Chiraz Houaidia
email: chiraz.houaidia@ensi-uma.tn

Hanen Idoudi
email: hanen.idoudi@ensi.rnu.tn

Adrien Van Den Bossche

Leila Azouz Saidane
email: leila.saidane@ensi-uma.tn

Thierry Val
email: val@irit.fr

Inter-flow and intra-flow interference mitigation routing in wireless mesh networks

Keywords:

In this paper, we address the problem of QoS support in an heterogeneous single-radio single-channel multi-rate wireless mesh network. We propose a new routing metric that provides information about link quality, based on PHY and MAC characteristics, including the link availability, the loss rate and the available bandwidth. This metric allows to apprehend inter-flow interferences and avoid bottleneck formation by balancing traffic load on the links. Based on the conflict graph model and calculation of maximal cliques, we define a method to estimate the available bandwidth of a path which considers, in addition, intra-flow interferences. Finally, we propose a routing protocol that supports this metric and we study by simulation its performances compared to different existing routing metrics and protocols. The results revealed the ability of our protocol (LARM) to support the network scalability as well as its ability to choose routes with high throughput and limited delay.

Introduction

Wireless Mesh Networks (WMNs) are a flexible, quickly deployable wireless networking solution that takes advantage from the absence of a rigid infrastructure. These networks are used to provide rural areas, where broadband infrastructure is not available, with a reliable Internet access based on multi-hop connections [START_REF] Akyildiz | Wireless mesh networks a survey[END_REF] .

To support next-generation applications with real-time requirements, WMNs must provide improved Quality of Service (QoS) guarantees [START_REF] Ashraf | Quality of service and routing in wireless mesh networks[END_REF][START_REF] Zhang | Qos routing for wireless ad hoc networks: problems, algorithms and protocols[END_REF] .

The problem of interconnecting the multi-hop wireless network to the backbone requires QoS guarantee not only over a single hop, but also over an entire wireless multi-hop path. Subsequently, the QoS parameters need to be propagated within the whole network, in order to extend reliability and high performance conditions.

Moreover, routing is considered as a key issue of QoS support over wireless multi-hop networks, since it determines whether the coming data traffic can be served on a high quality path or not.

On the other hand, in a WMN, a node transmits its data via a shared wireless channel, which has inherent broadcast and lossy characteristics. Data transmissions over this wireless channel are constrained by interference and limited bandwidth resources [START_REF] Buddhikot | Interference aware routing in multi--radio wireless mesh networks[END_REF] .

Besides the interference coming from the physical environment, a coming data traffic may interfere with two types of traffics, 1) neighboring traffic including the traffic cross the same node and adjacent nodes, commonly called inter-flow interference, 2) this data traffic itself (we call it self-traffic) along the path, commonly called intra-flow interference. To ensure accurate path quality estimation, both these two types of interference should be considered [START_REF] Buddhikot | Interference aware routing in multi--radio wireless mesh networks[END_REF] .

However, estimating interference in a wireless network and circumventing its effects is not a trivial task since the amount of interference depends on many factors including the radio propagation environment, spatial node distribution and MAC protocol dynamics. Therefore, adding interference-awareness to the routing protocol decisions is crucial.

Identifying paths with maximum available bandwidth is, also, one of the main issues concerning QoS in WMNs. The available path bandwidth is commonly defined as the maximum additional rate a flow can push before saturating its path [START_REF] Hou | Hop-by-hop routing in wireless mesh networks with bandwidth guarantees[END_REF] . As communications in WMNs are multi-hop, the bandwidth consumed by data flows and the available resources to a node are not local concepts, but are related to the neighboring nodes in carrier-sensing range [START_REF] Jia | Bandwidth guaranteed routing for ad-hoc networks with interference consideration[END_REF] . Hence, the challenge introduced here is how to estimate the available bandwidth for an incoming data flow on the entire path without violating the bandwidth guarantees of existing flows and with the only knowledge of the residual channel resources of each link. Then, estimating the widest path when accounting for intraflow and inter-flow interferences remain a major challenge for interference aware routing.

This paper focuses on the problem of identifying the maximum available bandwidth path from a source to a destination, which is also called the Maximum Bandwidth Problem (MBP) [START_REF] Chiu | Bandwidth-constrained routing problem in wireless ad hoc networks[END_REF] . We propose a new routing metric that captures the concept of link availability based on the PHY and MAC parameters, such as the loss rate, the available bandwidth, etc. We use the conflict graph model and calculation of maximal cliques to estimate the available bandwidth of a path when accounting for intra-flow interference. Finally, we propose a routing protocol that supports this metric and we study its performance by simulation compared to different existing routing metrics and protocols in the literature. Results showed that our proposal supports the network scalability and is well able to choose routes with high throughput and limited delay, thus, better delivery of data traffic.

The rest of this paper is organized as follows: After presenting an overview of the related works in Section 2 , we explain the design of our residual link capacity based routing metric with interference consideration in Section 3 . Section 4 describes our routing protocol in details, and Section 5 presents our extensive simulation results. We finally conclude our paper in Section 6 .

Related works

In the context of mesh networks, QoS guarantees are typically provided for bandwidth and/or end-to-end delay. The problem of QoS is important for these networks since they are typically used for providing broadband wireless Internet access to a large number of distributed users and networks. QoS support in multihop wireless networks has been studied extensively in literature and it has been addressed from a number of aspects. A large number of QoS solutions provide QoS by integrating wireless link quality metrics. New metrics, such as ETX [START_REF] De Couto | A high-throughput path metric for multi-Hop wireless routing[END_REF] , ETT [START_REF] Draves | Routing in multi-radio, multi-hop wireless mesh networks[END_REF] , WCETT [START_REF] Draves | Routing in multi-radio, multi-hop wireless mesh networks[END_REF] , etc. [START_REF] Ghannay | Multi-radio multi-channel routing metrics in IEEE 802.11s-based wireless mesh networks -And the winner is[END_REF] , are proposed towards a quality-aware routing, in order to more reflect the variations of link quality caused by transmission capacity, loss probability, interferences, etc. ETX represents the number of times a node expects to transmit and retransmit a packet for a successful delivery. ETT is an improved version of ETX where the ETT of a link is defined as the expected MAC layer duration for a successful transmission of a packet. By accounting for both the link capacity and quality of a link, this metric offers a better estimation and ensures both reliability and efficiency. Weighted Cumulative Expected Transmission Time (WCETT) is the first multi-channel metric for mesh networks. It is determined by the amount of time used by a frame to attend a destination and the maximum time period consumed on links sharing the same channel. The main motivation for WCETT was to specifically reduce intra-flow interference by minimizing the number of nodes on the same channel in the end-toend path.

Among these metrics, some improvements of ETX, such as ETT metric, only consider the total capacity of a wireless link and do not account for possible degradation of the bandwidth due to interferences or parallel data transmissions [START_REF] Houaidia | Novel link availability aware metrics for routing in wireless mesh networks[END_REF] . Some other proposals only treat the intraflow or the interflow interferences but not both at the same time. We have made, in a previous work [START_REF] Houaidia | Experimental performance analysis of routing metrics in wireless mesh networks[END_REF] , an experimental performance study of some of the proposed metrics and based on the results found we propose, in this work, new routing metrics specifically designed for wireless mesh networks.

From another perspective, a significant number of QoS solutions for wireless multi-hop networks provide QoS guarantees by integrating routing with resource reservation. Authors in [START_REF] Yang | Contention-aware admission control for ad hoc networks[END_REF] propose a Contention-aware Admission Control Protocol (CACP) which is an admission control algorithm for single-channel multi-hop networks based on the knowledge of local resources available at a node and the effect of admitting a new flow on the neighborhood nodes. Based on the carrier sensing mechanism at the MAC layer, they estimate the local available bandwidth at a node. The performance evaluation of CACP was made associated to DSR, but it is generic enough to work with almost any existing on-demand routing protocol for ad hoc networks. Similar solutions have been proposed [START_REF] Chen | QoS-Aware routing based on bandwidth estimation in mobile ad hoc networks[END_REF][START_REF] Ganz | Ad hoc QoS on-demand routing AQOR in mobile ad hoc networks[END_REF][START_REF] Ganz | Qos routing in mesh-based wireless networks[END_REF] in which the available bandwidth at a node is considered as the minimum bandwidth within a two-hop neighborhood of that node. Here also the route discovery process is coupled with admission control.

In another solution [START_REF] Liu-D | Available bandwidth estimation and admission control for qos routing in wireless mesh networks[END_REF] , authors propose two new mechanisms for available bandwidth estimation at a node. The first mechanism increases the carrier-sensing range so that flows which are near the boundary of the latter can also be taken into account. However increasing the carrier sensing range is not supported by current hardware as vendors do not allow low level access to the wireless firmware. In the second mechanism, they used the classical concept of packet probes and estimated available bandwidth using probe dispersion observed based on mathematical models. The authors of [START_REF] Liu-D | Available bandwidth estimation and admission control for qos routing in wireless mesh networks[END_REF] integrate bandwidth estimation and admission control with the on-demand LUNAR routing protocol. MARIA : Mesh Admission control and QoS Routing with Interference Awareness [START_REF] Lee | MARIA: interference-aware admission control and QoS routing in wireless mesh networks[END_REF] is another solution. With MARIA, a local conflict graph is computed at each node and an admission control is performed. Nodes, then, exchange their flow information periodically and compute their available residual bandwidth based on the local maximal clique constraints. Admission decision is made based on the residual bandwidth at each node. Authors also propose an on-demand routing protocol which incorporates the admission control during the Route Discovery phase. Some of the discussed existing QoS routing protocols operate with the knowledge of the available bandwidth of each link [START_REF] Li | Minimizing end-to-end delay: a novel routing metric for multi-radio wireless mesh networks[END_REF][START_REF] Li | Multi-hop effective bandwidth based routing in multi--radio wireless mesh networks[END_REF] . In these works, the available bandwidth of a path is computed, generally, based on the available bandwidth of each link on this path. Liu and Liao [START_REF] Liu | Interference-aware qos routing for multi-rate multi-radio multi-channel IEEE 802.11 wireless mesh networks[END_REF] gave a new link metric which is the available bandwidth of the link divided by the number of its interfering links. The path bandwidth is thus defined as the minimum value of the weights of all its composing links. In the mechanism described in [START_REF] Chen | Qos-aware routing based on bandwidth estimation for mobile ad hoc networks[END_REF] , the available bandwidth of a path is the minimum bandwidth among the links on the path divided by the number of hops on the path. Such formula is not able to reflect the exact path bandwidth. Authors in [START_REF] Xue | Ad hoc QOS on-demand routing (AQOR) in mobile ad hoc networks[END_REF] propose a protocol that checks the local available bandwidth of each node to determine whether it can satisfy the bandwidth requirement of a new data flow. Some works [START_REF] Liao | A TDMA-based bandwidth reservation protocol for QOS routing in a wireless mobile ad hoc networks[END_REF][START_REF] Shih | Dynamic bandwidth allocation for qos routing on TDMA-based mobile ad hoc networks[END_REF][START_REF] Zhu | Qos routing for mobile ad hoc networks[END_REF] consider the TDMA-based MAC model and focus on how to assign the available time slots on each link for a new flow in order to satisfy the bandwidth requirement of the new flow.

The algorithm suggested in [START_REF] Kala | Interference mitigation in wireless mesh networks through radio co-location aware conflict graphs[END_REF][START_REF] Kala | Radio co-location aware channel assignements for interference mitigation in wireless mesh networks[END_REF] is used to generate conflict graphs independently of the underlying interference model. Authors used the notion of radio co-location interference, which is caused and experienced by spatially co-located radios in multiradio multichannel (MRMC) WMNs. They experimentally validate the concept, and propose a new all-encompassing algorithm to create a radio co-location aware conflict graph. This novel conflict graph generation algorithm is demonstrated to be significantly superior and more efficient than the conventional approach, through theoretical interference estimates and comprehensive experiments. The results of an extensive set of simulations run on the IEEE 802.11g platform strongly indicate that the radio co-location aware conflict graphs are a marked improvement over their conventional counterparts.

Authors in [START_REF] Ramachandran | Interference Aware Channel Assignement in Multi-Radio Wireless Mesh Networks[END_REF] propose an interference-aware channel assignment algorithm and protocol for multi-radio wireless mesh networks that address overlapping channel assignments. The proposed solution utilizes a novel interference estimation technique imple-mented at each mesh router to minimize interference within the mesh network and between the mesh network and co-located wireless networks. An extension to the conflict graph model, the multi-radio conflict graph, is used to model the interference between the routers. They demonstrate their solutions practicality through the evaluation of a prototype implementation in a IEEE 802.11 testbed and they carried out an extensive evaluation via simulations.

These proposals of available bandwidth calculation cannot be solved in polynomial-time. Even though we can find the available bandwidth of a given path, it is not easy to identify a schedule that achieves that bandwidth since the scheduling problem is also NP-complete [START_REF] Zhu | Qos routing for mobile ad hoc networks[END_REF] .

Furthermore, most of these works consider the total offered link capacity and don't account for possible inter-flow interferences which is the main cause of link bandwidth degradation. In addition, in multi-hop WMNs, the consumed and the available resources to a data flow are not local concepts, but are related to the neighboring nodes in the end to end path. Then, we need a complete routing solution that gives accurate estimation of available bandwidth along a given path.

Our main goal is to develop a practical routing protocol that allows packets to go through the estimated widest path. This widest path is identified when considering both intra-flow and inter-flow interferences.

Residual link capacity based routing metric with interference consideration

The objective of any QoS solution is to provide applications with guarantees in terms of bandwidth, delay and jitter. Providing these guarantees in a distributed and wireless environment generally requires a clear estimation of available resources vis-a-vis individual requirements of each node, data flow or application. This knowledge is therefore crucial for any communication on a shared channel in the wireless network.

Since it is difficult to completely separate in time and frequency simultaneous transmissions in wireless networks, some transmissions will be produced at the same time and in the same frequency band. These simultaneous transmissions may, depending on the network topology, interfere and degrade network performances.

Most of the existing routing metrics in the literature consider only the total capacity of a wireless link and do not take into account the possible degradation of bandwidth due to interference or simultaneous data transmissions. Moreover, there are few proposals which address the problem of both intra-flow and inter-flow interference at once. We conducted, in a previous work [START_REF] Houaidia | Novel link availability aware metrics for routing in wireless mesh networks[END_REF][START_REF] Houaidia | Experimental performance analysis of routing metrics in wireless mesh networks[END_REF] , an experimental performance study of some of the existing metrics and based on its results we propose, in this work, a new routing metric specifically designed for wireless mesh networks. This metric aims to accurately measure the available capacity of a link when taking into account both the current use of the link and possible interferences with neighboring links. In fact, inter-flow interference occurs when different flows are being transmitted at the same time and then sharing the same available resource. In other words, the interflow interference affects the amount of residual channel resources on each link that will be allocated for a new flow [START_REF] Jia | Bandwidth guaranteed routing for ad-hoc networks with interference consideration[END_REF] .

We propose in the following, the study of a scenario to highlight this phenomenon.

In this simulation we consider six nodes configured as shown in Fig. 1 (a). The radio transmission range is set at 250 m and the interference zone is 550 m. Node A is out of the transmission range of node C, but in its interference zone. The node E is in the transmission zone of the node A and is out of the interference range of the node C. Three CBR traffic flows of 2 Mbps are established be-tween the nodes A and B, the nodes E and F and the nodes C and D. Transmissions are spaced by a period of 10 s. The links have different capacities, as shown in Fig. 1

(b).

As shown in Fig. 1 (b), after the transmission of flows 1 and 2, node C is still able to admit flow 3. However, since node A is in the interference zone of node C, flow 3 is capable of consuming the residual capacity of A which is, in fact, insufficient. In other words, even if the local bandwidth permits, node C does not have enough neighboring bandwidth (which is, the bandwidth available on the other links in the path) for flow 3.

To overcome the impact of inter-flow interference, we propose a first metric component called Residual Link Capacity based metric (RLC) given by the equation below:

RLC l = B l - T x l ω (1)
Where B l is the link bandwidth and Tx corresponds to the traffic occupying the link l in transmission and in reception during the time window ω. The routing protocol selects the link with the greater RLC. A route's RLC corresponds then to the minimum of RLCs of links composing the route.

RLC route = min ( RLC l ) l ∈ route (2)
Using this metric, each link is initialized to its bandwidth so that the routing protocol can choose from the start the route offering the greater bandwidth and thus supporting the greater traffic. Since it is based on real exchange of data in the network, the RLC based metric gives a real estimation and thus allows a more efficient routing. This routing metric is load sensitive i.e. the route decision changes when there are links with larger residual bandwidth which are more convenient to support larger data traffic.

This metric is improved to consider intra-flow interference. Intra-flow interference occurs when a data packet is being transmitted over multiple links along a path. In order to avoid conflict at the receiving node, some links may remain idle. We describe here after the interference model adopted in this work and the bandwidth estimation of each link. Our new metric Residual Link Capacity Based Routing Metric with Interference consideration (RLCI) is designed based on this information.

Preliminaries

In this section, we give an overview of the clique-based method for computing the available path bandwidth.

In wired networks, nodes are able to know the amount of available resources in the medium and how much bandwidth is being used. However, in wireless networks, where the medium is shared between multiple nodes, communication from one node may affect the bandwidth of neighboring nodes. Therefore, neighboring nodes should cooperate in a distributed manner to correctly identify the available resources which are no longer local concepts. Generally, we distinguish two types of interferences: intra-flow interferences and inter-flow interferences.

To model the interference relationship between links, one common method is the use of interference conflict graph. This method is used in several existing works [START_REF] Kodialam | Characterizing the capacity region in multi-radio multi-channel wireless mesh networks[END_REF][START_REF] Chiu | Bandwidth constrained routing problem in wireless ad hoc networks[END_REF] . Given a wireless network, each link becomes a node in the conflict graph. If two links in the wireless network interfere with each other i.e. cannot be active simultaneously, we put an edge between the corresponding nodes in the conflict graph. The example depicted in Fig. 2 illustrates the interference modeling using conflict graph. The wireless network based on a six-link chain topology is given in Fig. 2 (a) and the corresponding conflict graph is given in Fig. 2 (b). Assuming that all nodes have the same transmission or communication range R c and the same interference or sensing range R s as represented, we conclude that link 1 and 2, for example, conflict with each other because node b cannot transmit and receive at the same time. Link 1 and 3 are also conflicting because node c ′ s transmission will introduce enough interference for the reception at node b . An interference clique in the wireless network is a set of vertices that mutually conflict with each other. In the conflict graph, the corresponding nodes of these links form a complete subgraph.

A maximal interference clique is a complete subgraph that is not contained in any other complete subgraph. For example, 1, 2, 3 and 3, 4, 5 are maximal cliques while 1, 2 and 1, 3 are not maximal cliques.

Relying on this clique-based formulation, we describe below the method to capture bandwidth sharing among links within the path. Given a wireless network, we denote { Q 1 ; ...; Q k } as the maximal interference clique set of the network, C q as the capacity of a clique q, B ( l ) as the total bandwidth of link l and B ( p ) as the estimation of the available bandwidth of path p . Then, considering a path p = < l 1 , l 2 , . . . , l h >, the available bandwidth of the path p is estimated as follows [START_REF] Jia | Bandwidth guaranteed routing for ad-hoc networks with interference consideration[END_REF] :

B (p) = min q ∈ Q p C q ; C q = 1 l∈ q ( 1 B (l) ) (3)
The rationale behind the formula is: transmissions on the links in a clique cannot be concurrent but occur in a serial manner. Thus,

( 1 B (l) ) represents the time it takes for 1 Mbit data to traverse all the links in the clique q. C q is thus the bandwidth available over the clique q . The available bandwidth of the path is the bandwidth of the bottleneck clique. Let's illustrate the example in Fig. 2 However, the size of a maximal clique depends on how many links interfere with each other, which depends on the interference model adopted in the network. Due to the popularity of the 802.11 technology, we develop our work based on this MAC protocol. Both the two-way handshake DATA/ACK and the four-way handshake RTS/CTS/ DATA/ACK of 802.11 require the receiver of a data packet to send an ACK back to the sender of the data packet. Therefore, for a packet transmission to be successful, both the sender and the receiver should not be interfered by other nodes. a is interfered by another node b if a is within the interference range of b . In other words, the transmissions on links ( u ; v ) and ( s ; d ) are successful at the same time if and only if both s and d are outside the interference ranges of u and v . This model is referred as the bidirectional transmission model or Transmitter-Receiver Conflict Avoidance (TRCA) interference model and is adopted by many existing works [START_REF] Houaidia | Novel link availability aware metrics for routing in wireless mesh networks[END_REF] .

We define the transmission range of a node to be one hop, while the interference range to be r hops and to simplify our discussion, we set r = 2 but our results can be extended to any value of r. Then, according to this assumption, the interference modeling of chained nodes would be as shown in the Fig. 3 below: We also consider a single-radio single-channel configuration. We use the previous network in Fig. 3 to illustrate the TRCA interference model.

Based on the conflict graph in Fig. 2 b assuming r = 1 which is not the TRCA interference model we are using in this paper, when node a sends data to node b , node c is not allowed to transmit since it is in the interference range of b . This means that links 1 and 3 interfere with each other. Then, each maximal clique contains three consecutive links.

Under TRCA model, when node a sends data to node b , node d is not allowed to transmit since it is in the interference range of b . This means that links 1 and 4 interfere with each other. Then, each maximal clique contains four consecutive links (cf. Fig. 4 b).

Based on the TRCA interference model, since all maximal cliques in the conflict graph contain at least four interfering links, the formula for estimating the available bandwidth of a path p becomes as follows:

B (p) = min 1 ≤k ≤(h -4) C k ; C k = 1 1 B (k ) + 1 B (k +1) + 1 B (k +2) + 1 B (k +3) (4)

Metric design

In this section, we introduce our novel metric based on residual link capacity and accounting for both intra-flow and inter-flow interferences. The purpose of this metric is to measure accurately the residual capacity of each link when considering the possible conflict with eventually other transmissions occurring at the same time. The routing decision, then, will be based on links offering the greatest capacity, in other words, on widest paths. To avoid interflow interferences, we used the Residual Link Capacity (RLC) metric defined previously. This metric measures accurately the available bandwidth over a link since it captures the amount of data of all flows crossing the specified link. We apply, then, the clique based bandwidth estimation in order to consider possible intra-flow interferences. To model the interferences, we used the TRCA interference model described previously. This formulation guarantees a global and unique view inside the network i.e. that every node in the network will be aware of the widest neighboring links able to support additional traffic.

Each node first computes the residual capacity of its links. Then, for each path from this node to a destination node, it computes the available bandwidth over this path using the clique based formula introduced previously. We detail in Section 4 the routing protocol used to get information needed for the available path bandwidth calculation.

Given a wireless network, we denote { Q 1 ; ...; Q k } as the maximal interference clique set of the network, C q as the capacity of a clique q , RLC l as the Residual Link Capacity of link l and AB(p) as the estimation of the available bandwidth of path p . For each link l, RLC l is estimated as follows:

RLC l = T B l - T x l ω (5)
Where, T B l corresponds to the total available bandwidth of link l and T x l corresponds to the amount of data occupying the link l during the time window ω.

Then, considering a path p = < l 1 , l 2 , . . . , l h >, the available bandwidth of the path p is no longer the minimum of RLCs of links composing the path but it is estimated as follows [START_REF] Jia | Bandwidth guaranteed routing for ad-hoc networks with interference consideration[END_REF] :

AB (p) = min 1 ≤k ≤(h -4) C k ; C k = 1 RL C k + 1 RL C k + 1 + 1 RL C k + 2 + 1 RL C k + 3 -1 (6)
Hence, each node knows the residual capacity of neighboring links and is able to measure the widest path to a destination node while considering all possible interfering transmissions.

The main advantage of our routing metric RLCI consists of its consideration for both intra-flow and inter-flow interference. However, this metric is not isotonic and addresses the problem of local vision of available resources. We define the isotonicity property as introduced in [START_REF] Jia | Bandwidth guaranteed routing for ad-hoc networks with interference consideration[END_REF] : To illustrate this problem, we consider the example of the network in Fig. 5 where numbers on the links indicate the available link capacities in Mbits/s, the widest path from node a to node y is " a, b, c, d, y " because the path " a, e, f, g, y " presents a bottleneck (g,y) limited to 5 Mbits/s. However, from node x to node y , the widest path is instead " x, a, e, f, g, y " because it comprises the link (f,g) offering greater capacity.

According to our metric RLCI, the calculation of the available bandwidths on each path of this case study is given in the following equations :

AB (a, b, c, d, y ) = 1 10 + 1 10 + 1 10 + 1 10 -1 = 2 . 5 Mbits/s (7)
AB (a, e, f, g, y ) = = 2 . 22 Mbits/s [START_REF] Ghannay | Multi-radio multi-channel routing metrics in IEEE 802.11s-based wireless mesh networks -And the winner is[END_REF] In this case, a suitable routing mechanism is needed to unify the vision of all network nodes.

1 10 + 1 10 + 1 20 + 1 5 -1 = 2 .
The study of this performance metric requires integration into a routing protocol adapted to compensate for its non-isotonicity. The protocol specification and evaluation of performance will be detailed in the next section.

Link Availability based Routing Mechanism (LARM)

Design considerations and path selection

We studied in a previous work [START_REF] Houaidia | Impact of IEEE 802.11 PHY/MAC strategies on routing performance in wireless mesh networks[END_REF] through simulations the impact of PHY/MAC protocols on higher layers. This study has not been done to announce or promote one particular routing protocol as the "winner" protocol having the best performance but it is done to check the conditions where each of the proposed protocols achieves the highest efficiency.

Results have revealed a notable superiority in the general performance of proactive routing protocols when used with IEEE 802.11n based MAC layer, particularly when the network get denser.

Reactive routing protocols are better fitting high stability networks. They are not very suitable for mesh networks because of the delays required to establish a valid route for each data sending. In fact, even if mesh nodes are static, links between nodes evolve and their characteristics change permanently.

On the proactive side, OLSR, cannot meet the non-isotonicity of our metric because of the use of MPR sets which restrict vision to two hops and thus give a partial view of the network.

Hence, to capture accurately the variability of links throughput, we adopt a proactive routing protocol that we called Link Availability based Routing Mechanism -LARM. This protocol is based on Destination Sequenced Distance Vector (DSDV) routing protocol with a number of improvements that we can summarize in the following points:

-Explicit detection of neighbors with link quality computation, -Flexibility and dynamic routing decision adaptability with RLCI routing metric, -Triggered and aperiodic updates of routing table instead of periodic updates, -selection, according to an estimation of the offered bandwidth, a set of candidate paths to be diffused as best paths for neighboring nodes, -Updates controlled by a threshold between the old value of the routing metric and the new value in order to ensure stability of the routes and minimize routing overhead.

Moreover, since our routing metric RLCI is essentially based on the residual capacity of the first four hops to a destination node, we propose that each source node includes this portion of the route in the data packet.

Our protocol, as described, can lead to significant routing overhead which can degrade network performances especially when it gets denser. For that reason, we propose some solutions to reduce and optimize this overhead in terms of number and size of control messages.

1. The dissemination of new routing information is performed when needed and not periodically in order to avoid overloading the network by control messages containing unchanged or relatively stable information. In other words, a node broadcasts a new path to a destination only when the residual capacities of links forming the path have undergone considerable changes which could, then, change the routing decision. The estimation of this change and its importance is set according to a threshold value that we describe in detail later. 2. The threshold value of the RLC of a link is important because it influences, firstly, the knowledge of link state and secondly the overhead induced by updates. A frequent update of this metric allows adaptability of the routing decision and makes it able to react quickly and effectively to network scalability in terms of topology and load. However, a frequent update of this metric can lead to network overload by control messages. We propose a threshold value proportional to data traffic through a link and total capacity of the given link in order to estimate the link occupation and its availability when neglecting the RLC variations due to control messages exchange. 3. To avoid disseminate all possible paths to a given destination, we select only k potential candidate paths as best paths for neighbor nodes based on the available bandwidth on each path. This eliminates paths with very low throughput, reduces the size of control messages which better adapts our solution for scalability. 4. Since the calculation of the metric RLCI is essentially based on the residual capacity of the first four hops only, we suggest that each node broadcasts for each destination the four next hops from itself and their respective residual capacities. This reduces the generated overhead.

Routing information update

Below, we detail of our routing mechanism. We give, first, an overview of the routing information message which should be disseminated in the network in order to ensure reliable path selection. Afterward, we present the topology information database used to store routing information about all destination nodes in the network. We then describe, our packet forwarding mechanism which satisfies the consistency requirement. We apply Eq. ( 6) to estimate the available bandwidth of a path. To simplify our discussion, in the rest of our paper, we use available bandwidth instead of estimated available bandwidth when the context is clear. On the other hand, widest path refers to the path that has the maximum estimated available bandwidth.

First, all the nodes in the network exchange periodically HELLO messages to announce their neighborhood and to diffuse information about the amount of transmitted data during a time window ω on each link with symmetry consideration. Upon receipt of a HELLO message from a new neighbor, the node, using a cross-layer mechanism, calculates the residual capacity of the link connecting to it and insert a new entry in the routing table. When the neighbor originating the HELLO message is already known, we simply update the corresponding residual link capacity.

In the traditional distance-vector mechanism, a node only has to advertise the information of its own best path to its neighbors. Each neighbor can then identify its own best path. In Section 3 , we mentioned that if a node only advertises the widest path from its own perspective, its neighbors may not be able to find the widest path.

In our routing protocol, if a node finds a new path or detects changes in the estimated available path bandwidth, it will advertise this path information to its neighbors using a Routing Information Vector (RIV). Given a path p from a source node s to a destination node d , node s advertises the tuple ( s, d, NH, RLC NH , SNH, RLC SNH , TNH, RLC TNH , FNH, RLC FNH ). NH, SNH, TNH and FNH are the next hop, the second next hop, the third next hop and the fourth next hop on p from s , respectively. RLC NH , RLC SNH , RLC TNH and RLC FNH are the corresponding residual link capacities. Based on the information contained in this Routing Information Vector, each node knows the link quality about the first four hops of the identified path and can calculate its available path bandwidth using Eq. ( 6) .

Routing table

Each node maintains two tables: the traditional routing table used in DSDV and a topology information database where the node maintains all paths advertised by its neighbors or found by itself. In addition, for each path p , each node computes and maintains the available path bandwidth AB ( p ) and a timer indicating the delay from the last update.

Finally, the node indicates with a Boolean value, if this path p is the widest path or not according to its own local vision. This widest path should be stored in the routing table too. The format of the topology information database is as follows ( Table 1 ):

Based on DSDV, each routing entry is tagged with a sequence number which is originated by the destination, so that nodes can quickly distinguish stale routes from the new ones. Given two route entries from a source to a destination, the source always selects the one with the larger sequence number, which is newer, to be kept in the routing table.

After the network accepts a new flow or releases an existing connection, the local available bandwidth of each link will change, and thus the widest path from a source to a destination may evolve. In order to avoid frequent updates and network flooding with control messages, we define a RLC_Threshold proportional to the total capacity of a link. When the change of the residual capacity of a link is larger than this threshold, the node will advertise the new information to its neighbors. After receiving the new bandwidth information, the available bandwidth of a path to a destination may be recalculated.

Performance evaluation

In this section, we conduct the simulation experiments under NS-3 [START_REF]The Network Simulator NS-3[END_REF] to investigate the performance of our routing protocol for finding the maximum available bandwidth path. We carried on simulations in two distinct parts: a first part dedicated to the performance evaluation of our metric RLCI compared to existing metrics such as Hop-Count (HC), ETX and ALM. The second part is carried out to evaluate our routing mechanism LARM in comparison with other existing protocols such as OLSR, DSR, DSDV and HWMP. This choice considers different routing strategies, diverse routing metrics and distinct PHY/MAC layers associated. For all performance scenarios, we consider a single-radio single-channel configuration.

Routing metric evaluation

The following simulations show how the estimation of the available path bandwidth calculated by our metric RLCI achieves a gain in throughput by identifying widest paths, ensuring, thus, better traffic fluidity. We considered a random network topology with 40 nodes sharing an area of 2500 × 2500m 2 (see Fig. 6 ). We chose a random distribution of the nodes in order to obtain different density levels. This allows us to study several interference scenarios which is our first objective. In other words, our approach can be validated or verified in the best and worst interference case scenario. Many works [START_REF] Robinson | A performance study of deployment factors in wireless mesh networks[END_REF][START_REF] Reddy | Evaluation of channel assignment performance prediction techniques in random wireless mesh networks[END_REF] , however, have showed that strategic node placement in wireless mesh networks would give better network performances in terms of coverage, connectivity and fair mesh capacity.

We generated 12 TCP data flows with an average rate of 1 Mbits/s, i.e a traffic load of 30% of the total number of nodes. These data flows are transmitted at different times so as to have different estimations of the available bandwidth for each flow. The pairs of nodes in communication are selected according to the distance between them so as to have different route lengths. Fig. 7 represents, for each metric, the average throughput variation with the traffic charge injected in the network. The results show that the throughput decreases progressively with the traffic load. This decrease is due to data loss caused by possible collisions or saturated channel. Our metric RLCI incorporated in LARM protocol performs better than the other metrics with an important traffic load. Indeed, routing solutions based on shortest path quickly suffer from network congestion and do not propose adaptability strategy to such situation. With ETX, by measuring the loss rate on links, it enables the routing protocol to select paths with best delivery rates. However, as the loss rate estimation is based only on small probe messages and as it doesn't consider the impact of interference on data delivery, ETX may underestimate data loss and, then, do not guarantee optimal routing. RLCI implements several performance criteria at once, allowing it to establish the best performance compromise and thus assess the best link quality. Compared to ETX and ALM, RLCI is based on residual link capacity which enables it to quickly adapt the routing decision into links offering more available bandwidth and then better appropriate to support more traffic. Furthermore, RLCI considers the impact of intra-flow and inter-flow interferences which enables it to better fit situations of congestion or throughput degradation caused by neighborhood traffic.

To further detail the contribution of our metric RLCI, we represented in Fig. 8 the cumulative throughput per pair of nodes and this for different paths obtained with RLCI compared, respectively to, HC, ETX and ALM. Each data flow is represented by a point: the y-axis denotes the throughput values obtained for the metric RLCI, the x axis denotes the throughput obtained for the metric HC, ETX and ALM respectively. The function y = x corresponds to the case where the two metrics select the same path or paths achieving the same throughput. The points above the line y = x represents the data flow for which RLCI gives better performance as HC, ETX or ALM respectively. The results of the figure show better performance of RLCI compared to HC on most data flows. This is illustrated by the dense region above the line y = x. This region shows that routing based on the metric RLCI often finds paths with higher rates compared to HC based routing which is insensitive to link quality and traffic load.

Compared to ETX and ALM, the same observations are made. There are, however, some cases where ALM and ETX offer lar ger throughput than that obtained with RLCI. This statement is represented by the few points below the line y = x. However, the differences between the throughputs obtained are minimal in most cases. For example, a flow, providing a throughput equal to 512 Kbits/s with ETX, provides a throughput equal to about 460 Kbits/s with RLCI.

We randomly select 74 random multi-hop data flows and investigate the throughput of individual flow produced by the different routing metrics. Fig. 9 shows the simulation results of the flows which are sorted according to the throughput of our metric RLCI. This figure also shows the gap between the practical throughput and the estimated available bandwidth.

We first analyze the differences between the results of the considered metrics. We can observe that with HC, throughputs are the smallest. Indeed, based only on distance, it is more likely to select overloaded and interfering paths with poor link quality for data transmission. ETX and ALM perform better compared to HC. However, we can observe that they do not work well in some cases since ETX and ALM of a given path are both deduced from a simple addition of ETX and ALM of links forming the path. These metrics neglect the performance degradation caused by interference between successive links and may select a low available bandwidth path. Therefore, our metric is relatively more efficient for finding the high-throughput path. We now investigate why there is a difference between the practical throughput and the estimated available bandwidth. Fig. 8 shows that the practical throughput may be more than or less than the estimated one. First, our work develops an underestimate of the true available bandwidth because the theoretical calculation of our metric does not take into account of packet overheads and collisions in the MAC layer, which reduce the actual throughput in a real network. Another factor that leads to the practical throughput is less than the theoretical throughput is the assumption on interference range. We assume 2-hop interference but situations like Fig. 1 can happen. The practical throughput is thus smaller than the estimated path bandwidth. On the other hand, simulation results show that our approach gives an overestimation for almost all of the flows with large hop-count distance which can be explained by the fact that our metric is still based on traffic history. This traffic can change from instantly and so, a given link could be more available after significant traffic i.e it offers greater real bandwidth than the estimated one.

Routing mechanism evaluation

In this section, we evaluate the performance of our routing mechanism LARM with comparison to other existing protocols. The study is done under different strategies and PHY and MAC parameters in order to study the possible interaction and coexistence of such protocols in a heterogeneous multi-rate mesh environment and this, to figure out if the choice of combining PHY/MAC/NWK could affect the overall network performance and why. We considered a multitude of combinations of the protocol stack as shown in Fig. 10 .

Several proactive, reactive and hybrid routing protocols, implementing each a different routing metric, are considered : LARM(RLCI), DSR (HC), OLSR (ETX), DSDV (HC) and HWMP (ALM). We remind that HWMP is implemented at layer 2 but is considered here as a routing protocol for organizational reasons. Simulation parameters are given in Table 2 .

End to end delay

Fig. 11 shows the average end to end delay for the various combinations while increasing the number of deployed nodes and the It identifies the average time between sending a packet from a transmitter node and receiving it by the destination node, in milliseconds. This period includes all potential delays caused by queues, delays at the MAC level, retransmission, radio propagation and transfer time.

The results show that, for all combinations, the end to end delay increases with the size of the network, because in general, increasing the number of nodes in the network, neighborhood changes and the number of hops be-tween the source and the destination are also increasing. Thus, the delays caused by buffering delays and queues at intermediate nodes contribute significantly to the end to end delay.

This increase is particularly significant with DSR and HWMP protocols. This can be explained, from a routing point of view, on the one hand, by the reactive nature of these two protocols and particularly delays needed at each "route request" to establish valid routes. Moreover, since the DSR routing decision is based solely on the number of hops, this gives advantage to other protocols incorporating link quality metrics to achieve better performance particularly in terms of delivery delay.

Traffic Charge (%)

Traffic Charge (%) For dense topologies, networks based on the IEEE 802.11n MAC layer and MIMO technology provide, in most cases, faster delivery. This is mainly due to the link capacity and high throughput offered by the physical layer. In all conditions and traffic density, our protocol LARM performs the best thanks to the RLCI metric which incorporates the diversity of rates and benefits from large link capacities anddata aggregation mechanism of IEEE 802.11n MAC layer particularly in case of heavy traffic. Compared to OLSR, delay variation of our protocol LARM shows some similarity due to the proactive nature of both routing protocols. However, LARM, functioning with our metric RLCI, is more able to select widest paths and to ensure faster packet delivery. It offers an average end to end delay slightly lower than OLSR through its ability to borrow routes offering more throughput. Note that for the topologies of 20 nodes and 40 nodes, the values of the delays generated by LARM and OLSR are not null and are equal to, respectively, 0.921 ms and 1.756 ms for LARM and 0.927 ms and 2.276 ms for OLSR.

Loss rate

Fig. 12 shows the loss rate which is calculated from the number of lost data packets from all transmitted data packets. The results show that for all scenarios, the loss rate is affected by both the number of nodes and the traffic load.

In general, for low traffic, the loss rate is significantly affected by the routing decision. Routing protocols based on the hop count are less reliable because the shortest paths may contain saturated, interfering or breakable links causing, indeed, data corruption or packet loss.

Throughput

The average throughput is given in Fig. 13 . It is expressed in kbits/s and measures the total number of bits of received packets during the simulation period. Since the throughput variation with the network density is not significant, we have chosen to represent the evolution of this performance criteria based on the traffic load which is more representative. We used two representative topologies namely, a 40 nodes network (see Fig. 13 (a)) and a network of 80 nodes (see Fig. 13 (b)).

Results show that, depending on the traffic load, the throughput decreases gradually. This decrease reflects the losses previously identified.

Our protocol LARM performs the best with an important traffic load. It maintains a good throughput longer because of his choice of the most available lonks offering the greatest rates. Routing protocols based on hop count prefer the shortest paths and therefore the network quickly suffers from congestion and there is no reaction to address this situation. OLSR achieved better results compared to DSDV and DSR, thanks to the capacity of its ETX metric to measure the link loss rate, therefore, to choose links with the greatest delivery rates. However, since the ETX estimation is based only on small probe messages, this may lead it to underestimate data loss.

HWMP performance is very similar to DSR with a slight improvement. Indeed, the metric ALM considers, in addition to the error rate, link capacities. But this value is theoretical only and corresponds to the total capacity of the link.

Routing overhead

Fig. 14 shows the normalized routing overhead. It is calculated from the number of transmitted control packets among delivered data packets.

The similar appearance of most results leads us to conclude that the PHY/MAC layers have no significant impact on this performance metric. From a routing point of view, we can note from these results a significant overhead generated by DSDV compared to other protocols and this independently of the lower layers and the traffic load. This disparity is due to the massive exchange of routing information particularly in dense networks. The HWMP protocol also suffers from a significant overhead since it uses both types of control messages (proactive tree based dissemination and reactive one).

The exchange of routing information between the different nodes of the network would be the main factor in the overhead. Indeed, in a proactive routing strategy, controlled dissemination as the use of MPR sets with OLSR, significantly reduces the num-ber of control messages circulating in the network. LARM manages this overhead by using only triggered updates allowing it to generate less overhead compared to DSDV, but its performance is slightly lesser compared to OLSR, particularly in dense networks. Including the first 4 links of the path in the header of the data packet also generates additional overhead but remains negligible vis-a-vis the overhead induced by the exchange of routing tables. Since the calculation of this overhead is based solely on the number of messages, it can probably provide more degraded performance if the calculation was made according to the size of control messages.

The routing metric slightly affects this overhead. Indeed, as ETX and ALM use control messages for collecting routing data for the calculation of the metric which can be a source of additional overhead when there is important traffic or in case of a dense network. With its cross-layer mechanism, our protocol LARM avoids this source of overhead.

Synthesis

Performance evaluation conducted on the ns-3 simulator showed, in general, good performance of our metric RLCI incorporated into the protocol our LARM. In terms of end to end delay, our metric can choose links offering high throughput and enabling faster delivery of data packets. Coupled with a MAC layer offering high heterogeneous capacities, our routing mechanism reached, in most scenario and topologies, relatively brief delays compared to other protocols and metrics. By focusing on the less loaded links, our routing solution prevents the formation of bottlenecks which is usually the main cause of data loss. This feature explains the results of loss rate which remains fairly low compared to other solutions even in dense network and/or heavy traffic. The advantage of our metric in terms of throughput is double effect: first, our metric calculates the widest path toward a destination and gives a guarantee on the rate offered for each data transmission. On the other hand, the metric RLCI considers both intra-flow and inter-flow interferences, which permits a proper, accurate and continued estimation of available bandwidth on the network. By exploiting these aspects, the routing strategy based on this metric RLCI acquires adaptability to network scalability in terms of topology and load and thus offers QoS guarantees for terminal nodes.

However, in terms of overhead, our routing solution requires a significant exchange of routing information to ensure a coherent and unified view of available resources on the network. This overhead is inevitable but can be managed if we properly choose the parameters of our metric RLCI. Indeed, the overhead factor strongly depends on the update frequency of routing information on the network. This parameter represents a compromise between the accuracy of the calculated information and broadcasting costs of this information. For this purpose, we have provided, in a further work, a detailed study of this factor to measure its impact on the efficiency of our routing solution.

Conclusion

In this paper, we investigated the maximum available bandwidth path problem, which is an important issue to support quality-of-service in wireless mesh networks. We exploited PHY and MAC characteristics of the IEEE 802.11n layer to design an efficient routing metric that estimates the available path bandwidth when taking into account both intra-flow and inter-flow interferences.

Our metric measures, first, the residual link capacity in order to address the ability of links to support additional traffic and thus prevent the creation of bottlenecks. The route selection is based on the evolution of this metric over time. Based on the conflict graph model and calculation of maximal cliques, we proposed a method to estimate the available bandwidth when accounting for the neighborhood interferences. Finally, we defined a routing protocol that supports this metric.

Our solution also exploits link-diversity to perform loadbalancing at each wireless hop by spreading the traffic load of the incoming flow on multiple concurrent links while considering intra-flow interference.

The performances of this protocol were evaluated by simulation with comparison to other existing routing metrics and protocols (DSR-HC, OLSR-ETX, HWMP-ALM, DSDV-HC). Results showed substantial performance improvements compared to different existing routing protocols and metrics specially in terms of delay and throughput.

The isotonicity property of the routing metric would improve the efficiency of our solution particularly in terms of overhead, for that reason we propose in a further work, to study the feasibility of an isotonic path weight based on our RLCI metric. It might be interesting, also, to adapt our routing solution to an application taking advantage of a multi-channel context. In this case, one should distinguish interference on the same transmission channel to those that may occur on adjacent channels.

Fig. 1 .

 1 Fig. 1. Inter-flow interference : (a) Simulation topology; (b) Residual link capacity variations.

Fig. 2 .

 2 Fig. 2. Illustration for interference model. (a) The original graph, (b) The conflict graph.

3

 3 (a): Consider the path p = < a ; b; c; d; e ; f > . Let B(1), B(2), B(3), B(4) and B(5) of the network in Fig. 2 (a) be 10, 50, 25, 20 and 5 Mbps, respectively. There are two maximal cliques on this path which are {1, 2, 3} and {3, 4, 5}. C { 1 , 2 , 3 } . 44 Mbps The estimated available bandwidth of path p is : min { 6 . 25 , 3 . 44 } = 3 . 44 Mbps.

Fig. 3 .

 3 Fig. 3. TRCA interference model with r = 2.

Fig. 4 .

 4 Fig. 4. Conflict graph under TRCA interference model (a) The original graph, (b) The conflict graph.

5 = 3

 53 Where B ( k ) represents the available bandwidth of the link (l k , l k +1 ) . Further details about this clique-based estimation can be found in the following works[START_REF] Ghannay | Multi-radio multi-channel routing metrics in IEEE 802.11s-based wireless mesh networks -And the winner is[END_REF]. According to the example in Fig.1(a) and under TRCA interference model, the estimated path bandwidth of the path p = < a, b, c, d, e, f > is: Where : . 22 Mbps Then, B (p) = min { 4 . 76 , 3 . 22 } = 3 . 22 Mbps .

Fig. 5 .

 5 Fig. 5. Problem of local vision in the widest path estimation.

22 Mbits/s ( 8 ) 1 = 2

 812 AB (x, a, b, c, d, y) Mbits/s (9)AB (x, a, e, f, g, y )

Fig. 6 .Fig. 7 .

 67 Fig. 6. Network Topology: 40 nodes distributed over a surface of 2500 × 2500 m 2 .

Fig. 8 .

 8 Fig. 8. Per pair cumulated throughput of RLCI, (a) compared to HC; (b) compared to ETX; (c) compared to ALM.

Fig. 9 .

 9 Fig. 9. Throughput of data flows.

Fig. 10 .

 10 Fig. 10. Considered protocol stack.

Fig. 11 .

 11 Fig. 11. End to End Delay evolution with the traffic charge : (a) low traffic; (b) important traffic.

Fig. 12 .

 12 Fig. 12. Loss rate evolution with the traffic load : (a) low traffic; (b) important traffic.

Fig. 13 .

 13 Fig. 13. Throughput for a network topology of : (a) 40 nodes; (b) 80 nodes.

Fig. 14 .

 14 Fig. 14. Routing Overhead evolution with traffic charge. (a) low traffic; (b) important traffic.

Table 1

 1 Topology information base format.

	Dest.	Path	Next hop	Second next hop	Third next hop	Fourth next hop	AB(p)	Timer	Best path
	D	P 1	n 1	n 2	n 3	n 4	AB ( p 1 )	T ( p 1 )	0/1
			RLC ( s, n 1 )	RLC ( n 1 , n 2 )	RLC ( n 2 , n 3 )	RLC ( n 3 , n 4 )
		P 2	m 1	m 2	m 3	m 4	AB ( p 2 )	T ( p 2 )	0/1
			RLC ( s, m 1 )	RLC ( m 1 , m 2 )	RLC ( m 2 , m 3 )	RLC ( m 3 , m 4 )

Table 2

 2 Simulation parameters.

	Parameter	Value
	Simulation duration	200 s
	Topology	250 0 × 250 0 m
	Number of nodes	20-40-60-80-100
	Sensing range	250 m
	Frequency band	2.4 GHz
	Transmission power	30 dBm
	Propagation model	Nakagami-m
	Datarate	1Mbits/s
	Packet size	1024 Bytes
	Buffer size	100 packets
	OLSR-Hello interval	2 s
	OLSR-TC interval	5 s
	RANN interval	3s
	Proactive PREQ interval	3 s


Jérémie Clos
email: j.clos@rgu.ac.uk

Anil Bandhakavi
email: a.bandhakavi@rgu.ac.uk

Nirmalie Wiratunga
email: n.wiratunga@rgu.ac.uk

Guillaume Cabanac
email: guillaume.cabanac@univ-tlse3.fr

Predicting Emotional Reaction in Social Networks

Online content has shifted from static and documentoriented to dynamic and discussion-oriented, leading users to spend an increasing amount of time navigating online discussions in order to participate in their social network. Recent work on emotional contagion in social networks has shown that information is not neutral and affects its receiver. In this work, we present an approach to detect the emotional impact of news, using a dataset extracted from the Facebook pages of a major news provider. The results of our approach significantly outperform our selected baselines.

Introduction

With the rise of the social web, a majority of online content has shifted from being static and document-oriented to being highly dynamic and discussionoriented. With this shift, users have been spending more time navigating online discussions in order to stay informed with their social network. Recent work on emotion contagion in social networks [START_REF] Kramer | Experimental evidence of massivescale emotional contagion through social networks[END_REF] suggests that information is not neutral, and the way it is presented has an impact on the emotional state of its consumers. This demonstrates the importance of providing users with a way to control this content. In this work, we present a technique to predict the emotional impact of news on its consumers, using a dataset extracted from the Facebook pages of the New York Times, a major news network.

We highlight the novelty of our work with respect to existing research on textual emotion detection, before formalizing our problem and explaining our methodology. We evaluate our approach using two naive and two strong baselines. We conclude the paper by discussing our positive results and potential extensions of this work.

Related Work

Our work lies in the broader context of opinion mining. Most of the literature in this area aims to mine either the sentiment (positive and negative) or the basic emotions (anger, joy, . . . ) expressed in the content using computational models learned from labeled or distantly labeled sentiment or emotion corpora [START_REF] Binali | Computational approaches for emotion detection in text[END_REF][START_REF] Pang | Opinion mining and sentiment analysis[END_REF][START_REF] Ribeiro | Sentibench-a benchmark comparison of state-of-the-practice sentiment analysis methods[END_REF]. More recently work has also been done on the detection of emotion in a social network, but focusing on analyzing the emotion contained in text rather than its influence on others [START_REF] Qiyao | ULM: a user-level model for emotion prediction in social networks[END_REF].

The originality of our work lies in predicting emotion reactions induced in readers by emotional text. Whilst harnessing emotion rated content (e. g., news stories) like in [START_REF] Rao | Building emotional dictionary for sentiment analysis of online news[END_REF][START_REF] Staiano | DepecheMood: a lexicon for emotion analysis from crowdannotated news[END_REF], to learn word-emotion lexicons, we also go a step further and propose methods to adopt such lexicons for predicting emotion reactions towards emotional text (e. g., news posts). The task described in this work is thus inherently harder because of the latent factors that are implied in the process, e. g., a joyful news might be received with anger by a certain population if they already have a negative predisposition towards the entity concerned by the news, and inversely. Analyzing this bias, however, is beyond the scope of this work and is reserved for future research.

Method

Problem Definition

We now give a formal outline to the problem of emotion reaction prediction. Given a set of posts P in a social network (e. g., Facebook) and their corresponding emotion rating vectors R, where R i is the rating vector corresponding to the post P i , we aim to predict the emotion rating vector r ′ for an unseen post p ′ . The emotion ratings for each post in P are normalized to form a probability distribution across the different emotions. For example, a post friend met with an accident :( and its emotion ratings vector anger : 0.35, joy : 0.0, sadness : 0.55, surprise : 0.15, love : 0.0 .

Methods

Our approach contains two different steps. First we learn an emotion lexicon from emotion rated Facebook posts, in order to model the emotion distribution of that particular post. Secondly we train a multi-linear regression (MLR) model using the emotion distribution as predictors. The regression model is used to predict the emotion reaction distribution on unseen posts, thus providing a mapping from the emotional state of the post to the emotional state of the users that are reacting to it.

Lexicon for Emotion Reaction Detection

In this section we describe our proposed unigram mixture model (UMM) applied to the task of emotion lexicon (EmoLex) generation. We model real-world emotion data as a mixture of emotion bearing words and emotion-neutral (background) words. For example consider the tweet going to Paris this Saturday #elated #joyous, which explicitly connotes emotion joy. However, the word Saturday is evidently not indicative of joy. Further Paris could be associated with emotions such as love. Therefore our generative model assumes a mixture of two unigram language models to account for such word mixtures in documents. More formally our generative model describes the generation of documents connoting emotion e t as follows:

P (D e t , Z|θ e t ) = |D e t | i=1 w∈d i [(1 -Z w )λ e t P (w|θ e t ) + (Z w )(1 -λ e t )P (w|N )] c(w,d i ) (1)
where θ e t is the emotion language model and N is the background language model. λ e t the mixture parameter, c(w, d i ) the number of times word w occurs in document d i and Z w a binary hidden variable which indicates the language model that generated the word w.

We can estimate parameters θ e t and Z using expectation maximization (EM), which iteratively maximizes the complete data (D e t , Z) by alternating between two steps: E-step and M-step. The E and M steps in our case are as follows: E-step:

P (Z w = 0|D e t , θ (n) e t ) = λ e t P (w|θ (n) e t ) λ e t P (w|θ (n) e t ) + (1 -λ e t )P (w|N ) (2)
M-step:

P (w|θ (n+1) θ e t ) = |D e t | i=1 P (Z w = 0|D e t , θ (n) e t )c(w, d i )
w∈V

|D e t | i=1 (Z w = 0|D e t , θ (n) e t )c(w, d i ) (3)
where n indicates the EM iteration number. The EM iterations are terminated when an optimal estimate for the emotion language model θ e t is obtained. EM is used to estimate the parameters of the k mixture models corresponding to the emotions in E. The emotion lexicon EmoLex is learned by using the k emotion language models and the background model N as follows:

EmoLex(w i , θ e j ) = P (w i |θ (n) e j ) k t=1 [P (w i |θ e t )] + P (w i |N ) (4) EmoLex(w i , N ) = P (w i |N ) k t=1 [P (w i |θ e t )] + P (w i |N ) ( 5
)
where k is the number of emotions in the corpus, and EmoLex is a |V | × (k + 1) matrix, where |V | is the size of the vocabulary V .

Lexicon-Based Regression for Emotion Reaction Detection

In this section we describe the multilinear regression model built using feature vectors extracted using the EmoLex emotion lexicon. The model is built in two stages. In the first stage EmoLex is used to extract features to represent a post as a 5-dimensional emotion vector, using a simple average and aggregate approach, meaning that each component of the feature vector is computed as an average of the values of the corresponding component for each term in the post. More formally the feature vector d vec for a post d is extracted using the formulation described in Eq. 6.

d vec = w∈d EmoLex(w) × count(w, d) |d| (6)
Here EmoLex(w) represents the emotion vector corresponding to the word w, count(w, d) the frequency w in the post d and |d| the length of the post. In the second stage we build five separate MLR models, one for each target emotion. We now describe the MLR model for an arbitrary emotion e k .

Given a matrix of training vectors D n×5 = d 1 vec , d 2 vec , . . . , d n vec , and their corresponding user ratings vector R n×1 = r 1 e k , r 2 e k , . . . , r n e k , for emotion e k , the MLR model is defined in Eq. 7.

R = D × W + E (7)
In this equation W represents the coefficient matrix, which when multiplied with D becomes the fit of the regression model to the data. E is the vector that captures the deviation of the model. The objective is to learn the coefficient matrix W , which along with D, E, best estimates (i. e., with a minimal training error) the ratings vector R.

Evaluation

Given a set of emotionally charged Facebook posts, we investigate techniques to estimate the emotional reactions towards them, captured in the form of numerical ratings: the number of times people clicked on an emotion emoticon. We leverage a Facebook feature which allows users to react to any item published on a user timeline using an emoticon as shown in Fig. 1.

Fig. 1. Emotional reactions in Facebook stories

We evaluated our method using a stratified k-fold cross validation with 5 folds and the root mean square error (RMSE) as the performance metric. RMSE is a standard performance metric used when estimating continuous quantities, and is thus suited to our task. It is defined in Eq. 8 where Y is the vector of observed values, Ŷ the vector of predicted values and n the number of instances in the dataset.

RM SE(Y, Ŷ ) = n i=0 ( Ŷi -Y i ) 2 n (8)

Baselines

We use two naive baseline methods based on general corpus statistics (Uniform and Empirical) which do not learn any computational model on the training posts in order to predict the emotion distribution of unobserved posts, as well as two stronger contenders: one based on a simple lexicon with a trivial mapping (EmoLex) and one based on a linear regression trained on a Word2Vec embedding (Word2Vec+MLR).

1. Uniform assumes a completely uniform distribution over the target labels, so that no matter the input the output remains the following:

f (d) = 0.2; 0.2; 0.2; 0.2; 0.2 2. Empirical assumes that the distribution over the target labels is always the same as the empirical distribution observed in the training data, so that regardless of the input the output remains the following:

f (d) = f (e 1 ) |e| i=0 f (e i ) ; f (e 2 ) |e| i=0 f (e i ) f (e 3 ) |e| i=0 f (e i ) f (e 4 ) |e| i=0 f (e i ) f (e 5 ) |e| i=0 f (e i )
f (e i ) is the frequency of emotion i in the training corpus. 3. EmoLex simply uses the output of the emotion lexicon used to extract the feature vectors as a direct output. where EmoLex i (d) is the output of the lexicon for emotion i and document d. 4. Word2Vec+MLR uses word vectors from a Word2Vec embedding [START_REF] Mikolov | Distributed representations of words and phrases and their compositionality[END_REF], computed on a 400-dimensional embedding with a skipgram-10 model on a Wikipedia corpus, and trains a MLR on it.

D′ = v(t 1 ); v(t 2 ); ...; v(t n )
where v(t i ) is the embedding vector for term i belonging to the document.

Dataset

We used a dataset crawled from the comments on the Facebook page of the New York Times. As detailed in Table 1 emotions are not uniformly distributed in the dataset itself, but the distribution of emotions in the Facebook posts is strongly correlated with the distribution of emotions in the reactions (R = 0.8814 on a Pearson test). We also note that the coverage of our emotion lexicon is close from the coverage of the Word2Vec embedding despite the word embedding being computed on a general purpose resource.

Results

The results of our experiment, shown in Table 2 averaged over 5 folds show that our approach outperforms all the baselines. We note that while our approach outperforms all of the baselines by a significant margin (p < 0.05 on a pairwise two-tailed T-test computed on the 5 folds), the biggest margin remains between approaches that used an emotion mapping and approaches that did not. Hence, there is a correlation between the reactions of the users and the emotions displayed in the Facebook stories themselves, which leads more credence to preexisting works on online emotion contagion [START_REF] Kramer | Experimental evidence of massivescale emotional contagion through social networks[END_REF].

Conclusion

In this work we demonstrated the validity of our approach to predict the emotional reaction to a specific news item. We showed that the mapping from news item to an emotion space fed into a multilinear regression model outperformed both a direct mapping from the text (using Word2Vec and a multilinear regression) and an estimation from the text (using the EmoLex emotion lexicon). This work constitutes a first step towards building a generic model for estimating the emotional impact of news and providing users with a way to avoid being manipulated. Future extensions of this work will focus on diversifying the communication platforms used for spreading emotion-rich content, as well as studying the practical effect of such contagion on users.

f

  (d) = EmoLex 1 (d); EmoLex 2 (d); EmoLex 3 (d); EmoLex 4 (d); EmoLex 5 (d)

Table 1 .

 1 Descriptive statistics on the New York times dataset

	Corpus statistics Number of posts Average terms/sentence 22.34 5367	Emotion probability distribution Posts Reaction Anger 0.192 0.220 Joy 0.155 0.104
	EMOLEX coverage WORD2VEC coverage 16011 18792	Sadness Surprise Love	0.208 0.178 0.264	0.269 0.100 0.304

Table 2 .

 2 Results (lower is better)

		Method	RMSE
	Naive baselines Uniform	0.578
		Empirical	0.532
	Strong baselines EmoLex	0.510
		Word2Vec+MLR 0.531
	Approach	EmoLex+MLR	0.492


Simon Gorecki
email: simon.gorecki@u-bordeaux.fr

Gregory Zacharewicz
email: gregory.zacharewicz@u-bordeaux.fr

Nicolas Perry
email: nicolas.perry@ensam.eu

Using High Level Architecture in the SEE Project for industrial context

Keywords:

Nowadays, systems are becoming more and more complex. Therefore, Modeling & Simulation (M&S) are also growing in complexity. That's why it is important to form, train and graduate students and faculty. The National Aeronautics and Space Administration (NASA) host an international event of M&S oriented in, solving interoperability problems, Distributed Simulation (DS), and student cooperation. The goal is to use a DS standard: High Level Architecture (HLA) to simulate a moon base. Each team build a module of it and have to communicate with each other. This context will be propitious for student to learn HLA programming, in order to reuse it in an industrial manufacturing context. The use of HLA is interesting in order to couple already existent heterogeneous works.

Introduction

Nowadays modeling & Simulation (M&S) becomes increasingly important. They allow to represent any complex system, to study their functioning and their interactions with the environment. The simulation of a system will allow us to virtually design our subject to anticipate and avoid problems. With the growing technologies, the systems complexity is increasing and they became more and more complicate to simulate them. This makes appear distributed simulation (DS): one simulation divided multiple sub functions that are executed on separated computers. That solution allows to solve complexity problems, but it also raise interoperability problematics. There is a growing need to educate, train, and certify students, researchers, and faculty for M&S, DS and interoperability problematics [START_REF] Elfrey | SMACKDOWN: adventures in simulation standards and interoperability[END_REF] [START_REF] Falcone | Easing the Development of HLA Federates: The HLA Development Kit and Its Exploitation in the SEE Project[END_REF]. Zack Crues, a National Aeronautics and Space Administration (NASA) engineer has suggested to the Simulation Interoperability Standards Organizaion (SISO) Space Community Forum, launch an inter-university event which would learn to students about the importance of simulation interoperability and simulation. The SEE Project.

This project will serve as a springboard to study HLA mechanism in order to be reused in industrial domain. A French company has launched an innovative project for setting up a solar power plant. This project deals with different domains including risks. University of Bordeaux supports research in M&S and specifically in DS (Distributed Simulation). Most of these research works have created specific domain simulators. Each of these autonomous simulations is capable of representing a fragment of the global project. One of the last phases of this project is to assemble all these simulations to obtain a global simulation of the problem. [START_REF] Gorecki | Using High Level Architecture to combine simulations in company context mobile factory[END_REF] However, all these simulations use different technologies and manipulate heterogeneous data, which complicates the assembly. To solve these problems, we will use the HLA standard learned in the SEE project.

Distributed Simulation & High Level Architecture

In the computer simulation domain, distributed simulations are one of the most useful and powerful application. Indeed, they consist of several components (often associated with one or more functions) that can be processed by different processes. All these components are part of a single execution which can be relocated to a different computer / server, hence the term "distributed". This concept of functions relocation makes possible the loads distribute on different machines and thus increase the efficiency of a program.

One of the advantages of distributed simulation is to solve some interoperability problems. Interoperability is the ability of interactions between systems. This problematic appears when several systems highly dissimilar (by their internal structure, exchanged data format, or semantic data) must communicate. The interoperability problematic must be considered if interactions are at data level, service level or process level [START_REF] Zacharewicz | Multi Agent/HLA Enterprise Interoperability (Short-Lived Ontology Based)[END_REF]. Those problematics involves at least two entities which try to communicate. Consequently, establishing interoperability means to relate two systems together and remove incompatibilities. Incompatibility is the fundamental concept of interoperability. The concept 'incompatibility' has a broad sense and is not only limited to 'technical' aspect as usually considered in software engineering, but also 'information' and 'organisation' [START_REF] Zacharewicz | Multi Agent/HLA Enterprise Interoperability (Short-Lived Ontology Based)[END_REF].

Indeed, in distributed simulations the components are modular, they can have a heterogeneous architecture and exchange different structured messages, this allows to solve interoperability problems.

In our application case, this notion of distributed simulation will be used with the High Level Architecture standard (HLA) [START_REF]IEEE Standard for Modeling and Simulation (M&S) High Level Architecture (HLA) -Federate Interface Specification[END_REF]. It is a specification of software architecture. It defines a framework that allows to create global execution, consisting of distributed simulations. This framework defines how to create a "global" simulation consisting of several distributed simulations that can communicate with one another. It was originally created by the Office of Defense Modeling and Simulation (DMSO) of US Department of Defense (DoD) to facilitate the assembly of standalone simulations with a different architecture. The original goal was the reuse and interoperability of military applications, simulations and sensor. This standard is designed to resolve interoperability and reusability issues between software components.

HLA, is an architecture that enables several simulation systems to work together. This is called interoperability, a term that covers more than just sending and receiving data. The systems need to work together in such a way that they can achieve an overarching goal by exchanging services. Interoperability refers to the ability of interactions between systems. [START_REF] Zacharewicz | Short-Lived Ontology Approach for Agent/HLA Federated Enterprise Interoperability[END_REF] It extends beyond the boundaries of any single system, and involves at least two entities. Consequently, establishing interoperability means to relate two systems together and remove incompatibilities.

Another interesting aspect of this technology is synchronization. It enables to dynamically manage interoperability issues. Simulations exchange messages, it must be ensured that messages are sent at the right time, in the right order, and that they do not violate causal constraints. To do this, various systems for synchronization of processes and time management are proposed by HLA.

According to the HLA standard, each simulation participating to the application is called "federate". A federate interacts with other federates there are forming a group named HLA federation. All of these entities can communicate with each other through a Run-Time Infrastructure (RTI). It is the RTI that will manage the federation, authorize federates to communicate or not, and provide various services such as time management, file or data exchange, etc.

Agent based approach

In simulation, agent based simulation is one of the most popular approach for simulating systems. It is based on studding actions and interactions between autonomous components (agents). High Level Architecture (HLA) is based on Distributed Simulation, which is not dedicated to agent model. However, there are already in the past some approaches to use HLA in order to developed agent based model [START_REF] Lees | Distributed Simulation of Agent-Based Systems with HLA[END_REF].

4.

Run-Time Infrastructure (RTI)

A federation is composed of a set of federates and a Run-Time Infrastructure [START_REF] Falcone | Easing the Development of HLA Federates: The HLA Development Kit and Its Exploitation in the SEE Project[END_REF]. This RTI provide to federate all functionalities that are described by the specification. Federates can only interact through the RTI. By using HLA and throw the RTI, all federate are stand-alone simulations. That allow them to keep their own architecture which can be totally different to the others federates. This principle enables to solve interoperability problematics. By keeping they own architecture, and using a pivot-language, federates can be plug-in and plug-out to a federation that enable reusability. Federates can "Publish" to inform RTI and other federates about an intention to send information. They also can "Subscribe" to reflect some information created and updated by other federates. This is the basics communication mechanic. The data flow exchanged between all the federates are represented in the same form of classical object-oriented programming. There are two kind of object who are exchanged in HLA standard: Object Class and Interaction Class.

Object Class are time persistent during the simulation. They have attributes that can be updated. For example, in a simulation, an Object Class would be a car and his attribute would be a position, a speed, a name. Interaction are not persistent over time and can have parameters. For example, "Start car", "Stop car", "Accident" would be Interaction example.

This two kind of object are described by a XML file named Federation Object Model (FOM) attached to a federate. That's an important point: the FOM describe all the information that will be exchanged between federates. That the only think who will be shared between simulations. This has an impact on safety of this technology. The federate are totally autonomous, the only exchange between us is described by the XML file.

Each federate are single application which can be executed separately. Originally, HLA was created for reusability, all the component can be executed separately, or with others through a federation. Overall, HAL and the RTI provide information transport, federates time synchronization, does not violate causal rules.

SEE Project

The Simulation Exploration Experience (SEE) is a project initiated by Zack Crues, a National Aeronautics and Space Administration (NASA) engineer. He suggested that the Simulation Interoperability Standards Organization (SISO) Space Community Forum launch an inter-university event which would learn to students about the importance of simulation interoperability by doing it and, become more employable and job-ready than the average college graduate. Called 'Smackdown' [START_REF] Elfrey | SMACKDOWN: adventures in simulation standards and interoperability[END_REF], this project is supported by industry, academia and government. The first event las taken place in 2011 and since, it is renewed every year. In 2017, about 20 teams around the world has participated to the project like Massachusetts Institute of Technology (MIT), Florida Institute of Technology (FIT), Carolina State Colleges (interns at Johnson Space Center), University of Bordeaux (France), University of Genoa (Italy), University of Calabria (Italy), and many others.

The SEE Project meet industries, students, teachers and professional associations in a modeling and simulation (M&S) challenge. The goal is to run simulation of a virtual moon base where each team use the last release of HLA (1516e), and industry software, in order to create his own module. By using the HLA standard, the NASA provide a RTI and several federates that simulate a lunar environment. The RTI and all the federates are forming a lunar base federation. Each team has worked for month with the technical team, tutorials, and also together throw a forum in order to create or upgrade his own federate.

MÄK and Pitch Technologies provide to student evolved RTI software and his associated tutorials [START_REF] Möller | Developing an HLA Tutorial: Philosophy and Best Practices Proceedings of 2012 Fall Simulation Interoperability Workshop Simulation Interoperability Standards Organization[END_REF]. NASA provide also serval software like "HLA Starter kit" which simplify the HLA development [START_REF] Falcone | Easing the Development of HLA Federates: The HLA Development Kit and Its Exploitation in the SEE Project[END_REF]. It allows team to be more focused on data exchange between their federate by reducing the development complexity. NASA provide also Distributed Observer Network (DON3) that use a video game graphical engine in order to simulate the scenario in a 3D environment. This one is a special federate which permit to associate a 3D object model to each federate. By sharing throw FOM x, y and z coordinate, that allow to move 3D models in a virtual space. As the event is international, it is necessary to use remote connection to associate federates with RTI and VPN also provided by NASA.

Using all this different tools, the 2017 SEE Project enabled student from Liverpool to create and simulate an Asteroid harvest fleet allowing to mining resources. This team also improved his lunar orbit asteroid. University of Munich build a base which houses observatories and can serve as a means of exploration for the needs of lunar resources. The University of Calabria and Genoa created the Moon City Center (MCC) with interconnected modules provided by Orange Technical College, FACES, Sophia, Liverpool University as astronaut habitat, a standalone residence designed for long term comfort, a greenhouse to supply fresh vegetables for astronauts, a spacecraft Launchpad ready to launch a rocket to asteroid belt. And finally, the Florida Institute of Technology (FIT) created an exploration rover linked to the Bordeaux's Supply Depot. Both of them can communicate in two ways described in next section.

Application

As Bordeaux team, we worked on the Supply Depot Federate and particular on the communication with the FIT's rover as described in the Fig. 1.

Our objective was to communicate by using to two type of Object that used for communicate with HLA: Object Class and Interaction Class. For that, we had to find two type of exchange: one that need a persistent class that will be used with an Object Class, and another with ephemeral notion, used with an Interaction Class.

The ephemeral interaction (mechanism who will use Interaction Class) will be the radio communication between the two federates. Each message will instantiate an Interaction Class at his reception. After message reception, the federate will save and treat it. The class will destroy itself. For the persistent interaction (mechanism wo will use Object Class) will be the power exchange from the Supply Depot to the rover. At the federate initialization, this class will be created and when a power exchange will be needed the two federate will use this class for energy exchange. In our process algorithm, for trigger a power exchange (when the rover is low battery), the rover has to be near the supply depot and send to it an energy request message.

The Federation Object Model as the role of describe the two kind of object that we just describe. This specification is necessary for ensuring communication in HLA environment.

Supply Depot federate

Lunar Rover federate NASA RTI

FOM FOM

Lunar base federation We can see on the above UML Diagram (Fig 2 .) the lunar supply depot Federation Object model representation. As an object-oriented programming language, the "communication" class (left) inherit from the Interaction Class (HLAInteraction-Root), and the "power exchange" class (right) inherit from the Object Class (HLAObjectRoot). The parameters and attributes are specified below the class name. The diagram represents the description of the pivot format that enable HLA interoperability. Indeed, in our case, we are trying to establishing communication between two simulations (supply depot and rover) which are totally different. They haven't got the same structure but, by using the FOM technology, they will be able to communicate throw HLA.

From a technical point of view, federates are developed in java on Eclipse IDE. HLA concept are implemented throw several frameworks. The HLA library provided by Pitch allow to implement HLA functionalities. And the NASA HLA Starter kit is an overlay to simplify the HLA development.

For this project, the Bordeaux team has to build a 3D object model in order to be represented by the graphical engine (Distributed Observer Network). For this work, we used the Industrial design software SolidWorks with an additional plugin to export it in geometry definition file format (.obj).

We can see on the picture (Fig 3) a simulation screenshot that takes places in a past event with a supply depot having interaction with a rover. The visual render was made with DON3, a graphical engine developed by NASA. We can see on the above schema, the UML communication diagram sequences representing communication between the two federates, and the RTI. Supply Depot and Rover are designed for communication. Bordeaux team and FIT team had work together for establishing the scenario below.

In our case, the communication by messages allow to exchange text messages. Those messages aren't persistent over the time, so they will be set as Interaction class. One the message received, the object will be destroyed. His main function during the simulation is to test if federates are operational. Contrary to the other communication mean, transfer energy between the depot and the rover will be persistent over the simulation. It will be defined as Object class.

Industrial application

Once the SEE project completed, we will be able to apply HLA knowledges on an industrial context. This context is an franco-morocan company who want to design, develop and control a solar power plant. This project consists in installing solar panels fields in several countries in order to provide electricity in world areas which are not powered so far. In terms of implementation and difficulties caused by the project complexity, this innovation gave rise to several simulations that solve several problematics:

•

Simulation for the designing of solar transmitter supporting structure • Simulation for defining the structure foundations of the solar panels field, depending on the ground structure.

•

Study and dimensioning of the mobile factory dimensions, cost, etc, according to the demand • Study of project management method integrating risks. Calculating risks probabilities into project management However, those simulations where made by several persons, not in the same time, and with different languages. So, they aren't compatible with each other. That's why we will use the HLA in order to solve interoperability issues and assemble them in order to create a global simulation composed of each one of those simulations.

We can see on the left diagram above, a representation of all simulations process workflow needed. The three main simulations take and store data on a configurator which act as database and GUI. The risks management project isn't considered in this workflow because it act separately. On the right diagram above, we can see what could be the project architecture by using HLA technology. In that configuration, each simulation could be a federate connected to the RTI. The configurator federate acting as data center of the federation, and control center in order to drive scenarios.

Conclusion

The works developed in the frame of 2017 SEE Project has been a very instructive adventure at many levels for students. It has permitted to develop distributed simulation components. Also on social aspect, it has allowed to expose oneself to problems of interoperability through the simulation of lunar modules. It is also a very good exercise to understand and master the simulations distributed through the HLA specification. It also makes it possible to use widely used technologies such as Virtual Private Network (VPN) in order to communicate between each federated and the RTI hosted on NASA servers, or industrial design software like SolidWorks. Here the SEE project allowed us to study and understand the HLA mechanisms in order to apply it in an industrial context. The state of the art revealed that distributed simulation and the HLA standard can give an interesting answer to couple these heterogeneous works. The future step will consist in defining, according to HLA Federation Development Process (FEDEP), the behavior of each federate to assemble each simulation together.

Fig 1 .

 1 Fig 1. SEE Project federation.

Fig. 4 .

 4 Fig. 4. UML Communication diagram sequence

Table 2 .

 2 Lunar Supply Depot Federation Object Model (FOM)

Table 3 . SEE Project Simulation screenshot

 3

Table 5 . Global simulation as BPMN and HLA view

 5

	Solar transmitter feder-
	ate
	Foundations federate
	Mobile factory federate	RTI
	Configurator federate
	Project + risks
	Management federate

Acknowledgments. From a general point of view, this project brings much experience at the human level. Indeed, it has allowed us to work with major universities around the world. For that we would like to thank you NASA's engineers for their support and the developers of the SEE Project providing all participants with a memorable, interactive, problem-solving experience which can contribute importantly to the workforce of the future.