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33rd Annual IEEE Military Communications Conference on Affordable Mission Success - Meeting the Challenge (MILCOM)

作者： Dabideen, Stephen Ramanathan, Ram Dron, Will Leung, Alice Raytheon BBN Technol Cambridge MA 02138 USA

ISBN: (纸本)9781479967704

Network-wide broadcasting is a key requirement in military mobile ad hoc and sensor networks for supporting dissemination of routing control, situation reports, and other global traffic. Current methods for network-wide broadcasting are ill-suited for Multi-Channel Multi-Radio (MC-MR) networks because they do not exploit the presence of natural frequency groups for reducing the number of transmissions. We present Complex Activation Broadcasting (CAB) - a decentralized algorithm for broadcasting in real-world MC-MR networks that captures naturally occurring groups and builds a tree that exploits them. Our approach is based on representing a network as a simplicial complex, which unlike a graph, allows higher-order aggregations necessary for natively capturing groups. Using a special kind of simplicial complex called a neighborhood subcomplex, we present an algorithm for computing a broadcast tree. We have implemented our algorithm within the code base of a real-world military MANET, namely the DARPA WNaN network [1]. We compare the performance of our algorithm with the existing baseline. Results show that our algorithm uses 80 % less transmissions and 50% less receptions.

关键词： military communication mobile ad hoc networks telecommunication network routing telecommunication traffic trees (mathematics) wireless channels CAB DARPA WNaN network MC-MR networks broadcast tree complex activation broadcasting decentralized algorithm frequency groups global traffic higher-order aggregations military mobile ad hoc network multichannel multiradio networks natural frequency groups neighborhood subcomplex network-wide broadcasting real-world military MANET routing control dissemination simplicial complex situation reports wireless sensor networks Abstracts Broadcasting Channel allocation Face Mobile ad hoc networks Protocols Tin Mobile ad hoc networks broadcasting Military communications Taxi Channel allocation wireless channels telecommunication network routing telecommunication traffic Tin Wireless sensor networks protocols group frequency Face Face code base

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decentralized High-Reliability Energy Management Regime for Smart Building

Decentralized High-Reliability Energy Management Regime for ...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Xie, Peng Jia, Youwei Chen, Hongkun Wu, Jun Wang, Han Shi, Mengge Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen Peoples R China Wuhan Univ Sch Elect Engn & Automat Wuhan Peoples R China

ISBN: (纸本)9781665405072

This paper focuses on developing a fully decentralized energy management regime (EMR) for smart buildings to respond to the utility grid for peak-load shifting. The proposed EMR is constructed based on the robust tube model predictive control theory, which includes two stages: day-ahead scheduling and real-time operation. At the day-ahead scheduling stage, a smart building flexible resources robust scheduling problem is considered to ensure the reliability of power supply;at the real-time operation stage, a rolling correction based operational strategy is studied to minimize the deviation of actual and day-ahead scheduling. A multi-stage energy management model based on min- max robust optimization technology is constructed to provide quantitative decIsIOn support for the energy management of smart buildings. Moreover, the constructed mathematical model is solved by a fully decentralized algorithm based on consensus theory and the alternating direction method of multipliers for privacy security and " plug-and- play" purposes. Simulation and experimental results demonstrate that the effectiveness and computational efficiency of the proposed model and solving algorithm even in the presence of high system uncertainties.

关键词： smart building energy management tube model predictive control decentralized algorithm demand response

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decentralized Max-Min Resource Allocation for Monotonic Utility Functions

Decentralized Max-Min Resource Allocation for Monotonic Util...

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IEEE Conference on Computer Communications Workshops (IEEE INFOCOM)

作者： Wu, Shuang Peng, Xi Tian, Guangjian Huawei Technol Co Ltd Noahs Ark Lab Shenzhen Peoples R China Huawei Technol Co Ltd Theory Lab Shenzhen Peoples R China

ISBN: (纸本)9781665404433

We consider a decentralized solution to max-min resource allocation for a multi-agent system. Limited resources are allocated to the agents in a network, each of which has a utility function monotonically increasing in its allocated resource. We aim at finding the allocation that maximizes the minimum utility among all agents. Although the problem can be easily solved with a centralized algorithm, developing a decentralized algorithm in absence of a central coordinator is challenging. We show that the decentralized max-min resource allocation problem can be nontrivially transformed to a canonical decentralized optimization. By using the gradient tracking technique in the decentralized optimization, we develop a decentralized algorithm to solve the max-min resource allocation. The algorithm converges to a solution at a linear convergence rate (in a log-scale) for strongly monotonic and Lipschitz continuous utility functions. Moreover, the algorithm is privacy-preserving since the agents only transmit encoded utilities and allocated resource to their intermediate neighbors. Numerical simulations show the advantage of our problem reformulation and validate the theoretical convergence result.

关键词： max-min fairness decentralized algorithm resource allocation

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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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Multi-Consensus decentralized Accelerated Gradient Descent

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JOURNAL OF MACHINE LEARNING RESEARCH 2023年第1期24卷 1−50-1−50页

作者： Ye, Haishan Luo, Luo Zhou, Ziang Zhang, Tong Xi An Jiao Tong Univ Ctr Intelligent Decis Making & Machine Learning Sch Management Xian Peoples R China Fudan Univ Sch Data Sci Shanghai Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Hong Kong Univ Sci & Technol Comp Sci & Math Hong Kong Peoples R China

This paper considers the decentralized convex optimization problem, which has a wide range of applications in large-scale machine learning, sensor networks, and control theory. We propose novel algorithms that achieve optimal computation complexity and near optimal communication complexity. Our theoretical results give affirmative answers to the open problem on whether there exists an algorithm that can achieve a communication complexity (nearly) matching the lower bound depending on the global condition number instead of the local one. Furthermore, the linear convergence of our algorithms only depends on the strong convexity of global objective and it does not require the local functions to be convex. The design of our methods relies on a novel integration of well-known techniques including Nesterov's acceleration, multi-consensus and gradient-tracking. Empirical studies show the outperformance of our methods for machine learning applications.

关键词： consensus optimization decentralized algorithm accelerated gradient descent gradient tracking composite optimization

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Green technology innovation path based on blockchain algorithm

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2021年 31卷 100587-100587页

作者： Li, Donghan Northeastern Univ Sch Business Adm Shenyang 110819 Liaoning Peoples R China

The core of green technological innovation is to protect the ecology. The innovative introduction of ecological concepts into the existing ecosystem can ensure the sustainable development and progress of society. This paper mainly studies the path analysis of green technology innovation based on blockchain algorithm. Aiming at the high latency and low efficiency of pbft algorithm in green technology innovation, a low latency consensus algorithm ipbft based on an alternative voting mechanism is proposed. Explore the influencing factors of domestic realization of green technology innovation, clarify the reasons that hinder green technology innovation and give reasonable strategies to help domestic green technology innovation work to be carried out faster and better. This article compares the ipbft algorithm with the improved consensus algorithms cpbft, vpbft and pbft in recent years. Experimental results show that the consensus delay of the ipbft algorithm is much lower than that of the other three algorithms, and the average delay of the ipbft single consensus is only about 370ms. In different transaction volume and different node number throughput performance tests, the ipbft throughput in these two tests is better than the other three algorithms. In the view switching phase, the ipbft algorithm also takes less time than the other three algorithms. The above results show that the ipbft algorithm has lower consensus delay and higher throughput, which can reduce network communication overhead and save system transmission energy consumption. It can ensure the security and authenticity of information in the green technology innovation based on blockchain, and reduce system overhead.

关键词： Blockchain technology decentralized algorithm Green technology innovation Consensus algorithm

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Exploring multiple investment strategies for non-utility-owned DGs: A decentralized risked-based approach

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APPLIED ENERGY 2022年 326卷

作者： Yao, Haotian Xiang, Yue Liu, Junyong Sichuan Univ Coll Elect Engn Chengdu 610065 Peoples R China

The development of distributed generator (DG) and energy market has facilitated the investment in non-utility -owned DGs, leading to the necessity of decentralized optimization and finical risk management due to multiple uncertainties. To cope with these problems, this paper addresses the planning framework of non-utility-owned DGs considering multiple investment strategies, from the perspective of risk and profit. Initially, the autono-mous planning and operation strategy (APOS), and leasing planning and operation strategy (LPOS) are proposed, considering the different ownership of DG investment/operation rights and pricing mechanism. Then the DG planning problem is modeled as the independent decision-making stage of multiple DG investors and the global coordination stage of distribution system operator (DSO). Furthermore, in the DSO coordination problem, to accurately model the real-time uncertainties in DGs, load demand and main grid price, the conditional value at risk (CVaR) is adopted to manage the risk in profit (RIP). The effect of multiple investment strategies on the tradeoff between RIP and expected profit is analyzed. The planning problem is solved by a decentralized opti-mization approach that ensures the privacy protection and autonomous optimization of investors. Finally, results from the case study of the IEEE 33-bus system and IEEE 123-bus system demonstrate the superiority and effectiveness of the proposed method in dealing with the planning problem for multiple DG investors.

关键词： Conditional value -at -risk (CVaR) Distributed generator planning decentralized algorithm Multiple investment strategies Third -party stakeholders

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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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On honey bees and dynamic server allocation in Internet hosting centers

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ADAPTIVE BEHAVIOR 2004年第3-4期12卷 223-240页

作者： Nakrani, S Tovey, C Univ Oxford Comp Lab Oxford OX1 3QD England Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA 30332 USA

Internet centers host services for e-banks, e-auctions and other clients. Hosting centers then must allocate servers among clients to maximize revenue. The limited number of servers, costs of reallocating servers, and unpredictability of requests make server allocation optimization difficult. Based on the many similarities between server and honey bee colony forager allocation, we propose a new decentralized honey bee algorithm which dynamically allocates servers to satisfy request loads. We compare it against an omniscient optimality algorithm, a conventional greedy algorithm, and an algorithm that computes omnisciently the optimal static allocation. We evaluate performance on simulated request streams and commercial trace data. Our algorithm performs better than static or greedy for highly variable request loads, but greedy can outperform it under low variability. Honey bee forager allocation, though suboptimal for static food sources, may possess a counterbalancing responsiveness to food source variability.

关键词： internet hosting server allocation biomimicry heuristic honey bee foraging self-organization decentralized algorithm

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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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