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Joint Transmit Power and Trajectory Optimization for Two-Way Multi-Hop UAV Relaying Networks

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Joint Transmit Power and Trajectory Optimization for Two-Way...

IEEE International Conference on Communications Workshops, ICC

作者： Bing Li Shengjie Zhao Rongqing Zhang Hao Zhang Hanli Wang Liuqing Yang School of Software Engineering Tongji University Shanghai China National Mobile Communications Research Laboratory Southeast University China Ministry of Education Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China School of Electronic and Information Engineer Tongji University Shanghai China Department of Electrical & Computer Engineering Colorado State University CO USA

ISBN: (数字)9781728174402

ISBN: (纸本)9781728174419

Unmanned aerial vehicle (UAV) has been regarded as a promising means to supplement ground communications. As flying relays, UAVs can be rapidly and flexibly deployed to assist data transmissions in many practical scenarios. In this paper, we investigate a two-way multi-hop UAV relaying network, where there are two ground users as sources and multiple UAVs as relays to help the two ground sources exchange information. We first provide an efficient two-way multi-hop UAV relaying pattern, which can achieve a data rate of 2/4 data packets per time slot with decode-and-forward (DF) protocol. Then, we further formulate a joint transmit power and trajectory optimization problem for the UAVs in this two-way multi-hop relaying scenario. The formulated problem is non-convex which makes it difficult to solve directly, hence we propose an iterative algorithm to obtain an approximate optimal solution. Numerical results demonstrate that our proposed network achieves significant throughput gains.

关键词： Relays Protocols Throughput Trajectory optimization Data communication

Aggregation capacity of wireless sensor networks: Extended network case

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Aggregation capacity of wireless sensor networks: Extended n...

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

作者： Cheng Wang Changjun Jiang Yunhao Liu Xiang-Yang Li Shaojie Tang Huadong Ma Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science University of Tongji Shanghai China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China TNLIST School of Software Tsinghua University China Department of Computer Science Illinois Institute of Technology Chicago IL USA Department of Computer Science Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781424499199

A critical function of wireless sensor networks (WSNs) is data gathering. While, one is often only interested in collecting a relevant function of the sensor measurements at a sink node, rather than downloading all the data from all the sensors. This paper studies the capacity of computing and transporting the specific functions of sensor measurements to the sink node, called aggregation capacity, for WSNs. It focuses on random WSNs that can be classified into two types: random extended WSN and random dense WSN. All existing results about aggregation capacity are studied for dense WSNs, including random cases and arbitrary cases, under the protocol model (ProM) or physical model (PhyM). In this paper, we propose the first aggregation capacity scaling laws for random extended WSNs. We point out that unlike random dense WSNs, for random extended WSNs, the assumption made in ProM and PhyM that each successful transmission can sustain a constant rate is over-optimistic and unpractical due to transmit power *** derive the first result on aggregation capacity for random extended WSNs under the generalized physical model. Particularly, we prove that, for the type-sensitive perfectly compressible functions and type-threshold perfectly compressible functions, the aggregation capacities for random extended WSNs with n nodes are of order Θ ((log n) -β/2-1 ) and Θ (((log n) -β/2 )/(log log n)), respectively, where β >; 2 denotes the power attenuation exponent in the generalized physical model.

关键词： Wireless sensor networks Signal to noise ratio Interference Throughput Lattices Routing PROM

Provably Convergent Federated Trilevel Learning

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arXiv 2023年

作者： Jiao, Yang Yang, Kai Wu, Tiancheng Jian, Chengtao Huang, Jianwei Department of Computer Science and Technology Tongji University China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China Shanghai Research Institute for Intelligent Autonomous Systems China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Trilevel learning, also called trilevel optimization (TLO), has been recognized as a powerful modelling tool for hierarchical decision process and widely applied in many machine learning applications, such as robust neural architecture search, hyperparameter optimization, and domain adaptation. Tackling TLO problems has presented a great challenge due to their nested decision-making structure. In addition, existing works on TLO face the following key challenges: 1) they all focus on the non-distributed setting, which may lead to privacy breach;2) they do not offer any non-asymptotic convergence analysis which characterizes how fast an algorithm converges. To address the aforementioned challenges, this paper proposes an asynchronous federated trilevel optimization method to solve TLO problems. The proposed method utilizes µ-cuts to construct a hyper-polyhedral approximation for the TLO problem and solve it in an asynchronous manner. We demonstrate that the proposed µ-cuts are applicable to not only convex functions but also a wide range of non-convex functions that meet the µ-weakly convex assumption. Furthermore, we theoretically analyze the non-asymptotic convergence rate for the proposed method by showing its iteration complexity to obtain ϵ-stationary point is upper bounded by (Formula Presented). Extensive experiments on real-world datasets have been conducted to elucidate the superiority of the proposed method, e.g., it has a faster convergence rate with a maximum acceleration of approximately 80%. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

Greedy signal space recovery algorithm with overcomplete dictionaries in compressive sensing

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arXiv 2019年

作者： Zhu, Jianchen Zhao, Shengjie Shi, Qingjiang Arce, Gonzalo R. College of Electronics and Information Engineering Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China School of Software Engineering Tongji University Shanghai201804 China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

Compressive Sensing (CS) is a new paradigm for the efficient acquisition of signals that have sparse representation in a certain domain. Traditionally, CS has provided numerous methods for signal recovery over an orthonormal basis. However, modern applications have sparked the emergence of related methods for signals not sparse in an orthonormal basis but in some arbitrary, perhaps highly overcomplete, dictionary, particularly due to their potential to generate different kinds of sparse representation of signals. To this end, we apply a signal space greedy method, which relies on the ability to optimally project a signal onto a small number of dictionary atoms, to address signal recovery in this setting. We describe a generalized variant of the iterative recovery algorithm called Signal space Subspace Pursuit (SSSP) for this more challenging setting. Here, using the Dictionary-Restricted Isometry Property (D-RIP) rather than classical RIP, we derive a low bound on the number of measurements required and then provide the proof of convergence for the algorithm. The algorithm in noisy and noise-free measurements has low computational complexity and provides high recovery accuracy. Simulation results show that the algorithm outperforms best compared with the existing recovery algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Compressed sensing

An Improved Adaptive Exponential Smoothing Model for Short-term Travel Time Forecasting of Urban Arterial Street

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Acta Automatica Sinica 2008年第11期34卷 1404-1409页

作者： Zhi-Peng LI Hong YU Yun-Cai LIU Fu-Qiang LIU Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 P. R. China Intelligent Transportation System Department Shanghai Electrical Apparatus Research Institute Shanghai 200063 P. R. China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 P. R. China

Short-term forecasting of travel time is essential for the success of intelligent transportation system. In this paper, we review the state-of-art of short-term traffic forecasting models and outline their basic ideas, related works, advantages and disadvantages of each model. An improved adaptive exponential smoothing (IAES) model is also proposed to overcome the drawbacks of the previous adaptive exponential smoothing model. Then, comparing experiments are carried out under normal traffic condition and abnormal traffic condition to evaluate the performance of four main branches of forecasting models on direct travel time data obtained by license plate matching (LPM). The results of experiments show each model seems to have its own strength and weakness. The forecasting performance of IASE is superior to other models in shorter forecasting horizon (one and two step forecasting) and the IASE is capable of dealing with all kind of traffic conditions.

关键词： Travel time short-term forecasting license plate matching (LPM) exponential smoothing

Distributed Computation Offloading using Deep Reinforcement Learning in Internet of Vehicles

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Distributed Computation Offloading using Deep Reinforcement ...

IEEE International Conference on Communications in China (ICCC)

作者： Chen Chen Zheng Wang Qingqi Pei Ci He Zhibin Dou State Key Laboratory of Integrated Services Networks Xidian University Xi'an P.R.China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing China The Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education Shanghai China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang China

ISBN: (数字)9781728173276

ISBN: (纸本)9781728173283

In this paper, we first take the moving vehicles as a RP (resource pool), by which we proposed a distributed computation offloading scheme to fully utilize the available resources and reduce task execution time in I0V (Internet of Vehicles). After that, we divide a complex task into many sub-tasks and indicate that how to assign these small tasks to satisfy the task execution time in RP is a NP problem. The executing time of a task is modeled as the longest calculation time among all small tasks, which is actually a min-max problem. For a dynamically vehicular environment, a distributed computing offloading strategy based on deep reinforcement learning is proposed to find the best offloading scheme to minimize the execution time of a task. Numerical results demonstrate that our scheme is better than Partial Flooding Algorithm and can make full use of the available computing resources of surrounding vehicles by considering the mobility of vehicles, the delay of communication transmission, and the separability of the tasks, thus greatly reducing the execution time of the computing tasks.

关键词： Computational modeling Simulation Reinforcement learning User experience Delays Internet Task analysis

MVC-VPR: Mutual Learning of Viewpoint Classification and Visual Place Recognition

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arXiv 2024年

作者： Gu, Qiwen Wang, Xufei Zhang, Fenglin Zhao, Junqiao Tao, Siyue Ye, Chen Feng, Tiantian Jiang, Changjun Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The Shanghai Research Institute for Intelligent Autonomous System Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China School of Surveying and Geo-Informatics Tongji University Shanghai China

Visual Place Recognition (VPR) aims to robustly identify locations by leveraging image retrieval based on descriptors encoded from environmental images. However, drastic appearance changes of images captured from different viewpoints at the same location pose incoherent supervision signals for descriptor learning, which severely hinder the performance of VPR. Previous work proposes classifying images based on manually defined rules or ground truth labels for viewpoints, followed by descriptor training based on the classification results. However, not all datasets have ground truth labels of viewpoints and manually defined rules may be suboptimal, leading to degraded descriptor performance. To address these challenges, we introduce the mutual learning of viewpoint self-classification and VPR. Starting from coarse classification based on geographical coordinates, we progress to finer classification of viewpoints using simple clustering techniques. The dataset is partitioned in an unsupervised manner while simultaneously training a descriptor extractor for place recognition. Experimental results show that this approach almost perfectly partitions the dataset based on viewpoints, thus achieving mutually reinforcing effects. Our method even excels state-of-the-art (SOTA) methods that partition datasets using ground truth labels. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

Adaptive multilevel meshes for cloth simulation

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Journal of Information and Computational Science 2014年第11期11卷 3865-3872页

作者： Zhao, Jing Pei, Shanshan Lv, Mengya Li, Ying Tang, Yong Yang, Meiling Zhang, Mingmin Pan, Zhigeng The College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of HeBei Province Qinhuangdao China The College of Computer Science and Technology Zhejiang University Hangzhou China Institute of Service Engineering Hangzhou Normal University Hangzhou China

A novel method based on multilevel meshes is proposed to simulate the cloth draping process rapidly on a complex model. Considering of the difficulty of computing the realtime collision between cloth and complex models by normally methods, the draping process of cloth is divided into several phases and at each phase the mesh size is smaller than its previous one. The optimized 1-to-4 split is used to drive the rapid changes in the grid hierarchy and to simplify the calculations. We choose the widely used ellipsoid as the bounding cell to enclose the complex models in the environment. The ellipsoid avoids the complex structure of hierarchical bounding box and eliminates the impossible collision model area rapidly. Experiments demonstrate that this method can accelerate the draping process of cloth and decrease the number of iterations and the calculation of collision detection. Further mesh refinement improves the reality of cloth when it reaches the final position and ensures the realism of the simulation in real time. ©2014 Binary Information Press

关键词： Mesh generation

Data analytics for fog computing by distributed online learning with asynchronous update

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arXiv 2019年

作者： Li, Guangxia Zhao, Peilin Lu, Xiao Liu, Jia Shen, Yulong Shaanxi Key Laboratory of Network and System Security Xidian University China State Key Laboratory of Integrated Service Networks Xidian University China School of Computer Science and Technology Xidian University China Tencent AI Lab Tencent Inc. China Department of Electrical and Computer Engineering University of Alberta Canada

—Fog computing extends the cloud computing paradigm by allocating substantial portions of computations and services towards the edge of a network, and is, therefore, particularly suitable for large-scale, geo-distributed, and data-intensive applications. As the popularity of fog applications increases, there is a demand for the development of smart data analytic tools, which can process massive data streams in an efficient manner. To satisfy such requirements, we propose a system in which data streams generated from distributed sources are digested almost locally, whereas a relatively small amount of distilled information is converged to a center. The center extracts knowledge from the collected information, and shares it across all subordinates to boost their performances. Upon the proposed system, we devise a distributed machine learning algorithm using the online learning approach, which is well known for its high efficiency and innate ability to cope with streaming data. An asynchronous update strategy with rigorous theoretical support is applied to enhance the system robustness. Experimental results demonstrate that the proposed method is comparable with a model trained over a centralized platform in terms of the classification accuracy, whereas the efficiency and scalability of the overall system are improved. Copyright © 2019, The Authors. All rights reserved.

关键词： Fog computing