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Joint Optimization of Multi-UAV Deployment and Computational Offloading for Multi-Point Streaming Tasks

Joint Optimization of Multi-UAV Deployment and Computational...

引用

IEEE Conference on Wireless Communications and networking

作者： Jianlei Xu Yijun Guo Jianjun Hao Zijing Chen Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Multiple unmanned aerial vehicles (UAVs) computation offloading plays a major role in the sixth generation (6G) mobile networks for resolving conflicts between computation-intensive or time-sensitive tasks and sensor nodes (SNs) with limited capabilities. Emerging artificial intelligence businesses such as multimodal data confusion and multi-video streams processing bring new challenges to UAV-aided communication and computation networks due to their multi-point and streaming characteristics. This paper focuses on a multi-UAV aided multi-point streaming tasks scenario where the SNs generate subtasks comprising various workflows randomly in a service period. We model the sequential relationship between multi-point tasks and time constraints for different workflows and aim to minimize the average energy consumption of all SNs while satisfying different time constraints by jointly optimizing the UAV-SN association policy and UAV deployment locations. To address the non-convexity problem, we decompose the problem into two subproblems, which we then solve alternately with the Markov approximation algorithm and convex optimization until convergence. Numerical results show that the proposed algorithm can effectively lower the average energy consumption of all SNs by a maximum of 35.6% compared to other schemes while maintaining a successful service ratio of 93%.

关键词： Energy consumption Computational modeling Autonomous aerial vehicles Approximation algorithms Numerical models Time factors Task analysis

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Efficient Data Collection Scheme based on Information Entropy for Vehicular Crowdsensing

Efficient Data Collection Scheme based on Information Entrop...

引用

2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022

作者： Liu, Chuanwen Wang, Luhan Wen, Xiangming Liu, Luning Zheng, Wei Lu, Zhaoming Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing China

ISBN: (数字)9781665426718

ISBN: (纸本)9781665426718

Vehicular crowdsensing system plays an important role in monitoring the dynamic characteristics of real environment with the assistance of mobile edge computing (MEC). However, connected vehicles (CVs) continuously generate a huge amount of sensed data, which causes severe data redundancy and considerable communication overhead. To tackle the above challenge, we propose an efficient data collection scheme for vehicular crowdsensing to mitigate data redundancy and communication overhead while improving data quality. Particularly, we design a sensed data preprocessing mechanism to reduce data volume and guarantee data quality. Besides, a grid selection algorithm is proposed to select the regions with abundant information by taking advantage of information entropy theory. Then we design an online collecting parameter adjustment algorithm, which adjusts collecting parameters according to the result of grid selection algorithm and current data redundancy, thus communication overhead can be reduced. Finally, extensive simulations are conducted to evaluate the performance of proposed scheme. The simulation results demonstrate that our proposed scheme can significantly reduce communication overhead and data redundancy while meeting the data quality requirement of vehicular crowdsensing. © 2022 IEEE.

关键词： Data acquisition

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Visual Tracking Based on Siamese Neural network with Non-local Attention network 8

Visual Tracking Based on Siamese Neural Network with Non-loc...

引用

8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Li, Liying Wu, Muqing Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing China

ISBN: (纸本)9781665450515

In recent years, Siamese network has been widely studied due to its outperforming results in terms of speed and accuracy. However, how to effectively deal with the influence of large appearance changes, complex backgrounds and distractors in the tracking process is still a problem we need to face. In this paper, we propose a Siamese Neural network with Non-local Attention network, referred to as SiamNA. The proposed network consists of two parts. One is the backbone network based on Non-local attention module and the other is the Region Proposal network (RPN) based on adaptive multi-feature fusion. We find that the attention module can effectively capture the dependencies between elements in the sequence. We place this module after the backbone network to process the extracted features, which can effectively improve the target recognition ability and improve its robustness. We conduct experiments on four benchmarks including GOT-10k, VOT2016, OTB100 and VOT2018, and the results demonstrate that the proposed method is effective for improving accuracy and robustness. © 2022 IEEE.

关键词： Computer vision

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A multipath-assisted localization algorithm under 5G-based opensource testbed 9

A multipath-assisted localization algorithm under 5G-based o...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Lu, Yuye Zheng, Wei Chu, Xinghe Lu, Zhaoming Hu, Ruoqian Li, Meiling Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Taiyuan University of Science and Technology Taiyuan China

ISBN: (纸本)9798350317251

Indoor localization services are playing an important role in a variety of emerging industries. Traditional positioning methods suffer from insufficient accuracy or high deployment costs in indoor environments, and with the rapid development of cellular mobile communication systems, positioning under cellular networks has been more widely used due to its low cost and easy deployment. In the indoor environment, the number of available base stations is limited, and positioning by direct radius information provided by only one base station suffers from insufficient accuracy. In this paper, we designed a method for indoor localization based on cellular networks, which considering the localization information carried by the multi-path, to verify the performance of this algorithm in practical scene. We implement this algorithm in a 5G-based testbed based on software-defined radio (SDR) technologies. The results show that our algorithm can meet the requirements of indoor cases defined in 3GPP 38.855. © 2023 IEEE.

关键词： Testbeds

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3D Pose Estimation by Synthesizing Motions 8

3D Pose Estimation by Synthesizing Motions

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zhuang, Xin Wu, Muqing Zhao, Min Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing China

ISBN: (纸本)9781665450515

This paper addresses the problem of the insufficient generalization ability of 3D pose estimator to a single dataset. Existing pose estimators have different performance on different datasets. We attribute this to the insufficient diversity of 2D-3D pose pairs in the dataset. To this end, we propose MotionEnhance, an auto-augmentation framework that generates synthetic human 3D pose by learning the distribution of poses in the training set and uses them to fine-tune a 3D pose estimation. MotionEnhance introduces a human motion generator that synthesizes 3D pose with the inferred bone length and bone rotation and develops a discriminator accordingly to ensure the rationality of the action. The motion generator is based on Variational Auto-Encoder model to generate bone lengths and rotations. MotionEnhance learned the 3D pose distribution in the dataset to create the synthetic 3D pose and corresponding 2D coordinates. Motio- nEnhance is commonly used for different 3D pose estimators. Experiments show that the estimation error is reduced by 2.1mm MPJPE on the H3.6M dataset and by lO.3mm MPJPE on the 3DHP dataset after adding the MotionEnhance framework. © 2022 IEEE.

关键词： Signal encoding

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Secure Data Storage system Based on Consortium Blockchain and Hybrid Consensus Mechanism in Smart Grid 8

Secure Data Storage System Based on Consortium Blockchain an...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Hao, Wanyu Wu, Muqing Zhao, Min Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing China

ISBN: (纸本)9781665450515

In order to realize safe, reliable, and efficient smart grids, Wireless Sensor networks (WSNs) are widely deployed to collect and analyze grid data to monitor power grids. However, in the existing smart grids, sensing data in the WSNs are needed to be uploaded to a trusted central node. This centralized storage method is prone to security problems such as single point failure, data leakage and data tampering due to malicious attacks on the central node. To deal with these problems, this paper proposes a smart grid data security storage system. Use distributed storage blockchain technology to change the security issues of traditional centralized storage systems. In order to reduce network energy consumption, several data acquisition base stations are pre-selected in the network as nodes in the distributed network using the consortium blockchain technology. In order to improve consensus efficiency and ensure consensus security, the optimal grouping algorithm and randomly grouping algorithm are used to group nodes, the raft consensus mechanism is used within the group, and the Practical Byzantine Fault Tolerance (PBFT) consensus mechanism is used between groups. The security analysis shows that compared with the traditional centralized storage system, the storage system proposed in this paper has the advantages of safety, reliability and low energy consumption. © 2022 IEEE.

关键词： Blockchain

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Optimization of SVM and ANN Based on GAPA and Its Application in Short-Term Load Forecasting 6

Optimization of SVM and ANN Based on GAPA and Its Applicatio...

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6th IEEE International Conference on Energy Internet, ICEI 2022

作者： Zhang, Jingyi Wang, Yueting Jing, Wenpeng Lu, Zhaoming Wen, Xiangming Liu, Yong Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing China

ISBN: (纸本)9781665493277

Widespread employment of renewable energy such as wind and solar pushes power grids to move towards comprehensive data and predictive analysis. At present, a large number of researches have been conducted especially on machine learning methods to achieve load forecast. However, premature convergence and redundant iteration are two major defects of existing machine learning-based load forecasting methods, resulting in poor prediction effect and high time consumption. In this paper, a novel combined intelligent optimization algorithm based on genetic algorithm (GA), artificial fish swarm algorithm (AFSA) and particle swarm optimization (PSO) is proposed for optimizing machine learning-based load forecasting models. By replacing GA's mutation process with AFSA operator and PSO operator, the proposed algorithm named GA-AFSA-PSO Algorithm (GAPA) enhances both global search ability and local search ability, leading to its high prediction accuracy and fast convergence speed. To validate its effectiveness, GAPA is applied to the optimization of support vector machine (SVM) and artificial neural network (ANN) to predict one-day ahead load data. Moreover, two different sets of comparative tests are carried out to confirm the advantages of GAPA. The simulation results illustrate that, compared with GA, AFSA, PSO, AFSA-GA and GA-PSO, GAPA brings forth advancement in prediction accuracy, convergence rate and global search ability. © 2022 IEEE.

关键词： Genetic algorithms

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Multi-feature Fusion and Non-Local Operation for Vehicle Re-identification 8

Multi-feature Fusion and Non-Local Operation for Vehicle Re-...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zhang, Hongyi Wu, Muqing Zhao, Min Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing China

ISBN: (纸本)9781665450515

As one of the most important tasks in the computer vision, vehicle re-identification aims to retrieve and identify the same vehicle under different surveillance cameras, which plays a key role in urban road traffic safety and intelligent traffic management system. However, the large intra-class difference and high inter-class similarity are still main challenges, as well as the diversity in lighting conditions, camera's shooting angle, and occlusion degrees. In order to further improve the average accuracy and algorithm performance, this paper proposes a vehicle re-identification algorithm based on multi-feature fusion and non-local operation. We embed non-local operation into the ResNet50 network, and employ feature slicing and reorganization to obtain multiple feature branches. Besides, learning rate warm-up and cosine annealing scheduler are also used. The experimental results show that our proposed method achieves higher accuracy on two commonly used datasets VeRi-776 and VehicleID. © 2022 IEEE.

关键词： Vehicles

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MIMO Radar Aided Beam Selection in Millimeter Wave V2I Communications 8

MIMO Radar Aided Beam Selection in Millimeter Wave V2I Commu...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Li, Dianang He, Xinxin Lv, Jie Wang, Tao Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9781665450515

The multiple-in-multiple-out (MIMO) radar is deployed to assist the millimeter wave (mmWave) communication with low beam training overhead in vehicle-to-infrastructure (V2I), which has attracted increasing attention. This paper proposes a structured weighted radar aided beam selection. To be specific, since the spatial information provided by radar involves inevitable inaccuracy due to the angle deviation, we calculate the probability distribution of the codewords based on the deviation angle and use it for designing the training codebook. Furthermore, beam selection is formulated as prior weighted sparse signal recovery problem, where the prior structured-weight for communication is determined by the spatial congruence with radar channel, and the difference between two frequency bands is derived to correct the weight. The resulting prior structured weight is further deployed to the orthogonal matching pursuit algorithm to enhance its performance. Simulation results demonstrate that the proposed method provides the efficient and reliable beam selection performance of the mobility mmWave channel with fewer training overhead. © 2022 IEEE.

关键词： Millimeter waves

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Semantic-driven Computation Offloading and Resource Allocation for UAV-assisted Monitoring system in Vehicular networks 48

Semantic-driven Computation Offloading and Resource Allocati...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Sun, Xin Chen, Jiujiu Guo, Caili Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing 100876 China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

In the vehicular networks monitoring scenario where the unmanned aerial vehicle (UAV) assists the vehicle data collection and the edge-cloud server cooperates to complete the visual intelligent task (e.g. object detection), a large amount of video data needs to be transmitted. For task-oriented communication systems, traditional resource allocation schemes mainly focus on the network performance or user experience of video transmission, without considering the impact of semantic content on task performance. In this paper, we propose a semantic-driven computation offloading and resource allocation scheme, namely semantic-driven CO&RA. In the offloading decision, we propose a convolutional neural network (CNN) segmentation scheme, which makes full use of computing resources and better completes intelligent tasks. At the same time, facing the complex resource allocation problem, we design a multi-agent deep Q-network (DQN) algorithm. Last, the experimental results show that our proposed scheme has more advantages in the optimization of multiple objectives of latency, energy consumption and task performance. © 2022 IEEE.

关键词： Semantics

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