Vehicular Ad hoc Networks have received considerable attention in recent years and are considered as one of the most promising ad-hoc network technologies for intelligent transport systems. Vehicular Ad hoc Networks have special requirements and unique characteristics (e.g., special mobility patterns, short life links, rapid topology changes) which make the design of suitable routing protocols, a challenge. Consequently, an efficient routing protocol that fits with VANETs' requirements and characteristics is a crucial task to obtain a good performance in terms of average percentage of packet losses and average end-to-end packet delay. To attain this goal, we propose a novel probabilistic multimetric routing protocol (ProMRP) that is specially designed for VANETs. ProMRP estimates the probability for each neighbor of the node currently carrying the packet, to successfully deliver a packet to destination. This probability is computed based on four designed metrics: distance to destination, node's position, available bandwidth and nodes' density. Furthermore, an improved version of ProMRP called EProMRP is also proposed. EProMRP includes an algorithm that accurately estimates the current position of nodes in the moment of sending the packet instead of using the last updated position obtained from the previous beacon message. Simulations are carried out in a realistic urban scenario using OMNeT++/VEINS/SUMO, including real maps from the OpenStreetMaps platform. Simulation results show a better performance of ProMRP and EProMRP compared to recent similar proposals found in the literature in terms of packet losses and end-to-end packet delay, for different vehicles' densities.INDEX TERMS Probabilistic multimetric routing protocol, realistic urban scenarios, vehicular ad hoc networks.

The expected increase in the number of electric vehicles (EVs) in the coming years will contribute to reducing CO 2 pollution in our cities. Currently, EVs' users may suffer from distress due to long charging service times and overloaded charging stations (CSs). Critical traffic conditions (e.g., traffic jams) affect EVs' trip time (TT) towards CSs and thus influence the total trip duration. With this concern, Intelligent transport systems (ITS) and more specifically connected vehicle technologies, can leverage an efficient real-time EV charging service by jointly considering CSs status and traffic conditions in the city. In this work, we propose a scheme to manage EVs' charging planning, focusing on the selection of a CS for the energy-requiring EV. The proposed scheme considers anticipated charging slots reservations performed through a vehicular ad hoc network (VANET), which has been regarded as a cost-efficient communication framework. In specific, we consider two aspects: 1) the EV's total trip time towards its destination considering an intermediate charging at each candidate CS, and 2) the communication delay of the VANET routing protocol. First, in order to estimate the EV's total trip time, our CS selection scheme takes into account the average road speed, traffic lights, and route distance, along the path of the EV. The optimal CS that produces the minimum total charging service time (including the TT) is suggested to that energyrequiring EV. Then, we introduce two communication modes based on geographical routing protocols for VANETs to attain an anticipated charging slot reservation. Simulation results show that with our charging scheme EVs' charging service time is reduced and more EVs are successfully charged.

No abstract

No abstract

No abstract

Usually, simulations are the first approach to evaluate wireless and mobile networks due to the difficulties involved in deploying real test scenarios. Working with simulations, testing, and validating the target network model often requires a large number of simulation runs. Consequently, there are a significant amount of outcomes to be analyzed to finally plot results. One of the most extensively used simulators for wireless and mobile networks is OMNeT++. This simulation environment provides useful tools to automate the execution of simulation campaigns, yet single-scenario simulations are also supported where the assignation of resources (i.e., CPUs) has to be declared manually. However, conducting a large number of simulations is still cumbersome and can be improved to make it easier, faster, and more comfortable to analyze. In this work, we propose a large-scale simulations framework called simulations manager for OMNeT++ (SMO). SMO allows OMNeT++ users to quickly and easily execute large-scale network simulations, hiding the tedious process of conducting big simulation campaigns. Our framework automates simulations executions, resources assignment, and post-simulation data analysis through the use of Python's wide established statistical analysis tools. Besides, our tool is flexible and easy to adapt to many different network scenarios. Our framework is accompanied by a command-line environment allowing a fast and easy manipulation that allows users to significantly reduce the total processing time to carry out large sets of simulations about 25% of the original time. Our code and its documentation are publicly available at GitHub and on our website.INDEX TERMS Large-scale simulations, OMNeT++, results post-processing.

No abstract

No abstract