@proceedings {526,
	title = {A new modelling approach to represent the DCF mechanism of the CSMA/CA protocol},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {10378 LNCS},
	year = {2017},
	pages = {181-195},
	abstract = {<p>In this paper, a Markovian agent model is used to represent the behavior of wireless nodes based on CSMA/CA access method. This kind of network was usually modeled by means of bidimensional Markov Chains and more recently using semi-Markov process based models. Both these approaches are based on the assumptions of both full load network and independence of collision probability with respect to retransmission count of each packet. Our model inherently releases the latter hypothesis since it is not necessary to establish a constant collision probability at steady state. Here, we investigate the correctness of our approach analyzing the throughput of a network based on two IEEE 802.11g nodes when the amount of traffic sent by each one varies. Results have been compared with Omnet++ simulations and show the validity of the proposed model. {\^A}{\textcopyright} Springer International Publishing AG 2017.</p>
},
	keywords = {Access methods, Carrier sense multiple access, Collision probability, DCF mechanisms, Markov processes, Markovian agents, Network-based, Retransmissions, Semi markov process, Standards, Stochastic systems, Transport properties, Wireless nodes},
	isbn = {9783319614274},
	issn = {03029743},
	doi = {10.1007/978-3-319-61428-1_13},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021219346\&doi=10.1007\%2f978-3-319-61428-1_13\&partnerID=40\&md5=0dbb7a4c879561f94ca4992e4bc69e18},
	author = {Scarpa, M. and Serrano, S.}
}
@article {merlino2015tits,
	title = {A Crowd-Cooperative Approach for Intelligent Transportation Systems},
	journal = {Intelligent Transportation Systems, IEEE Transactions on},
	year = {2016},
	pages = {-},
	keywords = {Markovian agents, Mobile Crowdsensing, Route Planning, Stigmergy, Traffic Engineer- ing, Traffic Monitoring},
	issn = {1524-9050},
	author = {Davide Cerotti and Salvatore Distefano and Giovanni Merlino and Antonio Puliafito}
}
@proceedings {268,
	title = {Markovian agent modeling swarm intelligence algorithms in wireless sensor networks},
	journal = {PERFORMANCE EVALUATION},
	volume = {69},
	year = {2012},
	pages = {135{\textendash}149},
	abstract = {<p>Wireless Sensor Networks (WSN) are large networks of tiny sensor nodes that are usually randomly distributed over a geographical region. The network topology may vary in time in an unpredictable manner due to many different causes. For example, in order to reduce power consumption, battery operated sensors undergo cycles of sleeping{\textendash}active periods; additionally, sensors may be located in hostile environments increasing their likelihood of failure; furthermore, data might also be collected from a range of sources at different times. For this reason multi-hop routing algorithms used to route messages from a sensor node to a sink should be rapidly adaptable to the changing topology. Swarm intelligence has been proposed for this purpose, since it allows the emergence of a single global behavior from the interaction of many simple local agents. Swarm intelligent routing has been traditionally studied by resorting to simulation. The present paper aims to show that the recently proposed modeling technique, known as Markovian Agent Model (MAM), is suited for implementing swarm intelligent algorithms for large networks of interacting sensors. Various experimental results and quantitative performance indices are evaluated to support this claim. The validity of this approach is given a further proof by comparing the results with those obtained by using a WSN discrete event simulator.</p>
},
	keywords = {Gradient-based routing, Markovian agents, Performance evaluation, Swarm intelligence, Wireless sensor networks},
	doi = {10.1016/j.peva.2010.11.007},
	author = {Dario Bruneo and Marco Scarpa and Andrea Bobbio and Davide Cerotti and Marco Gribaudo}
}
