Marwen Abdennebi

Yacine Ghamri-Doudane

Adaptive CAC for SVC Video Traffic in IEEE 802.16 Networks

Call Admission Control is a key function that guarantees the Quality of Service (QoS) for users. In radio networks, this function is usually based on traffic models and ensures that sessions are admitted only if the estimated available bandwidth is enough for the entire call duration. For video on IEEE 802.16, the CAC function must ensure that the bandwidth to be reserved is compatible with the resource availability. For the enhanced SVC (Scalable Video Coding) systems, the CAC function must take into account all the layers and their characteristics. In this paper, we propose an enhanced CAC function for SVC that adapts the admission according to the statistical behaviour of the video sessions. The main goal is to use measurements in the 802.16 base station (BS) to update the traffic model of SVC video flows, this for the different layers of SVC flows. We then use the variability of the traffics generated to adapt the CAC according to the characteristics of incoming flows. To perform that, we use a Markovian model that adapts for each flow instead of using a generic static one as used in most of the papers. Performance evaluation is given to illustrate the interest of our proposal.

I. INTRODUCTION

IEEE 802.16 defines different services with different bandwidth reservation mechanism. Each defined service should map to the appropriate traffic and satisfy the Quality of Service (QoS) required by users. However, for each of those defined service, a Call Admission Control function, or CAC, must be added. CAC is a key function for IEEE 802.16 [START_REF]IEEE Standard for Local and Metropolitan Area Networks Part 16: Air Interface for Fixed Broadband Wireless Access Systems[END_REF] systems as it ensures that a new session will be accepted only if the Quality of Service can be guaranteed for the entire session duration. It is essential to ensure that this function is well implemented as it allows the operator to admit the maximum number of users in the system while keeping a good quality. For each service, this function must accept a session if, and only if, the QoS can be guaranteed.

In 802. [START_REF] Mcdonagh | Investigation of scalable video delivery using H.264 SVC on an LTE network[END_REF], for the particular case of real-time Polling Service (rtPS), the bandwidth reservation scheme must take into account the mean estimation and the probabilities of each bandwidth request and its variability in time. This estimation should be based on analytical traffic models for the sources. System modelling will give the probabilities of each traffic state and hence will allow the operator to guess the probabilities of each bandwidth request related to bitrate variations.

Usually, the corresponding algorithms use single traffic models, one model per traffic source. This approach does not take account of the variations between different kinds of video traffics and hence, will not be accurate for an efficient CAC. In our previous work [START_REF] Abdennebi | Adaptive CAC with Traffic Flows Classification for IEEE 802.16 Networks[END_REF], we proposed an approach that takes into account those differences between videos flows by classifying them into different categories, using 3 different kinds of traffic models. Measurements were done to classify flows in the appropriate category. However, this approach was applicable only for the simple case of AVC (Advanced Video Coding) video where the traffic characterising was done for a single layer.

We then propose in this paper to extend the adaptive CAC solution to SVC video flows by using models for each considered enhancement layer. In this case, we have to distinct between the layers of admitted video sources and have a traffic model for each category of enhanced layer. We then obtain an adaptive CAC that takes account of the number of video sessions in each category and for each layer, and hence adapts the admission decision as well as the rate through enhancement layers Hence, in the following, we first present a background study on IEEE 802.16 services, on SVC coding and on the CAC function for video streams. Then, section III describes our proposed adaptive CAC system and the measurements and processes it involves at the BS level. In section IV we show the benefits of our proposal in term of performances. Finally, in section V, we conclude the paper and propose some future research directions to improve and develop this work.

II. BACKGROUND

A. IEEE 802.16 System

Unlike for UGS (Unsolicited Grant Service), where the bandwidth request/grant mechanism is fixed for the entire session and hence the CAC function simply consists in + Laboratoire de Traitement et de Transport de l'Information (L2TI), Université de Paris 13 marwen.abdennebi@univ-paris13.fr admitting only sessions that fit the mean available bandwidth, for rtPS (and also for extend rtPS or ertPS), the bandwidth is not fixed and the request/grant vary during the session. Those variations must be taken into account before admitting a session to ensure that peaks of bitrates will not cause packet losses.

Hence, the CAC function [START_REF]An Efficient Call Admission Control for IEEE[END_REF] must be aware of the traffic models of requesting sources. Indeed, those models will give the bitrate variations and hence the bandwidth requests for the IEEE 802.16 frames. As the traffic models used will finally give the distribution of the bandwidth requests (in term of probabilities), it is very important that those models should be as accurate as possible, for both single and aggregated traffic sources. That consists in first computing, for each time interval, the probability of the bandwidth request related to each flow, this using the simple traffic model for the corresponding source [START_REF] Bianco | Bandwidth Allocation for Video Streaming in WiMAX Networks[END_REF]. Then, it should be compared with the available bandwidth for this service in the IEEE 802.16 frames that will be given using the aggregated traffic model for all admitted sources in rtPS. If the grant is possible for the defined number of frames (given from the polling interval), the bandwidth will be allocated, otherwise, the packets are delayed or even dropped.

B. SVC: Scalable Video Coding

SVC is an extension proposed to the H264/MPEG-4 compression standard to enhance the video quality using layered images [START_REF] Van Der Auwera | Traffic and Quality Characterization of Single-Layer Video Streams Encoded with the H.264/MPEG-4 Advanced Video Coding Standard and Scalable Video Coding Extension[END_REF]. Some works have been proposed to enhance performances of SVC flow transmissions on broadband radio networks, but most of them like [START_REF] Li | Scalable Video Multicast With Adaptive Modulation and Coding in Broadband Wireless Data Systems[END_REF] [9] in deals especially with the scheduling or the coding and modulation schemes. [START_REF] Van Der Auwera | Traffic and Quality Characterization of Single-Layer Video Streams Encoded with the H.264/MPEG-4 Advanced Video Coding Standard and Scalable Video Coding Extension[END_REF] This extension is based on the addition of enhancement layers that, when added, improve the quality of the video. The enhancement process is based on the addition of layer frames, in time and/or space. The obtained quality improvement is related to the number of enhancement layer (figure 1 above). Each enhancement layer decoding is dependency based, meaning that it needs the base and intermediate layers to be decoded.

In the following section, we consider the particular case of Coarse Grain Scalability, CGS, which consists in enhancing the quality through the increase of prediction adding the corresponding precision in each enhancement layer.

C. CAC for video traffic

Usual CAC algorithms often use the same model considering a single video stream. Video streaming are especially challenging for as the traffic flow rate and its variations are related to the motion level of the videos. We then demonstrate that the combined models will be more accurate than a single one. The most commonly used CAC computes the probability states of the modelled system and then determine the maximum number of sources that should be accepted [START_REF] Masri | Proposal of a Novel Bandwidth Management Framework for IEEE 802.16 Based on Aggregation[END_REF]. The main goal is then to ensure that the QoS condition is respected for the traffic sources in term of bandwidth/rate, delay, jitter and drops. The system can also accept or reject sessions "on the fly", according to bandwidth availability, but, this time, the system will not be able to accurately predict variations for this bandwidth and hence the guarantee would not be respected.

In fact, in rtPS, the regular polling of admitted sources implies various bandwidth requests in time and space related to the throughput variations of the corresponding streams. It means that almost all requests must be granted in order to satisfy the QoS in terms of loss rates and/or delays. Actually, for each time interval, the bandwidth requested should be compared to the available one (in terms of time slots and/or symbols in frame) to ensure the grant.

The figure 2 below illustrates the differences between the CAC methods, (a), (b), (c) we consider. The first is the simple one that takes into account the available bandwidth and static models to make the decision of accepting an incoming session. The second is the adaptive CAC we proposed in [START_REF] Abdennebi | Adaptive CAC with Traffic Flows Classification for IEEE 802.16 Networks[END_REF] and takes into account the measurements of flows to update the decision algorithm and make it more efficient. The third is the one we propose in this paper and which improve to the adaptive CAC the rate adaptation through adding enhancement layers. decision for SVC traffic taking advantage of the rate adaptation through enhancement layers. This adaptation uses flow measurements to classify them into 3 categories according bandwidth consumption, this for each considered SVC layers. This classification gives an idea about flows repartition among the models and is used by CAC for the admission decision and rate adaptation.

In the sub-sections below, we present the traffic model we use, then the streams measurements process and classification/mapping with the proposed models, and finally the adaptive CAC scheme. The whole process is summarized in the figure 3 of sub-section B.

A. Layer based Traffic Model

In this part, we propose to model each traffic layer of individual source and for base and enhanced layers. We use in this purpose as in [START_REF] Abdennebi | Adaptive CAC with Traffic Flows Classification for IEEE 802.16 Networks[END_REF] rate-based single dimension rate based Markovian chain. This model is appropriate for aggregated frames when the time scale of sampling/quantization is about 100 ms. This means that we can use this model in for the rtPS 802.16 as the bandwidth request concerns more than one MAC frames.

Actually, for video traffics, the rate levels and the rate variations differ from one flow to another according to the motion included in each video and is not related to the enhancement layer itself in CGS especially. As we use a Markovian process to model the rate, we will have, for each SVC video flow, one model for each layer.

The quantized video flows are modelled for each layer, base and enhanced. Each obtained model describes the bitrate evolution from one state to another. These states are the different value that the bitrate of each layer can take. Hence, the number of states will be the number of quantized values defined by the measurement process, with a maximum value defined for each model.

As described in figure below, each layer L of each flow is described by the process parameters λ L and µ L which are the mean arrival and service rates from one quantized bitrate to another.

3 ( ) ( ) 2 ( ) ( ) .( ) ( ) 1 ( ] [ 2 , 1 , 0 2 , 1 , 0 2 , 1 , 0 ) ( 2 , 1 , 0 2 2 , 1 , 0 2 , 1 , 0 2 , 1 , 0 2 , 1 , 0 2 2 , 1 , 0 2 , 1 , 0 2 , 1 , 0 2 , 1 , 0 2 , 1 , 0 2 , 1 , 0 2 , 1 , 0 τ µ λ τ µ λ µ λ µ λ λ + - = + = + = e Corr D n D Var nD D E
The state probabilities are obtained by solving the following linear equations system:

N L i L L i i N ) 1 ( ) ( . ρ ρ π +         = (4) with L L L µ λ ρ = and N L L ) 1 ( 1 0 ρ π + = { } 2 , 1 , 0 ∈ L (in our case)
The discrete values of modelled bitrate can have M+1 quantized levels. The durations T i L in each bitrate state i, for each layer L, are exponentially distributed:

L L L i i i n T µ λ . ) ( 1 + - = (5)
Those levels and their durations are used for the mapping process explained in the section below. The mapping is done through the performed measurements of levels and we also add for this mapping, the measurement of the average durations of those levels, i.e., in each state of the chain, this for each flow and layer.

B. Measurements and Streams Classification

The goal of this step is to classify the video session into the appropriate Markov chain model from the 3 defined categories by mapping it with the parameters one of the Markov chains corresponding to the category proposed.

With the adaptive proposed CAC, we aim to take account of the bitrate variability for the session admissions. It means that, according to each classification and flows repartition, we have a maximum number of sessions that will also vary. This classification/repartition is according to the bitrate, i.e., the motion levels, but also according to the enhancement layers (eLayers). We use for the traffic characterising 3 levels of motion as recommended in [START_REF] Lanfranchi | MPEG-4 Bandwidth Prediction for Broadband Cable Networks[END_REF] [START_REF] Koumaras | A Markov Modified Model of H.264 VBR Video Traffic[END_REF], which means 3 simple models, one for each flow category. We also consider 3 layers including the base layer combined to 3 categories of traffic models for each layer.

According to the traffic repartition the CAC must ensure a maximum number of admitted sources N if considering the base layer, N1 if considering the layer 1 and N2 for the layer 2.

For the CAC, we need to compute through measure the repartition of streams in each layer. We must determine η iL that denotes the relative number of flow in the traffic class i and the layer L. f flow in each traffic category for each layer.

If the repartition in each layer L is η 1L , η 2L , η 3L , we then have: Each traffic model, for each layer, is characterised by the rates levels and the Markov chain as explained in the subsection above. Morever, those models are also characterised by the mean rates states (levels) and the mean durations in those states. Those statistics give the PDF function (probability density function) which is used for mapping the measure with the model.

The figure 4 below summarizes this process, from the measurements step to the CAC update. The measurements are handled by the BS and should be able to give information on the statistical behaviour of video traffics for the classification. Then, those measurements are done for each admitted flow and this for each layer L that was allowed. The measurements are done for the bitrate, value and duration, as the traffic model is also bitrate based.

The rate measured is first quantized with the same level as for the models, which means that we have discrete values for the measures according the Markov chain states and hence the PDF functions.

Then we map the quantized streams measures (discrete rate levels) with the models to find the probability density function (rate levels for example) of the flow and finally classify it. The statistical behaviour is mapped to the histogram through the appropriate PDF and hence according to the parameters of the appropriate model.

Once identifying the parameters of the incoming flow, for all its enhancement layers, the classification is done into the appropriate traffic category i for each layer L.

After the classification, we are able to update the flow repartition η iL , then the global system model parameters and hence update the CAC function.

As the measurements are done for bitrate, the measurement duration must be enough to obtain the convergence of the parameters (the fact that the measured parameters reach a fixed value and does not diverge for more than n frames.). For the considered case of video flows, it means that this duration is in the scene time scale, which means at least seconds/minutes. It is important to note that for each flow measurements should be performed repeatedly to update the system.

Once the repartition determined, the CAC process takes place by computing the maximum number of sources that can be admitted in the system. This number can be different if we can cancel some layers in case of congestion. Hence, if N0, N1 and N2 is the maximum number of sources respectively in base layer, enhancement layer 1 and layer 2, we will have the CAC function, {N0, N1, N2} = f(η iL ), i is the traffic class and L the layer.

The proposed adaptive CAC will then, for each admitted flow, regularly update those parameters using measurements, and hence compute the repartitions parameters η iL . Once updated, the CAC function should compute, thanks to offered bandwidth and the Quality of Service parameter (delay or drop rate threshold), the new corresponding maximum number of sessions that the system can admit, i.e., N0, N1, or N2 depending on the layer.

C. CAC Adaptation

For each accepted flow, the measurements are done in order to guess its statistical characteristics after measures convergence. For the average value for example, it means that we can update the CAC after the measurement of one flow, until reaching a threshold value l defined according the measured rate for each layer L at each measure frame k (time).

l j rate j k rate k L L i j frame L i k frame L < - ∑ ∑ ∑ = - = = max 0 1 1 1 ) ( 1 ) ( 1 (6)
Then, the η iL could be determined and the corresponding rates probabilities in each state of the 3 Markov chains are given.

In the case of rtPS scheduling, the number of frames related to the bandwidth request/grant should be also taken into account as this number is related to the minimum time granularity for the quantization step. It means that this value is directly related to the number of bytes that the request concerns, and hence also the states probabilities of each considered flow.

Once the measurements convergence step is finished, we obtain the probabilities of each bitrate state through a histogram of the levels. In addition to this PDF function we also use state duration values, in order to guess the value of λ L and µ L . Equations ( 4) and ( 5) will be used to guess the individual flow characteristics, using the Markov behaviour of the quantized rate levels. The mapping is then deduced after the measurement of the mean durations in each state i and the probabilities distributions. As we have several parameters to be mapped, we use LMS (Least Mean Square) to classify flows in the appropriate category.

Once the classification obtained and the layer/flow mapped to the appropriate Markov chain, we should be able to find the appropriate CAC. After each update interval, we compute the updated states probabilities π i, j, k and this for each layers 0, 1, 2. The values of these states probabilities π i, j, k are obtained by multiplying single states by each other (π i, j, k is π i, .π j .π k ) and hence we find all the probabilities for all combined states that belong to the same layer.

For the CAC, the decision is done according to the mean bandwidth consumed (rate) and the bandwidth available. Depending on the layer rate this consumed bandwidth in this considered layer L (from 0 to 2) will be:

∑∑∑ = = = + + = 1 1 2 1 3 1 , , )] ( ) ( ) ( .[ N i N j N k L L L k j i L L k rate j rate i rate BW π (7)
This value must not exceed the available bandwidth allowed in the system (for all rtPS connections). This limitation is usually set according to objective parameters like delay, jitter and drop rates and/or to PSNR (Peak Signal to Noise Ratio).

IV. PERFORMANCE ANALYSIS

A. Performance Evaluation

In this part, we validate the proposed adaptive CAC and we evaluate its performances compared to a non-adaptive CAC. The goal is to show that the proposed scheme allows to admit more video sessions using the same allocated bandwidth and with no QoS degradation, i.e., with a mean QoS that stays under a defined threshold. This means that we can either admit more flows or even transmit more layers for video quality enhancement. At the same time, we show that we can also limit the admission in to avoid degradation while a non-adaptive CAC will not be able to detect this.

We added to the NS2 simulator a IEEE 802.16 module [START_REF] Freitag | Uplink Scheduling with Quality of Service in IEEE 802.16 Networks[END_REF]. We considered only the 802.16 rtPS service in which we are able to configure all the parameters of its reservation mechanism. The number of 802.16 MAC frames per bandwidth request was set to 5 and the MAC frame itself was 20 ms duration. This leads to set the quantization scale to 100ms (5 x 20 ms). Moreover, we focus only on the rtPS service with 10 Mbps allocated bandwidth in the MAC frame.

In this purpose, we use SVC video trace files downloaded from [START_REF] Ke | An Evaluation Framework for More Realistic Simulations of MPEG Video Transmission[END_REF]. Each file describe for each layer, the data frame type (I, B or P), its size and time the frame is generated. As in [START_REF] Abdennebi | Adaptive CAC with Traffic Flows Classification for IEEE 802.16 Networks[END_REF], we first analyse the statistical behaviour using Matlab. Those videos are various in type, from very high bitrates traffic (soccer games) to medium and low bitrates (with talks/news...)

Then, for each layer of each video session, we are able to find the appropriate parameters and hence classify it in the appropriate flow. Note that we considered only CGS (Coarse Grain Scalability) SVC videos traffic files for our simulations.

An example of a "NBC News" video [START_REF] Ke | An Evaluation Framework for More Realistic Simulations of MPEG Video Transmission[END_REF] flow in base layer is given in the figure 5. The measurement process should update the system model parameters, and hence the CAC in term of possible admissions. The measurements show that the convergence corresponds to about 3 minutes of measurements (illustrated by the green line) which is small enough compared to the session duration. After measurements mapping to the states probability functions, the classification is performed.

Below, the measurements for enhancement layer 2. Like for the base layer, the measurements mapping to the states probability functions and then classification are performed for enhancement layers. The appropriate adaptive CAC is obtained. Actually, the measurement process continues to update the system in case of strong variability.

The obtained models flow characteristics for "News" video category are given in the table 1 The same tables are obtained for the high rate traffic model that map for example to soccer games and for the medium rates model that is be used to characterise intermediate rates video streams like movies.

B. Adaptive CAC Performances and Results

To evaluate the performance of the proposed adaptive CAC, we used different combinations of traffic flows, with very different statistical behaviours and bitrates, for all the considered layers (from base to eLayer 2).

For each simulation time reference described in the plots, we increase the number of high data rate videos flows and we adapt the CAC in consequence.

We first adapt the CAC in term of sources admission and then in term of enhancement layer admissions. It means that in the first case, more sessions are accepted with the same quality, while in the second case, more layers are added, then better QoS is expected.

Figure 7 illustrates the first case and we clearly see that the CAC average number of session is increased thanks to the adaptation. Compared to the non-adaptive case, the number of admitted sources is increased in all cases, except in the beginning of the simulation where the number of flow is reduced to avoid losses. This is due to the fact that, as the convergence of measures is not completed yet, and to preserve QoS, the adaptive CAC should be restrictive in term of admission.

Figure 7. Admission for adaptive and non adaptive CAC However, except the beginning of the simulation, we are able to improve the CAC in term of flow admission. Unlike the static CAC that does not change the admission according the incoming flow traffic characteristics, the proposed adaptive CAC is able to use measurements to admit more session if needed or to restrict the admission to avoid the negative effect on QoS.

In figures 8 and 9 below, we compare the observed performances between adaptive and non adaptive, in case of adaptation in term of enhancement layers.

As explained above, the enhancement is obtained through two mechanisms which are highlighted in the obtained plots.

First, the CAC avoids having admitted flows that would degrade the quality of all admitted sessions. That will, in the other hand increases the number of blocked sessions like explained above, but it will keep the QoS of admitted streams acceptable for users.

Second (starting from the time reference 3.0), as the adaptation concerns the number of enhancement layers, we are able to increase the number of admitted sources. But since also adapting the rate through eLayers admissions, we are also able to reduce packet losses by reducing congestion and hence improve the mean quality of the admitted flows. This adaptation is performed thanks to the ability of the proposed system to react to the changes of the statistical behaviour of the admitted flows. Unlike the classical static CAC, the proposed one is able to reduce the session admission if possible and/or the enhancement layers for rate limitation and hence reducing losses. This reduction is significant, from about 2% to 1% in some cases (for some flow repartition configurations).

For the non adaptive case, the packet losses are not reduced significantly as the increased bandwidth is not always able to allow all enhancement layers to be admitted and hence causing packet losses.

The figure 9 gives the video quality results in term of PSNR. We clearly see that the system was able to add more layers in reaction to the bandwidth load. However, thanks to the system measurements and adaptations, we are able to increase the number of eLayers only if this does not affect the whole system performance. As expected from the packet losses results, we clearly see an enhancement for the PSNR and hence the quality perceived by the end user. Like for the losses, the enhancement is particularly observed for some traffic repartitions (up to 3 dB for the PSNR) in where our system is able to reduce losses while maintaining a good CAC at the same time.

V. CONCLUSION AND FUTURE WORK

In this paper we propose an adaptive CAC for enhanced SVC coding based on measurements and classification. This classification gives categories of traffic of similar statistical behaviour and can be done in addition to model parameters refinement. As illustrated by performance results, operator can enhance the QoS by admitting more enhancement layers for example and/or the Grade of Service (GoS) by admitting more flows without changing the mean QoS for users. The improvements are significant (up to 3 dB for PSNR) in term of QoS for users.

In future work, we are working on the impact of the SINR on CAC in IEEE 802.16 rtPS systems. We will then adapt the bandwidth allocation and the CAC, by flow and by layer, according to the radio conditions of each mobile node. Moreover, we are also working on an extension of our work to the 3GPP LTE (Long Term Evolution) and LTE Advanced air interface [START_REF] Mcdonagh | Investigation of scalable video delivery using H.264 SVC on an LTE network[END_REF].

Figure. 1 .

 1 Figure. 1. Example of two layers scalability, base and eL1, dependencies[START_REF] Van Der Auwera | Traffic and Quality Characterization of Single-Layer Video Streams Encoded with the H.264/MPEG-4 Advanced Video Coding Standard and Scalable Video Coding Extension[END_REF]

Figure 2 .

 2 Figure 2. Illustration of CAC methods differences

Figure 3 .

 3 Figure 3. Markov 3 process description of single SVC flow (base + 2 eL)

)

η 10 ,

 10 η 20 , η 30 for the base layer, η 11 , η 21 , η 31 for layer 1, and η 12 , η 22 , η 32 for the layer 2, This means that η 10 % of the flows have the characteristics of η 10 model in the base layer, η 21 % of the flows have the characteristics of η 21 model in the layer 1, etc. This leads us to 3 models for each layer, all bitrate based, to map well with bandwidth request/grant mechanism of IEEE 802.16.

Figure 4 .

 4 Figure 4. Adaptive CAC process description

Figure 5 .

 5 Figure 5. Bitrate (blue) and quantized rate (red) for base layer

Figure 6 .

 6 Figure 6. Bitrate (blue) and quantized rate (red) for enhancement layer 2

Figure 8 .

 8 Figure 8. Comparison between packets loss rates

Figure 9 .

 9 Figure 9. PSNR for adaptive and non adaptive CAC

Table 1 .

 1 below. Detected parameters of each layer for "News" video

	Base Layer	λ = 0.48 µ = 0.8	Mean rate = 0.30 Mbps
	Enhanced Layer 1	λ 1 = 0.56 µ 1 = 1.4	Mean rate = 0.67 Mbps
	Enhanced Layer 2	λ 2 = 1.5 µ 2 = 2.8	Mean rate = 1.94 Mbps


Marwane Ayaida
email: marwane.ayaida@univ-reims.fr

Mohtadi Barhoumi
email: mohtadi.barhoumi@univ-reims.fr

Hacène Fouchal
email: hacene.fouchal@univ-reims.fr

Yacine Ghamri-Doudane

Lissan Afilal
email: lissan.afilal@univ-reims.fr

HHLS: A Hybrid Routing Technique for VANETs

Keywords: VANETs, Location-based Services, Geographic Routing Protocols

In this paper, we propose a combination between a routing protocol Greedy Perimeter Stateless Routing (GPSR) and Hierarchical Location Service (HLS) that we denote Hybrid Hierarchical Location Service (HHLS). HLS and GPSR used to be combined in the original work with a direct method, i.e. GPRS takes care of routing packets and HLS is called to get the destination position when the target node position is not known or is not fresh enough. When a destination is quite far away from the sender, the exact position of the target is calculated, and an extra overhead is generated from sender to receiver. Our main purpose is to reduce this overhead in HHLS. We suggest to proceed as follows: when a packet has to be sent to the destination, it will be sent directly to the former position of the target instead of requesting for the exact position. When the packet is approaching the former position, the exact position request is then sent.

We have proposed a patch over the NS-2 simulator for HHLS according to our proposal. We have conducted experimentations which show promising results in terms of latency, packet delivery rate and overhead.

I. INTRODUCTION

VANETs (Vehicular Ad-hoc NETworks) are a special case of MANETs (Mobile Ad-hoc NETworks). Their major features is the high mobility of nodes. The immediate consequences are: topology changes and link disconnections.

Usual topology-based routing protocols have limited performances in such networks. Geographic routing protocols were designed to provide better performances for such networks. The main principle adopted by these protocols is that each node has to care about its actual geographic position (often achieved by a GPS) and the position of the targeted node to reach. With these protocols, the paradigm position-toposition is used. The Location-based Services is required to catch the destination position. The combination of this service with routing is quite natural in order to guarantee interesting performances. This combination called Hybrid Hierarchical Location Service (HHLS) was made between Greedy Perimeter Stateless Routing (GPSR) as a geographic routing protocol and Hierarchical Location Service (HLS) as a location-based service. Several experimentations were carried out over NS-2 network simulator. These experimentations demonstrate that the efficient combination between the geographic routing protocols and the location-based services enhanced the network performances while reducing the location overhead.

The remainder of the paper is organized as follows. Section II is dedicated to related works. Section III details our HHLS combination algorithm about GPRS and HLS, it describes also our experimentations and the obtained results. Section IV concludes the study and gives some hints about future works.

II. RELATED WORKS

A. Location Service

The location-based services can be classified into two classes: Flooding-based and Rendez-vous-based. The first class is composed of reactive and proactive services. In the proactive flooding-based location-based service, every node floods its geographic information through all the network periodically. Thus, all the nodes are able to update their location tables. Since this approach uses flooding and may surcharge the network by location update messages, several techniques to reduce the congestion were used. One of them is to tune the update frequency with the node mobility (the more nodes are moving fast, the higher update location frequency is used). An example of proactive flooding-based location-based service is the Distance Routing Effect Algorithm for Mobility (DREAM) [START_REF] Basagni | A distance routing effect algorithm for mobility (dream)[END_REF]. This routing includes a location-based service. Behind DREAM there are two ideas: the first one is, if a node is far it appears moving slower than a neighbor having the same speed. Therefore, the update frequency decreases with the distance to the node. The second idea is, a node with high mobility sends more update location packets. As a result, there are less packets than a simple flooding scheme without affecting the network performances. For the second group (i.e the reactive flooding-based location-based service), the location response is sent when receiving a location request. This avoids the overhead of useless location information of some nodes updated and never used. But, it adds high latencies not suitable in VANETs. As a good example, we cite the Reactive Location Service (RLS) [START_REF] Kasemann | A reactive location service for mobile ad hoc networks[END_REF].

In the second class (rendez-vous-based location service), all the nodes agree on a unique mapping of a node to other specific nodes. The geographic information are disseminated through the elected nodes called the "location servers". Thus, the location-based services proceed with two main operations:

1) Location Update: A node has to recruit location servers (chosen from other nodes) and needs to update its location through theses servers. The location servers are responsible of storing the geographic data of the relating nodes. 2) Location Request: When a node needs to know the location of another node, it broadcasts a location request.

The location server will replay as soon as it receives this request. There are two major approaches on the rendez-vous-based location services. In the quorum-based approach, as in [START_REF] Haas | Ad hoc mobility management with uniform quorum systems[END_REF], the location update is sent to a group of nodes (update quorum). The location query is sent to the same or a different group (query quorum). These two groups have not to be disjoint. Thus, the query reaches the first group which replies immediately. The challenge of this approach is to choose how to generate the query system [START_REF] Haas | Ad hoc mobility management with uniform quorum systems[END_REF].

The second approach is the hierarchical approach. The network is split into several levels. At each level a node selects location servers. The location query is forwarded up and down in the hierarchy. This principle reduces the forwarded packets and avoids flooding. The two major hierarchical services are: the Grid Location Service (GLS) [START_REF] Li | A scalable location service for geographic ad hoc routing[END_REF] and the Hierarchical Location Service (HLS) [START_REF] Kiess | Hierarchical location service for mobile ad-hoc networks[END_REF].

As GLS, HLS shares the network into several subsets called regions which are subdivided in hexagonal cells. Close regions are grouped into region level. This partition is fixed and known by all the participating nodes. The cell dimensions must be less than the range transmission. Thus, a node may be able to broadcast a message to all nodes in the same cell. Unlike GLS, a hashing function depending not only on the ID but also on the current position of the node is used to choose the responsible cell (where a node must select its location servers) at each region level. There are two different update methods:

• The direct method: to update its location information, a node sends frequently packets to the responsible cells.

A responsible cell may contain more than one location servers. This is the case only for close location servers (at the same region level). For others location servers the second methods is used. • The indirect method: To reduce the traffic, only the location of the responsible cell at level N-1 where is located the node is sent to the higher region level N. Instead of sending the node's geographic information, the coordinate of the responsible cell are sent when the node moves from a region level to another. Consequently, the traffic congestion generated by the node's movement is local at the first level and a few multi-hop long-distance location packets are sent to the top levels. If a node A needs to send data to a node B, A broadcasts a query location. When this location reaches a B's responsible cell and if A and B are in the same region level, a location server in the B's responsible cell sends a location reply. If they are not in the same region level, the location server forwards the packet toward the B's responsible cell in the lower level and so on until reaching the first level responsible cell. The latter generates a reply and sends it to the node A.

The location-based service used here for the combination is HLS since it has the best performances and the lowest overhead and complexity compared to GLS and RLS in [START_REF] Ayaida | A comparison of reactive, grid and hierarchical location-based services for vanets[END_REF].

B. Geographic Routing Protocols

Routing protocols algorithms must choose some criteria to make routing decisions, for instance the number of hops, latency, transmission power, bandwidth, etc. The topology-based routing protocols suffer from heavy discovery and maintenance phases, lack of scalability and high mobility effects (short links). However, geographic routing are suitable for large scale dynamic networks. The first routing protocol using the geographic information is the Location-Aided Routing (LAR) [START_REF] Ko | Location-aided routing (lar) in mobile ad hoc networks[END_REF]. This protocol used the geographic information in the route discovery. This latter is initiated in a Request Zone. If the request doesn't succeed, it initiates another request with a larger Request Zone and the decision is made on a routing table. The first real geographic routing protocol is the Greedy Perimeter Stateless Routing (GPSR) [START_REF] Karp | Gpsr: greedy perimeter stateless routing for wireless networks[END_REF]. It is a reactive protocol which forwards the packet to the destination's nearest neighbor (Greedy Forwarding approach) until reaching the destination. Therefore, it scales better than the topologybased protocols, but it does still not consider the urban streets topology and the existence of obstacles to radio transmissions. Another geographic routing protocol is the Geographic Source Routing (GSR) [START_REF] Lochert | A routing strategy for vehicular ad hoc networks in city environments[END_REF]. It combines geographical information and urban topology (street awareness). The sender calculates the shorter path (using Djikstra algorithm) to the destination from a map location information. Then, it selects a sequence of intersections (anchor-based) by which the data packet has to travel, thus forming the shortest path routing. To send messages from one intersection to another, it uses the greedy forwarding approach. The choice of intersections is fixed and does not consider the spatial and temporal traffic variations. Therefore, it increases the risk of choosing streets where the connectivity is not guaranteed, and then losing packets. Like GSR, Anchor-based Street and Traffic Aware Routing (A-STAR) [START_REF] Seet | A-STAR: A Mobile Ad Hoc Routing Strategy for Metropolis Vehicular ommunications[END_REF] is anchor-based. However, it reflects the streets characteristics. A connectivity rate is assigned to the roads depending on the capacity and the number of bus using it. This metric is used in addition to traditional metrics (distance, hops, latency) when making routing decisions. As a consequence, the streets taken by busses are not always the main roads where connectivity is ensured and the greedy approach does not consider the speed and direction for the next hop selection. This is why improved Greedy Traffic Aware Routing (GyTAR) [START_REF] Jerbi | An improved vehicular ad hoc routing protocol for city environments[END_REF] was designed as a geographical routing protocol adapted to urban environments and managing the traffic conditions. A sender selects dynamically an intersection (depending on the streets connectivity) through which a packet must be forwarded to reach the destination node. Between intersections, an improved greedy approach to forward packets between two intersections is used. This latter is based on the neighbors' speeds and directions. Despite GyTAR takes advantage from the urban roads characteristics, selects robust paths with high connectivity and minimizes the number of hops to reach an intersection; the main GyTAR drawbacks are that the connectivity information may be maintained by the infrastructures (RSSU: Road Side Service Unit) and it has a weak performances in spare networks. Besides, Geographic and Delay Tolerant Network with Navigation Assistance (GeoDTN+Nav) [START_REF] Cheng | Geodtn+nav: A hybrid geographic and dtn routing with navigation assistance in urban vehicular networks[END_REF] proposes a solution to the last problem. It switches between DTN (Delay Tolerant Network) and non DTN mode depending on the roads connectivity. In DTN mode and for a sparse network, the vehicle uses the carry-and-forward scheme. The packet will be stored until finding a possible forwarder. The main disadvantage of this protocol is that the performances are affected (high latencies) in a sparse network. This is due to the fact that the protocol tries constantly to switch between the DTN and non DTN mode when forwarding packets. In [START_REF] Taleb | A stable routing protocol to support its services in vanet networks[END_REF], authors introduced a scheme which enhances the stability of Intervehicular communications (IVC) and roadvehicle communications (RVC) communications in VANET networks. The key idea behind the proposed scheme is to group vehicles according to their moving directions. [START_REF] Yahya | A peer-to-peer based naming system for mobile ad hoc networks[END_REF] proposes a routing protocol adapted for mobile networks which could be used in the case of VANETs.

We have used the Greedy Perimeter Stateless Routing (GPSR) as the geographic routing protocol for the combination. However, the work still available with other geographic routing protocols.

III. HHLS: AN EFFICIENT GPRS & HLS COMBINATION

A. Description

In order to reduce the overhead of HLS and GPSR, we combine them in HHLS algorithm. The major difference between HHLS and HLS/GPSR is implemented in three functions. The first function Poslookup (defined in algo. 1) handles the querying of destination's position, it looks into the local cache memory of the current node and updates the packet information with the destination's position. Then, the second function GPSREmit (defined in algo. 2) manages the creation and emission of new packets, it verifies at first, whether the sender has fresh or non-fresh information about the destination's position and then starts the routing of packets. If not, the function starts a new position query and places the packet into a buffer while the query is taking place. The function forwardPacket (defined in algo. 3) handles the forwarding of packets, it is called whenever a packet reaches a intermediary node, it verifies whether this node has a fresher position of the target and eventually updates the packet's information with it. Otherwise, if the reached node is in the same region of the destination, we must launch a new query to retrieve the new position of the target.

B. Experimentations

1) Working Environment: The simulations were performed using the NS-2 simulator 2.33. Greedy Perimeter Stateless Routing (GPSR) [START_REF] Karp | Gpsr: greedy perimeter stateless routing for wireless networks[END_REF] is the used geographic routing protocol. The area chosen is a 2x2 km 2 of a real map representing a part of the French city Reims. This area is extracted from Open f reshness ← N ON F RESH; P repareP acket(packetT oSend);

P oslookup(packetT oSend);

if (freshness == FRESH) then

F orwardP acket(packetT oSend);

if (freshness == NONFRESH) then 8:

P repareP acket(packetT osend);

F orwardP acket(packetT oSend); LaunchP ositionQuery(destination);

12:

StickT oBuf f er(packetT oSend); At each simulation, each node starts 4 CBR traffics of 10 packets with a size of 128 B to 4 random destination nodes with one second of interval between each sending instant. The CBR traffic simulates, for example, an audio or a video streaming process. It may be used in monitoring applications for example, such as viewing the video stream from a camera located on a bus by the police car or the security agent vehicle. Also, this traffic could be used in entertainment applications to connect to the Internet or to play online video games.

2) Experimentation Results: The main result in our experimentation is the number of sent location requests shown in Figure . 1. Clearly, the number of location requests is reduced in HHLS. For example, there are 93% less location requests in HHLS compared to HLS with 20 nodes. This difference decreases when the number of nodes increases(25% with 100 nodes and 22% for 120 nodes). This is due to the traffic overload when the number of nodes increases. U pdateP acketDestination(packetT oSend); LaunchP ositionQuery(destination); StickT oBuf f er(packetT oSend); As a consequence of this location traffic, there are a lot of sent packets for location updates, requests, replies, handovers, etc. This produces an extra overhead. We measured the location-based service overhead in term of bandwidth consumption. The bandwidth consumed in the routing layer is presented in Figure . 2 and the one consumed in the MAC layer is shown in Figure . 3. In all these experimentations, the bandwidth needed for the routing layer and for the MAC layer is lower in HHLS than in HLS. This is due to the efficiency of the HHLS mechanism which launches the location query as late as possible.

Our main aim is to reduce the location overhead without affecting the network performances such as the packet delivery rate (PDR) and the latency. The PDR is defined as the ratio between the CBR packets received and the ones sent. It is depicted in Figure . 4. The PDR is almost better in HHLS than The PDR is at its lowest rate for HLS and HHLS with 20 nodes. This could be explained by the insufficient number of nodes and hence the weak connectivity of the network. Then, the PDR increases with the number of nodes up to 120 nodes, where the network is overloaded and then the PDR decreases again because of packet collisions. Another performance criterion is the average number of packet hops. The results of the average hops were not included because they was almost the same in both approaches HLS and HHLS.

Considering the results of these experimentations, we conclude that HHLS scheme has not only a lower cost (number of requests and consumed bandwidths) than HLS, but it enhances the network performances (PDRs and latencies). This confirms that mixing the geographical routing protocols and the location-based services reduces the overhead and also improves the performances instead of dealing with each issue alone.

IV. CONCLUSION & FUTURE WORKS

We have proposed a hybrid approach HHLS to handle the location service and to make the routing decisions in VANETs. In fact the approach provide one unique process which includes on one hand routing and on another hand location service. We implemented it in the NS-2 framework by means of an appropriate patch. We have conducted many experimentations with this patch in order to observe network performances. We have shown in this paper that a smart combination of HLS with GPRS could provide better results in terms of network performances in particular for the packet delivery rate, the latency and the overhead. As a future work, we intend to improve these performances by adding mobility prediction. Instead of sending the packet to old locations. We will estimate the new position using additional information such as speed, direction, etc. We intend also to improve the process by considering location updates after reaching target nodes when packets are routed.

Algorithm 1 HHLS :: Poslookup 1 :

 11 cacheT hreshold ← LocationCacheM axAge; 2: enum f reshness {N OP OSIT ION = 0, F RESH = 1, N ON F RESH = 2}; 3: procedure POSLOOKUP(packetT oSend)

Algorithm 3 3 :

 33 GPSR :: ForwardPackets 1: procedure FORWARDPACKET(packetT oSend) 2: if (freshness == FRESH) then ChooseN extBestHop(packetT oSend); 4: F orwardN extHop(packetT oSend); 5:

else 6 :

 6 if (Current Node freshness == FRESH) then 7:

8 :

 8 ChooseN extBestHop(packetT oSend); 9:F orwardN extHop(packetT oSend); 10:

else 11 :

 11 if (Current Region == Target Region) then 12:

Figure 1 .

 1 Figure 1. HLS requests Vs. HHLS requests

Figure 2 .

 2 Figure 2. HLS bandwidth Vs. HHLS bandwidth in routing layer

Figure 3 .

 3 Figure 3. HLS bandwidth Vs. HHLS bandwidth in MAC layer

Figure 4 .

 4 Figure 4. HLS PDR Vs. HHLS PDR

Figure 5 .

 5 Figure 5. HLS Latency Vs. HHLS Latency


Adriana Hava
email: adriana.hava@ucdconnect.ie

Yacine Ghamri-Doudane

John Murphy
email: j.murphy@ucd.ie

On Monitoring Overhead Impact in Wireless Mesh Networks

A wireless mesh network is characterized by dynamicity. It needs to be monitored permanently to make sure its properties remain within certain limits in order to provide Quality-of-Service to the end users or to identify possible faults. To establish in every moment what is the appropriate reporting interval of the measured information and the way it is disseminated are important tasks. It has to achieve information quickly enough to solve any issue but excessive as to affect the data traffic. The problem that arises is that the monitoring information needs to travel in the network along with the user traffic and thus, potentially causing congestion. Considering that a wireless mesh network has highly dynamic characteristics there is a need for a good understanding of the influences of disseminating monitoring information in the network along with user traffic. In this paper we provide an evaluation of the network performance while monitoring information is collected from network nodes. We study how different monitoring packet sizes and different reporting frequency of the information can impact the user traffic and compare these values to the case in which only user data travels across the network.

I. INTRODUCTION

Wireless Mesh Networks (WMNs) have become very popular in the recent years. They offer a cost-efficient solution for Internet access and applications in both municipal deployments and short-term small range deployments. Also, due to multi-hop capabilities they can be used to provide wireless Internet access in areas where cellular coverage is limited. Another reason for the tremendous growth of WMNs is that they can operate in the ISM band which reduces significantly the deployment cost unlike other technologies that use licensed spectrum.

A WMN is composed of a static set of mesh nodes that perform routing, radio relaying, and traffic forwarding between the clients and gateways. Clients may include mobile terminals such as laptops, vehicular on-board-computers, or Wi-Fi enabled cellular phones.

Many research works focus on providing appropriate Quality-of-Service (QoS) in Wireless Networks in order to offer demanding applications, such as Voice over IP ( [START_REF] Olariu | A Qos Based Call Admission Control And Resource Allocation Mechanism For Lte Femtocell Deployment[END_REF]) or Video on Demand. These imply measuring certain network parameters to monitor the network performance and allowing the derivation of QoS levels that can be offered by the system. The information collected from the network can also be used for other purposes, such as identifying network failures or for locating network bottlenecks. In order to achieve this goal, an efficient monitoring system must be deployed. This enables fast reaction to any change occurring in the network.

Many solutions have been proposed in the past for efficient monitoring of wired networks [START_REF] Chaudet | Optimal Positioning Of Active And Passive Monitoring Devices[END_REF], but they have not been designed with wireless mesh networks characteristics in mind. A study of these solutions shows they do not perform well in a wireless environment due to the dynamic nature [START_REF] Ishibashi | Topology and mobility considerations in mobile ad hoc networks[END_REF], interferences or limited bandwidth, specific to a wireless system. An efficient monitoring system that can provide an accurate network view using real-time monitoring data is required for QoS provisioning or network failures identification. One issue related to monitoring traffic parameters is that the packets containing the collected information have to travel up to a central collection point and contend for the medium with the normal data traffic. Thus the monitoring packets count as an overhead to the network, causing degradation of data services and the overall network performance.

As stated in [START_REF] Sailhan | Wireless Mesh Network Monitoring: Design, Implementation And Experiments[END_REF], monitoring implies two steps: the measurement of data, which can be active or passive, and the gathering of data, which can be classified into proactive or reactive. We consider a third phase, data dissemination, to be as important as the first two. This phase, which implies the reporting of the information collected by the mesh nodes to a collector, can be adjusted by changing the reporting frequency and the detail of reports. Therefore, in this paper we present a deeper analysis of the impact of monitoring on the performance of WMN through various simulations.

Section II describes the previous work done in this area and Section III presents our proposal for the evaluation of the monitoring overhead impact. The simulation setup used to assess the impact is described in Section IV. Finally, Section V discusses the evaluation results and Section VI presents the conclusions with some future work directions.

II. RELATED WORK

There has been many solutions proposed for efficient traffic monitoring and adaptation ( [START_REF] Muntean | Adaptive Pre-Recorded Multimedia Streaming[END_REF]) in wired networks [START_REF] Chaudet | Optimal Positioning Of Active And Passive Monitoring Devices[END_REF]. However, traffic monitoring in wireless mesh networks has become a topic of interest for researchers only recently. In the following, we describe the major work that had been performed in this topic.

The authors in [START_REF] Sailhan | Wireless Mesh Network Monitoring: Design, Implementation And Experiments[END_REF] propose a low overhead monitoring architecture customized for WMNs. Their approach is to automatically organize all nodes into a hierarchy of clusters dedicated to the delivery of monitoring data. Information is gathered while passively listening to the OLSR (Optimized Link State Routing) [START_REF] Clausen | Optimized Link State Routing Protocol (OLSR)[END_REF] protocol. The nodes in the structure cooperate and form a monitoring overlay which is able to adapt to the network characteristics. The evaluation is done using two performance metrics: robustness and scalability. The work in [START_REF] Sailhan | Wireless Mesh Network Monitoring: Design, Implementation And Experiments[END_REF] lacks in evaluating the proposed method in the presence of user traffic. This traffic may suffer interference from the monitoring data that is transmitted in the network.

In [START_REF] Kim | On Accurate Measurement Of Link Quality In Multi-Hop Wireless Mesh Networks[END_REF], Kim et al. introduce a scheme for accurate measurement of link quality in WMNs. Efficient and Accurate link-quality monitoR (EAR) uses three measurement schemes: passive, cooperative, and active monitoring. Their proposed solution maximizes the measurement accuracy by adopting dynamically one of the three schemes. The dissemination of link-quality information is not analyzed, thus it is unknown how the monitoring data interferes with the user traffic.

A probe-based monitoring architecture for IP flows in a WMN is presented in [START_REF] Huang | A Flow-Based Network Monitoring Framework For Wireless Mesh Networks[END_REF]. MeshFlow records are created on every mesh node on the path of a packet. These records are exported to a dedicated collector, which analyzes the data. The authors mention three methods for transmitting the records: dedicated cable line, antennas deployed around the entire backbone network, or in a multi-hop fashion along with the normal traffic transport. The first two methods increase the cost of deployment while the last method introduces overhead which is not analyzed in the paper.

Another method to reduce the overhead traffic is to identify the optimal placement of monitoring nodes. Chaudet et al. in [START_REF] Chaudet | Optimal Positioning Of Active And Passive Monitoring Devices[END_REF] found that in terms of deployment costs it may be more advantageous to monitor only 95% of the traffic, thus reducing the number of probes required. The work presented in [START_REF] Popi | Wimflow: A Distributed, Self-Adaptive Architecture For Flow Monitoring In Wireless Mesh Networks[END_REF] describes WiMFlow, a self-organized monitoring framework for WMNs. The proposed mechanism adapts the packet rate of control messages based on the topology changes in order to keep the overhead low.

Although all the above papers present different methods for collecting monitoring information they do not study the impact of disseminating this data. The studies presented in [START_REF] Gupta | A Study Of Overheads And Accuracy For Efficient Monitoring Of Wireless Mesh Networks[END_REF] are the only ones which investigate this problem. The authors study the effects of monitoring overheads on the forwarding of users FTP data traffic. They also check how it impacts the wireless mesh network performance in terms of packet loss and throughput. Three different approaches are presented: the monitor-selection, reporting interval, and threshold-based monitoring approach. For each of them the aggregated packet-loss percentage, end-to-end delay and percentage of packets retransmissions at the link layer are computed. We consider the aforementioned evaluation to be incomplete as some details are omitted: there is no mention about the number of wireless interfaces on each mesh node, about the chosen monitoring packet size for the approaches presented, and the most important, their results are not compared against the case when the monitoring is not enabled. Also we see a limitation in the assumption whether the monitoring packet size and reporting frequency should be connected. In our work, we disconnect the two parameters and analyze the impact on the user traffic if, for example, a larger packet is sent at low or high frequencies.

III. NETWORK MODEL AND EVALUATION METHODOLOGY

We consider that the most sensitive applications to networks fluctuations, but also the ones that require continuous monitoring, are video and voice data. Therefore, we focus our attention on the impact of disseminating the monitored information as overhead to UDP client traffic.

A. Network Model

Each mesh node is equipped with two 802.11g antennas for communication between mesh nodes and one 802.11a antenna for communication with the clients. We chose 802.11g for the communication inside the mesh network because of its larger distance range which is more suitable for an urban scenario. The 802.11a protocol was chosen for clients' connectivity because of the increased non-overlapping available channels.

For the mesh networking model we use the 802.11s standard and Hybrid Wireless Mesh Protocol (HWMP) as the MAC-layer routing protocol. The 802.11s standard is an extension of the 802.11 for arbitrary multi-hop topologies, where each mesh node operates as a link-layer router and cooperates with all the other mesh nodes in the process of frame forwarding. HWMP is based on the AODV routing protocol, but it works at the MAC layer for efficient path selection. HWMP works in two modes: proactive and reactive. In the reactive mode the path discovery starts when a source has data to transmit to an unknown destination. In the proactive mode a single mesh node is configured as root and if the route to a destination is unknown the data is sent to the root node which is responsible for forwarding it to the destination node.

B. Evaluation Methodology

The impact of monitoring data dissemination is evaluated through three different metrics: throughput, packet loss, and packet delay. These parameters are measured while varying the reporting frequency of measurement information and by varying the monitoring packet size. A more frequent reporting interval gives a better view about the network performance but it has a greater impact on the user traffic (because of higher contention for the medium). While in the case of a larger reporting interval the information granularity might be insufficient to perform real-time adaptation.

Therefore, this work gives a better insight of what is the trade-off of choosing between different combinations of monitoring packet sizes and reporting frequencies.

IV. EVALUATION RESULTS

A. Simulation Setup

NS-3, a discreet-event open-source network simulator, is used in our simulation setup. The topology on which the simulations are run is presented in Figure 1. Our two configurations are a 3x3 and a 4x4 grid topology consisting of mesh nodes placed at 100m grid step. The gateway is the node at the top-left of the grid. For the channel allocation we used two different configurations: spread channel policy, where different non-overlapping 20 MHz frequency channels are assigned to different mesh node interfaces, and a tiered channel distribution, where the channels are allocated as in Figure 5. Two types of mesh nodes are evaluated: equipped with one interface and with two interfaces.

In our simulation setup, the clients are spread across the wireless mesh network. User data packets are set at 1500 Bytes with different frequencies of sending data, equivalent to a user demand of 0.25, 0.50, 0.75, 1.00 and 1.25 Mbps for each client. The Nakagami fast fading propagation model is used, since it is suitable for urban scenarios for which our monitoring evaluation is designed.

For the monitoring traffic we chose reporting frequencies from the following values: 0.5, 1, 5, 10, and 15 seconds and the monitoring packet size was varied between 500, 1000, and 1500 Bytes. We decided to choose these reporting frequencies to have a larger variety of values and also based on other studies where the most common used value for reporting interval is one second. In summary, our configuration is presented in Table I. The results from Figure 2 depict the case of nine node grid where each user has a demand of 0.25 Mbps, Figure 3 for 0.75 Mbps user demand, and Figure 4 for 1.25 Mbps demand per user. These cases are chosen in order to evaluate the impact of reporting monitoring information in three different network traffic demand situations: for a low, medium, and high traffic volume.

All the figures present the average values for the overall throughput, end-to-end delay, and packet loss over five different simulation runs. The error bars represent the confidence interval. Each green bar indicates an average value obtained for a specific frequency from 0.5 seconds to 15 seconds, as per legend. Each set of five green bars corresponds to a monitoring packet size (PS) of 500 Bytes, 1000 Bytes and, respectively, 1500 Bytes. The upper plot of the figure indicates the impact on the user throughput when the monitoring reporting functionality is enabled. The middle plot depicts the impact on the end-to-end user delay, while the bottom plot indicates the impact on packet loss.

For the first case, where the user demand is 0.25 Mbps (Figure 2), it can be observed that for some cases, the reporting of monitoring data does influence the amount of throughput the clients receive. This situation happens when the spread channel allocation is used, for both reactive and proactive routing protocol, and for either one or two interfaces per node. It can be observed that when the frequency of reporting is the highest, i.e. 0.5 seconds, also the throughput drops faster compared with the case when the reporting frequency is set to lowest, i.e. 15 seconds. The same behaviour can be observed when measuring the end-to-end delay. If the frequency of reporting is set to lower values, the end-to-end delay increases compared to the case of higher reporting frequency. For the case of reactive routing, the end-to-end delay increases by up to 30% compared with the case when the reporting functionality is disabled.

In terms of packets lost, it can be observed that the size of the packets containing the monitoring data plays an important role. In the case of spread channel allocation, for smaller packets, i.e. 500 Bytes, the amount of packets lost is smaller compared to the case when packets of 1500 Bytes are used to transfer the monitoring information. It can be observed that the packet loss rate increases up to 10% when 500 Bytes packets are used and up to 30% when 1500 Bytes packets are used to encapsulate the information.

For the tiered channel allocation configurations, described in Figure 5, it can be observed that the impact on the throughput is much smaller compared to the case of spread channel allocation. The end-to-end delay maintains at the same value as when the reporting monitoring data functionality is disabled. A small increase in packet losses can be observed in the bottom plot, occurring only in the case of proactive routing.

Figure 3 depicts the results for 0.75 Mbps demand per user. A drop in throughput can be observed for all the configurations, but the highest impact is on the end-to-end delay. For all the configurations, the end-to-end delay increases compared to the case when the reporting functionality is not enabled. The highest impact is observed when the packet containing the monitoring information is set to 1500 Bytes.

Figure 4 presents the results for a user demand of 1.25 Mbps. The impact of sending monitoring data over the mesh network has a higher impact on the user throughput compared to the other two scenarios. On all configurations it can be observed an important decrease in throughput when the reporting functionality is enabled.

In order to have a deeper understanding on how reporting the information collected by each node to a central server will impact the clients, we extend the simulations on a 16 node mesh grid. The results are presented in Figure 6 and Figure 7.

It can be observed in Figure 6 for proactive routing and spread channel allocation configuration that the impact on the user throughput is much higher. For higher reporting frequencies, i.e. 0.5 seconds, the throughput drops by up to 20% compared to the case where there is only the client traffic running in the network. It can also be noticed a dependency on the size of the packet which is carrying the monitoring information. For larger packet sizes, i.e. 1500 Bytes, the throughput drops even more, up to 30%, compared to the case of 500 Bytes for the monitoring packet.

The end-to-end delay is also affected by the monitoring data dissemination through the network. The delay for the user packets will be higher for lower reporting frequencies and vice-versa. In one or two interfaces per node scenarios, the tiered channel allocation configuration has the same impact. The channel distribution allows the two interfaces to communicate independently of each other. However, if we consider the continuous path presented in Figure 5 between node A and node B, node M is not able to support another path between the two nodes as its both channels are involved for the initial path. This means that node B will behave as a single-channel node. Other possible routes between A and B could be established through nodes C or D but their channels are not available.

B. Discussions and Lessons Learned

Our study allowed us to highlight four main conclusions regarding the effect of the monitoring reporting functionality on the user traffic performance:

• The monitoring data reporting frequency plays an important role on the user traffic performance. In all the configurations presented, it has been observed that if the reporting frequencies are set to high values also the impact on the end-user traffic performance will increase. This can be observed in the measured throughput values which will be smaller compared to the case when the reporting functionality is disabled. Also the end-to-end delay is similarly strongly affected. For higher reporting frequencies the time a packet will spend in the mesh network will be longer. When the reporting frequency is set to high values, i.e. 15 seconds, the impact is almost zero.

• Along with the reporting frequency, a role is also played by the size chosen for the packet carrying the collected monitoring information. If the size of the monitoring packet is large, the impact on the user traffic performance is high. The packet size is strongly connected with the reporting frequency chosen. The highest impact on the user traffic performance has the combination of a large monitoring packet size and a high reporting frequency.

The throughput and the end-to-end delay are both affected in this case. Most importantly, the impact of the packet size is visible only for few cases, while the increase in the reporting frequency importantly impacts the user traffic performances for most of the studied wireless mesh network configurations.

• Both low traffic networks, i.e. 0.25 Mbps user demand, and congested networks, 1.25 Mbps user demand, are affected similarly by the presence of monitoring data. This means that even if the network is not congested, the reporting of monitoring data has a negative impact on the user traffic performance.

• The impact of the reporting of monitoring data is obviously increased when the network size increases.

Comparing the 3x3 grid case and the 4x4 grid case, it can be observed a more important decrease in the throughput and increase in the end-to-end delay in the 4x4 grid case when the monitoring reporting functionality is enabled. These results allow us to state that in order to mitigate the effect of the reporting of the monitoring data, there is a clear need to design new approaches that reduce the frequency of monitoring by enabling only some well selected nodes to perform the monitoring task. These nodes need to correctly adapt their reporting frequency.

V. CONCLUSIONS AND FUTURE WORKS

The paper presents a study about the impact of disseminating the monitoring information encapsulated into packets along with the user traffic. First, we analyze the user throughput, packet delivery ratio, and the packet delay when the monitoring functionality is disabled. Second, we study how these parameters change for various combinations of monitoring packet sizes and reporting frequencies on different mesh network configurations.

Most of the previous works look at different measurement techniques in wireless mesh networks but have omitted to analyze the consequences of disseminating the collected information along with the user data traffic. Thus, our work gives a deeper analysis of this problem.

Based on a number of simulations we show that there is a significant impact on the user traffic. We analyze how the network performance changes for various reporting frequencies and monitoring packet sizes.

Part of our future work will be to use these results for proposing new mechanisms to adapt the number of monitoring nodes based on the network conditions and also to modify in time the mesh nodes' reporting frequencies independently of each other.

Fig. 1 .

 1 Fig. 1. Nine-node grid topology

Fig. 5 .

 5 Fig. 5. Tiered channel allocation

Fig. 2 .Fig. 3 .Fig. 4 .

 234 Fig. 2. Simulation results for a 3x3 grid and 0.25 Mbps demand per user

Fig. 6 .Fig. 7 .

 67 Fig. 6. Simulation results for a 4x4 grid and 0.25 Mbps demand per user

ACKNOWLEDGMENT This work is partially supported by the European Union through the Marie Curie IAPP program under the grant agreement no. 230684: CarMesh: Ubiquitous Wireless Mesh Networks for Next-Generation Personal Digital Automotive Services and partially funded by Irish Research Council for Science, Engineering & Technology (IRCSET) via grant RS200902. This work was supported, in part, by Science Foundation Ireland grant 10/CE/I1855 to Lero -the Irish Software Engineering Research Centre (www.lero.ie)


INDICATORS OF TERRITORIAL COMPETITIVENESS

Csilla Filó

Université de Pécs

Indicators of territorial competitiveness

INTRODUCE

The 21 st century sees changes in modern society, social structure, territorial policy, public administration and other fields, generated by the European Union, which have a significant impact on the functioning and efficiency of society. For real competences to find their appropriate places and levels, a mature society are required as well as the investigation and improvement of the maturity of territorial levels prior to implementing any measures. To be able to induce these improvements, we first need to define the territorial unit where the process is to be launched. The broadest definition: A territorial unit is a (socio) geographical integration marked by a huge internal cohesion, a fundamental entity of our days. It is a socio-economic geographical unit based on the close cooperation and shared interests of its constituting parts, which cannot, however, always be defined by geographical boundaries.

The resulting definition of the improvable unit (region): A region is the functional connection and integration of geographically close urban areas, agglomerations, settlement units, and infrastructural networks characterized by the gradually growing importance of neighbourhood contacts. The basis of the creation of a region is the territorial blending of society and economy, and an awareness of close interrelatedness between local communities, based on historical grounds. To reveal the development potentials of a given territory, we need to determine the measurement units relating to the processes we are observing. This unit cannot be but the competitiveness of each area.

THE REGIONAL COMPETITIVENESS

To approach the notion of competitiveness from a regional perspective has become productive in many ways recently. On the one hand, as a consequence of developing in a globalized environment, it has become necessary for theories relating to the region to introduce an umbrella term, and on the other hand, the previous use of the term on the macro-and micro-level lacked the intermediary level that, as a localised level, could refer to an economic field for developments and investments. Competitiveness is often viewed as a key indicator of the success or failure of policy. The concept of competitiveness, however, while relatively clear when applied to enterprises, is more difficult to define and measure when applied to regions or countries. An industrial region, for example, is not directly competing against a predominantly agricultural region or a financial centre, so the measurement of its relative competitiveness is problematic. Moreover, the term itself tends to convey the impression of a win/lose situation, in which regions can improve their position only at the expense of others, whereas, in practice, there are mutual gains to be achieved from individual regions becoming more competitive.

UNDERSTANDING REGIONAL COMPETITIVENESS IN THE EU

The concept of competitiveness, as seen in the EU, could be summarised as "high and rising -standards of living and high rates of employment on sustainable basis".

The traditional measure of competitiveness/standards of living is generally calculated by the GDP per head, despite the fact that some other indicators should be defined in order to integrate the social, environmental, health and well-being dimensions. GDP per head can be broken into two main components: employment rate (proportion of working age population in work) and productivity (GDP per person employed).-Productivity is considered to be a good indicator of competitiveness following the assumption that productivity growth will increase competitiveness, which will in turn favour a higher growth of GDP. The growth of GDP will boost employment. Employment rate and productivity are closely linked to one another but they should be considered separately. Indeed, high level of one does not necessarily go with a high level of the other. For example, one can remind the period of "jobless growth" which struck the EU in the-80's, and which was characterised by high gains in productivity and poor gains in employment creation. Nevertheless, achievement of high employment growth and high productivity are not necessarily in conflict with one another. Indeed, if an economy growth above a given threshold-(2% for the EU) and this level is maintained over the long-term, there will always be net creation of employment. Therefore, we can say that in the long-term, there is always a positive relationship between productivity and employment creation. Lagging regions in Europe have always productivity below average while employment is most of the time (but not always) less important. Actually, in the EU, there are two types of lagging regions:

-regions with employment rates similar to the average but with lower productivity; -regions lagging in both productivity and employment.

The challenge for lagging regions is to increase/boost productivity by a mix policy without having adverse effect on employment. The policy of the EU regarding competitiveness is to practice a non-price competitiveness in order to keep and improve the leaving standards, the social and the moral values/models of its societies as a whole. Consequently, the EU places emphasis on structural factors underlying competitiveness which determine the long-term growth, in particular research and innovation, information technology and human capital. This commitment was confirmed by the ultimate objective of the Lisbon European Council, which has stated that "the EU becomes the most competitive and dynamic knowledge-based economy in the world over the decade, capable of sustainable economic growth with more and better jobs and greater social cohesion". There is a clear relationship between innovation, human capital and information technology when it comes to competitiveness. Indeed, the capacity to innovate is perceived as the best answer to global competition, the needs to adapt to technological changes and to constantly produce new products, which are elements characterizing the post-Fordist production system. The capacity to constantly innovate and to absorb innovations is directly linked to the level of education of the population. Information technology is a key element when it comes to knowledge development and policy. In summary, in the EU, developing regional competitiveness depends on modernizing and diversifying the productive structure (by developing knowledge-based economic activities and innovation) once a sufficient endowment of physical infrastructure and human capital is attained. This has to be achieved respecting a horizontal condition, which consists in achieving a sustainable development in environmental terms.

According to the Sixth Periodic Report on the Social and Economic Situation and Development of Regions in the European Union, the main determinants influencing the competitiveness of regions are the following:

-research and technological development -small and medium-sized enterprises -direct foreign capital investments -infrastructure and human capital -institutions and social capital The sixth regional report also includes the principal factors that usually characterize successful regions, namely, high rate of employment in business services and in the processing industry, the extent of innovative activities, favourable conditions of regional accessibility, skills of the workforce and a high proportion of university/college graduates.

--the capacity for economic structural change, especially the emergence of value-adding industries with multiplicative effect --high proportion of value-generating service sector (business services, research and development, higher education, culture) --typically knowledge-based production --strong innovative skills --the decision-making takes place in the successful region --a strong and thriving middle class --valuable settlement environment, an urban policy of high standards, and the availability of good quality public utilities and municipal services --successful conflict management --significant external (international) relations, integration into the system of towns and relations of an international region --increasing income and employment Measuring regional competitiveness has been traced back to four related economic categories: income generated in the region, labour productivity, employment rate and the openness. The notion of competitiveness obtained in this way cannot be used, however, to identify factors responsible for regional competitiveness or areas which are to be strengthened or developed by regional development policies and programmes for improved competitiveness. The pyramidal model of regional competitiveness seeks to provide a systematic account of these means and to describe the basic aspects of improved competitiveness [START_REF] Gardiner | Competitiveness, Productivity and Economic Growth across the European Regions[END_REF][START_REF] Lengyel | A regionalis versenykepessegrol (Regional Competitiveness)[END_REF][START_REF] Lengyel | The Pyramid Model: Enhancing Regional Competitiveness in Hungary[END_REF]. Factors influencing regional competitiveness can be divided into two groups of direct and indirect components. Of particular importance are programming factors with a direct and short-term influence on economic output, profitability, labour productivity and employment rate. But social, economic, environmental and cultural processes and parameters, the so-called 'success determinants', with an indirect, long-term impact on competitiveness are also to be taken into account [START_REF] Jensen-Butler | Competition between cities, Urban performance and the role of urban policy: A Theoretical Framework[END_REF]. Three levels can be distinguished with regard to the objectives of regional development programming and the various characteristics and factors influencing competitiveness:

-Basic categories of regional competitiveness (ex post indicators; revealed competitiveness): these categories measure competitiveness and include income, labour productivity, employment and openness. -Development (programming) factors of regional competitiveness (ex ante factors; improving competitiveness): factors with an immediate impact on basic categories. These can be used to improve regional competitiveness by means of institutions in short-term programming periods. -Success determinants of regional competitiveness (social and environmental conditions; sources of competitiveness): determinants with an indirect impact on basic categories and development (programming) factors. These determinants take shape over a longer period of time and their significance reaches beyond economic policymaking.

When characteristics determining competitiveness are placed on a chart one obtains the 'pyramidal model' of regional competitiveness (Figure ): the components of long-term success are to be found in the base, the middle layer is constituted by the development (programming) factors, the basic categories included in the standard definition of revealed competitiveness are located one level higher, while the standard of living and welfare of the region's population, the ultimate objective, forms the peak of the pyramid. Competitiveness depends on a wide range of factors and conditions. The five programming factors (priorities of the regional development strategy) of pyramidal model underlying competitiveness included in the Sixth Periodic Regional Report of the EU (EC 1999), however, exceptionally significant (Lengyel 2003[START_REF] Lengyel | The Pyramid Model: Enhancing Regional Competitiveness in Hungary[END_REF]. These development factors shape, to varying extent, economic output, labour productivity as well as employment.

Improving individual programming factors forms the object of regional policies. They are likely to improve the competitiveness of regions directly and in the short run by means of regional partners, local institutions.

Figure 1: The pyramid model of regional competitiveness (Based on: Lengyel (2003), Begg (1999), EC (1999), Jensen-Butler ( 1996))

The social and economic factors essential for long-term successfulness form the base of the model, the determining factors are in the middle and the ultimate goal of higher standards of living and better quality of life for the population of the region is at the top.

-Economic structure: in successful regions most of the people employed work in business services and the processing industry, which are characterised by high added value, intensive multiplicative effects and great flexibility. -Innovation culture: successful regions are characterised by extensive innovative activities, the effective diffusion of innovation, and a large number of patents. It should manifest itself not only in research and development institutions and universities; adequate business capacities and well-prepared, innovative small and medium-sized enterprises are equally important. -Regional accessibility: successful regions are easy to access, with convenient transport connections and geographical location. Supplemented by the transport -and communications infrastructure, these form an integral whole.

-Skills of workforce: in successful regions the proportion of qualified labour within the workforce is relatively high, which obviously requires an effective education system focussing on the actual demands of the labour market. -Social structure: in successful regions a strong and thriving middle class emerges, which helps the region develop by virtue of its discerning demand and higher income. -Centres of decision: successful regions are the regional bases of companies, they are units that undertake important activities of the enterprises operating there, and have decision-making competencies. New, innovative, strategic units are typically developed in the region or town where the company headquarters are found. -Quality of the environment: successful regions possess high-standard settlement environment (public safety, pleasing urban architecture, good quality accommodation, efficient public transport etc.) and healthy natural environment. -Social cohesion of the region: successful regions have an ability to manage conflicts, no matter whether they arise from economic structural change, dynamic economic growth or from inequalities among areas or communities within the region. The municipalities aim at cooperation and regional identity and local pride become stronger.

The indirect factors can be divided into two groups, one including those that determine short-term economic output (economic structure, innovation culture, regional accessibility, skills of work force) and the other including those that show their influence only in the long run (social structure, centres of decision, quality of environment, social cohesion of the region).

Following a different classification, we can put the indirect factors of the pyramid into five groups:

-a group of socio-economic activity (which includes the factors best expressing the economy and income potential of town): economic aspect, regional accessibility -a group of functional and organisational innovation: innovation culture, -a group referring to employment, intellectual resources and skills of the work force: skills of work force -a group relating to demographic factors and to the traditional and new functions of centres: centres of decision, social structure, social cohesion, -and treating quality of environment as a separate category, as it does not fit into any of the previous groups clearly

Regional competition can be seen as a process in which certain groups try to influence the development of regional or local economy through local policies, in explicit or often implicit ways, in competition with other regions. Consequently, competitiveness of a region is made up of several components, which can be described with various indicators.

CONCEPT OF TERRITORIAL COMPETITIVENESS

This concept of competitiveness aims to achieve a local specialization that enables different territories to cooperate in a context of general development.

Territorial competitiveness means:

-taking the area's resources into account in a bid for overall coherence;

-involving different players and institutions; -integrating business sectors into an innovation dynamic; -cooperating with other areas and linking up with regional, national and European policies as well as with the global context.

Our concept of territorial competitiveness has gained ground in academic, policy and practitioner circles. In particular, regional competitiveness has generated a large literature. These are described formerly. But the territorial competitiveness differs to regional competitiveness. This difference offers to approach. According to dynamism and spatial analyzing the territorial determination oversteps on the regional competitiveness. The base of regional competitiveness is the incoming and the product contrary with territorial competitiveness where the base is the innovate process in sustainable development.

Sum up, the territorial competitiveness diverges from regional competitiveness so that applies the methods of analysing but it considers in process the information concern this information will be jointed to challenge sustainable development.

Territorial competitiveness is an integrated and proactive approach to shaping the future of territories, regions and larger geographies -to some degree it can also be referred to as spatial planning. It goes beyond traditional regional policy as it brings together economic, social and environment opportunities and concerns as well as other factors which influence where activities takes place, how different places function and are connected, and what conditions are offered for living and doing business. Territorial competitiveness strategies can help exploring potentials for economic growth and jobs and at the same time support an enhanced quality of life by helping to meet the challenge of sustainable development. The field of sustainable development can be conceptually broken into three constituent parts: environmental sustainability, economic sustainability and social-political sustainability. Sustainable development policies encompass three general policy areas: economic, environmental and social.

CHALLENGE OF TERRITORIAL COMPETITIVENESS IN SUSTAINABLE DEVELOPMENT

CONCLUSION

The competitiveness territories have the high rate of qualify labour in the group of able to works. The education system is efficient because on the one part it fits for the creative and innovative activities the younger, on the other hand it follows the demand of labour market in flexibility. The successful and competitiveness regions have the level of knowledge base level and the labour culture, in additional the labour forces able to adapt plainly to changes (in trainings and courses the active labour force retrain able), ambitious with their environment.

In successful and competitiveness regions the new economic structure the knowledge base economical activities and the recovery of economic services enhance the social cohesion. Unschooleds are crowded out from labour market and the local society polarizes. The active gentility animates the regional development with utilize of higher incoming.

To sum up we can enhance the territorial development process with complex human resource survey. We can open up the social disadvantages in additional can find solution to this problem if we can survey the complex problems.

  Sustainable development is a socio-ecological process characterized by the fulfillment of human needs while maintaining the quality of the natural environment indefinitely. The linkage between environment and development was globally recognized in 1980, when the International Union for the Conservation of Nature published the World Conservation Strategy and used the term "sustainable development." The concept came into general usage following publication of the 1987 report of the Brundtland Commission -formally, the World Commission on Environment and Development. Set up by the United Nations General Assembly, the Brundtland Commission coined what was to become the most often-quoted definition of sustainable development as development that "meets the needs of the present generation without compromising the ability of future generations to meet their own needs." Although commendable, this definition is not operational and has created much antagonism and cognitive dissonance.