A Survey on Infrastructure-Based Vehicular Networks

Table 3

Comparison of deployment strategies.

Deployment	Summary
Analytic studies	Theoretical studies addressing specific issues allowing us to better understand the scenario before implementing simulations and trials. As a drawback, such formulations may not represent exactly the real environments and happenings
Deployment Strategies Based on the V2I Contact Probability	The V2I contact probability measures the expected number of contacts an average vehicle tends to experiment during a typical trip. As a drawback, the V2I contact probability is a generic measure that can help the deployment of simplistic applications. However, as we increase the complexity of vehicular applications, we demand a more complete set of measurements
Deployment Strategies for the Distribution of Content	Strategies for allocating RSUs in order to deliver large files, media, streaming, and gaming
Deployment Strategies Based on Clustering	The use of clustering strategies may help the network designer to understand the flow of vehicles and capture the most important zones in order to maximize the coverage with a given set of resources
Geometry-Based Deployment Strategies	Such strategies rely on geometric properties of the city in order to define the most promising locations for receiving RSUs. Such techniques are particularly promising when combined with Geographical Information Systems and Georeferenced Data, allowing full understanding of vehicular mobility. As a drawback, just a few authors have exploited such strategy, and, as far as we are concerned, any author has applied Geographical Information Systems for solving the deployment of RSUs
Deployment Strategies Based on Evolutionary Approaches	Evolutionary approaches involve the use of metaheuristics inspired by the process of natural selection commonly used to generate high-quality solutions to optimization and search problems by relying on bioinspired operators such as mutation, crossover, and selection
Linear Programming	Technique for the optimization of a linear objective function subject to linear equality and linear inequality constraints. As a drawback, solving realistic scenarios may be prohibitive given the required computational resources
Deployment Strategies based on the Maximum Coverage Problem (MCP)	In MCP, we have a collection of sets, each set holding specific elements. The same element can exist in multiple sets. The goal is to find the minimal collection of whose cardinality is maximal. Given its intrinsic nature, MCP is frequently used as an abstraction for the deployment problem. As a drawback, MCP modeling tends to have application as simplistic applications such as the dissemination of traffic warnings. However, more complex applications demand more sophisticated strategies for planning the roadside infrastructure