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Title	Crowdsensim 2.0: A Stateful Simulation Platform For Mobile Crowdsensing In Smart Cities
ID_Doc	16704
Authors	Montori F.; Cortesi E.; Bedogni L.; Capponi A.; Fiandrino C.; Bononi L.
Year	2019
Published	MSWiM 2019 - Proceedings of the 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems
DOI	http://dx.doi.org/10.1145/3345768.3355929
Abstract	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. © 2019 Association for Computing Machinery.
Author Keywords	Distributed algorithms; Mobile crowdsensing; Modeling; Simulation

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