Grégoire Danoy

A vehicular mobility model based on real traffic counting data

This paper proposes VehILux, a new vehicular mobility model based on real traffic counting data. It relies on two freely available sources of real information for the country of Luxembourg. The first source is traffic data collected by counting devices located on the Luxembourgian road network, while the second is geographical information about different types of areas: residential, industrial, commercial and other services. VehILux models vehicles commuting around the city of Luxembourg by considering two types of traffic, outer traffic with vehicles entering in the defined geographical area and inner traffic starting from residential zones located inside the geographical area. One part of the collected traffic data is used as input traffic, while another part is used to control the produced traffic and to fine-tune the model. VehILux is coupled with the microscopic road traffic simulator SUMO to produce realistic vehicular traces.

Achieving super-linear performance in parallel multi-objective evolutionary algorithms by means of cooperative coevolution

This article introduces three new multi-objective cooperative coevolutionary variants of three state-of-the-art multi-objective evolutionary algorithms, namely, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Multi-objective Cellular Genetic Algorithm (MOCell). In such a coevolutionary architecture, the population is split into several subpopulations or islands, each of them being in charge of optimizing a subset of the global solution by using the original multi-objective algorithm. Evaluation of complete solutions is achieved through cooperation, i.e., all subpopulations share a subset of their current partial solutions. Our purpose is to study how the performance of the cooperative coevolutionary multi-objective approaches can be drastically increased with respect to their corresponding original versions. This is specially interesting for solving complex problems involving a large number of variables, since the problem decomposition performed by the model at the island level allows for much faster executions (the number of variables to handle in every island is divided by the number of islands). We conduct a study on a real-world problem related to grid computing, the bi-objective robust scheduling problem of independent tasks. The goal in this problem is to minimize makespan (i.e., the time when the latest machine finishes its assigned tasks) and to maximize the robustness of the schedule (i.e., its tolerance to unexpected changes on the estimated time to complete the tasks). We propose a parallel, multithreaded implementation of the coevolutionary algorithms and we have analyzed the results obtained in terms of both the quality of the Pareto front approximations yielded by the techniques as well as the resulting speedups when running them on a multicore machine.

UAV fleet area coverage with network connectivity constraint

An Unmanned Aerial Vehicle (UAV) is an aircraft without onboard human pilot, which motion can be remotely and / or autonomously controlled. Using multiple UAVs, i.e. a fleet, offers various advantages compared to the single UAV scenario, such as longer mission duration, bigger mission area or the load balancing of the mission payload. For collaboration purposes, it is assumed that the UAVs are equipped with ad hoc communication capabilities and thus form a special case of mobile ad hoc networks. However, the coordination of one or more fleets of UAVs, in order to fulfill collaborative missions, raises multiples issues in particular when UAVs are required to act in an autonomous fashion. Thus, we propose a decentralised and localised algorithm to control the mobility of the UAVs. This algorithm is designed to perform surveillance missions with network connectivity constraints, which are required in most practical use cases for security purposes as any UAV should be able to be contacted at any moment in case of an emergency. The connectivity is maintained via a tree-based overlay network, which root is the base station of the mission, and created by predicting the future positions of one-hop neighbours. This algorithm is compared to the state of the art contributions by introducing new quality metrics to quantify different aspect of the area coverage process (speed, exhaustivity and fairness). Numerical results obtained via simulations show that the maintenance of the connectivity has a slight negative impact on the coverage performances while the connectivity performances are significantly better.

Energy-aware fast scheduling heuristics in heterogeneous computing systems

In heterogeneous computing systems it is crucial to schedule tasks in a manner that exploits the heterogeneity of the resources and applications to optimize systems performance. Moreover, the energy efficiency in these systems is of a great interest due to different concerns such as operational costs and environmental issues associated to carbon emissions. In this paper, we present a series of original low complexity energy efficient algorithms for scheduling. The main idea is to map a task to the machine that executes it fastest while the energy consumption is minimum. On the practical side, the set of experimental results showed that the proposed heuristics perform as efficiently as related approaches, demonstrating their applicability for the considered problem and its good scalability.

Aspects and trends in realistic VANET simulations

Realistic simulations of Vehicular Ad hoc Networks (VANETs) are necessary to evaluate novel technologies based on such networks and to prove benefits obtained from their implementation. This survey gathers from several research domains aspects that increase the quality of VANET simulations. It explains a multi-fold nature of VANETs and presents main building blocks of their simulation-traffic and network simulators. The paper proposes a comprehensive architecture for VANET simulation platform that focuses on producing reliable results. The architecture contains traffic and network simulators that communicate with each other in a dynamic and bi-directional way. The concept of a realistic traffic generator is introduced. It uses real-world data (e.g. maps, traffic volume counts) to model an activity-based traffic varying in time. The traffic generator aims at reproducing accurate vehicular traces for urban scenario. A higher level of realism can be obtained by modelling of human behaviour with intelligent agents and by the implementation of related subsystems, like traffic management and control or weather factors.

Evolutionary algorithm parameter tuning with sensitivity analysis

This article introduces a generic sensitivity analysis method to measure the influence and interdependencies of Evolutionary Algorithms parameters. The proposed work focuses on its application to a Parallel Asynchronous Cellular Genetic Algorithm (PA-CGA). Experimental results on two different instances of a scheduling problem have demonstrated that some metaheuristic parameters values have little influence on the solution quality. On the opposite, some local search parameter values have a strong impact on the obtained results for both instances. This study highlights the benefits of the method, which significantly reduces the parameter search space.

Connectivity Stability in Autonomous Multi-level UAV Swarms for Wide Area Monitoring

Many different types of unmanned aerial vehicles (UAVs) have been developed to address a variety of applications ranging from searching and mapping to surveillance. However, for complex wide-area surveillance scenarios, where fleets of autonomous UAVs must be deployed to work collectively on a common goal, multiple types of UAVs should be incorporated forming a heterogeneous UAV system. Indeed, the interconnection of two levels of UAVs---one with high altitude fixed-wing UAVs and one with low altitude rotary-wing UAVs---can provide applicability for scenarios which cannot be addressed by either UAV type. This work considers a bi-level flying ad hoc networks (FANETs), in which each UAV is equipped with ad hoc communication capabilities, in which the higher level fixed-wing swarm serves mainly as a communication bridge for the lower level UAV fleets, which conduct precise information sensing. The interconnection of multiple UAV types poses a significant challenge, since each UAV level moves according to its own mobility pattern, which is constrained by the UAV physical properties. Another important challenge is to form network clusters at the lower level, whereby the intra-level links must provide a certain degree of stability to allow a reliable communication within the UAV system. This article proposes a novel mobility model for the low-level UAVs that combines a pheromone-based model with a multi-hop clustering algorithm. The pheromones permit to focus on the least explored areas with the goal to optimize the coverage while the multi-hop clustering algorithm aims at keeping a stable and connected network. The proposed model works online and is fully distributed. The connection stability is evaluated against different measurements such as stability coefficient and volatility. The performance of the proposed model is compared to other state-of-the-art contributions using simulations. Experimental results demonstrate the ability of the proposed mobility model to significantly improve the network stability while having a limited impact on the wide-area coverage.

Power Allocation in Multibeam Satellite Systems: A Two-Stage Multi-Objective Optimization

Multibeam satellite systems offer flexibility that aims at efficiently reusing the available spectrum. To fully exploit the flexibility advantages, the payload resources-transmit power and bandwidth-must be efficiently allocated among multiple beams. This paper investigates the resource optimization problem in multibeam satellites. The NP-hardness and inapproximability of the problem are demonstrated motivating the use of metaheuristics. A systematic approach accomplishing the best traffic match is carried out. The additional requirement of minimizing the total power consumption is then considered, giving rise to a multi-objective optimization approach. The solutions to the a priori accomplished traffic matching optimization are used to enhance the efficiency of the multi-objective metaheuristic method proposed and, consequently, of the multibeam satellite system. The optimized performance is represented by the Pareto front, which provides trade-off points between total power consumption and rate achieved. This allows the decomposition of the problem into independent color-based sub-problems rendering the proposed two-stage optimization framework suitable for dimensioning the next generation multispot satellite systems.

A platform for realistic online vehicular network management

This paper introduces a platform for realistic and computationally efficient online vehicular networks simulation. It permits decentralized traffic management applications simulation as nodes mobility is modifiable at runtime thanks to the integration of two state-of-the-art network and traffic simulators. The platform embeds a tool that generates vehicular traces based on traffic counting data and ensures performance through a geographical decomposition of the network. Evidence of its performance is given on a Luxembougian traffic management scenario, using real road network and traffic data.

Evaluation of dynamic communities in large-scale vehicular networks

The topology of vehicular ad hoc networks (VANETs) is highly dynamic due to the mobility of the vehicles and changing traffic conditions in time and space. The development of any efficient service in such networks is challenging and requires thorough understanding of the characteristics and dynamics of the underlying communication topology. This work analyses the connectivity and the potential for creating communities in large-scale and realistic VANETs. In addition, the performance of a state-of-the-art decentralised community detection algorithm is studied in terms of how communities form and evolve when considering real-world vehicular mobility.