arXiv subjects

Mohammadreza Doostmohammadian

Publications and source records attributed to Mohammadreza Doostmohammadian.

At least 19 recordsLinked to original sources

arXiv2026

Distributed Algorithms for Filtering, Estimation, and Fault Detection over Cyber-Physical-Systems: A Tutorial and Survey

This survey provides overview of distributed estimation, filtering, and fault detection techniques in the context of cyber-physical systems (CPS). To establish a strong foundation, we first define essential aspects of linear dynamical systems and related graph theoretic concepts, emphasizing observability conditions that are key for local state estimation. After discussing consensus algorithms, this survey highlights single-time and double-time-scale consensus-based estimation and filtering approaches. We provide a detailed comparative analysis of these methodologies, examining their computational requirements, communication overhead, and performance characteristics in resource-constrained environments. We further explore different diffusion-based estimation techniques and observationally redundant designs to enhance resilience and robustness against failures and adversarial attacks. In addition, we investigate distributed fault detection methods that enable local isolation of faults over large-scale CPS. We present both stateless and stateful detection mechanisms, along with threshold-based techniques that balance detection accuracy and false alarm rates. These approaches are essential for maintaining system integrity and preventing cascading failures in critical infrastructure. This survey concludes with an exploration of diverse real-world applications. We examine implementation challenges and algorithm adaptations in smart grid and power networks, social systems, target tracking and localization, and intelligent transportation systems. By bridging theoretical insights with practical applications, this survey and tutorial provides valuable understanding and research directions in the field of distributed algorithm design for CPS, offering both newcomers and experienced researchers a comprehensive resource for addressing current challenges and future opportunities.

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arXiv2026

Resilient Monitoring of Social Dynamical Systems through Collaborative Multi-Agent Networks under Latency

Social dynamical networks significantly influence contemporary digital landscapes, affecting realms from social activism to public policy formulation. This paper investigates the use of multi-agent systems (MAS) to monitor and analyze these networks. Firstly, we propose a single-time-scale distributed inference model designed to effectively manage challenges such as latency and agent failure. Secondly, we provide sufficient conditions that ensure the stability of the proposed scheme. Notably, the observer gain design remains effective regardless of time delays. Thirdly, we develop a computationally efficient recovery mechanism for agent failures that relies on employing graph-theoretic approaches to restore network observability by replacing failed agents (due to sensing failure or unbounded delays, i.e., packet drops) by implementing computationally efficient graph-theoretic methods to assign observationally equivalent agent counterparts. Lastly, we illustrate the proposed scheme through a pedagogical example and real-world network applications.

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arXiv2026

Using Non-Lipschitz Signum-based Functions for Distributed Optimization and Machine Learning: Trade-off Between Con-vergence Rate and Optimality Gap

In recent years, the prevalence of large-scale data-sets and the demand for sophisti-cated learning models have necessitated the development of efficient distributed ma-chine learning (ML) solutions. Convergence speed is a critical factor influencing the practicality and effectiveness of these distributed frameworks. Recently, non-Lipschitz continuous optimization algorithms have been proposed to improve the slow conver-gence rate of the existing linear solutions. The use of signum-based functions is previ-ously considered in consensus and control literature to reach fast convergence in the prescribed time and also to provide robust algorithms to noisy/outlier data. However, as shown in this work, these algorithms lead to an optimality gap and steady-state re-sidual of the objective function in discrete-time setup. This motivates us to investigate the distributed optimization and ML algorithms in terms of trade-off between conver-gence rate and optimality gap. In this direction, we specifically consider the distributed regression problem and check its convergence rate by applying both linear and non-Lipschitz signum-based functions. We check our distributed regression approach by extensive simulations. Our results show that although adopting signum-based func-tions may give faster convergence, it results in large optimality gaps. The findings pre-sented in this paper may contribute to and advance the ongoing discourse of similar distributed algorithms, e.g., for distributed constrained optimization and distributed estimation.

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arXiv2026

Resilient Consensus-Based Target Tracking under False Data Injection Attacks in Multi-Agent Networks

Distributed target tracking in multi-agent networks plays a critical role in cooperative sensing and autonomous navigation. However, it faces significant challenges in highly dynamic and adversarial setups. This study aims to enhance the resilience of decentralized target tracking algorithms against measurement faults and cyber-physical threats, especially false data injection attacks. We propose a consensus-based estimation algorithm that integrates a nearly-constant-velocity model with saturation-based filtering to suppress impulsive measurement variations and promote robust, distributed state estimation. To counteract adversarial conditions, we incorporate a dynamic false data injection detection and isolation mechanism that uses innovation thresholds to identify and disregard suspicious measurements before they can degrade the global estimate. The effectiveness of the proposed algorithms is demonstrated through a series of simulation-based case studies under both benign and adversarial conditions. The results show that increased network connectivity and higher consensus iteration rates improve estimation accuracy and convergence speed, while properly tuned saturation filters achieve a practical balance between fault suppression and accurate estimation. Furthermore, under localized, coordinated, and transient false data injection attacks, the detection mechanism successfully identifies compromised agents and prevents their data from corrupting the distributed global estimate. Overall, this study illustrates that the proposed algorithm provides a simplified fault-tolerant solution that significantly enhances the accuracy and resilience of distributed target tracking without imposing excessive communication or computational burdens.

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arXiv2026

On the Observability and Redundancy of Intelligent Transportation Networks

The safety and reliability of intelligent-transportation-systems (ITS) can be greatly enhanced through adding redundancy in the information-sharing network of the vehicles. In this paper, we first model the mixed traffic of human-driven and autonomous vehicles as a distributed system observability problem over a network of communicating vehicles. We clearly show that a strongly-connected network with minimum n links (with n as network size) is sufficient for the observability of mixed traffic network. Then, we present graph-theoretic results on adding redundancy to the changing network of vehicles to make it resilient to the failure of certain number of vehicles/sensors or their data-sharing links. Finally, we employ distributed observer design to validate our results over a simple mixed traffic example.

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arXiv2026

Distributed Observer-based Fault Detection over Intelligent Networked Multi-Vehicle Systems

Decentralized strategies are of interest for local decision-making over multi-vehicle networks. This paper studies mixed traffic networks of human-driven and autonomous vehicles with partial sensor measurements. The idea is to enable the group of connected autonomous vehicles (CAVs) to track the state of a group of human-driven vehicles (HDVs) via distributed consensus-based observers/estimators. Particularly, we make no assumption that the group of HDVs is locally observable in the direct neighborhood of any CAV. Then, the main contribution is to design local residual-based fault detection and isolation (FDI) at every CAV to detect possible faults/attacks in the sensor measurements. This distributed detection strategy enables every CAV to locally find possible anomalies in its taken sensor measurement with no need for a central processing unit. Two FDI logics are proposed with and without considering the history of the residuals. These FDI techniques are based on probabilistic threshold design on the residuals (in contrast to the existing deterministic threshold FDI techniques) with no assumption that the noise is of bounded support. This is more realistic in real-world multi-vehicle transportation systems.

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arXiv2026

Impact of Clustering on the Observability and Controllability of Complex Networks

The increasing complexity and interconnectedness of systems across various fields have led to a growing interest in studying complex networks, particularly Scale-Free (SF) networks, which best model real-world systems. This paper investigates the influence of clustering on the observability and controllability of complex SF networks, framing these characteristics in the context of structured systems theory. In this paper, we show that densely clustered networks require fewer driver and observer nodes due to better information propagation within clusters. This relationship is of interest for optimizing network design in applications such as social networks and intelligent transportation systems. We first quantify the network observability/controllability requirements, and then, through Monte-Carlo simulations and different case studies, we show how clustering affects these metrics. Our findings offer practical insights into reducing control and observer nodes for sensor/actuator placement, particularly in resource-constrained setups. This work contributes to the understanding of network observability/controllability and presents techniques for improving these features through alterations in network structure and clustering.

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arXiv2025

Machine Learning and CPU (Central Processing Unit) Scheduling Co-Optimization over a Network of Computing Centers

In the rapidly evolving research on artificial intelligence (AI) the demand for fast, computationally efficient, and scalable solutions has increased in recent years. The problem of optimizing the computing resources for distributed machine learning (ML) and optimization is considered in this paper. Given a set of data distributed over a network of computing-nodes/servers, the idea is to optimally assign the CPU (central processing unit) usage while simultaneously training each computing node locally via its own share of data. This formulates the problem as a co-optimization setup to (i) optimize the data processing and (ii) optimally allocate the computing resources. The information-sharing network among the nodes might be time-varying, but with balanced weights to ensure consensus-type convergence of the algorithm. The algorithm is all-time feasible, which implies that the computing resource-demand balance constraint holds at all iterations of the proposed solution. Moreover, the solution allows addressing possible log-scale quantization over the information-sharing channels to exchange log-quantized data. For some example applications, distributed support-vector-machine (SVM) and regression are considered as the ML training models. Results from perturbation theory, along with Lyapunov stability and eigen-spectrum analysis, are used to prove the convergence towards the optimal case. As compared to existing CPU scheduling solutions, the proposed algorithm improves the cost optimality gap by more than $50\%$.

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arXiv2025

Distributed Allocation and Resource Scheduling Algorithms Resilient to Link Failure

Distributed resource allocation (DRA) is fundamental to modern networked systems, spanning applications from economic dispatch in smart grids to CPU scheduling in data centers. Conventional DRA approaches require reliable communication, yet real-world networks frequently suffer from link failures, packet drops, and communication delays due to environmental conditions, network congestion, and security threats. We introduce a novel resilient DRA algorithm that addresses these critical challenges, and our main contributions are as follows: (1) guaranteed constraint feasibility at all times, ensuring resource-demand balance even during algorithm termination or network disruption; (2) robust convergence despite sector-bound nonlinearities at nodes/links, accommodating practical constraints like quantization and saturation; and (3) optimal performance under merely uniformly-connected networks, eliminating the need for continuous connectivity. Unlike existing approaches that require persistent network connectivity and provide only asymptotic feasibility, our graph-theoretic solution leverages network percolation theory to maintain performance during intermittent disconnections. This makes it particularly valuable for mobile multi-agent systems where nodes frequently move out of communication range. Theoretical analysis and simulations demonstrate that our algorithm converges to optimal solutions despite heterogeneous time delays and substantial link failures, significantly advancing the reliability of distributed resource allocation in practical network environments.

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arXiv2025

Delay-Tolerant Augmented-Consensus-based Distributed Directed Optimization

Distributed optimization finds applications in large-scale machine learning, data processing and classification over multi-agent networks. In real-world scenarios, the communication network of agents may encounter latency that may affect the convergence of the optimization protocol. This paper addresses the case where the information exchange among the agents (computing nodes) over data-transmission channels (links) might be subject to communication time-delays, which is not well addressed in the existing literature. Our proposed algorithm improves the state-of-the-art by handling heterogeneous and arbitrary but bounded and fixed (time-invariant) delays over general strongly-connected directed networks. Arguments from matrix theory, algebraic graph theory, and augmented consensus formulation are applied to prove the convergence to the optimal value. Simulations are provided to verify the results and compare the performance with some existing delay-free algorithms.

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arXiv2025

Distributed Automatic Generation Control subject to Ramp-Rate-Limits: Anytime Feasibility and Uniform Network-Connectivity

This paper considers automatic generation control over an information-sharing network of communicating generators as a multi-agent system. The optimization solution is distributed among the agents based on information consensus algorithms, while addressing the generators' ramp-rate-limits (RRL). This is typically ignored in the existing linear/nonlinear optimization solutions but they exist in real-time power generation scenarios. Without addressing the RRL, the generators cannot follow the assigned rate of generating power by the optimization algorithm; therefore, the existing solutions may not necessarily converge to the exact optimal cost or may lose feasibility in practice. The proposed solution in this work addresses the ramp-rate-limit constraint along with the box constraint (limits on the generated powers) and the coupling-constraint (generation-demand balance) at all iteration times of the algorithm. The latter is referred to as the anytime feasibility and implies that at every termination point of the algorithm, the balance between the demand and generated power holds. To improve the convergence rate of the algorithm we further consider internal signum-based nonlinearity. We also show that our solution can tolerate communication link removal. This follows from the uniform-connectivity assumption on the communication network.

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arXiv2025

Momentum-based Accelerated Algorithm for Distributed Optimization under Sector-Bound Nonlinearity

Distributed optimization advances centralized machine learning methods by enabling parallel and decentralized learning processes over a network of computing nodes. This work provides an accelerated consensus-based distributed algorithm for locally non-convex optimization using the gradient-tracking technique. The proposed algorithm (i) improves the convergence rate by adding momentum towards the optimal state using the heavy-ball method, while (ii) addressing general sector-bound nonlinearities over the information-sharing network. The link nonlinearity includes any sign-preserving odd sector-bound mapping, for example, log-scale data quantization or clipping in practical applications. For admissible momentum and gradient-tracking parameters, using perturbation theory and eigen-spectrum analysis, we prove convergence even in the presence of sector-bound nonlinearity and for locally non-convex cost functions. Further, in contrast to most existing weight-stochastic algorithms, we adopt weight-balanced (WB) network design. This WB design and perturbation-based analysis allow to handle dynamic directed network of agents to address possible time-varying setups due to link failures or packet drops.

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arXiv2025

On the Design of Resilient Distributed Single Time-Scale Estimators: A Graph-Theoretic Approach

Distributed estimation in interconnected systems has gained increasing attention due to its relevance in diverse applications such as sensor networks, autonomous vehicles, and cloud computing. In real practice, the sensor network may suffer from communication and/or sensor failures. This might be due to cyber-attacks, faults, or environmental conditions. Distributed estimation resilient to such conditions is the topic of this paper. By representing the sensor network as a graph and exploiting its inherent structural properties, we introduce novel techniques that enhance the robustness of distributed estimators. As compared to the literature, the proposed estimator (i) relaxes the network connectivity of most existing single time-scale estimators and (ii) reduces the communication load of the existing double time-scale estimators by avoiding the inner consensus loop. On the other hand, the sensors might be subject to faults or attacks, resulting in biased measurements. Removing these sensor data may result in observability loss. Therefore, we propose resilient design on the definitions of $q$-node-connectivity and $q$-link-connectivity, which capture robust strong-connectivity under link or sensor node failure. By proper design of the sensor network, we prove Schur stability of the proposed distributed estimation protocol under failure of up to $q$ sensors or $q$ communication links.

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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

Momentum-based Distributed Resource Scheduling Optimization Subject to Sector-Bound Nonlinearity and Latency

This paper proposes an accelerated consensus-based distributed iterative algorithm for resource allocation and scheduling. The proposed gradient-tracking algorithm introduces an auxiliary variable to add momentum towards the optimal state. We prove that this solution is all-time feasible, implying that the coupling constraint always holds along the algorithm iterative procedure; therefore, the algorithm can be terminated at any time. This is in contrast to the ADMM-based solutions that meet constraint feasibility asymptotically. Further, we show that the proposed algorithm can handle possible link nonlinearity due to logarithmically-quantized data transmission (or any sign-preserving odd sector-bound nonlinear mapping). We prove convergence over uniformly-connected dynamic networks (i.e., a hybrid setup) that may occur in mobile and time-varying multi-agent networks. Further, the latency issue over the network is addressed by proposing delay-tolerant solutions. To our best knowledge, accelerated momentum-based convergence, nonlinear linking, all-time feasibility, uniform network connectivity, and handling (possible) time delays are not altogether addressed in the literature. These contributions make our solution practical in many real-world applications.

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arXiv2025

Fully Distributed and Quantized Algorithm for MPC-based Autonomous Vehicle Platooning Optimization

Intelligent transportation systems have recently emerged to address the growing interest for safer, more efficient, and sustainable transportation solutions. In this direction, this paper presents distributed algorithms for control and optimization over vehicular networks. First, we formulate the autonomous vehicle platooning framework based on model-predictive-control (MPC) strategies and present its objective optimization as a cooperative quadratic cost function. Then, we propose a distributed algorithm to locally optimize this objective at every vehicle subject to data quantization over the communication network of vehicles. In contrast to most existing literature that assumes ideal communication channels, log-scale data quantization over the network is addressed in this work, which is more realistic and practical. In particular, we show by simulation that the proposed log-quantized algorithm reaches optimal convergence with less residual and optimality gap. This outperforms the existing literature considering uniform quantization which leads to a large optimality gap and residual.

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arXiv2025

Distributed Observer Design for Tracking Platoon of Connected and Autonomous Vehicles

Intelligent transportation systems (ITS) aim to advance innovative strategies relating to different modes of transport, traffic management, and autonomous vehicles. This paper studies the platoon of connected and autonomous vehicles (CAV) and proposes a distributed observer to track the state of the CAV dynamics. First, we model the CAV dynamics via an LTI interconnected system. Then, a consensus-based strategy is proposed to infer the state of the CAV dynamics based on local information exchange over the communication network of vehicles. A linear-matrix-inequality (LMI) technique is adopted for the block-diagonal observer gain design such that this gain is associated in a distributed way and locally to every vehicle. The distributed observer error dynamics is then shown to follow the structure of the Kronecker matrix product of the system dynamics and the adjacency matrix of the CAV network. The notions of survivable network design and redundant observer scheme are further discussed in the paper to address resilience to link and node failure. Finally, we verify our theoretical contributions via numerical simulations.

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arXiv2025

Log-Scale Quantization in Distributed First-Order Methods: Gradient-based Learning from Distributed Data

Decentralized strategies are of interest for learning from large-scale data over networks. This paper studies learning over a network of geographically distributed nodes/agents subject to quantization. Each node possesses a private local cost function, collectively contributing to a global cost function, which the considered methodology aims to minimize. In contrast to many existing papers, the information exchange among nodes is log-quantized to address limited network-bandwidth in practical situations. We consider a first-order computationally efficient distributed optimization algorithm (with no extra inner consensus loop) that leverages node-level gradient correction based on local data and network-level gradient aggregation only over nearby nodes. This method only requires balanced networks with no need for stochastic weight design. It can handle log-scale quantized data exchange over possibly time-varying and switching network setups. We study convergence over both structured networks (for example, training over data-centers) and ad-hoc multi-agent networks (for example, training over dynamic robotic networks). Through experimental validation, we show that (i) structured networks generally result in a smaller optimality gap, and (ii) log-scale quantization leads to a smaller optimality gap compared to uniform quantization.

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