Andrea Zanella

Bio: Andrea Zanella is an academic researcher from University of Padua. The author has contributed to research in topics: Network packet & Throughput. The author has an hindex of 37, co-authored 279 publications receiving 9860 citations. Previous affiliations of Andrea Zanella include Nokia & University of California, Los Angeles.

Internet of Things for Smart Cities

Abstract: The Internet of Things (IoT) shall be able to incorporate transparently and seamlessly a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the development of a plethora of digital services. Building a general architecture for the IoT is hence a very complex task, mainly because of the extremely large variety of devices, link layer technologies, and services that may be involved in such a system. In this paper, we focus specifically to an urban IoT system that, while still being quite a broad category, are characterized by their specific application domain. Urban IoTs, in fact, are designed to support the Smart City vision, which aims at exploiting the most advanced communication technologies to support added-value services for the administration of the city and for the citizens. This paper hence provides a comprehensive survey of the enabling technologies, protocols, and architecture for an urban IoT. Furthermore, the paper will present and discuss the technical solutions and best-practice guidelines adopted in the Padova Smart City project, a proof-of-concept deployment of an IoT island in the city of Padova, Italy, performed in collaboration with the city municipality.

Long-range communications in unlicensed bands: the rising stars in the IoT and smart city scenarios

Abstract: Connectivity is probably the most basic building block of the IoT paradigm. Up to now, the two main approaches to provide data access to things have been based on either multihop mesh networks using short-range communication technologies in the unlicensed spectrum, or long-range legacy cellular technologies, mainly 2G/GSM/GPRS, operating in the corresponding licensed frequency bands. Recently, these reference models have been challenged by a new type of wireless connectivity, characterized by low-rate, long-range transmission technologies in the unlicensed sub-gigahertz frequency bands, used to realize access networks with star topology referred to as low-power WANs (LPWANs). In this article, we introduce this new approach to provide connectivity in the IoT scenario, discussing its advantages over the established paradigms in terms of efficiency, effectiveness, and architectural design, particularly for typical smart city applications.

Long-Range Communications in Unlicensed Bands: the Rising Stars in the IoT and Smart City Scenarios

Abstract: Connectivity is probably the most basic building block of the Internet of Things (IoT) paradigm. Up to know, the two main approaches to provide data access to the \emph{things} have been based either on multi-hop mesh networks using short-range communication technologies in the unlicensed spectrum, or on long-range, legacy cellular technologies, mainly 2G/GSM, operating in the corresponding licensed frequency bands. Recently, these reference models have been challenged by a new type of wireless connectivity, characterized by low-rate, long-range transmission technologies in the unlicensed sub-GHz frequency bands, used to realize access networks with star topology which are referred to a \emph{Low-Power Wide Area Networks} (LPWANs). In this paper, we introduce this new approach to provide connectivity in the IoT scenario, discussing its advantages over the established paradigms in terms of efficiency, effectiveness, and architectural design, in particular for the typical Smart Cities applications.

IoT: Internet of Threats? A Survey of Practical Security Vulnerabilities in Real IoT Devices

Abstract: The Internet of Things (IoT) is rapidly spreading, reaching a multitude of different domains, including personal health care, environmental monitoring, home automation, smart mobility, and Industry 4.0. As a consequence, more and more IoT devices are being deployed in a variety of public and private environments, progressively becoming common objects of everyday life. It is hence apparent that, in such a scenario, cybersecurity becomes critical to avoid threats like leakage of sensible information, denial of service (DoS) attacks, unauthorized network access, and so on. Unfortunately, many low-end IoT commercial products do not usually support strong security mechanisms, and can hence be target of—or even means for—a number of security attacks. The aim of this article is to provide a broad overview of the security risks in the IoT sector and to discuss some possible counteractions. To this end, after a general introduction to security in the IoT domain, we discuss the specific security mechanisms adopted by the most popular IoT communication protocols. Then, we report and analyze some of the attacks against real IoT devices reported in the literature, in order to point out the current security weaknesses of commercial IoT solutions and remark the importance of considering security as an integral part in the design of IoT systems. We conclude this article with a reasoned comparison of the considered IoT technologies with respect to a set of qualifying security attributes, namely integrity, anonymity, confidentiality, privacy, access control, authentication, authorization, resilience, self organization.

Experimental comparison of RSSI-based localization algorithms for indoor wireless sensor networks

Abstract: In this paper, we investigate the actual performance of some of the best known localization algorithms when deployed in real-world indoor environments. Among the plethora of possible localization schemes, we focus on those based on radio signal strength measurements only, since they do not require extra circuitry that would result in higher cost and energy consumption. For a fair comparison, we have first gathered thousands of radio signal strength measurements in two different indoor environments. To estimate the channel model parameters and to compare the different localization algorithms these data have been used.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Coverage control for mobile sensing networks

Abstract: This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications

Abstract: Fog/edge computing has been proposed to be integrated with Internet of Things (IoT) to enable computing services devices deployed at network edge, aiming to improve the user’s experience and resilience of the services in case of failures. With the advantage of distributed architecture and close to end-users, fog/edge computing can provide faster response and greater quality of service for IoT applications. Thus, fog/edge computing-based IoT becomes future infrastructure on IoT development. To develop fog/edge computing-based IoT infrastructure, the architecture, enabling techniques, and issues related to IoT should be investigated first, and then the integration of fog/edge computing and IoT should be explored. To this end, this paper conducts a comprehensive overview of IoT with respect to system architecture, enabling technologies, security and privacy issues, and present the integration of fog/edge computing and IoT, and applications. Particularly, this paper first explores the relationship between cyber-physical systems and IoT, both of which play important roles in realizing an intelligent cyber-physical world. Then, existing architectures, enabling technologies, and security and privacy issues in IoT are presented to enhance the understanding of the state of the art IoT development. To investigate the fog/edge computing-based IoT, this paper also investigate the relationship between IoT and fog/edge computing, and discuss issues in fog/edge computing-based IoT. Finally, several applications, including the smart grid, smart transportation, and smart cities, are presented to demonstrate how fog/edge computing-based IoT to be implemented in real-world applications.

An Introduction to Deep Learning for the Physical Layer

Abstract: We present and discuss several novel applications of deep learning for the physical layer. By interpreting a communications system as an autoencoder, we develop a fundamental new way to think about communications system design as an end-to-end reconstruction task that seeks to jointly optimize transmitter and receiver components in a single process. We show how this idea can be extended to networks of multiple transmitters and receivers and present the concept of radio transformer networks as a means to incorporate expert domain knowledge in the machine learning model. Lastly, we demonstrate the application of convolutional neural networks on raw IQ samples for modulation classification which achieves competitive accuracy with respect to traditional schemes relying on expert features. This paper is concluded with a discussion of open challenges and areas for future investigation.