2022 3rd Information Communication Technologies Conference (ICTC)最新文献_第2页

Time and Energy optimization Scheme of Task Offloading for Single-Cell MEC-D2D Networks 单cell MEC-D2D网络任务卸载的时间和能量优化方案

2022 3rd Information Communication Technologies Conference (ICTC) Pub Date : 2022-05-06 DOI: 10.1109/ictc55111.2022.9778638

Zhiying Peng, Ju Liu, Zheng Dong, Zhichao Gao, Qian Zhang

{"title":"Time and Energy optimization Scheme of Task Offloading for Single-Cell MEC-D2D Networks","authors":"Zhiying Peng, Ju Liu, Zheng Dong, Zhichao Gao, Qian Zhang","doi":"10.1109/ictc55111.2022.9778638","DOIUrl":"https://doi.org/10.1109/ictc55111.2022.9778638","url":null,"abstract":"With the advent of the era of the Internet of Things (IoT), massive amounts of data generated by computationally intensive mobile devices have brought a huge burden on network bandwidth resources. To tackle it, both device-to-device (D2D) and multi-access edge computing (MEC) technologies can further improve the computation capability of cellular networks. In this paper, we consider a single-cell wireless network based on MEC- D2D technology to maximize the utilization of computational resources of the edge server while minimizing the average task completion time. To that end, we minimize the offloading energy consumption of users when the task is indivisible. More specifically, we propose two computation offloading schemes by solving the computational resources allocation problem and the task offloading strategy of the edge server. We formulate the first optimization scheme of joint average time and resource allocation (JATRA) to minimize the average task completion time which is formulated by a 0-1 integer programming problem. By seeking the factor of computation resource allocation, the joint average time and energy consumption (JATEC) optimization scheme is carried out to minimize the energy consumption of task offloading. As the result evaluated by numerous simulations shown, the schemes we present significantly reduce the average task completion time and energy consumption compared with the baseline schemes.","PeriodicalId":123022,"journal":{"name":"2022 3rd Information Communication Technologies Conference (ICTC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123698001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Improved DCT-Based Channel Estimation Algorithm with Channel PDP Modeling 一种改进的基于dct的信道估计算法和信道PDP建模

2022 3rd Information Communication Technologies Conference (ICTC) Pub Date : 2022-05-06 DOI: 10.1109/ictc55111.2022.9778367

Junzi Luo, Dongming Wang

引用次数: 2

Evolutionary Game of Supply Chain Alliance Based on Blockchain 基于区块链的供应链联盟演化博弈

2022 3rd Information Communication Technologies Conference (ICTC) Pub Date : 2022-05-06 DOI: 10.1109/ictc55111.2022.9778845

Chenglong Cao, Xiaoling Zhu

引用次数: 0

Sparse Signal Detection and Fingerprint Feature Recognition Based on Fast 2D DFRFT 基于快速二维DFRFT的稀疏信号检测与指纹特征识别

2022 3rd Information Communication Technologies Conference (ICTC) Pub Date : 2022-05-06 DOI: 10.1109/ictc55111.2022.9778395

Jun-gang Yang, Jinshun Shen

引用次数: 0

ICTC 2022 Cover Page ICTC 2022封面

2022 3rd Information Communication Technologies Conference (ICTC) Pub Date : 2022-05-06 DOI: 10.1109/ictc55111.2022.9778368

引用次数: 0

A Novel Method for Studying 3D-MANET Capacity Based on Zipf’s Law 基于Zipf定律的3D-MANET容量研究新方法

2022 3rd Information Communication Technologies Conference (ICTC) Pub Date : 2022-05-06 DOI: 10.1109/ictc55111.2022.9778767

Zairan Cheng, Ying Liu

{"title":"A Novel Method for Studying 3D-MANET Capacity Based on Zipf’s Law","authors":"Zairan Cheng, Ying Liu","doi":"10.1109/ictc55111.2022.9778767","DOIUrl":"https://doi.org/10.1109/ictc55111.2022.9778767","url":null,"abstract":"Emerging three-dimensional (3D) mobile ad hoc networks paradigms such as flying ad hoc network and 3Dvehicular ad hoc network have contributed significantly to various fields. Accurately characterizing the throughput capacity of 3D-MANET is becoming increasingly critical. For the convenience of model solving, the prior work has typically been analyzed based on the uniform or specific probability distribution of network nodes, but ignoring such the node distribution is difficult to describe the real application scene. With was formerly different, in this paper, we propose a cell-gridded 3D network model in which movement and distribution of nodes follow Zipf’s law with exponent $varphi$ (many scholars have proved this law to conform to the movement law of the actual world). Moreover, our model can cover a variety of distribution scenarios with changes in $varphi$ value. We derived the packet delivery rate and network throughput capacity scaling law under the two-hop relay routing algorithm with the help of Zipf’s law and classical capacity analysis theories and discussed the impact of $varphi$ on capacity. Additionally, our research findings indicate that if we can fully utilize network users’ movement and distribution status information in different areas and reasonably divide network cells, it will be expected to improve network throughput capacity performance.","PeriodicalId":123022,"journal":{"name":"2022 3rd Information Communication Technologies Conference (ICTC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124413371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Fragmentation-Aware Load-Balanced RMSCA Algorithm in Space-Division Multiplexing Elastic Optical Networks 空间复用弹性光网络中碎片感知负载均衡RMSCA算法

2022 3rd Information Communication Technologies Conference (ICTC) Pub Date : 2022-05-06 DOI: 10.1109/ictc55111.2022.9778371

Qiuwang Lan, Yifeng Cai, Shangjun Chen, Xin Chen, J. Shen

引用次数: 1

Research on Autonomous Decision-Making of UCAV Based on Deep Reinforcement Learning 基于深度强化学习的无人飞行器自主决策研究

2022 3rd Information Communication Technologies Conference (ICTC) Pub Date : 2022-05-06 DOI: 10.1109/ictc55111.2022.9778652

L. Wang, Hongtao Wei

引用次数: 2

Rolling Bearing Health State Assessment Based on K-Means and Ensemble HMM 基于k均值和集合HMM的滚动轴承健康状态评估

2022 3rd Information Communication Technologies Conference (ICTC) Pub Date : 2022-05-06 DOI: 10.1109/ictc55111.2022.9778234

Xinmeng Cai, Longsheng Cheng, Qi-Feng Yao

引用次数: 0

Short-Term PV Output Prediction Method Based on Feature Analysis and Multi-model Fusion 基于特征分析和多模型融合的光伏短期产量预测方法

2022 3rd Information Communication Technologies Conference (ICTC) Pub Date : 2022-05-06 DOI: 10.1109/ictc55111.2022.9778479

Yuansheng Song, Teng Zhao, Ziru Niu, Jin Du, Fanghui Jiang, Fangyue Zhai

{"title":"Short-Term PV Output Prediction Method Based on Feature Analysis and Multi-model Fusion","authors":"Yuansheng Song, Teng Zhao, Ziru Niu, Jin Du, Fanghui Jiang, Fangyue Zhai","doi":"10.1109/ictc55111.2022.9778479","DOIUrl":"https://doi.org/10.1109/ictc55111.2022.9778479","url":null,"abstract":"In the future, photovoltaic power generation will usher in a larger market. The rapid development of artificial intelligence provides a new solution for photovoltaic power generation forecasting. In this paper, combined with the current cutting-edge theoretical research in the field of artificial intelligence, a short-term photovoltaic power generation power prediction method based on multi-model fusion Stacking ensemble learning is proposed. Random Forest and correlation coefficient feature importance analysis were used to determine the important climate factors as the input characteristics of the prediction model. On this basis, multiple machine learning models with good single prediction performance and certain differences are integrated into the Stacking ensemble learning photovoltaic output prediction model. The base learner of the model includes XGBoost tree ensemble algorithm and GRU neural network algorithm. To prevent overfitting, the meta-learner consists of the LSSVM algorithm with relatively simple complexity and high accuracy. The calculation example uses the photovoltaic power and climate data provided by the Australian Solar Energy Research and Development Center to verify the effectiveness of the algorithm. The prediction results show that the Stacking model has higher prediction accuracy than the traditional single model which can better track the fluctuation of output power.","PeriodicalId":123022,"journal":{"name":"2022 3rd Information Communication Technologies Conference (ICTC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126698512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1