M
Marco Zennaro
Researcher at International Centre for Theoretical Physics
Publications - 173
Citations - 2810
Marco Zennaro is an academic researcher from International Centre for Theoretical Physics. The author has contributed to research in topics: Wireless sensor network & Computer science. The author has an hindex of 24, co-authored 161 publications receiving 2252 citations. Previous affiliations of Marco Zennaro include Royal Institute of Technology & University of California, Berkeley.
Papers
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Proceedings ArticleDOI
A Modular Software Infrastructure for Distributed Control of Collaborating UAVs
Allison Ryan,Xiao Xiao,Sivakumar Rathinam,John Tisdale,Marco Zennaro,Derek Caveney,Raja Sengupta,J. Karl Hedrick +7 more
TL;DR: A UAV software architecture and hardware platform that have demonstrated single-user control of a ∞eet of aircraft, distributed task assignment, and vision-based navigation is presented.
Proceedings ArticleDOI
Enabling the Internet of Things in developing countries: Opportunities and challenges
TL;DR: An overview of the enablement of IoT in developing countries is presented and it will be shown that some of the challenges can be turned into opportunities and that IoT has good chances to succeed in developing nations.
Proceedings ArticleDOI
Cloud based patient prioritization as service in public health care
TL;DR: The results obtained from an experimental prototype reveal the field readiness of the off-the-shelf bio-sensor technology used by the system, the performance achieved when using a solar powered subsystem, the relative communication capabilities provided by its protocols and the network engineering feasibility of the planned community health care network.
Proceedings ArticleDOI
CSL: A Language to Specify and Re-specify Mobile Sensor Network Behaviors
Joshua Love,Jerry Jariyasunant,Eloi Pereira,Marco Zennaro,Karl Hedrick,Christoph M. Kirsch,Raja Sengupta +6 more
TL;DR: Experimental results show that the CSL Execution Engine performs CSL controllers to be simple, intuitive and scalable with the addition of very little overhead.
Journal ArticleDOI
Rainfall-Induced Landslide Prediction Using Machine Learning Models: The Case of Ngororero District, Rwanda.
TL;DR: Two prediction modelling approaches, namely random forest (RF) and logistic regression (LR) are proposed, which use rainfall datasets as well as various other internal and external parameters for landslide prediction and hence improve the accuracy.