Baltasar Beferull-Lozano

Nordic Machine Intelligence (NMI) 5(3) p. 1-14 Doi: https://doi.org/10.5617/nmi.12551

Signal Processing 233 p. 109930-109930 Doi: https://doi.org/10.1016/j.sigpro.2025.109930

2025 IEEE Statistical Signal Processing Workshop (SSP) p. 306-310 Doi: https://doi.org/10.1109/ssp64130.2025.11073282

IEEE Transactions on Cognitive Communications and Networking (TCCN) 12 p. 846-863 Doi: https://doi.org/10.1109/TCCN.2025.3570469

This paper leverages transfer learning (TL) on a mixture of experts (MoE) model for indoor radio map estimation. The proposed MoE combines location-based and location-free experts through a gating network exploiting their complementary benefits. To estimate the radio map in a new wireless environment, the learned model of another sufficiently similar wireless environment is transferred and fine-tuned with additional data from the new wireless environment. The proposed data-driven similarity measure predicts the amount of training data needed for TL. Results demonstrate that the proposed method achieves similar accuracy, in terms of mean square error (MSE), to a model trained without TL while only requiring 5-40% of measurement data to adapt to several varying wireless environments. The test environments include both the office area and the cafe area wireless environments. As expected, the proposed MoE method outperforms both experts as well as the state-of-the-art methods, in both the presence and the absence of noise in location-based and location-free features.

European Signal Processing Conference (EUSIPCO)

Vector autoregressive (VAR) processes are simple yet remarkably versatile discrete statistical models used to characterize the dynamics of a collection of variables. Although implicitly, any given VAR process assumes the highest rate, or resolution, at which those variables may vary. Hence, dynamic variations at finer rates are out of the explainability of such models by design. This paper proposes a new method to overcome this drawback. Specifically, it describes how to increase the resolution associated with any VAR process such that the process mean is unchanged and the forecast estimates at the rates given by the original resolution, and the long-run associated credible intervals, are closely preserved. Our experiments confirm the viability of the proposed method for multivariate time series analysis.

IEEE Transactions on Signal Processing 72 p. 5454-5469 Doi: https://doi.org/10.1109/TSP.2024.3503063

The vector autoregressive (VAR) model is extensively employed for modelling dynamic processes, yet its scalability is challenged by an overwhelming growth in parameters when dealing with several hundred time series. To overcome this issue, inductive priors (e.g., data structure) can be leveraged to restrict the parameter space while still effectively modelling the time series. We present simplicial VAR models to mitigate the curse of dimensionality in VAR models, demonstrating also their utility in capturing the dynamics of time series defined over higher-order network structures such as edges and trian- gles. The proposed models use simplicial convolutional filters to facilitate parameter sharing across simplicial signals and capture structure-aware spatio-temporal dependencies among them. We also develop a joint simplicial-temporal Fourier transform to analyze the spectral characteristics of the models, depicting them as simplicial-temporal filters. We focus on streaming signals from real-world time-varying networks and develop an online algorithm for learning simplicial VAR models with a sublinear dynamic regret bound, ensuring convergence under reasonable assumptions. Through experiments on synthetic networks, water distribution networks, and collaborating agents, we demonstrate that the proposed models attain competitive signal modelling accuracy with orders of magnitude fewer parameters than VAR models.

IEEE Conference on Industrial Electronics and Applications 19 Doi: https://doi.org/10.1109/ICIEA61579.2024.10665191

IEEE Conference on Industrial Electronics and Applications Doi: https://doi.org/10.1109/ICIEA61579.2024.10665270

This paper introduces a framework for teaching robot fine-manipulation skills by combining haptic teleoperation with Probabilistic Movement Primitives (ProMPs). Addressing the challenges of executing precise tasks such as snap-joint manipulation, our framework utilises ROS2 alongside a cartesian compliance controller for task space operation. This setup facilitates the intuitive guidance of robots by human operators, capturing the detailed movements critical for fine-manipulation tasks. Through experiments with the UR5e 6-DOF collaborative robot arm and a 3DSystems Touch haptic device, we illustrate the accurate representation and replication of the human demonstrated tasks.

IEEE Xplore Digital Library Doi: https://doi.org/10.1109/ICIT58233.2024.10540954

This paper addresses the control-theoretic approach of an autonomous fog computing platform to process the of-ftoaded mobile robot vision data for warehouse applications. The approach entails polytopic linear parameter varying (PLPV) modelling of the fog platform and the design of an event-triggered control to process the vision data within a specified time frame called service time, by auto-scaling the computing nodes. The PLPV model is developed using dynamic mobile robot vision data with image resolution and frame rate as parameters. The developed theory is experimentally validated on a mobile robot that navigates in a warehouse application environment, and the results are presented to show the efficacy of the proposed controller in meeting the service time requirements.

IEEE Transactions on Signal and Information Processing over Networks 9 p. 691-704 Doi: https://doi.org/10.1109/TSIPN.2023.3324569

Identifying the topology underlying a set of time series is useful for tasks such as prediction, denoising, and data completion. Vector autoregressive (VAR) model-based topologies capture dependencies among time series and are often inferred from observed spatio-temporal data. When data are affected by noise and/or missing samples, topology identification and signal recovery (reconstruction) tasks must be performed jointly. Additional challenges arise when i) the underlying topology is time-varying, ii) data become available sequentially, and iii) no delay is tolerated. This study proposes an online algorithm to overcome these challenges in estimating VAR model-based topologies, having constant complexity per iteration, which makes it interesting for big-data scenarios. The inexact proximal online gradient descent framework is used to derive a performance guarantee for the proposed algorithm, in the form of a dynamic regret bound. Numerical tests are also presented, showing the ability of the proposed algorithm to track time-varying topologies with missing data in an online fashion.

Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON) Doi: https://doi.org/10.1109/IECON51785.2023.10312543

While Deep Learning based methods can solve complex problems by employing Neural Networks to act as powerful function approximators, they often suffer from inflexibility in terms of deployment beyond the training scenario and also include irrelevant data priors in the form of an ordered array of input values. This problem is quite evident in the field of MultiAgent Reinforcement Learning (MARL), where most research covers methods that are trained on a fixed number of agents, restricted by the fixed size of the input vector. In this paper, we argue that this is not a reasonable assumption, both in terms of the inflexible amount of environmental information and the restrictive nature of the structure of the information. We explore DeepSets and Set Transformers as two powerful set function approximators to address the problem of cardinality invariance and permutation invariance in the observation space of a reinforcement learning agent. We explain Set-Input Reinforcement Learning (SIRL) in detail and evaluate the performance of the DeepSets and Set Transformer methods through simulated experiments on a challenging multi-agent environment, that otherwise yields sub-optimal policies through traditional function approximation approaches. We demonstrate that both DeepSets and Set Transformer based encoders scale well to increasing the number of agents from training to evaluation.

IEEE Open Journal of Signal Processing 4 p. 61-70 Doi: https://doi.org/10.1109/OJSP.2023.3241580

An online topology estimation algorithm for nonlinear structural equation models (SEM) is proposed in this paper, addressing the nonlinearity and the non-stationarity of real-world systems. The nonlinearity is modeled using kernel formulations, and the curse of dimensionality associated with the kernels is mitigated using random feature approximation. The online learning strategy uses a group-lasso-based optimization framework with a prediction-corrections technique that accounts for the model evolution. The proposed approach has three properties of interest. First, it enjoys node-separable learning, which allows for scalability in large networks. Second, it offers privacy in SEM learning by replacing the actual data with node-specific random features. Third, its performance can be characterized theoretically via a dynamic regret analysis, showing that it is possible to obtain a linear dynamic regret bound under mild assumptions. Numerical results with synthetic and real data corroborate our findings and show competitive performance w.r.t. state-of-the-art alternatives.

2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring) Doi: https://doi.org/10.1109/VTC2023-Spring57618.2023.10200979

Accurate estimation of radio maps is important for various applications of wireless communications, such as network planning, and resource allocation. To learn accurate radio map models, one needs to have accurate knowledge of transmitter and receiver locations. However, it is difficult to obtain accurate locations in practice, especially, in scenarios having a high degree of wireless multi-path. Alternatively, time of arrival (ToA) features, which are easier to obtain, can be employed for estimating radio maps. To this end, this paper investigates the application of transfer learning method using ToA features for estimating radio maps under indoor wireless communications. The performance is compared with the scenarios where only the locations of receivers and both ToAs and locations of receivers, are used for estimating radio maps, assuming that locations are known. Due to the changes in propagation characteristics, a radio map model learned in a specific wireless environment cannot be directly employed in a new wireless environment. To address this issue, a data-driven transfer learning method is designed that transfers and fine-tunes a deep neural network model learned for a radio map from a source wireless environment to other distinct (target) wireless environments. Our proposed method predicts the training data required in the new wireless environments using a data-driven similarity measure. Our results demonstrate that using ToA (location-free) features results in a superior performance for estimating radio maps in terms of the necessary number of sensor measurements for estimating radio maps with a good accuracy, as compared to a location-based approach, where it may be difficult to have accurate location estimations. It leads to a saving of 70-90% of the necessary sensor measurement data for a mean square error (MSE) of 0.004.

IEEE Signal Processing Letters 31 p. 121-125 Doi: https://doi.org/10.1109/LSP.2023.3343185

—The performance of reproducing kernel Hilbert space-based methods is known to be sensitive to the choice of the reproducing kernel. Choosing an adequate reproducing kernel can be challenging and computationally demanding, especially in data-rich tasks without prior information about the solution domain. In this paper, we propose a learning scheme that scalably combines several single kernel-based online methods to reduce the kernel-selection bias. The proposed learning scheme applies to any task formulated as a regularized empirical risk minimization convex problem. More specifically, our learning scheme is based on a multi-kernel learning formulation that can be applied to widen any single-kernel solution space, thus increasing the possibility of finding higher-performance solutions. In addition, it is parallelizable, allowing for the distribution of the computational load across different computing units. We show experimentally that the proposed learning scheme outperforms the combined single-kernel online methods separately in terms of the cumulative regularized least squares cost metric.

2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Doi: https://doi.org/10.1109/ICASSP49357.2023.10096095

Vector autoregressive (VAR) model is widely used to model time-varying processes, but it suffers from prohibitive growth of the parameters when the number of time series exceeds a few hundreds. We propose a simplicial VAR model to mitigate the curse of dimensionality of the VAR models when the time series are defined over higher-order network structures such as edges, triangles, etc. The proposed model shares parameters across the simplicial signals by leveraging the simplicial convolutional filter and captures structure-aware spatio-temporal dependencies of the time-varying processes. Targetting the streaming signals from the real-world nonstationary networks, we develop a group-lasso-based online strategy to learn the proposed model. Using traffic and water distribution networks, we demonstrate that the proposed model achieves competitive signal prediction accuracy with a significantly less number of parameters than the VAR models.

IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society Doi: https://doi.org/10.1109/IECON51785.2023.10311938

Multi-agent deep reinforcement learning (MADRL), where a group of agents inside multi-agent systems cooperate to achieve a common goal, has been shown useful in many applications such as collaborative robots, autonomous driving or video games involving teams. In this paper, we propose two multi-agent deep reinforcement learning (MADRL) frameworks for value function factorization built by using Graph Convolutional Neural Networks (GCNN) based on neighborhood graph filters (NGFs). These MADRL frameworks are based on the paradigm of centralized training with decentralized execution (CTDE). In this work, we show that the superior stability of the NGFs as compared to standard graph filters leads also to superior performance for the MADRL algorithms. In the first MADRL framework, the NGF-based GCNN is used to predict the local q-value at each of the agents, while in the other one, the NGF-based GCNN is used to mix the local q-values to generate a global Q-value. We have compared the performance of NGF-based GCNNs over state-of-the-art graph neural networks for value function factorization in the MADRL framework for the StarCraft II and Coalition Structure Generation problems. The results show that the proposed MARL frameworks outperform the existing state-of-art architectures.

IEEE Transactions on Signal Processing 71 p. 4317-4329 Doi: https://doi.org/10.1109/TSP.2023.3329946

The task of reconstructing smooth signals from streamed data in the form of signal samples arises in various applications. This work addresses such a task subject to a zerodelay response; that is, the smooth signal must be reconstructed sequentially as soon as a data sample is available and without having access to subsequent data. State-of-the-art approaches solve this problem by interpolating consecutive data samples using splines. Here, each interpolation step yields a piece that ensures a smooth signal reconstruction while minimizing a cost metric, typically a weighted sum between the squared residual and a derivative-based measure of smoothness. As a result, a zerodelay interpolation is achieved in exchange for an almost certainly higher cumulative cost as compared to interpolating all data samples together. This paper presents a novel approach to further reduce this cumulative cost on average. First, we formulate a zero-delay smoothing spline interpolation problem from a sequential decision-making perspective, allowing us to model the future impact of each interpolated piece on the average cumulative cost. Then, an interpolation method is proposed to exploit the temporal dependencies between the streamed data samples. Our method is assisted by a recurrent neural network and accordingly trained to reduce the accumulated cost on average over a set of example data samples collected from the same signal source generating the signal to be reconstructed. Finally, we present extensive experimental results for synthetic and real data showing how our approach outperforms the abovementioned state-of-the-art.

Internet of Things: Engineering Cyber Physical Human Systems 22 Doi: https://doi.org/10.1016/j.iot.2023.100770

Energy Harvesting plays a crucial role in improving the network lifetime of an IoT application. Radio Frequency (RF) energy is one of the most prominent ambient energy sources available that can be used for harvesting energy. Moreover, a number of dedicated RF-energy transmitters is required for sufficient energy transfer to the power deficit IoT nodes. However, the appropriate positioning of these RF-energy transmitters in the network is a challenging issue. In this context, a network-aware RF-energy transmitter positioning scheme is proposed in this work. This RFenergy transmitter deployment scheme considers different network performance parameters such as energy-hole information, node-connectivity information, and data routing information for RF-energy transmitter placement. Apart from that, the amount of RF-energy harvested is constrained to the RF-energy carried by the propagating RF-waves. Moreover, the wireless medium uncertainties result in severe attenuation of the propagating RF-waves. In this regard, a robust hybrid RF-wave propagation model is also proposed. The proposed model amalgamates different wave propagation mechanisms for signal strength prediction in the propagating RF-waves. This helps in improving the modelling accuracy of the RF-waves. The proposed model also predicts the amount of residual RF-energy available in the propagating RF-wave for harvesting. This model is also validated against the practical datasets generated from experiments. The proposed transmitter positioning scheme, along with the hybrid RF-wave propagation model, overcome the energy hole issue in the IoT networks and improves energy efficiency. Extensive simulations are conducted under various network scenarios to evaluate the energy harvesting performance of the proposed methods. It is observed that the proposed network-aware RF-energy harvesting scheme improves the network performance in terms of energy harvesting, network coverage, and the lifetime of IoT network.

IEEE Transactions on Signal and Information Processing over Networks 9 p. 315-328 Doi: https://doi.org/10.1109/TSIPN.2023.3271148

In the context of graph signal processing, the existing distributed approaches for implementing linear network operators rely on the notion of graph shift matrix, which captures the local structure of the graph. Most of the existing approaches consider only a restricted set of linear network operators. However, in this paper, we focus on approximating general linear network operators as fast as possible after a finite number of local exchanges, with a negligible error. We propose a new distributed successive method based on designing a sequence of different graph shift matrices, which are optimized to approximate the desired network operator in an approximately minimal number of iterations. We also consider the robustness of the distributed computation of linear operators against graph perturbations. For this, we first analyze the effect of graph perturbations on our successive method and then, we incorporate the effect of graph perturbations in our design by proposing an online kernel-based estimator, which enables the nodes of the network to estimate the missing values caused by graph perturbations across iterations via available information received from neighbor nodes. Our numerical results demonstrate the superior performance of our methods over the existing state-of-the-art approaches.

IEEE Transactions on Signal Processing 71 p. 2027-2042 Doi: https://doi.org/10.1109/TSP.2023.3282068

Online topology estimation of graph-connected time series is challenging in practice, particularly because the dependencies between the time series in many real-world scenarios are nonlinear. To address this challenge, we introduce a novel kernel-based algorithm for online graph topology estimation. Our proposed algorithm also performs a Fourier-based random feature approximation to tackle the curse of dimensionality associated with kernel representations. Exploiting the fact that real-world networks often exhibit sparse topologies, we propose a group-Lasso based optimization framework, which is solved using an iterative composite objective mirror descent method, yielding an online algorithm with fixed computational complexity per iteration. We provide theoretical guarantees for our algorithm and prove that it can achieve sublinear dynamic regret under certain reasonable assumptions. In experiments conducted on both real and synthetic data, our method outperforms existing state-of-the-art competitors.

IEEE Transactions on Communications 70(9) p. 6066-6082 Doi: https://doi.org/10.1109/TCOMM.2022.3194193

Underlay Device-to-Device (D2D) communications improve the spectral efficiency by simultaneously allowing direct communication between D2D-users on the same channels as cellular users (CU). However, most related works consider perfect Channel State Information (CSI) with single-antenna transmissions and usually assign each channel to one D2D pair. In this work, we formulate an optimization problem for maximizing the aggregate rate of all D2D pairs and CUs in single and multiple antenna configurations under imperfect CSI, by optimizing channel and power resources. Our formulation guarantees probability of outage below a specified threshold and fairness in channel allocation across D2D pairs. The resulting problem is a stochastic-mixed-integer-non-convex problem, we solve it approximately by alternating between power-allocation and channel-assignment sub-problems. The stochastic objective and outage constraints are addressed by the concept of order-of-statistics in the single-antenna case and the Bernstein-type inequality in the multiple-antenna configuration. The power-allocation sub-problem is solved by exploiting a quadratic-transformation, while the channel-assignment sub-problem is solved by integer relaxation. Furthermore, two computationally efficient algorithms are proposed to approximately solve the problem in a partially decentralized manner. We also establish convergence guarantees for the different algorithms proposed in this work. Simulation results show that the proposed approach achieves higher throughput compared to the state-of-the-art alternatives.

Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON) Doi: https://doi.org/10.1109/IECON49645.2022.9969046

Multi-Agent Reinforcement Learning (MARL) algorithms based on the Centralised Training Decentralised Execution (CTDE) approach have seen a great deal of interest in recent years. Most of the recent works focus on a specific class of environments built on the StartCraft Multi-Agent Challenge (SMAC) environment suite. However, experiments with the PettingZoo multi-particle environments show poor performance in an interesting subset of tasks. In this paper, the nature of these environments and the reward structures are analyzed. It shows that poor performance in these tasks is not due to a lack of representation power in the individual Q function or mixing functions, but rather a result of convergence to a suboptimal equilibrium of dual channel rewards and the issue of agent-reward decoupling that can be a common problem for many MARL environments. We present reward curriculum-based versions of QMIX and VDN as a solution to these problems and compare their results with the standard algorithms. The results show a clear performance gain in terms of a common cumulative reward metric.

2022 IEEE Wireless Communications and Networking Conference (WCNC) p. 1479-1484 Doi: https://doi.org/10.1109/WCNC51071.2022.9771636

In this paper, we investigate the application of transfer learning to train a Deep Neural Network (DNN) model for joint channel and power allocation in underlay device-todevice (D2D) communication. Based on the traditional optimization solutions, generating training dataset for scenarios with perfect channel state information (CSI) is not computationally demanding, compared to scenarios with imperfect CSI. Thus, a transfer learning-based approach can be exploited to transfer the DNN model trained for the perfect CSI scenarios to the imperfect CSI scenarios. We also consider the issue of defining the similarity between two types of resource allocation tasks. For this, we first determine the value of outage probability for which two resource allocation tasks are same, that is, for which our numerical results illustrate the minimal need of relearning from the transferred DNN model. For other values of outage probability, there is a mismatch between the two tasks and our results illustrate a more efficient relearning of the transferred DNN model. Our results show that the learning dataset required for relearning of the transferred DNN model is significantly smaller than the required training dataset for a DNN model without transfer learning.

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) Doi: https://doi.org/10.1109/SPAWC51304.2022.9834020

Graph convolutional neural networks (GCNNs) have emerged as a promising tool in the deep learning community to learn complex hidden relationships of data generated from non-Euclidean domains and represented as graphs. GCNNs are formed by a cascade of layers of graph filters, which replace the classical convolution operation in convolutional neural networks. These graph filters, when operated over real networks, can be subject to random perturbations due to link losses that can be caused by noise, interference and adversarial attacks. In addition, these graph filters are executed by finite-precision processors, which generate numerical quantization errors that may affect their performance. Despite the research works studying the effect of either graph perturbations or quantization in GCNNs, their robustness against both of these problems jointly is still not well investigated and understood. In this paper, we propose a quantized GCNN architecture based on neighborhood graph filters under random graph perturbations. We investigate the stability of such architecture to both random graph perturbations and quantization errors. We prove that the expected error due to quantization and random graph perturbations at the GCNN output is upper-bounded and we show how this bound can be controlled. Numerical experiments are conducted to corroborate our theoretical findings.

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) Doi: https://doi.org/10.1109/SPAWC51304.2022.9833974

This paper investigates the problem of transfer learning in radio map estimation for indoor wireless communications, which can be exploited for different applications, such as channel modelling, resource allocation, network planning, and reducing the number of necessary power measurements. Due to the nature of wireless communications, a radio map model developed under a particular environment can not be directly used in a new environment because of the changes in the propagation characteristics, thus creating a new model for every environment requires in general a large amount of data and is computationally demanding. To address these issues, we design an effective novel data-driven transfer learning procedure that transfers and fine-tunes a deep neural network (DNN)-based model for a radio map learned from an original indoor wireless environment to other different indoor wireless environments. Our method allows to predict the amount of training data needed in new indoor wireless environments when performing the operation of transfer learning using our similarity measure. Our simulation results illustrate that the proposed method achieves a saving of 60-70% in sensor measurement data and is able to adapt to a new wireless environment with a small amount of additional data.

IEEE Transactions on Communications 70(9) p. 6066-6082 Doi: https://doi.org/10.1109/TCOMM.2022.3194193

Proceedings of the Northern Lights Deep Learning Workshop 3 Doi: https://doi.org/10.7557/18.6236

Aquaponic systems provide a reliable solution to grow vegetables while cultivating fish (or other aquatic organisms) in a controlled environment. The main advantage of these systems compared with traditional soil-based agriculture and aquaculture installations is the ability to produce fish and vegetables with low water consumption. Aquaponics requires a robust control system capable of optimizing fish and plant growth while ensuring a safe operation. To support the control system, this work explores the design process of Deep Learning models based on Recurrent Neural Networks to forecast one hour of pH values in small-scale industrial Aquaponics. This implementation guides us through the machine learning life-cycle with industrial time-series data, i.e. data acquisition, pre-processing, feature engineering, architecture selection, training, and model verification.

30th European Signal Processing Conference (EUSIPCO 2022) p. 687-691 Doi: https://doi.org/10.23919/EUSIPCO55093.2022.9909681

Extracting causal graph structures from multivariate time series, termed topology identification, is a fundamental problem in network science with several important applications. Topology identification is a challenging problem in real-world sensor networks, especially when the available time series are partially observed due to faulty communication links or sensor failures. The problem becomes even more challenging when the sensor dependencies are nonlinear and nonstationary. This paper proposes a kernel-based online framework using random feature approximation to jointly estimate nonlinear causal dependencies and missing data from partial observations of streaming graph-connected time series. Exploiting the fact that real-world networks often exhibit sparse topologies, we propose a group lasso-based optimization framework for topology identification, which is solved online using alternating minimization techniques. The ability of the algorithm is illustrated using several numerical experiments conducted using both synthetic and real data.

IEEE Transactions on Vehicular Technology 71(3) p. 3075-3091 Doi: https://doi.org/10.1109/TVT.2022.3145011

Most recent works in device-to-device (D2D) underlay communications focus on the optimization of either power or channel allocation to improve the spectral efficiency, and typically consider uplink and downlink separately. Further, several of them also assume perfect knowledge of channel-state-information (CSI). In this paper, we formulate a joint uplink and downlink resource allocation scheme, which assigns both power and channel resources to D2D pairs and cellular users in an underlay network scenario. The objective is to maximize the overall network rate while maintaining fairness among the D2D pairs. In addition, we also consider imperfect CSI, where we guarantee a certain outage probability to maintain the desired quality-of-service (QoS). The resulting problem is a mixed integer non-convex optimization problem and we propose both centralized and decentralized algorithms to solve it, using convex relaxation, fractional programming, and alternating optimization. In the decentralized setting, the computational load is distributed among the D2D pairs and the base station, keeping also a low communication overhead. Moreover, we also provide a theoretical convergence analysis, including also the rate of convergence to stationary points. The proposed algorithms have been experimentally tested in a simulation environment, showing their favorable performance, as compared with the state-of-the-art alternatives.

IEEE Signal Processing Letters 30 p. 115-119 Doi: https://doi.org/10.1109/LSP.2022.3221846

An online algorithm for missing data imputation for networks with signals defined on the edges is presented. Leveraging the prior knowledge intrinsic to real-world networks, we propose a bi-level optimization scheme that exploits the causal dependencies and the flow conservation, respectively via (i) a sparse line graph identification strategy based on a group-Lasso and (ii) a Kalman filtering-based signal reconstruction strategy developed using simplicial complex (SC) formulation. The advantages of this first SC-based attempt for time-varying signal imputation have been demonstrated through numerical experiments using EPANET models of both synthetic and real water distribution networks.

Intelligent Technologies and Applications: 4th International Conference, INTAP 2021, Grimstad, Norway, October 11–13, 2021, Revised Selected Papers p. 17-30 Doi: https://doi.org/10.1007/978-3-031-10525-8_2

A method for nonlinear topology identification is proposed, based on the assumption that a collection of time series are generated in two steps: i) a vector autoregressive process in a latent space, and ii) a nonlinear, component-wise, monotonically increasing observation mapping. The latter mappings are assumed invertible, and are modeled as shallow neural networks, so that their inverse can be numerically evaluated, and their parameters can be learned using a technique inspired in deep learning. Due to the function inversion, the backpropagation step is not straightforward, and this paper explains the steps needed to calculate the gradients applying implicit differentiation. Whereas the model explainability is the same as that for linear VAR processes, preliminary numerical tests show that the prediction error becomes smaller.

2022 14th International Conference on COMmunication Systems & NETworkS (COMSNETS) p. 590-594 Doi: https://doi.org/10.1109/COMSNETS53615.2022.9668343

Abstract—Various applications of wireless 5G networks demand high throughput for improving the Quality of Service. Power management of the beams plays a key role in improving the throughput in 5G-mmWave networks. However, resource constraints make beam generation a challenging issue. In this work, cluster-based 3D beam power management schemes are proposed for improving the throughput in resource-Constrained wireless networks. Firstly, the network users are divided into a number of groups. 3D beam patterns are generated from the transmitter to maximize the throughput of users based on clustering information. The proposed cluster-based beam management scheme considers a limited amount of power resources at the transmitter. Available transmit power is distributed optimally to every cluster in the network. The performance is improved further by identifying the outliers in the clusters. The tradeoff between the transmit power and users served decides the threshold on outlier detection. The performance of proposed cluster-based 3D beam patterns is evaluated under different network scenarios using numeric simulations. The proposed method shows significant improvements in terms of throughput, network coverage, average signal-to-noise ratio (SNR), and transmit power consumption. The performance of the proposed methods is also compared with the alternative schemes under power-constrained scenarios.

2022 56th Asilomar Conference on Signals, Systems, and Computers p. 829-833 Doi: https://doi.org/10.1109/IEEECONF56349.2022.10051993

We design a sequential non-linear risk-aware estimator based on particle filtering to compute estimates and approximate the system state posterior distribution. For this purpose, we consider the risk given by the expected variance of the squared error between the system state and the estimate conditioned on the observations. We compare the proposed estimator with existing risk-neutral estimators in terms of error variance, mean squared error, and execution time per iteration. The comparison is carried out using a simulation of a Recirculating Aquaculture System, a case study for non-linear critical systems, where the performance in estimation for low probability events becomes an important aspect. Our simulation results demonstrate a competitive estimation performance while ensuring a lower risk. 1 1 The reader can find the code for the proposed estimators in github.com/uia-wisenet/Risk_Aware_MMSE_PF.

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Doi: https://doi.org/10.1109/VTC2022-Fall57202.2022.10013001

2022 56th Asilomar Conference on Signals, Systems, and Computers Doi: https://doi.org/10.1109/IEEECONF56349.2022.10051968

IEEE Transactions on Signal Processing 70 p. 643-658 Doi: https://doi.org/10.1109/TSP.2021.3139208

Distributed graph filters have recently found applications in wireless sensor networks (WSNs) to solve distributed tasks such as reaching consensus, signal denoising, and reconstruction. However, when implemented over WSNs, the graph filters should deal with network limited energy constraints as well as processing and communication capabilities.Quantization plays a fundamental role to improve the latter but its effects on distributed graph filtering are little understood. WSNs are also prone to random link losses due to noise and interference. In this instance, the filter output is affected by both the quantization error and the topological randomness error, which, if it is not properly accounted in the filter design phase, may lead to an accumulated error through the filtering iterations and significantly degrade the performance. In this paper, we analyze how quantization affects distributed graph filtering over both time-invariant and time-varying graphs. We bring insights on the quantization effects for the twomost common graph filters: the finite impulse response (FIR) and autoregressive moving average (ARMA) graph filter. Besides providing a comprehensive analysis, we devise theoretical performance guarantees on the filter performancewhen the quantization stepsize is fixed or changes dynamically over the filtering iterations. For FIR filters, we show that a dynamic quantization stepsize leads to more reduction of the quantization noise than in the fixed-stepsize quantization. For ARMAgraph filters,we showthat decreasing the quantization stepsize over the iterations reduces the quantization noise to zero at the steady-state. In addition, we propose robust filter design strategies that minimize the quantization noise for both time-invariant and time-varying networks. Numerical experiments on synthetic and two real data sets corroborate our findings and show the different trade-offs between quantization bits, filter order, and robustness to topological randomness.

IEEE Transactions on Green Communications and Networking (ITGCN) 6(2) p. 1009-1022 Doi: https://doi.org/10.1109/TGCN.2021.3135351

IEEE Transactions on Communications 69(5) p. 3409-3422 Doi: https://doi.org/10.1109/TCOMM.2021.3058374

Multicast device-to-device communications operating underlay with cellular networks is a spectral efficient technique for disseminating data to nearby receivers. However, due to the critical challenge of having an intelligent interference coordination between multicast groups along with the cellular network, it is necessary to judiciously perform resource allocation for the combined network. In this work, we present a framework for a joint channel and power allocation strategy to maximize the sum rate of the combined network while guaranteeing minimum rate to individual groups and cellular users. The objective function is augmented by an austerity function that penalizes excessive assignment of low rate channels. The formulated problem is a mixed-integer-non-convex program, which requires exponential complexity to obtain the optimal solution. To tackle this, we exploit fractional programming and integer relaxation to obtain a parametric convex approximation. Based on sequential convex approximation approach, we first propose a centralized algorithm that ensures convergence to a limit point. Next, we propose a distributed algorithm in which via dual decomposition, separable sub-problems are formulated to be solved at the respective groups in cooperation with the base station. We provide convergence guarantees of the proposed solutions and demonstrate their merits by simulations, showing improvement in network throughput.

Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing p. 5225-5229 Doi: https://doi.org/10.1109/ICASSP39728.2021.9415098

2021 29th European Signal Processing Conference (EUSIPCO) Doi: https://doi.org/10.23919/EUSIPCO54536.2021.9615973

2021 IEEE International Conference on Autonomous Systems (ICAS) p. 197-201 Doi: https://doi.org/10.1109/ICAS49788.2021.9551168

Pumps consume a significant amount of energy in a water distribution network (WDN). With the emergence of dynamic energy cost, the pump scheduling as per user demand is a computationally challenging task. Computing the decision variables of pump scheduling relies over mixed integer optimization (MIO) formulations. However, MIO formulations are NP-hard in general and solving such problems is inefficient in terms of computation time and memory. Moreover, the computational complexity of solving such MIO formulations increases exponentially with the size of the WDN. As an alternative, we propose a data-driven approach to estimate the decision variables of pump scheduling using deep neural networks (DNN). We evaluate the performance of our trained DNN relative to a state-of-the-art MIO solver, and conclude that our DNN based approach can be used to minimize the pump switching and cost incurred due to dynamic energy in a given WDN with much lower complexity.

2021 IEEE Data Science and Learning Workshop (DSLW) Doi: https://doi.org/10.1109/DSLW51110.2021.9523399

Estimating the unknown causal dependencies among graph-connected time series plays an important role in many applications, such as sensor network analysis, signal processing over cyber-physical systems, and finance engineering. Inference of such causal dependencies, often know as topology identification, is not well studied for non-linear non-stationary systems, and most of the existing methods are batch-based which are not capable of handling streaming sensor signals. In this paper, we propose an online kernel-based algorithm for topology estimation of non-linear vector autoregressive time series by solving a sparse online optimization framework using the composite objective mirror descent method. Experiments conducted on real and synthetic data sets show that the proposed algorithm outperforms the state-of-the-art methods for topology estimation.

IEEE Transactions on Signal Processing 70 Doi: https://doi.org/10.1109/TSP.2021.3139208

Distributed graph filters have recently found appli- cations in wireless sensor networks (WSNs) to solve distributed tasks such as reaching consensus, signal denoising, and recon- struction. However, when implemented over WSNs, the graph filters should deal with network limited energy constraints as well as processing and communication capabilities. Quantization plays a fundamental role to improve the latter but its effects on distributed graph filtering are little understood. WSNs are also prone to random link losses due to noise and interference. In this instance, the filter output is affected by both the quantization error and the topological randomness error, which, if it is not properly accounted in the filter design phase, may lead to an accumulated error through the filtering iterations and significantly degrade the performance. In this paper, we analyze how quantization affects distributed graph filtering over both time-invariant and time-varying graphs. We bring insights on the quantization effects for the two most common graph filters: the finite impulse response (FIR) and autoregressive moving average (ARMA) graph filter. Besides providing a comprehensive anal- ysis, we devise theoretical performance guarantees on the filter performance when the quantization stepsize is fixed or changes dynamically over the filtering iterations. For FIR filters, we show that a dynamic quantization stepsize leads to more control on the quantization noise than the fixed-stepsize quantization. For ARMA graph filters, we show that decreasing the quantization stepsize over the iterations reduces the quantization noise to zero at the steady-state. In addition, we propose robust filter design strategies that minimize the quantization noise for both time- invariant and time-varying networks. Numerical experiments on synthetic and two real data sets corroborate our findings and show the different trade-offs between quantization bits, filter order, and robustness to topological randomness.

Machine Learning for Signal Processing Doi: https://doi.org/10.1109/MLSP52302.2021.9596512

Online topology estimation of graph-connected time series is challenging, especially since the causal dependencies in many real-world networks are nonlinear. In this paper, we propose a kernel-based algorithm for graph topology estimation. The algorithm uses a Fourier-based Random feature approximation to tackle the curse of dimensionality associated with the kernel representations. Exploiting the fact that the real-world networks often exhibit sparse topologies, we propose a group lasso based optimization framework, which is solve using an iterative composite objective mirror descent method, yielding an online algorithm with fixed computational complexity per iteration. The experiments conducted on real and synthetic data show that the proposed method outperforms its competitors.

Machine Learning for Signal Processing Doi: https://doi.org/10.1109/MLSP52302.2021.9596115

Kernel-based approaches have achieved noticeable success as non-parametric regression methods under the framework of stochastic optimization. However, most of the kernel-based methods in the literature are not suitable to track sequentially streamed quantized data samples from dynamic environments. This shortcoming occurs mainly for two reasons: first, their poor versatility in tracking variables that may change unpredictably over time, primarily because of their lack of flexibility when choosing a functional cost that best suits the associated regression problem; second, their indifference to the smoothness of the underlying physical signal generating those samples. This work introduces a novel algorithm constituted by an online regression problem that accounts for these two drawbacks and a stochastic proximal method that exploits its structure. In addition, we provide tracking guarantees by analyzing the dynamic regret of our algorithm. Finally, we present some experimental results that support our theoretical analysis and show that our algorithm has a favorable performance compared to the state-of-the-art.

IEEE Transactions on Signal Processing 69 p. 2056-2069 Doi: https://doi.org/10.1109/TSP.2021.3066787

2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC) Doi: https://doi.org/10.1109/CCNC49032.2021.9369595

IEEE Sensors Journal 21(23) p. 26447-26469 Doi: https://doi.org/10.1109/JSEN.2021.3121506

There is a growing demand to equip Smart Water Networks (SWN) with advanced sensing and computation capabilities in order to detect anomalies and apply autonomous event-triggered control. Cyber-Physical Systems (CPSs) have emerged as an important research area capable of intelligently sensing the state of SWN and reacting autonomously in scenarios of unexpected crisis development. Through computational algorithms, CPSs can integrate physical components of SWN, such as sensors and actuators, and provide technological frameworks for data analytics, pertinent decision making, and control. The development of CPSs in SWN requires the collaboration of diverse scientific disciplines such as civil, hydraulics, electronics, environment, computer science, optimization, communication, and control theory. For efficient and successful deployment of CPS in SWN, there is a need for a common methodology in terms of design approaches that can involve various scientific disciplines. This paper reviews the state of the art, challenges, and opportunities for CPSs, that could be explored to design the intelligent sensing, communication, and control capabilities of CPS for SWN. In addition, we look at the challenges and solutions in developing a computational framework from the perspectives of machine learning, optimization, and control theory for SWN.

2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) Doi: https://doi.org/10.1109/SPAWC48557.2020.9154329

ICC 2020 - 2020 IEEE International Conference on Communications (ICC) Doi: https://doi.org/10.1109/ICC40277.2020.9149079

Robust beamforming is an efficient technique to guarantee the desired receiver performance in the presence of erroneous channel state information (CSI). However, the application of robust beamforming in underlay device-to-device (D2D) communication still requires further investigation. In this paper, we investigate resource allocation problem for underlay D2D communications by considering multiple antennas at the base station (BS) and at the transmitters of D2D pairs. The proposed design problem aims at maximizing the aggregate rate of all D2D pairs and cellular users (CUs) in downlink spectrum. In addition, our objective is augmented to achieve a fair allocation of resources across the D2D pairs. Further, assuming elliptically bounded CSI errors, the formulation ensures maintaining signal to interference plus noise ratio (SINR) above a specified threshold. The derived optimization problem results in a mixed integer non-convex problem and requires exponential complexity to obtain the optimal solution. We perform a semi-definite relaxation (SDR) to handle the stochastic SINR constraints by using the S-Lemma, obtaining a number of linear matrix inequalities. The non-convexity is addressed by introducing slack variables and performing a quadratic transformation to obtain sub-optimal beamformers via alternating optimization. The solution for channel assignments to D2D pairs is obtained by convex relaxation of the integer constraints. Finally, we demonstrate the merit of the proposed approach by simulations in which we observe higher and more robust network throughput, as compared to previous state-of-the-art.

IET Communications 15(1) p. 51-59 Doi: https://doi.org/10.1049/cmu2.12057

Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications(ICIEA 2020) p. 939-944 Doi: https://doi.org/10.1109/ICIEA48937.2020.9248365

Identifying dependencies among variables in a complex system is an important problem in network science. Structural equation models (SEM) have been used widely in many fields for topology inference, because they are tractable and incorporate exogenous influences in the model. Topology identification based on static SEM is useful in stationary environments; however, in many applications a time-varying underlying topology is sought. This paper presents an online algorithm to track sparse time-varying topologies in dynamic environments and most importantly, performs a detailed analysis on the performance guarantees. The tracking capability is characterized in terms of a bound on the dynamic regret of the proposed algorithm. Numerical tests show that the proposed algorithm can track changes under different models of time-varying topologies.

IEEE Vehicular Technology Conference (VTC) Doi: https://doi.org/10.1109/VTC2020-Fall49728.2020.9348681

IEEE/CIC International Conference on Communications in China - Workshops (CIC/ICCC) Doi: https://doi.org/10.1109/ICCWorkshops49005.2020.9145202

IEEE Internet of Things Journal 7(12) Doi: https://doi.org/10.1109/JIOT.2020.3010610

Wireless sensor networks (WSNs) are considered as a major technology enabling the Internet-of-Things (IoT) paradigm. The recent emerging graph signal processing field can also contribute to enabling the IoT by providing key tools, such as graph filters (GFs), for processing the data associated with the sensor devices. GFs can be performed over WSNs in a distributed manner by means of a certain number of communication exchanges among the nodes. But, WSNs are often affected by interferences and noise, which leads to view these networks as directed, random and time-varying graph topologies. Most of the existing works neglect this problem by considering an unrealistic assumption that claims the same probability of link activation in both directions when sending a packet between two neighboring nodes. This work focuses on the problem of operating graph filtering in random asymmetric WSNs. We show first that graph filtering with finite impulse response GFs (node-invariant and node-variant) requires having equal connectivity probabilities for all the links in order to have an unbiased filtering, which cannot be achieved in practice in random WSNs. After this, we characterize the graph filtering error and present an efficient strategy to conduct graph filtering tasks over random WSNs with node-variant GFs by maximizing accuracy, that is, ensuring a small bias-variance tradeoff. In order to enforce the desired accuracy, we optimize the filter coefficients and design a cross-layer distributed scheduling algorithm (CDSA) at the MAC layer. Extensive numerical experiments are presented to show the efficiency of the proposed solution as well as the CDSA for the denoising application.

IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops 2020 Doi: https://doi.org/10.1109/PIMRC48278.2020.9217293

Multicast device-to-device (D2D) communications operating underlay with cellular networks is a spectral efficient technique for disseminating data to the nearby receivers. However, due to critical challenges such as, mitigating mutual interference and unavailability of perfect channel state information (CSI), the resource allocation to multicast groups needs significant attention. In this work, we present a framework for joint channel assignment and power allocation strategy to maximize the sum rate of the combined network. The proposed framework allows access of multiple channels to the multicast groups, thus improving the achievable rate of the individual groups. Furthermore, fairness in allocating resources to the multicast groups is also ensured by augmenting the objective with a penalty function. In addition, considering imperfect CSI, the framework guarantees to provide rate above a specified outage for all the users. The formulated problem is a mixed integer nonconvex program which requires exponential complexity to obtain the optimal solution. To tackle this, we first introduce auxiliary variables to decouple the original problem into smaller power allocation problems and a channel assignment problem. Next, with the aid of fractional programming via a quadratic transformation, we obtain an efficient power allocation solution by alternating optimization. The solution for channel assignment is obtained by convex relaxation of integer constraints. Finally, we demonstrate the merit of the proposed approach by simulations, showing a higher and a more robust network throughput. Index Terms—D2D multicast communications, resource allocation, imperfect CSI, fractional programming.

IEEE Transactions on Signal Processing 69 p. 220-225 Doi: https://doi.org/10.1109/TSP.2020.3042940

Causality graphs are routinely estimated in social sciences, natural sciences, and engineering due to their capacity to efficiently represent the spatiotemporal structure of multi-variate data sets in a format amenable for human interpretation, forecasting, and anomaly detection. A popular approach to mathematically formalize causality is based on vector autoregressive (VAR) models and constitutes an alternative to the well-known, yet usually intractable, Granger causality. Relying on such a VAR causality notion, this paper develops two algorithms with complementary benefits to track time-varying causality graphs in an online fashion. Their constant complexity per update also renders these algorithms appealing for big-data scenarios. Despite using data sequentially, both algorithms are shown to asymptotically attain the same average performance as a batch estimator which uses the entire data set at once. To this end, sublinear (static) regret bounds are established. Performance is also characterized in time-varying setups by means of dynamic regret analysis. Numerical results with real and synthetic data further support the merits of the proposed algorithms in static and dynamic scenarios.

2020 IEEE Global Communications Conference (GLOBECOM) Doi: https://doi.org/10.1109/GLOBECOM42002.2020.9322198

In order to estimate the channel gain (CG) between the locations of an arbitrary transceiver pair across a geographic area of interest, CG maps can be constructed from spatially distributed sensor measurements. Most approaches to build such spectrum maps are location-based, meaning that the input variable to the estimating function is a pair of spatial locations. The performance of such maps depends critically on the ability of the sensors to determine their positions, which may be drastically impaired if the positioning pilot signals are affected by multipath channels. An alternative location-free approach was recently proposed for spectrum power maps, where the input variable to the maps consists of features extracted from the positioning signals, instead of location estimates. The location-based and the location-free approaches have complementary merits. In this work, apart from adapting the location-free features for the CG maps, a method that can combine both approaches is proposed in a mixture-of-experts framework.

IEEE Transactions on Signal Processing 69 Doi: https://doi.org/10.1109/TSP.2020.3038528

A number of inference problems with sensor networks involve projecting a measured signal onto a given subspace. In existing decentralized approaches, sensors communicate with their local neighbors to obtain a sequence of iterates that asymptotically converges to the desired projection. In contrast, the present paper develops methods that produce these projections in a finite and approximately minimal number of iterations. Building upon tools from graph signal processing, the problem is cast as the design of a graph filter which, in turn, is reduced to the design of a suitable graph shift operator. Exploiting the eigenstructure of the projection and shift matrices leads to an objective whose minimization yields approximately minimum-order graph filters. To cope with the fact that this problem is not convex, the present work introduces a novel convex relaxation of the number of distinct eigenvalues of a matrix based on the nuclear norm of a Kronecker difference. To tackle the case where there exists no graph filter capable of implementing a certain subspace projection with a given network topology, a second optimization criterion is presented to approximate the desired projection while trading the number of iterations for approximation error. Two algorithms are proposed to optimize the aforementioned criteria based on the alternating-direction method of multipliers. An exhaustive simulation study demonstrates that the obtained filters can effectively obtain subspace projections markedly faster than existing algorithms.

15th IEEE Conference on Industrial Electronics and Applications p. 1171-1175 Doi: https://doi.org/10.1109/ICIEA48937.2020.9248169

Bollard is a vital component of mooring system. It is the anchor point for mooring ropes to be fixed in order to secure the vessel or ship. An algorithm that translates the segmented mask of bollard output from masked R-CNN along with bounding box and associated class probability to its corresponding edge coordinate and finally to the single reference point for efficient detection and classification of bollard towards autonomous mooring is presented. At first stage, Mask R-CNN framework is trained with custom built bollard. The model obtained from the training is inferred with real data resulting in instance segment of bollard. The segmented mask obtained contains relatively large amount of the data points representing the whole area of bollard, which typically is not desirable. In order to precisely localize the bollard with one reference co-ordinate, the proposed algorithm is applied to segmented mask. Firstly, it translates the segmented mask to only four co-ordinate points, where each point correspond to the edge of bollard. Further, from the edges, the reference point is estimated. This causes significant reduction in point of interest (POI) and has potential to reduce the error encountered during pose estimation of the bollard in 3D thus making the autonomous mooring more precise and accurate.

28th European Signal Processing Conference (EUSIPCO 2020) p. 2085-2089 Doi: https://doi.org/10.23919/Eusipco47968.2020.9287537

There is a clear need for efficient hyperparameter optimization (HO) algorithms for statistical learning, since commonly applied search methods (such as grid search with N-fold cross-validation) are inefficient and/or approximate. Previously existing gradient-based HO algorithms that rely on the smoothness of the cost function cannot be applied in problems such as Lasso regression. In this contribution, we develop a HO method that relies on the structure of proximal gradient methods and does not require a smooth cost function. Such a method is applied to Leave-one-out (LOO)-validated Lasso and Group Lasso, and an online variant is proposed. Numerical experiments corroborate the convergence of the proposed methods to stationary points of the LOO validation error curve, and the improved efficiency and stability of the online algorithm

IEEE Transactions on Signal Processing 67(15) p. 4013-4026 Doi: https://doi.org/10.1109/TSP.2019.2923151

Spectrum cartography constructs maps of metrics such as channel gain or received signal power across a geographic area of interest using spatially distributed sensor measurements. Applications of these maps include network planning, interference coordination, power control, localization, and cognitive radios to name a few. Since existing spectrum cartography techniques require accurate estimates of the sensor locations, their performance is drastically impaired by multipath affecting the positioning pilot signals. This phenomenon occurs especially in indoor or dense urban scenarios. To overcome such a limitation, this paper introduces a novel paradigm for spectrum cartography, where estimation of spectral maps relies on features of these positioning signals rather than on location estimates. Specific learning algorithms are built on this approach and offer a markedly improved estimation performance than those of the existing approaches relying on localization, as demonstrated by simulation studies in indoor scenarios.

2019 IEEE 5th World Forum on Internet of Things (WF-IoT) Doi: https://doi.org/10.1109/WF-IoT.2019.8767353

2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) Doi: https://doi.org/10.1109/SPAWC.2019.8815521

2019 IEEE 5th World Forum on Internet of Things (WF-IoT) Doi: https://doi.org/10.1109/WF-IoT.2019.8767196

2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) Doi: https://doi.org/10.1109/SPAWC.2019.8815544

arXiv.org

We study decentralized designing of the graph shift operators to implement linear transformations between graph signals. Since this operator captures the local structure of the graph, the proposed method of this paper gives rise to decentralized linear network operators. Unfortunately, existing decentralized approaches either consider some special instances of linear transformations or confine themselves to some known graph shift operators reduced family of the designing linear transformations task. To remedy these limitations, this paper develops a framework for computing a wide class of linear transformations in a decentralized fashion by relying on the notion of graph shift operator. To this end, a set of successive graph shift operators is implemented to compute linear transformations in a small number of iterations (as fast as possible).

International Symposium on Advanced Networks and Telecommunication Systems (ANTS) 2019- Doi: https://doi.org/10.1109/ANTS47819.2019.9118165

International Symposium on Advanced Networks and Telecommunication Systems (ANTS) 2019- Doi: https://doi.org/10.1109/ANTS47819.2019.9117975

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Doi: https://doi.org/10.1109/GlobalSIP45357.2019.8969270

Distributed graph filters can be implemented over wireless sensor networks by means of cooperation and exchanges among nodes. However, in practice, the performance of such graph filters is deeply affected by the quantization errors that are accumulated when the messages are transmitted. The latter is paramount to overcome the limitations in terms of bandwidth and computation capabilities in sensor nodes. In addition to quantization errors, distributed graph filters are also affected by random packet losses due to interferences and background noise, leading to the degradation of the performance in terms of the filtering accuracy. In this work, we consider the problem of designing graph filters that are robust to quantized data and time-varying topologies. We propose an optimized method that minimizes the quantization error, while ensuring an accurate filtering over time-varying graph topologies. The efficiency of the proposed theoretical findings is validated by numerical results in random wireless sensor networks.

IEEE Transactions on Signal and Information Processing over Networks 5(2) p. 320-333 Doi: https://doi.org/10.1109/TSIPN.2018.2873070

Wireless Sensor Networks have been revealed as a powerful technology to solve many different problems through sensor nodes cooperation. One important cooperative process is the so-called average gossip algorithm, which constitutes a building block to perform many inference tasks in an efficient and distributed manner. From the theoretical designs proposed in most previous work, this algorithm requires instantaneous symmetric links in order to reach average consensus. However, in a realistic scenario wireless communications are subject to interferences and other environmental factors, which results in random instantaneous topologies that are, in general, asymmetric. Consequently, the estimation of the average obtained by the gossip algorithm is a random variable, which its realizations may significantly differ from the average value. In the present work, we first derive a sufficient conditions for any MAC protocol to guarantee that the expected value of the obtained consensus random variable is the average of the initial values (unbiased estimator), while the variance of the estimator is minimum. Then, we propose a cross-layer and distributed link scheduling protocol based on carrier sense, which besides avoiding collisions, ensures both an unbiased estimation and close to minimum variance values. Extensive numerical results are presented to show the validity and efficiency of the proposed approach.

13th IEEE International Conference on Advanced Networks and Telecommunications Systems, 2019 (IEEE ANTS 2019) p. 1-6

13th IEEE International Conference on Advanced Networks and Telecommunications Systems, 2019 (IEEE ANTS 2019) p. 1-5

VLSI Design 2018-January p. 455-456 Doi: https://doi.org/10.1109/VLSID.2018.110

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Doi: https://doi.org/10.1109/GlobalSIP.2018.8646531

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Doi: https://doi.org/10.1109/GlobalSIP.2018.8646607

2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) Doi: https://doi.org/10.1109/SPAWC.2018.8445848

Wireless sensor networks (WSN s) are often characterized by random and asymmetric packet losses due to the wireless medium, leading to network topologies that can be modeled as random, time-varying and directed graphs. Most of existing works related to graph filtering in the context of WSNs assume that the probability of delivering an information from one node to a neighbor node is the same as in the reverse direction. This assumption is not realistic due to the typical link asymmetry in WSNs caused by interferences and background noise. In this work, we analyze the problem of applying stochastic graph filtering over random time-varying asymmetric network topologies. We show that it is possible to perform stochastic graph filtering under asymmetric links with node-variant graph filters, while optimizing a trade-off between the expected error (bias) and the variance of the error, with respect to performing graph filtering over a fixed static topology given by a certain connectivity radius of the nodes.

IEEE Transactions on Signal and Information Processing over Networks 5(1) p. 113-126 Doi: https://doi.org/10.1109/TSIPN.2018.2866324

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) p. 4124-4128 Doi: https://doi.org/10.1109/ICASSP.2018.8461849

Consensus algorithms are iterative methods that represent a basic building block to achieve superior functionalities in increasingly complex sensor networks by facilitating the implementation of many signal-processing tasks in a distributed manner. Due to the heterogeneity of the devices, which may present very different capabilities (e.g. energy supply, transmission range), the energy often becomes a scarce resource and the communications turn into directed. To maximize the network lifetime, a magnitude that in this work measures the number of consensus processes that can be executed before the first node in the network runs out of battery, we propose a topology optimization methodology for directed networks. Numerical results corroborate the merits of this work.

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) p. 3789-3793 Doi: https://doi.org/10.1109/ICASSP.2018.8461962

In this paper, we consider a joint topology and radio resource optimization for device-to-device (D2D) based mobile social networks. The considered social network is an interest based which is modeled as a d -intersection binomial random graph. The Radio network is also modeled as a random graph where an edge between any two distinct nodes is activated with a certain probability that is equivalent to the probability of exceeding a certain signal to interference ratio for that link. The entire network is then modeled as an intersection graph between the social and radio induced graphs. Thereafter, network topology is optimized such that enabled social edges satisfy certain network connectivity constrains under specific radio environment characteristics. Radio resource allocation is performed to maximize the radio resource utilization exploiting both social ties awareness among the network nodes and knowledge of channel gains among users' locations. We formulate our radio resource allocation problem as a semidefinite program over a graph representing the network topology. Simulation based numerical results are shown in terms of achieved link efficiency and optimized topology parameters.

Smart Water Grids p. 41-69 Doi: https://doi.org/10.1201/b21948

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) p. 4639-4643 Doi: https://doi.org/10.1109/ICASSP.2018.8462110

A significant number of linear inference problems in wireless sensor networks can be solved by projecting the observed signal onto a given subspace. Decentralized approaches avoid the need for performing such an operation at a central processor, thereby reducing congestion and increasing the robustness and the scalability of the network. Unfortunately, existing decentralized approaches either confine themselves to a reduced family of subspace projection tasks or need an infinite number of iterations to obtain the exact projection. To remedy these limitations, this paper develops a framework for computing a wide class of subspace projections in a decentralized fashion by relying on the notion of graph filtering. To this end, a methodology to obtain the shift matrix and the corresponding filter coefficients that provide exact subspace projection in a nearly minimal number of iterations is proposed. Numerical experiments corroborate the merits of the proposed approach.

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) p. 3684-3688 Doi: https://doi.org/10.1109/ICASSP.2018.8462424

Since the spectral efficiency of wireless communications is already close to its fundamental bounds, a significant increase in spatial efficiency is required to meet future traffic demands. Device-to-device (D2D) communications provide such an increase by allowing nearby users to communicate directly without passing their packages through the base station. To fully exploit the benefits of this paradigm, proper channel assignment and power allocation algorithms are required. The main limitation of existing schemes, which restrict D2D transmitters to operate on a single channel at a time, is circumvented by the joint channel assignment and power allocation algorithm proposed in this paper. This algorithm relies on convex relaxation to efficiently obtain nearlyoptimal solutions to the mixed-integer program arising in this context. Numerical experiments corroborate the merits of the proposed scheme relative to state-of-the art alternatives.

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) p. 3549-3553 Doi: https://doi.org/10.1109/ICASSP.2018.8461731

Spectrum cartography constructs maps of metrics such as channel gain or received signal power across a geographic area of interest using measurements of spatially distributed sensors. Applications of these maps include network planning, interference coordination, power control, localization, and cognitive radio to name a few. Existing spectrum cartography methods necessitate knowledge of sensor locations, but such locations cannot be accurately determined from pilot positioning signals (such as those in LTE or GPS) in indoor or dense urban scenarios due to multipath. To circumvent this limitation, this paper proposes localization-free cartography, where spectral maps are directly constructed from features of these positioning signals rather than from location estimates. The proposed algorithm capitalizes on the framework of kernel-based learning and offers improved prediction performance relative to existing alternatives, as demonstrated by a simulation study in a street canyon.

2018 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS) p. 104-107 Doi: https://doi.org/10.1109/iSES.2018.00031

This article presents the design and prototype implementation of a low-cost and short-range self-powered wireless IoT device based on energy harvesting for both indoor and outdoor applications. Prototyped devices are deployed in a star network configuration with a custom protocol. Based on measurements, devices achieve a line-of-sight range of 228.5m above 40m from the ground level. Nodes are powered based on energy harvesting from a small 0.36W solar panel and 120mAh lithium button cell as storage elements. The test in the well-lit room shows an average harvested power of 941.94μW over a period of 2.5 days, while under the low lighting conditions showed an average of 212μW over a period of 24h. From measurements, a fully charged rechargeable 120mAh cell lasts for 278 days with 55s transmission interval. Temperature, visible lights level and relative humidity sensors are integrated into the nodes.

2018 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS) p. 215-218 Doi: https://doi.org/10.1109/iSES.2018.00054

This paper presents the design and prototype implementation of long-range self-powered wireless IoT devices using nRF52840 based on energy harvesting. The test-bed is setup in both star and multi-hop configurations with optimized custom protocols. In both network configurations, nodes consume less power than what is harvested in an indoor light environment using a small 0.36W rated monocrystalline solar panel. The average power by which the battery was charged during the test was 941.94μW in an indoor environment. Nodes are able to operate for 12 months using a fully charged 120mAh rated rechargeable coin cell battery with 55s transmission interval. Based on measurements, a line of sight range of 1.8km is obtained using coded transmissions. Sensors of temperature, relative humidity and visible light are integrated into the nodes.

2018 31st International Conference on VLSI Design and 2018 17th International Conference on Embedded Systems (VLSID) p. 455-456 Doi: https://doi.org/10.1109/VLSID.2018.110

This paper presents a proof of concept for self-powered Internet of Things (IoT) device, which is maintenance free and completely self-sustainable through energy harvesting. These IoT devices can be deployed in large scale and placed anywhere as long as they are in range of a gateway, and as long as there is sufficient light levels for the solar panel, such as indoor lights. A complete IoT device is designed, prototyped and tested. The IoT device can potentially last for more than 5 months (transmission interval of 30 seconds) on the coin cell battery (capacity of 120mAh) without any energy harvesting, sufficiently long for the dark seasons of the year. The sensor node contains ultra-low power sensors for temperature, humidity and light levels, with the possibility of adding several more sensors.

2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) Doi: https://doi.org/10.1109/ANTS.2018.8710171

5th IEEE Global Conference on Signal and Information Processing (GlobalSIP 2017) p. 608-612 Doi: https://doi.org/10.1109/GlobalSIP.2017.8309031

The large number of nodes forming current sensor networks has made essential to introduce distributed mechanisms in many traditional applications. In the emerging field of graph signal processing, the distributed mechanism of information potentials constitutes a distributed graph filtering process that can be used to solve many different problems. An important limitation of this algorithm is that it is inherently iterative, which implies that the nodes incur in a repeated communication cost along the exchange periods of the filtering process. Since sensor nodes are battery powered and radio communications are one of the most energy demanding operations, in this work, we propose to redesign the network topology in order to reduce the total energy consumption of the filtering process. An accurate energy model is proposed and extensive numerical results are presented to show the efficiency of our methodology according to this energy model.

2017 25th European Signal Processing Conference (EUSIPCO) Doi: https://doi.org/10.23919/EUSIPCO.2017.8081227

Graph filters, which are considered as the workhorses of graph signal analysis in the emerging field of signal processing on graphs, are useful for many applications such as distributed estimation in wireless sensor networks. Many of these tasks are based on basic distributed operators such as consensus, which are carried out by sensor devices under limited energy supply. To cope with the energy constraints, this paper focuses on designing the network topology in order to maximize the network lifetime and reduce the energy consumption when applying graph filters. The problem is a complex combinatorial problem and in this work, we propose two efficient heuristic algorithms for solving it. We show by simulations that they provide good performance in terms of the network lifetime and the total energy consumption of the filtering process.

Computational Advances in Multi-Sensor Adaptive Processing, IEEE 7th International Workshop on (CAMSAP) Doi: https://doi.org/10.1109/CAMSAP.2017.8313212

Computational Advances in Multi-Sensor Adaptive Processing, IEEE 7th International Workshop on (CAMSAP) Doi: https://doi.org/10.1109/CAMSAP.2017.8313211

An important problem in data sciences pertains to inferring causal interactions among a collection of time series. Upon modeling these as a vector autoregressive (VAR) process, this paper deals with estimating the model parameters to identify the underlying causality graph. To exploit the sparse connectivity of causality graphs, the proposed estimators minimize a group-Lasso regularized functional. To cope with real-time applications, big data setups, and possibly time-varying topologies, two online algorithms are presented to recover the sparse coefficients when observations are received sequentially. The proposed algorithms are inspired by the classic recursive least squares (RLS) algorithm and offer complementary benefits in terms of computational efficiency. Numerical results showcase the merits of the proposed schemes in both estimation and prediction tasks.

18th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2017) Doi: https://doi.org/10.1109/SPAWC.2017.8227752

In this paper, we experimentally validate the functionality of a developed algorithm for spectrum cartography using adaptive Gaussian radial basis functions (RBF). The RBF are strategically centered around representative centroid locations in a machine learning context. We assume no prior knowledge about neither the power spectral densities (PSD) of the transmitters nor their locations. Instead, the received signal power at each location is estimated as a linear combination of different RBFs. The weights of the RBFs, their Gaussian decaying parameters and locations are jointly optimized using expectation maximization with a least squares loss function and a quadratic regularizer. The performance of adaptive RBFs based spectrum cartography is shown through measurements using a universal software radio peripheral, a customized node and LabView framework. The obtained results verify the ability of adaptive RBF to construct spectrum maps with an acceptable performance measured by normalized mean square error (NMSE).

2017, 11th International Conference on Signal Processing and Communication Systems (ICSPCS) Doi: https://doi.org/10.1109/ICSPCS.2017.8270459

2017, 11th International Conference on Signal Processing and Communication Systems (ICSPCS) Doi: https://doi.org/10.1109/ICSPCS.2017.8270477

Association for Computing Machinery (ACM)

Proceedings of the 3rd International Workshop on Cyber-Physical Systems for Smart Water Networks p. 7-10 Doi: https://doi.org/10.1201/b21948-2

The appearance of biofilm has become a serious problem in many reverse osmosis based systems such as the ones found in water treatment and desalination plants. In these systems, the use of traditional techniques such as pretreatment or dozing biocides are not effective when the biofilm reaches an irreversible attachment phase. In this work, we present a framework for the use of a WSN as an estimator of the biofilm evolution in a reverse osmosis membrane so that effective solutions can be applied before the irreversible phase is attained. This design is addressed in a complete distributed and decentralized fashion, and subject to realistic constraints where cooperation between nodes is performed under unreliable links.

2017 IEEE International Instrumentation and Measurement Technology Conference Doi: https://doi.org/10.1109/I2MTC.2017.7969681

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) p. 3599-3603 Doi: https://doi.org/10.1109/ICASSP.2017.7952827

EURASIP Journal on Advances in Signal Processing 2016(1) Doi: https://doi.org/10.1186/s13634-016-0360-0

Big data, characterized by huge volumes of continuously varying streams of information, present formidable challenges in terms of acquisition, processing, and transmission, especially when one considers novel technology platforms such as the Internet-of-Things and Wireless Sensor Networks. Either by design or by physical limitations, a large number of measurements never reach the central processing stations, making the task of data analytics even more problematic. In this work, we propose Singular Spectrum Matrix Completion (SS-MC), a novel approach for the simultaneous recovery of missing data and the prediction of future behavior in the absence of complete measurement sets. The goal is achieved via the solution of an efficient minimization problem which exploits the low rank representation of the associated trajectory matrices when expressed in terms of appropriately designed dictionaries obtained by leveraging the theory of Singular Spectrum Analysis. Experimental results in real datasets demonstrate that the proposed scheme is well suited for the recovery and prediction of multiple time series, achieving lower estimation error compared to state-of-the-art schemes.

Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing 2016-May p. 4438-4442 Doi: https://doi.org/10.1109/ICASSP.2016.7472516

2016 IEEE International Conference on Distributed Computing in Sensor Systems p. 187-192 Doi: https://doi.org/10.1109/DCOSS.2016.38

Cyber-Physical Systems: From Theory to Practice p. 181-223 Doi: https://doi.org/10.1201/b19290-12

Distributed Computing in Sensor Systems (DCOSS) p. 108-114 Doi: https://doi.org/10.1109/DCOSS.2015.11

ACM Digital Library

Proceedings of the 1st ACM International Workshop on Cyber-Physical Systems for Smart Water Networks Doi: https://doi.org/10.1145/2738935.2738938

IEEE Journal on Selected Topics in Signal Processing 9(2) p. 292-303 Doi: https://doi.org/10.1109/JSTSP.2014.2370932

IEEE Vehicular Technology Conference (VTC) Proceedings 2015 Doi: https://doi.org/10.1109/VTCSpring.2015.7145783

IEEE Transactions on Signal Processing 63(12) p. 3046-3061 Doi: https://doi.org/10.1109/TSP.2015.2416676

ICC 2014 - Proceedings of 2014 IEEE International Conference on Communications p. 269-275 Doi: https://doi.org/10.1109/ICC.2014.6883330

Average consensus algorithms are an essential tool in wireless sensor networks for multiple estimation tasks, being the convergence time and the energy consumption of these algorithms critical for their usability. Most existing work in the related literature focuses on improving these two parameters, assuming generally unicast communications, which are neither realistic nor efficient given the wireless nature of these networks. Instead, broadcast communications allow a greater instantaneous exchange of information between the network nodes, accelerating the consensus and saving energy in communications. In this work, we propose two methods that optimize the network topology to simultaneously improve the total power consumption per iteration, the maximum power consumption per node and the convergence time in a broadcast scenario. The first method is applied to continuous systems, while the second one is more suitable for discrete systems. Numerical results are presented to show the validity and efficiency of the proposed methods.

2014 IEEE Wireless Communications and Networking Conference (WCNC) p. 1715-1720 Doi: https://doi.org/10.1109/WCNC.2014.6952488

ICC 2014 - Proceedings of 2014 IEEE International Conference on Communications p. 2436-2442 Doi: https://doi.org/10.1109/ICC.2014.6883688

The so-called scale free property is a feature of many complex systems, including the Internet and World Wide Web, where most nodes have just a few links whereas the rest have a huge number of them. The traditional models conceived to reproduce the formation of these systems do not take into account the topological features of the growing network. Nevertheless, the resulting topology has a major influence on important properties of the network, such as synchronization and consensus time of nodes, network robustness, etc. In this work, we propose a new growth model that, starting from a random deployment of nodes, performs a wiring process using a metric that balances the degree of the nodes (preferential attachment), the euclidean distance (power consumption) and the algebraic connectivity (consensus time among others). Numerical results show that our proposal not only captures the properties of scale free networks, but also achieves a final topology that leads to more energy-efficient processes than traditional approaches.

2014 Proceedings of the 22nd European Signal Processing Conference (EUSIPCO) p. 131-135

Wireless Sensor Networks are a recent technology where the nodes cooperate to obtain, in a totally distributed way, certain function of the collected data. An important example of these distributed processes is the average gossip algorithm, which allows the nodes to obtain the global average by only using local data exchanges. This process is traditionally slow, but can be accelerated by introducing geographic information or by exploiting the broadcast nature of the wireless medium. However, when a gossip protocol utilizes long geographic routes or broadcast communications, its convergence is not easily guaranteed due to asymmetry in communications. Alternatively, we propose an asymmetric version of the gossip algorithm that exploits residual information involved in each asymmetric exchange. Our asymmetric gossip algorithm achieves convergence faster than existing studies in the related literature. Numerical results are presented to show clearly the validity and efficiency of our approach.

2014 IEEE Global Communications Conference (GLOBECOM) p. 473-479 Doi: https://doi.org/10.1109/GLOCOM.2014.7036853

IEEE Transactions on Signal Processing 61(24) p. 6355-6370 Doi: https://doi.org/10.1109/TSP.2013.2284486

This paper considers the problem of power-efficient distributed estimation of vector parameters related to localized phenomena so that both sensor selection and routing structure in a Wireless Sensor Network (WSN) are jointly optimized to obtain the best possible estimation performance at a given querying node, for a given total power budget. First, we formulate our problem as an optimization problem and show that it is an NP-Hard problem. Then, we design two algorithms: a Fixed-Tree Relaxation-Based Algorithm (FTRA) and a very efficient Iterative Distributed Algorithm (IDA) to optimize the sensor selection and routing structure. We also provide a lower bound for our optimization problem and show that our IDA provides a performance that is close to this bound, and it is substantially superior to the previous approaches presented in the literature. An important result from our work is the fact that because of the interplay between communication cost and estimation gain when fusing measurements from different sensors, the traditional Shortest Path Tree (SPT) routing structure, widely used in practice, is no longer optimal. To be specific, our routing structure provides a better trade-off between the overall power efficiency and estimation accuracy. Comparing to more conventional sensor selection and fixed routing algorithms, our proposed algorithms yield a significant amount of energy saving for the same estimation accuracy.

97th IEEE Vehicular Technology Conference (VTC-Spring)

IEEE Asilomar Conference on Signals, Systems, and Computers

IEEE Transactions on Signal Processing 70 p. 2842-2843 Doi: https://doi.org/10.1109/TSP.2022.3176492

56th Asilomar Conference on Signals, Systems, and Computers 2022

28th European Signal Processing Conference (EUSIPCO 2020)

2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA)

Temadag ved Arendal International School

IEEE ANTS 2019, 16 – 19 December, Goa, India

IEEE ANTS 2019, 16 – 19 December, Goa, India

Graph Signal Processing Workshop

Due to their capacity to condense the spatiotemporal structure of a data set in a format amenable for human inter-pretation, forecasting, and anomaly detection, causality graphsare routinely estimated in social sciences, natural sciences, andengineering. A popular approach to mathematically formalizecausality is based on vector autoregressive (VAR) models, whichconstitutes an alternative to the well-known but usually in-tractable Granger causality. Relying on such a VAR causalitynotion, this paper develops two algorithms with complementarybenefits to track time-varying causality graphs in an onlinefashion. Despite using data in a sequential fashion, both al-gorithms are shown to asymptotically attain the same averageperformance as a batch estimator with all data available atonce. Moreover, their constant complexity per update rendersthese algorithms appealing for big-data scenarios. Theoreticaland experimental performance analysis support the merits ofthe proposed algorithms. Remarkably, no probabilistic modelsor stationarity assumptions need to be introduced, which endows the developed algorithms with considerable generality.

Graph Signal Processing Workshop

2019 IEEE 5th World Forum on Internet of Things (WF-IoT)

2019 IEEE 5th World Forum on Internet of Things (WF-IoT)

IEEE Global Conference on Signal and Information Processing (GlobalSIP)

31st International Conference on VLSI Design (VLSID 2018) concurrently with 17th International Conference on Embedded Systems (ES 2018)

GlobalSIP 2018

IEEE International Conference on Advanced Networks and Telecommunication Systems (IEEE ANTS 2018), Indore, India.

Godt tenkt!

4th IEEE International Symposium on Smart Electronic Systems (IEEE iSES 2018), Hyderabad, India.

4th IEEE International Symposium on Smart Electronic Systems (IEEE iSES 2018), Hyderabad, India.

ICASSP 2018

11th International Conferences on Signal Processing and Communication Systems (ICSPCS'2017)

2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

IEEE International Conference on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017

IEEE Global Conference on Signal and Information Processing (GlobalSIP)

11th International Conference on Signal Processing and Communication Systems (ICSPCS'2017)

The 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2017)

This paper presents a framework for spectrum cartography based on the use of adaptive Gaussian radial basis functions (RBF) centered around a specific number of centroid locations, which are determined, jointly with the other RBF parameters, by the available measurement values at given sensor locations in a specific geographical area. The spectrum map is constructed non-parametrically as no prior knowledge about the transmitters is assumed. The received signal power at each location (over a given bandwidth and time period) is estimated as a weighted contribution from different RBF, in such a way that the both RBF parameters and the weights are jointly optimized using an alternating minimization method with a least squares loss function and a quadratic regularization term. Our method is evaluated through simulations, showing a performance (in terms of normalized MSE) that is comparable to semi-parametric methods, and even superior as the number of sensors or RBF increases.

International Conference on Distributed Computing in Sensor Systems (DCOSS 2016)

International Conference on Acoustic Speech and Signal Processing (ICASSP) 2016

International Conference on Distributed Computing in Sensor Systems DCOSS

Parameter estimation is one of the most common tasks performed by Wireless Sensor Networks, where the deployed nodes try to infer some information about the parameter of interest from the collected observations, usually corrupted by noise. Local estimations are then improved by cooperation between neighboring nodes. This cooperation usually involves successive data exchanges and local information updates until a global consensus value is reached. Therefore, the quality of the global estimator depends on the amount of collected observations, hence the number of active nodes. However, the inherent iterative nature of the consensus process involves a certain energy consumption. Since the devices composing the network are usually battery powered, nodes becoming inactive due to battery depletion emerge as a serious problem. In this work, we aim to maximize the lifetime of the most energy demanding nodes, such that the quality of the global estimator is kept above a certain threshold. To this end, we optimize the network topology by considering both the duration of each consensus process, given by the algebraic connectivity of the network, and the power consumption per iteration of the most demanding nodes. Numerical results are provided to demonstrate the validity and efficiency of our methodology.

Offshore Data Community Workshop

1st International Workshop on Cyber-Physical Systems for Smart Water Networks

The correct estimation of biolm formation in industrial environments, such as reverse osmosis plants, has become a topic of great interest. The occurrence of this natural process is the cause of huge economic losses due to a decrease of performance and maintenance costs in these plants. Current solutions based on water pretreatment or the dozing of biocides are not eective due to the lack of information about the state of the biolm in the water system. In this work, we propose the use of a wireless sensor network that, based on the measurement of the biolm thickness grown at the substratum of each sensor, estimates the biomass concentration within the biolm, and, eventually, the thickness of the biolm in the RO membrane. Our solution is performed in a total distributed fashion such that the information does not need to be routed to any central entity, reducing infrastructure costs and endowing the solution robustness against central entities failures.

Digitalkonferansen Change IT 2015

GLOBECOM 2014

Wireless Sensor Networks have been identified as a promising technology to efficiently perform distributed monitoring, tracking and control tasks. In order to accomplish them, since fast decisions are generally required, high values of throughput must be obtained. Additionally, a high packet reception rate is important to avoid wasting energy due to unsuccessful transmissions. These communication requirements are more easily satisfied by exploiting the broadcast nature of the wireless medium, which allows several simultaneous receptions through a unique node transmission. We propose a Medium Access Control protocol that ensures, simultaneously, high values of throughput and a high packet reception rate in broadcast scenarios. To this extent, we first propose a utility function to measure the performance of the network in terms of these two parameters. Then, we design a medium access policy that, relying on local and adaptive decisions of the nodes, obtains greater values of this utility function than existing approaches based on fixed communication thresholds. Eugenio Celada Funes Department of Information and Communication Technologies and CIEM, Office: A4-023, University of Agder, Jon Lilletuns vei 9, 4879, Grimstad Norway. E-mail: eugenio.celada@uia.no eucefu@gmail.com Tel: +47 37233816