A Game Theory Approach for Congestion Control in Vehicular Ad Hoc Networks

The continuous transfer of messages in vehicular ad hoc networks leads to a heavy network traffic load. This causes congestion in the wireless channel which degrades the reliability of the network and significantly affects the Quality of Service (QoS) parameters such as packet loss, throughput and average delay. Therefore, it is vital to adapt the transmitting data rates in a way that ensure that acceptable performance is achieved and that there is reliable communication of information between vehicles in smart cities. This means the information will be delivered in a timely manner to the drivers, which in turn allows implementation of efficient solutions for improved mobility and comfort in intelligent transportation systems. In this paper, congestion control in the communication channel has been formulated as a non-cooperative game approach and the vehicles act as players in the game to request a high data rate in a selfish way. The solution of the optimal game is presented by using Karush-Kuhn-Tucker conditions and Lagrange multipliers. Simulation results show that the proposed method improves network efficiency in the presence of congestion by an overall average of 50.40%, 49.37%, 58.39% and 36.66% in terms of throughput, average delay, number of lost packets and total channel busy time as compared to Carrier-Sense Multiple Access with Collision Avoidance mechanism

Improving the operating efficiency of the more electric aircraft concept through optimised flight procedures

The increasing awareness of the environmental risks and costs due to the growing demand in aviation has prompted both academic and industrial research into short-term and long-term technologies which could help address the challenges. Among these, the more electric aircraft has been identified as a key design concept which would make aircraft more environmentally friendly and cost effective in the long run. Moreover, the notion of free-flight and optimised trajectories has been identified as a key operational concept which would help curb the environmental effects of aircraft as well as reduce overall costs. The research in this paper presents a methodology in which these two concepts can be coupled to study the benefits of more electric aircraft (MEA) flying optimised trajectories. A wide range of issues from aircraft performance, engine performance, airframe systems operation, power off-take penalties, emission modelling, optimisation algorithms and optimisation frameworks has been addressed throughout the study. The case study is based on a popular short haul flight between London Heathrow and Amsterdam Schiphol. The culmination of the study establishes the advantage of the MEA over conventional aircraft and also addresses the enhanced approach to the classical aircraft trajectory optimisation problem. The study shows that the operation procedures to achieve a minimum fuel burn are significantly different for a conventional aircraft and MEA. Trajectory optimisation reduced the fuel burn by 17.4% for the conventional aircraft and 12.2% for the more electric compared to the respective baseline cases. Within the constraints of the study, the minimum fuel burn trajectory for the MEA consumed 9.9% less fuel than the minimum fuel burn trajectory for the conventional aircraft

Fast multi-objective optimisation of a micro-fluidic device by using graphics accelerators

The development of technology that uses widely available and inexpensive hardware for realworld cases is presented in this work. This is part of a long-term approach to minimise the impact of aviation on the environment and aims to enable the users both from industrial and academic background to design more optimal mixing devices. Here, a Multi-Objective Tabu Search is combined with a flow solver based on the Lattice Boltzmann Method (LBM) so as to optimise and simulate the shape and the flow of a micro-reactor, respectively. Several geometrical arrangements of a micro-reactor are proposed so as to increase the mixing capability of the device while minimising the pressure losses and to investigate related flow features. The computational engineering design process is accelerated by harnessing the high computational power of Graphic Processor Units (GPUs). The ultimate aim is to effectively harvest and harness computing cycles while performing design optimisation studies that can deliver higher quality designs of improved performance within shorter time intervals

Biobjective optimisation of preliminary aircraft trajectories

The protection of the environment against pollutants produced by aviation is of great concern in the 21st century. Among the multiplicity of proposed solutions, modifying flight profiles for existing aircraft is a promising approach. The aim is to deliver and understand the trade-off between environmental impact and operating costs. This work will illustrate the optimisation process of aircraft trajectories by minimising fuel consumption and flight time for the climb phase of an aircraft that belongs to A320 category. To achieve this purpose a new variant of a multi-objective Tabu Search optimiser was evolved and integrated within a computational framework, called GATAC, that simulates flight profiles based on altitude and speed. © 2013 Springer-Verlag

The design and implementation of a GPUenabled multi-objective Tabu-search intended for real world and high-dimensional applications

Metaheuristics is a class of approximate methods based on heuristics that can effectively handle real world (usually NP-hard) problems of high-dimensionality with multiple objectives. An existing multiobjective Tabu-Search (MOTS2) has been re-designed by and ported onto Compute Unified Device Architecture (CUDA) so as to effectively deal with a scalable multi-objective problem with a range of decision variables. The high computational cost due to the problem complexity is addressed by employing Graphics Processing Units (GPUs), which alleviate the computational intensity . The main challenges of the re-implementation are the effective communication with the GPU and the transparent integration with the optimization procedures. Finally, future work is proposed towards heterogeneous applications, where improved features are accelerated by the GPUs. © The Authors. Published by Elsevier B.V

Multi-objective optimization of a fluid structure interaction benchmarking

The integration and application of a new multi-objective tabu search optimization algorithm for Fluid Structure Interaction (FSI) problems are presented. The aim is to enhance the computational design process for real world applications and to achieve higher performance of the whole system for the four considered objectives. The described system combines the optimizer with a well established FSI solver which is based on the fully implicit, monolithic formuFlation of the problem in the Arbitrary Lagrangian-Eulerian FEM approach. The proposed solver resolves the proposed uid-structure interaction benchmark which describes the self-induced elastic deformation of a beam attached to a cylinder in laminar channel ow. The optimized ow characteristics of the aforementioned geometrical arrangement illustrate the performance of the system in two dimensions. Special emphasis is given to the analysis of the simulation package, which is of high accuracy and is the core of application. The design process identifies the best combination of ow features for optimal system behavior and the most important objectives. In addition, the presented methodology has the potential to run in parallel, which will significantly speed-up the elapsed time. Finite Element Method (FEM), Fluid-Structure Interaction (FSI), Multi-Ojective Tabu search (MOTS2). Copyright © 2013 Tech Science Press

Developing an Open-source Platform for Intelligent Intersection Management Studies

Traffic management is a key component of road transport systems. Envisaging a new age of road transport systems accommodating intelligent, connected, and autonomous vehicles, many novel intersection control algorithms have been proposed in the literature. These algorithms are often implemented using bespoke software and tested over custom built network models because of their complexity and the lack of freely accessible software tools. This in turn makes them difficult to evaluate and benchmark. To solve this issue, in this paper, we present the Traffic Control Test Bed project, the objective of which is to develop an open source microsimulation platform for the evaluation of intersection control algorithms. The platform provides a library of road network models together with an intuitive synthetic road network generator for user-defined layouts. It facilitates and streamlines the parallel execution of simulations. Outputs and performance indicators are monitored and visualised by the platform both during runtime and at post processing stage. We demonstrate the usage of the platform with a case study evaluating two simple signal optimisation methods. As well as being an arena for traffic control algorithms, the open source property of the platform also invites contributions from the wider research community to improve execution validity and efficiency of traffic control systems