Analysis of an Electric Vehicle Charging System Along a Highway

To reduce carbon emission, the transportation sector evolves toward replacing internal combustion vehicles by electric vehicles (EV). However, the limited driving ranges of EVs, their long recharge duration and the need of appropriate charging infrastructures require smart strategies to optimize the charging stops during a long trip. These challenges have generated a new area of studies that were mainly directed to extend the classical Vehicle Routing Problem (VRP) to a fleet of commercial EVs. In this paper, we propose a different point of view, by considering the interaction of private EVs with the related infrastructure, focusing on a highway trip. We consider a highway where charging stations are scattered along the road, and are equipped withmultiple chargers. Using Fluid Stochastic Petri Nets (FSPN), the paper compares different decision policies when to stop and recharge the battery to maximize the probability of a car to reach its destination and minimize the trip completion time

Markovian Agent models with applications to wireless sensor networks

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 for wireless sensor networks deployed in environmental protection

Wireless Sensor Networks (WSN) are distributed interacting systems formed by many similar tiny sensors communicating to gather information from the environment and transmit it to a base station. The present paper presents an analytical modeling and analysis technique based on Markovian Agents (MAs) and discusses a very complex scenario in which a WSN is deployed in a wide open area to monitor the outbreak of a fire and send a warning signal to a base station. The models is composed by four classes of MA modeling, respectively: the fire propagation, the high temperature front propagation, the sensor nodes and the sink; and four types of messages. It is shown that, even if the overall state space of the models is huge, nevertheless an analytical solution is feasible, by exploiting the locality of the interactions among MAs, based on a message passing mechanism combined with a perception function

Fire prevention by means of WSN: A preliminary propagation study using Interactive Markovian Agents

none3INFD. Cerotti;M. Gribaudo;A. BobbioD., Cerotti; Gribaudo, Marco; A., Bobbi

Modeling Techniques for Pool Depletion Systems

The evolution of digital technologies and software applications has introduced a new computational paradigm that involves the concurrent processing of jobs taken from a large pool in systems with limited computational capacity. Pool Depletion Systems is a framework proposed to analyze this paradigm where an optimal admission policy for jobs allocation is adopted to improve the performance of the system. Markov analysis and discrete event simulation, two techniques adopted to study Pool Depletion Systems framework, may require a long time before providing results, especially when dealing with complex systems. For this reason, a fluid approximation technique is presented in this chapter; in fact, it can provide results in a very short time, slightly decreasing their accuracy

Markovian Agent Models: A Dynamic Population of Interdependent Markovian Agents

A Markovian Agent Model (MAM) is an agent-based spatio-temporal analytical formalism aimed to model a collection of interacting entities, called Markovian Agents (MA), guided by stochastic behaviours. An MA is characterized by a finite number of states over which a transition kernel is defined. Transitions can either be local, or induced by the state of other agents in the system. Agents operate in a space that can be either continuous, or composed by a discrete number of locations. MAs may belong to different classes and each class can be parametrized depending on the location in the geographical (or abstract) space. In this work, we provide a very general analytical formulation of an MAM that encompasses many forms of physical dependencies among objects and many ways in which the spatial density may change in time. We revisit recent literature to show how previous works can be cast in terms of this more general MAM formulation