WISENET at University of Agder will lead the new INTPART project INCAPS, where our Center will collaborate with several highly reputed and top Indian Universities. This project establishes a long-term collaboration between top ranked Indian Universities, including 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, University of Agder (UiA), Norwegian University of Science and Technology (NTNU) and Norwegian Institute for Water Research (NIVA) in world-class research and education says project manager, Prof. Linga Reddy Cenkeramaddi.
Key participants from University of Agder are researchers from WISENET Lab, including Prof. Linga Reddy Cenkeramaddi, Prof. Baltasar Beferull Lozano and Prof. Daniel Romero. Other key researchers involved in the project are, Prof. Kimmo Kansanen from NTNU, Dr. Christopher Harman from NIVA, Prof. Phaneendra K. Yalavarthy from IISc Bangalore, Prof. Abhinav Kumar from IIT Hyderabad, and Prof. Soumya J from BITS Hyderabad.
Only the foremost established and excellent research communities can apply for this INTPART grant and this is highly competitive as the competition is among the world renowned research centers. Major goals are to strengthen the competitiveness and innovation capacity, solving major societal challenges and developing high-quality academic environments. WISENET Lab obtains this grant by competing with the best research centers across the nation. Main objective is to create sustainable partnerships with the best international institutions and research communities across the globe.
This project considers broad areas of research which include smart sensing for autonomous systems, mmWave sensors based system design, analog, digital and mixed signal circuit design, prototyping of wireless communication systems, low-altitude UAVs tracking and communications, de-centralized wireless communications, in-network processing and intelligence for heterogeneous wireless sensor and communication networks, machine learning and deep learning for autonomous systems, data analytics, energy harvesting based smart electronic systems, smart water networks and inference methods for timely detection and prediction, cognitive control and adaptive learning in autonomous cyber-physical systems.
by Netlab Admin
On October 25 the WP6 Leader prof. Baltasar Beferull Lozano organized a workshop on the fundamentals of Graph Signal Processing, together with his colleagues Dr. Daniel Romero and Dr. Luis Miguel Lopez Ramos. Several participants from industry attended, in addition to researchers from UiA. The agenda for the workshop was as follows:
10-12: Basics of Graph Signal Processing, possible applications in different domains (within the SFI OM project) and Feedback from industrial partners.
13-14: Brief review of already well-functioning cooperation cases with industrial partners in WP6.
14-15: Exploration of new cooperation opportunities with industrial partners, associated use cases and strategies.
Wisenet Lab was invited to present his work and vision at the Godt tenkt! Conference.
Title: Data Science in O&G meets Graph Signal Processing
Speaker: Baltasar Beferull Lozano
Godt tenkt! is an Innovation Conference that brings together the oil and gas industry. New technologies and smart solutions are crucial for the Norwegian oil and gas industry to be able to renew and improve. Lundin Norway and the Norwegian Petroleum Association will therefore like to gather those who work with tomorrow’s solutions.
Students Rolf Arne Kjellby, Svein Erik Løtveit & Thor Eirik Johnsrud, did their Master Thesis under main supervision of Associate professor Linga Reddy Cenkeramaddi and co-supervision of Assistant Professor Geir Jevne. Project task is defined by Assoc. Prof. Linga Reddy Cenkeramaddi. Students designed and developed self-powered and ultra-low powered wireless IoT devices for indoor and outdoor applications. These nodes are tested and work up to 1.8 km and can be deployed in remote places where the accessibility is limited. The nodes can also be deployed in harsh-weather conditions without requiring any maintenance. Designed nodes are of professional market-quality, market-ready, efficient, self-powered and maintenance free. Many possibilities for further research based on these nodes including a start-up company.
Automation of indoor climate is becoming increasingly popular for both household and industrial use. Through automation, comfort increases and power consumption decreases. In order to deploy an automation system, sensors are required.
This master thesis proposes two wireless sensor nodes based on ATmega328p along with the nRF24l01+ transceiver and nRF52840 with various capabilities in both star and multi-hop network configurations. The designed nodes are fully self-powered through energy harvesting, and the nodes are completely self sustainable with no wires, and no user intervention is required during the lifetime of the components. In addition, these nodes do not require any maintenance and can be deployed in remote places. The wireless sensor nodes can be deployed anywhere as long as they are in range of a gateway or nodes that can forward towards a gateway, and as long as there is sufficient light level for the solar panel, such as indoor lights. Fully functional wireless sensor nodes are designed and tested, and compared the performance of both star and multi-hop topologies. The developed nodes consume less power than what is harvested in both indoor and outdoor environments.
Wisenet Lab has been granted a UAV project called “Low-altitude UAV Communication and Tracking (LUCAT)", under Indo-Norwegian researcher projects within ICT call (INDNOR), in collaboration with IISc Bangalore, India.
The project will strengthen research collaboration between Norway and India within smart environments, communication technologies and address societal challenges by improving environmental monitoring and security and robustness in energy infrastructures.
Total number of applications processed for the call: 50 Number of projects awarded funding: 5 Total funding of LUCAT project: 10.3 MNOK (NFR funding: 10 MNOK)
by Julia Cervera-Vallés
Big Data meets Graph Signal Processing Baltasar Beferull-Lozano, professor, Universitetet i Agder
Oslo: Møte 21. februar 2018 at Norske Videnskaps-Akademi
There has been significant recent progress in the development of signal processing tools that operate directly over graph-structured data. Many datasets in multiple real applications can be modeled flexibly in terms of the so-called graph signals, where each node of the graph represents essentially one or several data time-series and the links connecting the nodes represent some type of space-time dependencies among the various data time-series. The emergence of very diverse and ubiquitous computing, communicating and sensing devices has led to an era where huge amounts of data (big data) are generated constantly. Examples of applications include industrial plants, cyber-physical networked sensor systems, water distribution networks, connectivity brain networks, finance networked systems and social networks, among others. This talk will provide an overview about the recent area of Graph Signal Processing, exploring recent results, challenges and applications. We will illustrate the key concepts and cover different data processing methods for machine learning tasks over graph signals, their computational efficiency, and their impact in several important applications.
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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:
by Baltasar Beferull Lozano
The WISENET Lab has obtained also funding for the 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 Lab 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.
The skills generated by this projects will be a long-term resource for digitalization in business and public sector, 23 projects are on to the second round when the Research Council is to strengthen basic ICT research in Norway. UiA WISENET leads one of the projects that are in for the second round.
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.
The authors: Leila Ben Saad, Cesar Asensio-Marco and Baltasar Beferull-Lozano
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 the sensor nodes are battery powered and radio communications are 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.
The authors: Leila Ben Saad, Thilina Weerasinghe and Baltasar Beferull-Lozano
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 when applying graph filters. None of the existing works in the literature have studied such problem when graph filters are used. 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 and increase significantly the network lifetime.
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