Michele Girolami

Istituto di Scienza e Tecnologie dell'Informazione “Alessandro Faedo”
Area della Ricerca CNR di Pisa
Via G. Moruzzi 1
56124 PISA - Italy
Room C70A

email: michele <dot> girolami [at] isti <dot> cnr <dot> it
phone: +39 050 315 2950
fax: +39 050 315 2040

Ph.D. in Computer Science
Dipartimento di Informatica - Universita' di Pisa
Largo B. Pontecorvo 3, 56127 Pisa, Italy

Ph.D thesis

Visualizza il profilo di Michele Girolami su LinkedIn Strava

On the way


  • ACTIVAGE: ACTivating InnoVative IoT smart living environments for AGEing well, H2020, H2020-IOT-2016-2017, starting Jan 2017
  • SocializeME, un progetto di cooperazione tra enti di ricerca e scuole superiori, Fondazione Cassa Risparmio Lucca, started on August 2016

Papers (2016)

  • S. Chessa, M. Girolami, L. Foschini, R. Ianiello, A. Corradi and P. Bellavista, “Mobile Crowd Sensing Management with the ParticipAct Living Lab”, Pervasive and Mobile Computing, Elsevier
  • F. Potortì, A. Crivello, M. Girolami, P. Barsocchi, E. Traficante, “Wi-Fi probes as digital crumbs for crowd localisation”, International Conference on Indoor Positioning and Indoor Navigation (IPIN), 4-7 October 2016, Alcalá de Henares, Spain
  • P. Barsocchi, A, Crivello, M. Girolami, F. Mavilia, E. Ferro, “Are You in or Out? Monitoring the Human Behavior Through an Occupancy Strategy”, IEEE MoCS (ISCC) 2016
  • M. Girolami, S. Chessa, M. Dragone, M. Bouroche, V. Cahill, “Using Spatial Interpolation in the Design of a Coverage Metric for Mobile Crowdsensing Systems”,IEEE MoCS (ISCC) 2016
  • M. Girolami, S. Chessa, F. Di Rienzo, F. Paparella, and A. M. Caruso, “Signals from the depths: Properties of percolation strategies with the argo dataset,” in 21st IEEE Symposium on Computers and Communication (ISCC 2016), Messina, Italy, Jun. 2016.
  • M. Girolami, S. Chessa, A. Corradi, Luca Foschini, “Empowering Mobile Crowdsensing through Social and Ad-Hoc Networking,” IEEE Commun. Magazine, 2016.
  • J. A. Alvarez-Garcia, A. Arcos Garcia, S. Chessa, L. Fortunati, M. Girolami, “Detecting Social Interactions in Working Environments through Sensing Technologies”, ISAmI 2016
  • M.Girolami, S. Basagni, F. Furfari and S. Chessa, “SIDEMAN: Service Discovery in Mobile Social Networks”, Ad Hoc & Sensor Wireless Networks 2016, to appear

2017 Events

2016 Events

Research activities

Internet of Things (IoT) is a novel paradigm that is rapidly gaining the attention of many researchers and industrial actors. The term is broadly used to describe the architectures designed for interconnecting smart objects (or simply things or objects), and to identify the Internet-based technologies enabling such architectures. I have been focusing on two Mobile Social Networking as the cutting-edge of the mobile computing and of distributed computation frontiers as well as on Mobile Crowd Sensing.

Mobile Social Networking In a Mobile Social Network (MSN, http://en.wikipedia.org/wiki/Opportunistic_Mobile_Social_Networks) devices (or nodes) enter or leave without notice. New nodes provide some services (or more generally resources) that other nodes can discover and later on access. The goal of the service discovery is to find the resources available on the network according to a query expressed by the user. A common pattern for service discovery algorithm is the active-mode. The description of the service is matched against the query and, eventually, a response is sent back to the user. Many service discovery protocols have been already proposed for different application domains. Notable examples include the pioneering but still widely adopted protocols Jini, UPnP and SLP). These protocols are mainly focused on administrated and infrastructure-based IP networks, but they do not consider typical requirements of MSNs, such as mobility, dynamic network connectivity, social behavior of humans and hardware or software constraints. My research is focused on exploiting the social dimension of humans. I'm investigating how to understand and detect social interactions among people so that to optimize the diffusion of information in a MSN.

Mobile Crowd Sensing (MCS) I also study how to combine the participatory and the opportunistic MCS approaches together so that to increase the amount of data that can be retrieved from a sensing campaign. More specifically, the technique we study aim at exploiting the homophily degree among people, such as the people’s interests and their mobility. Analyzing and measuring the homophily of people allows to detect communities of “similar” individuals that might potentially offer sensing information useful for the MCS objectives.

Device Integration The device (or object) integration is a core task in the IoT scenario. It arises from the observation that the objects in a SE have different hardware and software features. Some objects have high hardware/software capabilities that allow them to be easily integrated with each other. By converse, other objects are designed for very specific tasks, and they have poor hardware/software capabilities. The classes of low-power objects are not able to support complex protocols, hence they are not able to fully interact with more powerful devices. Such low-power devices require an integration gateway in order to cooperate with other objects


Supervisor of Emanuele Simonelli, Master Thesis on Computer Science, University of Pisa





* Reti Mobili: reti ad Hoc e di Sensori (RHS) rhs2016.zip
* Sensor networks, internet of things and smart environments (PhD course) rhs2016.zip




michele-girolami.txt · Last modified: 2017-01-18 15:59 by michele
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