WISENET is participating in the National DIGIPRO Center initiative:
The DigiPro Center initiative focuses on the digitalization of the process industry, which involves a variety of areas, such as smart sensing, data science, machine learning, and smart data transmission in harsh environments of the process industry. All the major companies of the process industry in Norway are involved.
by Baltasar Beferull Lozano
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:
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.
Congratulations to Mohamed Elnourani for the journal paper publication in IEEE Transactions on Communications:
M. Elnourani, S. Deshmukh, B. Beferull-Lozano, “Distributed Resource Allocation in Underlay Multicast D2D Communications”, To appear in IEEE Transactions on Communications, 2021
Congratulations to Bakht Zaman for the journal paper publication in IEEE Transactions on Signal Processing:
B. Zaman, L. M. Lopez-Ramos, D. Romero, B. Beferull-Lozano, “Online Topology Identification from Vector Autoregressive Time-series”, IEEE Transactions on Signal Processing, Vol. 69, pp. 210-225, 2020.
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.
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).
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:
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
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: http://cds.iisc.ac.in/faculty/yalavarthy/.
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.
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.
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:
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:
The papers are:
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.
GUEST LECTURE, WEDNESDAY 16 TH OF AUGUST 2017 TIME: 10:30 11:45, ROOM C2 041 PROFESSOR ANTONIO ORTEGA UNIVERSITY OF SOUTHERN CALIFORNIA LEARNING GRAPHS FROM DATA
Abstract 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 areas Bio 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.
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 has been appointed as of March 15, 2016, Senior Associate Editor for IEEE Transactions on Signal Processing.
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.
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 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 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.
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 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.
The WISENET Lab is currently growing in size with the hiring of three new PhD. and three new Postdoctoral researchers.
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