@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 {251,
	title = {An intelligent swarm of Markovian Agents},
	journal = {Springer Handbook of Computational Intelligence},
	year = {2012},
	pages = {1{\textendash}22},
	publisher = {Springer-Verlag},
	address = {Berlino},
	abstract = {<p>We define a Markovian Agent Model (MAM) as an analytical model formed by a spatial collection of interacting Markovian Agents (MAs), whose properties and behavior can be evaluated by numerical techniques. MAMs have been introduced with the aim of providing a flexible and scalable framework for distributed systems of interacting objects, where both the local properties and the interactions may depend on the geographical position. MAMs can be proposed to model biologically inspired systems since they are suited to cope with the four common principles that govern swarm intelligence: positive feedback, negative feedback, randomness, multiple interactions. In the present work, we report some results of a MAM for a Wireless Sensor Network (WSN) routing protocol based on swarm intelligence, and some preliminary results in utilizing MAs for very basic Ant Colony Optimization (ACO) benchmarks.</p>
},
	author = {Dario Bruneo and Marco Scarpa and Andrea Bobbio and Davide Cerotti and Marco Gribaudo}
}
@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}
}
