Cristiano M. Silva

Deployment of roadside units based on partial mobility information

This work presents an algorithm for deployment of roadside units based on partial mobility information. We propose the partition of the road network into same size urban cells, and we use the migration ratios between adjacent urban cells in order to infer the better locations for the deployment of the roadside units. Our goal is to identify those α locations maximizing the number of distinct vehicles experiencing at least one V2I contact opportunity. We compare our strategy to two deployment algorithms: MCP-g relies on full mobility information (full knowledge of the vehicles trajectories), while MCP-kp does not assume any mobility information at all. Results demonstrate that our strategy increases the number of distinct vehicles contacting the infrastructure in 6.8% when compared to MCP-kp. On the other hand, MCP-g overcomes our strategy by 8.5%. We must evaluate whether the 8.5% improvement worthies tracking the trajectories of vehicles. Complementary, the marginal contribution of adding a new roadside unit becomes much more assertive when employing our strategy, enabling a better evaluation of the return on investments by network designers. Such guarantees are not provided by MCP-kp, and they are too weak in MCP-g.

Evaluating the Performance of Heterogeneous Vehicular Networks

There are several kinds of envisioned vehicular applications: video delivery, accidents detection, dissemination of traffic announcements, and so forth. Such applications demand minimal (and possibly distinct) Quality of Service guarantees that must couple the vehicular network. Since the vehicular networks will become reality soon, we demand strategies for planning and managing such networks. In this work we propose the concept of a Delta Network: the Delta Network is a metric for evaluating the performance of heterogeneous vehicular networks. By using the concept of a Network Delta, we expect network providers being able to measure and compare the performance of distinct heterogeneous vehicular networks (Delta is technology-independent). Finally, the concept of a Delta Network may also be applied to couple vehicular applications and vehicular networks in order to support the network provider in the decision of deploying (or not) a novel vehicular application.

Design of roadside infrastructure for information dissemination in vehicular networks

This work presents a probabilistic constructive heuristic to design the roadside infrastructure for information dissemination in vehicular networks. We formulate the problem as a Probabilistic Maximum Coverage Problem (PMCP) and we use them to maximize the number of vehicles in contact with the infrastructure. We compare our approach to a non-probabilistic MCP in simulated urban areas considering Manhattan-style topology with variable traffic conditions. The results reveal that our approach (Probabilistic MCP) increases the number of contacts between vehicles and dissemination points, optimizes the allocation of dissemination points, distributes the dissemination points in a layout that better fits the traffic flow and provides more regularity in the number of contacts experienced by vehicles.

Designing mobile content delivery networks for the internet of vehicles

Abstract Content delivery is a key functionality for developing the Internet of Vehicles. In such networks, vehicles act as sensors of the urban mobility by constantly exchanging messages with another vehicles, the cellular network, and also the infrastructure (roadside units). However, the task of delivering content in such dynamic network is far from trivial. In this work, we investigate the development of Content Delivery Networks (CDN) in the context of vehicular networks. Roadside units support the communication by replicating and delivering contents to vehicles within their range of coverage. Initially, we devise a strategy for measuring the performance of the content delivery in vehicular networks. Then, we use the proposed metric for designing a deployment strategy allowing us to identify the better locations for deploying the roadside units in order to properly support the dissemination of a variety of contents, each content requiring specific levels of performance. We compare our deployment strategy to the intuitive strategy of allocating roadside units at the densest locations of the road network. The results demonstrate that our strategy requires less roadside units than the baseline for non-massive deployments in order to achieve similar levels of performance, incurring in less costs when setting up the content-delivery infrastructure.

A genetic algorithm for deploying roadside units in VANETs

In this work we propose a genetic algorithm, Delta-GA, for solving the allocation of Roadside Units (RSUs) in a Vehicular Network. Our goal is to find the minimum set of RSUs in order to meet a Deployment Δ^ρ1_ρ2. The Deployment Δ^ρ1_ρ2 is a metric for specifying minimal communication guarantees from the infrastructure supporting the Vehicular Network. We compare Delta-GA to two baseline algorithms, Delta-g and Delta-r, to solve the Deployment Δ^ρ1_ρ2. Our results demonstrate that Delta-GA requires less Roadside Units in order to achieve the same deployment efficiency.

Non-Intrusive Planning the Roadside Infrastructure for Vehicular Networks

In this article, we describe a strategy for planning the roadside infrastructure for vehicular networks based on the global behavior of drivers. Instead of relying on the trajectories of all vehicles, our proposal relies on the migration ratios of vehicles between urban regions in order to infer the better locations for deploying the roadside units. By relying on the global behavior of drivers, our strategy does not incur in privacy concerns. Given a set of α available roadside units, our goal is to select those α-better locations for placing the roadside units in order to maximize the number of distinct vehicles experiencing at least one V2I contact opportunity. Our results demonstrate that full knowledge of the vehicle trajectories are not mandatory for achieving a close-to-optimal deployment performance when we intend to maximize the number of distinct vehicles experiencing (at least one) V2I contact opportunities.

Delta-r: A novel and more economic strategy for allocating the roadside infrastructure in vehicular networks with guaranteed levels of performance

In this work we propose Delta-r, a new greedy heuristic for solving the allocation of roadside units in order to meet a Δρ2ρ1-Deployment. The Δρ2ρ1-Deployment is a metric for specifying minimal levels of performance from the infrastructure supporting vehicular networks. As far as we are concerned, this is the first QoS-bounded deployment strategy considering both the contact probability, and the contact duration. We compare Delta-r to two baselines: DL allocates the roadside units at the densest locations of the road network, while Delta-g uses the absolute V2I contact time. Differently from Delta-r, our proposal evaluates the deployment performance when using the relative V2I contact time considering vehicles and locations of the road network. Our results demonstrate Delta-r requiring less roadside units to achieve the same performance of the infrastructure supporting the V2I communication.

Design of roadside communication infrastructure with QoS guarantees

There are several kinds of envisioned vehicular applications: video delivery, accidents detection, dissemination of traffic announcements, and so forth. Such applications demand minimal (and possibly distinct) QoS guarantees that must couple the vehicular network. Given that vehicular networks will soon become reality, we demand strategies for planning and managing such networks. In this work we propose Delta (A), a QoS-based strategy for planning the roadside infrastructure supporting a vehicular network. Thus, the network provider may employ our strategy to design a new network, compare the performance of distinct vehicular networks, and even evaluate the adherence between vehicular applications and the network. Delta is based on two metrics: i) connectivity duration, and ii) percentage of vehicles presenting such connectivity duration. For instance, if a given vehicular application requires that 20% of the vehicles are connected during 30% of the trip, we say that such application requires a deployment Delta (0.3, 0.2). Complementary, we also present Delta-g, a greedy heuristic for solving Delta. A deployment Delta (0.1, 0.1) requires the coverage of 0.09% of the road network, while a deployment Delta (0.9, 0.9) requires 21.67% of coverage.

Smart Traffic Light for Low Traffic Conditions

This work presents a novel traffic device (LaNPro) that avoids the stop of vehicles at junctions under low traffic conditions. To the best of our knowledge, this is the first smart traffic light designed for low traffic conditions. LaNPro is a security solution to preserve the physical integrity of drivers in countries with high social discrepancy. The server-side of the solution is deployed as a module of a smart traffic light, and it senses the presence of vehicles along the road through input devices (radars, cameras, road sensors, wireless communication) to assign the right of way. While any smart traffic light is able to manage low traffic intersections, we argue that they are not specialized devices to perform such task, and thus they may lack important optimizations. The main aggregated value of our approach is the ability to handle low traffic conditions, and that involves several challenges. Results show that our proposal may ensure the non-stop crossing of intersections having an expected traffic volume equal or less than λ = 0.10 vehicles per second, assuming intersections composed of 2, 3, or 4 lanes, road segments 200 m long, intersections 10 m wide, and vehicles 5 m long traveling at an average speed of μ = 40 km/h with standard deviation σ = 4 km/h.

Managing Infrastructure-Based Vehicular Networks

In this thesis work the authors exploit the management of infrastructure-based vehicular networks. The authors begin to work by investigating the most basic problem faced by the network designers when planning an infrastructure-based vehicular network: given a road network, a flow, and α available RSUs, where the RSUs must be located in order to maximize the network performance? The authors propose a novel approach for locating the RSUs: the authors develop a deployment algorithm based on partial mobility information. By partial mobility information, we mean: i) density of vehicles along the road network; and, ii) migration ratios from distinct locations of the road network.