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Scheduling EV Charging Having Demand With Different Reliability Constraints

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第10期24卷 11018-11029页

作者： Shao, Shiping Sartipizadeh, Hossein Gupta, Abhishek Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA Ford Motor Co Dearborn MI 48126 USA

We develop a new algorithm, based on approximate dynamic programming, to efficiently schedule the charging processes of a large number of electric vehicles (EVs) with flexibility, which allows the EV charging service provider to charge the EVs between their minimum and target state of charge (SoC) within a time window. We assume that the charging demands have known distributions and the charging process of EVs can be scheduled preemptively. We model the scheduling problem as a dynamic program with state-dependent constraints and solve it utilizing the fitted value iteration. We propose a multi-stage decoupled algorithm to reduce the dimensions in the fitted value iteration to reduce the computation complexity, and provide sufficient conditions for achieving optimality.

关键词： Dynamic scheduling Electric vehicle charging Heuristic algorithms Costs Schedules approximation algorithms Reliability EV charging approximate dynamic programming fitted value iteration

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An Overall Evaluation on Benefits of Competitive Influence Diffusion

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IEEE TRANSACTIONS ON BIG DATA 2023年第2期9卷 653-664页

作者： Guo, Jianxiong Zhang, Yapu Wu, Weili Univ Texas Dallas Erik Jonsson Sch Engn & Comp Sci Dept Comp Sci Richardson TX 75080 USA Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China

Influence maximization (IM) is a representative and classic problem that has been studied extensively before. The most important application derived from the IM problem is viral marketing. Take us as a promoter, we want to get benefits from the influence diffusion in a given social network, where each influenced (activated) user is associated with a benefit. However, there is often competing information initiated by our rivals that diffuses in the same social network at the same time. Consider such a scenario, a user is influenced by both our information and our rivals' information. Here, the benefit from this user should be weakened to a certain degree. How to quantify the degree of weakening? Based on that, we propose an overall evaluation on benefits of influence (OEBI) problem. We prove the objective function of the OEBI problem is not monotone, not submodular, and not supermodular. Fortunately, we can decompose this objective function into the difference of two submodular functions and adopt a modular-modular procedure to approximate it with a data-dependent approximation guarantee. Because of the difficulty to compute the exact objective value, we design a group of unbiased estimators by exploiting the idea of reverse influence sampling, which can improve time efficiency significantly without losing its approximation ratio. Finally, numerical experiments on real datasets verified the effectiveness of our approaches regardless of performance and efficiency.

关键词： Social networking (online) approximation algorithms Linear programming Computational modeling Greedy algorithms Heuristic algorithms Big Data Overall evaluations influence maximization submodularity modular-modular proceduce sampling techniques social networks approximation algorithm

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Pliability and Approximating Max-CSPs

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JOURNAL OF THE ACM 2023年第6期70卷 1-43页

作者： Romero, Miguel Wrochna, Marcin Zivny, Stanislav Pontificia Univ Catolica Chile Av Vicuna Mackenna 4860 Santiago 7821092 Chile Univ Warsaw Banacha 2 PL-02097 Warsaw Poland Univ Oxford Wolfson Bldg Parks Rd Oxford OX1 3QD England

We identify a sufficient condition, treewidth-pliability, that gives a polynomial-time algorithm for an arbitrarily good approximation of the optimal value in a large class of Max-2-CSPs parameterised by the class of allowed constraint graphs (with arbitrary constraints on an unbounded alphabet). Our result applies more generally to the maximum homomorphism problem between two rational-valued structures. The condition unifies the two main approaches for designing a polynomial-time approximation scheme. One is Baker's layering technique, which applies to sparse graphs such as planar or excluded-minor graphs. The other is based on Szemeredi's regularity lemma and applies to dense graphs. We extend the applicability of both techniques to new classes of Max-CSPs. However, we prove that the condition cannot be used to find solutions (as opposed to approximating the optimal value) in general. Treewidth-pliability turns out to be a robust notion that can be defined in several equivalent ways, including characterisations via size, treedepth, or the Hadwiger number. We show connections to the notions of fractional-treewidth-fragility from structural graph theory, hyperfiniteness from the area of property testing, and regularity partitions from the theory of dense graph limits. These may be of independent interest. In particular, we show that a monotone class of graphs is hyperfinite if and only if it is fractionally-treewidth-fragile and has bounded degree.

关键词： Constraint satisfaction problems homomorphism problems approximation algorithms PTAS treewidth fractional-treewidth-fragility hyperfiniteness

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Generalization Analysis of CNNs for Classification on Spheres

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第9期34卷 6200-6213页

作者： Feng, Han Huang, Shuo Zhou, Ding-Xuan City Univ Hong Kong Dept Math Kowloon Hong Kong Peoples R China City Univ Hong Kong Sch Data Sci Dept Math Kowloon Hong Kong Peoples R China City Univ Hong Kong Liu Bie Ju Ctr Math Sci Kowloon Hong Kong Peoples R China

Deep learning based on deep convolutional neural networks (CNNs) is extremely efficient in solving classification problems in speech recognition, computer vision, and many other fields. But there is no enough theoretical understanding about this topic, especially the generalization ability of the induced CNN algorithms. In this article, we develop some generalization analysis of a deep CNN algorithm for binary classification with data on spheres. An essential property of the classification problem is the lack of continuity or high smoothness of the target function associated with a convex loss function such as the hinge loss. This motivates us to consider the approximation of functions in the $L_p$ space with 1 <= p <= infinity. We provide rates of $L_p$ -approximation when the approximated function lies in a Sobolev space and then present generalization bounds and learning rates for the excess misclassification error of the deep CNN classification algorithm. Our novel analysis is based on efficient cubature formulae on spheres and other tools from spherical analysis and approximation theory.

关键词： Convolutional neural networks Indexes Extraterrestrial measurements Urban areas Fasteners approximation algorithms Learning systems Classification problems convolutional neural networks (CNNs) deep learning generalization error bounds spherical analysis

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Dependency-Aware Task Scheduling in TrustZone Empowered Edge Clouds for Makespan Minimization

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

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IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2023年第3期8卷 423-434页

作者： Li, Yuepeng Zeng, Deze China Univ Geosci Sch Comp Sci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430079 Hubei Peoples R China

Task offloading to edge servers has become a promising solution to tackle the computation resource poverty of the end devices. However, the zero-trust edge computing platform is highly challenged by the growing concern on security and privacy. Thus, Trust Execution Environment (TEE), like TrustZone, is advocated to empower edge clouds to enable secure task offloading. To explore TrustZone, the inevitable involvement of data encryption and decryption operations makes existing offloading strategies not applicable any more, especially when the task dependency is considered. In addition, TrustZone has distinguishable task scheduling paradigm as one CPU core does not allow multitask coexist at the same time. Taking the above issues into consideration, we investigate a dependency-aware task offloading problem for makespan minimization in TrustZone empowered edge clouds. By inventing an extended graph to describe the task execution process, we provide a formal statement to the problem and prove its NP-hardness. We then propose a Customized List Scheduling (CLS) based approximate algorithm and theoretically analyze its achievable performance. Extensive testbed based experiment results show that our approximation algorithm can effectively reduce the makespan and significantly outperforms existing state-of-the-art offloading approaches in TrustZone empowered edge clouds.

关键词： Task analysis Servers Program processors Encryption Cryptography approximation algorithms Scheduling Secure task offloading TrustZone edge computing

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Synthesis of Clustered Concentric Ring Arrays Through Joint Optimization of Multi-Parameters

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2023年第1期71卷 840-851页

作者： Yang, Jianhua Yang, Feng Yang, Peng Xing, Zhiyu Univ Elect Sci & Technol China Sch Elect Sci & Engn Chengdu 611731 Peoples R China

This article proposes a novel hybrid methodology that addresses the clustering of concentric ring arrays (CRAs) through joint optimization of ring radii, clusters partitions, and clusters excitations. It takes advantage of convex programming (CP) with respect to the excitation variables due to their convex nature. For the optimization of rings radii and clusters partitions, a subproblem is formulated by mix-integer programming with linear constraints, which is effectively resolved using the developed technique of mixed-integer linear-constraint genetic algorithm (MILCGA). A set of representative numerical experiments show the effectiveness of the proposed method and illustrate its potentialities, representing a promising solution for CRAs to obtain a suitable tradeoff between costs, robustness, flexibility, and beam control performances.

关键词： Optimization Genetic algorithms Costs Antenna radiation patterns Mutual coupling Linear antenna arrays approximation algorithms Clustered arrays concentric ring arrays (CRAs) joint optimization

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Placing Wireless Chargers With Limited Mobility

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第6期22卷 3589-3603页

作者： Dai, Haipeng Wang, Xiaoyu Lin, Xuzhen Gu, Rong Shi, Shuyu Liu, Yunhuai Dou, Wanchun Chen, Guihai Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Jiangsu Peoples R China Peking Univ Beijing 100080 Peoples R China

Several recent works have studied mobile charging under the "one-to-many" charging pattern where a single charger can charge multiple devices simultaneously. However, most of them focus on path planning and charging time allocation, but overlook the underlying dependence of the charging efficiency on initial deployment positions of chargers. This paper studies the problem of Placing directional wIreless chargers with Limited mObiliTy (PILOT), that is, maximize the overall charging utility for a set of static rechargeable devices on a 2D plane by determining deployment positions, stop positions and orientations, and portions of time for all deployed chargers that can move in a limited area after their deployment. To the best of our knowledge, we are the first to study placement of mobile directional chargers under the "one-to-many" pattern. To address PILOT, we propose a(1/2 - epsilon)-approximation algorithm. First, we present a method to approximate nonlinear charging power of chargers, and further propose an approach to construct Maximal Covered Set uniform subareas to reduce the infinite continuous search space for stop positions and orientations to a finite discrete one. Second, we present geometrical techniques to further reduce the infinite solution space for candidate deployment positions to a finite one without performance loss, and transform PILOT to a mixed integer nonlinear programming problem. Finally, we propose a linear programming based greedy algorithm to address it. Simulation and experimental results show that our algorithm outperforms six comparison algorithms by 19.74% similar to 500.01%.

关键词： Wireless communication Wireless sensor networks Mobile computing Sensors approximation algorithms Wireless power transfer Resource management Placement directional charging limited mobility wireless power transfer approximation algorithm

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A Two-Stage GCN-Based Deep Reinforcement Learning Framework for SFC Embedding in Multi-Datacenter Networks

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2023年第4期20卷 4297-4312页

作者： Xiao, Da Zhang, J. Andrew Liu, Xin Qu, Yiwen Ni, Wei Liu, Ren Ping Univ Technol Sydney Sch Elect & Data Engn Sydney NSW 2007 Australia Univ Calif Davis Dept Comp Sci Davis CA 95616 USA CSIRO Data61 Sydney NSW 2122 Australia

Network Function Virtualization (NFV), which decouples network functions from hardware and transforms them into Virtual Network Functions (VNFs), is a crucial technology for data center (DC) networks. A service function chain (SFC) is composed of an ordered set of VNFs and virtual links (VLs) connecting them. To optimize the resource allocation in DC networks, we need to efficiently map SFCs onto the physical network. Nevertheless, the dynamics and diversity of SFC requests in multi-datacenter (MDC) networks pose a significant challenge in embedding SFCs. To overcome this challenge, we design a two-stage graph convolutional network (GCN) assisted deep reinforcement learning (DRL) scheme. This framework aims to maximize the overall acceptance ratio of SFC requests while minimizing the total cost in an MDC network. In the first stage, we propose a GCN-based DRL algorithm as a coarse granularity solution to the SFC embedding problem from the macro perspective. This solution outlines a local observation scope (LOS) for each agent in the multi-agent system of the second stage, where all agents simultaneously handle SFC requests from their respective DCs using a multi-agent framework from the micro perspective. Numerical evaluations show that, compared to state-of-the-art methods, the proposed scheme improves the acceptance ratio by approximately 13% compared with the Kolin algorithm and 18% compared with the DQN algorithm and saves the cost by around 28% compared with the Kolin and the DQN.

关键词： Costs Load modeling approximation algorithms Service function chaining Resource management Feature extraction Deep learning Network function virtualization multi-datacenter multi-agent graph convolutional network

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Upper and lower bounds on approximating weighted mixed domination

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THEORETICAL COMPUTER SCIENCE 2023年 939卷 292-302页

作者： Xiao, Mingyu Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China

A mixed dominating set of a graph G = (V, E) is a mixed set D of vertices and edges, such that for every edge or vertex, if it is not in D, then it is adjacent or incident to at least one vertex or edge in D. The mixed domination problem is to find a mixed dominating set with a minimum cardinality. It has applications in system control and some other scenarios and it is NP-hard to compute an optimal solution. This paper studies approximation algorithms and hardness of the weighted mixed dominating set problem. The weighted version is a generalization of the unweighted version, where all vertices are assigned the same nonnegative weight wv and all edges are assigned the same nonnegative weight we, and the question is to find a mixed dominating set with a minimum total weight. Although the mixed dominating set problem has a simple 2-approximation algorithm, few approximation results for the weighted version are known. The main contributions of this paper include:1. for we > wv, a 2-approximation algorithm;2. for we > 2wv, inapproximability within ratio 1.3606 unless P = NP and within ratio 2 under UGC;3. for 2wv > we > wv, inapproximability within ratio 1.1803 unless P = NP and within ratio 1.5 under UGC;4. for we < wv, inapproximability within ratio (1 -epsilon) ln | V | unless P = NP for any c > 0.(c) 2022 Elsevier B.V. All rights reserved.

关键词： approximation algorithms Inapproximability Mixed dominating set Vertex cover

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A Distributed Algorithm for Controlling Continuous and Discrete Variables in a Radial Distribution Grid

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IEEE ACCESS 2023年 11卷 2488-2499页

作者： Klemets, Jonatan Ralf Axel Degefa, Merkebu Zenebe SINTEF Energy Res N-7034 Trondheim Norway

The increased integration of distributed energy resources (DERs) in the distribution network with intermittent generation profiles will likely make voltage regulation a difficult task. However, DERs bring both challenges and opportunities, as they can provide renewable forms of local energy and act as voltage regulating components. The DERs are usually interfaced with power electronic devices, in which both their active and reactive power outputs can be regulated and treated as continuous control variables. In contrast, other voltage regulatory devices such as On-Load Tap-Changing (OLTC) transformers are often controlled by making discrete tap changes. Thus, appropriate control strategies are required to control and coordinate the DERs with other voltage regulatory devices. In this work, a distributed control strategy based on the Alternating Direction Method of Multipliers (ADMM) is developed, which controls both the continuous and the discrete variables in a distribution grid. The proposed control strategy is compared to a centralized and a local control architecture, where optimal parameters have been computed for the local controllers. Finally, a simulation study is made for the three different control architectures using a modified CIGRE medium voltage network. The results showed significant improvements in the daily voltage profiles while also reducing the power losses by over 30% when using an optimal control strategy.

关键词： Voltage control Convex functions Decentralized control Reactive power Mathematical models approximation algorithms Optimization optimal control distributed control optimal power flow reactive power

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