Pascal Chatonnay

A Friis-based calibrated model for WiFi terminals positioning

Two types of applications use indoor positioning, services linked with mobility, such as guided tour or meeting systems, and the active security of a wireless network which locates intrusive unauthorized mobile terminals. Indoor positioning cannot be managed by a geostationary system like GPS. In fact, current researches are being conducted to conceive indoor positioning using wireless networks such as WiFi. We study such a mechanism and compare the accuracy of our results to other solutions. Our model is based on the Friis relation, which expresses signal strength as a function of distance, in a free space environment. The Friis-based model is adapted to fit the conditions of implementation. The positioning function is combined with a mobility prediction mechanism and constitutes the mobility service in a video on demand system called MoVie (mobile video).

Indoor Wi-Fi positioning: techniques and systems

If outdoor positioning is widely treated and quite precise, positioning indoors or, more generally, in heterogeneous environments, as well as mobility prediction, requires important devices. New wireless technologies (e.g., Wi-Fi, Ultra Wide Band) combine the mobility of terminals with large bandwidth. Terminal mobility is one of the major pillars of applications attempting to become context-aware, and a large bandwidth enables new services such as multimedia contents streaming towards mobile terminals. Being context-aware and able to provide services in a mobile environment requires the knowledge of spatial and temporal data about the terminal. The key phase in the achievement of mobility management is the positioning process. We propose a layered positioning system based on a model combining a reference point-based approach with a trilateration-based one. Several layers of refinement are offered based on the knowledge of the topology and devices deployed. The more data are known, the better adapted to its area the positioning system can be.

Comparison of three algorithms for solving the convergent demand responsive transportation problem

Led by computer science and geography laboratories, this paper presents three algorithms for solving the convergent demand responsive transport problem (CDRTP). Two of them are exact: the first one is based on a dynamic programming algorithm to enumerate exhaustively the sprawling spanning trees and the second one is based on a depth first search algorithm. The third one is stochastic and uses a steady state genetic algorithm. These approaches address the problems of scalability and flexibility, are compared and discussed

A self-adaptive multiagent evolutionary algorithm for electrical machine design

This paper presents a self-adaptive algorithm that hybridises evolutionary and multiagent concepts. Each evolutionary individual is implemented as a simple agent capable of re-production and predation. The transitions between these two states depend on the agents local environment. Thus, no explicit global process is defined to select neither the mates nor the preys. The convergence of the algorithm emerges from the behaviour of the agents. This brings interesting properties, such as population size self-regulation. Two sets of experimental results are provided: a comparison with Saw-Tooth Algorithm and micro-GA using four classical functions and an optimisation of the efficiency and the weight of an electrical motor. Some possible evolutions and prospects are finally proposed.

Permanent magnet motor multiobjective optimization using multiple runs of an evolutionary algorithm

This paper presents an original method of permanent magnet motor optimal design. The permanent magnet machines optimization must respect multiple constraints. Efficiency and weight have a large influence on the design. These two constraints can be found in several vehicular applications: propulsion motors, electrical fans for combustion engine, driving motors for ancillaries, driving motors for air-circuit fuel-cell compressor...Indeed, in all those embedded applications, the efficiency must be maximal to limit the energy consumption and the mass or the volume must be as low as possible. In this paper, the authors focus on an original multi-objective optimization algorithm well adapted to the previous problem. The method is based on multiplying runs of a new genetic algorithm specialized in broadly covering the solution space around target objectives. This algorithm is an improved variant of previously developed algorithms. The efficiency of these algorithms was proven by comparing with a deterministic algorithm (SQP) and a reference multi-objective genetic algorithm (NSGA-II). The presented algorithm is first validated on a study case from the literature: the dimensioning of a slotless permanent magnet machine. Then experimental results of the complete method applied on a permanent magnet motor are highlighted in a multi-objective point of view.

PredictiveMobility Models based on Kth Markov Models

With the massive arrival of wireless networks, the mobility of the terminals increases with the interconnections. New problems, such as mobile multimedia content streaming, arise with the emergence of new mobile multimedia services. In this paper, we present a mobility model based on the Markov models, especially the all-Kth Markov model. We present three predictive models: the K-past model, the K-to-J past model and its improvement, the K-to-1 past* model. The whole are pertinent solutions to tackle mobility patterns. We validate our approach firstly with various realistic benchmarks on data related to indoor WiFi positioning systems

Hand-Over Video Cache Policy for Mobile Users

Nowadays, wireless links are shared between clients and the available bandwidth fluctuates, so the video stream must be adaptive in order to optimize the transmitted quality. In this article, we present a tool called MoVie to distribute multimedia streaming on large wireless networks. The two main aspects of MoVie deal with network access and managing systems. The management of video streams is realized using video caches linked to a physical area. This article describes the policy used to take into account the mobility of the clients and the hierarchical aspect of the streams by preparing the neighboring caches to receive roaming clients.

Introduction of a combination vector to optimise the interpolation of numerical phantoms

Phantoms are 3-dimensional (3D) numerical representations of the contours of organs in the human body. The quality of the dosimetric reports established when accidental overexposures to radiation occur is highly dependent on the phantoms reliability with respect to the subject. EquiVox is a Case-Based Reasoning platform which proposes an interpolation of the 3D Lung Contours (3DLC) of subjects during its adaptation phase. This interpolation is conducted by an Artificial Neural Network (ANN) trained to learn how to interpolate the 3DLC of a learning set (LS). ANN is a well-suited tool when known results are numerous. Since the cardinality of our learning set is restrained, the imperfections of each 3DLC have a great impact on interpolations. Thus, we explored the possibility of ignoring some of the 3DLC of LS via implementation of a new learning algorithm which associated Combination Vectors (CV) to LS. The results proved that this method could optimise interpolation accuracy. Furthermore, this study highlights the fact that some of the 3DLC were harmful for some interpolations whereas they increased the accuracy of others.

Friis and iterative trilateration based WiFi devices tracking

The spreading of the WiFi networks allows new applications. New problems bound to the mobility of the terminals arise. This article addresses the service continuity in mobility which requires terminal positioning. The solution proposed is iterative trilateration for which the distances are computed according to the signal strength. This solution is implemented in GeoMoVie, the mobility component in a multimedia content streaming platform. It is tested and validated with a heterogeneous indoor test bed. It is also advantageously compared with related work approaches.

New Notation and Classification Scheme for Vehicle Routing Problems

Vehicle Routing Problems have been some of the most studied problems in combinatorial optimisation because they have many applications in transportation and supply chain. They are usually known as Vehicle Routing Problems or VRPs. The related literature is quite large and diverse both in terms of variants of the problems and in terms of solving approaches. To identify the different variants of routing problems, authors generally use initialisms, in which various prefixes and suffixes indicate the presence of different assumptions or constraints. But this identification based on initialisms is inefficient. For example, two variants of a problem may be identified by the same abbreviation, whereas different abbreviations may be assigned to the same problem. This paper proposes a new notation and a new formalism to identify and to classify instances of routing problems. This contribution aims at filling in the gaps of the current identification system. The goal is to allow everyone to position his work accurately in the literature, and to easily identify approaches and results comparable to his research. The proposed notation is inspired by the scheduling formalism. It has four fields (π /α /β /γ ), respectively describing the type and horizon of the problem, the system structure, resources and demands, constraints and objectives to be optimized. 26 papers from the literature chosen for their disparity are classified using this notation to illustrate its usefulness and a software tool is proposed to make its use easier.