Dissimilarity Metric Based on Local Neighboring Information and Genetic Programming for Data Dissemination in Vehicular Ad Hoc Networks (VANETs)  
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1 Engineering Department, Loyola Andalusia University, 14004 Seville, Spain; [EMAIL_ADDRESS]  
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2 Department of Information Security Engineering, Soonchunhyang University, Asan 31538, Korea; [EMAIL_ADDRESS]  
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2 Department of Information Security Engineering, Soonchunhyang University, Asan 31538, Korea; [EMAIL_ADDRESS]  
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3 Electronic Engineering Department, University of Seville, 41004 Seville, Spain; [EMAIL_ADDRESS]  
Many topological parameters have been used in VANETs to heuristically calculate the suitable nodes' retransmission probability such as the distance among nodes [17; 18; 19], local density estimators [20] like the number of neighbors, and the relative speed of nodes [21], among others [1]. Although it is not clear which one is best, it can be stated that the Euclidean distance is a good candidate for VANETs, since it is expected that in the near future the majority of cars will include a positioning sensor like Global Positioning System (GPS). Therefore, vehicles will be able to exploit their relative distance to disseminate information in a multi-hop fashion. Generally, the main benefit behind using the Euclidean distance to disseminate messages in a VANET is to give higher priority to those distant vehicles placed at the edge of the vehicles' communication area. Consequently, the neighbors of a vehicle positioned at the edge of the wireless communication range are likely to be selected as forwarding nodes. Conversely, neighbor nodes located at a closer distance with respect to a source node will be silent to avoid redundant messages and network congestion. This mechanism is commonly known as silencing approach [22; 23]. Most of the existing data dissemination algorithms for VANETs employ the ratio among the distance between two vehicles (sender and receiver) and the vehicle's wireless communication range, which is considered to have the same value for all the vehicles in the VANET [18; 19], to determine the potential forwarding nodes. However, such hypothesis of being aware of the actual value of the vehicle's wireless communication range cannot be applicable in real VANETs due to a number of significant and uncontrollable factors. In general, vehicles in VANETs may have different wireless communication ranges depending on their onboard sensors [19]. Notice that it may even occur in the unlikely case that all vehicles use the same wireless transceiver because of production discrepancies. Furthermore, the vehicle's wireless communication range in actual VANET scenarios may depend on uncontrollable and extremely changeable parameters, such as density of vehicles, interferences, and obstacles [24], among others. For these reasons, alternative approaches that select distant nodes using the vehicle's communication range for such selection have been proposed [24; 25; 26]. On this line, dissimilarity metrics or distances, which employ the relation between the neighbors of two vehicles in order to calculate the similarity and/or dissimilarity between them [24; 27], can be promising alternatives. The main rationale behind using dissimilarity metrics based on neighboring information is that two vehicles are similar if they share many neighbors, and therefore, two vehicles are likely to be similar if they are located at a closer distance. Conversely, the vehicles are dissimilar if they do not share neighbors. It has been demonstrated that classical dissimilarity metrics correlate moderately well with the Euclidean distance [24]. In general, the dissimilarity between two vehicles increases with respect to the relative Euclidean distance between them. Thus, dissimilarity metrics can be suitable estimators of the relative Euclidean distance among vehicles in VANETs.