Time-varying Communication Networks Research Articles

A wave delay neural network for solving label-constrained shortest route query on time-varying communication networks.

The focus of the research is on the label-constrained time-varying shortest route query problem on time-varying communication networks. To the best of our knowledge, research on this issue is still relatively limited, and similar studies have the drawbacks of low solution accuracy and slow computational speed. In this study, a wave delay neural network (WDNN) framework and corresponding algorithms is proposed to effectively solve the label-constrained time-varying shortest routing query problem. This framework accurately simulates the time-varying characteristics of the network without any training requirements. WDNN adopts a new type of wave neuron, which is independently designed and all neurons are parallelly computed on WDNN. This algorithm determines the shortest route based on the waves received by the destination neuron (node). Furthermore, the time complexity and correctness of the proposed algorithm were analyzed in detail in this study, and the performance of the algorithm was analyzed in depth by comparing it with existing algorithms on randomly generated and real networks. The research results indicate that the proposed algorithm outperforms current existing algorithms in terms of response speed and computational accuracy.

Distributed Constrained Optimization With Delayed Subgradient Information Over Time-Varying Network Under Adaptive Quantization.

In this article, we consider a distributed constrained optimization problem with delayed subgradient information over the time-varying communication network, where each agent can only communicate with its neighbors and the communication channel has a limited data rate. We propose an adaptive quantization method to address this problem. A mirror descent algorithm with delayed subgradient information is established based on the theory of Bregman divergence. With a non-Euclidean Bregman projection-based scheme, the proposed method essentially generalizes many previous classical Euclidean projection-based distributed algorithms. Through the proposed adaptive quantization method, the optimal value without any quantization error can be obtained. Furthermore, comprehensive analysis on the convergence of the algorithm is carried out and our results show that the optimal convergence rate can be obtained under appropriate conditions. Finally, numerical examples are presented to demonstrate the effectiveness of our results.

A Generic Algorithm Framework for Distributed Optimization Over the Time-Varying Network With Communication Delays

In this paper, we study the distributed optimization problem (DOP) over the time-varying communication network topology with communication delays. The existing distributed optimization algorithms (DOAs) cannot be directly applied to DOPs over the time-varying network with communication delays. In this paper, by adding new virtual agents and redefining communication links, some delay properties are realized and existing DOAs can then be reused to solve the DOPs. Thus, a generic algorithm framework is established which can apply to many important DOAs, such as the mirror descent algorithm and dual averaging algorithm, to solve DOPs over the time-varying network with communication delays. The convergence results of the DOAs are established and verified under the generic algorithm framework.

Distributed economic dispatch algorithm in smart grid based on event-triggered and fixed-time consensus methods

In this paper, a distributed economic dispatch algorithm based on event-triggered and fixed-time consensus is proposed for optimal dispatching among generators in smart grid. Aiming to quickly achieve economic dispatch in distributed smart grid and significantly reduce the frequency of communication between generators, the algorithm can address the supply–demand balance between the generation side and the consumption side, as well as the capacity constraints of the generators. Different from the relevant work, the proposed incremental cost can converge in fixed-time and the settling time is independent of the initial state of the generators. It will provide more flexibility for the system. Meanwhile, a fully distributed event-triggered strategy is adopted, which can reduce the interactive information between generators and avoid the waste of communication resources. Furthermore, the proposed algorithm is robust to the time-varying communication network topology. Finally, several examples are presented that validate the effectiveness of the algorithm.

A distributed optimization approach via symmetric group actions under time-varying communication networks

The widespread adoption of edge computing has revolutionized data processing by decentralizing computational power, resulting in faster response times, decreased network latency, and enhanced privacy. This paradigm shift is crucial due to the increasing demand for real-time analytics and the growth of Internet of Things devices. Optimizing resource allocation and decision-making in on-board computing presents challenges, especially in dynamic and heterogeneous environments. Distributed optimization techniques address these challenges by allowing multiple agents to collaborate and make decisions based on local knowledge. This paper focuses on distributed optimization in multi-agent systems with time-varying communication networks and explores the advantages of considering symmetric group action to reach global optimization. In particular, we focus our study into a class of Integer Linear Programming (ILP) for modeling real-world optimization problems in decentralized environments. The paper introduces a novel approach that leverages group actions and probabilistic selection of initial states to enhance convergence to desirable solutions. The method guarantees feasibility while minimizing computational effort for the agents. Additionally, numerical simulations have been conducted to validate the proposed algorithm, demonstrating its efficacy in optimizing resource allocation for a class of scheduling problems.

Distributed zeroth-order optimization: Convergence rates that match centralized counterpart

Zeroth-order optimization has become increasingly important in complex optimization and machine learning when cost functions are impossible to be described in closed analytical forms. The key idea of zeroth-order optimization lies in the ability for a learner to build gradient estimates by queries sent to the cost function, and then traditional gradient descent algorithms can be executed replacing gradients by the estimates. For optimization over large-scale multi-agent systems with decentralized data and costs, zeroth-order optimization can continue to be utilized to develop scalable and distributed algorithms. In this paper, we aim at understanding the trend in performance transitioning from centralized to distributed zeroth-order algorithms in terms of convergence rates, and focus on multi-agent systems with time-varying communication networks. We establish a series of convergence rates for distributed zeroth-order subgradient algorithms under both one-point and two-point zeroth-order oracles. Apart from the additional node-to-node communication cost due to the distributed nature of algorithms, the established rates in convergence are shown to match their centralized counterpart. We also propose a multi-stage distributed zeroth-order algorithm that better utilizes the learning rates, reduces the computational complexity, and attains even faster convergence rates for compact decision set.

Bilateral Peer-to-Peer Energy Trading via Coalitional Games

In this paper, we propose a bilateral peer-to-peer (P2P) energy trading scheme under single-contract and multi-contract market setups, both as an assignment game, a special class of coalitional games. The proposed market formulation allows for efficient computation of a market equilibrium while keeping the desired economic properties offered by the coalitional games. Furthermore, our market model allows buyers to have heterogeneous preferences (product differentiation) over the energy sellers, which can be economic, social, or environmental. To address the problem of scalability in coalitional games, we design a novel distributed negotiation mechanism that utilizes the geometric structure of the equilibrium solution to improve the convergence speed. Our algorithm enables market participants (prosumers) to reach a consensus on a set of “stable” and “fair” bilateral contracts which encourages prosumer participation. The negotiation process is executed with virtually minimal information requirements on a time-varying communication network that in turn preserves privacy. We use operator-theoretic tools to rigorously prove its convergence. Numerical simulations illustrate the benefits of our negotiation protocol and show that the average execution time of a negotiation step is much faster than the benchmark.

Toward distributed control of networked omnidirectional automated ground vehicles in complex environment with delay communication

The utilization of multiple small-sized automated ground vehicles (AGVs) with omnidirectional movement for cooperative transportation of large objects in manufacturing and logistics is a growing area of research. Utilizing multiple AGVs can improve system flexibility and efficiency compared to using a single large AGV, particularly in clustered and narrow spaces. This study presents a control framework for multiple AGVs to cooperatively transport an object in a cluster environment. The framework utilizes the rapidly-exploring random tree (RRT∗) algorithm to plan a feasible path through the environment, and a new distributed synchronization control algorithm for each AGV to track its desired trajectory and synchronize with its neighbors. The proposed control framework can be applied in cases with time-varying communication delays and networks with directed spanning tree connections, making it more versatile than existing synchronization control methods. Simulation and experimental results are provided to validate the proposed framework.

Fast Coordination of Distributed Energy Resources Over Time-Varying Communication Networks

In this article, we consider the problem of optimally coordinating the response of a group of distributed energy resources (DERs) controlled by distributed agents so they collectively meet the electric power demanded by a collection of loads while minimizing the total generation cost and respecting the DER capacity limits. This problem can be cast as a convex optimization problem, where the global objective is to minimize a sum of convex functions corresponding to individual DER generation cost while satisfying 1) linear inequality constraints corresponding to the DER capacity limits and 2) a linear equality constraint corresponding to the total power generated by the DERs being equal to the total power demand. We develop distributed algorithms to solve the DER coordination problem over time-varying communication networks with either bidirectional or unidirectional communication links. The proposed algorithms can be seen as distributed versions of a centralized primal–dual algorithm. One of the algorithms proposed for directed communication graphs has a geometric convergence rate even when communication out-degrees are unknown to agents. We showcase the proposed algorithms using the standard IEEE 39-bus test system and compare their performance against other ones proposed in the literature.

Event-triggered scheme for finite-time distributed economic dispatch in smart grids

This paper considers the finite-time distributed economic dispatch problem in smart grids: the power generated by individual generators are designed to satisfy a certain demand while minimizing the total generation cost in a distributed manner, which guarantees the convergence in finite time. The proposed method facilitates the solution of real time power dispatch problems. First, a class of distributed economic dispatch algorithm is proposed to achieve the optimal solution in finite-time with and without capacity limitations. Second, in order to reduce the information exchange requirements, a distributed, asynchronous event-triggered communication scheme is established which is free of Zeno with guaranteed finite-time convergence. Furthermore, both proposed algorithms are robust to the time-varying communication networks. Simulation results illustrate the effectiveness and scalability of the distributed algorithms.

A Decentralized Primal-Dual Method for Constrained Minimization of a Strongly Convex Function

We propose decentralized primal-dual methods for cooperative multiagent consensus optimization problems over both static and time-varying communication networks, where only local communications are allowed. The objective is to minimize the sum of agent-specific convex functions over conic constraint sets defined by agent-specific nonlinear functions; hence, the optimal consensus decision should lie in the intersection of these private sets. Under the strong convexity assumption, we provide convergence rates for suboptimality, infeasibility, and consensus violation in terms of the number of communications required; examine the effect of underlying network topology on the convergence rates.

An Asynchronous Gradient Descent Based Method for Distributed Resource Allocation With Bounded Variables

This article considers the distributed resource allocation problem over a network of n agents with individual optimization functions and coupled resource constraints. The optimization variables are bounded for each agent. We propose an asynchronous distributed gradient descent based method, which is able to tackle time-varying communication networks and communication problems. The main ideas are twofold. First, the degree of freedom of the network is reduced to deal with the global constraint. Second, we utilize delay compensation to counteract the delay of asynchronous communication. We prove that the convergence rate is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(\frac{1}{k})$</tex-math></inline-formula> and analyze both the communication complexity and the time complexity. This method is applied into the parallel pump control problem in HVAC system and shows good numerical results.

Consensus-based distributed economic dispatch for Multi Micro Energy Grid systems under coupled carbon emissions

The promotion of carbon neutrality targets will significantly influence the energy management of multi micro energy grid (MMEG) systems that can supply flexible local loads using various distributed energy resources. So far, few works incorporate the carbon emissions management with the fully-distributed coordination of MMEG systems to protect the environment and individual privacy and improve the energy efficiency. To fill this gap, in this paper, a hierarchical optimization framework is proposed for the MMEG system to solve an environmental economic dispatch problem, such that the energy coordination strategy could be obtained in a fully-distributed way. At the upper level, under a local and time-varying communication network, we develop an aggregate model based on a real-time price policy for the MMEG system. On this basis, a fully distributed consensus-based gradient projection method is proposed to minimize the total system generation cost with the coupled emission constraint satisfied, wherein a piecewise linearization technique is applied to handle the non-convexity and non-smoothness of the cost function caused by valve-point loading effects (VPLE) of thermal units. At the lower level, we apply an accelerated distributed augmented Lagrangian (ADAL) method to deal with a cost minimization optimization problem for users, such that each user could implement its own optimal strategy simultaneously based on the obtained aggregated strategy from the upper level. The effectiveness of the proposed approach is demonstrated by numerical simulations on an MMEG test system.

Multi-Agent Natural Actor-Critic Reinforcement Learning Algorithms

Multi-agent actor-critic algorithms are an important part of the Reinforcement Learning (RL) paradigm. We propose three fully decentralized multi-agent natural actor-critic (MAN) algorithms in this work. The objective is to collectively find a joint policy that maximizes the average long-term return of these agents. In the absence of a central controller and to preserve privacy, agents communicate some information to their neighbors via a time-varying communication network. We prove convergence of all the three MAN algorithms to a globally asymptotically stable set of the ODE corresponding to actor update; these use linear function approximations. We show that the Kullback–Leibler divergence between policies of successive iterates is proportional to the objective function’s gradient. We observe that the minimum singular value of the Fisher information matrix is well within the reciprocal of the policy parameter dimension. Using this, we theoretically show that the optimal value of the deterministic variant of the MAN algorithm at each iterate dominates that of the standard gradient-based multi-agent actor-critic (MAAC) algorithm. To our knowledge, it is the first such result in multi-agent reinforcement learning (MARL). To illustrate the usefulness of our proposed algorithms, we implement them on a bi-lane traffic network to reduce the average network congestion. We observe an almost 25% reduction in the average congestion in 2 MAN algorithms; the average congestion in another MAN algorithm is on par with the MAAC algorithm. We also consider a generic 15 agent MARL; the performance of the MAN algorithms is again as good as the MAAC algorithm.

Formation control of non-identical multi-agent systems

The problem considered in this work is formation control for non-identical linear multi-agent systems (MASs) under a time-varying communication network. The size of the formation is scalable via a scaling factor determined by a leader agent. Past works on scalable formation are limited to identical agents under a fixed communication network. In addition, the formation scaling variable is updated under a leader-follower network. Differently, this work considers a leaderless undirected network in addition to a leader-follower network to update the formation scaling variable. The control law to achieve scalable formation is based on the internal model principle and consensus algorithm. A biased reference output, updated in a distributed manner, is introduced such that each agent tracks a different reference output. Numerical examples show the effectiveness of the proposed method.

Payoff Distribution in Robust Coalitional Games on Time-Varying Networks

In this paper, we consider a sequence of transferable utility (TU) coalitional games where the coalitional values are unknown but vary within certain bounds. As a solution to the resulting family of games, we formalise the notion of Our main contribution is to design two distributed algorithms, namely, distributed payoff allocation and distributed bargaining, that converge to a consensual payoff distribution in the robust CORE. We adopt an operator-theoretic perspective to show convergence of both algorithms executed on time-varying communication networks. An energy storage optimization application motivates our framework for coalitional games.

Distributed Vehicle to Grid Integration Over Communication and Physical Networks With Uncertainty Effects

This paper proposes a distributed framework for vehicle grid integration taking into account the communication and physical networks. To this end, we model the electric vehicle behavior that includes times of departure and arrival, state of charge, required energy, and its objectives, e.g., avoid battery degradation and willingness to pay. Next, we formulate the centralised day ahead distribution market which explicitly represents the physical system, supports unbalanced three phase networks with delta and wye connections, and incorporates the charging needs of electric vehicles under a uni- and a bi-directional charging scheme. The solution of the centralised market requires knowledge of electric vehicle information in terms of desired energy, departure and arrival times that electric vehicle owners are reluctant in providing. Moreover, the computational effort required to solve the day ahead distribution market in cases of numerous electric vehicles is very intensive. As such, we propose a distributed solution of the day ahead distribution market clearing mechanism over a time-varying communication network where no private information is exchanged. We also incorporate uncertainty of electric vehicle charging needs and provide guarantees on the probability of constraint satisfaction. We illustrate the proposed vehicle grid integration framework through the 13-bus, 33-bus, and 141-bus distribution feeders.

Decentralized Fictitious Play in Near-Potential Games With Time-Varying Communication Networks

We study the convergence properties of decentralized fictitious play (DFP) for the class of near-potential games where the incentives of agents are nearly aligned with a potential function. In DFP, agents share information only with their current neighbors in a sequence of time-varying networks, keep estimates of other agents' empirical frequencies, and take actions to maximize their expected utility functions computed with respect to the estimated empirical frequencies. We show that empirical frequencies of actions converge to a set of strategies with potential function values that are larger than the potential function values obtained by approximate Nash equilibria of the potential game. This result establishes that DFP has identical convergence guarantees in near-potential games as the standard fictitious play in which agents observe the past actions of all the other agents.

Multi-commodity Optimization of Peer-to-peer Energy Trading Resources in Smart Grid

Utility maximization is a major priority of prosumers participating in peer-to-peer energy trading and sharing (P2P-ETS). However, as more distributed energy resources integrate into the distribution network, the impact of the communication link becomes significant. We present a multi-commodity formulation that allows the dual-optimization of energy and communication resources in P2P-ETS. On one hand, the proposed algorithm minimizes the cost of energy generation and communication delay. On the other hand, it also maximizes the global utility of prosumers with fair resource allocation. We evaluate the algorithm in a variety of realistic conditions including a time-varying communication network with signal delay signal loss. The results show that the convergence is achieved in a fewer number of time steps than the previously proposed algorithms. It is further observed that the entities with a higher willingness to trade the energy acquire more satisfactions than others.

Subgradient averaging for multi-agent optimisation with different constraint sets

We consider a multi-agent setting with agents exchanging information over a possibly time-varying network, aiming at minimising a separable objective function subject to constraints. To achieve this objective we propose a novel subgradient averaging algorithm that allows for non-differentiable objective functions and different constraint sets per agent. Allowing different constraints per agent simultaneously with a time-varying communication network constitutes a distinctive feature of our approach, extending existing results on distributed subgradient methods. To highlight the necessity of dealing with a different constraint set within a distributed optimisation context, we analyse a problem instance where an existing algorithm does not exhibit a convergent behaviour if adapted to account for different constraint sets. For our proposed iterative scheme we show asymptotic convergence of the iterates to a minimum of the underlying optimisation problem for step sizes of the form ηk+1, η>0. We also analyse this scheme under a step size choice of ηk+1, η>0, and establish a convergence rate of O(lnkk) in objective value. To demonstrate the efficacy of the proposed method, we investigate a robust regression problem and an ℓ2 regression problem with regularisation.