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Domenico Ciuonzo - Data Fusion in Wireless Sensor Networks: A statistical signal processing perspective

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Domenico Ciuonzo Data Fusion in Wireless Sensor Networks: A statistical signal processing perspective
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The role of data fusion has been expanding in recent years through the incorporation of pervasive applications, where the physical infrastructure is coupled with information and communication technologies, such as wireless sensor networks for the internet of things (IoT), e-health and Industry 4.0. In this edited reference, the authors provide advanced tools for the design, analysis and implementation of inference algorithms in wireless sensor networks.

The book is directed at the sensing, signal processing, and ICTs research communities. The contents will be of particular use to researchers (from academia and industry) and practitioners working in wireless sensor networks, IoT, E-health and Industry 4.0 applications who wish to understand the basics of inference problems. It will also be of interest to professionals, and graduate and PhD students who wish to understand the fundamental concepts of inference algorithms based on intelligent and energy-efficient protocols.

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About the editors

Domenico Ciuonzo was born in Aversa, Italy, on June 29, 1985. He received the B.Sc. and M.Sc. (both summa cum laude) degrees in computer engineering and the Ph.D. degree in electronic engineering from the University of Campania L. Vanvitelli, Aversa, Italy, in 2007, 2009, and 2013, respectively. In 2011, he was involved in the Visiting Researcher Programme of NATO CMRE, La Spezia, Italy. In 2012, he was a visiting scholar with the Electrical and Computer Engineering Department, University of Connecticut, Storrs, CT, USA. In 2015 and 2016, he held visiting appointments at the Department of Electronics and Telecommunications, Norwegian University of Science and Technology, Trondheim, Norway. In 2018, he was a Visiting Researcher at Centre Tecnologic de Telecomunicacions de Catalunya (CTTC), Castelldefels, Spain. From 2013 to 2014, he was a Post-Doctoral Researcher with the Department of Industrial and Information Engineering, University of Campania, L. Vanvitelli. From 2014 to 2016, he was a Post-Doctoral Researcher with the Department of Electrical Engineering and Information Technology, University of Naples Federico II, Italy. Since 2017, he is a Researcher at NM-2 s.r.l., Naples, Italy. His reviewing activity was recognized by the IEEE Communications Letters, the IEEE Transactions on Communications, and the IEEE Transactions on Wireless Communications which nominated him Exemplary Reviewer in 2013/2017, 2014 and 2017, respectively. Since 2016, he is an IEEE Senior Member. In 2018, he achieved the national Italian qualification for Associate Professor in Telecommunications from the Italian Ministry of University and Research (MIUR). Since 2014, he is/has been member of the editorial board of different IEEE, IET and Elsevier journals. His research interests fall within the areas of data fusion, statistical signal processing, wireless sensor networks, machine learning and network analytics.

Pierluigi Salvo Rossi was born in Naples, Italy, on April 26, 1977. He received the Dr. Eng. degree in telecommunications engineering (summa cum laude) and the Ph.D. degree in computer engineering, in 2002 and 2005, respectively, both from the University of Naples Federico II, Italy. From 2005 to 2008, he worked as a postdoc at the Department of Computer Science and Systems, University of Naples Federico II, Italy, at the Department of Information Engineering, Second University of Naples, Italy, and at the Department of Electronics and Telecommunications, Norwegian University of Science and Technology, Trondheim, Norway. From 2008 to 2014, he was an assistant professor (tenured in 2011) in telecommunications at the Department of Industrial and Information Engineering, Second University of Naples, Italy. From 2014 to 2016, he was an associate professor in signal processing with the Department of Electronics and Telecommunications, Norwegian University of Science and Technology, Trondheim, Norway. From 2016 to 2017, he was a full professor in signal processing with the Department of Electronic Systems, Norwegian University of Science and Technology, Trondheim, Norway. Since 2017, he is a principal engineer with the Department of Advanced Analytics and Machine Learning, Kongsberg Digital AS, Norway. He held visiting appointments at the Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA, at the Department of Electrical and Information Technology, Lund University, Sweden, at the Department of Electronics and Telecommunications, Norwegian University of Science and Technology, Trondheim, Norway, and at the Excellence Center for Wireless Sensor Networks, Uppsala University, Sweden. He is an IEEE senior member and serves as a senior editor for the IEEE Communications Letter (since 2016) and an associate editor for the IEEE Transactions on Wireless Communications (since 2015). He was an associate editor for the IEEE Communications Letter (from 2012 to 2016) and a Guest Editor for Elsevier Physical Communication (2012). His research interests fall within the areas of communications, machine learning, signal processing and sensor networks.

Acknowledgment

We would like to thank Val Moliere and Olivia Wilkins for their precious help and administrative support during the editorial preparation of the book.

Domenico Ciuonzo

Network Measurement and Monitoring (NM2), Naples, Italy

Pierluigi Salvo Rossi

Kongsberg Digital AS, Trondheim, Norway

Chapter 1
Generalized score-tests for decision fusion with sensing model uncertainty

Domenico Ciuonzo

1Network Measurement and Monitoring (NM2), Italy

2Department of Advanced Analytics and Machine Learning, Kongsberg Digital AS, Norway

3ECE Department, University of Connecticut, USA

This chapter investigates distributed detection of a phenomenon of interest (POI) via decision fusion in wireless sensor networks (WSNs). The decisions are collected by a fusion center (FC), which is in charge of performing a more accurate global decision. So as to account for a realistic scenario, it is assumed that the POI presents a signature with limited spatial extent, and its exact location and emitted amplitude (or energy) are not known. More specifically, when the POI is present, the sensors observe a signal with an attenuation depending on the distance between the sensor and the (unknown) target position, embedded in Gaussian noise. The unavailability of a completely specified model defeats the applicability of the well-known (optimal) likelihood-ratio (LR) test (LRT). As a consequence, in the general case, the FC is usually in charge of solving a composite hypothesis test and the generalized LRT (GLRT) is commonly employed. Unfortunately, in these scenarios, its complexity is typically high. Accordingly, the present chapter discusses the development of generalized score tests as alternatives with reduced computational complexity. After a brief recall of the GLRT for the considered problems, fusion rules corresponding to generalized versions of well-known score tests are introduced, based on Davies framework, since the resulting problems include nuisance parameters only under the POI-present hypothesis. The focus is on two relevant signal models, i.e., the cases of random and deterministic unknown signals, leading to one-sided and two-sided testing, respectively. Finally, a convincing (semi-theoretical) rationale for threshold-optimization is presented and analyzed.

1.1 Uncertainty in decision fusion sensing model

WSNs have attracted significant attention due to their potential improved capabilities in performing detection and estimation, with a wide range of applications, comprising battlefield surveillance, reconnaissance, security, traffic, and environmental monitoring [] or revealing a dangerous emission, such as an oil-spill source measured by an underwater sensor network or a radioactive source from a set of Geiger counters.

Due to stringent bandwidth and energy constraints, it is often assumed that each sensor quantizes its own observation with a single bit before transmission to the FC, which takes a global decision. In this context, the optimal test (under Bayesian/NeymanPearson frameworks) at each sensor is known to be a one-bit quantization of the local LR, i.e., a LRT.

Unfortunately in most cases, due to a lack of knowledge of the parameters of the POI to be detected, it is not possible to compute the LRT at each sensor. Also, even when the sensors can compute their local LRT, the search for local quantization thresholds is well-known to be exponentially complex [] or represents the estimated decision regarding the detection event, based on the local measurement. In both cases, the bits from the sensors are collected by the FC and combined via a specifically designed fusion rule aiming at improved detection performance.

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