Building an interoperable space for smart agriculture

The digital transformation in agriculture introduces new challenges in terms of data,knowledge and technology adoption due to critical interoperability issues,and also challenges regarding the identification of the most suitable data sources to be exploited and the information models that must be used.DEMETER(Building an Interoperable,Data-Driven,Innovative and Sustainable European Agri-Food Sector)addresses these challenges by providing an overarching solution that integrates various heterogeneous hardware and software resources(e.g.,devices,networks,platforms)and enables the seamless sharing of data and knowledge throughout the agri-food chain.This paper introduces the main concepts of DEMETER and its reference architecture to address the data sharing and interoperability needs of farmers,which is validated via two rounds of 20 large-scale pilots along the DEMETER lifecycle.This paper elaborates on the two pilots carried out in region of Murcia in Spain,which target the arable crops sector and demonstrate the benefits of the deployed DEMETER reference architecture.

TinyML-Based Fall Detection for Connected Personal Mobility Vehicles

A new wave of electric vehicles for personal mobility is currently crowding public spaces.They offer a sustainable and efficient way of getting around in urban environments,however,these devices bring additional safety issues,including serious accidents for riders.Thereby,taking advantage of a connected personal mobility vehicle,we present a novel on-device Machine Learning(ML)-based fall detection system that analyzes data captured from a range of sensors integrated on an on-board unit(OBU)prototype.Given the typical processing limitations of these elements,we exploit the potential of the TinyML paradigm,which enables embedding powerful ML algorithms in constrained units.We have generated and publicly released a large dataset,including real riding measurements and realistically simulated falling events,which has been employed to produce different TinyML models.The attained results show the good operation of the system to detect falls efficiently using embedded OBUs.The considered algorithms have been successfully tested on mass-market low-power units,implying reduced energy consumption,flash footprints and running times,enabling new possibilities for this kind of vehicles.

From Network Functions to NetApps: The 5GASP Methodology

As the 5G ecosystem continues its consolidation,the testing and validation of the innovations achieved by integrators and verticals service providers is of preponderant importance.In this line,5GASP is a European H2020-funded project that aims at easing the idea-to-market process through the creation of an European testbed that is fully automated and self-service,in order to foster rapid development and testing of new and innovative 5G Network Applications(NetApps).The main objective of this paper is to present the 5GASP’s unified methodology to design,develop and onboard NetApps within the scope of different vertical services,letting them use specific 5G facilities.Besides,we examine the whole 5GASP process in a tutorial fashion by adopting a specific use case focusing on the integration of a virtual On-Board Unit(vOBU)service that permits offloading processing from the attached vehicle and serving data-access requests.As demonstrated,the presented workflow permits the agile,rigorous,and safe development,testing and certification of NetApps,which will enable valuable in-network services for 5G and beyond infrastructures.

Parking Availability Prediction with Coarse-Grained Human Mobility Data

Nowadays,the anticipation of parking-space demand is an instrumental service in order to reduce traffic congestion levels in urban spaces.The purpose of our work is to study,design and develop a parking-availability predictor that extracts the knowledge from human mobility data,based on the anonymized human displacements of an urban area,and also from weather conditions.Most of the existing solutions for this prediction take as contextual data the current road-traffic state defined at very high temporal or spatial resolution.However,access to this type of fine-grained location data is usually quite limited due to several economic or privacy-related restrictions.To overcome this limitation,our proposal uses urban areas that are defined at very low spatial and temporal resolution.We conducted several experiments using three Artificial Neural Networks:Multilayer Perceptron,Gated Recurrent Units and bidirectional Long Short Term Memory networks and we tested their suitability using different combinations of inputs.Several metrics are provided for the sake of comparison within our study and between other studies.The solution has been evaluated in a real-world testbed in the city of Murcia(Spain)integrating an open human-mobility dataset showing high accuracy.A MAPE between 4%and 10%was reported in horizons of 1 to 3 h.

Advancing 5G Network Applications Lifecycle Security:An ML-Driven Approach

As 5th Generation(5G)and Beyond 5G(B5G)networks become increasingly prevalent,ensuring not only networksecurity but also the security and reliability of the applications,the so-called network applications,becomesof paramount importance.This paper introduces a novel integrated model architecture,combining a networkapplication validation framework with an AI-driven reactive system to enhance security in real-time.The proposedmodel leverages machine learning(ML)and artificial intelligence(AI)to dynamically monitor and respond tosecurity threats,effectively mitigating potential risks before they impact the network infrastructure.This dualapproach not only validates the functionality and performance of network applications before their real deploymentbut also enhances the network’s ability to adapt and respond to threats as they arise.The implementation ofthis model,in the shape of an architecture deployed in two distinct sites,demonstrates its practical viability andeffectiveness.Integrating application validation with proactive threat detection and response,the proposed modeladdresses critical security challenges unique to 5G infrastructures.This paper details the model,architecture’sdesign,implementation,and evaluation of this solution,illustrating its potential to improve network securitymanagement in 5G environments significantly.Our findings highlight the architecture’s capability to ensure boththe operational integrity of network applications and the security of the underlying infrastructure,presenting asignificant advancement in network security.