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Hybrid decentralized Data Analytics in Edge-Computing-Empowered IoT Networks

IEEE INTERNET OF THINGS JOURNAL 2021年第9期8卷 7706-7716页

作者： Zhao, Liang Li, Fangyu Valero, Maria Kennesaw State Univ Dept Informat Technol Marietta GA 30060 USA Kennesaw State Univ Dept Elect & Comp Engn Marietta GA 30067 USA

Edge computing is emerging as a new infrastructure for Internet-of-Things (IoT) networks by placing computation and analytics near to where data are generated. This article presents a novel data analytics framework for edge computing. The framework is based on a new decentralized algorithm, which enables all the nodes to obtain the global optimal model without sharing raw data. The resulting scheme executes in a hybrid mode: local IoT nodes send computed information to edge nodes. The edge nodes cooperate with each other by exchanging analytics with their neighbors only. The presenting approach is analyzed and evaluated on various applications and the experimental results demonstrate the effectiveness of the proposed methodology in providing fast data analytics to edge computing infrastructure.

关键词： Data analytics decentralized algorithm edge computing Internet of Things (IoT)

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A New Method for Peer Matching and Negotiation of Prosumers in Peer-to-Peer Energy Markets

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IEEE TRANSACTIONS ON SMART GRID 2021年第3期12卷 2472-2483页

作者： Khorasany, Mohsen Paudel, Amrit Razzaghi, Reza Siano, Pierluigi Monash Univ Dept Elect & Comp Syst Engn Melbourne Vic 3800 Australia Univ British Columbia Okanagan Fac Appl Sci Sch Engn Kelowna Kelowna BC V1V 1V7 Canada Univ Salerno Dept Management & Innovat Syst I-84084 Fisciano Italy

This article presents a scalable mechanism for peer-to-peer (P2P) energy trading among prosumers in a smart grid. In the proposed mechanism, prosumers engage in a non-mediated negotiation with their peers to reach an agreement on the price and quantity of energy to be exchanged. Instead of concurrent bilateral negotiation between all peers with high overheads, an iterative peer matching process is employed to match peers for bilateral negotiation. The proposed negotiation algorithm enables prosumers to come to an agreement, given that they have no prior knowledge about the preference structure of their trading partners. A greediness factor is introduced to model the selfish behavior of prosumers in the negotiation process and to investigate its impact on the negotiation outcome. In order to recover the costs related to power losses, a transaction fee is applied to each transaction that enables the grid operator to recover incurred losses due to P2P trades. The case studies demonstrate that the proposed mechanism discourages greedy behavior of prosumers in the negotiation process as it does not increase their economic surplus. Also, it has an appropriate performance from the computation overheads and scalability perspectives.

关键词： Peer-to-peer computing Economics Optimization Indexes Computational complexity Smart grids Scalability decentralized algorithm market design multi-issue negotiation peer-to-peer market prosumer smart grids transaction fee

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decentralized Radio Resource Adaptation in D2D-U Networks

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IEEE INTERNET OF THINGS JOURNAL 2021年第8期8卷 6720-6732页

作者： Yin, Rui Wu, Zheyi Liu, Shengli Wu, Celimuge Yuan, Jiantao Chen, Xianfu Zhejiang Univ City Coll Sch Informat & Elect Engn Hangzhou 310018 Peoples R China KwaZulu Natal Univ Sch Elect Elect & Comp Engn ZA-4041 Durban South Africa Zhejiang Univ Sch Informat & Elect Engn Hangzhou 310027 Peoples R China Univ Electrocommun Grad Sch Informat & Engn Tokyo 1828585 Japan Valt Teknillinen Tutkimuskeskus Tech Res Ctr Finl Oulu 90570 Finland

Unlike the conventional device-to-device (D2D) networks, the unlicensed D2D (D2D-U) pairs can not only reuse the licensed channels with the base station (BS) but also share the unlicensed channels with the WiFi stations. One challenge arises from the fact that the co-channel interference on licensed channels and the collision probability on unlicensed channels may cause extra power consumption at the terminals. Accordingly, we first propose a channel access method for the D2D-U pairs on unlicensed channels. Then, a decentralized joint spectrum and power allocation scheme is designed to minimize the power consumption at D2D-U pairs. Different from the existing distributed schemes, the proposed scheme can guarantee the global minimization of power consumption across the D2D-U pairs. Simulation results validate the theoretical analysis and verify the performance from the proposed scheme.

关键词： Resource management Device-to-device communication Wireless fidelity 5G mobile communication Power demand Cellular networks Computational complexity decentralized algorithm new radio (NR) radio resource management unlicensed D2D (D2D-U)

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decentralized Failure-Tolerant Optimization of Electric Vehicle Charging

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IEEE TRANSACTIONS ON SMART GRID 2021年第5期12卷 4068-4078页

作者： Aravena, Ignacio Chapin, Steve J. Ponce, Colin Lawrence Livermore Natl Lab Livermore CA 94550 USA

We present a decentralized failure-tolerant algorithm for optimizing electric vehicle (EV) charging, using charging stations as computing agents. The algorithm is based on the alternating direction method of multipliers (ADMM) and it has the following features: (i) It handles coupling constraints for capacity, peak demand, and ancillary services. (ii) It does not require a central agent collecting information and performing coordination (e.g., an aggregator), instead all agents exchange information and computations are carried out in a fully decentralized fashion. (iii) It can withstand the failure of any number of computing agents, as long as the remaining computing agents are in a connected communications network. We construct this algorithm by reformulating the optimal EV charging problem in a decomposable form, amenable to ADMM, and then developing efficient decentralized solution methods for the subproblems dealing with coupling constraints. We conduct numerical experiments on industry-scale synthetic EV charging datasets, with up to 1 152 charging stations, using a high-performance computing cluster. The experiments demonstrate that the proposed algorithm can solve the optimal EV charging problem fast enough to permit the integration of EV charging with real-time electricity markets, even in the presence of failures.

关键词： Electric vehicle charging Processor scheduling Games Real-time systems Job shop scheduling Optimization Couplings Electric vehicle charging ADMM decentralized algorithm fail-proof algorithm

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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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Multi-consensus decentralized accelerated gradient descent

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 14474-14523页

作者： Haishan Ye Luo Luo Ziang Zhou Tong Zhang Center for Intelligent Decision-Making and Machine Learning School of Management Xi'an Jiaotong University Xi'an China School of Data Science Fudan University Shanghai China Department of Computing The Hong Kong Polytechnic University Hong Kong China Computer Science & Mathematics The Hong Kong University of Science and Technology Hong Kong China

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

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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 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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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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Cooperative energy management of a community of smart-buildings: A Blockchain approach

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2020年第0期117卷 105643-000页

作者： Van Cutsem, Olivier Dac, David Ho Boudou, Pol Kayal, Maher Ecole Polytech Fed Lausanne Dept Elect Engn STI CH-1005 Lausanne Switzerland

Demand Response (DR) is progressively moving from a centralized, unidirectional structure to a set of advanced decentralized mechanisms that better balance distributed supply and demand. This paper presents a decentralized cooperative DR framework to manage the daily energy exchanges within a community of Smart-Buildings, in the presence of local Renewable Energy Sources (RES). The proposed algorithm taps into the flexibility of the participants to let them decide of a day-ahead community power profile, and subsequently ensures the forecast tracking during the next day. In practice, the algorithm is fully decentralized by the Blockchain technology, that enables a trusted communication medium among the participants and enforces autonomous monitoring and billing via Smart-Contracts. With such an energy management framework, participating Smart-Buildings can together aim at a common objective, such as carbon-free resources usage or aggregated grid services, without depending on a centralized aggregator/utility. Simulations on realistic Swiss building models demonstrate that nearly all the renewable production resources could be harnessed locally through the presented framework, compared to selfish individual optimization. Under a quadratic cost of grid electricity, the considered community profile could dramatically be flattened, hence avoiding costly peaks at the grid interface. A scalability analysis shows that, considering the current public Ethereum Blockchain, the framework could handle a community size of up to 100 Smart-Buildings.

关键词： Cooperative energy management Smart-buildings Smart-community Blockchain Demand response Smart-contract Linear optimization decentralized algorithm

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