@article {Distefano2015629,
	title = {QoS Assessment of Mobile Crowdsensing Services},
	journal = {Journal of Grid Computing},
	volume = {13},
	number = {4},
	year = {2015},
	note = {cited By 2},
	pages = {629-650},
	publisher = {Springer Netherlands},
	abstract = {<p>The wide spreading of smart devices drives to develop distributed applications of increasing complexity, attracting efforts from both research and business communities. Recently, a new volunteer contribution paradigm based on participatory and opportunistic sensing is affirming in the Internet of Things scenario: Mobile Crowdsensing (MCS). A typical MCS application considers smart devices as contributing sensors able to produce geolocalized data about the physical environment, then collected by a remote application server for processing. The growing interest on MCS allows to think about its possible exploitation in commercial context. This calls for adequate methods able to support MCS service providers in design choices, implementing mechanisms for the quality of service (QoS) assessment while dealing with complex time-dependent phenomena and churning issues due to contributors that unpredictably join and leave the MCS system. In this paper, we propose an analytical modeling framework based on stochastic Petri nets to evaluate QoS metrics of a class of MCS services. This method requires to extend the Petri net formalism by specifying a marking dependency semantics for non-exponentially distributed transitions. The approach is then applied to an MCS application example deriving some QoS measures that can drive quantitative evaluation and characterization of the {\textquotedblleft}crowd{\textquotedblright} behavior. {\textcopyright} 2015, Springer Science+Business Media Dordrecht.</p>
},
	keywords = {crowdsensing, Digital storage, Distributed applications, Marking dependency, Non-Markovian, Performability, Petri nets, Quality of service, Quality of service (QoS) assessments, Quantitative evaluation, Semantics, Stochastic systems, Time dependent phenomena},
	issn = {15707873},
	doi = {10.1007/s10723-015-9338-7},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958155341\&doi=10.1007\%2fs10723-015-9338-7\&partnerID=40\&md5=3bd7e36a37ab06a3acabb37a21b90ab1},
	author = {Salvatore Distefano and Francesco Longo and Marco Scarpa}
}
@proceedings {Distefano201377,
	title = {Investigating mobile crowdsensing application performance},
	journal = {Proceedings of the 3rd ACM International Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications (DIVANet), Co-located with the 16th ACM Int. Conf. on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM)},
	year = {2013},
	note = {cited By 0; Conference of 3rd ACM Int. Symp. on Design and Analysis of Intelligent Vehicular Networks and Applications, DIVANet 2013, Held in Conjunction with the 16th ACM Int. Conf. on Modeling, Analysis and Simulation of Wireless and Mobile Systems, MSWiM 2013 ; Conference Date: 3 November 2013 Through 8 November 2013; Conference Code:101342},
	pages = {77-83},
	publisher = {ACM},
	address = {Barcelona, Spain, 3-8 November 2013},
	abstract = {<p>Mobile Crowdsensing (MCS) is an emerging distributed paradigm lying at the intersection between the Internet of Things and the volunteer/crowd-based approach. MCS applications are usually deployed on contributing nodes such as smart devices and mobiles, equipped by sensing resources that sample the physical environment and provide the sensed data, once filtered, aggregated and preprocessed, to the MCS application server. The MCS opportunistic approach unlocks new form of pervasive, participatory sensing applications, acquiring interests also in business contexts that call for adequate techniques and tools to drive architects and developers in MCS application design. Aim of this paper is to evaluate the performance of an MCS application though a stochastic model able to stochastically represent the overall MCS environment, thus providing a valid support to MCS application development. The Petri nets formalism is used due to its expressiveness and the capabilities to represent complex, dependent, non-Markovian, phenomena usually characterizing MCS environments. A specific MCS application is then evaluated to demonstrate the effectiveness of the proposed technique on a real case study. {\textcopyright} 2013 ACM.</p>
},
	keywords = {Application development, Application performance, Complex networks, crowdsensing, Design, Digital storage, Internet of Things (IOT), Participatory sensing applications, Performance, Petri nets, Physical environments, Stochastic models, Techniques and tools},
	isbn = {9781450323581},
	doi = {10.1145/2512921.2512931},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84889685098\&partnerID=40\&md5=15df22b992482173437dcd8080bbbc94},
	author = {Salvatore Distefano and Francesco Longo and Marco Scarpa}
}
