Monica Nicoli

Bio: Monica Nicoli is an academic researcher from Polytechnic University of Milan. The author has contributed to research in topics: Communication channel & Wireless sensor network. The author has an hindex of 23, co-authored 139 publications receiving 1961 citations. Previous affiliations of Monica Nicoli include Siemens & Uppsala University.

Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks

Abstract: Federated learning (FL) is emerging as a new paradigm to train machine learning models in distributed systems. Rather than sharing, and disclosing, the training dataset with the server, the model parameters (e.g. neural networks weights and biases) are optimized collectively by large populations of interconnected devices, acting as local learners. FL can be applied to power-constrained IoT devices with slow and sporadic connections. In addition, it does not need data to be exported to third parties, preserving privacy. Despite these benefits, a main limit of existing approaches is the centralized optimization which relies on a server for aggregation and fusion of local parameters; this has the drawback of a single point of failure and scaling issues for increasing network size. The paper proposes a fully distributed (or server-less) learning approach: the proposed FL algorithms leverage the cooperation of devices that perform data operations inside the network by iterating local computations and mutual interactions via consensus-based methods. The approach lays the groundwork for integration of FL within 5G and beyond networks characterized by decentralized connectivity and computing, with intelligence distributed over the end-devices. The proposed methodology is verified by experimental datasets collected inside an industrial IoT environment.

Hidden Markov Models for Radio Localization in Mixed LOS/NLOS Conditions

Abstract: This paper deals with the problem of radio localization of moving terminals (MTs) for indoor applications with mixed line-of-sight/non-line-of-sight (LOS/NLOS) conditions. To reduce false localizations, a grid-based Bayesian approach is proposed to jointly track the sequence of the positions and the sight conditions of the MT. This method is based on the assumption that both the MT position and the sight condition are Markov chains whose state is hidden in the received signals [hidden Markov model (HMM)]. The observations used for the HMM localization are obtained from the power-delay profile of the received signals. In ultrawideband (UWB) systems, the use of the whole power-delay profile, rather than the total power only, allows to reach higher localization accuracy, as the power-profile is a joint measurement of time of arrival and power. Numerical results show that the proposed HMM method improves the accuracy of localization with respect to conventional ranging methods, especially in mixed LOS/NLOS indoor environments

Wireless sensor networks for traffic management and road safety

Abstract: Wireless sensor networks (WSN) employ self-powered sensing devices that are mutually interconnected through wireless ad-hoc technologies. This study illustrates the basics of WSN-based traffic monitoring and summarises the possible benefits in Intelligent Transport Systems (ITS) applications for the improvement of quality and safety of mobility. Compared with conventional infrastructure-based monitoring systems, this technology facilitates a denser deployment of sensors along the road, resulting in a higher spatial resolution of traffic parameter sampling. An experimental data analysis reported in this study shows how the high spatial resolution can enhance the reliability of traffic modelling as well as the accuracy of short-term traffic state prediction. The analysis uses the data published by the freeway performance measurement system of the University of California-Berkeley and the California Department of Transportation. A microscopic cellular automata model is used to estimate traffic flow and occupancy over time on a road segment in which a relevant traffic-flow anomaly is detected. The analysis shows that the estimate accuracy improves for increasing number of active sensors, as feasible in the case of WSN-based monitoring systems.

Device-Free Radio Vision for Assisted Living: Leveraging wireless channel quality information for human sensing Wireless propagation is conventionally considered as the enabling tool for transporting information

Abstract: It?s not difficult. Every time I lift my arm, it distorts a small electromagnetic field that is maintained continuously across the room. Slightly different positions of my hand and fingers produce different distortions and my robots can interpret these distortions as orders. I only use it for simple orders: Come here! Bring tea! and so on.

Implicit Cooperative Positioning in Vehicular Networks

Abstract: Absolute positioning of vehicles is based on Global Navigation Satellite Systems (GNSSs) combined with on-board sensors and high-resolution maps. In cooperative intelligent transportation systems, the positioning performance can be augmented by means of vehicular networks that enable vehicles to share location-related information. This paper presents an implicit cooperative positioning (ICP) algorithm that exploits the Vehicle-to-Vehicle (V2V) connectivity in an innovative manner, avoiding the use of explicit V2V measurements such as ranging. In the ICP approach, vehicles jointly localize non-cooperative physical features (such as people, traffic lights, or inactive cars) in the surrounding areas, and use them as common noisy reference points to refine their location estimates. Information on sensed features are fused through V2V links by a consensus procedure, nested within a message passing algorithm, to enhance the vehicle localization accuracy. As positioning does not rely on explicit ranging information between vehicles, the proposed ICP method is amenable to implementation with off-the-shelf vehicular communication hardware. The localization algorithm is validated in different traffic scenarios, including a crossroad area with heterogeneous conditions in terms of feature density and V2V connectivity, and a real urban area by using Simulation of Urban MObility (SUMO) for traffic data generation. Performance results show that the proposed ICP method can significantly improve the vehicle location accuracy compared to the stand-alone GNSS, especially in harsh environments, such as in urban canyons, where the GNSS signal is highly degraded or denied.

IEEE Standard for Local and Metropolitan Area Networks Part 16: Air Interface for Fixed Broadband Wireless Access Systems Draft Amendment: Management Information Base Extensions

Abstract: This document provides updates to IEEE Std 802.16's MIB for the MAC, PHY and asso- ciated management procedures in order to accommodate recent extensions to the standard.

Industrial Internet of Things: Challenges, Opportunities, and Directions

Abstract: Internet of Things (IoT) is an emerging domain that promises ubiquitous connection to the Internet, turning common objects into connected devices. The IoT paradigm is changing the way people interact with things around them. It paves the way for creating pervasively connected infrastructures to support innovative services and promises better flexibility and efficiency. Such advantages are attractive not only for consumer applications, but also for the industrial domain. Over the last few years, we have been witnessing the IoT paradigm making its way into the industry marketplace with purposely designed solutions. In this paper, we clarify the concepts of IoT, Industrial IoT, and Industry 4.0. We highlight the opportunities brought in by this paradigm shift as well as the challenges for its realization. In particular, we focus on the challenges associated with the need of energy efficiency, real-time performance, coexistence, interoperability, and security and privacy. We also provide a systematic overview of the state-of-the-art research efforts and potential research directions to solve Industrial IoT challenges.

Ranging With Ultrawide Bandwidth Signals in Multipath Environments

Abstract: Over the coming decades, high-definition situationally-aware networks have the potential to create revolutionary applications in the social, scientific, commercial, and military sectors Ultrawide bandwidth (UWB) technology is a viable candidate for enabling accurate localization capabilities through time-of-arrival (TOA)-based ranging techniques These techniques exploit the fine delay resolution property of UWB signals by estimating the TOA of the first signal path Exploiting the full capabilities of UWB TOA estimation can be challenging, especially when operating in harsh propagation environments, since the direct path may not exist or it may not be the strongest In this paper, we first give an overview of ranging techniques together with the primary sources of TOA error (including propagation effects, clock drift, and interference) We then describe fundamental TOA bounds (such as the Cramer-Rao bound and the tighter Ziv-Zakai bound) in both ideal and multipath environments These bounds serve as useful benchmarks in assessing the performance of TOA estimation techniques We also explore practical low-complexity TOA estimation techniques and analyze their performance in the presence of multipath and interference using IEEE 802154a channel models as well as experimental data measured in indoor residential environments

Space-Time Block Coding for Wireless Communications

Abstract: II