المؤلفون: Staffolani, Alessandro, Darvariu, Victor-Alexandru, Foschini, Luca, Girolami, Michele, Bellavista, Paolo, Musolesi, Mirco

المصدر: IEEE Transactions on Network and Service Management , 2023 (2024) (In press).

مصطلحات موضوعية: O-RAN, reinforcement learning, resource allocation, multi-objective optimization

الوصف: Open Radio Access Network (O-RAN) is an emerging paradigm proposed for enhancing the 5G network infrastructure. O-RAN promotes open vendor-neutral interfaces and virtualized network functions that enable the decoupling of network components and their optimization through intelligent controllers. The decomposition of base station functions enables better resource usage, but also opens new technical challenges concerning their efficient orchestration and allocation. In this paper, we propose Proactive Resource Orchestrator based on Reinforcement Learning (PRORL), a novel solution for the efficient and dynamic allocation of resources in O-RAN infrastructures. We frame the problem as a Markov Decision Process and solve it using Deep Reinforcement Learning; one relevant feature of PRORL is that it learns demand patterns from experience for proactive resource allocation. We extensively evaluate our proposal by using both synthetic and real-world data, showing that we can significantly outperform the existing algorithms, which are typically based on the analysis of static demands. More specifically, we achieve an improvement of 90% over greedy baselines and deal with complex trade-offs in terms of competing objectives such as demand satisfaction, resource utilization, and the inherent cost associated with allocating resources.

وصف الملف: text

العلاقة: https://discovery.ucl.ac.uk/id/eprint/10189737/1/tsnm24_prorl.pdfTest; https://discovery.ucl.ac.uk/id/eprint/10189737Test/

الإتاحة: https://discovery.ucl.ac.uk/id/eprint/10189737/1/tsnm24_prorl.pdfTest
https://discovery.ucl.ac.uk/id/eprint/10189737Test/

المؤلفون: Girolami, Michele, Mavilia, Fabio, Furfari, Francesco, Barsocchi, Paolo

المساهمون: European Union - Next Generation EU

المصدر: IEEE Journal of Indoor and Seamless Positioning and Navigation ; volume 2, page 36-50 ; ISSN 2832-7322

الإتاحة: https://doi.org/10.1109/jispin.2023.3345268Test
http://xplorestaging.ieee.org/ielx7/9955032/10378862/10366502.pdf?arnumber=10366502Test

المؤلفون: Furfari, Francesco, Girolami, Michele, Barsocchi, Paolo

المصدر: IEEE Journal of Indoor and Seamless Positioning and Navigation ; volume 2, page 130-142 ; ISSN 2832-7322

الإتاحة: https://doi.org/10.1109/jispin.2024.3377146Test
http://xplorestaging.ieee.org/ielx7/9955032/10378862/10471883.pdf?arnumber=10471883Test

المؤلفون: Girolami, Michele, La Rosa, Davide, Barsocchi, Paolo

المساهمون: European Union, Office of Population Affairs, Regione Toscana

المصدر: Data in Brief ; volume 53, page 110215 ; ISSN 2352-3409

مصطلحات موضوعية: Multidisciplinary

الإتاحة: https://doi.org/10.1016/j.dib.2024.110215Test
https://api.elsevier.com/content/article/PII:S2352340924001860?httpAccept=text/xmlTest
https://api.elsevier.com/content/article/PII:S2352340924001860?httpAccept=text/plainTest

المؤلفون: Girolami, Michele, La Rosa, Davide, Barsocchi, Paolo

المساهمون: Office of Population Affairs, European Commission, Regione Toscana

المصدر: Ad Hoc Networks ; volume 154, page 103367 ; ISSN 1570-8705

الإتاحة: https://doi.org/10.1016/j.adhoc.2023.103367Test
https://api.elsevier.com/content/article/PII:S1570870523002871?httpAccept=text/xmlTest
https://api.elsevier.com/content/article/PII:S1570870523002871?httpAccept=text/plainTest

المؤلفون: Mastropietro, Alfonso, Palumbo, Filippo, Orte, Silvia, Girolami, Michele, Furfari, Francesco, Baronti, Paolo, Candea, Ciprian, Röcke, Christina, Tarro, Lucia, Sykora, Martin, Porcelli, Simone, Rizzo, Giovanna

المصدر: Mastropietro, Alfonso; Palumbo, Filippo; Orte, Silvia; Girolami, Michele; Furfari, Francesco; Baronti, Paolo; Candea, Ciprian; Röcke, Christina; Tarro, Lucia; Sykora, Martin; Porcelli, Simone; Rizzo, Giovanna (2023). A multi-domain ontology on healthy ageing for the characterization of older adults status and behaviour. Journal of Ambient Intelligence and Humanized Computing, 14(7):8725-8743.

مصطلحات موضوعية: Institute of Psychology, Healthy Longevity Center, 150 Psychology, General Computer Science

الوصف: Ageing is a multi-factorial physiological process and the development of novel IoT systems, tools and devices, specifically targeted to older people, must be based on a holistic framework built on robust scientific knowledge in different health domains. Furthermore, interoperability must be guaranteed using standardized frameworks or approaches. These aspects still largely lack in the specific literature. The main aim of the paper is to develop a new ontology (the NESTORE ontology) to extend the available ontologies provided by universAAL-IoT (uAAL-IoT). The ontology is based on a multidomain healthy ageing holistic model, structuring well-assessed scientific knowledge, specifically targeted to healthy older adults aged between 65 and 75. The tool is intended to support, and standardize heterogeneous data about ageing in compliance with the uAAL-IoT framework. The NESTORE ontology covers all the relevant concepts to represent 3 significant domains of ageing: (1) Physiological Status and Physical Activity Behaviour; (2) Nutrition; and (3) Cognitive and Mental Status and Social Behaviour. In total, 12 sub-ontologies were modelled with more than 60 classes and sub-classes referenced among them by using more than 100 relations and around 20 enumerations. The proposed ontology increases the uAAL collection by 40%. NESTORE ontology provides innovation both in terms of semantic content and technological approach. The thorough use of this ontology can support the development of a decision support system, to promote healthy ageing, with the capacity to do dynamic multi-scale modelling of user-specific data based on the semantic annotations of users’ profile.

وصف الملف: application/pdf

العلاقة: https://www.zora.uzh.ch/id/eprint/218182/1/Mastropietro2021_Article_AMulti-domainOntologyOnHealthy.pdfTest; urn:issn:1868-5137

الإتاحة: https://doi.org/10.5167/uzh-21818210.1007/s12652-021-03627-6Test
https://www.zora.uzh.ch/id/eprint/218182Test/
https://www.zora.uzh.ch/id/eprint/218182/1/Mastropietro2021_Article_AMulti-domainOntologyOnHealthy.pdfTest

المؤلفون: Girolami, Michele, Furfari, Francesco, Barsocchi, Paolo, Mavilia, Fabio

المساهمون: ChAALenge Project, European Union–Next Generation EU, in the context of the National Recovery and Resilience Plan, Investment Partenariato Esteso PE8 “Conseguenze e sfide dell’invecchiamento,” Project Age-IT

المصدر: IEEE Access ; volume 11, page 81763-81776 ; ISSN 2169-3536

الإتاحة: https://doi.org/10.1109/access.2023.3301126Test
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10201845.pdf?arnumber=10201845Test

المؤلفون: Girolami, Michele, Cipullo, Erminia, Colella, Tommaso, Chessa, Stefano

المصدر: Journal of Ambient Intelligence & Smart Environments; 2024, Vol. 16 Issue 2, p241-268, 28p

مصطلحات موضوعية: MOBILE apps, CROWDSENSING, DATA modeling, PERSONALLY identifiable information, DRONE aircraft

مستخلص: Mobile CrowdSensing (MCS) is a computational paradigm designed to gather sensing data by using personal devices of MCS platform users. However, being the mobility of devices tightly correlated with mobility of their owners, the locations from which data are collected might be limited to specific sub-regions. We extend the data coverage capability of a traditional MCS platform by exploiting unmanned aerial vehicles (UAV) as mobile sensors gathering data from low covered locations. We present a probabilistic model designed to measure the coverage of a location. The model analyses the user's trajectories and the detouring capability of users towards locations of interest. Our model provides a coverage probability for each of the target locations, so that to identify low-covered locations. In turn, these locations are used as targets for the StationPositioning algorithms which optimizes the deployment of k UAV stations. We analyze the performance of StationPositioning by comparing the ratio of the covered locations against Random, DBSCAN and KMeans deployment algorithm. We explore the performance by varying the time period, the deployment regions and the existence of areas where it is not possible to deploy any station. Our experimental results show that StationPositioning is able to optimize the selected target location for a number of UAV stations with a maximum covered ratio up to 60%. [ABSTRACT FROM AUTHOR]

: Copyright of Journal of Ambient Intelligence & Smart Environments is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Girolami, Michele, Kocian, Alexander, Chessa, Stefano

المصدر: Journal of Ambient Intelligence & Humanized Computing; Jun2024, Vol. 15 Issue 6, p2941-2951, 11p

مستخلص: The expected spatial coverage of a crowdsensing platform is an important parameter that derives from the mobility data of the crowdsensing platform users. We tackle the challenge of estimating the anticipated coverage while adhering to privacy constraints, where the platform is restricted from accessing detailed mobility data of individual users. Specifically, we model the coverage as the probability that a user detours to a point of interest if the user is present in a certain region around that point. Following this approach, we propose and evaluate a centralized as well as a distributed implementation model. We examine real-world mobility data employed for assessing the coverage performance of the two models, and we show that the two implementation models provide different privacy requirements but are equivalent in terms of their outputs. [ABSTRACT FROM AUTHOR]

: Copyright of Journal of Ambient Intelligence & Humanized Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Barsocchi, Paolo, Chessa, Stefano, Foschini, Luca, Belli, Dimitri, Girolami, Michele

المساهمون: Barsocchi, Paolo, Chessa, Stefano, Foschini, Luca, Belli, Dimitri, Girolami, Michele

مصطلحات موضوعية: CrowdSensing, Multi-access Edge Computing, Mobile Edge, Community Detection

الوصف: The combination of the edge computing paradigm with Mobile CrowdSensing (MCS) is a promising approach. However, the selection of the proper edge nodes is a crucial aspect that greatly affects the performance of the extended architecture. This work studies the performance of an edge-based MCS architecture with ParticipAct, a real-word experimental dataset. We present a community-based edge selection strategy and we measure two key metrics, namely latency and the number of requests satisfied. We show how they vary by adopting three evolutionary community detection algorithms, TILES, Infomap and iLCD configured by changing several configuration settings. We also study the two metrics, by varying the number of edge nodes selected so that to show its benefit.

وصف الملف: ELETTRONICO

العلاقة: info:eu-repo/semantics/altIdentifier/isbn/978-1-7281-8298-8; info:eu-repo/semantics/altIdentifier/wos/WOS:000668970504078; ispartofbook:GLOBECOM 2020 - 2020 IEEE Global Communications Conference; IEEE Global Communications Conference, GLOBECOM 2020; firstpage:1; lastpage:6; numberofpages:6; serie:. IEEE GLOBAL COMMUNICATIONS CONFERENCE; https://hdl.handle.net/11585/811875Test; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85101215492; https://ieeexplore.ieee.org/document/9348085Test

الإتاحة: https://doi.org/10.1109/GLOBECOM42002.2020.9348085Test
https://hdl.handle.net/11585/811875Test
https://ieeexplore.ieee.org/document/9348085Test