arXiv subjects

Themistoklis Charalambous

Publications and source records attributed to Themistoklis Charalambous.

At least 37 records · Page 2Linked to original sources

arXiv2025

Distributed Optimization with Efficient Communication, Event-Triggered Solution Enhancement, and Operation Stopping

In modern large-scale systems with sensor networks and IoT devices it is essential to collaboratively solve complex problems while utilizing network resources efficiently. In our paper we present three distributed optimization algorithms that exhibit efficient communication among nodes. Our first algorithm presents a simple quantized averaged gradient procedure for distributed optimization, which is shown to converge to a neighborhood of the optimal solution. Our second algorithm incorporates a novel event-triggered refinement mechanism, which refines the utilized quantization level to enhance the precision of the estimated optimal solution. It enables nodes to terminate their operation according to predefined performance guarantees. Our third algorithm is tailored to operate in environments where each message consists of only a few bits. It incorporates a novel event-triggered mechanism for adjusting the quantizer basis and quantization level, allowing nodes to collaboratively decide operation termination based on predefined performance criteria. We analyze the three algorithms and establish their linear convergence. Finally, an application on distributed sensor fusion for target localization is used to demonstrate their favorable performance compared to existing algorithms in the literature.

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arXiv2025

Distributed Optimization with Gradient Tracking over Heterogeneous Delay-Prone Directed Networks

In this paper, we address the distributed optimization problem over unidirectional networks with possibly time-invariant heterogeneous bounded transmission delays. In particular, we propose a modified version of the Accelerated Distributed Directed OPTimization (ADD-OPT) algorithm, herein called Robustified ADD-OPT (R-ADD-OPT), which is able to solve the distributed optimization problem, even when the communication links suffer from heterogeneous but bounded transmission delays. We show that if the gradient step-size of the R-ADD-OPT algorithm is within a certain range, which also depends on the maximum time delay in the network, then the nodes are guaranteed to converge to the optimal solution of the distributed optimization problem. The range of the gradient step-size that guarantees convergence can be computed a priori based on the maximum time delay in the network.

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arXiv2025

A Linear Push-Pull Average Consensus Algorithm for Delay-Prone Networks

In this paper, we address the average consensus problem of multi-agent systems for possibly unbalanced and delay-prone networks with directional information flow. We propose a linear distributed algorithm (referred to as RPPAC) that handles asynchronous updates and time-varying heterogeneous information delays. Our proposed distributed algorithm utilizes a surplus-consensus mechanism and information regarding the number of incoming and outgoing links to guarantee state averaging, despite the imbalanced and delayed information flow in directional networks. The convergence of the RPPAC algorithm is examined using key properties of the backward product of time-varying matrices that correspond to different snapshots of the directional augmented network.

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arXiv2025

HARQ-based Quantized Average Consensus over Unreliable Directed Network Topologies

In this paper, we propose a distributed algorithm (herein called HARQ-QAC) that enables nodes to calculate the average of their initial states by exchanging quantized messages over a directed communication network. In our setting, we assume that our communication network consists of unreliable communication links (i.e., links suffering from packet drops). For countering link unreliability our algorithm leverages narrowband error-free feedback channels for acknowledging whether a packet transmission between nodes was successful. Additionally, we show that the feedback channels play a crucial role in enabling our algorithm to exhibit finite-time convergence. We analyze our algorithm and demonstrate its operation via an example, where we illustrate its operational advantages. Finally, simulations corroborate that our proposed algorithm converges to the average of the initial quantized values in a finite number of steps, despite the packet losses. This is the first quantized consensus algorithm in the literature that can handle packet losses and converge to the average. Additionally, the use of the retransmission mechanism allows for accelerating the convergence.

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arXiv2024

Distributed Target Tracking based on Localization with Linear Time-Difference-of-Arrival Measurements: A Delay-Tolerant Networked Estimation Approach

This paper considers target tracking based on a beacon signal's time-difference-of-arrival (TDOA) to a group of cooperating sensors. The sensors receive a reflected signal from the target where the time-of-arrival (TOA) renders the distance information. The existing approaches include: (i) classic centralized solutions which gather and process the target data at a central unit, (ii) distributed solutions which assume that the target data is observable in the dense neighborhood of each sensor (to be filtered locally), and (iii) double time-scale distributed methods with high rates of communication/consensus over the network. This work, in order to reduce the network connectivity in (i)-(ii) and communication rate in (iii), proposes a distributed single time-scale technique, which can also handle heterogeneous constant data-exchange delays over the static sensor network. This work assumes only distributed observability (in contrast to local observability in some existing works categorized in (ii)), i.e., the target is observable globally over a (strongly) connected network. The (strong) connectivity further allows for survivable network and $q$-redundant observer design. Each sensor locally shares information and processes the received data in its immediate neighborhood via local linear-matrix-inequalities (LMI) feedback gains to ensure tracking error stability. The same gain matrix works in the presence of heterogeneous delays with no need of redesigning algorithms. Since most existing distributed estimation scenarios are linear (based on consensus), many works use linearization of the existing nonlinear TDOA measurement models where the output matrix is a function of the target position.

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arXiv2024

Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement

In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorithms is essential. In our paper we addresses the challenge of unconstrained distributed optimization. In our scenario each node's local function exhibits strong convexity with Lipschitz continuous gradients. The exchange of information between nodes occurs through $3$-bit bandwidth-limited channels (i.e., nodes exchange messages represented by a only $3$-bits). Our proposed algorithm respects the network's bandwidth constraints by leveraging zoom-in and zoom-out operations to adjust quantizer parameters dynamically. We show that during our algorithm's operation nodes are able to converge to the exact optimal solution. Furthermore, we show that our algorithm achieves a linear convergence rate to the optimal solution. We conclude the paper with simulations that highlight our algorithm's unique characteristics.

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arXiv2024

Average Consensus over Directed Networks in Open Multi-Agent Systems with Acknowledgement Feedback

In this paper, we address the distributed average consensus problem over directed networks in open multi-agent systems (OMAS), where the stability of the network is disrupted by frequent agent arrivals and departures, leading to a time-varying average consensus target. To tackle this challenge, we introduce a novel ratio consensus algorithm (OPENRC) based on acknowledgement feedback, designed to be robust to agent arrivals and departures, as well as to unbalanced directed network topologies. We demonstrate that when all active agents execute the OPENRC algorithm, the sum of their state variables remains constant during quiescent epochs when the network remains unchanged. By assuming eventual convergence during such quiescent periods following persistent variations in system composition and size, we prove the convergence of the OPENRC algorithm using column-stochasticity and mass-preservation properties. Finally, we apply and evaluate our proposed algorithm in a simulated environment, where agents are departing from and arriving in the network to highlight its resilience against changes in the network size and topology.

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arXiv2024

ARQ-based Average Consensus over Directed Network Topologies with Unreliable Communication Links

In this paper, we address the discrete-time average consensus problem in strongly connected directed graphs, where nodes exchange information over unreliable error-prone communication links. We enhance the Robustified Ratio Consensus algorithm by exploiting features of the (Hybrid) Automatic Repeat ReQuest - (H)ARQ protocol used for error control of data transmissions, in order to allow the nodes to reach asymptotic average consensus even when information is exchanged over error-prone directional networks. This strategy, apart from handling time-varying information delays induced by retransmissions of erroneous packets, can also handle packet drops that occur when exceeding a predefined packet retransmission limit. Invoking the (H)ARQ protocol allows nodes to: (a) exploit the incoming error-free acknowledgement feedback to initially acquire or later update their out-degree, (b) know whether a packet has arrived or not, and (c) determine a local upper-bound on the delays imposed by the retransmission limit. By augmenting the network's corresponding weight matrix, we show that nodes utilizing our proposed (H)ARQ Ratio Consensus algorithm can reach asymptotic average consensus over unreliable networks, while improving their convergence speed and maintaining low values in their local buffers compared to the current state-of-the-art.

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arXiv2024

Internal and String Stability of an Observer-based Controller for Vehicle Platooning under the MPF Topology

In this paper, we study the internal stability and string stability of a vehicle platoon under the constant time headway spacing (CTHS) policy and the multiple-predecessor-following (MPF) vehicle-to-vehicle information flow topology. More specifically, we depart from the conventional Proportional-Integral-Derivative (PID) controller design for such systems and we propose the design of an observer-based controller. For designing our observer-based controller, we first design a distributed observer, with which each follower estimates their position, speed and acceleration error with respect to the leader. The observer is designed by means of constructing an observer matrix whose parameters should be determined. Next, we simplify the design of the matrix of the observer in such a way that the design boils down to choosing a single scalar value; this design further simplifies the structure of the controller, whose simplicity enables the derivation of string stability conditions by means of a frequency response method. Subsequently, the string stability conditions for a given time headway, are transformed to conditions for the controller parameters. We obtain controller parameters that satisfy the stability conditions by designing a novel heuristic search algorithm. Furthermore, we extend the search algorithm by incorporating a bisection-like algorithm, which allows to obtain (within some deviation tolerance) the minimum available value of the time headway. Finally, we provide insights about how to finalize the observer-based controller parameters from above algorithms to avoid the peaking phenomenon. The performance of the proposed observer-based controller is demonstrated via illustrative examples. Additionally, a comparison with a widely-used PID controller for MPF topology shows that our proposed observer-based controller has better convergence performance.

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arXiv2024

Distributed Estimation and Control for LTI Systems under Finite-Time Agreement

This paper considers a strongly connected network of agents, each capable of partially observing and controlling a discrete-time linear time-invariant (LTI) system that is jointly observable and controllable. Additionally, agents collaborate to achieve a shared estimated state, computed as the average of their local state estimates. Recent studies suggest that increasing the number of average consensus steps between state estimation updates allows agents to choose from a wider range of state feedback controllers, thereby potentially enhancing control performance. However, such approaches require that agents know the input matrices of all other nodes, and the selection of control gains is, in general, centralized. Motivated by the limitations of such approaches, we propose a new technique where: (i) estimation and control gain design is fully distributed and finite-time, and (ii) agent coordination involves a finite-time exact average consensus algorithm, allowing arbitrary selection of estimation convergence rate despite the estimator's distributed nature. We verify our methodology's effectiveness using illustrative numerical simulations.

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arXiv2023

Distributed Optimization with Quantized Gradient Descent

In this paper, we consider the unconstrained distributed optimization problem, in which the exchange of information in the network is captured by a directed graph topology, thus, nodes can only communicate with their neighbors. Additionally, in our problem, the communication channels among the nodes have limited bandwidth. In order to alleviate this limitation, quantized messages should be exchanged among the nodes. For solving this distributed optimization problem, we combine a gradient descent method with a distributed quantized consensus algorithm (which requires the nodes to exchange quantized messages and converges in a finite number of steps). Specifically, at every optimization step, each node (i) performs a gradient descent step (i.e., subtracts the scaled gradient from its current estimate), and (ii) performs a finite-time calculation of the quantized average of every node's estimate in the network. As a consequence, this algorithm approximately mimics the centralized gradient descent algorithm. We show that our algorithm asymptotically converges to a neighborhood of the optimal solution with linear convergence rate. The performance of the proposed algorithm is demonstrated via simple illustrative examples.

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arXiv2023

Asynchronous Distributed Optimization via ADMM with Efficient Communication

In this paper, we focus on an asynchronous distributed optimization problem. In our problem, each node is endowed with a convex local cost function, and is able to communicate with its neighbors over a directed communication network. Furthermore, we assume that the communication channels between nodes have limited bandwidth, and each node suffers from processing delays. We present a distributed algorithm which combines the Alternating Direction Method of Multipliers (ADMM) strategy with a finite time quantized averaging algorithm. In our proposed algorithm, nodes exchange quantized valued messages and operate in an asynchronous fashion. More specifically, during every iteration of our algorithm each node (i) solves a local convex optimization problem (for the one of its primal variables), and (ii) utilizes a finite-time quantized averaging algorithm to obtain the value of the second primal variable (since the cost function for the second primal variable is not decomposable). We show that our algorithm converges to the optimal solution at a rate of $O(1/k)$ (where $k$ is the number of time steps) for the case where the local cost function of every node is convex and not-necessarily differentiable. Finally, we demonstrate the operational advantages of our algorithm against other algorithms from the literature.

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arXiv2023

Distributed Optimization via Gradient Descent with Event-Triggered Zooming over Quantized Communication

In this paper, we study unconstrained distributed optimization strongly convex problems, in which the exchange of information in the network is captured by a directed graph topology over digital channels that have limited capacity (and hence information should be quantized). Distributed methods in which nodes use quantized communication yield a solution at the proximity of the optimal solution, hence reaching an error floor that depends on the quantization level used; the finer the quantization the lower the error floor. However, it is not possible to determine in advance the optimal quantization level that ensures specific performance guarantees (such as achieving an error floor below a predefined threshold). Choosing a very small quantization level that would guarantee the desired performance, requires {information} packets of very large size, which is not desirable (could increase the probability of packet losses, increase delays, etc) and often not feasible due to the limited capacity of the channels available. In order to obtain a communication-efficient distributed solution and a sufficiently close proximity to the optimal solution, we propose a quantized distributed optimization algorithm that converges in a finite number of steps and is able to adjust the quantization level accordingly. The proposed solution uses a finite-time distributed optimization protocol to find a solution to the problem for a given quantization level in a finite number of steps and keeps refining the quantization level until the difference in the solution between two successive solutions with different quantization levels is below a certain pre-specified threshold.

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arXiv2023

A Privacy-Preserving Finite-Time Push-Sum based Gradient Method for Distributed Optimization over Digraphs

This paper addresses the problem of distributed optimization, where a network of agents represented as a directed graph (digraph) aims to collaboratively minimize the sum of their individual cost functions. Existing approaches for distributed optimization over digraphs, such as Push-Pull, require agents to exchange explicit state values with their neighbors in order to reach an optimal solution. However, this can result in the disclosure of sensitive and private information. To overcome this issue, we propose a state-decomposition-based privacy-preserving finite-time push-sum (PrFTPS) algorithm without any global information, such as network size or graph diameter. Then, based on PrFTPS, we design a gradient descent algorithm (PrFTPS-GD) to solve the distributed optimization problem. It is proved that under PrFTPS-GD, the privacy of each agent is preserved and the linear convergence rate related to the optimization iteration number is achieved. Finally, numerical simulations are provided to illustrate the effectiveness of the proposed approach.

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arXiv2023

Machine learning based iterative learning control for non-repetitive time-varying systems

The repetitive tracking task for time-varying systems (TVSs) with non-repetitive time-varying parameters, which is also called non-repetitive TVSs, is realized in this paper using iterative learning control (ILC). A machine learning (ML) based nominal model update mechanism, which utilizes the linear regression technique to update the nominal model at each ILC trial only using the current trial information, is proposed for non-repetitive TVSs in order to enhance the ILC performance. Given that the ML mechanism forces the model uncertainties to remain within the ILC robust tolerance, an ILC update law is proposed to deal with non-repetitive TVSs. How to tune parameters inside ML and ILC algorithms to achieve the desired aggregate performance is also provided. The robustness and reliability of the proposed method are verified by simulations. Comparison with current state-of-the-art demonstrates its superior control performance in terms of controlling precision. This paper broadens ILC applications from time-invariant systems to non-repetitive TVSs, adopts ML regression technique to estimate non-repetitive time-varying parameters between two ILC trials and proposes a detailed parameter tuning mechanism to achieve desired performance, which are the main contributions.

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arXiv2023

Scheduling Policies for AoI Minimization with Timely Throughput Constraints

In 5G and beyond communication systems, the notion of latency gets great momentum in wireless connectivity as a metric for serving real-time communications requirements. However, in many applications, research has pointed out that latency could be inefficient to handle applications with data freshness requirements. Recently, Age of Information (AoI) metric, which can capture the freshness of the data, has attracted a lot of attention. In this work, we consider mixed traffic with time-sensitive users; a deadline-constrained user, and an AoI-oriented user. To develop an efficient scheduling policy, we cast a novel optimization problem formulation for minimizing the average AoI while satisfying the timely throughput constraints. The formulated problem is cast as a Constrained Markov Decision Process (CMDP). We relax the constrained problem to an unconstrained Markov Decision Process (MDP) problem by utilizing the Lyapunov optimization theory and it can be proved that it is solved per frame by applying backward dynamic programming algorithms with optimality guarantees. In addition, we provide a low-complexity algorithm guaranteeing that the timely-throughput constraint is satisfied. Simulation results show that the timely throughput constraints are satisfied while minimizing the average AoI. Simulation results show the convergence of the algorithms for different values of the weighted factor and the trade-off between the AoI and the timely throughput.

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arXiv2023

Distributed Optimization for Quadratic Cost Functions over Large-Scale Networks with Quantized Communication and Finite-Time Convergence

We propose two distributed iterative algorithms that can be used to solve, in finite time, the distributed optimization problem over quadratic local cost functions in large-scale networks. The first algorithm exhibits synchronous operation whereas the second one exhibits asynchronous operation. Both algorithms share salient features. Specifically, the algorithms operate exclusively with quantized values, which means that the information stored, processed and exchanged between neighboring nodes is subject to deterministic uniform quantization. The algorithms rely on event-driven updates in order to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage. Finally, once the algorithms converge, nodes distributively terminate their operation. We prove that our algorithms converge in a finite number of iterations to the exact optimal solution depending on the quantization level, and we present applications of our algorithms to (i) optimal task scheduling for data centers, and (ii) global model aggregation for distributed federated learning. We provide simulations of these applications to illustrate the operation, performance, and advantages of the proposed algorithms. Additionally, it is shown that our proposed algorithms compare favorably to algorithms in the current literature. Quantized communication and asynchronous updates increase the required time to completion, but finite-time operation is maintained.

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arXiv2023

Maximum Correntropy Criterion Kalman Filter For Indoor Quadrotor Navigation Under Intermittent Measurements

We present a multisensor fusion framework for the onboard real-time navigation of a quadrotor in an indoor environment. The framework integrates sensor readings from an Inertial Measurement Unit (IMU), a camera-based object detection algorithm, and an Ultra-WideBand (UWB) localisation system. Often the sensor readings are not always readily available, leading to inaccurate pose estimation and hence poor navigation performance. To effectively handle and fuse sensor readings, and accurately estimate the pose of the quadrotor for tracking a predefined trajectory, we design a Maximum Correntropy Criterion Kalman Filter (MCC-KF) that can manage intermittent observations. The MCC-KF is designed to improve the performance of the estimation process when is done with a Kalman Filter (KF), since KFs are likely to degrade dramatically in practical scenarios in which noise is non-Gaussian (especially when the noise is heavy-tailed). To evaluate the performance of the MCC-KF, we compare it with a previously designed Kalman filter by the authors. Through this comparison, we aim to demonstrate the effectiveness of the MCC-KF in handling indoor navigation missions. The simulation results show that our presented framework offers low positioning errors, while effectively handling intermittent sensor measurements.

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