and improves by 5.5% on ScanObjectNN. At the same time, our method reduces the number of parameters by 91% and FLOPs by 50% compared to PCT on the ModelNet40 dataset. © 2022 IEEE.

关键词： Classification (of information)

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Adaptive Climbing Strategy of Snake Robot Based on Compliance Control

Adaptive Climbing Strategy of Snake Robot Based on Complianc...

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Intelligent Robotics and Systems (ISoIRS), International Symposium on

作者： Huizhuo Yang Jiyuan Dong Meng Wang Shiyong Meng Lei Yang Jiashuo Xue School of Electronic and Information Engineering Tiangong University Tianjin China Department of Computer Science and Technology Tiangong University Tianjin China Department of Software Engineering Tiangong University Tianjin China

ISBN: (数字)9798350364798

ISBN: (纸本)9798350364804

Snake robots, exhibiting a high degree of redundancy, are a class of biomimetic robots capable of forming a helical rolling gait for climbing trees and pipes. The surface structure of naturally grown trees is irregular, which may hinder the snake robots from achieving optimal surface conformation. Additionally, the presence of numerous branching structures in pipes introduces unpredictability in pipe climbing. To address these challenges, we propose an adaptive climbing strategy for snake robots based on compliant control. By adjusting a pre-set elasticity coefficient, the snake robot modulates the output torque of each joint, thereby altering the target angle of the joint. This adjustment enables the snake robot to adapt to unstructured environments. We conducted an experiment on climbing curved pipes to verify the effectiveness of this method. The result demonstrate that under unknown conditions, the robustness of the snake robot’s climbing motion is enhanced by modifying the pre-set elasticity coefficient based on compliant control. This finding underscores the potential of compliant control in improving the adaptability and performance of Snake robots in complex environments.

关键词： Torque Shape Snake robots Pipelines Redundancy Adaptive algorithms Elasticity

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