arXiv subjects

Gabriele Oliva

Publications and source records attributed to Gabriele Oliva.

At least 19 recordsLinked to original sources

arXiv2026

Model-Based Detection of Anomalous Events in Submarine Cables Using Distributed Deformation Sensing and Kalman Filtering

Submarine power and telecommunication cables constitute critical global infrastructure, yet they remain vulnerable to mechanical damage caused by maritime activities and intentional tampering. Continuous monitoring of these assets is therefore essential for early detection of anomalous events. This paper proposes a model-based framework for real-time anomaly detection in submarine cables using spatially distributed deformation measurements along the cable. The cable is modeled as a tensioned structure governed by a damped wave equation with fixed boundary conditions. A finite-dimensional state-space representation is obtained through spatial discretization, enabling the use of a Kalman filter to estimate the cable's dynamic state under stochastic environmental disturbances. Anomaly detection is then formulated as a statistical hypothesis test applied to the innovation sequence of the filter. Compared with purely data-driven alarms, the proposed framework provides an interpretable residual signal whose threshold can be related to a prescribed false-alarm probability. Numerical simulations demonstrate that the proposed framework can reliably identify localized disturbances while remaining robust to ambient environmental excitation.

eess.SY↗

arXiv2026

Geometry-Informed Maritime Anomaly Detection Using Probabilistic Roadmaps

Maritime anomaly detection is essential for navigational safety and for the protection of critical underwater infrastructure. This paper proposes a geometry-informed supervised framework for detecting anomalous vessel trajectories in the Baltic Sea using Automatic Identification System (AIS) data. A Probabilistic Roadmap (PRM) is constructed over the navigable maritime domain and used as a structural prior to project trajectories onto feasible corridors. This representation enables the extraction of interpretable voyage-level features capturing route efficiency, geometric deviation from nominal paths, kinematic variability, and proximity to submarine cables. To address the scarcity of labeled anomalous events, synthetic anomalies are generated through controlled trajectory perturbations and infrastructure-aware distortions, producing a balanced dataset for supervised training. A Random Forest classifier is trained on the resulting feature set and evaluated under cross-validation and a held-out test split. Experimental results show stable generalization performance, achieving a test ROC AUC of 0.837, indicating the effectiveness of embedding navigational feasibility constraints into the anomaly detection process. The proposed approach provides an interpretable and operationally relevant framework for infrastructure-aware maritime monitoring in geometrically complex environments.

eess.SY↗

arXiv2026

Distributed Containment of a Compromised Agent through Repulsive Cages

UAV swarms and cyber-physical multi-agent systems are increasingly deployed in safety-critical missions that require coordinated motion, distributed decision making, and autonomy. A major security risk arises when a legitimate agent is hijacked and driven by adversarial high-level commands. Rather than focusing on detection and isolation of malicious agents, we exploit a structural property common in autonomous platforms: low-level collision-avoidance modules are typically implemented as independent safety layers and may remain active even under high-level compromise. Building on this property, we propose a distributed containment framework that uses the compromised agent's uncompromised avoidance response as an indirect actuation channel. Defender agents select their geometric configuration to shape the repulsive field experienced by the target, with the goal of keeping it inside a prescribed admissible region and, when required, steering it toward a desired destination. The interaction is modeled as an online Stackelberg game in which defenders act as leaders and the adversary reacts by choosing the target command. Using support-function and normal-cone arguments, we derive an exact geometric characterization of robust one-step containment and introduce the notion of a repulsive cage. These results define a centralized Stackelberg oracle and motivate a fully distributed online approximation based on local communication and dynamic field estimation. We prove sublinear dynamic-regret bounds with respect to the centralized benchmark, quantifying the effect of network-induced estimation errors and temporal variability of the stage-wise optimum. Simulations validate the approach and corroborate the theory.

eess.SY↗

arXiv2026

An Innovation-Based Approach to Detect Stealthy Disturbance Attacks in Maritime Monitoring

Modern maritime navigation and control systems rely on digital sensing, estimation, and communication pipelines that fuse GNSS, radar, inertial, and AIS data through approaches such as Kalman-filter-based estimators. While these technologies are essential for safety and efficiency, their growing interconnection also exposes vessels to faults and cyber-physical anomalies. This paper introduces a Statistical Detection Suite (SDS) to detect malicious stealthy disturbances. Specifically, the SDS operates directly on the innovations of Kalman filters, providing a lightweight yet statistically grounded layer of anomaly monitoring within maritime estimation frameworks. The SDS jointly evaluates whitened innovations through four complementary checks: (i) bias, (ii) covariance consistency via the normalized innovation squared (NIS), (iii) Gaussianity, and (iv) temporal independence via portmanteau statistics. The analysis further examines how an adversary can craft stealthy finite-impulse-response (FIR) Gaussian disturbances that can evade classical chi-square checks, formulating an optimization-based design that balances stealth and trajectory impact. An evaluation in maritime navigation scenarios illustrates how the SDS exposes colored spoofing attacks that bypass traditional methods, highlighting the role of innovation-based monitoring in strengthening maritime resilience against cyber-physical threats.

eess.SY↗

arXiv2025

Average Consensus with Dynamic Quantization Framing and Finite-Time Termination over Limited-Bandwidth Directed Networks

This paper proposes a deterministic distributed algorithm, referred to as PP-ACDC, that achieves exact average consensus over possibly unbalanced directed graphs using only a fixed and a priori specified number of quantization bits. The method integrates Push-Pull (surplus) consensus dynamics with a dynamic quantization framing scheme combining zooming and midpoint shifting, enabling agents to preserve the true global average while progressively refining their quantization precision. We establish a rigorous convergence theory showing that PP-ACDC achieves asymptotic (exact) average consensus on any strongly connected digraph under appropriately chosen quantization parameters. Moreover, we develop a fully distributed and synchronized finite-time termination mechanism, and we provide a formal proof on the detection of $\epsilon$-convergence to the average within a finite number of iterations. Numerical simulations corroborate the theoretical results and demonstrate that PP-ACDC achieves reliable, communication-efficient, and precise average consensus even under very tight bit budgets, underscoring its suitability for large-scale and resource-constrained multi-agent systems operating over directed networks.

eess.SY↗

arXiv2025

Multi-cluster distributed optimization in open multi-agent systems over directed graphs with acknowledgement messages

In this paper, we tackle the problem of distributed optimization over directed networks in open multi-agent systems (OMAS), where agents may dynamically join or leave, causing persistent changes in network topology and problem dimension. These disruptions not only pose significant challenges to maintaining convergence and stability in distributed optimization algorithms, but could also break the network topology into multiple clusters, each one associated with its own set of objective functions. To address this, we propose a novel Open Distributed Optimization Algorithm with Gradient Tracking (OPEN-GT), which employs: (a) a dynamic mechanism for detecting active out-neighbors through acknowledgement messages, and (b) a fully distributed max-consensus procedure to spread information regarding agent departures, in possibly unbalanced directed networks. We show that when all active agents execute OPEN-GT, the optimization process in each formed cluster remains consistent, while the agents converge to their cluster-wide optimal solution if there exists a time after which the network remains unchanged. Finally, we validate our approach in a simulated environment with dynamically changing agent populations, demonstrating its resilience to network variations and its ability to support distributed optimization under OMAS dynamics.

eess.SY↗

arXiv2025

Average Consensus with Dynamic Compression in Bandwidth-Limited Directed Networks

In this paper, the average consensus problem has been considered for directed unbalanced networks under finite bit-rate communication. We propose the Push-Pull Average Consensus algorithm with Dynamic Compression (PP-ACDC) algorithm, a distributed consensus algorithm that deploys an adaptive quantization scheme and achieves convergence to the exact average without the need of global information. A preliminary numerical convergence analysis and simulation results corroborate the performance of PP-ACDC.

eess.SY↗

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.

eess.SY↗

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.

eess.SY↗

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.

eess.SY↗

arXiv2022

Distributed Finite Time k-means Clustering with Quantized Communucation and Transmission Stopping

In this paper, we present a distributed algorithm which implements the $k$-means algorithm in a distributed fashion for multi-agent systems with directed communication links. The goal of $k$-means is to partition the network's agents in mutually exclusive sets (groups) such that agents in the same set have (and possibly share) similar information and are able to calculate a representative value for their group.During the operation of our distributed algorithm, each node (i) transmits quantized values in an event-driven fashion, and (ii) exhibits distributed stopping capabilities. Transmitting quantized values leads to more efficient usage of the available bandwidth and reduces the communication bottleneck. Also, in order to preserve available resources, nodes are able to distributively determine whether they can terminate the operation of the proposed algorithm. We characterize the properties of the proposed distributed algorithm and show that its execution (on any static and strongly connected digraph) will partition all agents to mutually exclusive clusters in finite time. We conclude with examples that illustrate the operation, performance, and potential advantages of the proposed algorithm.

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arXiv2020

Incomplete Analytic Hierarchy Process with Minimum Weighted Ordinal Violations

Incomplete pairwise comparison matrices offer a natural way of expressing preferences in decision making processes. Although ordinal information is crucial, there is a bias in the literature: cardinal models dominate. Ordinal models usually yield non-unique solutions; therefore, an approach blending ordinal and cardinal information is needed. In this work, we consider two cascading problems: first, we compute ordinal preferences, maximizing an index that combines ordinal and cardinal information; then, we obtain a cardinal ranking by enforcing ordinal constraints. Notably, we provide a sufficient condition (that is likely to be satisfied in practical cases) for the first problem to admit a unique solution and we develop a provably polynomial-time algorithm to compute it. The effectiveness of the proposed method is analyzed and compared with respect to other approaches and criteria at the state of the art.

math.OC↗

arXiv2019

A Dimensionality-Reduction Strategy to Compute Shortest Paths in Urban Water Networks

The efficient computation of shortest paths in complex networks is essential to face new challenges related to critical infrastructures such as a near real-time monitoring and control and the management of big size systems. In particular, using information on the minimum paths in water distribution networks (WDNs) allows to track the diffusion of contaminants and to quantify the resilience and criticality of the system. This is, ultimately, approached by considering dynamically changing path-weights that depend on the flow or on other information available at run-time. These analyses tipically include all the WDN assets but reducing the high degree of physical details with a minimum lost of key information for their performance assessment. This paper proposes a strategy to compute minimum paths that is based on a dimensionality-reduction process. Specifically, the network is partitioned into communities and suitably modified to obtain a reduced complexity representation (e.g., in terms of number of nodes and links). The paper shows how this novel, reduced representation is equivalent to the traditional network on computing the shortest paths. The proposed approach is validated considering two utility networks as case studies. The results show that the proposed method provides the exact solution for the shortest path with a computational-time reduction consistently over 50\% and up to 90\% for some cases. Furthermore, the application of the proposal on WDNs partitioning shows both hydraulic and economic advantages thanks to their monitoring and controlling at near real-time.

physics.soc-ph↗

arXiv2019

Distributed estimation and control of node centrality in undirected asymmetric networks

Measures of node centrality that describe the importance of a node within a network are crucial for understanding the behavior of social networks and graphs. In this paper, we address the problems of distributed estimation and control of node centrality in undirected graphs with asymmetric weight values. In particular, we focus our attention on $\alpha$-centrality, which can be seen as a generalization of eigenvector centrality. In this setting, we first consider a distributed protocol where agents compute their $\alpha$-centrality, focusing on the convergence properties of the method; then, we combine the estimation method with a consensus algorithm to achieve a consensus value weighted by the influence of each node in the network. Finally, we formulate an $\alpha$-centrality control problem which is naturally decoupled and, thus, suitable for a distributed setting and we apply this formulation to protect the most valuable nodes in a network against a targeted attack, by making every node in the network equally important in terms of {\alpha}-centrality. Simulations results are provided to corroborate the theoretical findings.

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arXiv2017

Distributed and Proximity-Constrained C-Means for Discrete Coverage Control

In this paper we present a novel distributed coverage control framework for a network of mobile agents, in charge of covering a finite set of points of interest (PoI), such as people in danger, geographically dispersed equipment or environmental landmarks. The proposed algorithm is inspired by C-Means, an unsupervised learning algorithm originally proposed for non-exclusive clustering and for identification of cluster centroids from a set of observations. To cope with the agents' limited sensing range and avoid infeasible coverage solutions, traditional C-Means needs to be enhanced with proximity constraints, ensuring that each agent takes into account only neighboring PoIs. The proposed coverage control framework provides useful information concerning the ranking or importance of the different PoIs to the agents, which can be exploited in further application-dependent data fusion processes, patrolling, or disaster relief applications.

cs.RO↗

arXiv2014

Distributed k-means algorithm

In this paper we provide a fully distributed implementation of the k-means clustering algorithm, intended for wireless sensor networks where each agent is endowed with a possibly high-dimensional observation (e.g., position, humidity, temperature, etc.) The proposed algorithm, by means of one-hop communication, partitions the agents into measure-dependent groups that have small in-group and large out-group "distances". Since the partitions may not have a relation with the topology of the network--members of the same clusters may not be spatially close--the algorithm is provided with a mechanism to compute the clusters'centroids even when the clusters are disconnected in several sub-clusters.The results of the proposed distributed algorithm coincide, in terms of minimization of the objective function, with the centralized k-means algorithm. Some numerical examples illustrate the capabilities of the proposed solution.

cs.LG↗

arXiv2012

Network Localization by Shadow Edges

Localization is a fundamental task for sensor networks. Traditional network construction approaches allow to obtain localized networks requiring the nodes to be at least tri-connected (in 2D), i.e., the communication graph needs to be globally rigid. In this paper we exploit, besides the information on the neighbors sensed by each robot/sensor, also the information about the lack of communication among nodes. The result is a framework where the nodes are required to be bi-connected and the communication graph has to be rigid. This is possible considering a novel typology of link, namely Shadow Edges, that account for the lack of communication among nodes and allow to reduce the uncertainty associated to the position of the nodes.

eess.SY↗

arXiv2011

On the Stability of Linear Discrete-Time Fuzzy Systems

In this paper the linear and stationary Discrete-time systems with state variables and dynamic coefficients represented by fuzzy numbers are studied, providing some stability criteria, and characterizing the bounds of the set of solutions in the case of positive systems.

eess.SY↗