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An LSTM-based distributed scheme for data transmission reduction of IoT systems

NEUROCOMPUTING 2022年第0期485卷 166-180页

作者： Fathalla, Ahmed Li, Kenli Salah, Ahmad Mohamed, Marwa F. Hunan Univ Coll Informat Sci & Engn Changsha 410082 Peoples R China Suez Canal Univ Fac Sci Dept Math Ismailia Egypt Zagazig Univ Coll Comp & Informat Zagazig Egypt Suez Canal Univ Fac Computes & Informat Ismailia Egypt

The growth of the number of connected devices in Internet of Things (IoT) systems causes a huge increase in network traffic. Thus, there is a significant demand for systems that can predict the measurements of the distributed IoT-based applications to mitigate the increasing network traffic. Existing methods utilized a distributed scheme, i.e., dual prediction schemes (DPS), to achieve this task. The idea of this scheme is based on deploying a predictive model on the data sources (i.e., sensors) and the fusion center in a distributive manner. The state-of-the-art results can be simply reached using predictive models, e.g., adaptive filters. Deep learning-based approaches are not well utilized to address this problem. In this context, we proposed a distributed scheme utilizing fog and edge computing technologies. The proposed DPS includes an LSTM predictive model to reduce the data transmission instances of connected IoT devices;it is called LSTM-DPS. In addition, we proposed an updating mechanism that updates the LSTM model according to a set of tracking parameters that observes the model behavior during the deployment due to the changes of data properties through time. The proposed updating mechanism guarantees that the deployed LSTM model is identical at the data source and fusion center. The LSTM-DPS is evaluated using two real datasets. The obtained results show that the LSTM-DPS outperforms state-ofthe-art methods in terms of communication reduction ratios. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Deep learning distributed scheme Dual prediction Edge computing IoT LSTM Transmission reduction

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A distributed scheme developed for eco-hydrological modeling in the upper Heihe River

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Science China Earth Sciences 2015年第1期58卷 36-45页

作者： YANG DaWen GAO Bing JIAO Yang LEI Hui Min ZHANG YanLin YANG HanBo CONG ZhenTao State Key Laboratory of Hydro-science and Engineering Department of Hydraulic EngineeringTsinghua University School of Water Resources and Environment China University of Geosciences Cold and Arid Regions Environmental and Engineering Research Institute Chinese Academy of Sciences

Modeling the hydrological processes at catchment scale requires a flexible distributed scheme to represent the catchment to- pography, river network and vegetation pattern. This study has developed a distributed scheme for eco-hydrological simulation in the upper Heihe River. Based on a 1 km x 1 km grid system, the study catchment is divided into 461 sub-catchments, whose main streams form the streamflow pathway. Furthermore, a 1 km grid is represented by a number of topographically similar ＂hillslope-valley＂ systems, and the hillslope is the basic unit of the eco-hydrological simulation. This model is tested with a simplified hydrological simulation focusing on soil-water dynamics and streamflow routing. Based on a 12-year simulation from 2001 to 2012, it is found that variability in hydrological behavior is closely associated with climatic and landscape condi- tions especially vegetation types. The subsurface and groundwater flows dominate the total river runoff. This implies that the soil freezing and thawing process would significantly influence the runoff generation in the upper Heihe basin. Furthermore, the runoff components and water balance characteristics vary among different vegetation types, showing the importance of coupling the vegetation pattern into catchment hydrological simulation. This paper also discusses the model improvement to be done in future study.

关键词： distributed scheme catchment discretization streamflow pathway hillslope parameterization the upper Heihe River

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distributed opinion competition scheme with gradient-based neural network in social networks

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SCIENTIFIC REPORTS 2024年第1期14卷 1-11页

作者： Feng, Zhuowen Xing, Yuru Wang, Guancheng Guangdong Ocean Univ Coll Literature & News Commun Zhanjiang 524088 Peoples R China Guangdong Ocean Univ Coll Elect & Informat Engn Zhanjiang 524088 Peoples R China

In the context of social networks becoming primary platforms for information dissemination and public discourse, understanding how opinions compete and reach consensus has become increasingly vital. This paper introduces a novel distributed competition model designed to elucidate the dynamics of opinion competitive behavior in social networks. The proposed model captures the development mechanism of various opinions, their appeal to individuals, and the impact of the social environment on their evolution. The model reveals that a subset of opinions ultimately prevails and is adopted. Key elements of social networks are quantified as parameters, with parameter variations representing the dynamics of opinions. Furthermore, a modified gradient-based neural network is designed as the evolutional law of the opinion, whose stability and convergence are confirmed by theoretical analysis. Additionally, experiments simulate real-world competitive scenarios, demonstrating practical applications for the model. This model can be widely applied to various filed in social networks, offering a new perspective for understanding and predicting competition phenomenon in complex social systems. Overall, this work provides a structured and systematic approach to understanding opinion dynamics, which greatly enhances our ability to analyze competitive behaviors and anticipate the outcomes of diverse viewpoints in social networks.

关键词： Opinion competition k-winners-take-all (k-WTA) Social network Recurrent neural network (RNN) distributed scheme

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distributed access and offloading scheme for multiple UAVs assisted MEC networks 98

Distributed access and offloading scheme for multiple UAVs a...

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98th IEEE Vehicular Technology Conference (VTC-Fall)

作者： He, Saifei Cheng, Ming Pan, Yijin Lin, Min Zhu, Wei-Ping Nanjing Univ Posts & Telecommun Sch Telecommun & Informat Engn Nanjing 210003 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada

ISBN: (纸本)9798350329285

Unmanned aerial vehicles (UAVs) have improved the capacity and coverage of wireless networks. Mobile edge computing (MEC) has provided substantial computation capability to user equipment (UEs). The integration of UAV and MEC can take advantages of both to provide flexible computation service. In UAV assisted MEC networks, delay and energy consumption are two main concerns, which are conflicting to a certain extent. This paper investigates delay and energy consumption jointly in a multiple UAVs assisted MEC network. A cost function is defined to balance the delay and the energy consumption. The user access, task offloading, and computational resource allocation are jointly considered to minimize the long-term cost. To tackle this difficult problem, we formulate the long-term problem into sequential decision problem and treat all UEs as intelligent agents. Each UE decides its access UAV, task offloading proportion, and required edge computation resource to minimize the its own cost. Moreover, the optimal task offloading proportion and required computation resource can be obtained in closed-form given user access so that the action space can be significantly reduced. Then, an adversarial multi-armed bandit based algorithm is employed at each UE and a distributed scheme is proposed to solve the joint optimization problem. Simulation results validate the effectiveness and robustness of the distributed scheme and show its superiority to benchmarks.

关键词： Adversarial multi-armed bandit distributed scheme long-term cost mobile edge computing unmanned aerial vehicle

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A distributed scheme for Image Splicing Detection

A Distributed Scheme for Image Splicing Detection

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12th International Workshop on Digital-Forensics and Watermarking (IWDW)

作者： Zhao, Xudong Wang, Shilin Li, Shenghong Li, Jianhua Lin, Xiang Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Sch Informat Secur Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9783662438862;9783662438855

In order to capture more splicing traces and to improve the robustness to anti-forensics, combining different kinds of features are adopted for image detection work in recently years. However, the combined features inevitably increase the feature dimensionality and the computational complexity. In this paper, we propose a distributed approach to reducing the computational complexity introduced by the high-dimensional features in image splicing detection. We introduce first-order noncausal model to the splicing detection work and give the distributed solution to this model. The noncausal model is split into several small tasks which are solved simultaneously by the distributed scheme. Experimental results over the public Columbia Image Splicing Detection Evaluation Dataset show that the distributed noncausal model could differentiate between splicing images and natural ones effectively.

关键词： Image splicing detection distributed scheme First-order noncausal model

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Multi-Agent Reinforcement Learning Based distributed Multi-User Scheduling and Beamforming Design in Multi-Cell Systems

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第3期74卷 4432-4444页

作者： Bai, Shaozhuang Gao, Zhenzhen Liao, Xuewen Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Shaanxi Peoples R China

In multi-cell systems, joint multi-user scheduling and beamforming (MUS-BF) design are two fundamental problems that are usually studied separately in the existing literature. In this work, we focus on the joint MUS-BF design with the goal of minimizing the network experience delay. Since the objective function is long-term and non-convex, we first equate the network experience delay with the long-term average network experience delay cost, which can be optimized by reinforcement learning techniques. Then, we propose a distributed MUS-BF scheme based on multi-agent reinforcement learning to minimize the long-term average network experience delay cost, where the adaptive multi-deep Q network (DQN) architecture is designed at each base station (BS) to find suitable MUS-BF with low computational complexity. Each BS is able to train its own multi-DQN and implement appropriate MUS-BF based on the local information. Simulation results validate that the adaptive multi-DQN architecture decreases 70% computational complexity of the proposed algorithm without compromising performance compared to the non-adaptive design. Compared to existing schemes, the proposed scheme achieves superior performance with the lowest information overhead, making it a more appealing solution for real-time MUS-BF in delay-sensitive scenarios.

关键词： Multi-cell system multi-user scheduling beam- forming design beam- forming design network experience delay distributed scheme distributed scheme multi-agent reinforcement learning multi-agent reinforcement learning multi-agent reinforcement learning

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distributed Energy Cooperation in Multicell Wireless Powered Communication Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第5期73卷 7188-7203页

作者： Li, Guang-Ju Nie, Xiaokai Huang, Yongming Yu, Wenwu Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Sch Math Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211102 Peoples R China Southeast Univ Sch Automat Key Lab Measurement & Control Complex Syst Engn Minist Educ Nanjing 210096 Peoples R China Southeast Univ Shenzhen Res Inst Shenzhen 518057 Peoples R China Southeast Univ Sch Informat Sci & Engn Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Pervas Commun Ctr Purple Mt Labs Nanjing 211111 Peoples R China

This paper investigates a general multicell wireless powered communication network, in which multiple access points (APs) provide energy to the Internet of Things devices (IoT-Ds), and the IoT-Ds utilize the harvested energy to send information back to their associated AP. Within the network, a scheme enabling information and energy cooperation among APs is proposed to realize data transmission among cells as well as mitigate the risk of energy supply-demand imbalance. Based on such cooperation scheme, APs with surplus energy can share energy with other APs with deficient power supply, and the energy efficiency maximization problem is studied for the considered network of two practical scenarios including APs with energy-storage devices (ESDs) and without ESDs. To solve the optimization problems, a novel distributed scheme is designed to jointly the optimize energy cooperation strategy and time allocation, where each AP communicates only with neighboring APs to independently update its own optimal decision. Different from the centralized methods requiring global parameters such as all channel status information (CSI) that may be unavailable, APs in the developed distributed scheme jointly undertake the computational tasks without knowing all CSI. This results in faster convergence and higher scalability for multicell networks compared to the centralized schemes. Finally, simulation examples of two scenarios are presented to validate the performance of the proposed distributed scheme.

关键词： Electrostatic discharges Throughput Wireless communication Resource management Optimization Couplings Internet of Things distributed scheme energy cooperation energy efficiency multicell WPCNs

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A distributed multiple sample testing for massive data

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JOURNAL OF APPLIED STATISTICS 2023年第3期50卷 555-573页

作者： Xie Xiaoyue Shi Jian Song Kai Chinese Acad Sci Acad Math & Syst Sci Beijing Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing Peoples R China Beijing Inst Technol Sch Management & Econ Beijing Peoples R China

When the data are stored in a distributed manner, direct application of traditional hypothesis testing procedures is often prohibitive due to communication costs and privacy concerns. This paper mainly develops and investigates a distributed two-node Kolmogorov-Smirnov hypothesis testing scheme, implemented by the divide-and-conquer strategy. In addition, this paper also provides a distributed fraud detection and a distribution-based classification for multi-node machines based on the proposed hypothesis testing scheme. The distributed fraud detection is to detect which node stores fraud data in multi-node machines and the distribution-based classification is to determine whether the multi-node distributions differ and classify different distributions. These methods can improve the accuracy of statistical inference in a distributed storage architecture. Furthermore, this paper verifies the feasibility of the proposed methods by simulation and real example studies.

关键词： distributed scheme hypothesis testing fraud detection classification

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Cluster mechanism for sensing data report using robust collaborative distributed spectrum sensing

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2022年第4期25卷 2541-2556页

作者： Mokhtar, Rania A. Saeed, Rashid A. Alhumyani, Hesham Khayyat, Mashael Abdel-Khalek, S. Taif Univ Coll Comp & Informat Technol Dept Comp Engn POB 11099 At Taif 21944 Saudi Arabia Univ Jeddah Dept Informat Syst & Technol Coll Comp Sci & Engn Jeddah Saudi Arabia Taif Univ Dept Math Coll Sci POB 11099 At Taif 21944 Saudi Arabia

In cooperative mobile communications, spectrum sensing performance may encounter difficulties, which alert with many reporting errors, especially in dense network scenarios. In such networks, the decision fusion process for cooperating users becomes very complex, which requires sensing heavy traffic that needs a large bandwidth. To enhance the reliability of robust cooperative spectrum sensing, the paper proposed a new data fusion scheme based on clustering algorithm and distributed detection, in addition to an adapted threshold based on controlled false alarm probability. The proposed algorithm is dedicated to a highly Rayleigh faded environment to improves the channel errors. The results show that the use of two stages process of distribution clusters and selection fusion node (FN)s gives 0.42 error improvement. The results of the receiver operating characteristic (ROC) curve show an improvement in both false alarms and detection probabilities. Moreover, the sensitivity is also enhanced by 0.95.

关键词： Clustering algorithm Spectrum sensing Cooperative communications distributed scheme False alarm probability Data fusion Cognitive radio Detection probability

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A distributed scheme of energy-efficient 3D-beamforming

A distributed scheme of energy-efficient 3D-beamforming

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10th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2014)

作者： Minghuan Tang Gaofeng Cui Xun Zou Weidong Wang Yinghai Zhang Information & Electronics Technology Lab Beijing University of Posts and Telecommunications Key Laboratory of Universal Wireless Communications Ministry of Education China

ISBN: (纸本)9781510804166

Energy efficiency and spectral efficiency are two important aspects of wireless system. In this paper, a distributed scheme of energy-efficient power control is addressed jointly with 3D-beamforming (3D-BF), named as energy-efficient 3D-beamforming (EE-3DBF). In consideration of the high complexity to deal with power control and 3D-BF simultaneously, EE-3DBF is divided into limited coordination downtilt constraint 3D-BF (LCDC-3DBF) and energy-efficient power control (EEPC). LCDC-3DBF is applied to add downtilt constrain to restrain inter-cell inter-ference in cell edge. EEPC is employed to choose transmit power for each base station (BS) to gain energy efficiency. Simulation result demonstrates that EE-3DBF improves both energy efficiency and the throughput of system.

关键词： 3D-Beamforming distributed scheme Energy Efficiency Power Control Spectral Efficiency Energy efficiency Power control spectral efficiency CARRIER SYSTEM Transmitter power

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