In recent years, a new versatile analytical technique has emerged whose main idea is to model a distributed system by means of interacting agents, so that each agent maintains its local properties but at the same time modies its behaviour according to the in uence of the interaction with the other agents. In this way, the analysis of each agent alone incorporates the eect of the interdependencies. In the present model each agent selects its actions based on the current state and is represented by a continuous time Markov chain (CTMC). We refer to this kind of agents as Markovian Agents (MA) [1{3] for which the innitesimal generator has a xed local component, that may depend on the geographical position of the MA, and a component that depends on the interactions with other MAs.
Markovian Agent models with applications to wireless sensor networks
BOBBIO, Andrea;CEROTTI, DAVIDE;GRIBAUDO, Marco
2014-01-01
Abstract
In recent years, a new versatile analytical technique has emerged whose main idea is to model a distributed system by means of interacting agents, so that each agent maintains its local properties but at the same time modies its behaviour according to the in uence of the interaction with the other agents. In this way, the analysis of each agent alone incorporates the eect of the interdependencies. In the present model each agent selects its actions based on the current state and is represented by a continuous time Markov chain (CTMC). We refer to this kind of agents as Markovian Agents (MA) [1{3] for which the innitesimal generator has a xed local component, that may depend on the geographical position of the MA, and a component that depends on the interactions with other MAs.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
