Philippe Canalda

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.

Wi-Fi-based indoor positioning: Basic techniques, hybrid algorithms and open software platform

In urbanized and indoor environments, outdoor positioning systems, such as Global Navigation Satellite Systems (GNSSs), are often inaccurate and adaptations of such systems to those contexts are expensive and hard to deploy. Nowadays, a lot of indoor positioning techniques have been studied, but it it quite difficult to objectively evaluate and compare their accuracies in the same environment.

Open Wireless Positioning System: A Wi-Fi-Based Indoor Positioning System

Wireless network positioning is the main pillar of the continuity of rich and mobile multimedia applications. Good position accuracy is particularly difficult to obtain in urban or leafy areas and indoors or in mixed (both indoor and outdoor) environments. A system proposing such positioning must localize any mobile terminal accurately within hostile environments and ideally be low-cost and easy to deploy. We propose an indoor positioning system, based on the IEEE 802.11 wireless network. This system, named OWLPS (Open WireLess Positioning System), implements several of the major mobile position computation algorithms and techniques: fin- gerprinting location, topology-based and viterbi-like algorithm, propagation models. These algorithms result from community work and our personal researches. Index Terms—Wireless LAN, Radio position measurement, Indoor radio communication

WiFi GPS based combined positioning algorithm

If nowadays, positioning becomes more and more accurate, and covers better and better a territory (indoor and outdoor), it remains territories where traditional (and basic) positioning system (GPS, gsm or WiFi) and hybrid ones (GPS- gsm, GPS-WiFi, GPS-WiFi-gsm, …) are insufficient and requires research investment treating combined positioning. In this paper we propose a GPS-WiFi combined positioning algorithm, based on trilateration technique. Real experiments and other simulation are conduced and demonstrate accuracy gains, even where various criteria dilution of precision (GPS dops criteria, or ours WiFi geometrical and signal attenuations dop proposal, or hybrid dop ones) indicate all the disruption of positioning service. A testbed scenario issued from a real urban campus environment validates not only our GPS-WiFi combined positioning algorithm but also an implementation of pertinent positioning techniques and dops criteria. This work constitutes a further step to better position everywhere and to ensure continuity of a positioning service.

OwlPS: A Self-calibrated Fingerprint-Based Wi-Fi Positioning System

Owl Positioning System (OwlPS) is an indoor positioning system based on the IEEE 802.11 radio network (Wi-Fi). Since 2004, our team OMNI develops and experiments various techniques (both from the literature and from our own work) for indoor and outdoor positioning. We mainly exploit signal strength fingerprinting and indoor propagation models, helped by information such as the building’s map, the mobile’s path, etc. The latest version of the system (v1.2) includes a self-calibration mechanism, that avoids the time-consuming manual fingerprinting phase and allows taking into account dynamically the changes of the environment (human, climatic, etc.) when computing the location of the mobile terminals.

Benchmark measurements for Wi-Fi signal strength-based positioning system

In this paper, we propose a methodology for recording the signal strength of Wi-Fi signals to provide a benchmark for offline use. The parameters which are studied and seem important are the input data of the recording system. These parameters are classified into three categories, the type of packets transmitted, the propagation context and the environment. From the recorded measurements, comparing similarity functions between the reference map and measurements will be based on the same observed values. The analysis will therefore be reliable and reproducible. Signal measuring is always done with the same hardware to be objective. A set of factors is studied in order to measure the real impact on the received signal strength signal accurately.

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

A Very First Geometric Dilution of Precision Proposal for Wireless Access Mobile Networks

This paper provides a description of an hybrid system merging the advantages of a GNSS such as the GPS and a wireless system such as Wi-Fi. Once a general mathematical presentation concerning all the multilateration based positioning systems, GPS, GSM and Wi-Fi is done, the paper analyses the accuracy of multilateration techniques processing absolute and/or relative distance measurements between the mobile terminal and multiple base stations such as BTS or Access Points. Based on those formulas, a simulations are done and then analysed. An interpretation of the results obtained over simulations concerning the location estimation from a geometric point of view are also givenEigenvalue decomposition (EVD) is a widely-used factorization tool to perform principal component analysis, and has been employed for dimensionality reduction and pattern recognition in many scientific and engineering applications, such as image processing, text mining and wireless communications. EVD is considered computationally expensive, and as software implementations have not been able to meet the performance requirements of many real-time applications, the use of reconfigurable computing technology has shown promise in accelerating this type of computation. In this paper, we present an efficient FPGA-based double-precision floating-point architecture for EVD, which can efficiently analyze large-scale matrices. Our experimental results using an FPGA-based hybrid acceleration system indicate the efficiency of our novel array architecture, with dimension-dependent speedups over an optimized software implementation that range from 1.5x to 15.45x in terms of computation time.A critical research issue is to lower the energy consumption of a virtualized data center by means of virtual machine placement optimization while satisfying the resource requirements of the cloud services. In this paper, we focus on different existing schemes and on the energy-aware virtual machine placement optimization problem of a heterogeneous virtualized data center. We attempt to explore a better alternative approach to minimizing the energy consumption, and we observe that particle swarm optimization (PSO) has considerable potential. However, the PSO must be improved to solve an optimization problem. The improvement includes redefining the parameters and operators of the PSO, adopting an energy-aware local fitness first strategy and designing a novel coding scheme. Using the improved PSO, an optimal virtual machine replacement scheme with the lowest energy consumption can be found. Experimental results indicate that our approach significantly outperforms other approaches, and can lessen 13%-23% energy consumption in the context of this paper.With the rapid development of Internet of Things and Big Data analysis, the computing mode of the 21st century is undergoing profound reform. But these technologies bring great challenges such as more multiple-dimensional and more numerous information with wide-area and heterogeneous sensor networks to classical context-aware frameworks at the same time. The IOV (Internet of Vehicles) applications is one kind of the typical IOT (Internet of Things) applications and the data involved in them are more and more big which need more complex querying or analyzing methods. Therefore, we have researched the big data problems in IOV applications and put forward the clouding based big data space-time analytics methods for contexts storing and contexts querying to improve the analysis efficiency of the systems. By these methods, we can improve the capability of the complex IOV applications for dealing the numerous contexts.

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.