Anthony E. Ohazulike

On the applicability of fair and adaptive data dissemination in traffic information systems

Vehicular Ad hoc Networks (VANETs) are expected to serve as support to the development of not only safety applications but also information-rich applications that disseminate relevant data to vehicles. Due to the continuous collection, processing, and dissemination of data, one crucial requirement is the efficient use of the available bandwidth. Firstly, the rate of message transmissions must be properly controlled in order to limit the amount of data inserted into the network. Secondly, messages must be carefully selected to maximize the utility (benefit) gain of vehicles in the neighborhood. We argue that such selection must aim at a fair distribution of data utility, given the possible conflicting data interests among vehicles. In this work, we propose a data dissemination protocol for VANETs that distributes data utility fairly over vehicles while adaptively controlling the network load. The protocol relies only on local knowledge to achieve fairness with concepts of Nash Bargaining from game theory. We show the applicability of the protocol by giving example of utility functions for two Traffic Information Systems (TIS) applications: (i) parking-related and (ii) traffic information applications. The protocol is validated with both real-world experiments and simulations of realistic large-scale networks. The results show that our protocol presents a higher fairness index and yet it maintains a high level of bandwidth utilization efficiency compared to other approaches.

Fair and adaptive data dissemination for Traffic Information Systems

Vehicular Ad-hoc Networks (VANETs) are expected to serve as support to the development of not only safety applications but also information-rich applications that disseminate relevant data to vehicles. Due to the continuous collection, processing, and dissemination of data, one crucial requirement is the efficient use of the available bandwidth. Firstly, the rate of message transmissions must be properly controlled in order to limit the amount of data inserted into the network. Secondly, messages must be carefully selected to maximize the utility (benefit) gain of vehicles in the neighborhood. We argue that such selection must aim at a fair distribution of data utility, given the possible conflicting data interests among vehicles. In this work, we propose a data dissemination protocol for VANETs that distributes data utility fairly over vehicles while adaptively controlling the network load. The protocol relies only on local knowledge to achieve fairness with concepts of Nash Bargaining from game theory. Simulation results show that our algorithm presents a higher fairness index and yet it maintains a high level of bandwidth utilization efficiency compared to other approaches. In addition, the rate of transmissions is adaptively controlled as new information about the environment is collected.

Achieving Data Utility Fairness in Periodic Dissemination for VANETs

In addition to safety, Vehicular Ad-hoc Networks (VANETs) enable the development of new information-rich applications that disseminate relevant data to vehicles. One key challenge in such networks is to use the available bandwidth efficiently when there is: (i) a short connectivity time due to the rapidly changing road environment, and (ii) bandwidth congestion due to continuous collection and dissemination of data. Numerous solutions were proposed to alleviate bandwidth congestion by using transmission power and beaconing rate control. However, the reduction of data messages transmitted by using priority- based data selection mechanisms has not been fully explored. In this work, we propose a periodic data dissemination protocol for non-safety applications which distributes data utility fairly among vehicles with conflicting data interests. Furthermore, given a defined maximum network load allowed, only the least relevant data is suppressed. Fairness is achieved using the concept of Nash Bargaining from game theory. Simulation results show that our approach leads to an efficient bandwidth utilization in terms of utility per message received and higher fairness index compared with other approaches.

Multi-objective optimization of traffic externalities using tolls

Genetic algorithms (GAs) are widely accepted by researchers as a method of solving multi-objective optimization problems (MOPs), at least for listing a high quality approximation of the Pareto front of a MOP. In traffic management, it has been long established that tolls can be used to optimally distribute traffic in a network with aim of combating some traffic externalities such as congestion, emission, noise, safety issues. Formulating the multi-objective toll problem as a one point solution problem fails to give the general overview of the objective space of the MOP. Therefore, in this paper we develop a game theoretic approach that gives the general overview of the objective space of the multiobjective problem and compare the results with those of the wellknown genetic algorithm non-dominated sorting genetic algorithm II (NSGA-II). Results show that the game theoretic approach presents a promising tool for solving multi-objective problems, since it produces similar non-dominated solutions as NSGA-II, indicating that competing objectives (or stakeholders in the game setting) can still produce Pareto optimal solutions. Most fascinating is that a range of non-dominated solutions is generated during the game, and almost all generated solutions are in the neighborhood of the Pareto set. This indicates that good solutions are generated very fast during the game.

Multiple Stakeholders in Road Pricing: A Game Theoretic Approach

We investigate a game theoretic approach as an alternative to the standard multi-objective optimization models for road pricing. Assuming that various, partly conflicting traffic externalities (congestion, air pollution, noise, safety, etcetera) are represented by corresponding players acting on a common network, we obtain a non-cooperative game where each player pursues a different road pricing strategy to control a specific externality. The game is actually a Stackelberg game, but now with multiple leaders/actors in the upper level determining link tolls, and road users as followers in the lower level, adapting their route choice to the tolls imposed. This chapter reviews our earlier results on the game theoretic models, and the existence of Nash Equilibrium (NE). In order to cope with the fact that NE may not exist in the game, we propose a “first-best taxation” scheme, allowing the government to enforce pre-described NE (analogous first-best pricing schemes). We further discuss the stability of this taxing mechanism.