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Multi-objective network resource allocation method based on fractional PID control

SIGNAL PROCESSING 2025年 227卷

作者： Ni, Xintong Wei, Yiheng Zhou, Shuaiyu Tao, Meng Southeast Univ Sch Math Nanjing 211189 Peoples R China

In this paper, a fractional proportional-integral-derivative (PID) distributed optimization algorithm is proposed to solve the network resource allocation problem. The algorithm combines fractional calculus and the concept of PID control, which improves the convergence rate and increases the freedom, flexibility and potential with multiple parameters compared with the existing algorithms. Meanwhile, the results of simulation study verified the efficiency and superiority of the algorithm.

关键词： Multi-objective optimization Resource allocation distributed algorithms PID Fractional calculus

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Quantized Privacy-Preserving algorithms for Homogeneous and Heterogeneous Networks With Finite Transmission Guarantees

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2025年第1期12卷 325-337页

作者： Rikos, Apostolos I. Hadjicostis, Christoforos N. Johansson, Karl H. Hong Kong Univ Sci & Technol Guangzhou Artificial Intelligence Thrust Informat Hub Guangzhou 511453 Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Clear Water Bay Hong Kong Peoples R China Univ Cyprus Dept Elect & Comp Engn CY-1678 Nicosia Cyprus KTH Royal Inst Technol Div Decis & Control Syst SE-10044 Stockholm Sweden Digital Futures SE-10044 Stockholm Sweden

Privacy protection is increasingly critical in various applications due to the prevalence of networked systems. In this article, we focus on preserving the privacy of nodes' initial states while computing their average in a network. Curious nodes attempt to identify the initial states of other nodes without interfering in the computation. To address this challenge, we propose two privacy-preserving algorithms. The first algorithm operates over homogeneous networks, i.e., nodes have consistent processing delays and are able to communicate in a synchronous fashion. The second algorithm operates over heterogeneous networks, i.e., nodes have varying processing delays and communicate in an asynchronous fashion. Our algorithms exhibit efficient communication, finite-time convergence, and operation termination after convergence, making them suitable for resource-constrained environments. We also present topological conditions under which our algorithms enable privacy preservation. Finally, we apply our algorithms to a smart grid system and compare their performance against other algorithms, emphasizing their advantages in communication efficiency, convergence rate, and privacy preservation.

关键词： Privacy Convergence Smart grids Control systems Network systems Heterogeneous networks Delays distributed algorithms finite-time convergence finite transmission privacy-preserving average consensus quantized communication smart grid

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Move-optimal arbitrary pattern formation by mobile robots on rectangular grid using near-optimal spatial area

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THEORETICAL COMPUTER SCIENCE 2025年 1038卷

作者： Sharma, Avisek Ghosh, Satakshi Goswami, Pritam Sau, Buddhadeb Jadavpur Univ Dept Math Kolkata 700032 West Bengal India Int Inst Informat Technol IIIT Bhubaneswar Dept Basic Sci & Humanities Bhubaneswar 751003 Odisha India Sister Nivedita Univ Dept Comp Sci & Engn Kolkata 700156 West Bengal India

Arbitrary pattern formation (APF) is a well-studied problem in swarm robotics. To the best of our knowledge, the problem has been considered in two different settings: one in a euclidean plane and another in an infinite grid. This work deals with the problem in an infinite rectangular grid setting. The previous works in literature dealing with the APF problem in an infinite grid had a fundamental issue. These deterministic algorithms use a lot of spatial area in the grid to solve the problem, mainly to maintain the asymmetry of the configuration and avoid any collision. These solution techniques cannot be useful if there is a spatial constraint in the application field. In this work, we consider luminous robots (each robot equipped with a light that can take three colors) to avoid symmetry, but we carefully designed a deterministic algorithm that solves the APF problem using the minimal required spatial area in the grid if the initial pattern is asymmetric. The robots are autonomous, identical, and anonymous, and they operate in Look-Compute-Move cycles under a fully-asynchronous scheduler. The APF algorithm proposed in [1] by Bose et al. can be modified using luminous robots so that it uses minimal spatial area, but that algorithm is not move-optimal. The algorithm proposed in this paper not only uses minimal spatial area but is also asymptotically move-optimal.

关键词： distributed algorithms Arbitrary pattern formation Rectangular grid Robot with lights Optimal algorithms

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Maximum independent set formation on a finite grid by myopic robots

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THEORETICAL COMPUTER SCIENCE 2025年 1031卷

作者： Das, Raja Sharma, Avisek Sau, Buddhadeb Jadavpur Univ Dept Math Kolkata India

This work deals with the Maximum Independent Set (MAXIS) formation problem in a finite rectangular grid by autonomous robots. Suppose we are given a set of identical robots, where each robot is placed on a node of a finite rectangular grid G such that no two robots are on the same node. The MAXIS formation problem asks to design an algorithm and each robot will move autonomously after executing the algorithm and terminate at a node such that after a finite time the set of nodes occupied by the robots is a maximum independent set of G. We assume that robots are anonymous and they execute the same distributed algorithm. Previous works solved this problem using one or several door nodes through which the robots enter the grid or the graph one by one and occupy the required nodes. In this work, we propose a deterministic algorithm that solves the MAXIS formation problem in a more generalized scenario, i.e., when the total number of required robots to form a MAXIS are arbitrarily placed on the grid. The proposed algorithm works under a semi-synchronous scheduler using robots with only two hop visibility range and only three colors.

关键词： Myopic robot Maximum independent set Finite grid Autonomous robots Robot with lights distributed algorithms

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Expanding the Role of DSOs in Establishing Flexible Local Markets: A Brokerage Perspective

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IEEE TRANSACTIONS ON POWER SYSTEMS 2024年第1期39卷 1148-1161页

作者： Zhang, Chaorui Li, Jiayong Zhang, Ying-Jun Angela Xu, Zhao Chinese Univ Hong Kong Dept Informat Engn Hong Kong 999077 Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China Shenzhen Res Inst Res Inst Smart Energy Shenzhen Peoples R China Hong Kong Polytech Univ Dept Elect Engn Hong Kong 999077 Peoples R China

As more distributed energy resources are deployed into distribution networks, supply and demand matching between prosumers becomes a new challenge for distribution system operators (DSOs) due to the intermittency of DERs. Recent studies begin to expand the responsibilities of DSOs towards local generation and consumption optimization, such as establishing a flexible local market. However, the difficulty of boosting local power exchange while reducing operation costs still remains unsolved. In this article, we propose a new way of power trading management for DSOs from a brokerage perspective. Specifically, DSOs endeavor to coordinate prosumers in the local market by setting a fair transactive price as incentive signals. Meanwhile, to compensate for the operation cost (e.g., power line loss), DSOs need to charge commission. We investigate how to compute the optimal transactive price in a privacy-preserving way by using a distributed algorithm and set a fair service fee, which attracts more power transactions in the local market as well as reduces the operation cost of DSOs. Moreover, to accelerate the distributed algorithm, we derive an optimal transactive price updating rule for DSOs, and the closed-form expression of the optimal strategy for each prosumer with respect to the transactive price. Numerical results on Southern California Edison 56-bus feeder and IEEE 123-node test feeder show that the proposed power trading management for DSOs from a brokerage perspective leads to a TRIPLE win. With the proposed mechanism, the prosumers' payoffs largely increase by as much as 192.85%. Then, the prosumers are more willing to exchange power locally, which significantly reduces the increasing transmission burden in transmission networks of power systems. Moreover, DSOs obtain compensation by charging a service commission and reduce their operating costs.

关键词： distributed algorithms distribution system operators local electricity markets optimal power flow prosumers

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Frequency Regulation From distributed Energy Resource Using Cloud-Edge Collaborations Under Wireless Environments

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IEEE TRANSACTIONS ON SMART GRID 2022年第1期13卷 367-380页

作者： Zhang, Jiawei Wang, Peng Zhang, Ning Tsinghua Univ State Key Lab Power Syst Dept Elect Engn Beijing 100084 Peoples R China

The increasing integration of variable renewable energy increases the demand for a power system's frequency regulation resources. The decreasing share of controllable power generators makes the frequency regulation resources insufficient. Simultaneously, distributed energy resources (DER) on the distribution network side, such as storage devices, electric vehicles, and controllable building energy systems, have excellent potential to provide frequency support. However, power system dispatchers cannot control these resources in the same manner as conventional power sources. These sources have power output uncertainties and cannot directly access power system communication networks. This paper proposes a cloud-edge collaboration and wireless communication coordination framework to facilitate DER frequency regulations. The cloud balances the power uncertainty of controllability in edges together with computing burden, wire, and wireless communication loads. Also, the edges adjust storage's power outputs to achieve frequency regulations and covering PV power fluctuations. A numerical case study demonstrates the effectiveness of the proposed method. The solution holds 95.5% frequency regulation adjustable power capacity of theoretical maximum on average. Meanwhile, the computing costs reduce by 36.3%, and wire communication only operates in 0.35% of a day as a supplement (reduces by 99.65%).

关键词： Frequency control Base stations Wireless communication Regulation Wires Photovoltaic systems Power systems Edge computing frequency regulation mobile communications renewable energy distributed algorithms load balance

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Optimal Utility Design of Greedy algorithms in Resource Allocation Games

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2024年第10期69卷 6592-6604页

作者： Konda, Rohit Chandan, Rahul Grimsman, David Marden, Jason R. Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA Brigham Young Univ Dept Comp Sci Provo UT 84602 USA

Designing fast, distributed algorithms for multiagent problems is vital for many novel application domains. Greedy algorithms have been shown in many multiagent contexts to be an efficient approach to arrive at good solutions quickly. In this work, we ask the following: Is there any way to improve the performance of greedy algorithms without sacrificing the linear run-time guarantees? For this, we take inspiration from incentive design in the game-theoretic literature. In this work, we consider a modified version of the greedy algorithm where agents do not optimize against the global objective. Instead, each agent is prescribed an auxiliary utility function (which may differ from the original objective function) in which it optimizes under. By designing the utility functions properly, we show in this work that the resulting performance guarantees of the greedy algorithm can increase significantly. We study this approach in the context of resource-allocation games, which are used to model a rich variety of engineering applications. Interestingly, the performance guarantees from the modified greedy algorithm can be significantly close to the best centralized performance guarantees. The main technical contribution of the article is the characterization of the performance guarantees through a linear program construction.

关键词： Greedy algorithms Games Resource management Standards distributed algorithms Wireless communication Task analysis Algorithm design distributed systems game theory optimization resource allocation

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Flexible Vertical Federated Learning With Heterogeneous Parties

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第12期35卷 17878-17892页

作者： Castiglia, Timothy Wang, Shiqiang Patterson, Stacy Ab Initio Software Lexington MA 02421 USA IBM Corp Thomas J Watson Res Ctr Yorktown Hts NY 10598 USA Rensselaer Polytech Inst Dept Comp Sci Troy NY 12180 USA

We propose flexible vertical federated learning (Flex-VFL), a distributed machine algorithm that trains a smooth, nonconvex function in a distributed system with vertically partitioned data. We consider a system with several parties that wish to collaboratively learn a global function. Each party holds a local dataset;the datasets have different features but share the same sample ID space. The parties are heterogeneous in nature: the parties' operating speeds, local model architectures, and optimizers may be different from one another and, further, they may change over time. To train a global model in such a system, Flex-VFL utilizes a form of parallel block coordinate descent (P-BCD), where parties train a partition of the global model via stochastic coordinate descent. We provide theoretical convergence analysis for Flex-VFL and show that the convergence rate is constrained by the party speeds and local optimizer parameters. We apply this analysis and extend our algorithm to adapt party learning rates in response to changing speeds and local optimizer parameters. Finally, we compare the convergence time of Flex-VFL against synchronous and asynchronous VFL algorithms, as well as illustrate the effectiveness of our adaptive extension.

关键词： algorithms computational and artificial intelligence convergence distributed algorithms federated learning machine learning mathematics neural networks nonconvex optimization stochastic gradient descent (SGD)

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A Safe First-Order Method for Pricing-Based Resource Allocation in Safety-Critical Networks

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2025年第1期12卷 521-533页

作者： Turan, Berkay Hutchinson, Spencer Alizadeh, Mahnoosh Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA

We introduce a novel algorithm for solving network utility maximization (NUM) problems that arise in resource allocation schemes over networks with known safety-critical constraints, where the constraints form an arbitrary convex and compact feasible set. Inspired by applications where customers' demand can only be affected through posted prices and real-time two-way communication with customers is not available, we require an algorithm to generate "safe prices." This means that at no iteration should the realized demand in response to the posted prices violate the safety constraints of the network. Thus, in contrast to existing distributed first-order methods, our algorithm, called safe pricing for NUM (SPNUM), is guaranteed to produce feasible primal iterates at all iterations. At the heart of the algorithm lie two key steps that must go hand in hand to guarantee safety and convergence: first, applying a projected gradient method on a shrunk feasible set to get the desired demand, and second, estimating the price response function of the users and determining the price so that the induced demand is close to the desired demand. We ensure safety by adjusting the shrinkage to account for the error between the induced demand and the desired demand. In addition, by gradually reducing the amount of shrinkage and the step size of the gradient method, we prove that the primal iterates produced by the SPNUM achieve a sublinear static regret of O ( log (T)) after T time steps.

关键词： Resource management Pricing Gradient methods Optimization Safety Vectors Iterative algorithms Convergence Network systems Convex functions distributed algorithms multi-agent systems optimization methods safety smart devices

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Fixed-Time Projection Algorithm for distributed Constrained Optimization on Time-Varying Digraphs

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第1期67卷 390-397页

作者： Chen, Gang Yang, Qing Song, Yongduan Lewis, Frank L. Chongqing Univ Coll Automat State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China Univ Texas Arlington Arlington TX 76118 USA

This article studies the distributed convex optimization problem with a common decision variable, a global inequality constraint, and local constraint sets over a time-varying multiagent network, the objective function of which is a sum of agents' local convex cost functions. To solve such problem, a penalty-based distributed continuous-time subgradient algorithm with time-varying gain is developed for each agent to seek the saddle point of the penalty Lagrangian function. It is shown that an exact primal optimal solution can be obtained with certain assumption on time-varying gain. Moreover, the proposed algorithm adopts fixed-time projection scheme to ensure that for any initial state value, each local state estimate converges to its convex constraint set within fixed time. Finally, numerical examples are provided to show the effectiveness of the theoretical results.

关键词： Linear programming distributed algorithms Cost function Convex functions Convergence Indexes Time factors Continuous-time subgradient algorithm distributed optimization fixed-time projection time-varying digraph

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