Brahim Belaoucha

Théodore Papadopoulo

Large brain effective network from EEG/MEG data and dMR information

Over the past 30 years, neuroimaging has become a predominant technique. One might envision that over the next years it will play a major role in disclosing the brain's functional interactions. In this work, we use information coming from diffusion magnetic resonance imaging (dMRI) to reconstruct effective brain network from two functional modalities: electroencephalography (EEG) and magnetoencephalography (MEG).

I. INTRODUCTION

MEG and EEG imaging techniques record the brain cortical activity in real time. Localizing active regions from these measurements, in the source distributed model, involves solving an ill-posed inverse problem. Prior on the source space must be set to obtain a unique solution.

Minimum norm estimate (MNE) is one of the most used inverse solvers because it leads to a linear inverse operator [START_REF] Wang | Magnetic source images determined by a lead-field analysis: the unique minimum-norm least-squares estimation[END_REF][START_REF] Hämäläinen | Interpreting magnetic fields of the brain: minimum norm estimates[END_REF]. It results to widely extended active regions which are, biologically, not plausible. Among other solvers, we can find the minimum current estimate (MCE). It provides a focal reconstruction but is non-smooth in time because the sparse prior is applied at each time sample independently. Mixed-norm estimate (MxNE) [START_REF] Gramfort | Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods[END_REF] was introduced to overcome this limitation. It provides spatial focality, by applying l 1 -norm on the source space, and temporal smoothness, by using l 2 -norm on time courses.

Functional connectivity analysis has been traditionally implemented at the sensor level, but lately, a number of studies have begun to use source-space analysis. It has the potential of providing more accurate information regarding regions' functional in-teractions [START_REF] Fukushima | MEG source reconstruction based on identification of directed source interactions on whole-brain anatomical networks[END_REF][START_REF] Sekihara | Source-Space Connectivity Analysis Using Imaginary Coherence[END_REF][START_REF] Belaoucha | Multivariate autoregressive model constrained by anatomical connectivity to reconstruct focal sources[END_REF]. Some of the functional measures (e.g. Directed Transfer Function (DTF) and Partial Directed Coherence (PDC)) can be derived from multivariate autoregressive model (MAR) which is more than a combination of purely temporal and purely spatial correlation, as it also takes into account the time-lagged correlation between sources.

In [START_REF] Fukushima | MEG source reconstruction based on identification of directed source interactions on whole-brain anatomical networks[END_REF], the authors constrain the dynamics of the sources by a MAR model. They assume that there exist only one time-lag, which depends on the length of anatomical connections, between a pair of regions. In [START_REF] Belaoucha | Multivariate autoregressive model constrained by anatomical connectivity to reconstruct focal sources[END_REF], the authors use a variant of MxNE constrained with a MAR model of order 1 to reconstruct regions' interactions. In both works, complex time courses can not be reconstructed because of limits in the degree of freedom of the model. In this work, we generalize the work of [START_REF] Belaoucha | Multivariate autoregressive model constrained by anatomical connectivity to reconstruct focal sources[END_REF] by constraining the dynamics of the cortical regions with higher MAR model whose matrix elements are constrained by the anatomical connectivity and neighbors. We call it iterative source and dynamics reconstruction of order p (iSDR(p)). We test and compared our method with real and synthetic data.

II. METHOD

Under the quasi-static approximation of the Maxwell's equations, the EEG/MEG data M t is a linear superposition of the source signals J t at the same time t:

M t = GJ t + ε t , (1)
where G ∈ R m×n (m: size of sensor array, n: size of source space) is the lead-field matrix and ε t is N (0, Σ c ). To reduce the problem complexity, we assume a constant activation per cortical region. This reduces the size of the lead-field to m × K, where K is the number of cortical regions. We use the mutual nearest neighbor parcellation algorithm presented in [START_REF] Bellec | Identification of large-scale networks in the brain using fMRI[END_REF][START_REF] Belaoucha | Cortical Surface Parcellation via dMRI using Mutual Nearest Neighbor Condition[END_REF] to divide the cortical surface into functional regions from dMRI. The source dynamics is constant during a window T and it follows MAR model of order p:

J t = p i=1 A i J t-i + ω t (2)
where ω t is N (0, Σ s ), p is a positive integer that defines the order of the MAR model, A i is a K ×K matrix which relates sources' current values to its pasts. The only nonzero effective interactions in A corresponds to either structurally connected or neighboring regions. The behavior of the MAR model is obtained from the companion matrix Φ:

Φ =     A1 A2 • • • Ap-1 Ap I . . . I     (3)
where I is the K × K identity matrix. The MAR model is stable (stationary) if all eigenvalues of Φ are inside the unit circle and nonstationary if it has eigenvalues on the unit circle. The model is unstable if at least one eigenvalue is outside the unit circle.

The sources estimates J are obtained by minimizing the following functional given a MAR model:

U (J) = M v -G d J v 2 2 + λ J 21 (4)
where

Mv = [Mp+1, • • • , MT ], Jv = vec(J) = vec([J1, • • • , JT -1]) and J 21 = i j J(i, j) 2
i.e. l 2 -norm over time and l 1 -norm over space.

G d =    G 1 G 2 • • • Gp . . . . . . . . . . . . G 1 • • • G p-1 Gp    (5)
where G i = GA i . The MAR elements are obtained by fitting the source estimates to the MAR model:

argmin A 1 ,•••,Ap T t=p+1 Jt - p i=1 AiJt-i 2 2 (6)
We solve iteratively Eq 4 and 6 until J k -J k-1 < where J k is the reconstructed sources at iteration k.

III. DATA

MEG and EEG were measured using 306 system (102 magnetometers, 204 planar gradiometers), and 70 EEG channels recorded EEG data simultaneously. Stimuli were presented in six 7.5 min runs. The face stimuli contain two sets of 300 gray scale photographs, half from unfamiliar people (unknown to the participants) and the remaining from famous people. In a third condition, 150 photographs of scrambled faces are obtained from either famous or unfamiliar people. The reader is referred to [START_REF] Wakeman | A multi-subject, multi-modal human neuroimaging dataset[END_REF] for more details.

In this work, we are interested in localizing face recognition areas. For this reason, we use only the measurement acquired when using photos of famous people subtracted to the ones obtained when using scrambled faces. Low pass filter of cut-off frequency 35 Hz was used to smooth the data. The MEG/EEG forward problem, lead field matrix G, is obtained using OpenMEEG [START_REF] Kybic | A common formalism for the Integral formulations of the forward EEG problem[END_REF][START_REF] Gramfort | OpenMEEG: opensource software for quasistatic bioelectromagnetics[END_REF].

IV. RESULTS AND DISCUSSION

A. Simulation

We use a random lead-field matrix of size 20 × 500 and whose elements are obtained from a normal distribution N (0, 1). Each region is connected randomly to 4 different regions. We activate 50 random pairs of regions, one pair at a time, 100 times at different noise levels (signal-to-noise ratio (SNR) = {15, 10, 5} dB) using the following simple MAR model: , with J 0 (S 1 ) = 6.15, J 0 (S 2 ) = -3.64,

J 1 (S 1 ) = -J 1 (S 2 ) = 5nA i.e. J t = A 1 J t-1 + A 2 J t-2 .
We estimate the sources and their interactions by initializing A 2 and A 1 with different values to have an insight about the effect of the MAR's initialization on the resulting estimate. We use the following combinations: A 2 = βI and A 1 = αI with (α, β) ∈ {(1, 0), ( 34 , 1 4 ), ( 1 2 , 1 2 )}. The results are presented in Fig [START_REF] Wang | Magnetic source images determined by a lead-field analysis: the unique minimum-norm least-squares estimation[END_REF]. Fig ( 1a) represents the reconstruction error computed as J gt -J r 2 , where J gt and J r are the simulated ground truth and reconstructed activation, using MxNE and iSDR(p=2) respectively. The estimated initial values of S 1 and S 2 obtained from iSDR(p=2) are shown in Fig ( 1b). Fig ( 1c) represents the nonzero eigenvalues of the estimated companion matrix Φ.

iSDR(p) outperforms MxNE especially in low SNR. For (α, β) = (1, 0), iSDR favors the initial values (J t=0 ) to be close to 0, see blue dots in Fig ( 1b), due to initializing A 2 = 0 i.e no interactions between t and t -2. Setting β to nonzero improves both the temporal reconstruction of signals and their dynamics (eigenvalues of Φ). iSDR could reconstruct accurately two eigenvalues, right side of the plane in Fig ( 1c). The remaining two are affected by the choice of (α, β). The results are less dispersed when (α, β) = ( 12 , 1 2 ) (see left side of Fig ( 1c)). B. Real data iSDR(p=2) is applied to MEG data of 9 subjects from [START_REF] Wakeman | A multi-subject, multi-modal human neuroimaging dataset[END_REF]. In [START_REF] Bukowski | Cerebral lateralization of face-sensitive areas in left-handers: Only the FFA does not get it right[END_REF], the authors show that subjects may have right or bilateral fusiform gyrus (FF) activation if they are right or left handed, respectively. In Several works [START_REF] Wakeman | A multi-subject, multi-modal human neuroimaging dataset[END_REF][START_REF] Fukushima | MEG source reconstruction based on identification of directed source interactions on whole-brain anatomical networks[END_REF] show that activation in the FF between 170 and 200 ms is observed when performing face recognition task which coincides with what we observe in this work. In the majority of subjects, regions in the superior posterior temporal lobe, medial parietal, temporal pole (TP), orbitofrontal (OF) are found to be active. This coincides with the findings of other works with the same dataset [START_REF] Wakeman | A multi-subject, multi-modal human neuroimaging dataset[END_REF][START_REF] Fukushima | MEG source reconstruction based on identification of directed source interactions on whole-brain anatomical networks[END_REF]. We have found interactions between TP and frontal lobe (FL), left and right FL, inferior occipital and temporal gyrus with OF.

We applied our method only with MAR model of order 2. Our approach provides functionally plausible results when tested with MEG data provided by [START_REF] Wakeman | A multi-subject, multi-modal human neuroimaging dataset[END_REF].

V. CONCLUSION

In this work, we proposed a source reconstruction method constrained by multivariate autoregressive model from functional imaging EEG/MEG data. Application to real data of other mental tasks must be investigated and compared to physiological literature to validate our method. Information criterion can be used to obtain the order of MAR model.

Fig. 1 :

 1 Fig. 1: In (a), mean, over 100 simulations, of reconstruction error Er of MxNE and iSDR(p=2) with different initialization of A and SNR levels. (b) Estimated initial values of S1 and S2 using iSDR(p = 2). (c) Eigenvalues of the companion matrix. The results are shown at 3 different SNR. Black dots represent ground truth values.

Fig. 2 :

 2 Fig. 2: Reconstructed activation in the right fusiform gyrus (RFF) of 7 subjects, left panel, and left FF (RFF), of 2 subjects using iSDR(p = 2). The temporal courses are normalized with their l2-norm for subjects comparison.

Fig 2 ,

 2 we show the estimated temporal course of the right/left FF (R/LFF). In our work, seven subjects show higher activation in the RFF, Fig (2) left, while only two show higher activation in LFF, Fig (2) right. Due to page limitation, we show only the effective connectivity of Subject 1, Fig (3).

Fig. 3 :

 3 Fig. 3: Effective network of A1, left, and A2, right, obtained from iSDR(p=2) and MEG data [9] during face recognition task. Red spheres represent center of active cortical regions. Edges represent uni/bidirectional interactions. Self-interactions are not shown for visualization clarity. Edge's color represents its strength.

ACKNOWLEDGMENT

This work has received funding from ERC under the European Union's Horizon 2020 research and innovation program (ERC Advanced Grant agreement No 694665 : CoBCoM) and French ANR contract Vibrations. We thank Dr. Daniel Wakeman and Prof. Richard Henson for providing the data.


A J Fuentes Su Arez

E Hubert

Scaolding skeletons using spherical Voronoi diagrams

Keywords: mesh generation, skeletal models, 3D modeling, Voronoi diagrams

Given a skeleton made of line segments we describe how to obtain a coarse mesh (or scaold) of a surface surrounding it. We emphasize in this abstract the key result that allows us to complete the approach in [3] that could not treat skeletons with cycles.

1 Introduction keletons re used in Qh grphis for modeling nd nimting rtiulted shpesF he user n design omplex shpe y skething simple nd intuitive geometri ojet tht is the input to surfe generting lgorithmF sn the ontext of this pper skeleton is set of sptil line segments tht do not interset exept t the endpointsD then lled jointsF st is used to mthemtilly dene surfe round the skeletonX y onvolutionD y osetD or other kind of distne surfe IQF yne surfe is properly dened it must e trnslted into n lterntive representtion either for visuliztion @meshA or for lol editing @xfsD splinesD etAF he issue is tht usully the new representtion is not relted to the originl skeleton nd so the user loses ontrol over the modelF o overome this skeletonEsed mesh genertion method is neededF he ide developed here is to onstrut orse4 mesh round the skeletonD s regulr s possileD nd resemling the struture of the skeletonF his mesh is then used to generte the representtion of the mthemtil surfeF pollowing the terminology of II we ll this proess scaolding nd the orresponding orse mesh scaoldF yne of the proposed ides for the mesh reonstrution ws the method of fEmeshes WD it retes retngulr ge round eh piee of the skeleton tht re stithed together to mke losed meshF he min drwk of this method is tht the stithing proess ould led to irregulrities t the jointsF eentlyD f&rentzen et lF Q proposed method lled w @keleton to udEdominnt polygonl weshA going in the opposite diretionX they rst dene the mesh points round the joints nd then rerete qud sed tuulr4 polyhedrl surfe round eh line segmentF w onsists of two stepsX @iA Partition the unit sphere entered t the joints into ellsD one for eh inident line segmentY eh ell is dened y n ordered set of points on the sphereF @iiA grete sleevesD iFeF link the verties of the ells t the extremities of eh line segmentF por the retion of the sleeves the extreml ells must e omptileD tht is they must hve the sme numer of vertiesF he prtition of the sphere in f&rentzen et lF Q n e reognized to e oronoi digrm on the sphereF sn order to mth the ells they propose n lgorithm for inserting dditionl verties on the oundry of the ells in suh wy tht ll the sleeves n e simultneously retedF et this lgorithm does not work in the presene of ylesF purthermore there is no nlysis on the optimlity of w with respet to the numer of linksF he ontriution we emphsize in this strt is tht we prove the proE ess outlined ove is fesile even in the presene of ylesF e present method whih uses snteger viner rogrming nd omputes n optiml soE lution minimizing the numer of verties for the prtition of the sphere t the jointsF pesiility is proven thnks to numeril hrteriztion y ivin IP of grphs omintorilly equivlent to insried polyhedronsF yur minimiztion riterion is wht gives the orsest4 meshD mong those sed on oronoi prtitions of the spheres t the jointsF st is worth mentioning tht nother prtition of the sphere is introdued in IID prtition into qudrnglesD whih therefore mkes the mthing of ells trivilD the vet eing tht the onvexity of the ells is not gurnteedF 2 Formalization of the geometrical problem sn this pper skeleton is nite set of sptil line segments stisfying the following propertyX ny two line segments interset t most t one of their endpointsF e skeleton denes nturlly grph G S a @V S ; E S A @following the nottion in TAD y identifying the set of verties V S with the set of ll endpoints in D nd the set of edges E S with the set itselfF he grph G S will e lled the skeleton graphF en edge e P E S onneting two nodes a; b P V S n e lterntively represented s abF sf e a ab we sy tht e is incident to a @or bA nd will e denoted e ( a @or e ( bAF s e denotes the line segment in ssoited to eF he unit sphere entered t node v P V S is denoted S v D nd e v a fs e S v j e P E S ; e ( vg is the set of the intersetion points of the line segments inident to v with the unit sphere entered t vF he Voronoi diagram P of e v on the sphereD denoted or@e v AD prtitions the sphere S v into regions fR v e g e(v D where @S v s e A P R v e D tht re delimited y rs of gret irles IDIHF e region R v e is thus well dened y n ordered set of points on S v tht we ll the ell C v e F his set of points onsists of the end points of the rs delimiting R v e nd t lest one dditionl point per r to remove miguityF he numer of points hosen on r of C v e will e lled the number of subdivisions for tht rF e scaold u S is dened s pir @P S ; ¨SAD stisfying @IA P S a fg v j v P V S g where eh g v a fC v e j v P V S ; e P E S ; e ( vg is fmily of cells representing prtition of S v ording to or@e v AF @PA ¨S a f e j e P E S g is fmily of ijetions e etween C a e nd C b e for e a abF por e ( v nd C v e a @p 1 ; p 2 ; : : : ; p n A we sy tht p i is linked with e @p i AF he term sold is visully4 justied y the followingX if we try to visulize u S y drwing line segment etween ny pir of linked elements of P S we n see struture tht resemles set of solds4 round the skeleton F yur gol is to prove tht the oronoi ells n lwys e sudivided so s to uild soldF por tht we need the ells C a e nd C b e @e a abA to hve identil numer of pointsF he point insertion strtegy desried in Q does not work in the presene of yles in the skeletonF e model the prolem of mthing the ells s n snteger viner rogrm @sA SF e shll prove rst tht the s is fesileF hus we n provide n optiml solution using the frnhEndEfound lgorithm SD pge PUVF vet e e set of points on P dimensionl sphere nd or@eA its P dimensionl spheril oronoi digrmF he Delaunay triangulation hel@eA is the dul of or@eA PF or@eA is lso dul to the sptil onvex hull of e in the surrounding Q dimensionl spe RDVF his mens tht the heluny tringultion is omintorilly equivlent to the onvex hullF por eh v P V S let E v e the set of edges of hel@e v AF ih edge in E v represents ommon oundry etween two regions in or@e v AF por f P E v we dene positive integer x v f lled weight representing the numer of sudivisions to e done to the orresponding rF he numer of elements in ell C v e @v P V S D e P E S nd e ( vA is given y g ; V e P

¨S

x v h P Z; x v h ! I; Vh P E v ; v P V S @IA sf this s is fesile then it will hve t lest one optiml solutionF he fesiility of the system @IA is the min result presented in this strtD whih we stte in the following theoremF Theorem 3.1 The set of linear equations @IA has a solution with all the entries x v h in the set of positive integers.

he ide of the proof is to show tht for v P V S D the local liner system X f2Ev f((Sa\se)

x v f a v Ve ( v; e a ab P E S @PA hs solution in the positive integers with v lso positive integerF he vlues xv f long with v stisfying @PA will e lled local solutionF sndeed ssuming there re lol solutions @x v f ; v A for ll v P V S then there is glol solution

x f given y x f a

x v f a @= v A ¡ xv f where a Q u2V S u F he existene of rel positive solution for the lol system @PA omes from the ft tht hel@e v A is omintorilly equivlent to the onvex hull of e v whih is n insrile polyhedronD nd the following numeril hrterE iztion due to ivin IP @sttement extrted from UAF Proposition 3.2 If a graph is of inscribable type then weights w can be assigned to its edges such that:

(i) For each edge e, H < w@eA < I=P.

(ii) For each vertex v, the total weight of all edges incident to v is equal to 1. yne then rgues tht if there is rel positive solution then there must e lso n integer positive solutionF 4 Application and examples he method outlined in this pper hs een implemented nd used suessE fully for solding skeletonsF st hs een tested for wide vriety of models inluding losed skeletonsF st is integrted in projet iming t the visulE iztion of onvolution surfesF ome results re shown in pigure IF

  ab P E S the ijetion e P ¨S tells us tht the ells C a e nd C b e in u S must hve the sme numer of elementsF yn the other hnd the totl numer of links in u S is given y the sum of ll the weightsD thus our s is formlized s

Fig. 1 .

 1 Fig. 1. Example skeletons and scaolds (skeletons in red, endpoints in green, Voronoi cells in white, links in cyan, scaold-based mesh in dark green).

This project has received funding from the European Union's Horizon

research and innovation programme under the Marie Sk lodowska-Curie grant agreement No 675789.2 Email: alvaro.fuentes-suarez@inria.fr


Ondrej Krejcar

Development of Localization Module for Various Smart Devices Platforms

Keywords: WiFi, localization, multilateration, Gaussian filter, vector map

This paper deals with the localization module extension for various platform to support locating in the interior. Any mobile devices (vehicles) can be equipped with a GPS module for location, but in areas with high density in the building interior or city centers a location using GPS is not suitable due to insufficient signal. The solution for these sites can be seen in use of existing WiFi infrastructure. The result of described paper is software module for mobile device. This localization module communicates with the WiFi module for position evaluation of mobile device based on maps and signal strengths of visible WiFi APs.

Introduction

Modern digital world where we live is Smarter and Smarter, whereas information systems (including mobile devices) are more sophisticated, complex and Smarter. Operation systems are nicer, smoother and can predict user needs what means "Smarter" [START_REF] Mikulecky | Remarks on Ubiquitous Intelligent Supportive Spaces[END_REF].

Current modern mobile phones are recognized as smart devices so they are called "Smart Phones" (Classic design of mobile phones is going to side track). There are several different operation systems existing for Smartphone devices as Windows Mobile, Apple iPhone iOS, Google Android, etc. However each one is trying to implement as much Smart capabilities as possible.

By the term mobile device we understand in this paper not only mobile device known as Smart Phone, but all kind of mobile devices as vehicles, robots, etc. These mobile devices are now used for various tasks from basic types (e.g. car now carry a wide variety of activities, from washing) through the sophisticated ones (e.g. automatic machining of materials) to an intelligent robot machines for separate survey and or collection of environmental samples. The mass deployment is now mainly in areas of human activities that are stereotyped and are relatively easy to replace by automate, or vice versa, in areas that are inaccessible to humans, eventually dangerous or other reason.

In particular, advances in computer technology and machine perception now makes it possible to construct mobile devices that does not operate stereotype on the basis of a predetermined procedure, but on the basis of interaction with the environment through sensors. These devices are then characterized by some degree of "intelligence" and are capable of sensing data based on a certain level of deciding on further action. The basic problem of autonomous mobile devices is their unique location in space. These devices can be equipped with an array of sensors that enable it to perform various kinds of transformations based on sensors measurement (which provide a position in space).

We can use odometry or different types of distance measurement by ultrasonic or laser sensors. These relative localization methods are unfortunately inaccurate (e.g. accumulation error) and often they cannot determine the position (space symmetry, the same corridor in different floors, etc.). As a solution for such cases an absolute positioning methods can be used.

A prototype of mobile exploration vehicle was developed for testing purposes [START_REF] Kotzian | User Perspective Adaptation Enhancement Using Autonomous Mobile Devices[END_REF]. The vehicle use a number of sensors for orientation in the 3D space. To determine the absolute position in space, vehicle also use a standard localization based on the GPS module to determine the absolute position in space. Location is based on Multilateration. For correct and accurate location in 2D space it is needed to get a line of sight with at least 3 satellites and a sufficient signal. Civilian GPS uses the frequency of 1575.42 MHz (UHF). The use of this technology is unfortunately limited to the above-mentioned line of sight [START_REF] Brida | A Novel Optimizing Algorithm for DV based Positioning Methods in ad hoc Networks[END_REF]. To locate in these areas is offering the use of WiFi, or some other technology of signal transmitting [START_REF] Liou | Manifold construction by local neighborhood preservation[END_REF]. The aim of this work is to develop a universal SW localization module which allows localization of various kinds of mobile devices in the interior just using known WiFi network architecture and known WiFi AP position in 3D space.

Problem Definition

Wireless localization can be deployed with greater or lesser success in virtually every situation where we have a greater number of broadcast stations, which can be distinguished from each other and determine their distance [START_REF] Krejcar | Problem Solving of Low Data Throughput on Mobile Devices by Artefacts Prebuffering[END_REF]. Resolution stations can be either based on agent based access with using different transmission channels, carrier frequency, or by a unique identifier (e.g. MAC address) [START_REF] Juszczyszyn | Agent-based approach for distributed intrusion detection system design[END_REF].

Location in case of GSM is not sufficiently accurate for its refinement, although it could be used fingerprints, but for this application it is not appropriate.

GPS is already used in the vehicle, but not usable in buildings. Galileo will encounter the same problem, and then again this solution is not applicable.

Localization with RFID requires the installation of a large number of readers, and is useful for monitoring product, but not for "continuous" localization.

Bluetooth and ZigBee achieve similar localization features such as WiFi and are quite commonly used in industry. However, in general the extension is not sufficient and would be pre-installed in the planned deployment destination of the vehicle. It is not acceptable, and for this reason will be selected a WiFi, which can be used today almost ubiquitous infrastructure.

For the actual location it is especially important to recalculate the value of the RSSI of WiFi signal on the distance from the transmitter [START_REF] Brida | An Experimental Evaluation of AGA Algorithm for RSS Positioning in GSM Networks[END_REF]. Because the signal is burdened by losses, needs to be filtered before processing. Filtration is carried out with Gaussian filter and calculates the actual position with multilateration [START_REF] Chilamkurti | Enabling Wireless Technologies for Green Pervasive Computing[END_REF]. For barrier-free environment, where we expect a smooth sloping curve, can be used to predict losses spread basic empirical model. By modifying the basic empirical model a Lost-Path model can be obtained, which further expands upon the relationship of the antenna gain, loss and slow the spread of losses. Lost-path model is used further as the basis for localization model [START_REF] Chilamkurti | Green Networking for Major Components of Information Communication Technology Systems[END_REF].

Implementation

Localization module for the vehicle is the main part of this work. It is written in C++. For debugging under Windows, the application is platform independent, although the target device is based on Linux. The emphasis is on code portability and re-usability. Classes that are not directly related to location core are written in the form of components, independent of other classes and have been tested and tuned separately. The properties of C++ are used and concept of event-driven application is used. In application exist outside the main thread another 4, so taking the effort to stay as long as possible in sleep, that location did not take much of needed computing power for other processes in the vehicle.

Principles of Operation

The application has two primary inputs and 1 output. The input is the address to the WiFi module and the second input is path to the file with a map. The output is the approximate position with tolerances. When application is started, the map file is loaded into memory and immediately closed. It can be further used by other processes of the vehicle. We created also a parallel thread which periodically reads the information of visible APs and store it to list of pairs MAC address and RSSI values. The list of pairs is forwarded to the process that the steam expands by information from the map. This information is a coordinates, antenna gain, path loss and propagation exponent. If any of this information is missing, the default value is used. You can also enter the following and that least some influence of the environment. Couples who do not have information in the map are removed.

The next stage is to record RSSI history and Gaussian filtering RSSI. The result of this step is a list of AP items, which have position, filtered RSSI value and other necessary data to calculate multilateration. Multilateration first sorts the items according to signal strength and then remove items with a very weak signal. These APs are typically far and the signal is strongly affected by the fraud and bring it calculation error, which is too large. In addition, they can be after the Frances break. It is left to only a certain maximum number of items that the calculation did not last too long. Matrix H and B are created from the remaining items. The calculation is then performed using the multilateration formula listed above. The application is built on several basic encapsulated classes which are separated by interfaces. For communication between them and between the individual threads are used events. Application architecture demonstrates the diagram of components [Fig. 1].

Fig. 1. UML diagram of application components.

WiFiLocator -Application is based on the class WiFiLocator. When application is started, instances of classes GaussianLocationEngine, MapLoaderMAP and ScanSourceAP are created. ScanSourceAP -The class takes care of communication with the WiFi module. After its launch, the module is switched to AT mode and initializes. MapLoaderMAP -it cares about reading and parsing the map. The output is a list (vector) of AP descriptors. GaussianLocationEngine -The actual positioning engine. After the engine is called, Gaussian filter is started, and then calculation of locating with multilateration.

Testing of Developed Solution

Test of Gaussian filter influence is based on data from measurements with HP iPAQ hx4700. AP was used as the ZyXEL P660HW-T3 device. The [Fig. 2] shows the unfiltered samples and filtered samples with Gaussian filter with different sizes and sigma parameter of kernel. Simulations showed the assumption that with increasing size and scattering kernel it is a greater suppression of fast losses, but with the growing size of the kernel the system response is worse. The kernel size of 20 is seen that the suppression of leakage is not fast enough, while at 200 the other hand, system response is too slow. As a pretty good compromise it seems to be size of kernel as one hundredth. With this size and zero variance a time delay decrease to half of the size, i.e. 50 samples. This finding implies the need to lay claim to the fastest possible scanning the surroundings and thus the sampling RSSI [START_REF] Brida | On the accuracy of weighted proximity based localization in wireless sensor networks[END_REF].

Localization accuracy

To verify the accuracy, the scheme has been formulated with 4 transmitter (AP). Each transmitter remote from each other about 50 m. The transmitters emit a signal, the attenuation of the load of 10% error with normal distribution on the side of distance. Despite these transmitters were planned route, which passed by a localized point and it did with 10,000 scans. Gaussian filter kernel size is 100 and dispersion 50. The situation demonstrates [Fig. 3]. The black dashed line is the actual trajectory. The blue trajectory is localized and gray points indicate the location, which represent locations without filter.

Conclusions

In this paper a development of localization module has been described. This module solves the problem with absolute localization inside buildings. It is a compact and scalable solution with a median accuracy of 3 meters in tested environment. As a source for location data of surrounding APs an extended vector of mobile device map were used. WiFi module which was connected by serial port allows scanning the surrounding visible WiFI AP. The result is the location information including estimated errors. Further direction of this project can be seen in implementation of Monte Carlo localization method, otherwise known as particle filter. This method should itself allow more accurate positioning of WiFi, but also to introduce other information from the sensors to form a single, redundant, robust and above all precise positioning mechanism [START_REF] Brida | On the accuracy of weighted proximity based localization in wireless sensor networks[END_REF], [START_REF] Vybiral | Devices for Position Detection[END_REF].

Fig. 2 .

 2 Fig. 2. Application of Gaussian filter to the measured data.

Acknowledgement This work was supported by "SMEW -Smart Environments at Workplaces", Grant Agency of the Czech Republic, GACR P403/10/1310. We also acknowledge support from student Ales Kurecka in development of testing module and in several technical problems they grown during the development phase.


Mohtasim Abbassi
email: mohtasimabbassi@yahoo.com

Shahbaz Khan

M Rahman

A Distributed Signaling Fast Mobile IPv6 Scheme for Next Generation Heterogeneous IP Networks

Keywords: Mobility Management, NGWNs, FMIPv6, DS-FMIPv6

The Next-Generation Wireless Networks (NGWNs) are believed to exhibit a heterogeneous environment which imposes various challenges, among them Mobility Management is most crucial. The FMIPv6 protocol has been standardized by IETF to address certain shortcomings of the baseline Mobile IPv6. However, it does not directly support vertical handovers. Therefore, research is still going on to develop more effective IPv6 based vertical handoff protocols for efficient support of frequent and seamless roaming. This paper presents a new protocol named Distributed Signaling Fast Mobile IPv6 (DS-FMIPv6) which effectively addresses reduction of handoff latency, signaling costs and power overheads for a better support of seamless & frequent vertical handoffs.

Introduction

The Next-Generation wireless networks are extensively recommended to be all-IPbased. Therefore, the IPv6-based Mobility Management protocols are of significant importance. The FMIPv6 [START_REF] Koodli | Fast Handover for Mobile IPv6[END_REF] proposed by IETF aims towards improving the performance of the fundamental MIPv6 protocol [START_REF] Johnson | Mobility Support in IPv6[END_REF]. These protocols are however hindered by several drawbacks of which the handoff latency is most important. Moreover, in case of frequent handovers, signaling and power overheads incurred by these schemes become even more critical. These protocols also require improvements to become suitable enough to efficiently fulfill several stringent requirements like context transfers during vertical handoffs.

In this paper, a vertical handoff protocol named Distributed Signaling FMIPv6 (DS-FMIPv6) is presented. The signaling involved in this handoff protocol is distributed among MN, Current Access Router (CAR) and the New Access Router (NAR)hence the scheme is named as Distributed Signaling FMIPv6 scheme. The performance of FMIPv6 is further improved in DS-FMIPv6 by carrying out selected signaling procedures in advance i.e. prior to the start of actual handover and by increasing the functionality of routers. The protocol operation relies on a network selection algorithm evaluated at the Current Access Router (CAR) which evaluates the best suitable network beforehand for the current MN status. This algorithm is assumed to encompass all necessary parameters required for network selection. Detailed analysis shows that DS-FMIPv6 scheme significantly reduces the handoff latency along with signaling and power overheads when an MN undergoes frequent handovers. The rest of paper is organized as follows: in section 2, an overview of IETF-proposed FMIPv6 protocol along with its few extensions is presented. Section 3 presents the proposed DS-FMIPv6 protocol. Performance analysis of DS-FMIPv6 is explained in Section 4. Finally, section 5 concludes the paper and sets directions for future research in connection with this topic.

Fast Mobile IPv6 & its extensions:

Fast Mobile IPv6:

FMIPv6 is proposed to reduce handoff delay and to minimize the services disruption incurred with MIPv6 during handoffs. The most invigorating feature of FMIPv6 is that it relies on link layer information (L2 Trigger) to predict or to rapidly respond to handoff events (Predictive and Reactive mode respectively). When an MN detects its movement towards NAR, by using L2 Trigger, it exchanges Router Solicitation for Proxy Advertisement (RtSolPr) and Proxy Router Advertisement (PrRtAdv) messages with Previous Access Router (PAR) to configure a new Care-of Address (nCoA). The MN sends FBU to PAR in order to associate its previous CoA with New CoA. In order to prevent routing failure, a bidirectional tunnel between PAR and NAR is established by using Handover Initiate (HI) and Handover Acknowledgment (HAck) messages. On communicating FBU to PAR, the MN undergoes L2 handoff procedures. After successful L2 Handoff the MN announces its presence on new link by using Unsolicited Neighbour Advertisement (UNA) message. The NAR at this stage starts delivering packets to the MN. The Binding Updates to HA & CN are carried out in similar fashion as for MIPv6.

Enhancements in Fast Mobile IPv6:

Various enhancements in FMIPv6 are proposed with different aims and objectives, but their efficiency is hindered by certain limitations. An overview of few schemes is presented in this sub-section. In [START_REF] Li | An Enhanced Fast Handover with low latency for Mobile IPv6[END_REF], the handoff latency of FMIPv6 is improved by making certain fundamental enhancements in various processes involved. The movement detection & tunnel establishment procedures are merged, resulting in reduction of latency because of DAD procedure. The Binding Update to the HA/CN is brought forward to the time before the layer 2 handover which further improves the latency problem. In [START_REF] Zhang | Intelligent Fast Handover Scheme for Mobile IPv6-based Wireless Local Area Networks[END_REF], an Intelligent FMIPv6 (iFMIPv6) scheme is designed specifically for WLANs. This scheme addresses the reduction in Scanning Delay, where the Access Point will have Pre-knowledge of its neighbourhood and the MN is intelligent enough knowing its mobility pattern and thus the next stop of its movement. Therefore, at the time of handoff, the MN only needs to scan the selected AP by probing the corresponding operating channel, thus reducing the overall scanning delay. In [START_REF] Menezes | An Efficient Handover Scheme based on Fast Mobile IPv6[END_REF], an efficient handover scheme between WLAN and 3G-UMTS is proposed utilizing Pre-Authentication in order to achieve a lossless vertical handover. An efficient handoff decision algorithm provides three link layer triggers for setting off the MN to detect the movement to a new subnet, to start Pre-Authentication, and to initiate Fast Binding Update. This Pre-Authentication concept reduces the overall service disruption time to a reasonable level. In [START_REF] Jo | A Cross-Layer Vertical Handover between Mobile WiMAX and 3G Networks[END_REF], FMIPv6 based cross layer optimized vertical handover scheme is proposed for Mobile WiMAX, UMTS and CDMA networks. In this scheme, certain Layer 2 and Layer 3 messages are reordered or combined to eliminate redundant signaling. In [START_REF] Kim | An Enhancement of FMIPv6 for Packet Radio Networks which Supports the QoS Provisioning on the MIPv6[END_REF], pseudo-binding FMIPv6 (pFMIPv6) scheme is presented which addresses the fast moving issue of the MN. The Access Router informs mobile node about the existence of other networks around the current network. The MN in turn creates several CoAs and initiates pseudo Fast Binding Update (pFBU) with all its neighbouring networks, even though it does not detect the signal of the next network yet. In this way, time deficiency involving these HO procedures for a fast moving MN is reduced to a high extent.

The DS-FMIPv6 Protocol:

The signaling involved in DS-FMIPv6 is majorly derived from the MIPv6 and FMIPv6. The FBAck message is omitted and a new message Binding Update Initiate (BUI) is defined for its efficient protocol operation. Following steps constitute the protocol operation of DS-FMIPv6 i.

Any change in status of MN (e.g. its average velocity, direction or imminent active application) is reported to the CAR through an enhanced RtSolPr (eRtSolPr) message. The RtSolPr message in FMIPv6 is enhanced to form eRtSolPr by defining a new option field which carries information about the above defined parameters. The Link Local Address (LLA) of MN is also communicated using LLA option. ii.

The CAR evaluates the handoff decision algorithm (HDA) using MN's information from eRtSolPr message and neighbouring networks information from the Router's cache. The prospective new network of MN is evaluated and new Care-of Address (nCoA) is formulated using LLA and network prefix. [START_REF] Thomson | IPv6 Stateless Address Autoconfiguration[END_REF] iii.

The CAR now initiates the Duplicate Address Detection (DAD) to test the uniqueness of the CoA, by exchanging pHI (pseudo HI) and pHAck (pseudo HAck) messages with NAR. This exchange of messages also establishes a tunnel between CAR and NAR, which initially remains inactivated. The establishment of tunnel beforehand can be more effective in reduction of packet loss during handovers. iv.

CAR sends ePrRtAdv (enhanced PrRtAdv) message to inform MN its most suitable network by communicating its new CoA to it. Acquiring nCoA, the MN sends FBU message to inform CAR that it is ready to handoff to new network. The CAR thus discontinues forwarding traffic to it and starts forwarding packets to NAR through already established tunnel.

The CAR also sends a Binding Update Initiate (BUI) message to NAR piggybacking CTD (Context Transfer Data) [START_REF] Loughney | Context Transfer Protocol (CXTP)[END_REF] message in order to inform it that a new MN is about to join it, and instructs it to initiate the Binding Update procedure with CN/HA. vi.

The MN, on the other hand, scans new network and initiates Layer 2 (L2) handoff. It then announces its presence on the NAR through UNA message. CTAR (Context Transfer Activate Request) message is also piggybacked on it, which carries an authorization token to ascertain the reliability of the Context Transfer. At this instant the protocol operation involving MN completes and MN is ready to receive data packets from NAR routed through CAR. vii.

The Binding Update procedure on NAR begins when the pHoTI message piggybacked on pBU HA (pseudo-BU HA ) is sent to HA. This allows the simultaneous initiation of pBU procedure to HA and Return Routability (RR). The HA then forwards pHoTI to CN(s), and responds the NAR by pBA HA (pseudo-BA HA ) message. viii.

The NAR also sends the pCoTI (pseudo CoTI) message to the CN. The response of pHoTI and pCoTI message from CN is pHoT and pCoT messages respectively. The pHoT message is routed to NAR via HA. The RR procedure completes at this stage. ix.

After completion of RR, the NAR can now send the pseudo-Binding Update to Correspondent Node (pBU CN ). The CN in turn responds with a pBA CN (pseudo-BU CN ) message to NAR. At this instant the NAR will start receiving data traffic directly from CN(s). This completes the DS-FMIPv6 protocol operation. The signaling flow between different entities is shown in the Figure 1.

4 Performance Analysis:

Handoff Latency:

The Handoff Latency is referred to as the time interval from the moment that packets cannot be sent or received to the moment that the mobile node can communicate directly with CN via new Access Router [START_REF] Li | An Enhanced Fast Handover with low latency for Mobile IPv6[END_REF]. The overall handoff delay for FMIPv6 and DS-FMIPv6 are given as;

Signaling Cost

The Advanced DAD procedure [START_REF] T-Trong | Enhanced Vertical Handover in Mobile IPv6 with Media Independent Handover Services and Advance Duplicate Address Detection[END_REF] incorporated in DS-FMIPv6 eliminates the probability of any Reactive Mode. Here for comparison, we will assume successful anticipation of handover and will compare the signaling of predictive mode of FMIPv6 with DS-FMIPv6. Evaluation of a supportive handoff decision algorithm at Access Router in DS-FMIPv6 facilitates MN to turn on only the suitable network interface at the time of handoff. Traditionally, there can be three other possibilities for interface activation of MN i.e. all interfaces active all the time, active periodically, or at the time of handoff. The power consumption for each of these cases denoted by P D , P A , P P and P H respectively is mathematically formulated as;

For comparison, we consider an MN undergoing 15 handoffs among four networks with different power consumptions in each state [START_REF] Salawu | Energy Optimization Mechanism for Mobile Terminals using Vertical Handoff between WLAN and CDMA2000 Networks[END_REF][START_REF] Perrucci | On the Impact of 2G and 3G Network Usage for Mobile Phones' Battery Life[END_REF]. Results show that interface management in DS-FMIPv6 incurs the lowest possible power consumption. The IETF solutions proposed for Mobility Management are not compact enough to support vertical handoffs. Also their handoff delays adversely affect delay sensitive and real-time applications. Other issues of signaling Cost and Power consumption at MNs become equally crucial in case of recurring handoffs. In this paper, a novel DS-FMIPv6 protocol is proposed which effectively reduces the handoff latency imposed by FMIPv6 from 35-75%. The proposed scheme also promises the lowest possible signaling cost & power consumption of MNs. In future, the obtained numerical results will be validated through simulations by modeling the protocol on a suitable simulator in order to provide a better analysis of these metrics.

Fig. 1 .

 1 Fig. 1. Signaling Sequence of DS-FMIPv6 Scheme v.Acquiring nCoA, the MN sends FBU message to inform CAR that it is ready to handoff to new network. The CAR thus discontinues forwarding traffic to it and starts forwarding packets to NAR through already established tunnel.The CAR also sends a Binding Update Initiate (BUI) message to NAR piggybacking CTD (Context Transfer Data)[START_REF] Loughney | Context Transfer Protocol (CXTP)[END_REF] message in order to inform it that a new MN is about to join it, and instructs it to initiate the Binding Update procedure with CN/HA. vi.The MN, on the other hand, scans new network and initiates Layer 2 (L2) handoff. It then announces its presence on the NAR through UNA message. CTAR (Context Transfer Activate Request) message is also piggybacked on it, which carries an authorization token to ascertain the reliability of the Context Transfer. At this instant the protocol operation involving MN completes and MN is ready to receive data packets from NAR routed through CAR. vii.The Binding Update procedure on NAR begins when the pHoTI message piggybacked on pBU HA (pseudo-BU HA ) is sent to HA. This allows the simultaneous initiation of pBU procedure to HA and Return Routability (RR). The HA then forwards pHoTI to CN(s), and responds the NAR by pBA HA (pseudo-BA HA ) message. viii.The NAR also sends the pCoTI (pseudo CoTI) message to the CN. The response of pHoTI and pCoTI message from CN is pHoT and pCoT messages

Fig. 2 .

 2 Fig. 2. Percentage Reduction of Handoff Latency in DS-FMIPv6 in comparison to FMIPv6

Fig. 3 .

 3 Fig. 3. Signaling Cost comparison of FMIPv6 and DS-FMIPv6

Fig. 4 .

 4 Fig. 4. Power Reduction by DS-FMIPv6 Interface Management

Table 1 : Example Scenario for Power Consumption comparison

 1


Miroslav Voznak
email: miroslav.voznak@vsb.cz

Adrian Kovac
email: kovaca@ktl.elf.stuba.sk

Michal Halas
email: halas@ktl.elf.stuba.sk

Effective Packet Loss Estimation on VoIP Jitter Buffer

Keywords: Jitter buffer, Dejitter buffer, MOS, E-Model, Packet loss, Packet drop, Pareto distribution, Pareto/D/1/K, VoIP

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Voice over IP communication gains still greater importance in telecommunications industry. Lack of synchronization in comparison to TDM (Time-division Multiplexing) brings concerns about variable conditions on network which cause packet loss and fluctuations in delay (jitter). Jitter causes excess packet loss on receiving buffers depending on the buffer size and delay variance. When using E-Model as an objective method based purely on network parameters for speech quality assessment, effects of jitter are not incorporated in original E-Model. Estimated MOS (Mean Opinion Score) can be positively biased or be too optimistic about call quality under real IP network conditions. In our paper we propose methods of numerical approximation of general jitter buffer behavior. These approximations are used to quantify effective network packet loss P plef taking three additional network parameters on the input: jitter buffer size [ms]; voice packet size [ms] and network delay variance (jitter) [ms].

Selected Time-Related IP Network Parameters

Under time-related network parameters we understand packet transmission delay, mean interarrival time difference -jitter and secondarily packet loss, which can be understood as infinite delay of packet delivery. Based on practical experience of voice perception and human conversational model, ITU-T G.114 [START_REF]ITU-T Recommendation G.114: One-way Transmission Time[END_REF] defines the recommended value of Mouth-to-Ear delay which consists of partial delays occurring at different stages of communication path. The delay distribution on IP networks can be successfully modeled and described by Pareto distribution which give better results compared to Weibull, Poisson or Log-Normal distribution [START_REF] Koh | Loss Probability Behavior of Pareto/M/1/K Queue[END_REF]- [START_REF] Dang | Fractal Analysis and Modeling of VoIP Traffic[END_REF], [START_REF] Mirtchev | Study Of Queuing Systems With A Generalized Departure Process[END_REF]. Pareto distribution belongs to family of geometrical and long-tailed distributions that characterize statistical set where most values are clumped around the beginning of the interval.

Equation [START_REF]ITU-T Recommendation G.114: One-way Transmission Time[END_REF] shows PDF and equation ( 2) CDF functions of generalized Pareto distribution (GPD).

( ) ( ) ( ) 1 , , 1 1 x F x ξ ξ µ σ ξ µ σ   -     -   = -+     ( ) ( ) ( ) 1 1
, ,

1 1 x f x ξ ξ µ σ ξ µ σ σ   --     -   = +     (1), (2)
Where σ = scale, ξ = shape and µ = location parameter (min. value of random variable with Pareto distribution), µ is an offset of Pareto curve from zero and represents minimal network delay T a-min . Jitter J [ms] is calculated in real-time as floating average of 16 samples of differences between interarrival times (timestamps) of consecutively received packets contained in RTP (Real Time Protocol) header, the calculation is defined in RFC 1889 and given by equation ( 3) where each difference is calculated according to equation (4). Jitter value is transferred in RTCP (Real Time Control Protocol) protocol header as one of the QoS parameters.

J = J + (|D (i-1,i) | -J) / 16 [ms] and

D (i, j) = (R j -R i ) -(S j -S i ) [ms] (3), (4)
Where R are timestamps of packet reception time, S when packet was sent and indices i,j are consecutive packet numbers.According to G.1020 [START_REF] Morton | ): Performance Parameter Definitions for Quality of Speech and other Voiceband Applications Utilizing IP Networks[END_REF] an alternative approach of jitter based on determination of the Mean Absolute Packet Delay Variation with regard to a short term average or minimum value (adjusted absolute packet delay variation) offers more accurate calculation short-term jitter better describing relationship to jitter buffer behavior. In [START_REF] Morton | ): Performance Parameter Definitions for Quality of Speech and other Voiceband Applications Utilizing IP Networks[END_REF] the short term jitter is computed for current packet (i) whose delay is designated t i . Packet (i) is compared to a running average estimate of the mean delay using 16 previous packet delays and assigned either a positive or negative deviation value. These characteristics are calculated according to equations ( 5), ( 6) and ( 7) [START_REF] Morton | ): Performance Parameter Definitions for Quality of Speech and other Voiceband Applications Utilizing IP Networks[END_REF], where a Mean Delay is expressed in relation ( 5), a Positive dealy deviation in [START_REF]G.107: The E-Model, a computational model for use in transmission planning, rev. 8[END_REF] and Negative deviation in [START_REF] Voznak | E-model Modification for Case of Cascade Codecs Arrangement[END_REF].

D i = (15 × D i-1 + t i -1) / 16
(5)

P i = t i -D i if t i > Di and N i = D i -t i if t i < Di (6), (7)
If t i = D i , then both P i and N i are zero. Mean Absolute Packet Delay Variation 2 (MAPDV2) for packet i is computed as (8) [START_REF] Morton | ): Performance Parameter Definitions for Quality of Speech and other Voiceband Applications Utilizing IP Networks[END_REF]:

MAPDV2 = mean(P i ) + mean(N i ) (8)
Variance of input stream packet delivery causes problems with synchronous playback. Receiver can wait finite amount of time for data to be delivered [START_REF] Broitman | Determination of Optimal Size of the De-jitter Buffer of a Receiving Router[END_REF], [START_REF]International Telecommunications Union, STUDY GROUP 12 -Delayed Contribution 98: Analysis, Measurement and Modelling of Jitter[END_REF]. All data that are not lost in the network but arrive later than expected are considered lost, hence we use the term Effective packet loss to describe all losses in data path. Effective packet loss is encountered at the input of audio decoder and is equal to or greater than measured network packet loss advertised through RTCP packets. We analyze additional loss on jitter buffer and its inclusion into E-Model further.

E-Model as Objective Measurement Method

E-Model defined by ITU-T G.107 is widely accepted objective method used for estimation of VoIP call quality [START_REF]G.107: The E-Model, a computational model for use in transmission planning, rev. 8[END_REF], [START_REF] Voznak | E-model Modification for Case of Cascade Codecs Arrangement[END_REF]. E-Model uses set of selected input parameters to calculate intermediate variable, R-factor, which is mostly converted to MOS-CQ value. Input parameters contribute to the final estimation of quality in additive manner as expressed in [START_REF] Kovac | E-model MOS Estimate Improvement through Jitter Buffer Packet Loss Modelling[END_REF].

R = R o -I s -I d -I e-eff + A . ( 9
)
Where R o represents the basic SNR, circuit and room noise; I s represents all impairments related to voice recording such as quantization distortion, low voice volume, compression artifacts; I d covers degradations caused by audio signal delay including side tone echo; I e-eff impairment factor represents all degradations caused by network transmission path, including end-to-end delay, packet loss, codec compression artifacts and PLC (Packet Loss Concealment) masking capabilities and A is an advantage factor of particular communication technology. We focus at I e-eff parameter, which is calculated as in [START_REF] Kovac | Impact of Jitter on Speech Quality Estimation in Modified E-model[END_REF]:

I e-eff = I e + (95 -I e ) × P pl / (P pl + B pl ) .

(

) 10
Where I e represents impairment factor given by codec compression and voice reproduction capabilities, B pl is codec robustness describing immunity of particular codec against random losses and quantifying its PLC masking qualities. These values are listed for narrowband codecs in ITU-T G.113 appendix [START_REF]Planning Values for the Equipment Impairment Factor Ie and Packet-Loss Robustness Factor Bpl, ITU-T Recommendation G.113[END_REF]. We propose to substitute P pl parameter for an overall effective packet loss P plef according to equations [START_REF] Mirtchev | Study Of Queuing Systems With A Generalized Departure Process[END_REF] and [START_REF] Broitman | Determination of Optimal Size of the De-jitter Buffer of a Receiving Router[END_REF]. In this document we refer to "Modified E-Model" as to model based on original ITU-T G.107 with P pl packet loss substituted by proposed P plef . Both P pl and P plef values lies in interval <0,1>.

P plef = 1 -(1 -P pl ).(1 -P jitter ) and P jitter = (P plef -P pl )/(1 -P pl ) (11), (12)

Where P jitter is net packet loss on jitter buffer. According to the type of buffer used, P plef should be substituted by either P loss_wo or P loss_wr respectively.

Jitter Effects Simulation and Measurements of Effective Packet Loss

Measurements, which were carried out, proved the jitter buffer behavior described in chapter 2 and helped to find a best fitting function modeling the jitter buffer packet loss P jitter . We have simulated MOS dependence of several codecs such as G.711 both µ-law and A-law with PLC, G723.1 (ACELP and MP-MLQ), G.726, G.729 on QoS parameters which were presented by One-way delay T a ∈{0, 20, 50, 100, 150, 200, 300, 400} [ms]; Network packet loss P pl ∈{0, 1, 2, 3, 5, 7, 10, 15, 20} [%] and Network jitter of 20, 40 and 80 ms with Pareto distribution.VoIP traffic was emulated using IxChariot software and the transport network presented a computer with two network cards with WANem software implementing Pareto distribution on an output packet stream. The stream was received with third computer with IxChariot endpoint, the situation is depicted in Fig. 1. For each codec and combination of QoS parameters values were simulated one-minute VoIP calls and repeated three times, from which data for evaluation were collected, overall 189 combinations of parameters were simulated [START_REF] Kovac | E-model MOS Estimate Improvement through Jitter Buffer Packet Loss Modelling[END_REF], [START_REF] Kovac | Impact of Jitter on Speech Quality Estimation in Modified E-model[END_REF]. Based on simulation results and measurements we have determined optimal shape parameter ξ giving the smallest overall MSE error of differences between measured and estimated P loss_wo and P loss_wr by equations ( 13) and ( 14). Optimal value of sought shape parameter ξ is around values -0.1 to -0.2 depending on actual network traffic characteristics, but in general value of -0.1 proved itself to give good results across wide range of LAN IP networks.

(

)

ξ σ µ ξ 1 wo _ loss x 1 P -       - + = ( ) 2 1 . x 1 P 1 wr _ loss ξ σ µ ξ -       - + = (13), (14)
After substitution of equations ( 13) and ( 14) into [START_REF] Broitman | Determination of Optimal Size of the De-jitter Buffer of a Receiving Router[END_REF], parameters ξ = -0.1 and µ = 0 we get equation for jitter buffer packet loss without reordering (equation ( 15), x = buffer size in [ms]) and with reordering capability (16) where x = packet size in [ms]:

10 wo _ loss x 1 . 0 1 P       - + = σ 2 1 . x 1 . 0 1 P 10 wr _ loss       - + = σ (15), (16)
To use these calculated losses in should be used in place of parameter proposed equation for equipment impairment factor calculation in

P plef = 1 -(1 I
Graph in Fig. 2 show implementation of ITU modifications in case of G.729 additional parameters: network jitter, buff Proposed modifications give more accurate MOS showing good correlation with real network conditions with jitter as opposed to too optimistic original E ceived similar results for other measured codecs.

Conclusions

We

Fig. 1 .

 1 Fig. 1. Test bench for VoIP MOS evaluation with E-Model.

Fig. 2 .

 2 Fig. 2. MOS,

  I e + (95 -I e ) × P plef / (P plef -B pl ) shows comparison of: measured MOS, MOS estimated by original implementation of ITU-T E-Model and MOS estimate using E-Model with proposed in case of G.729 codec. Modified E-Model takes into account three additional parameters: network jitter, buffer size [ms] and audio packet length [ms]. Proposed modifications give more accurate MOS showing good correlation with real network conditions with jitter as opposed to too optimistic original E-Model ceived similar results for other measured codecs. MOS, G.729 codec, network jitter 20 ms, buffer 40 ms Model improvement based on the substitution of packet loss par effective packet loss P plef and we perceive it as the main contribution this paper. Since the jitter J is calculated as floating average of 16 packets, we co sider the characteristics constant if they are similar over recent 16 samples. When considering codec with 20 ms audio per packet, so all values are balanced after 320 s. The transient response of real-time E-Model shows sufficiently fast recovery rate for practical purposes below 1 second. was supported by IT4Innovations Centre of Excellence project, reg. no. CZ.1.05/1.1.00/02.0070 within Operational Program 'Research and Development for project VEGA No. 1/0186/12 "Modeling of Multimedia Traffic Parameters in IMS Networks" conducted at Slovak University of Technology Brati comparison of: measured MOS, MOS estimated by original Model with proposed Model takes into account three er size [ms] and audio packet length [ms]. Proposed modifications give more accurate MOS showing good correlation with real Model, we resubstitution of packet loss paraand we perceive it as the main contribution is calculated as floating average of 16 packets, we consider the characteristics constant if they are similar over recent 16 samples. When all values are balanced after 320 sufficiently fast recovery rate was supported by IT4Innovations Centre of Excellence project, reg. no. rational Program 'Research and Development for project VEGA No. 1/0186/12 "Modeling of Multimedia Traffic at Slovak University of Technology Bratis-

	To use these calculated losses in E-Model we propose to use equation (17 should be used in place of parameter P pl (network packet loss). Equation (18 proposed equation for equipment impairment factor calculation in E-Model I (1 -P pl ) × (1 -P dejitter ) = P pl + P dejitter -P pl × P dejitter 18) is final I e-eff . (17) I e,eff = 17), which (18)
	propose the E-Model improvement based on the
	meter P pl by the effective packet loss
	of this paper. Since the
	sider the characteristics constant if they are similar over recent 16 samples. When
	considering codec with 20 ms audio per packet,
	ms. The transient response of real
	for practical purposes below 1 second
	Acknowledgement
	This work was supported by IT4Innovations Centre of Excellence project, reg. no.
	CZ.1.05/1.1.00/02.0070 within Ope
	Innovations' and by project VEGA No. 1/0186/12 "Modeling of Multimedia Traffic
	Parameters in IMS Networks"
	lava.


Antonio Ghezzi
email: antonio1.ghezzi@polimi.it

A proposal of Business Model Design parameters for Future Internet Carriers

Keywords: Future Internet, Carriers, Business Model Design, Strategy, Interconnections 1

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The Internet's future development is not only heavily depending on its technological evolution, but also on business sustainability for the interconnection ecosystem the Web relies on, where various players characterized by fairly different economics are coexisting [START_REF] Clark | Tussle in Cyberspace: Defining Tomorrow's Internet[END_REF].

Current Internet technologies and business rules for network interconnection are proving to be no longer able to support the sustainable development of all actors along the value network, i.e. from the application to the network service providers. While Over-The-Top (OTT) providers develop high performance applications that create new revenues opportunities for them, Carriers have to cope with more and more constrainable traffic they cannot control and charge in order to recover corresponding extra network investment costs [START_REF] Zwickl | On the Quantification of Value Networks: A Dependency Model for Interconnection Scenarios[END_REF]. Indeed, peering agreements remain static and insensitive with respect to Quality of Service parameters, and operators rely mainly on the revenues coming from flat rate pricing in access networks.

To lighten this quite gloomy picture, Internet Service Providers (ISP) or Carriers are required to elaborate innovative strategies, in turn resulting in original tactical choices at a business model design level. The present study, through collecting the multifaceted perspectives of 10 Carriers (integrated by the complementary view of 5 OTTs), aims at disclosing such strategic and tactical decisions (either already consolidated in the current market practices, or innovatively derived from original stances) and present them in a comprehensive framework for to support Carriers in the business model design process from a Future Internet perspective.

Methodology

The research leverages on two main methodological pillars: literature review and multiple case studies. The literature review focused on the significant and quite innovative stream of the broad Strategic Management theory. i.e. Business Model Design, posing the theoretical bases for the proposed Carriers model's building blocks.

With reference to the empirical research methodology, case studies are defined [START_REF] Yin | Case Study Research: Design and Methods[END_REF] as "empirical inquiries that investigates a contemporary phenomenon within its real-life context, especially when the boundaries between phenomenon and context are not clearly evident; and in which multiple sources of evidence are used". Qualitative research methodology is particularly suitable for reaching the research objectives, which aim at understanding the complex phenomenon of Business Model design within a given industry -that is, the Internet Interconnections Industry -characterised by a high level of dynamicity and competitive turbulence, and at thus building new theory, or extending existing theories, on such context [START_REF] Walsham | Interpretive case-studies in IS research -nature and methods[END_REF].

To accomplish the previously identified research propositions, exploratory case studies based on semi-structured interviews are performed on a set of 15 companies operating in the broad Internet Interconnection market, and belonging to two major categories: Carriers (10 cases: British Telecom; Deutsche Telekom; France Telecom; Telecom Italia; Telefonica; Telenor; Fastweb; Tiscali; Colt; Libero Infostrada) and OTT/Content Providers (5 cases: Akamai; Google; Yahoo; Buongiorno; Dada).

The interviews focused on addressing which are the most critical choices to be made at a Business Model design level for Carriers. The semi-structured nature of the interview allows to start from some key issues identified through the literature, but also to let any innovative issue emerge from the open discussion.

As argued in [START_REF] Meredith | Building operation management theory through case and field research[END_REF], a multiple case studies approach reinforces the generalisation of results obtained, and allows to perform a cross analysis on Business Models characteristics and their combinations, due to the presence of extreme cases, polar types or niche situations within the theoretical sample. As the validity and reliability of case studies rest heavily on the correctness of the information provided by the interviewees and can be assured by using multiple sources or looking at data in multiple ways, several sources of evidences or research methods are to be employed: interviews (to be considered the primary data source), analysis of internal documents (both official and informal), study of secondary sources on the firm (i.e. research reports, websites, newsletters, white papers, databases, and conferences proceedings). This combination of sources allows obtaining perceptual triangulation, essential for assuring rigorous results in qualitative research.

Literature review

The concept of business model generally refers to the "architecture of a business" or the way firms structure their activities in order to create and capture value [START_REF] Rappa | Business Models on the Web: Managing the digital enterprise[END_REF] [12] [START_REF] Weill | Place to Space: Migrating to E-Business Models[END_REF].

As a literature stream, business model design has evolved from a piecemeal approach that looked for the single identification of typologies or taxonomies of models [START_REF] Amit | Value creation in e-business[END_REF] [10] [START_REF] Tapascott | Digital Capital: Harnessing the Power of Business Webs[END_REF] [14] to one searching for the development of a clear and unambiguous ontology [START_REF] Osterwalder | The Business Model Ontology. A proposition in a design science approach[END_REF] -that is, the definition of the basic concepts of a theory -that could be employed as a generalized tool for supporting strategy analysis of firms. In parallel, business model has become an extensive and dynamic concept, as its focus has shifted from the single firm to the network of firms, and from the sole firm's positioning within the network to its entire interrelations and hierarchies [START_REF] Ballon | Business modelling revisited: the configuration of control and value[END_REF].

It is widely accepted in literature that a business model shall be analyzed through a multi-category approach, as a combination of multiple design dimension, elements or building blocks. However, the proposed dimensions and interrelations are quite diverse, and the existing body of knowledge shows a lack of homogeneity [START_REF] Johnson | Reinventing your business model[END_REF].

Noteworthy attempts of providing a unified and consistent framework can be found in several works. Yu [START_REF] Yu | An integrated framework of business models for guiding electronic commerce applications and case studies[END_REF] mentions different critical business model components such as assets, markets, customers, competitors, products, services, costs, prices, revenues, profits, market shares, economic scales, marketing strategies, competitive advantages. According to Hedman and Kalling [START_REF] Hedman | The business model concept: theoretical underpinnings and empirical illustrations[END_REF], the conceptual business model should include elements such as customers and competitors, the offering, activities and organization, resources and factor market interactions. Osterwalder, in his proposed business model design template [START_REF] Osterwalder | The Business Model Ontology. A proposition in a design science approach[END_REF], identifies four key dimensions for a business model: infrastructure; offering; customers; and finance. Morris and others [START_REF] Morris | The entrepreneurs business model: toward a unified perspective[END_REF] propose a six-component framework for characterizing a business model, regardless of venture type, which comprehends: value creation; value target; internal source of advantage; firm market positioning; value capture; entrepreneur's time, scope and size ambitions. Ballon [START_REF] Ballon | Business modelling revisited: the configuration of control and value[END_REF] holds that the recurrent parameters a business model is built on can be brought back to the general concepts of value -value proposition and financial configuration -and control -inter-firm or value network relationships -. For Johnson and others [START_REF] Johnson | Reinventing your business model[END_REF] a business model consists of four interlocking elements that, taken together, create and deliver value: customer value; profit formula; key resources; and key processes. Recently, Zott and Amit [START_REF] Zott | Business model design: an activity system perspective[END_REF] suggest two sets of parameters that systems designers need to consider: design elements -content, structure and governance -that describe the architecture of an activity system; and design themes -novelty, lock-in, complementarities and efficiency -that describe the sources of the activity system's value creation.

Notwithstanding the presence of several alternative frameworks for business model design, the literature review allows to claim most studies converge and focus on the concept of value: either it is the value a firm plans to offer to its customers (i.e. value proposition); or the value it creates, shares and competes for with other external actors in its market, depending on the level and nature of its value network integration; or, in the end, the value as a result of the proper selection of a cost structure and a revenue model [START_REF] Ghezzi | How to get Strategic Planning and Business Model Design wrong: the case of a Mobile Technology Provider[END_REF].

Business Model Design Parameters for Future Internet Carriers

The research carried out through the multiple case studies allowed to shed light on the core business model design parameters for Carriers in the Internet Interconnections market. The findings are synthesized in the BM Framework below provided, which identifies three macro-dimensions. The macro-dimensions are in turn divided into 12 parameters. The framework is mainly directed to Carriers or Internet Service Providers (ISPs) as the referencing actor For each and every parameter, the "value range" is identified, i.e. the extremes values or key alternatives the variables can assume, which also represent the major tradeoff between opposite choices; the main strategic implications deriving from alternative parameters adoption are also discussed.

Business Model Parameter

Value Range (Trade-off) Strategic Implications

Value Proposition

Product/Service Deliv-

Basic

Discussion and conclusions

The framework proposed for supporting the business model crafting for Future Internet Carriers discloses a number of emerging strategic and tactical trends ongoing within the Internet Interconnection market. Carriers' value proposition is moving from basic connectivity provisioning to the elaboration of an ASQ offer towards end users or OTTs/CPs, in order to incorporate a larger share of the overall market value. To avoid being gradually left aside in a more and more peripheral value network position, Carriers should also strive to maintain direct customer ownership and a coverage of the Internet value added services layer, by becoming enablers and partners of the OTTs/CPs services offer which requires higher and higher quality of service, from several perspectives (e.g. bandwidth; low latency); peering interconnections, as well as Cloud/CDN solutions, should be deployed wisely by taking into fair consideration the strategic and tactical implications of such technological configurations. Carriers' current financial configuration should be redesigned by evaluating the option of introducing innovative revenue sharing models and traffic charging schemes (e.g. Sending party pays model, Congestion charging) towards OTTs/CPs, with the ultimate goal of enhancing the business' financial sustainability.

These abovementioned considerations prove the reference model (solidly grounded in business model design theory) holds a straightforward value for Carriers' top managers, who can adopt it as a checklist of strategic and tactical decisions to be taken, and directly associate them to their implications.

Future studies should aim at testing the validity of the model within different firm samples, and, in turn, assess the relationship between specific choices in the business model parameters combination and firm performance.

1 .

 1 Value Proposition parameters. It addresses the firm's strategic positioning by considering key business dimensions. (a) Product/Service delivered (basic connectivity vs. ASQ vs. Content) (b) Target customer (Content Provider/OTT vs. End User) (c) Customer value (basic connectivity vs. Assured Service Quality vs. Content) (d) Resources and Competencies (technology-oriented vs. content-oriented) 2. Value Network parameters. It addresses the firm's level of integration within its referencing Value Network system. (a) Vertical Integration (Infrastructure layer vs. Internet Service Layer) (b) Customer Ownership (Direct vs. Indirect) (c) Interconnection modality (prevalent: transit vs. peering) (d) Content delivery model (Client-Server vs. Cloud vs. CDN) 3. Financial Configuration parameters. It addresses the firm's configuration generating revenue and cost streams. (a) Revenue Model (Single Transaction vs. Subscription) (b) Revenue Sharing Model (Present vs. Absent) (c) Traffic Charging scheme (Receiving Party Network Pays model vs. Congestion-charging model vs. Sending Party Network Pays model) (d) Cost Model (concentrated vs. distributed)

Table 1 .

 1 Business Model Design Parameters for Future Internet Carriers

	connectivity	Traditional business for ISPs.
			Easier diffusion/substitution.
	Assured	Service	Higher potential margins from connectivity.
	Quality (ASQ)	Service differentiation

Acknowledgments

The research leading to these results has received funding from the European's Community Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 248567 for the 'ETICS' Project. Further information is available at www.ict-etics.eu. The authors would like to thank all members of the ETICS project for many valuable discussions and feedback.


Ivan Zelinka
email: ivan.zelinka@vsb.cz

Donald Davendra
email: donald.davendra@vsb.cz

Lenka Skanderova
email: lenka.skanderova.st@vsb.cz

Visualization of Complex Networks Dynamics -Case Study

Keywords: complex networks, network dynamics, coupled map lattices, chaos, and visualization, control of complex systems

In this article is discussed novel method of the so-called complex networks dynamics and its visualization by means of so called coupled map lattices method. The main aim of this research is to demonstrate possibility to visualize complex network dynamics by means of the same method, that is used spatiotemporal chaos modeling. It is suggested here to use coupled map lattices system to simulate complex network so that each site is equal to one vertex of complex network. Interaction between network vertices is in coupled map lattices equal to the strength of mutual influence between system sites. Also another results from previous experiments, where dynamics of evolutionary algorithms has been converted to complex network and consequently to CML, are mentioned at the end. All results has been properly visualized and explained.

Introduction

In this article, we try to merge two completely different (at first glance) areas of research: complex networks and chaotic systems visualization. Large-scale networks, exhibiting complex patterns of interaction amongst vertices exist in both nature and in man-made systems (i.e., communication networks, genetic pathways, ecological or economical networks, social networks, networks of various scientific collaboration, Internet, World Wide Web, power grid etc.). The structure of complex networks thus can be observed in many systems. The word "complex" networks [START_REF] Dorogovtsev | Evolution of Networks[END_REF], [START_REF] Boccaletti | Complex Networks: Structure and Dynamics[END_REF] comes from the fact that they exhibit substantial and non-trivial topological features, with patterns of connection between vertices that are neither purely regular nor purely random. Such features include a heavy tail in the degree distribution, a high clustering coefficient, and hierarchical structure, amongst other features. In the case of directed networks, these features also include reciprocity, triad significance profile and other features.

As an example of another exhibition of the existence of complex networks are evolutionary algorithms, whose dynamics can be visualized like complex network structure and dynamics.

The main idea of our research is to show in this article that the dynamics of complex network can be analyzed and visualized like a coupled map lattices (CML), which is usually used for visualization of the chaotic systems. CML system can be understood like a row (1D case) or field of N×M mechanical oscillators, which are mutually joined and influent themselves via nonlinear coupling parameter ε. Their behavior is then visualized as done in Figure 1. Different levels of colors (or levels of gray) represent different "energy" (phase) of each oscillator. Complex network science is very popular in the last 10 years. Root of it comes from social complex networks, like citation networks etc. During the time it become to be very popular and interdisciplinary important scientific field of research. It has been discovered, that complex network structure and its dynamics, is hidden in the background of many processes that are not, on the first glance, related to the complex networks. Lets note for example classical examples like citation networks, social networks kind of facebook, interactions on the stock exchange, complex communication or power networks, interactions and stress level between people in the closed groups (space flights, submarine missions) etc. are. Another side of coin we can note (from our research) complex network, that is hidden behind dynamics of evolutionary algorithms, see Figure 3 and for full report [START_REF] Zelinka | Investigation on Relations Between Complex Networks and Evolutionary Algorithm Dynamics[END_REF]. This is typical example of formed complex network that show interactions between individuals in the population of the evolutionary algorithm. Each node on Figure 3 is in fact individual from population and links amongst them represent its activities (i.e. which individual has been selected to be parent etc.). Based on fact, that complex networks and its dynamics can be discovered almost everywhere today, question how to control, analyze or predict them is of course important one. Of course, a lot of standard techniques exist today there. All those techniques, see for example [START_REF] Meyn | Control Techniques for Complex Networks[END_REF], are based on classical mathematical tools, or on heuristic methods, see [START_REF] Zelinka | Investigation on Realtime Deterministic Chaos Control by Means of Evolutionary Algorithms[END_REF], [START_REF] Senkerik | Optimization of feedback control of chaos by evolutionary algorithms[END_REF] and [START_REF] Zelinka | Investigation on real-time deterministic chaos control by means of evolutionary algorithms[END_REF]. As mentioned before, our aim in this article is to show, that there is quite simple approach how to visualize complex networks and its growth and dynamic in the time so, that well-known methods from chaos control can be used. This approach is discussed in the next section.

Complex Networks Dyamics And Its Vizualization

Our method of visualization is based on fact that simplest version of CML (i.e. 1D version) is usually depicted like a row of mutually joined sites, where each site is nonlinearly joined with its nearest sites. Our vision of equivalence between CML and complex network is quite simple. Each vertex is equivalent to the site in the CML.

Comparing to the standard CML, sites in complex network CML (CNCML) are not joined to the nearest site, but to the sites equal to the complex network vertices. Thus sites in CNCML are not joined symmetrically (i.e. from site X to Y and vice versa) and between different sites is random pattern of connections, which can change in the time. Lets see as an example Figure 1. Vertex No. 12 have feedback to itself (red selfloop), then it influent vertices 7 (double edge), 21 and are influenced by vertices (CNCML sites) 18 and 19. As it is visible, based on our interpretation (CN = CNCML), CNCML is more complex version of the classical simplified version of the CML, however still CML in general, so all techniques of control and analysis shall be working on such a version of CML. Our experiments of CNCML visualization were based on approach that on the beginning has been generated complex network with big number of vertices and no another vertex has been added after all. Only weights on the randomly selected edges have been modified. In all cases has been CNCML calculated and visualized in such a way, that for each CN vertex (i.e. CNCML site) has been calculated according to [START_REF] Zelinka | Investigation on Realtime Deterministic Chaos Control by Means of Evolutionary Algorithms[END_REF]. Different levels of vertices (sites) excitation are depicted by different colors. When compared with Figure 2, it is clearly visible, that our proposed kind of visualization is usable. In Figure 2 and Figure 3 it is observable, that CNCML visualization shows complex and obviously nonlinear behavior of tested CNs. Different kind of behavior (different colors) is clearly visible. Dynamics of the complex network with edges deactivation by real number coefficient. Different colors (gray levels) including white one represent different vertices (sites) activation.

(

) 5
Figure 2 CML of the network with 20 vertices in 500 iterations.

Conclusions

In this paper we have proposed method how to visualize and simulate (model) complex network dynamics by means of the CML systems. CML modeling is well known in the domain of so-called spatiotemporal deterministic chaos. For analysis and control of the CML systems has been developed numerous techniques, based on classical mathematics as well as on the heuristic methods. Based on this fact we have used and tested method, how to visualize CN dynamics as a CML system. As a summarization and conclusion can be state this:

 The model of complex network, used for simulations here, has been based on randomly initialized network with different number of vertices.  Edges between vertices have been also initialized randomly, however, roulette method from genetic algorithms, have been used to add the new edge to the vertex, and/or increase or decrease importance of existing edge. Roulette method has been used in order to prefer richer vertices (i.e. vertices with more incoming edges) and thus support in this way the main idea of complex networks with small world phenomenon.  One kind of dynamics for CN visualization has been used. It has been based on preliminary fact, that we already have developed network, with constant number of vertices and dynamic itself depends only on the edge weights evolution.  All substantial and typical results are visualized via Figure 3 -Figure 2 and it can be stated that our technique of CNCML is usable and complex behavior of CN can be visualized in this way. It is also obvious, that methods of analysis and control of CMLs can be also used for CNCML, because conversion classical CN to the CNCML basically create specific, more complex version, of the CML and there are no restriction of use of contemporary methods on such a version of CML. On the other side, in the case of big computational complexity for complex and big CNCMLs, heuristics, like evolutionary algorithms [START_REF] Zelinka | Real-time deterministic chaos control by means of selected evolutionary algorithms[END_REF], [START_REF] Zelinka | Investigation on Realtime Deterministic Chaos Control by Means of Evolutionary Algorithms[END_REF], [START_REF] Senkerik | Optimization of feedback control of chaos by evolutionary algorithms[END_REF], [START_REF] Zelinka | Investigation on real-time deterministic chaos control by means of evolutionary algorithms[END_REF] can be easily used.

Another, more advanced results of this approach are represented in [START_REF] Zelinka | Does Evolutionary Algorithms Dynamics Create a Complex Networks Structures?[END_REF]. The main motivation of this research is whether it is possible to visualize and simulate underlying dynamics of an evolutionary process as a complex network. Based on preliminary results (based only on 2 algorithms in 10 versions and 16 test function out of 17) it can be stated that its possible use and benefit of this approach is according to us novel approach to evaluate and control evolutionary dynamics. Based on numerically demonstrated fact (no mathematical proof has been made) that EAs dynamics can be visualized like complex networks we believe that there is new research area for study of EAs dynamics and its possible control via techniques of complex network control [START_REF] Meyn | Control Techniques for Complex Networks[END_REF]. The next research shall be focused on collection of the data from existing real CNs, based on [START_REF] Zelinka | Does Evolutionary Algorithms Dynamics Create a Complex Networks Structures?[END_REF] also exact analysis, when chaotic regimes are observable in CNCML, will be done and also what kind of routes to chaos (intermittence, crisis, bifurcations) are there shall be interesting. Another open research issue is to use of selected evolutionary algorithms on CNCML control with "reverse" investigation, what exact impact has such a control on the original CN structure (basically control here means that EAs have to change structure of CN).

Figure 1

 1 Figure 1 Example: complex network with more vertices (left), typical CML behavior with chaotic and deterministic "windows", after iteration 6000 it is due to the control fully stabilized (right)

Figure 3

 3 Figure 3 Dynamics of the complex network visualized like CML system with edges deactivation by real number coefficient. Different colors including white one represent different vertices (sites) activation.

Acknowledgement

This work was supported by IT4Innovations Centre of Excellence project, reg. no. CZ.1.05/1.1.00/02.0070 supported by Operational Program 'Research and Development for Innovations' funded by Structural Funds of the European Union and state budget of the Czech Republic.