New Paper Accepted in IEEE Signal Processing Letters

Congratulations to E. Ruiz and B. Beferull-Lozano, for the acceptance of a journal paper in IEEE Signal Processing Letters, 2023.

E. Ruiz-Moreno, B. Beferull-Lozano, “An Online Multiple Kernel Parallelizable Learning Scheme”, Accepted, To appear IEEE Signal Processing Letters, 2023.


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.

New paper accepted in IEEE Transactions on Signal Processing

Congratulations to E. Ruiz, L. M. López-Ramos, B. Beferull-Lozano, for the acceptance of a journal paper in IEEE Transactions on Signal Processing, 2023.

E. Ruiz, L. M. López-Ramos, B. Beferull-Lozano, “A Trainable Approach to Zero-delay Smoothing Spline Interpolation”, IEEE Transactions on Signal Processing, 2023.


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 zero-delay 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 zero-delay 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.

New paper accepted in IEEE Transactions on Signal and Information Processing over Networks

Congratulations to B. Zaman, L. M. López-Ramos, B. Beferull-Lozano for the acceptance of a journal paper in IEEE Transactions on Signal and Information Processing over Networks, 2023.

B. Zaman, L. M. Lopez-Ramos, B. Beferull-Lozano, “Online Joint Topology Identification and Signal Estimation from Streams with Missing Data”, Accepted, To appear in IEEE Transactions on Signal and Information Processing over Networks, 2023.

Short description of the paper: 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.

New Paper accepted in IEEE Transactions on Signal Processing

Congratulations to R. T. Money, J. P. Krishan, B. Beferull-Lozano for the acceptance of a journal paper in IEEE Transactions on Signal Processing, 2023.

- R. T. Money, J. P. Krishnan, B. Beferull-Lozano, “Sparse Online Learning with Kernels using Random Features for Estimating Nonlinear Dynamic Graphs”, To appear in IEEE Transactions on Signal Processing, Vol. 71, pp. 2027-2042, June 2023.

Figures: (upper) Estimation of Topology for Edvard Grieg O&G Platform, (middle) Brain connectivity estimation for different Epilepsy stages, (lower) NMSE performance for our algorithm (RFNL-TIRSO).

Short description of the paper: 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.

New Paper Accepted in IEEE Transactions on Signal and Information Processing over Networks

Congratulations to S. Mollaebrahim and B. Beferull-Lozano for the acceptance of a journal paper in IEEE Transactions on Signal and Information Processing over Networks, 2023.

- S. Mollaebrahim, B. Beferull-Lozano, “Distributed Linear Network Operators via Successive Graph Shift Matrices”, To appear in IEEE Transactions on Signal and Information Processing over Networks, 2023.

Figures: Normalized Mean Projection Error (NMPE) Performance of the proposed algorithm vs. existing state-of-the-art algorithms, for the Subspace Projection problem.

Short description of the paper:
 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.

Best Paper Award at IEEE ISMODE 2022

Congratulations to K. Roy, L. Miguel-Lopez (SimulaMet) and B. Beferull-Lozano, E. Isufi (Delft University) for the Best Paper Award at IEEE ISMODE 2022.

K. Roy, L. Miguel-Lopez, B. Beferull-Lozano, Joint Learning of Topology and Invertible Non-linearities from Multiple Time Series, IEEE International Conference on Machine Learning, Optimization, and Data Science (ISMODE), 2022 (Best Paper Award).

New Paper Accepted in IEEE Open Journal of Signal Processing

Congratulations to R. Money, J. Krishnan (SimulaMet), B. Beferull-Lozano, E. Isufi (Delft University) for the acceptance of a journal paper in IEEE Open Journal of Signal Processing.

R. Money, J. Krishnan, B. Beferull-Lozano, E. Isufi, “Scalable and Privacy-aware Online Learning of Non-linear Structural Equation Models”, IEEE Open Journal of Signal Processing, 2023.

Figure: Dependencies between companies in stock market (S&P 500 index) during and after COVID

Title: Scalable and Privacy-aware Online Learning of Non-linear Structural Equation Models

Short description of the paper:
 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 correction 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.

New Paper Accepted in IEEE Signal Processing Letters

Congratulations to R. Money, J. Krishnan (SimulaMet), B. Beferull-Lozano, E. Isufi (Delft University) for the acceptance of a journal paper in IEEE Signal Processing Letters (Early access at IEEE Xplore).

Title: Online Missing Data Imputation of Edge Flows

Short description of the paper:
A novel online algorithm for missing data imputation for networks with signals defined on the edges is presented in this paper. Leveraging the prior knowledge intrinsic to most real world networks, we propose a bi-level optimization scheme that includes: (i) a sparse line graph identification strategy by solving a group-Lasso-based optimization framework via composite objective mirror descent to exploit the causal dependencies among the signals and (ii) a Kalman-filtering-based signal reconstruction strategy developed using simplicial complex (SC) formulation to exploit the flow conservation. To the best of our knowledge, this is the first SC-based attempt for time-varying signal imputation, whose advantages have been demonstrated through numerical experiments conducted using EPANET models of both synthetic and real water distribution networks.

New Paper Accepted in IEEE Transactions on Communications

Congratulations to M. Elnourani, S. Deshmukh, B. Beferull-Lozano for the acceptance of a journal paper publication in IEEE Transactions on Communications.

Title: “Resource Allocation for Underlay Interfering D2D Networks with Multi-antenna and Imperfect CSI”, Journal: IEEE Transactions on Communications.

Short description of the paper:

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.

New paper accepted in IEEE Transactions on Signal Processing

Congratulations to Leila Ben Saad, Baltasar Beferull-Lozano and Elvin Isufi for the journal paper "Quantization Analysis and Robust Design for Distributed Graph Filters" accepted for publication in IEEE Transactions on Signal Processing.

Figure: Squared norm of unquantized and quantized filtered streaming graph signals for the change point detection problem.

Title: Quantization Analysis and Robust Design for Distributed Graph Filters, Journal: IEEE Transactions on Signal Processing, Vol. 70, pp. 643 - 658, Dec. 2021.

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 two most common graph filters: the finite impulse response (FIR) and auto-regressive moving average (ARMA) graph filter. Besides providing a comprehensive analysis, 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.

WISENET - Part of National DIGIPRO Center

WISENET is participating in the National DIGIPRO Center initiative:

Centre web site:

The DigiPro Centre initiative focuses on the digitalization of the process industry, which involves a variety of areas, such as smart sensing, data science, machine learning, and communication network intelligence in harsh environments of the process industry. All the major companies of the process industry in Norway are involved.

Seven WISENET papers published! - Spring 2021

The WISENET Center has been very productive scientifically during the Spring Semester 2021, generating seven international high quality scientific publications, including also a Best Paper Award!:

These are the publications that have been generated:

New paper accepted in IEEE Transactions on Signal Processing

Congratulations to Siavash Mollaebrahim for his second journal paper accepted in IEEE Transactions on Signal Processing! :

S. Mollaebrahim, B. Beferull-Lozano, "Design of Asymmetric Shift Operators for Efficient Decentralized Subspace Projection" (Accepted), To appear in IEEE Transactions on Signal Processing, 2021.

Exchange students WISENET - Top Indian Institutions, NFR INTPART INCAPS Project

The INTPART Project Indo-Norwegian collaboration in Autonomous Cyber-Physical Systems (INCAPS) is led by the WISENET Centre and involves several exchange students between Norway and India. The first exchange Master student has been Prabhat Rai from IIT Hyderabad. Associate Prof. Linga Reddy Cenkeramaddi is coordinating this Indo-Norwegian collaboration network with the support of several other researchers from the Center.

The INTPART Programme promotes International Partnerships for Excellence Education, Research and Innovation, developing long-term relations between Norwegian higher education and research institutions and strong internationally recognized research groups and institutions from other countries.

The project INCAPS involves a wide variety of research topics, such as smart sensing for autonomous systems, mmWave sensors, prototypes of wireless communication systems, low-altitude UAV tracking and communication, machine learning and artificial intelligence.

BS. Agam Bhatia BML, BS. Aarav Pandya BML, MS. Prabhat Kumar Rai IITH, BS. Aveen Dayal BML, BS. Mehak Jindal BML, MS. Jyoti Bhatia IITI

The Consortium of INCAPS includes the following world-class research and education Institutions: University of Agder (Coordinator), Indian Institute of Science (IISc), Bangalore, Indian Institute of Technology, Hyderabad (IITH), International Institute of Information Technology, Hyderabad (IIITH) and Birla Institute of Technology and Science (BITS), Hyderabad and Norwegian Universities and research Institutes, Norwegian University of Science and Technology (NTNU) and Norwegian Institute for Water Research (NIVA).


WISENET Centre & Mechatronics Centres lead NFR INTPART Project INMOST

The Mechatronics and WISENET Centre at University of Agder will lead the new INTPART Project INMOST which will allow to build a strong collaboration in several research areas with various highly reputed and top Indian Universities.

This project will establish a multi-international network for interdisciplinary research in Intelligence for offshore Mechatronics systems, including Artificial Intelligence, Machine Learning, Data Science, Deep learning, cognitive process control, Autonomous sensor systems, Wireless sensors networks,  Cyber-Physical Systems and Condition-based monitoring for cranes, vessels, drilling and O&G production systems, wind turbines and other offshore Mechatronics systems.

The project will build an international network to enable PhDs, researchers and students acquiring this interdisciplinary background. It will also obtain complementary transferable skills for careers in academia and industry. Participation in different scenarios with the use of multiple platforms from the consortium, and the supervision from leading research partners will allow to acquire critical knowledge with a holistic approach. This will enhance the R&D competitiveness in offshore industry both in Norway and India.

The main objectives are:

  • Strengthen collaborative network between industry (both public and private enterprises) and academia and develop models for technology transfer, innovation and entrepreneurship.
  • Increased value creation by using smart sensing, machine and deep learning techniques, robotics and advanced control in offshore mechatronics systems.
  • Facilitate education and knowledge sharing through better mobility for students and researchers.
  • Create an arena for generation of research and innovation projects.
  • Increased utilization of research infrastructure in Norway and India.

Full project name: INTPART Project Indo-Norwegian collaboration in Intelligent Offshore Mechatronics Systems (INMOST)

Funding: INTPART Programme, Research Council of Norway

Principal Investigators: Prof. Jing Zhou, Prof. Linga Reddy Cenkeramaddi, Prof. Baltasar Beferull-Lozano, Prof. Ajit Jha

Participant Institutions: University of Agder (Coordinator), NTNU, NORCE, IIT Indore, NIT Goa, IIT Tirupati, IIT Palakkad.

Period: 2020 – 2023

Prof. Phaneendra Yalavarthy, IISc Bangalore, visits the WISENET Center

Phaneendra Yalavarthy is visiting WISENET center as visiting associate professor from June 14, 2019 to July 15, 2019 and main cooperating partner of International Indo-Norwegian partnership funded by Research Council of Norway through NFR projects INCAPS and LUCAT, between WISENET and IISc, Bangalore. His research interests including computational methods in medical imaging and inverse problems. His visit during this period will include the kick-off of NFR projects INCAPS and LUCAT and will be also delivering a Guest Lecture on June 26, 2019.

Phaneendra Yalavarthy received B.Sc. and M.Sc. degrees in physics from Sri Sathya Sai University, Puttaparthy, India in 1999 and 2001 respectively. He also obtained a M.Sc. degree in Engineering from Indian Institute of Science, Bangalore, India in 2004. He received a Ph.D., working as a U.S. Department of Defense Breast Cancer Pre-doctoral Fellow, in biomedical computation from Dartmouth College, Hanover, USA in 2007. He worked as a post-doctoral research associate in the Department of Radiation Oncology, School of Medicine, Washington University in St. Louis, USA from 2007-2008. Currently he is working as an associate professor in the Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India. He served as the chair of the department for the period 2016-2018. He is currently serving as the chair of Office of International Relations (OIR) and Institute coordinator of Prime Minister’s Research Fellowship (PMRF) scheme at IISc, Bangalore. He is a recipient of the Apple Laureate award in the year 2009. He also received Department of Atomic Energy young scientist research award in 2010 and a coauthor of the work chosen for the International Society for Magnetic Resonance in Medicine (ISMRM) Merit Award (Summa Cum Laude) in 2012. He is a recipient of Indian National Academy of Engineering (INAE) young engineer award and Innovative Young Biotechnologist Award (Government of India) for the year 2013. He also received the National Academy of Sciences, India (NASI)-Young Scientist Platinum Jubilee Award in the year 2014. He spent one year (2015) of sabbatical at Health and Medical Equipment (HME) Division of Samsung R&D Institute, Bangalore, working on post-processing algorithms for clinical applications of MR Imaging. His research interests include Computational methods in medical imaging, medical image processing (reconstruction/analysis), physiological signal processing, diffuse optical imaging, and photoacoustic imaging. He is a senior member of IEEE & SPIE and serves as an associate editor of IEEE Transactions on Medical Imaging. For more details:

Best Master Thesis Award in Information and Communication Technologies (ICT) - UiA, 2019

Master Thesis: "Design and implementation of wake-up radios for long-range wireless IoT devices"

Master Student Anders Frøytlog, did his Master Thesis under the main supervision of Associate professor Linga Reddy Cenkeramaddi and co-supervision of Assistant Professor Magne Arild Hauglund. Project task is defined by Assoc. Prof. Linga Reddy Cenkeramaddi. The solutions (especially DC-MAC protocol along with wakeup radio) proposed in the thesis greatly reduce the power consumption in the long-range wireless IoT devices. Summary of the thesis can be found below.

Summary of the thesis: As the development within Internet of things (IoT) increases rapidly and the market starts to utilize its potential, an enormous effort is being made in both academia and industry to optimize solutions according to the market demands. The demands vary from case to case and some of them include high data rate, long battery lifetime, low latency and long range/area coverage depending on application scenarios. The numerous use cases and demands for IoT resulted in various IoT technologies.

In many IoT applications, especially Wireless IoT applications, energy-efficiency and battery lifetime are the most important performance metrics. The wireless access mechanisms used in current technologies utilize Duty-cycling (DC) to reduce power consumption. DC allows a node to turn the radio on and off in specific intervals in order to reduce power consumption. These DC-MAC protocols suffer from overhearing, idle listening or unnecessary transmission of advertisement packets. The different protocols may also include long delay time caused by the inactive period in the MAC protocol. The recent research and development of Wake-up Radios (WuRs) address some of these problems. A WuR is a simple low power radio receiver which always listens to the channel to detect a Wake-up Call (WuC). A wake-up radio receiver (WuRx) is attached to the main radio which is always OFF, except when it is supposed to send data. The WuRx and the main radio (MR) are two parts of an IoT node. The use of WuRx eliminates the unnecessary power consumption caused by idle listening and reduce the overhearing consumption as well as the latency. Many articles have been published about WuRs. However, most of the current WuR solutions focus on short range applications. The objective of this thesis is to design a WuRx for long-range applications (10km to 15km range), implement a WuRx and evaluate the results and compare to existing solutions.

System Overview

To reduce the power consumption in long-range IoT applications, a WuRx has been proposed, tested and evaluated. Performance of the proposed WuRx integrated with LoRaWAN node is compared to a LoRaWAN node without a WuRx. A DC-MAC protocol which is combined with a WuRx to reduce the power consumption is also investigated. The result of this thesis is a Long-Range IoT node with an average power consumption of only 0.032mA.

6 WISENET papers accepted at IEEE ICASSP 2018!

The WISENET Lab has managed to get 6 papers accepted at the IEEE ICASSP 2018 international conference. The IEEE ICASSP is the top international conference in the area of signal and data processing. These are the papers accepted:

  • Y. Teganya, L. M. Lopez-Ramos, D. Romero, B. Beferull-Lozano, "Localization-Free Power Cartography", IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.
  • M. Elnourani, M. Hamid, D. Romero, B. Beferull-Lozano, "Underlay Device-to-Device Communications on Multiple Channels", IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.
  • M. Hamid, B. Beferull-Lozano, "Joint Topology and Radio Resource Optimization for Device-to-Device Based Mobile Social Networks", IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.
  • T. Weerasinghe, D. Romero, C. Asensio-Marco, B. Beferull-Lozano, "Fast Distributed Subspace Projection via Graph Filters", IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.
  • C. Asensio-Marco, B. Beferull-Lozano, "Energy efficient consensus over directed graphs", IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.
  • D. Ramírez, D. Romero, J. Vía, R. López-Valcarce, I. Santamaría, "Locally optimal invariant detector for testing equality of two power spectral densities", IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.

IKTPLUSS project INDURB funded!

The WISENET Lab has obtained also funding for the project "Data-driven cyber-physical networked systems for autonomous cognitive control
and adaptive learning in industrial & urban water environments (INDURB)" project from the IKTPLUSS Program, Research Council of Norway. The project INDURB focuses on data-driven cyber-physical networked systems for autonomous cognitive control in water environments.

Only two long-term research projects (out of more than 100 project proposal applications) were selected in the whole IKTPLUSS Call, one of them INDURB, led by the WISENET Centre at UiA.

The INDURB project involves highly multidisciplinary research across different areas, and in addition to international cooperation, it will have a direct strong impact on both the Sørlandet region and national-wise through the various water-related circuits and problems.

News published about INDURB project at UiA web site can be found here

Two IEEE CAMSAP papers accepted

Two IEEE CAMSAP papers accepted:

The papers are:

  • B. Zaman, L.M. López-Ramos, D. Romero, B. Beferull-Lozano, “Online Topology Estimation for Vector Autoregressive Processes in Data Networks”, International Conference on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017.
  • M. Ramezani, B. Beferull-Lozano, “Graph Recursive Least Squares Filter for Topology Inference in Causal Data Processes”, International Conference on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017.

Prof. Antonio Ortega (USC) visits WISENET

Antonio Ortega, Professor, University of Southern California, visited the WISENET Lab from August 15th to August 18th. Prof. Ortega gave a very interesting Talk "Learning Graphs from Data" and attended several presentations given by PhD students in our Lab related to Graph Signal Processing, providing useful feedback.


TIME: 10:30 11:45,
ROOM C2 041

There has been significant recent progress in the development of tools
for graph signal processing, including methods for sampling and transforming
graph signals In many applications, a graph needs to be learned from data
before these graph signal processing methods can be applied A standard
approach for graph learning is to estimate the empirical covariance from the data
and then compute an inverse covariance ( matrix under desirable
structural constraints We present recent results that allow us to solve these
problems under constraints that encompass a broad class of generalized graph
Laplacians These methods are computationally efficient, can incorporate
sparsity constraints, and can also be used to optimize weights for a given known
topology We illustrate these ideas with examples in image processing and other
Antonio Ortega received the Telecommunications Engineering degree from
the Universidad Politecnica de Madrid, Madrid, Spain in 1989 and the Ph D in
Electrical Engineering from Columbia University, New York, NY in 1994 In 1994
he joined the Electrical Engineering department at the University of Southern
California ( where he is currently a Professor and has served as Associate
Chair He is a Fellow of the IEEE 2007 and a member of ACM and APSIPA He is
currently a member of the Board of Governors of the IEEE Signal Processing
Society and the inaugural Editor in Chief of the APSIPA Transactions on Signal
and Information Processing He has received several paper awards, including
most recently the 2016 Signal Processing Magazine award and was a plenary
speaker at ICIP 2013 His recent research work is focusing on graph signal
processing, machine learning, multimedia compression and wireless sensor
networks About 40 PhD students have completed their PhD thesis under his
supervision at USC and his work has led to over 300 publications in international
conferences and journals, as well as several patents.

Our Paper “Adaptive consensus-based distributed Kalman filter for WSNs with random link failures” accepted at IEEE DCOSS 2016.

Wireless Sensor Networks have emerged as a very powerful tool for the monitoring and control, over large areas, of diverse phenomena. One of the most appealing properties of these networks is their potentiality to perform complex tasks in a total distributed fashion, without requiring a central entity. In this scenario, where nodes are constrained to use just local information and communicate only with one-hop neighbors, iterative consensus algorithms enjoy great popularity due to their simplicity. In this work, we propose a consensus-based distributed implementation of a Kalman filter for state estimation, in a sensor network whose connections are subject to random failures. As a result of this unreliability, the agreement value of the consensus process is a random variable. Under these conditions, we ensure that the estimator is unbiased, and adaptively compute the gain of the filter by considering the statistical properties of the consensus process. To the best of our knowledge, this is the first time that the distributed implementation of the Kalman filter is addressed by considering the random error introduced by the consensus. We present some numerical results that confirm the validity of our approach.

Prof. Baltasar Beferull-Lozano appointed as Senior Associate Editor for IEEE Trans. on Signal Processing

Prof. Baltasar Beferull-Lozano has been appointed as of March 15, 2016, Senior Associate Editor for IEEE Transactions on Signal Processing.

Our project WISECART has been selected to be funded within the FRIPRO TOPPFORSK Programme!!

The Research Council of Norway has published the evaluation results here. Our project WISECART is one of the only two research projects funded in Norway within the FRIPRO TOPPFORSK Programme, in the area of Information and Communication Technologies. These are excellent news for the WISENET Lab, as well as for the Department of Information and Communication Technologies and the Faculty of Engineering and Science at University of Agder.

Our Paper "Adaptive consensus-based distributed detection in WSN with unreliable links" accepted at IEEE ICASSP 2016

Event detection is a crucial tasks in wireless sensor networks. The importance of a fast response makes distributed strategies, where nodes exchange information just with their one hop neighbors to reach local decisions, more adequate than schemes where all nodes send observations to a central entity. Distributed detectors are usually based on average consensus, where all nodes iteratively communicate to asymptotically agree on a final result. In a realistic scenario, communications are subject to random failures, which impacts the performance of the consensus. We propose an alternative detector, which adapts to the statistical properties of the consensus and compensate deviations from the average. Simulation results show that this adaptive detector improves the performance and approximates to the one of the optimal detector.

Prof. Baltasar Beferull elected as Member of Agder Academy of Sciences and Letters, Norway

Prof. Baltasar Beferull was elected as new Member of the Agder Academy of Sciences and Letters, Norway. The ceremony will take place on October 30th, 2015, at Klubben, in Kristiansand.

The main mission of the Academy is to contribute to strengthening the scientific activity in Norway, and increasing the general understanding of the vital importance of science in society. This mission is accomplished in particular by organizing public meetings, conferences and seminars, and by focusing on outstanding academic achievements through the awarding of academic prizes.

Prof. Baltasar Beferull invited to the NI RF & Communications 5G Round Table 2015 in Vienna

Prof. Baltasar Beferull has been invited to attend the National Instruments RF & Communications 5G Round Table 2015 in Vienna. This invitation-only event aims at bringing together a network of leading European researchers and educators active in the area of new communication technologies. This year, the event will have a strong focus on technologies supporting vehicular connectivity, 5G research and development and for prototyping as well as test bed implementations.

Key topics covered this year:

  • Massive MIMO
  • Dense networks
  • PHY enhancements
  • mmWave
  • Vehicular connectivity
  • LabVIEW Communications System Design Suite

This two-day event is limited to a small number of preeminent group leaders and professors, to allow for effective networking and feedback with NI executives, R&D representatives and senior experts as well as experienced Communications platform users. The aim is to prioritize case studies that discuss the use of LabVIEW Communications to accelerate research by reducing the time needed to get from research concept to real-time test bed implementation.

WISENET is co-organizing CySWater 2016

WISENET is co-organizing together with ICS-FORTH (Signal Processing Lab) the 2nd International Workshop on Cyber-Physical Systems for Smart Water Networks (CySWater2016) during the CPS (Cyber-Physical Systems) Week in Viena.

The objective of the 2nd International Workshop on Cyber-physical Systems for Smart Water Networks (CySWater2016) is to bring together for the second time researchers and engineers from the fields of Communications/Networking, Learning/Processing, and Control and practitioners from the Water Industry to both share their experiences, as well as formulate novel CPS paradigms for fulfilling the vision of Smart Water Networks (SWN). Emphasis will be given to both theoretical modelling, as well as modern exemplars that respond to different aspects of the water life-cycle.

The proposed, multi-disciplinary agenda attempts both to stimulate the research and engineering questions, as well as to solicit CPS-based solutions, for addressing the problem of water crisis. As such CySWater aspires to serve as the application-driven forum, where CPS modelling, deployment, and evaluation are tailored to the specific needs of an emerging societal challenge. Therefore CySWater both fits perfectly the purposes of the CPS Week, and complements the agenda of the CPS Week conferences.

Topics of interest include, but are not limited to:

Data Acquisition, Processing & Learning: Infrastructure & smart sensor devices for SWN; Signal sampling, classification & anomaly detection for SWN; Decentralized multi-sensor fusion, learning & data analytics.

Communications, Networking & Control: Underground & underwater CPS; Mobile network agents for large-scale CPS; Networked Control Systems architecture, incl. cloud computing aspects; CPS and security for SWN.

Analysis, Performance, & Applications: Testbeds, field studies, & performance analysis; Novel CPS application paradigms for modern water applications, s.a. water treatment, water recycle and reuse, waste/sewage/storm water management; Standardization and policies for enabling SWN.

WISENET Lab staff growing

The WISENET Lab is currently growing in size with the hiring of three new PhD. and three new Postdoctoral researchers.

SMART-RIG Research Project begins

Off-shore – On-shore Collective Analytics & Intelligence based on Heterogeneous Wireless Networks

Full project name: Off-shore–On-shore Collective Analytics & Intelligence for condition-based monitoring in drilling & operations using heterogeneous networks (SMART-RIG)


Funding: Researcher Project, Research Council of Norway

Principal Investigator: Prof. Baltasar Beferull-Lozano

Topic: This project is motivated by the grand challenge of providing a new ICT solution for collective off-shore–on-shore intelligence for predictive CBM of drilling rigs, covering: a) the distributed acquisition of sensor signals, including data pre-processing,adaptive sampling rate optimization and collaborative calibration capabilities (Network Tier 1), b) in-network cooperative processing and distributed context-aware intelligence to perform essential data analytics tasks such as local event prediction and detection, low-level feature extraction, decision-making support and learning, c) design of semantic sensor management tools at micro-server nodes (Network Tier 2), with higher resources in terms of computation and communication capabilities, providing data aggregation across different inter-related subsystems and an intermediate medium-to-high level inference about the data collection, i.e., high-level diagnostics (fault detection, isolation and cause identification) and prognostics (fault & degradation prediction), tracking continuous consistency with the on-shore high-level analytics running at the servers (Network Tier 3) of Control Centers, providing recommendations or direct actions if necessary. The proof of concept will be demonstrated with the support from Lundin, Frigstad and MHWirth.

Role: Coordination and Leader of all technical WorkPackages.

Period: 2015 – 2019