Conferences

@inproceedings{odbedagamarobesovigafe:2024:case,

title = {Optimizing Tomato Production through IoT-based Smart Data Collection and Analysis},

author = {Giulia Oddi and Laura Belli and Luca Davoli and Martina Galaverni and Ilaria Marchioni and Margherita Rodolfi and Deborah Beghé and Federico Solari and Giuseppe Vignali and Tommaso Ganino and Gianluigi Ferrari},

doi = {10.1109/CASE59546.2024.10711738},

year  = {2024},

date = {2024-10-23},

urldate = {2024-01-01},

booktitle = {2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)},

pages = {45-50},

address = {Bari, Italy},

abstract = {The always-growing diffusion and adoption of Internet of Things (IoT) technologies is revolutionizing heterogeneous scenarios (e.g., home, industry, safety, etc.), including the agricultural/farming: this paves the way to the Smart Agriculture (SA) paradigm. In detail, this approach leverages the exploitation of IoT smart objects (e.g., sensors, actuators, ground robots, and flying drones) to optimize and improve agricultural practices, eventually improving both sustainability and efficiency. This paper presents an IoT-based data collection and analysis architecture expedient to acquire and manage IoT data streams generated from the field. The proposed approach has been applied to a real experimental use case to optimize tomato production in the Äzienda Sperimentale Stuard" farm located in Parma, Italy. The obtained results highlight the feasibility, sustainability, and gain margins (i.e., in terms of cost/benefits) returned by such a deployment in a real scenario, enabling farmers to make informed decisions based on on-field data acquisition (e.g., reducing water consumption during the growing season).},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{darabefe:2024:foodops,

title = {CoAP-based Digital Twin Modelling of Heterogeneous IoT Scenarios},

author = {Luca Davoli and Hafiz Humza Mahmood Ramzan and Gianluigi Laura Ferrari Belli},

doi = {10.46354/i3m.2024.foodops.016},

year  = {2024},

date = {2024-10-20},

urldate = {2024-01-01},

booktitle = {10th International Food Operations and Processing Simulation Workshop (FoodOPS 2024)},

pages = {1-5},

address = {Tenerife, Spain},

abstract = {Modern societies nowadays require more and more abstraction efforts to hide the complexity of underlying systems and infrastructures. To this end, the concept of Digital Twin (DT) has recently emerged as a key enabler for the digital transformation of well-established architectures toward their virtual representation, opening to intelligent processing capabilities (e.g., monitoring, simulation, prediction, optimization). Aside from defining DTs to enhance these services, another key paradigm that is noteworthy of attention is the Internet of Things (IoT), enabling data and information collection through heterogeneous textitsmart devices (often equipped with sensors and actuators). Thus, combining DTs and IoT together with the Constrained Application Protocol (CoAP) as communication protocol (with its native features), will allow to define scalable and lightweight replicas of real systems, and exploit key features (e.g., service and resource discovery) to provide end users with smart solutions. In this paper, a modelling paradigm for heterogeneous IoT scenarios, based on the definition of a DT for each entity involved in a specific context to be mapped, is detailed. This will allow to textita-priori estimate the behaviour of an IoT ecosystem and provide well-known interaction endpoints to request data from/pushing information to the hidden lower layers of the same ecosystem.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nabedafe:2024:cits,

title = {Hyperledger Fabric in Precision Agriculture: A Study on Data Integrity and Availability},

author = {Anum Nawaz and Laura Belli and Luca Davoli and Gianluigi Ferrari},

doi = {10.1109/CITS61189.2024.10608019},

year  = {2024},

date = {2024-07-30},

urldate = {2024-01-01},

booktitle = {2024 International Conference on Computer, Information and Telecommunication Systems (CITS)},

pages = {1-8},

address = {Girona, Spain},

abstract = {The increasing severity of weather events and growing demands for food pose significant challenges to farming and agricultural activities. Over the past decades, the deployment of data acquisition and Internet of Things (IoT)-oriented technologies has emerged as a relevant solution to face these issues, with a primary reason behind this digital agricultural revolution being the cost-effectiveness of data collection in various areas (e.g., soil conditions, crop development, weather patterns). On the basis of these technological advancements, in this paper we discuss on a fully-distributed blockchain-based IoT-oriented agricultural monitoring system based on an integrated Hyperledger Fabric framework. The proposed platform is designed to maximize the efficiency of the approach, to analyze several potential benefits (including, as an example, possible increased food production on reduced land areas, with lower input requirements and a diminished environmental impact), and to effectively aggregate and interpret data into actionable insights for farmers and policymakers. We then propose a preliminary system's deployment, which is instrumental to reflect on platform's scalability, inclusiveness, and modularity. The obtained results highlight its suitability to enhance precision agriculture with secure tracking features.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{mapadafe:2024:gem,

title = {Benchmarking MLCommons Tiny Audio Denoising with Deployability Constraints},

author = {Armin Mazinani and Danilo Pietro Pau and Luca Davoli and Gianluigi Ferrari},

doi = {10.1109/GEM61861.2024.10585695},

issn = {2766-6530},

year  = {2024},

date = {2024-07-11},

urldate = {2024-01-01},

booktitle = {2024 IEEE Gaming, Entertainment, and Media Conference (GEM)},

pages = {1-4},

address = {Turin, Italy},

abstract = {Speech enhancement is a critical field in audio signal processing given its essentiality to overcome obstacles related to loud and damaged speech signals. Due to the revolutionary capa-bilities of Deep Learning (DL) models, there has been significant interest on benchmarking them and studying their suitability for tiny embedded systems. In this paper, we thoroughly examine the growing field of voice improvement, with a specific emphasis on the use of DL-based techniques under consideration by the MLCommons standardization. In particular, among the others, the Legendre Memory Unit (LMU) model achieves an average Scale-Invariant Signal-to-Distortion Ratio (SISDR) on 8.613 in 627 KiB of FLASH memory, making it deployable on tiny microcontrollers by requiring only 7 ms per inference run.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{gamabegaodberodafe:2024:ebc,

title = {Poster: Evaluation of Hop Cone Maturation through Internet of Things (IoT) and Smart Farming Technologies. A Preliminary Study},

author = {Martina Galaverni and Ilaria Marchioni and Laura Belli and Tommaso Ganino and Giulia Oddi and Deborah Beghé and Margherita Rodolfi and Luca Davoli and Gianluigi Ferrari},

year  = {2024},

date = {2024-05-30},

urldate = {2024-01-01},

booktitle = {39th EBC Congress},

pages = {1-1},

address = {Lille, France},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{olmadafestmacano:2023:ew,

title = {Simulation-based Analysis of Experimental 5G NR Downlink CSI-RSRP-based Handover Performance},

author = {Eleonora Oliosi and Sunil Mathew and Luca Davoli and Gianluigi Ferrari and Nicolo Strozzi and Filippo Manghi and Fabio Casoli and Andrea Notari},

url = {https://ieeexplore.ieee.org/document/10477096},

year  = {2024},

date = {2024-03-20},

urldate = {2024-03-20},

booktitle = {28th European Wireless Conference (European Wireless 2023)},

pages = {8-13},

address = {Rome, Italy},

abstract = {In the last years, 5G New Radio (NR), introduced in 2019 in the 3rd Generation Partnership Project (3GPP) Release 15, has become the global radio standard for 5G networks. The increasing number of available 5G gNodeBs (gNBs) makes handover management crucial, especially from the point of view of the Quality of Experience (QoE) of a User Equipment (UE). In order to analyse the handover behaviour in 5G networks, we derive a Matlab-based simulator for location-based handover management. As a first step, an accurate simulation model of an experimental 5G NR communication system is derived. In detail, the performance results are expressed in terms of Reference Signal Received Power (RSRP) 5G NR signal quality metric, measured on the Channel State Information (CSI) Reference Signal (RS) on a DownLink (DL) transmission from a gNB to a mobile UE. In order to derive an accurate simulator, we rely on an experimental testbed based on the use of a commercial wireless 5G embedded modem positioned in a moving vehicle and collecting geolocalized RSRP values.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{bedafe:2024:iiwot,

title = {A Cloud-Oriented Indoor-Outdoor Real-Time Localization IoT Architecture for Industrial Environments},

author = {Laura Belli and Luca Davoli and Gianluigi Ferrari},

doi = {10.1109/CCNC51664.2024.10454636},

issn = {2331-9860},

year  = {2024},

date = {2024-03-18},

urldate = {2024-03-18},

booktitle = {2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)},

pages = {1-6},

address = {Las Vegas, NV, USA},

abstract = {Localization services for precise and continuous monitoring of the locations of both humans and vehicles in industrial environments are among the most relevant applications in Industrial Internet of Things (IIoT) contexts, to maximize safety and optimize operational activities. Unfortunately, localization in industrial scenarios is particularly challenging because targets can generally move freely in both indoor and outdoor areas. In this paper, we propose a localization monitoring architecture based on a prototypical wearable IoT device equipped with Ultra-Wide Band (UWB), inertial, and GNSS/RTK technologies for seamless localization in heterogeneous environments. We focus on a Web of Things (WoT) approach, verifying suitability and limitations in a real use case scenario. Our approach shows that the proposed architecture can effectively enhance the safety of workers, detecting potentially dangerous events and triggering alarms (e.g., via smart buzzers or gas concentration warning devices) based on a cloud WoT architecture.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{dabefeloaz:2024:iiwot,

title = {An Edge Computing-Oriented WoT Architecture for Air Quality Monitoring in Mobile Vehicular Scenarios},

author = {Luca Davoli and Laura Belli and Gianluigi Ferrari and Elisa Londero and Paolo Azzoni},

doi = {10.1109/CCNC51664.2024.10454646},

issn = {2331-9860},

year  = {2024},

date = {2024-03-18},

urldate = {2024-03-18},

booktitle = {2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)},

pages = {1-7},

address = {Las Vegas, NV, USA},

abstract = {Nowadays, the need to efficiently process information in Internet of Things (IoT)-oriented heterogeneous scenarios has increased significantly, e.g., in all scenarios where unobtrusive environmental monitoring is beneficial for the involved people (e.g., inside public transport vehicles, indoor workplaces and offices, large public infrastructures, etc.). This objective typically requires the combination of heterogeneous IoT systems, which need to efficiently share information, e.g., through the Web of Things (WoT) paradigm. In this paper, we propose an edge computing-oriented flexible WoT architecture, with distributed intelligence, for air quality monitoring and prediction inside a public transport bus. Our results show that the proposed architecture allows seamless integration of heterogeneous IoT systems according to a WoT perspective, exploiting the device/edge/fog computing continuum and using containerized and secure processing modules.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{maoldafestno:2023:icrvitm,

title = {On the Reduction of Unnecessary Handovers Using 5G Small Cells in a 5G NR Environment},

author = {Sunil Mathew and Eleonora Oliosi and Luca Davoli and Gianluigi Ferrari and Nicolò Strozzi and Andrea Notari},

doi = {10.1109/IC-RVITM60032.2023.10435126},

year  = {2024},

date = {2024-02-21},

urldate = {2023-01-01},

booktitle = {2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management (IC-RVITM)},

pages = {1-6},

address = {Bangalore, India},

abstract = {Nowadays, the deployment of 5G Non-StandAlone (NSA) networks has led to significant enhancements in the User Equipment (UE) experience, in particular in terms of latency reduction and throughput increase. To this end, 5G gNodeBs (gNBs) offer broad coverage but may face challenges in areas with low signal strength, resulting in a Quality of Service (QoS) degradation and too short connections to distant gNBs (denoted as Unnecessary HandOvers, UHOs) due to geographic peculiarities. In order to tackle these issues, in this paper the use of cost-effective 5G small cells in critical areas is considered, aiming at (i) boosting coverage, (ii) enhancing UE QoS, and (iii) avoiding UHOs, thus improving HO performance and UE QoS.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stpadani:23:icar,

title = {Redundant and Loosely Coupled LiDAR-Wi-Fi Integration for Robust Global Localization in Autonomous Mobile Robotics},

author = {Nikolaos Stathoulopoulos and Emanuele Pagliari and Luca Davoli and George Nikolakopoulos},

doi = {10.1109/ICAR58858.2023.10406402},

issn = {2572-6919},

year  = {2024},

date = {2024-02-01},

urldate = {2023-12-01},

booktitle = {2023 21st International Conference on Advanced Robotics (ICAR)},

pages = {121-127},

address = {Abu Dhabi, United Arab Emirates},

abstract = {This paper presents a framework addressing the challenge of global localization in autonomous mobile robotics by integrating LiDAR-based descriptors and Wi-Fi finger-printing in a pre-mapped environment. This is motivated by the increasing demand for reliable localization in complex scenarios, such as urban areas or underground mines, requiring robust systems able to overcome limitations faced by traditional Global Navigation Satellite System (GNSS)-based localization methods. By leveraging the complementary strengths of LiDAR and Wi-Fi sensors used to generate predictions and evaluate the confidence of each prediction as an indicator of potential degradation, we propose a redundancy-based approach that enhances the system's overall robustness and accuracy. The proposed framework allows independent operation of the LiDAR and Wi-Fi sensors, ensuring system redundancy. By combining the predictions while considering their confidence levels, we achieve enhanced and consistent performance in localization tasks.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{madapafe:2023:iotais,

title = {Accurate Classification of Sport Activities Under Tiny Deployability Constraints},

author = {Armin Mazinani and Luca Davoli and Danilo Pietro Pau and Gianluigi Ferrari},

doi = {10.1109/IoTaIS60147.2023.10346056},

issn = {2832-1383},

year  = {2023},

date = {2023-12-14},

urldate = {2023-01-01},

booktitle = {2023 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)},

pages = {261-267},

address = {Bali, Indonesia},

abstract = {Human Activity Recognition (HAR) plays a prominent role in various domains, such as healthcare, surveillance, and sports. In this paper, our goal is to identify the most accurate Deep Learning (DL) algorithm under tiny deployability constraints. Our results show that a Recurrent Neural Network (RNN) given by the combination of a one-dimensional Convolutional Neural Network (ID-CNN) with Bi-directional Gated Recurrent Unit (Bi-GRU) is the most attractive solution, with respect to Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and the recently introduced Legendre Memory Unit (LMU). The algorithm performance is investigated over a publicly available dataset consisting of 19 different daily activities. According to the obtained results, 1D-CNN-BiGRU has an average accuracy within 0.2% of that of BiGRU (the RNN with highest accuracy) with an execution time more than 4 times shorter.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{madafe:2023:brains,

title = {Deep Learning-Based Cryptocurrency Price Prediction: A Comparative Analysis},

author = {Armin Mazinani and Luca Davoli and Gianluigi Ferrari},

doi = {10.1109/BRAINS59668.2023.10317011},

year  = {2023},

date = {2023-11-17},

urldate = {2023-01-01},

booktitle = {2023 5th Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS)},

pages = {1-8},

address = {Paris, France},

abstract = {In recent years, cryptocurrencies have gained a lot of popularity in the financial markets and now, in addition to investing on them, it is possible to use them as a common currency to meet daily needs. Given the complex nature of financial markets and their reliance on different parameters to determine stocks' and assets' prices, the ability to predict prices is important for investment decisions, especially with respect to cryptocurrencies. To this end, Deep Learning (DL)-based algorithms can be viable solutions, owing to their use as time series forecasting tools. In this paper, we investigate the applicability of DL algorithms to forecast the prices of three cryptocurrencies, namely Bitcoin, Ethereum, and Ripple. We evaluate the performance of the proposed approach, in terms of short-term and long-term prediction accuracy (considering proper error metrics).},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{madapafe:2023:icitri,

title = {Air Quality Estimation with Embedded AI-Based Prediction Algorithms},

author = {Armin Mazinani and Luca Davoli and Danilo Pietro Pau and Gianluigi Ferrari},

doi = {10.1109/ICITRI59340.2023.10249864},

year  = {2023},

date = {2023-09-19},

urldate = {2023-09-19},

booktitle = {2023 International Conference on Information Technology Research and Innovation (ICITRI)},

pages = {87-92},

address = {Jakarta, Indonesia},

abstract = {Air pollution is one of the main criticalities in cities with large populations. Therefore, accurate air quality prediction is crucial to control the environmental pollution and to maintain healthy living conditions for the citizens. To this end, particulate matters (e.g., PM2.5) have been recognised as one of the most important pollutants with a detrimental impact on human health. In this paper, we investigate the trade-off between estimation accuracy and computational complexity of Machine Learning (ML) and Deep Learning (DL) algorithms in predicting air pollution (in terms of PM2.5 concentration), in order to investigate their applicability to Internet of Things (IoT)-oriented applications. Six DL methods are implemented and evaluated, considering various time lags. DL approaches are shown to outperform ML approaches—in the DL case, two distinct optimizers, namely ADAM and Root Mean Squared Propagation (RMSProp), are considered. Among all algorithms evaluated, GRU had a RMSE of 20.02, while SimpleRNN reduced the MACs number by 98.90% over GRU and with an accuracy drop of 7.5%.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stprbobedafevi:2023:foodops,

title = {Selection of 4.0 sensors for small holders: the compromise between the advantages and the costs of the implementation},

author = {Roberta Stefanini and Luca Preite and Eleonora Bottani and Laura Belli and Luca Davoli and Gianluigi Ferrari and Giuseppe Vignali},

doi = {10.46354/i3m.2023.foodops.007},

year  = {2023},

date = {2023-09-19},

urldate = {2023-01-01},

booktitle = {Proceedings of the 9th International Food Operations and Processing Simulation Workshop (FoodOPS 2023)},

pages = {1-7},

address = {Athens, Greece},

abstract = {The agricultural sector involves various environmental impacts, related to soil exploitation, water consumption and greenhouse gasses emissions. The advent of 4.0 technologies could help reduce them, e.g., by using sensors that constantly control the field. However, these solutions are often implemented by big producers that can easily bear their costs. Thence, in the case of small holders, can the benefits achievable with 4.0 technologies justify their implementation costs? To answer this question, an Italian field with three rows of tomatoes has been investigated as a case study. A row with a traditional irrigation system has been compared to two rows with a 60% irrigation scenario monitored with 4.0 sensors. Overall, one environmental sensor, three crop analysis sensors, three flowmeters, three valves and one network infrastructure have been selected and introduced. The key findings of the work allow for quantifying the amount of water that small holders can save; the positive Net Present Value recommends the investment, with a Pay Back Period of 1.9 years. In the next steps, additional 4.0 sensors will be tested in the agricultural supply chain of some selected small holders in the Mediterranean area, to check whether the 4.0 implementation could not only reduce water consumption, but also improve storage conditions and reduce wastage.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{madabefera:2023:aeit,

title = {Thermal Camera-based Driver Monitoring in the Automotive Scenario},

author = {Veronica Mattioli and Luca Davoli and Laura Belli and Gianluigi Ferrari and Riccardo Raheli},

doi = {10.23919/AEITAUTOMOTIVE58986.2023.10217235},

year  = {2023},

date = {2023-08-23},

urldate = {2023-08-23},

booktitle = {2023 AEIT International Conference on Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE)},

pages = {1-6},

address = {Modena, Italy},

abstract = {Driver monitoring in the automotive scenario is a key task in preventing hazardous events, such as road accidents. To this purpose, driver monitoring systems aim at providing prompt assistance in case of anomalies, such as alterations of the driver’s psycho-physiological status. The driving performance may, indeed, be negatively affected by high levels of stress and workload, that might be caused by different factors.In this paper, a novel in-vehicle monitoring system to extract the skin temperature of a driver, as one of the main indicators of his/her psycho-physiological status, is proposed. Thermal imaging techniques and video processing algorithms are jointly employed to record and extract temperature-related information in a non-invasive and contactless way. Temperature variations detected on the subject’s face are regulated by the activity of the Autonomic Nervous System (ANS) and represent, indeed, an important index of perceived stress levels. The feasibility of the proposed method is assessed both in simulated and real driving scenarios on the basis of experimental results.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10305879,

title = {Optical Character Recognition Using Optimized Convolutional Networks*},

author = {Anum Nawaz and Muhammad Irfan and Tomi Westerlund},

doi = {10.1109/FMEC59375.2023.10305879},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC)},

pages = {107-114},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{padacife:2020:isaect,

title = {A Modular Multi-interface Gateway for Heterogeneous IoT Networking},

author = {Emanuele Pagliari and Luca Davoli and Antonio Cilfone and Gianluigi Ferrari},

doi = {10.1109/ISAECT50560.2020.9523689},

year  = {2021},

date = {2021-09-01},

urldate = {2021-09-01},

booktitle = {2020 International Symposium on Advanced Electrical and Communication Technologies (ISAECT)},

pages = {1-6},

address = {Marrakech, Morocco},

abstract = {The massive deployment of Internet of Things (IoT) architectures and applications, in many fields over the last decade, has accelerated research efforts on low-power wireless connectivity protocols. Moreover, many standards have been introduced, highlighting the need to make data flow among different communication protocols and network interfaces feasible. To this end, devices like Gateways (GWs) play a crucial role in many IoT applications and will impact future developments and possibilities. In this paper, the design and deployment of a new modular and scalable GW architecture solution, suitable for a wide plethora of use case scenarios and useful as a starting point for many possible improvements and applications, is proposed. Experimental performance results are discussed, showing the roles of different interfaces in specific use cases in which the proposed this solution may be applied.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokey,

title = {A Time-aware Data Clustering Approach to Predictive Maintenance of a Pharmaceutical Industrial Plant},

author = {Gabriele Calzavara and Eleonora Oliosi and Gianluigi Ferrari},

doi = {10.1109/ICAIIC51459.2021.9415206},

year  = {2021},

date = {2021-04-01},

urldate = {2021-04-01},

booktitle = {2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)},

pages = {454-458},

address = {Jeju Island, Korea (South)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{cocidafe:2020:metroagrifor,

title = {AI at the Edge: a Smart Gateway for Greenhouse Air Temperature Forecasting},

author = {Gaia Codeluppi and Antonio Cilfone and Luca Davoli and Gianluigi Ferrari},

doi = {10.1109/MetroAgriFor50201.2020.9277553},

year  = {2020},

date = {2020-12-08},

booktitle = {2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)},

pages = {348--353},

address = {Trento, Italy},

abstract = {Controlling and forecasting environmental variables (e.g., air temperature) is usually a key and complex part in a greenhouse management architecture. Indeed, a greenhouse inner micro-climate, which is the result of an extensive set of inter-related environmental variables influenced by external weather conditions, has to be tightly monitored, regulated, and, some-times, forecast. Nowadays, Wireless Sensor Networks (WSNs) and Machine Learning (ML) are two of the most successful technologies to deal with this challenge. In this paper, we discuss how a Smart Gateway (GW), acting as a collector for sensor data coming from a WSN installed in a greenhouse, could be enriched with a Neural Network (NN)-based prediction model allowing to forecast a greenhouse’s inner air temperature. In the case of missing sensor data coming from the WSN, the proposed prediction algorithm, fed with meteorological open data (gathered from the DarkSky repository), is run on the GW in order to predict the missing values. Despite the model is especially designed to be lightweight and executable by a device with constrained capabilities, it can be adopted either at Cloud or at GW level to forecast future air temperature’s values, in order to support the management of a greenhouse. Experimental results show that the NN-based prediction algorithm can forecast greenhouse air temperature with a Root Mean Square Error (RMSE) of 1.50°C, a Mean Absolute Percentage Error (MAPE) of 4.91%, and a R2 score of 0.965.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{BaYoFe_ICCKE20,

title = {Software-defined control of emergency vehicles in smart cities},

author = {Nazila Bagheri and Saleh Yousefi and Gianluigi Ferrari},

year  = {2020},

date = {2020-11-04},

booktitle = {International Conference on Computer and Knowledge Engineering (ICCKE)},

pages = {6 pages},

address = {Ferdowsi University of Mashhad, Iran},

abstract = {One of the most fundamental challenges in nowadays transportation systems is the appropriate management of emergencies resulting from accidents. The purpose of this paper is to utilize vehicular communication technologies and integrate them with the software defined idea to reduce the time required by the emergency vehicle to arrive at the accident scene from the emergency center (i.e., rescue time). In this context, one of the main approaches is traffic light preemption in favor of emergency vehicles: the timing of traffic lights along the rescue route is dynamically adjusted to minimize the number of RED lights met by the emergency vehicles. Most of existing methods of preemption are based on local decision making at each individual traffic light. However, in this paper, the use of a central controller for traffic light scheduling leads to higher efficiency due to the higher knowledge of street traffic and intersection conditions. The proposed method is evaluated using the OMNET++ and SUMO tools over part of the city of Tabriz, Iran. The simulation results demonstrate that the proposed method can reduce the average rescue time even by more than 50% in some cases.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{klkrda:2020,

title = {LoRaWAN with HSM as a Security Improvement for Agriculture Applications},

author = {Reinhard Kloibhofer and Erwin Kristen and Luca Davoli},

editor = {António Casimiro and Frank Ortmeier and Erwin Schoitsch and Friedemann Bitsch and Pedro Ferreira},

doi = {10.1007/978-3-030-55583-2_13},

isbn = {978-3-030-55583-2},

year  = {2020},

date = {2020-09-08},

booktitle = {Computer Safety, Reliability, and Security. SAFECOMP 2020 Workshops},

volume = {12235},

pages = {176-188},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {The digital future in agriculture has started a long time ago, with Smart Farming and Agriculture 4.0 being synonyms that describe the change in this domain. Digitalization stands for the needed technology to realize the transformation from conventional to modern agriculture. The continuously monitoring of all environmental data and the recording of all work parameters enables data collections, which are used for precise decision making and the planning of in-time missions. To guarantee secure and genuine data, appropriate data security measures must be provided.



This paper will present a research work in the EU AFarCloud project. It introduces the important LoRaWAN data communication technology for the transmission of sensor data and to present a concept for improving data security and protection of sensor nodes. Data and device protection are becoming increasingly important, particularly around LoRaWAN applications in agriculture.



In the first part, a general assessment of the security situation in modern agriculture, data encryption methods, and the LoRaWAN data communication technology, will be presented.



Then, the paper explains the security improvement concept by using a Hardware Secure Module (HSM), which not only improves the data security but also prevents device manipulations. A real system implementation (Security Evaluation Demonstrator, SED) helps to validate the correctness and the correct function of the advanced security improvement.



Finally, an outlook on necessary future works declares what should be done in order to make the digital agriculture safe and secure in the same extent as Industrial Control Systems (ICSs) will be today.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{cidafe:2019:isaect,

title = {Virtualizing LoRaWAN Nodes: a CoAP-based Approach},

author = { Antonio Cilfone and Luca Davoli and Gianluigi Ferrari},

doi = {10.1109/ISAECT47714.2019.9069691},

year  = {2019},

date = {2019-11-29},

booktitle = {2019 International Symposium on Advanced Electrical and Communication Technologies (ISAECT)},

pages = {1-6},

publisher = {IEEE},

abstract = {In the near future, Internet of Things (IoT) will play a relevant role in people’s lives and one of the main challenges will be the integration of heterogeneous networks. Among widely adopted network technologies, the development of the so-called Low-Power Wide-Area Networks (LPWANs), in particular Long Range WAN (LoRaWAN) is attracting a significant interest from both academic and industrial worlds. The integration of LoRaWAN with other communication technologies represents a fundamental requirement for a successful rapid and large-scale diffusion of IoT paradigms, such as Smart Farming, Smart Factory, and Smart City. The aim of this paper is two-fold: we propose (i) a mechanism for automatic discovery of the sensors a LoRa device is equipped with; and (ii) a novel networking architecture, based on cloud computing and node virtualization, to enable the interaction of LoRaWAN end-nodes with other IP-based IoT devices. Our solution does not impact LoRaWAN networking and enables a seamless interaction between LoRaWAN end-nodes and other Constrained Application Protocol (CoAP)-based nodes.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{cocidafe:2019:metroagrifor,

title = {VegIoT Garden: a modular IoT Management Platform for Urban Vegetable Gardens},

author = {Gaia Codeluppi and Antonio Cilfone and Luca Davoli and Gianluigi Ferrari},

doi = {10.1109/MetroAgriFor.2019.8909228},

isbn = {978-1-7281-3611-0},

year  = {2019},

date = {2019-11-21},

booktitle = {2019 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)},

pages = {121-126},

publisher = {IEEE},

address = {Portici, Italy},

abstract = {Nowadays, the agricultural sector is facing challenges especially because of an extensive range of grueling trends. In this context, new highly technological applications—such as Internet of Things (IoT), Precision Agriculture (PA), and blockchain—are enabling Smart Agriculture (SA), which holds the promise to support future needs. In this extended abstract, a low-cost, modular, and energy-efficient IoT platform for SA, denoted as VegIoT Garden, based on Commercial-Off-The-Shelf (COTS) devices, adopting short- and long-range communication protocols (IEEE 802.11 and LoRa), and aiming at enhancing the management of vegetable gardens through the collection, monitoring, and analysis of sensor data, related to relevant parameters of growing plants (i.e., air and soil humidity and temperature), is presented. The infrastructure is completed with an Internet-enabled Home Node (HN) and an iOS-based mobile App, developed in order to simplify data visualization and plants’ status monitoring. The proposed IoT system has been validated in a real scenario (a vegetable garden) for more than a week: the collected data highlighted possible causes for a disease contracted by vegetables (namely, tomato’s blossom-end root), thus validating VegIoT Garden.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{cadamaciyuwafe:2019:iswcs,

title = {RSSI-based Methods for LOS/NLOS Channel Identification in Indoor Scenarios},

author = {Fabrizio Carpi and Luca Davoli and Marco Martalò and Antonio Cilfone and Yingjie Yu and Yi Wang and Gianluigi Ferrari},

doi = {10.1109/ISWCS.2019.8877315},

isbn = {978-1-7281-2527-5},

year  = {2019},

date = {2019-10-22},

booktitle = {2019 16th International Symposium on Wireless Communication Systems (ISWCS)},

pages = {171-175},

publisher = {IEEE},

address = {Oulu, Finland},

abstract = {In this paper, we investigate classification methods aiming at identifying the Line-Of-Sight (LOS) or Non-LOS (NLOS) condition of a wireless channel. Our approach is based on the computation of statistical features over N consecutive channel measurements at the receiver (namely, N Received Signal Strength Indicator, RSSI, values). First, threshold classification criteria, on the considered features, are derived in order to perform LOS/NLOS identification. The thresholds’ values are tuned according to the "behaviour" of the statistical features in the considered environment. This method is compared to a sample-based (whose aim is to detect the data distribution) and a machine learning-based approaches. Although our approach is general, we present experimental results for IEEE 802.11 indoor channels. Our results show that simple threshold-based classification criteria on the considered statistical features may yield approximately 85÷90% LOS/NLOS classification accuracy, making them an attractive strategy for future 5G systems.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{AmFe_ICTCS2019,

title = {Resilience analysis of time-varying networks with addition and deletion of nodes},

author = {Michele Amoretti and Gianluigi Ferrari},

url = {http://ceur-ws.org/Vol-2504/paper24.pdf},

year  = {2019},

date = {2019-10-01},

booktitle = {Italian Conference on Theoretical Computer Science (ICTCS 2019)},

pages = {12 pages},

series = {CEUR WORKSHOP PROCEEDINGS},

abstract = {Most real world networks are dynamic, in the sense that nodes are added/deleted over time and connections among them evolve as well. Modeling the node degree distribution of such networks is very important, as it allows to characterize their resilience in terms of probability of node isolation. Unfortunately, in most cases this is highly challenging. In this paper, we propose an analytical framework for modeling the node degree distribution while taking into account node lifetime statistics. We provide exact solutions for two special cases of networks with preferential attachment, and we present simulation results that confirm the analytical ones.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{9006635,

title = {Edge AI and Blockchain for Privacy-Critical and Data-Sensitive Applications},

author = {A. Nawaz and T. N. Gia and J. Peña Queralta and T. Westerlund},

doi = {10.23919/ICMU48249.2019.9006635},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

booktitle = {2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU)},

pages = {1-2},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{daprve:softcom:2018,

title = {NEMO: A Flexible Java-based Network Emulator},

author = {Luca Davoli and Yanina Protskaya and Luca Veltri},

url = {https://ieeexplore.ieee.org/document/8555769},

doi = {10.23919/SOFTCOM.2018.8555769},

issn = {1847-358X},

year  = {2018},

date = {2018-12-03},

booktitle = {2018 26th International Conference on Software, Telecommunications and Computer Networks (SoftCOM)},

pages = {1-6},

abstract = {Network emulation provides the capability to evaluate applications on standalone systems, representing a trade-off between real infrastructures and simulators, providing a lowlayer virtual network, and yet allowing real high-layer application code to be executed. Moreover, network emulation is useful to study and evaluate the behavior of applications in different conditions(maybe sometimes difficult to reach in real networks), in turn allowing a rapid deployment of hybrid real hardware/virtual network topologies. In this paper, a modular, flexible and highly scalable Java-based network emulator, denoted as NEMO, is proposed. NEMO can be integrated with external open-source Java applications or used as a virtualization mechanism, able to run third-party Java binary applications on a virtual node attached to either a virtual network or a real external network, in a completely transparent way for the end-user. Moreover, NEMO allows to plug in user-defined network components and run virtual networks composed by millions of nodes on a single end-user machine, as well as on a distributed infrastructure.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{StPaFe_IPIN18,

title = {A novel step detection and step length estimation algorithm for hand-held smartphones},

author = {Nicolò Strozzi and Federico Parisi and Gianluigi Ferrari},

url = {https://ieeexplore.ieee.org/document/8533807},

doi = {10.1109/IPIN.2018.8533807},

year  = {2018},

date = {2018-10-01},

booktitle = {2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN 2018)},

publisher = {IEEE},

address = {Nantes, France},

abstract = {In this paper, we present an innovative inertial navigation system based on the data collected through the Inertial Measurement Unit (IMU) embedded in a commercial smartphone. We propose an innovative step detection algorithm which is independent of the holding mode, the only assumption being that the device is hand-held (i.e., the user is texting/navigating or phoning) and its movement is related to the upper body displacement during walking. We also present a new approach able to automatically calibrate the step length estimation formula according to the smartphone positioning. The developed algorithms have been validated through a test campaign in which we have evaluated the system performance considering three different smartphone models and different path lengths. The obtained results show that the maximum step detection error is always below 4% (average: 2.08%; standard deviation: 1.82%) whereas the maximum path length estimation error is below 8.1% (average: 3.6%; standard deviation: 1.81%) in all the considered cases.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{alphand2018iotchain,

title = {IoTChain: A blockchain security architecture for the Internet of Things},

author = {Olivier Alphand and Michele Amoretti and Timothy Claeys and Simone Dall'Asta and Andrzej Duda and Gianluigi Ferrari and Franck Rousseau and Bernard Tourancheau and Luca Veltri and Francesco Zanichelli},

url = {https://ieeexplore.ieee.org/document/8377385},

doi = {10.1109/WCNC.2018.8377385},

year  = {2018},

date = {2018-04-01},

booktitle = {IEEE Wireless Communications and Networking Conference (WCNC 2018)},

pages = {1-6},

publisher = {IEEE},

address = {Barcelona, Spain},

abstract = {In this paper, we propose IoTChain, a combination of the OSCAR architecture [1] and the ACE authorization framework [2] to provide an E2E solution for the secure authorized access to IoT resources. IoTChain consists of two components, an authorization blockchain based on the ACE framework and the OSCAR object security model, extended with a group key scheme. The blockchain provides a flexible and trustless way to handle authorization while OSCAR uses the public ledger to set up multicast groups for authorized clients. To evaluate the feasibility of our architecture, we have implemented the authorization blockchain on top of a private Ethereum network. We report on several experiments that assess the performance of different architecture components.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{davoli:2017:iswcs,

title = {An Anonymization Protocol for the Internet of Things},

author = {Luca Davoli and Yanina Protskaya and Luca Veltri},

url = {http://ieeexplore.ieee.org/document/8108159/},

doi = {10.1109/ISWCS.2017.8108159},

issn = {2154-0225},

year  = {2017},

date = {2017-11-16},

booktitle = {2017 International Symposium on Wireless Communication Systems (ISWCS)},

pages = {459--464},

organization = {IEEE},

abstract = {The Internet of Things (IoT) is expected to pervasively interconnect billions of devices, denoted as “smart objects”, in an Internet-like structure, which will extend the current Internet, enabling new forms of interactions between objects based on social relationships. In such a scenario, security is a difficult and challenging task, and proper mechanisms should be defined without introducing too much protocol overhead and processing load. In particular, in this paper we focus on the anonymity of the communications and we propose a solution particularly suitable for such a constrained scenario. In the proposed solution IoT nodes form an Onion Routing anonymity network completely based on a datagram transport (e.g., over UDP). Confidentiality is completely enforced by the anonymity network and no other security protocols, such as IPSec or DTLS, are required. The proposed solution has been also implemented and tested.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{7886029,

title = {Respiratory rate monitoring by maximum likelihood video processing},

author = {Davide Alinovi and Gianluigi Ferrari and Francesco Pisani and Riccardo Raheli},

doi = {10.1109/ISSPIT.2016.7886029},

year  = {2016},

date = {2016-12-01},

booktitle = {2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)},

pages = {172-177},

abstract = {A novel video processing-based method for remote estimation of the respiratory rate (RR) is proposed. Relying on the fact that breathing involves quasi-periodic movements, this technique employs a generalized model of pixel-wise periodicity and applies a maximum likelihood (ML) criterion. The system first selects suitable regions of interest (ROI) mainly affected by respiratory movements. The obtained ROI are jointly analyzed for the estimation of the fundamental frequency, which is strictly related to the RR of the patient. A large motion detection algorithm is also applied, in order to exclude, from RR estimation, ROI possibly affected by unrelated large movements. The RRs estimated by the proposed system are compared with those extracted by a pneumograph and a previously proposed video processing algorithm. The results, albeit preliminary, show a good agreement with the pneumograph and a clear improvement over the previously proposed algorithm.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{StPaFe_IPIN2016,

title = {A multifloor hybrid inertial/barometric navigation system},

author = {Nicolò Strozzi and Federico Parisi and Gianluigi Ferrari},

url = {http://dx.doi.org/10.1109/IPIN.2016.7743703



http://ieeexplore.ieee.org/document/7743703/},

isbn = {978-1-5090-2425-4 },

year  = {2016},

date = {2016-10-09},

booktitle = {2016 IEEE International Conference on Indoor Positioning and Indoor Navigation (IPIN)},

abstract = {This paper describes the development of a multifloor hybrid inertial/barometric navigation system. The prototype integrates several Magnetic Angular Rate and Gyroscope (MARG) sensors and a barometer. The inertial (MARG) sub-system, by properly processing the signals collected by the MARG sensors placed on the test subject's feet, reconstructs the two-dimensional navigation pattern by applying a Zero velocity UPdaTe (ZUPT) technique. Three different sensors' configurations are investigated in order to find the best performing set-up. A simpler configuration with a single MARG sensor is also considered to derive a reference performance benchmark without multiple MARG sensor fusion. The barometer, connected via usb to a Freakduino board, is used to detect the floor change. The fusion of inertial and barometric signals allows to fully reconstruct the movement of a person in both indoor and outdoor environments. The main goal of the proposed system is to allow accurate personal navigation without any external reference (i.e., radio signals, satellite signals, etc.). Considering a closed path, the relative distance error between the starting point and the final estimated position is below 2.5% of the total traveled distance.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{7785154,

title = {A P2P virtual core-network architecture for next-generation mobility networks},

author = {Andrea G. Forte and Wei Wang and Luca Veltri and Gianluigi Ferrari},

doi = {10.1109/CSCN.2016.7785154},

year  = {2016},

date = {2016-10-01},

booktitle = {2016 IEEE Conference on Standards for Communications and Networking (CSCN)},

pages = {1-7},

abstract = {We propose a new virtualized Peer-to-Peer (P2P) core-network architecture where each user gets its own private copy of the core network. This enables higher security and novel services, that cannot be deployed in today's architecture. We describe the new architecture in detail, presenting some of its many advantages and novel services. Lastly, we discuss some architectural options and their tradeoff by analyzing voice-call traffic in a large U.S. cellular network provider.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{7593065,

title = {Pragmatic code-aided phase synchronization in iterative multi-sample receivers},

author = {Muhammad Asim and Marco Martalò and Gianluigi Ferrari and Riccardo Raheli},

doi = {10.1109/ISTC.2016.7593065},

issn = {2165-4719},

year  = {2016},

date = {2016-09-01},

booktitle = {2016 9th International Symposium on Turbo Codes and Iterative Information Processing (ISTC)},

pages = {1-5},

abstract = {In this paper, we consider communications impaired by phase noise and we propose an iterative multi-sample receiver, where the received signal is sampled with more than one sample per symbol interval. The approach is pragmatic in the sense that demodulation/decoding is performed separately from synchronization and relies on “off-the-shelf” subblocks. In particular, we extend a recently proposed Maximum A-posteriori Probability (MAP)-based algorithm for phase synchronization by exploiting oversampling at the receiver. Our simulation results show an improved performance with respect to a “classical” receiver, where phase synchronization relies on one sample per symbol interval only, for high phase noise scenarios.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{7794892,

title = {Tradeoff between energy consumption and detection capabilities in collaborative cognitive wireless networks},

author = {Marco Martalò and Gianluigi Ferrari and Andrea Abrardo},

doi = {10.1109/PIMRC.2016.7794892},

issn = {2166-9589},

year  = {2016},

date = {2016-09-01},

booktitle = {2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)},

pages = {1-6},

abstract = {In this paper, we analyze a cognitive wireless scenario, where a primary wireless network (PWN) coexists with a cognitive (or secondary) wireless network (CWN). The PWN uses licensed spectrum and the nodes of the CWN cooperate to detect idle subchannels (not used by the PWN's nodes), possibly taking into account the knowledge of their positions'. On the basis of this scenario, we present a simple, yet effective, framework to analyze the tradeoff between the CWN detection capabilities, i.e., the probability of detecting an unused lincensed subchannel, and the energy consumption needed to detect this subchannel. To this end, we introduce a novel performance indicator, denoted as detection energy efficiency. Our results show that there is an optimal working point, i.e., an optimal number of collaborating CWN nodes that allows to achieve the highest detection energy efficiency.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{salsano:noms2016,

title = {PMSR - Poor Man's Segment Routing, a minimalistic approach to Segment Routing and a Traffic Engineering use case},

author = {Stefano Salsano and Luca Veltri and Luca Davoli and Pier Luigi Ventre and Giuseppe Siracusano},

url = {https://ieeexplore.ieee.org/document/7502864},

doi = {10.1109/NOMS.2016.7502864},

year  = {2016},

date = {2016-07-06},

booktitle = {NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium},

pages = {598-604},

address = {Istanbul, Turkey},

abstract = {The current specification of the Segment Routing (SR) architecture requires enhancements to the intradomain routing protocols (e.g. OSPF and IS-IS) so that the nodes can advertise the Segment Identifiers (SIDs). We propose a simpler solution called PMSR (Poor Man's Segment Routing), that does not require any enhancement to routing protocol. We compare the procedures of PMSR with traditional SR, showing that PMSR can reduce the operation and management complexity. We analyze the set of use cases in the current SR drafts and we claim that PMSR can support the large majority of them. Thanks to the drastic simplification of the control plane, we have been able to develop an open source prototype of PMSR. In the second part of the paper, we consider a Traffic Engineering use case, starting from a traditional flow assignment optimization problem, which allocates hop-by-hop paths to flows. We propose a SR path assignment algorithm and prove that it is optimal with respect to the number of segments allocated to a flow.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{PaFeBaDICiMa16,

title = {Accurate gait analysis in post-stroke patients using a single inertial measurement unit},

author = {Federico Parisi and Gianluigi Ferrari and Alessio Baricich and Marco D'Innocenzo and Carlo Cisari and Alessandro Mauro},

url = {http://dx.doi.org/10.1109/BSN.2016.7516284},

year  = {2016},

date = {2016-06-01},

booktitle = {2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)},

pages = {335--340},

organization = {IEEE},

abstract = {Improving independent mobility in post-stroke patients is one of the main goals of most rehabilitation strategies. While quantitative gait assessment is crucial to provide a meaningful feedback on the recovery progress, the irregularity of hemiparetic walking prevents the use of classical Inertial Measurement Unit (IMU)-based gait analysis algorithms. In this paper, we propose a novel low-cost system, which relies on a single wearable IMU attached to the lower trunk, to estimate spatio-temporal gait parameters of both hemiparetic and healthy subjects. A new procedure for temporal features' computation and two modified versions of well-known step length (i.e., spatial features) estimators are derived. In both cases, we exploit dynamic calibration constants, related to the “power” of an individual gait pattern, to deal with the typical asymmetry and inter-subject variability of hemiparetic gait. The spatio-temporal features estimated with the proposed methods are compared with ground-truth parameters extracted by an optoelectronic system. The obtained results show very high correlations between estimated and reference values.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{StPaFe_BSN16,

title = {On single sensor-based inertial navigation},

author = {Nicolò Strozzi and Federico Parisi and Gianluigi Ferrari},

url = {http://dx.doi.org/10.1109/BSN.2016.7516278



http://ieeexplore.ieee.org/document/7516278/},

year  = {2016},

date = {2016-01-01},

booktitle = {2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)},

pages = {300-305},

abstract = {In this paper, we compare two novel algorithms for pedestrian navigation based on signals collected by a single wearable Magnetic, Angular Rate, and Gravity (MARG) sensor. The two navigation algorithms, denoted as Enhanced Pedestrian Dead Reckoning (EPDR) and De-Drifted Propagation (DDP), require the placement of the MARG sensor on the foot or on the chest of the test subject, respectively. Different methods for gait characterization are compared, evaluating navigation dynamics by using data collected through an extensive experimental campaign. The main goal of this research is to investigate the peculiarities of different inertial navigation algorithms, in order to highlight the impact of the sensor's placement, together with inertial sensor issues. Considering a closed path (i.e., ending at the starting point), the relative distance error between the starting point and the final estimated position is about 2% of the total travelled distance for both DDP and EPDR navigation algorithms. On the other hand, the error between the initial heading angle and the final estimated one is approximately 10° for EPDR and 7° for DDP, respectively.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Caetal_MeMeA16,

title = {SmartCED: An Android application for neonatal seizures detection},

author = {Luca Cattani and Harpreet P. Saini and Gianluigi Ferrari and Francesco Pisani and Riccardo Raheli},

url = {http://dx.doi.org/10.1109/MeMeA.2016.7533708},

year  = {2016},

date = {2016-01-01},

booktitle = {2016 IEEE International Symposium on Medical Measurements and Applications (MeMeA)},

pages = {1-6},

abstract = {In this paper, we present Smartphone-based Contactless Epilepsy Detector (SmartCED): an Android monitoring application able to diagnose neonatal clonic seizures and warn about their possible occurrences in realtime. SmartCED has, however, wider applicability so that it could also be used on adult patients. The main goal is to implement a wire-free and low-cost epilepsy diagnostic system, executing all the necessary processing directly on the smartphone. Seizures' recognition is based on a well-known statistical criterion, namely Maximum Likelihood (ML). As clonic seizures are characterized by quasi-periodic movements of some body parts, it is possible to detect the presence of a seizure by evaluating this periodicity from the video stream of the smartphone's camera. The heavy computational processing is carried out in the native code (C language) to enhance the performance. SmartCED presents a user-friendly interface in order to extend its use even to unskilled staff. In fact, although it integrates complex software from the technical point of view, the user has just to: start the App, “frame the patient”, and start monitoring with a simple touch.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{TrFePiRa_ISPLC16,

title = {Performance of LDPC coded modulations in Power Line Communications},

author = {Carlo Tripodi and Gianluigi Ferrari and Riccardo Pighi and Riccardo Raheli},

url = {http://dx.doi.org/10.1109/ISPLC.2016.7476294},

year  = {2016},

date = {2016-01-01},

booktitle = {2016 International Symposium on Power Line Communications and its Applications (ISPLC)},

pages = {25-30},

abstract = {We discuss the performance of different coding schemes based on the use of short LDPC codes, which are suitable for the application in Power Line Communications (PLC) systems, where low latency and high spectral efficiency is requested. We identify the class of LDPC codes appropriate for this purpose in the codes used in IEEE 802.16e standard (WiMAX). We then propose different coding schemes and conduct a performance analysis in terms of their capability of achieving the capacity in an Additive White Gaussian Noise (AWGN) channel, while keeping low the overall latency of the encoding/decoding process. We also consider the presence of bursty impulse noise, in order to investigate a typical PLC scenario. The analysis proves that WiMAX standard LDPC codes, used with proper coding schemes, achieve competitive trade-off between spectral and energy efficiency, maintaining the compatibility with the considered latency constraint.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{EWSDN2015,

title = {Traffic Engineering with Segment Routing: SDN-Based Architectural Design and Open Source Implementation},

author = {Luca Davoli and Luca Veltri and Pier Luigi Ventre and Giuseppe Siracusano and Stefano Salsano},

url = {https://ieeexplore.ieee.org/document/7313628},

doi = {10.1109/EWSDN.2015.73},

year  = {2015},

date = {2015-11-04},

booktitle = {2015 Fourth European Workshop on Software Defined Networks (EWSDN)},

pages = {111-112},

abstract = {Traffic Engineering (TE) in IP carrier networks is one of the functions that can benefit from the Software Defined Networking paradigm. However traditional per-flow routing requires a direct interaction between the SDN controller and each node that is involved in the traffic paths. Segment Routing (SR) may simplify the route enforcement delegating all the configuration and per-flow state at the border of the network. In this work we propose an architecture that integrates the SDN paradigm with SR based TE, for which we have provided an open source reference implementation. We have designed and implemented a simple TE/SR heuristic for flow allocation and we show and discuss experimental results.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{PaFeGiCoCiAzAlMa15BSN,

title = {On the correlation between UPDRS scoring in the leg agility, sit-to-stand, and gait tasks for parkinsonians},

author = {Federico Parisi and Gianluigi Ferrari and Matteo Giuberti and Laura Contin and Veronica Cimolin and Corrado Azzaro and Giovanni Albani and Alessandro Mauro},

url = {http://dx.doi.org/10.1109/BSN.2015.7299401},

year  = {2015},

date = {2015-06-09},

booktitle = {Proc. of 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)},

address = {Cambridge, MA, USA},

abstract = {Recently, we have proposed a unified approach, based on the use of a Body Sensor Network (BSN) formed by a few body-worn wireless inertial nodes, for automatic assignment of Unified Parkinsons Disease Rating Scale (UPDRS) scores in the following tasks: Leg Agility (LA), Sit-to-Stand (S2S), and Gait (G). Unlike our previous works and the majority of the works appeared in the literature, where UPDRS tasks are investigated singularly, in the current paper we carry out a comparative investigation of the LA, S2S, and G tasks. In particular, we focus on the correlation between UPDRS values assigned to the three tasks by both an expert neurologist and our automatic system. We also consider an aggregate UPDRS score in order to highlight the relevance of each task in the assessment of the gravity of the Parkinson;s Disease (PD).},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{7249069,

title = {Reduced-complexity synchronization for high-order coded modulations},

author = {Marco Martalò and Gianluigi Ferrari and Muhammad Asim and Jonathan Gambini and Christian Mazzucco and Giacomo Cannalire and Sergio Bianchi and Riccardo Raheli},

url = {https://dx.doi.org/10.1109/ICC.2015.7249069},

issn = {1550-3607},

year  = {2015},

date = {2015-01-01},

booktitle = {2015 IEEE International Conference on Communications (ICC)},

pages = {4721-4726},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{7454360,

title = {Information rate analysis of the oversampled phase-noise channel},

author = {Marco Martalò and Antonino Gervasi and Carlo Tripodi and Riccardo Raheli},

url = {https://dx.doi.org/10.1109/ISWCS.2015.7454360},

year  = {2015},

date = {2015-01-01},

booktitle = {2015 International Symposium on Wireless Communication Systems (ISWCS)},

pages = {346-350},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}