no code implementations • 28 Feb 2024 • Apostolos I. Rikos, Themistoklis Charalambous, Karl H. Johansson
Our proposed algorithm is the first algorithm that achieves max-consensus in a deterministic manner (i. e., nodes always calculate the maximum of their states regardless of the nature of the probability distribution of the packet drops).
no code implementations • 27 Feb 2024 • Camilla Fioravanti, Evagoras Makridis, Gabriele Oliva, Maria Vrakopoulou, Themistoklis Charalambous
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.
no code implementations • 8 Sep 2023 • Apostolos I. Rikos, Wei Jiang, Themistoklis Charalambous, Karl H. Johansson
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.
no code implementations • 2 Apr 2023 • Apostolos I. Rikos, Andreas Grammenos, Evangelia Kalyvianaki, Christoforos N. Hadjicostis, Themistoklis Charalambous, Karl H. Johansson
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.
no code implementations • 29 Nov 2022 • Apostolos I. Rikos, Themistoklis Charalambous, Christoforos N. Hadjicostis, Karl H. Johansson
We present two distributed algorithms which rely on quantized operation (i. e., nodes process and transmit quantized messages), and are able to calculate the exact solutions in a finite number of steps.
no code implementations • 20 Nov 2022 • Apostolos I. Rikos, Wei Jiang, Themistoklis Charalambous, Karl H. Johansson
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).
no code implementations • 29 Sep 2022 • Evagoras Makridis, Themistoklis Charalambous, Christoforos N. Hadjicostis
In this paper, we address the discrete-time average consensus problem, where nodes exchange information over unreliable communication links.
no code implementations • 30 Aug 2022 • Mohammadreza Doostmohammadian, Wei Jiang, Themistoklis Charalambous
Every node locally updates its state toward optimizing a global allocation cost function via received information of its neighbouring nodes even when the data exchange over the network is heterogeneously delayed at different links.
no code implementations • 30 Aug 2022 • Mohammadreza Doostmohammadian, Usman A. Khan, Alireza Aghasi, Themistoklis Charalambous
This paper considers distributed resource allocation and sum-preserving constrained optimization over lossy networks, where the links are unreliable and subject to packet drops.
no code implementations • 30 Aug 2022 • Mohammadreza Doostmohammadian, Alireza Aghasi, Apostolos I. Rikos, Andreas Grammenos, Evangelia Kalyvianaki, Christoforos N. Hadjicostis, Karl H. Johansson, Themistoklis Charalambous
This paper considers a network of collaborating agents for local resource allocation subject to nonlinear model constraints.
no code implementations • 26 Apr 2022 • Mohammadreza Doostmohammadian, Themistoklis Charalambous
We present the minimal conditions on the remaining sensor network (after link/node removal) such that the distributed observability is still preserved and, thus, the sensor network can track the (single) maneuvering target.
no code implementations • 4 Apr 2022 • Mohammadreza Doostmohammadian, Themistoklis Charalambous
Instead, only instantaneous deviation of the residuals raises the alarm in the stateless case without considering the history of the sensor outputs and estimation data.
no code implementations • 28 Mar 2022 • Mohammadreza Doostmohammadian, Maria Vrakopoulou, Alireza Aghasi, Themistoklis Charalambous
Motivated by recent development in networking and parallel data-processing, we consider a distributed and localized finite-sum (or fixed-sum) allocation technique to solve resource-constrained convex optimization problems over multi-agent networks (MANs).
no code implementations • 20 Sep 2021 • Mohammadreza Doostmohammadian, Houman Zarrabi, Hamid R. Rabiee, Usman A. Khan, Themistoklis Charalambous
First, for performance analysis in the attack-free case, we show that the proposed distributed estimation is unbiased with bounded mean-square deviation in steady-state.
no code implementations • 10 Sep 2021 • Mohammadreza Doostmohammadian, Alireza Aghasi, Maria Vrakopoulou, Themistoklis Charalambous
A general nonlinear $1$st-order consensus-based solution for distributed constrained convex optimization is proposed with network resource allocation applications.
no code implementations • 1 Jul 2021 • Yiyang Chen, Wei Jiang, Themistoklis Charalambous
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.
no code implementations • 22 May 2021 • Mohammadreza Doostmohammadian, Themistoklis Charalambous, Miadreza Shafie-khah, Hamid R. Rabiee, Usman A. Khan
Observability and estimation are closely tied to the system structure, which can be visualized as a system graph--a graph that captures the inter-dependencies within the state variables.
no code implementations • 22 May 2021 • Mohammadreza Doostmohammadian, Themistoklis Charalambous, Miadreza Shafie-khah, Nader Meskin, Usman A. Khan
This paper considers distributed estimation of linear systems when the state observations are corrupted with Gaussian noise of unbounded support and under possible random adversarial attacks.
no code implementations • 12 May 2021 • Nikolaos Nomikos, Spyros Zoupanos, Themistoklis Charalambous, Ioannis Krikidis, Athina Petropulu
Mobile networks are experiencing tremendous increase in data volume and user density.
no code implementations • 3 May 2021 • Wei Jiang, Kun Liu, Themistoklis Charalambous
First, for all agents whose state matrix has no eigenvalues with positive real parts, a communication-delay-related observer, which is used to construct the controller, is designed for followers to estimate the leader's state information.
no code implementations • 7 Apr 2021 • Tahmoores Farjam, Themistoklis Charalambous
In this paper, we study distributed channel triggering mechanisms for wireless networked control systems (WNCSs) for conventional and smart sensors, i. e., sensors without and with computational power, respectively.
no code implementations • 1 Apr 2021 • Mohammadreza Doostmohammadian, Usman A. Khan, Mohammad Pirani, Themistoklis Charalambous
Classical distributed estimation scenarios typically assume timely and reliable exchanges of information over the sensor network.
no code implementations • 1 Apr 2021 • Mohammadreza Doostmohammadian, Alireza Aghasi, Themistoklis Charalambous, Usman A. Khan
In this paper, we consider the binary classification problem via distributed Support-Vector-Machines (SVM), where the idea is to train a network of agents, with limited share of data, to cooperatively learn the SVM classifier for the global database.
no code implementations • 10 Mar 2021 • Tahmoores Farjam, Henk Wymeersch, Themistoklis Charalambous
This property is then exploited for developing our distributed deterministic channel access scheme.
no code implementations • 15 Dec 2020 • Mohammadreza Doostmohammadian, Alireza Aghasi, Mohammad Pirani, Ehsan Nekouei, Usman A. Khan, Themistoklis Charalambous
The idea is to optimally allocate the resources among the group of agents by minimizing the overall cost function subject to fixed sum of resources.
no code implementations • 2 Aug 2015 • Zhenhua Zou, Anders Gidmark, Themistoklis Charalambous, Mikael Johansson
While the idea of RF-EH is appealing, it is not always beneficial to attempt to harvest energy; in environments where the ambient energy is low, nodes could consume more energy being awake with their harvesting circuits turned on than what they can extract from the ambient radio signals; it is then better to enter a sleep mode until the ambient RF energy increases.