## 1 Introduction  
The two main concerns with the increasing number of vehicles on the roads are congestion and safety. In the USA alone, congestion accounts for 115 billion dollars in fuel costs [1], with similar figures in other developed countries. Worldwide traffic casualties amount to 1.17 million per year [2]. In this context, Intelligent Transportation Systems (ITS) aim at enhancing transportation efficiency and safety through the use of advanced information processing, communications, control, as well as new electronic technologies. Sensing the environment is a major aspect of ITS, as well as of other novel applications in future vehicular scenarios. Traditionally these systems have relied on different alternatives [3]. One group frequently employed to detect traffic flows comprises intrusive sensors, including sensors such as inductive loops, magnetometers, pneumatic road tubes and diverse kinds of weigh-in-motion sensors. However, the installation and maintenance of these sensors has important associated costs, since large sections of the road need to be torn up, disrupting traffic flow. Other non-intrusive sensors can also be used, such as video cameras, radars, acoustic arrays and ultrasonic sensors, which can be placed above ground. Their main drawbacks are that they are usually large-sized, power-hungry sensors and may be affected by different environmental conditions. In addition, both intrusive and non-intrusive sensors are expensive and associated with difficult installation, classically requiring wired infrastructures and power lines for energy supply. This leads to the deployment of those sensors only at critical locations, which work independently of each other. The information they produce must be transmitted to distant Traffic Management Centers (TMCs) for centralized data processing, which require the transmission of high amounts of data through expensive communication infrastructures. In general, this results in unacceptable data dissemination delays which limit the utilization of the system for vehicle safety applications requiring a quick response (even real time in most of the cases). An alternative to these highly centralized solutions is the use of a cooperative approach where processing is performed _in-situ_ among distributed devices, enabling faster reaction times. In addition, if this is combined with wireless communications, some of the inconveniences derived from the emplacement of nodes may be alleviated. Vehicular _Ad Hoc_ Networks (VANETs) are an example of such combinations [4]. In a VANET, moving vehicles, as well as roadside infrastructure, become nodes of a highly dynamic mobile network that can disseminate relevant information over long distances and collaborate to offer drivers and users improved distributed vehicular services. Nevertheless, VANETs only monitor road conditions opportunistically, that is, when a vehicle is nearby, and their proper behavior is conditioned by the number of vehicles traveling as well as by the penetration rate of such technology into vehicles. Wireless Sensor Networks (WSNs) are a technology which is becoming more mature and is gaining momentum as one of the enabling technologies for the Future Internet. Therefore, it is being applied ubiquitously and, in particular, to ITS. They consist of medium to large networks of inexpensive wireless sensor nodes capable of sensing, processing and distributing information acquired from the environment through the collaborative effort of nodes [5]. WSNs provide significant advantages both in cost as well as in distributed intelligence. On the one hand, installation and maintenance expenses are reduced because of the use of cheap devices which do not require wiring. Furthermore, distributedintelligence enables the development of diverse real-time traffic safety applications not feasible with centralized solutions.