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Communication-Efficient decentralized Subspace Estimation

IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2022年第3期16卷 516-531页

作者： Jiao, Yuchen Gu, Yuantao Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

Machine learning techniques have been widely used in communication systems because of their superior performance. Among them, the classical machine learning algorithm Principal Component Analysis (PCA) aims to estimate the principal subspace of the received signals, and thus is also known as subspace estimation. It is often used in Direction of Arrival (DoA) tasks. Recently, the widespread deployment of networks has motivated researchers to solve the problem of subspace estimation in decentralized settings. An important concern in the design of decentralized algorithms is the communication complexity, because communication consumes much energy, while the sensors in the network are generally energy-limited. Specifically, communication complexity refers to the minimum amount of transmitted variables to obtain an estimate with an error smaller than epsilon. For existing algorithms, this complexity is O(log(2)(1/epsilon)). In this paper, we improve this complexity to be O(log(1/epsilon)) by designing a new algorithm named decentralized Subspace Estimation (DSE). Such improvement is achieved by using the gradient tracking technique. We theoretically analyze the influence of the network connectivity and the eigen-gap of the data on communication complexity. In addition, we use DSE to solve the DoA estimation problem. Experiments verify the effectiveness of the algorithm and the correctness of the theoretical result.

关键词： decentralized algorithm subspace estimation communication complexity average consensus wireless sensor networks

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Game Theoretic-Based Joint Logistics and Energy Service Utilization Strategy for Electric Ships Considering Ship to Port Behavior

Game Theoretic-Based Joint Logistics and Energy Service Util...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Lin, Annie Wen, Shuli Zhu, Miao Ding, Zhaohao Shanghai Jiao Tong University Key Laboratory of Control of Power Transmission and Conversion Ministry of Education Shanghai China Shanghai Jiao Tong University Yazhou Bay Institute of Deepsea SCI-TECH Hainan China North China Electric Power University School of Electrical and Electronic Engineering Beijing China

ISBN: (纸本)9781665471640

Maritime electrification is reshaping the interaction between ships and ports from pure logistics to an integrated energy-logistics coupling. However, the proliferation of electrified ships (ESs) connected to ports, if not managed effectively, can result in port congestion and inefficient resource utilization. Unlike land-based electric cars, which only require energy services when parked at charging stations, ESs docking at ports necessitate the port call service including both energy service and logistics service for cargo handling. To address these issues, this paper proposes a generalized Nash game framework to coordinate energy and logistics service utilization strategy for ESs. Each shipowner aims to minimize their own economic cost, while satisfying their specific energy and logistics demands and adhering to global coupling constraints. Notably, ESs can also participate in arbitrage by selling electricity back to the port during port stay, which can be referred to as ship to port behavior (S2P). Moreover, the existence and uniqueness of the generalized Nash equilibrium are proven. In addition, due to selfishness and privacy concerns, shipowners are reluctant to disclose private constraints and personal objectives to others, so a decentralized algorithm is developed to seek the generalized Nash equilibrium. Simulation results demonstrate the effectiveness and efficiency of the proposed method. © 2023 IEEE.

关键词： decentralized algorithm electric ship (ES) energy and logistics coordination generalized Nash equilibrium port call service ship to port (S2P)

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decentralized Text-Based Person Re-Identification in Multi-Camera Networks

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IEEE ACCESS 2024年 12卷 172125-172148页

作者： Agyeman, Rockson Rinner, Bernhard Univ Klagenfurt Inst Networked & Embedded Syst A-9020 Klagenfurt Am Worthersee Austria

Text-based person re-identification (re-ID) is an emerging research domain in multi-camera surveillance systems. It involves identifying individuals across different camera views using textual descriptions as queries. Although text-based person re-ID holds significant potential for surveillance systems, its practical applications are limited by the high computational demands of existing algorithms. This is because most state-of-the-art algorithms prioritize identification accuracy over resource efficiency. Therefore, surveillance systems based on existing algorithms rely heavily on centralized architectures, where a central application server aggregates videos from camera nodes, generates galleries from the videos, and performs person identification. However, such centralized systems face challenges in large-scale deployments, including scalability issues, high bandwidth utilization, and processing bottlenecks. This paper presents a decentralized approach to text-based person re-ID to overcome these limitations. First, it introduces U-TextReIDNet, a resource-efficient model designed to identify persons in multi-person images on the NVIDIA Jetson Nano embedded board. U-TextReIDNet achieves Top-1 accuracies of 54.02% on the CUHK-PEDES dataset and 38.45% on the RSTPReid dataset. With only 38.77 million parameters, U-TextReIDNet is significantly smaller than most existing text-based person re-ID models. Using U-TextReIDNet, we implement a decentralized system that distributes the person identification task across camera nodes, transmitting only videos containing persons of interest to the command station. Additionally, we developed a prototype of this decentralized system, and conducted performance and usability tests using real human subjects. The prototype successfully performs real-time person re-ID, reduces bandwidth utilization, improves system scalability, and eliminates processing bottlenecks.

关键词： Cameras Visualization Identification of persons Videos Servers Feature extraction Computer architecture Surveillance Scalability Privacy decentralized algorithm multi-camera networks multi-modal re-identification model real-time video analytics text-based person re-identification

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Efficient decentralized Multirobot Task Allocation for Maximizing Total Assignments 22

Efficient Decentralized Multirobot Task Allocation for Maxim...

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22nd International Conference on Control, Automation and Systems (ICCAS)

作者： Wang, Shengli Liu, Youjiang Qiu, Yongtao Zhou, Jie Tsinghua Univ Dept Engn Phys Beijing 100084 Peoples R China China Acad Engn Phys Inst Elect Engn Mianyang 621900 Sichuan Peoples R China

ISBN: (纸本)9788993215243

This paper addresses the decentralized task allocation problem of multirobot systems, in which the objective is to maximize the total task assignments, i.e., the number of tasks that can be successfully executed by all robotic vehicles under the time constraints of tasks and battery limits of vehicles. Based on the state-of-the-art performance impact (PI) algorithm, a novel extension named PI for minimizing traveling time (PI-minTravel) is proposed in this paper. With the proposed PI-minTravel, tasks that are close enough to the last task of each vehicle are assigned to the vehicle first, so that the total traveling time of all vehicles can be minimized. Due to the limited fuel of each vehicle, less traveling time will leave more time to execute tasks, then more tasks can be executed, especially when the ratio of tasks to vehicles is high. Extensive simulation results show that the proposed PI-minTravel can assign more tasks and converge within fewer iterations compared with PI algorithm, while it can assign fewer tasks but converge within much fewer iterations compared with PI for maximizing assignments (PI-maxAss) algorithm.

关键词： multirobot system task allocation decentralized algorithm search and rescue

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Request-Response and Censoring-Based Energy-Efficient decentralized Change-Point Detection With IoT Applications

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IEEE INTERNET OF THINGS JOURNAL 2021年第8期8卷 6771-6788页

作者： Gu, Yuantao Jiao, Yuchen Xu, Xingyu Yu, Quan Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China

Change-point detection (CPD) from streaming data is a fundamental problem in statistical signal processing and has wide applications in wireless sensor networks and the Internet of Things (IoT), such as environmental monitoring and physical activity detection. In various detection schemes, decentralized detection without fusion center is becoming increasingly popular for IoT applications since it enjoys the benefit of strong robustness and security. However, it faces the big challenge of high energy consumption. In this work, we propose an energy-efficient decentralized CPD algorithm called request-response and censoring-based cumulative sum. Specifically, we design a novel communication strategy based on request-response and censoring scheme, which could help the sensors extract useful information from the neighbor sets with a low amount of communication. Furthermore, to provide a guideline on selecting the parameters involved in our algorithm, we theoretically analyze the performance of the proposed algorithm under a simplification for the most interesting cases, in terms of two standard criteria average running length (ARL) and expected detection delay (EDD). Numerical simulations are conducted to verify the effectiveness of the proposed algorithm. Moreover, we test the feasibility of the proposed algorithm in the physical activity CPD task. The experimental results on the real-world data set demonstrate its high energy efficiency and small detection delay.

关键词： Sensors Wireless sensor networks Task analysis Hidden Markov models Internet of Things Data models Delays Censoring change-point detection (CPD) cumulative sum (CUSUM) decentralized algorithm request– response

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Multi-agent reinforcement learning for multi-area power exchange

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 235卷

作者： Xi, Jiachen Garcia, Alfredo Chen, Yu Christine Khatami, Roohallah Texas A&M Univ Dept Ind & Syst Engn College Stn TX 77840 USA Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada Southern Illinois Univ Sch Elect Comp & Biomed Engn Carbondale IL USA

Increasing renewable integration leads to faster and more frequent fluctuations in the power system net-load (load minus non-dispatchable renewable generation) along with greater uncertainty in its forecast. These can exacerbate the computational burden of centralized power system optimization (or market clearing) that accounts for variability and uncertainty in net load. Another layer of complexity pertains to estimating accurate models of spatio-temporal net-load uncertainty. Taken together, decentralized approaches for learning to optimize (or to clear a market) using only local information are compelling to explore. This paper develops a decentralized multi-agent reinforcement learning (MARL) approach that seeks to learn optimal policies for operating interconnected power systems under uncertainty. The proposed method incurs less computational and communication burden compared to a centralized stochastic programming approach and offers improved privacy preservation. Numerical simulations involving a three-area test system yield desirable results, with the resulting average net operation costs being less than 5% away from those obtained in a benchmark centralized model predictive control solution.

关键词： Power system Reinforcement learning Uncertainty decentralized algorithm Actor-critic algorithm

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A paradigm shift in appointment Scheduling: Introducing a decentralized integrated Online booking system

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 257卷

作者： Seyedi, Pardis Eshghi, Kourosh Carter, Michael W. Sharif Univ Technol Dept Ind Engn Tehran Iran Univ Toronto Dept Mech & Ind Engn Toronto ON Canada

Long wait times, coordination, and integration are persistent issues in many health systems, hindering effective management of client waitlists and delaying treatment. The traditional approach of independent scheduling at each center does not allow for a coordinated response to demand fluctuations. An Integrated Online Booking system (IOB) for outpatient services is designed in this study, presenting the first central intake-based appointment system integrated with an optimization approach and a novel decentralized and distributed appointment system. This system considers the independence of centers while better responding to demand and improving overall system performance. The optimization component of the IOB system is expanded in this paper to include multiple sites, patient preferences, priorities, and wait time targets. It becomes a large-scale problem as all requests from a geographical region are combined into one stream. A decomposable algorithm based on the Alternating Direction Method of Multipliers (ADMM) is used for this system. The study employs real large-scale MRI data from all hospitals in Ontario, Canada, to demonstrate the effectiveness of the IOB system, providing insights into its potential impact on healthcare operations. The results indicate that the IOB system can outperform common appointment scheduling approaches in terms of reducing patient wait times, balancing utilization rates, improving referral patterns, and enhancing system efficiency. The proposed IOB system can bring added value to the market by speeding up diagnosis and treatment processes while improving the efficiency of healthcare systems. This platform can be considered as Software as a Service (SaaS), providing a scalable and accessible solution for managing appointments across various sectors, and leveraging blockchain technology to ensure secure, transparent, and tamper-proof appointment records. This study provides insights for decision-makers, highlighting the importance o

关键词： Operations research Appointment scheduling decentralized algorithm Healthcare management Service Operations Management

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Integrated Electrical, Heating, and Water Distribution System to Accommodate Wind Power

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2021年第2期12卷 1100-1114页

作者： Li, Chunyan Yao, Yiming Xie, Kaigui Hu, Bo Niu, Tao Chongqing Univ Sch Elect Engn Chongqing 400044 Peoples R China Chongqing Univ Chongqing 400044 Peoples R China

With the rapid development of communication technology, the cooperation between electrical systems and other energy systems has become possible to promote wind power accommodation. As a common kind of energy carrier, the water distribution system has not been researched so far to help accommodate wind power when integrated with other energy systems. This paper proposes a novel model of integrated electrical, heating, and water distribution system to accommodate wind power. The optimal scheduling of heat pump and water pump are studied to make full use of the abundant wind power. The heat transmission loss is considered and thus the best siting of the heat pump can be determined. The demand response program is also included in the schedule structure to make the modeling more comprehensive. Moreover, a novel two-layer decentralized optimization algorithm is proposed to protect the data privacy of each energy system. The effectiveness of the proposed integrated system model and the two-layer decentralized algorithm are verified on two test systems. The results indicate that system integrations can reduce the wind power curtailment significantly and decrease the operation cost of each energy system. Besides, the proposed two-layer algorithm performs well in terms of the algorithm efficiency and scalability.

关键词： Wind power generation Water heating Heat pumps Resistance heating Cogeneration Mathematical model Wind power integrated energy system heating system water distribution system decentralized algorithm

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DeEPCA: decentralized Exact PCA with Linear Convergence Rate

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JOURNAL OF MACHINE LEARNING RESEARCH 2021年第1期22卷 1-27页

作者： Ye, Haishan Zhang, Tong Xi An Jiao Tong Univ Sch Management Ctr Intelligent Decis Making & Machine Learning Xian Peoples R China Hong Kong Univ Sci & Technol Comp Sci & Math Kowloon Clear Water Bay Hong Kong Peoples R China

Due to the rapid growth of smart agents such as weakly connected computational nodes and sensors, developing decentralized algorithms that can perform computations on local agents becomes a major research direction. This paper considers the problem of decentralized principal components analysis (PCA), which is a statistical method widely used for data analysis. We introduce a technique called subspace tracking to reduce the communication cost, and apply it to power iterations. This leads to a decentralized PCA algorithm called DeEPCA, which has a convergence rate similar to that of the centralized PCA, while achieving the best communication complexity among existing decentralized PCA algorithms. DeEPCA is the first decentralized PCA algorithm with the number of communication rounds for each power iteration independent of target precision. Compared to existing algorithms, the proposed method is easier to tune in practice, with an improved overall communication cost. Our experiments validate the advantages of DeEPCA empirically.

关键词： decentralized algorithm Principal Component Analysis Subspace Tracking

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Adaptive Kernel Learning in Heterogeneous Networks

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2021年 7卷 423-437页

作者： Pradhan, Hrusikesha Bedi, Amrit Singh Koppel, Alec Rajawat, Ketan Indian Inst Technol Dept Engn Kanpur 208016 Uttar Pradesh India US Army Res Lab Adelphi MD 20783 USA

We consider learning in decentralized heterogeneous networks: agents seek to minimize a convex functional that aggregates data across the network, while only having access to their local data streams. We focus on the case where agents seek to estimate a regression function that belongs to a reproducing kernel Hilbert space (RKHS). To incentivize coordination while respecting network heterogeneity, we impose nonlinear proximity constraints. The resulting constrained stochastic optimization problem is solved using the functional variant of stochastic primal-dual (Arrow-Hurwicz) method which yields a decentralized algorithm. In order to avoid the model complexity from growing linearly over time, we project the primal iterates onto subspaces greedily constructed from kernel evaluations of agents' local observations. The resulting scheme, dubbed Heterogeneous Adaptive Learning with Kernels (HALK), allows us, for the first time, to characterize the precise trade-off between the optimality gap, constraint violation, and the model complexity. In particular, the proposed algorithm can be tuned to achieve zero constraint violation, an optimality gap of O(T-1/2 + alpha) after T iterations, where the number of elements retained in the dictionary is determined by 1/alpha. Simulations on a correlated spatio-temporal field estimation validate our theoretical results, which are corroborated in practice for networked oceanic sensing buoys estimating temperature and salinity from depth measurements.

关键词： Nonparametric regression decentralized algorithm statistical learning kernel methods stochastic optimization online learning stochastic primal-dual method

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