Antonio Cilfone’s research while affiliated with University of Parma and other places

In this work, a container-based architecture for the integration of Long Range Wide Area Network (LoRaWAN) end nodes—e.g., used to monitor industrial machines or mobile entities in specific environments—with Internet Protocol (IP)-based networks is proposed and its performance is investigated. To this end, we exploit the native service and resource discovery support of the Constrained Application Protocol (CoAP), as well as its light traffic requirements, owing to its use of User Datagram Protocol (UDP) rather than Transmission Control Protocol (TCP). This approach (i) adapts transparently (with no impact) to both private and public LoRaWAN networks, (ii) enables seamless interaction between LoRaWAN-based and CoAP-based nodes, through a logical “virtualization” of LoRaWAN nodes at server side, and (iii) enables routing among LoRaWAN end nodes, overcoming LoRaWAN's absence of inter-node communication and lack of compliance (at the end nodes' side) with IP. Two virtualization approaches are proposed: (i) virtualization of a single end node (represented as a CoAP server) per container and (ii) virtualization of multiple end nodes (as CoAP servers) per container. Finally, deployments of the proposed virtualization architectures, using both a laptop and an Internet of Things (IoT) device (e.g., a Raspberry Pi), are considered, highlighting how the best solution relies on the use of several containers, with more than one CoAP server per container.

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.

Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced.

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.

The ongoing diffusion of Internet of Things (IoT) technologies is opening new possibilities, and one of the most remarkable applications is associated with the smart city paradigm, which is continuously evolving. In general, it can be defined as the integration of IoT and Information Communication Technologies (ICT) into city management, with the aim of addressing the exponential growth of urbanization and population, thus significantly increasing people’s quality of life. The smart city paradigm is also strictly connected to sustainability aspects, taking into account, for example, the reduction of environmental impact of urban activities, the optimized management of energy resources, and the design of innovative services and solution for citizens. Abiding by this new paradigm, several cities started a process of strong innovation in different fields (such as mobility and transportation, industry, health, tourism, and education), thanks to significant investments provided by stakeholders and the European Commission (EC). In this paper, we analyze key aspects of an IoT infrastructure for smart cities, outlining the innovations implemented in the city of Parma (Emilia Romagna region, Italy) as a successful example. Special attention is dedicated to the theme of smart urban mobility.

Presently, the adoption of Internet of Things (IoT)-related technologies in the Smart Farming domain is rapidly emerging. The ultimate goal is to collect, monitor, and effectively employ relevant data for agricultural processes, with the purpose of achieving an optimized and more environmentally sustainable agriculture. In this paper, a low-cost, modular, and Long-Range Wide-Area Network (LoRaWAN)-based IoT platform, denoted as “LoRaWAN-based Smart Farming Modular IoT Architecture” (LoRaFarM), and aimed at improving the management of generic farms in a highly customizable way, is presented. The platform, built around a core middleware, is easily extensible with ad-hoc low-level modules (feeding the middleware with data coming from the sensors deployed in the farm) or high-level modules (providing advanced functionalities to the farmer). The proposed platform has been evaluated in a real farm in Italy, collecting environmental data (air/soil temperature and humidity) related to the growth of farm products (namely grapes and greenhouse vegetables) over a period of three months. A web-based visualization tool for the collected data is also presented, to validate the LoRaFarM architecture.

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.