Reference : CrowdSenSim 2.0: a Stateful Simulation Platform for Mobile Crowdsensing in Smart Cities
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/41599
CrowdSenSim 2.0: a Stateful Simulation Platform for Mobile Crowdsensing in Smart Cities
English
Montori, Federico [University of Bologna]
Cortesi, Emanuele [University of Bologna]
Bedogni, Luca [University of Bologna]
Capponi, Andrea mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Fiandrino, Claudio [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Bononi, Luciano [University of Bologna]
Nov-2019
MSWIM '19: Proceedings of the 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, Miami Beach, FL, USA, 2019.
Association for Computing Machinery
Yes
International
978-1-4503-6904-6
New York
NY
22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM)
November 2019
Miami Beach, FL
USA
[en] mobile crowdsensing ; simulation ; modeling ; distributed algorithms
[en] Mobile crowdsensing (MCS) has become a popular paradigm for data collection in urban environments. In MCS systems, a crowd supplies sensing information for monitoring phenomena through mobile devices. Typically, a large number of participants is required to make a sensing campaign successful. For such a reason, it is often not practical for researchers to build and deploy large testbeds to assess the performance of frameworks and algorithms for data collection, user recruitment, and evaluating the quality of information. Simulations offer a valid alternative. In this paper, we present CrowdSenSim 2.0, a significant extension of the popular CrowdSenSim simulation platform. CrowdSenSim 2.0 features a stateful approach to support algorithms where the chronological order of events matters, extensions of the architectural modules, including an additional system to model urban environments, code refactoring, and parallel execution of algorithms. All these improvements boost the performances of the simulator and make the runtime execution and memory utilization significantly lower, also enabling the support for larger simulation scenarios. We demonstrate retro-compatibility with the older platform and evaluate as a case study a stateful data collection algorithm.
http://hdl.handle.net/10993/41599
10.1145/3345768.3355929
https://doi.org/10.1145/3345768.3355929

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