Giuseppe Piro
email: g.piro@poliba.it

Vincenzo Ciancaglini
email: vincenzo.ciancaglini@inria.fr

Enabling real-time TV broadcast services over CCN networks

Introduction

Due to the growing importance that content sharing applications are experiencing in our everyday life [START_REF] Ahlgren | Content, connectivity, and cloud: ingredients for the network of the future[END_REF], the emerging Content Centric Networking (CCN) paradigm [START_REF] Jacobson | Networking named content[END_REF] represents the most attractive solution for driving the current host-based Internet system towards a novel architecture focused around the content-centric concept. Basically, the communication in CCN requires the adoption of only two types of messages, namely Interest and Data [START_REF] Jacobson | Networking named content[END_REF]. A user may ask for a specific content by issuing an Interest, which is routed across the network towards nodes in posses of the required information, thus triggering them to reply with Data packets. While routing operations are performed by the strategy layer only for Interest packets, Data messages are sent back to requesting users just following the reverse path of the Interest, allowing every intermediate node to cache the forwarded content.

During past few years, CCN obtained a very warm attention in the scientific community. This is testified by the presence of several studies that have already investigated caching policies and data-transfer performances [START_REF] Carofiglio | Modeling data transfer in content-centric networking[END_REF][4] [START_REF] Rossi | On sizing CCN content stores by exploiting topological information[END_REF], congestion control issues [START_REF] Grieco | AIMD and CCN: past and novel acronyms working together in the Future Internet[END_REF], and routing strategies [START_REF] Tortelli | CCN forwarding engine based bloom filters[END_REF] [START_REF] You | Dipit: A distributed bloom-filter based pit table for CCN nodes[END_REF]. More recently, instead, thanks to the growing demand for TV services, some research activities are focusing the attention also to the design of sophisticated techniques enabling real-time TV broadcast services over CCN networks. Unlike Video-On-Demand, the real-time video distribution has to deal with a specific class of problems to ensure the timely delivery of an ordered stream of chunks. Moreover, video chunks have to be received within a given time interval (the playout delay), before being actually played. A chunk not delivered before such time deadline will result in degradation of the rendered video. Bearing in mind such issues, handling real-time streaming services in CCN networks represents a very difficult challenge. In this regard, only few preliminary works have been proposed in literature. The architecture presented in [START_REF] Li | Time-shifted TV in content centric networks: the case for cooperative in-network caching[END_REF] has been designed for mapping HTTP-based streaming applications in a CCN. A novel cooperative caching strategy enabling time-shifted TV services has been discussed in [START_REF] Xu | Live streaming with content centric networking[END_REF]. A timebased Interest protocol is proposed in [START_REF] Park | Time-based interest protocol for real-time content streaming in content-centric networking (CCN)[END_REF] in which a user sends a specific Interest message asking for a group of contents generated by the server during a specific time interval. Similarly to the previous paper, also in [START_REF] Li | MERTS: A more efficient real-time traffic support scheme for Content Centric Networking[END_REF] is proposed a mechanism through which a user may request for multiple Data packets by issuing one Interest message. Unfortunately, all of these works do not evaluate the effectiveness of conceived ideas in complex and realistic environments and, more in general, they do not propose (and test) any sophisticated strategy that adapts real-time services in high loaded networks.

In our very recent contribution [START_REF] Ciancaglini | CCN-TV: a data-centric approach to real-time video services[END_REF] we designed a novel architecture, namely CCN-TV, supporting real-time streaming services through the data-centric approach. In this paper, we extend our previous work by testing the CCN-TV system in a more complex network scenario. In addition, a depth analysis of the role that some of the main components of a CCN node, i.e., the cache and the Pending Interest Table (PIT) table, have in the presence of real-time services, as well as the comparison with respect to a baseline scenario where these features are not implemented, will be provided too.

The CCN-TV Architecture

In CCN-TV we consider a network of nodes requesting different real-time video streams, identified by a channelID, served by one or more broadcast servers. Unlike canonical UDP/TCP-based streaming, in CCN-TV each video is divided in consecutive chunks, identified by a progressive chunk number, that have to be requested individually, via a dedicated Interest. This fundamental aspect naturally supports the implementation of a flow control mechanism through which each user can explicitly request for new chunks just when the old ones have been received (or in the case they are not more useful because out of delay). In line with these premises, a channel bootstrap phase, a flow control strategy, and an efficient mechanism for retransmitting Interest packets have been designed within the CCN-TV architecture. For enabling these functionalities we need to extend the basic structure of the Interest packet by introducing an additional Status field marking if the Interest is related to the channel bootstrap phase or to a retransmission. In the case it is necessary to be conformed to classical CCN messages, this field can be easily replaced by an additional entry in the content name.

The channel bootstrap phase

Due to video codec requirements, a video stream can be visualized at the user side only once a specific I-Frame has been received. Therefore, to bootstrap a TV channel, a client has to find the closest server and gather from it the chunk (and the corresponding chunkID) of the last generated I-Frame. To this end, it sends an Interest packet for the URI: [domain]/[channelID], with the Status field set to BOOTSTRAP and a Nonce field containing a uniquely generated value. In this way, the message will travel unblocked until the first good stream repository, that will answer with a Data packet providing information about the first chunk of the last generated I-Frame. Once the user received this Data packet, it will request subsequent chunks, using a sliding window mechanism detailed in the following.

The flow control mechanism

A sliding window mechanism has been properly designed for enabling the user to request subsequent chunks of a video content. First, let us define Pending Chunk and Pending Window as the chunk whose Interest has been sent by the node and the window containing W different pending chunks not yet received, respectively. In details, together with the chunkID, we store in the Pending Window the timestamp of the first request and the timestamp of the last retransmission. Hence, whenever a new data message is received, or if the node does not receive any data for at least windowTimeout seconds, the following operations are performed: (1) purge the Pending Window from all the chunks who are expired, i.e., who have already been played;

(2) retransmit all chunks that have not been received within the windowTimeout; (3) transmit, for each slot that got freed by the received or expired chunks, the Interest for a new one.

Interest routing

Normally, a CCN node does not propagate Interest packets related to contents already requested by other users in the past but not yet satisfied with corresponding Data packets [START_REF] Jacobson | Networking named content[END_REF]. The PIT table is used to keep track of Interest packets that have been forwarded upstream towards content sources, combining them with the respective arrival faces, thus allowing the properly delivery of backward Data packets sent in response to Interests. It is important to note that this mechanism prevents the propagation of retransmitted Interest packets, thus compromising the right behavior of CCN-TV. In order to force the propagation of retransmitted Interests, the Status field is set to Retransmission: this configuration would impose nodes along the routing path to propagate it versus the router that can satisfy this request (i.e., by skipping the usual CCN mechanism).

Performance evaluation of CCN-TV

We evaluated performances of CCN-TV architecture, through computer simulations carried out with ccnSim, an open source and scalable chunk level simulator of CCN, built on top of the Omnet++ framework [START_REF] Rossini | Large scale simulation of CCN networks[END_REF].

Network configuration and system parameters

Differently from [START_REF] Ciancaglini | CCN-TV: a data-centric approach to real-time video services[END_REF], we considered a more complex network architecture composed by 68 routers connected among them according to the Deutsche Telekom topology [15]. A CCN node is directly installed to each router and no TCP or UDP encapsulation has been implemented. We assume the presence of only one small video-streaming provider that offers 5 parallel real-time transmissions to remote clients, each one connected to one router of the Deutsche Telekom network. In every simulation round, each video content is mapped to a video stream compressed using H.264 at an average coding rate randomly chosen in the range [250,2000] kbps. On the other hand, every client chooses to watch one specific TV channel based on its popularity, which has been modeled through the Zipf distribution (in line with [START_REF] Ciancaglini | CCN-TV: a data-centric approach to real-time video services[END_REF] we set α=1). In our tests, we adopted the optimal routing strategy based on the shortest path approach, already available within ccnSim [START_REF] Rossini | Large scale simulation of CCN networks[END_REF]. Moreover, three caching strategies have been considered in our study: no-cache, LRU, and FIFO. When well-known LRU or FIFO policies are adopted, we set the size of the cache to 210 Mbits, i.e., a typical value for SRAM memories already available in the commerce [START_REF] Perino | A reality check for content centric networking[END_REF]. The nocache policy is intended to evaluate the performance of the CCN without using any caching mechanism. Furthermore, a baseline scenario, in which the no-cache policy is enabled and the PIT table is totally disabled (this means that each user establishes with the service provider a unicast communication and the server should generate a dedicated Data packet for each generated Interest), has been considered as reference configuration. Regarding the flow control mechanism, the window size W has been set to 10, ensuring that faces of the server are almost fully loaded in all considered scenarios. The transmission queue length associated to each face, Q, has been set in order to be larger than Q = L C × τ, where L C and τ represent the link capacity and the maximum propagation delay in the considered network topology, respectively. In order to evaluate performances of CCN-TV under various system configurations, we considered different settings of the bandwidth dedicated to real-time services (set in the range [40-100] Mbps), the playout delay (chosen in the range [START_REF] Xu | Live streaming with content centric networking[END_REF][START_REF] Park | Time-based interest protocol for real-time content streaming in content-centric networking (CCN)[END_REF][START_REF] Li | MERTS: A more efficient real-time traffic support scheme for Content Centric Networking[END_REF][START_REF] Ciancaglini | CCN-TV: a data-centric approach to real-time video services[END_REF][START_REF] Rossini | Large scale simulation of CCN networks[END_REF][15][START_REF] Perino | A reality check for content centric networking[END_REF][17][18][19][20] s), and the windowTimeout (chosen in the range [1/10-1/2] of the playout delay). To conclude, each simulation lasts 300s and all results have been averaged over 15 simulations.

Simulation results

The chunk loss ratio, which represents the percentage of chunks that have not been received in time (i.e., before the expiration of the playout delay) by clients, is the first important parameter that we reported in Fig. 1 which describes how CCN-TV settings affect the quality of service offered to end users. We note that the amount of discarded chunks is very influenced by the playout delay: the highest playout delay allows the client to receive more Data packets before the expiration of the time deadline, thus reducing the amount of discarded chunks. In addition, the reduction of link capacities leads to a higher number of lost chunks, due to increased latencies induced by network congestion. By handling unicast communications, the baseline scenario generates the highest network congestion level, thus registering the worst performances. From this finding emerges the important role that both cache and PIT table have on network performances. In order to estimate the Quality of Experience perceived by end users, we have also computed Peak Signal to Noise Ratio (PSNR) of received video flows (results are shown in Fig. 2). In line with previous results, the PNSR is higher in the same case in which the chunk loss ratio is lower. This means that quality of TV broadcast services ameliorates when we increase the playout delay and the link capacity. Also in this case, we remark that no-cache, LRU, and FIFO caching policies outperform always the baseline scenario. To provide a further insight, we also reported in Fig. 3 the percentage of Interest packets sent by users and directly received by the service provider. In the baseline scenario, the total amount of generated Interests reach the remote server, thus excessively overloading its faces. By enabling the PIT table, even without implementing any caching mechanism, the system is able to halve the traffic load at the server side, thus improving significantly network performances. Finally, the traffic load handled by the server further reduces when a cache policy is activated. Anyway, it is evident that, in the presence of real-time flows, the cache does not represent an important CCN feature because it is not able to guarantee a notable improvement of system performances with respect to the case it is not used. On the other hand, we noticed that the PIT plays a more relevant role. In fact, in presence of live video streaming services, clients that are connected to a channel request same chunks simultaneously. In this case, a CCN router has to handle multiple Interest messages that, even though sent by different users, are related to the same content. According to the CCN paradigm, such a node will store all of these requests into the PIT, waiting for the corresponding Data packet. As soon as the packet is received, the router will forward it to all users that have requested the chunk in the past. According to these considerations, the use of the cache will not produce a relevant gain of network performances. Indeed, the PIT helps reducing the burden at the server side by avoiding that many Interest packets for the same chunk are routed to the server.

Conclusion

In this work, we investigate performances of the CCN-TV architecture, which has been properly designed for offering real-time TV broadcast services in CCN networks, under different system settings. Besides having demonstrated the effectiveness of the discussed architecture, presented results have highlighted that, differently from any caching policies, the PIT table has a fundamental role in reducing the burden at the server side in the presence of real-time streaming services.

Giuseppe

Figure 1 .

 1 Chunk Loss Ratio when the playout delay has been set to (a) 10s and (b) 20s.

Figure 2 .

 2 PSNR of received video flows when the playout delay has been set to (a) 10s and (b) 20s.

Figure 3 .

 3 Figure 3. Percentage of Interest packets received by the remote server.

  Piro is a postdoctoral researcher at Politecnico di Bari, Italy. His main research interests include quality of service in wireless networks, network simulation tools, 4G cellular systems, nanonetwork communications, and future Internet architectures. Piro has a PhD in electronics engineering from Politecnico di Bari. He founded the LTE-Sim project and is a developer of Network Simulator 3. Vincenzo Ciancaglini is a PhD candidate at INRIA Sophia Antipolis Méditerranée, France. His research topics include the interoperability of content-cased systems, future internet architectures and media streaming systems. Ciancaglini has a MSc in Electrical Engineering, from Stockholm's Royal Institute of Technology, Sweden and a MSc in Telecommunications Engineering form Politecnico di Torino, Italy.


MATERIALS AND METHODS

MD simulation in aqueous solution. The crystal structure of the parallel [dTGGGGT] 4 G-quadruplex (PDB entry 352D) [START_REF] Phillips | The Crystal Structure of a Parallel-Stranded Guanine Tetraplex at 0.95 A Resolution[END_REF] was relaxed performing a 50 ns MD run in explicit water. The system was immersed in a truncated octahedron box of TIP3P [START_REF] Jorgensen | Comparison of Simple Potential Functions for Simulating Liquid Water[END_REF] water molecules, with a minimum distance of one edge of the box and one solute atom equal to ca. 10 Å; 17 additional sodium ions were added to neutralize the negative charge of the quadruplex. To optimize and equilibrate the system, we design a multistep protocol consisting of 5000 steps of minimization and 300-ps of equilibration, where we performed a gradual heating of the system and removal of positional restraints on DNA. Going into details, the minimization step was split in three rounds: in the first one we performed 500 steps of steepest descendent followed by 1500 steps of conjugate gradient, applying an harmonic restraints of 100 kcal/molÅ 2 on the solute; in the second round we decreased the conjugate gradient to 1000 steps and the harmonic restraints on the solute to 50 kcal/molÅ 2 and in the final round we decreased it to 10 Kcal/molÅ 2 . As regard the equilibration step, it was split in five 20-ns rounds of restricted MD simulations, followed by a final round of 200-ns of unrestricted MD simulation. In the first 20-ps round, the system was kept at constant volume and gently heated up to 300 K, while the DNA was weakly restrained by a 10 Kcal/molÅ 2 harmonic potential. The following four 20-ns rounds were performed at isothermal isobaric ensemble (P= 1 atm, T= 300 K), with the DNA constraints gradually decreasing to 10, 5, 2.5, and 1 Kcal/molÅ 2 , until no constraints were applied to the final 200-ps equilibration round. Finally, the 50-ns unrestrained MD production was performed with pmemd module of AMBER12. Periodic boundary conditions and the Particle Mesh Ewald (PME) method [START_REF] Darden | Particle Mesh Ewald: An N⋅log(N) Method for Ewald Sums in Large Systems[END_REF] were used, together with a 2.0 fs time step, with bonds involving hydrogens constrained by the SHAKE algorithm. [START_REF] Ryckaert | Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of N-Alkanes[END_REF] The radial cut-off for the non-bonded interaction was 8 Å in both equilibration and production runs.

MD simulation in gas phase. Four snapshots (specifically those at 1, 5, 10 and 50 ns) of the solution simulation were selected to create the starting structures needed for four independent gas phase MD runs. Water molecules and external counter ions were removed; then, the charge state of the models was adapted with that one observed in ESI-MS experiments (corresponding to -5 for the predominant charge state), by applying the "distributed model" described by Rueda et al., [START_REF] Rueda | G-Quadruplexes Can Maintain Their Structure in the Gas Phase[END_REF] that requires the equal distribution of the total charge along all the phosphates. After that, the structures were relaxed with 5000 steps of steepest descendent, followed by 15000 steps of conjugate gradient, without applying any harmonic restraints. A thermalization period of 1-ns was then performed, and followed by a 500-ns unrestrained MD production run with the sander module of AMBER12. The temperature was kept constant at 298 K using a weakcoupling algorithm. [START_REF] Berendsen | Molecular Dynamics with Coupling to an External Bath[END_REF] No non-bonded interactions radial cut-off was applied; SHAKE [START_REF] Ryckaert | Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of N-Alkanes[END_REF] algorithm was used to constrain all the bonds involving hydrogen atoms, and the integration time step was 1.0 fs.

Figure S1 :

 S1 Figure S1: Key simulation results obtained from 50-ns MD simulation of [dTGGGGT] 4 in solution. Plots following the stability of the models during the dynamic run, through the variation of the structural descriptors of the system: (a) radii of gyration; (b) root mean square deviations of all backbone atoms, the thymines, and the G-tetrads; (c) root mean square fluctuations of the thymines (black), and the guanines (white); (d) interatomic distances between all guanine N1-O6 (grey) and N2-N7 atoms (black) participating to the Hoogsteen H-bonds of the G-tetrads.

Figure S2 :

 S2 Figure S2: Key simulation results obtained from 500-ns MD simulation of replica 1 of [d(TGGGGT) 4 + 3NH 4 ] 5-in the gas-phase. Plots following the stability of the models during the dynamic run, through the variation of the structural descriptors of the system: (a) radii of gyration; (b) root mean square deviations of all backbone atoms, the thymines, and the G-tetrads; (c) root mean square fluctuations of the thymines (black), and the guanines (white); (d) interatomic distances between all guanine N1-O6 (grey) and N2-N7 atoms (black) participating to the Hoogsteen H-bonds of the G-tetrads.

Figure S3 :

 S3 Figure S3: Key simulation results obtained from 500-ns MD simulation of replica 2 of [d(TGGGGT) 4 + 3NH 4 ] 5-in the gas-phase. Plots following the stability of the models during the dynamic run, through the variation of the structural descriptors of the system: (a) radii of gyration; (b) root mean square deviations of all backbone atoms, the thymines, and the G-tetrads; (c) root mean square fluctuations of the thymines (black), and the guanines (white); (d) interatomic distances between all guanine N1-O6 (grey) and N2-N7 atoms (black) participating to the Hoogsteen H-bonds of the G-tetrads..

Figure S4 :

 S4 Figure S4: Key simulation results obtained from 500-ns MD simulation of replica 3 of [d(TGGGGT) 4 + 3NH 4 ] 5-in the gas-phase. Plots following the stability of the models during the dynamic run, through the variation of the structural descriptors of the system: (a) radii of gyration; (b) root mean square deviations of all backbone atoms, the thymines, and the G-tetrads; (c) root mean square fluctuations of the thymines (black), and the guanines (white); (d) interatomic distances between all guanine N1-O6 (grey) and N2-N7 atoms (black) participating to the Hoogsteen H-bonds of the G-tetrads.

Figure S5 :

 S5 Figure S5: Key simulation results obtained from 500-ns MD simulation of replica 4 of [d(TGGGGT) 4 + 3NH 4 ] 5-in the gas-phase. Plots following the stability of the models during the dynamic run, through the variation of the structural descriptors of the system: (a) radii of gyration; (b) root mean square deviations of all backbone atoms, the thymines, and the G-tetrads; (c) root mean square fluctuations of the thymines (black), and the guanines (white); (d) interatomic distances between all guanine N1-O6 (grey) and N2-N7 atoms (black) participating to the Hoogsteen H-bonds of the G-tetrads.


Pierre Stéphan
email: pierre.stephan@cnrs-bellevue.fr

Serge Suanez
email: serge.suanez@univ-brest.fr

Bernard Fichaut
email: bernard.fichaut@univ-brest.fr

S Suanez -B

Fichaut

Long-, mid-and short-term evolution of coastal gravel spits of Brittany, France

Gravel spits of Brittany have experimented a long morphosedimentary evolution over the last millenia. Based on analysis of several back-barrier holocene sediment stratigraphies, distinct phases of construction and barrier breakdown were recognized, indicating the role played by storminess and sediment supply during the late-holocene period. Over the last centuries and decades, a deficit of sediment bubget affecting several gravel spits is highlighted. Therefore, actual coastal evolution of most of them is mainly dominated by cannibalization, landward retreat by rollover and complete destruction of the spits in some places. This coastal erosion is related to the lack of significant offshore sediment input or from the erosion of unconsolided cliffs. Locally, anthropogenic forcing have axacerbated the erosion processes by sediment minings and/or the construction of hard defense structures. For the swash-aligned gravel spits, frequency and magnitude of overwash processes is controlling the rate of landward retreat by rollover. This morphodynamic behaviour is illustrated by topomorphological surveys realised between 2002 and 2012 on Sillon de Talbert spit which has experienced a complete crestal removal during the 10 March 2008 Johanna storm. Although this event have a 50-100yrs occurence, the barrier has exhibited a rapid crestal rebuilding by overtopping processes, illustrating the great resilience of the spit. Thus, coastal erosion management strategies mainly based on hard defense structures are gradually abandoned for new management policies based on soft operations. Nowadays, gravel spits of Brittany are also considered as a geological heritage and management plans are establishing by local authorities.

GENERAL SETTING

In Brittany, gravel beaches are located mainly on the northern and western coast (fig. 1). Their construction is mainly due to the shoreward removing of periglacial deposits initially accumulated on the coastal shelf during the post-glacial marine transgression. The southern part of Brittany is characterized by sandy beaches and coastal dunes, except locally where a coarse material is provided by erosion of cliffs formed by Pleistocene deposits (head). Nowadays, unconsolidated cliffs are considered as the most significant source of coarse sediments in Brittany [START_REF] Guilcher | Les cordons littoraux de la rade de Brest[END_REF][START_REF] Guilcher | Coastal evolution in western, southwestern and northern Brittany as a regional test of impact of sea level rise[END_REF]. In the Bay of Brest, the highly weathered of shale cliffs also contribute locally to feed the gravel barriers. In the Bay of Brest, the indentations of the jagged coastline were favorable to the construction of a numerous smale-scale barriers and spits with a high morphological diversity (fig. 2A to 2D). The lenght and the volume of gravel spits in the bay of Brest never exceed 700 m and 100.000 m 3 respectively. Nevertheless, the Sillon de Talbert spit studied in the north of Brittany forms the bigger accumulation reaching 3.2 km long and sediment volume estimated at 1.23 x 10 6 m 3 (fig. 2E and2F). Like most spits on the Channel coasts, formation of the Sillon de Talbert spit began at about 6000 cal.yr.BP when the relative sea-level rise decreased [START_REF] Morzadec-Kerfourn | Coastline changes in the Armorican Massif (France) during the Holocene[END_REF]. This accumulation was enhanced by the presence of many rocky outcrops scattered on a wide seaward-inclined abrasion platform. A few hundred metres seaward from the spit, a peat deposit shows at the surface, in which a Final Neolithic archaeological site dating from around 2800 BC was uncovered. This human settlement, then located in the sheltered back-barrier area, bears witness to the considerable movement of the sediment accumulation due to the sea-level rise during the past millennia.

Gravel spits of Brittany belong to "composite gravel beach" type [START_REF] Carter | The morphodynamics of coarse clastic beaches and barriers: a short-and longterm perspective[END_REF]Jennings and Schulmeister 2002). The beachface is characterised by two distinct morphosedimentary parts separated by a break in slope at the mid tide level. The lower part of the beachface is dissipative with a low slope gradient (0.01%), and takes the form of a sandy or rocky platform. The upper part of beachface is reflective and corresponds to the gravel barrier itself, mainly composed by sandy-gravel material and a pebble fraction dominating on the crest. Interstitial sandy fraction is important (around 50%). Slopes values are between 5% and 17% according to the different types of sediment assemblages that form the barriers. Gravel spits present generally a single ridge exceeding the high-water-spring-tide level of a few decimeters. Nevertheless, the crest is potentially affected by episodic overwash processes during storm events combined with high-tide level. Some spits present multiple ridge-crest morphology in their distal part related to longshore sediment supply. Behind the spits, back-barrier environments are most often characterised by salt-marshes infilled by sandy to peaty mud.

The coast of Brittany is considered with macrotidal environment, with a maximum tidal ranging from 10.85 m on the Sillon de Talbert, to 7.6 m in the Bay of Brest, and 6 m on the southern coast. Dominant swells mainly come from the west (fig. 1) and show a notable increase during the winter. However, wave conditions vary locally depending on the shallow bathymetry. The spit of Sillon de Talbert appears to be affected by the most energetic swell conditions. The modal height (Hs) is between 1 and 1.5 m, and the modal period (Tpic) between 9 and 10 seconds. During storm events, the wave height can sometimes reach up to 9 m and the period up to 20 seconds. In these conditions, the Sillon de Talbert acts as a natural barrier against wave attack, offering a sheltered environment for the Bréhat Archipelago to the east and preventing flooding of low-lying Lanros peninsula to the south. The other spits of Brittany are located in more sheltered environments as shown the spits of the bay of Brest. The spits of Dibenn and Bétahon are protected by several shoals and islands. The spit of Linkin is also sheltered from dominant waves by a rocky promontory. Finally, the Bay of Brest is a fetch-limited environment and the waves height never exceed 1 m, whatever the wind conditions (Stéphan, 2011a). According to their general orientation to incident waves, spits are swash-or drift-aligned (fig. 1).

LONG-TERM (10 3 YR) EVOLUTION OF GRAVEL SPITS OF BRITTANY

The lithostratigraphy and biostratigraphy of several back-barrier sediment sequences were examined to reconstruct the late-holocene evolution of four gravel spits located in the bay of Brest. Benthic foraminifera assemblages were analysed to identify the exposure of the salt-marshes and to determine the phases of construction and breakdown of gravel spits. The characterization and the chronology of environmental conditions associated to sedimentary units were improved by using a foraminifera-based transfer function and twenty radiocarbon dates (Stéphan 2011a).

The results show that the stratigraphy of sediment infillings studied at Porzguen and Troaon saltmarshes are transgressives and reveal a similar general pattern of sedimentation. A basal well-humified peat containing reeds remnants (Phragmites australis) and detrital wood fragments overlays a weathered shale pre-Holocene surface on a maximum thickness of 1.7 m. The age of this basal unit is between 6250 cal.yr BP and 5000 cal.yr BP. Benthic foraminifera assemblages are dominated by Trochammina inflata and, secondarily by Jadammina macrescens and Haplophragmoides wilberti and are attributed by the transfert function to the upper limit of the tide levels. The basal peat units are overlayed by an organic-rich silty-clay 4 m thick containing root fragments of salt-marsh plants. Foraminifera assemblages are dominated by Trochammina inflata and Jadammina macresens. The contact between the basal peat and this overlying organic mud is gradational and reflect an increase of marine influence in a salt-marsh environment protected by a coastal barrier. At Porzguen marsh, the foraminifra assemblages are homogenous until the actual surface of the mash indicating no significant change of the back-barrier environment and a great stability of the gravel spit during the last millenia. At Troaon marsh, however, this unit contains two thin silty-sand horizons characterized by numerous shell fragments and dominated by Haynesina germanica and Elphidium species. These horizons reflect a change in the hydrodynamic conditions of the back-barrier environment related to the erosion of the initial gravel barrier. These coastal changes are not dated precisely but could be related to a phase of high strominess recognised around 3000 cal.yr BP in western part of France. Barriers breakdowns are identified in most late-holocene stratigraphies and correspond to a period of major climatic changes, known as the Bond Cold events [START_REF] Bond | A pervasive millennial-scale cycle in North Atlantic Holocene and glacial climates[END_REF].

At Arun marsh, the sediment infilling has a maximum thickness of 4 m. At the seaward marges of the salt-marsh, the back-barrier sediment sequence is characterised by a set of coarse washover fan deposits interstratified with a silty-clay layers dominated by Jadammina macrescens and Trochammina inflata assemblages. Two barrier erosion and breakdown phases associated to washover deposits are dated around 2000-1800 cal.yr BP and 1000 cal.yr BP respectively. Here, long-term coastal changes are more important because the spit is exposed to more energetic wave conditions.

On the Loc'h spit, the sedimentary sequence is characterised by a coarse gravels unit corresponding to an initial gravel barrier. Two radiocardon dates obtained from wood fragments located at the bottom and at the top of an underlying sandy-clay organic-rich layer give an age of AD 889-1023 and AD 1023-1157, respectively [START_REF] Stéphan | Mise au point sur l'évolution géomorphologique et le devenir des flèches de galets du Loc'h de Landévennec (Bretagne, France)[END_REF]. Analysis of fossil foraminifera assemblages and use of the foraminifera-based transfer function demonstrates that the underlying deposit corresponds to a salt-marsh environment initially protected by an initial gravel barrier. The destruction of this barrier appears to take place from the 11 th -12 th centuries and could be related to important variations in sediment supply from periglacial cliffs erosion, associated to extreme storm events. Storm deposits from the end of the early middle ages have been recognized by [START_REF] Giot | La dune ancienne de la baie d'Audierne[END_REF] and [START_REF] Devoy | Coastal stratigraphies as indicators of environnemental changes upon European Atlantic coasts in the Late Holocene[END_REF] in other coastal stratigraphies in the western part of Brittany. This period is obviously characterised by high storminess in the western France.

The back-barrier sediment sequences studied in the bay of Brest indicate that actual gravel spits have recorded a long morphosedimentary evolution over the last millenia. Distinct phases of construction and barrier breakdown were recognized, indicating a variability of storminess and sediment supllies during late-holocene period. However, morphogenic events are not synchronous because the high spatial variability of barrier exposition to the wind-waves in a fetch-limited environment. At Porzguen marsh, sediment sequence highlights a great stability of the gravel spit due to very sheltered conditions.

HISTORICAL EVOLUTION OF GRAVEL SPITS

Ancient maps of the spits located in south Brittany which have been achieved at the beginning of the 19 th century show a trend to landward retreat. In the bay of Brest, a landward migration is also suspected on swash-aligned gravel spits but the rates of migration are difficult to assess. On the Sillon de Talbert, however, coastal changes are more significant. On the first detailed maps drawn in 1666 and 1675, the gravel spit is represented as a welded barrier attached to the islands of the Olone Archipelago. A major morphological change can be seen on the Cassini's map, achieved in the second half of the 18th century. On that map, the Sillon de Talbert is deconnected from the Olone Archipelago by a 200 to 300 m long breach, transforming the barrier into a spit. This dislocation is dated in the early 18 th century, probably due to the violent storm on 26 November 1703. This storm heavily hit the British Isles and western France and is supposed to have cost the lives of between 8,000 and 15,000 people. In England, this storm alone is thought to have killed 1,700 people and to have sunk 12 Royal Navy vessels. It is considered as the most violent storm of the past centuries [START_REF] Lamb | Historic storms of the North Sea, British Isles and Northwest Europe[END_REF]. The transformation of the initial welded barrier into a spit marked the beginning of a slow cannibalistic process throughout the 19 th and 20 th centuries, which was accompanied by continuous landward retreat of the spit by rollover. The cannibalistic trend on the Sillon de Talbert is linked to a gradual change in its general direction in relation to incident waves, from a swash-aligned to a more drift-aligned position, leading to an increase in longshore drift. This sediment transport fed the distal end, which widened out through the formation of successive accretion ridges on the lee side of the tip. In return, the proximal end experienced a sediment deficit due to sediment loss by drift.

Over the last decades, the quantification of the recent mobility of spits in Brittany shows a sediment budget deficit (fig. 3). This sediment depletion is illustrated by cannibalization (e.g. Dibenn spit, fig. 4A), landward retreat by rollover (e.g. Sillon de Talbert spit, fig. 4B) and, in some places, complete destruction of the gravel-dominated spits (e.g. Faou spit, fig. 4C). Located on the southern part of Brittany, Betahon spit is the only case of recent progradation with a mean rate of seaward migration around 0.25 m.yr -1 (fig. 3). The progradation is related to the construction of a foredune during 1980's by important sediment supply from the west of the bay. Six spits located in the inner parts of the bay of Brest are stable due to their very sheltered position and fetchlimited conditions. It is the case of Porzguen spit which also showed an exceptional stability during the last millennia. Seven spits have experienced a slow landward migration by rollover because of occasional impact of overwash events induicing, in this case, rates of retreat never exceed 0.2 m.yr -1 . Seven spits show macro-and microscale cannibalization trends, reflecting scarcity of the longshore sediment supply. Four spits recorded a rapid rollover with rates of landward migration between 0.25 and 1 m.yr -1 . The highest migration rates are recorded on the Sillon de Talbert (fig. 4B). Finally, a case of complete destrcution was observed on Faou spit during last decades. After a rapid landward retreat around 0.6m/yr between 1948 and 2004, two large breaches were openned during the 1970's leading to the gradual lowering of the crest and the sediment dispersion in the back-barrier zone by waves (fig. 4C). Ultimately, the morphological evolutions measured over last decades reveal a scarcity of coarse grain sediment along the Brittany coastline.

THE QUESTION OF SEDIMENT SUPPLY FROM PERIGLACIAL CLIFF EROSION AND SEDIMENT BUDGET

The recent mobility of the gravel spits highlights the lack of significant sediment input. Because the unconsolidated cliffs formed by periglacial deposits (head) are nowadays the main sediment sources of coarse sediment to the coast, the recent volumes provided by their erosion were assessed closed to three study areas. Cliff erosion rates were determined near Betahon and Dibenn spits and in the bay of Caro (Bay of Brest) by analysing changes of the backscar (cliff-top) plotted from aerial photographs. Volumes were calculated as [START_REF] Shuisky | Basic principles of sediment budget study in the coastal zone[END_REF] and have been estimated at 570 m 3 .yr -1 in the bay of Betahon. This supply explains the recent seaward migration of the spit. However, the bay of Betahon appears to be an exception at regional scale. Generally in Brittany, the retreat of unconsolidated cliffs is slow. For instance, erosion of Dibenn cliffs provides a sediment volume estimated at 20 m 3 .yr -1 which is not sufficient to prevent the cannibalization of the spit. In the bay of Brest, taking the exemple of Caro studied site, the maxima of volumes of sediment supply provided by unconsolidated cliff erosion is estimated at only 48 m 3 .yr -1 .

Gravel barriers are formed by fossil deposits which have within a context of sediment abundance and a decreasing of Holocene of sea-level rise around 6000 cal.yr BP. In the eastern part of the Channel, authors have shown that a large amount of the gravel accumulations were originated from sea erosion of a periglacial head deposits present on the continental shelf during the post-glacial transgression [START_REF] Bray | Littoral cell definition and budgets for central southern England[END_REF]. This sediment source was gradually depleted during the last milleniums because of the decrease of the relative sea-level rise. On Nova Scotia coast (Canada), the authors have explained the erosion of gravel barrier and spit by the absence of major sea-level fluctuations during the past millennia [START_REF] Forbes | Morphodynamic evolution, self-organisation, and instability of coarse-clastic barriers on paraglacial coast[END_REF][START_REF] Orford | Organisational controls, typologies and time scales of paraglacial gravel-dominated coastal systems[END_REF]. In that context, no sediment supply on the foreshore by cliff erosion is realised. Stéphan (2011c) showed that in western Brittany (Bay of Brest), rates of relative sea-level rise have gradually declined since 2700 cal.yr.BP and are below 1 mm.yr -1 for the last millennium. This rise of sea-level is not sufficient to enable a widespread of large amout of sediments. Field observations indicate that the base of unconsolided cliffs is nowdays rarely reached by waves attack (Stéphan 2011a). Furthermore, the presence of a large platform in front of the current cliffs promotes the dissipation of wave energy before it reaches the foot of the cliff.

IMPACT OF HUMAN FORCINGS

In Brittany, anthropogenic forcing have often been overstated to explain coastal erosion of gravel barriers. Sediment mining has played a role only in places. Pebbles were only extracted for industrial use in the Bay of Audierne during the Second World War by the Wehrmacht Todt Organisation, to built the german defense (Atlantic Wall). In this area, sediment extraction represented a volume of around 1.10 6 m 3 . For the Sillon de Talbert, no data is available to estimate the volumes extracted, but historical documents indicate that during the 18 th and 19 th centuries, the barrier was also used for pebble mining. However, this practice was prohibited in the early 20 th century. In the bay of Brest, sediment mining undertaken during the 1960's by oyster-farming activity has caused a reduction of 10% and 50% of the total sediment volumes of Mengleuz and Kersanton spits respectively.

Jetties constructed downdrift have led to a reduction of longshore sediment supply towards the Troaon, Auberlac'h (fig. 2A) and Mengleuz (fig 2C) spits. This process is explaining cannibalization of Troaon spit and the erosion of Mengleuz and Auberlac'h spits. On the Sillon de Talbert, following the 5 April 1962 storm, major defense management of the spit was undertaken. In 1967, the breaches in the dune were artificially plugged. A riprap jetty was built between 1974 and 1982 in order to protect the dune section and to trap eastward sediment transit. A frontal rocky armor was also built at the crest of the spit over a distance of 1500 m to prevent overwash and rollover processes. However, the armor perturbated the natural self-organisation of the spit and increased wave reflection causing the erosion of the lower beachface. During winter 1989-90, despite the existence of this defense structure, serie of storms caused considerable lowering of the barrier, the formation of new breaches and the retreat of the spit. In 2004, the main part of the frontal armor was removed by local authorities (fig. 2G and2H).

MORPHODYNAMIC BEHAVIOURS OF GRAVEL BARRIERS AT MID-TERM

Natural forcing, such as waves and tide, was analysed between 1953 and 2011 in the bay of Brest (Stéphan 2011c) and between 1979 and 2011 on the Sillon de Talbert spit [START_REF] Stéphan | Long-term morphodynamic evolution of the Sillon de Talbert gravel barrier spit, Brittany, France[END_REF]). This analysis was based on the reconstruction of high water levels inducing crest erosion and/or overwash processes. On the Sillon de Talbert spit, the rate of landward retreat by rollover is strongly controlled by overwash frequency and magnitude. Between 1979 and 2010, Unit 1 (see on fig. 4B) was affected by very few overwash episodes, which explains the stability of this spit section from the 1970's. Unit 2 was more frequently affected by overwash, in particular after the 1989-90 winter storms which created two main breaches in this section. At the same time, the increase of overwash events during the 1990's can also be explained by the lowering of the crest. Units 3 and 4 showed more frequent overwash events between 1979 and 2010. The most severe ones were generated by 1989-90 winter storms and the 10 March 2008 Johanna storm. During these two storm periods, landward retreat by sluicing overwash reached -15 m and -13.2 m for unit 3 and -17 m and -14.7 m for Unit 4. Several secondary discrete overwash events were also recorded during the spit's low mobility phases.

In the bay of Brest, spit mobility over the last decades was not so clearly linked to overwash events. Nevertheless, three overwash regimes are identified according to barrier alignment (fig. 5). A first overwash regime (R1), characterized by low frequency and high magnitude of overwash events, is driving swash-aligned gravel spit processes causing slow landward retreat by rollover. A second regime (R2), defined by high frequency and low magnitude of overwash events, is drinving drift-aligned gravel spit evolution dominated by relative stability and cannibalization. A third regime (R3) concerns swash-aligned spits frequently reached by energetic overwash events causing rapid landward retreat by rollover or complete destruction of spits.

SHORT-TERM EVOLUTIONS : BARRIER RESPONSES TO EXTREME STORM EVENTS

Between 2002 to 2012, morphological evolution of five gravel barriers affected by erosion was annualy surveyed using DGPS. The role played by overwash dynamic was also studied by analysing hydrodynamic conditions. Results show different pluri-annual morphogenetic phases. On Sillon de Talbert spit, the major storm event of 10 Marsh 2008 caused a barrier average retreat up to 10 m and a crest lowering reaching -1.5 m. Volume of removed sediments due to barrier retreat were estimated to 120,000 m 3 (10% of the total volume of the spit). The morphogenetic impact of this extreme event is explained by the conjunction of high spring tide levels and energetic wave conditions (hs = 9.5 m) inducing high surge level on the coast. The occurrence of this event is between 50 and 100 years on the coast of Brittany. This sluicing overwash phase occurred after a period of stability related to a low morphogenetic condition between 2002 and 2007. After the Johanna storm event, crestal overtoppping phase corresponding to the progressive spit recovery was recorded. The "ridge rebuilding activity" as defined by [START_REF] Orford | Gravel-Dominated coastal barrier reorganisation variability as a function of coastal susceptibility and barrier resilience[END_REF], was undertaken between March 2008 and September 2012, showing the great resilience of the barrier. During this post-storm recovery period, the average of crest elevation was up to 1 m, reaching locally 2 m. These different morphogenetic phases were also recognised on other coastal areas along the Brittany coastline (Suanez et al. 2011;[START_REF] Suanez | Effects of natural and human forcing on mesoscale shoreline dynamics of Saint-Michel-en-Grève Bay (Brittany, France)[END_REF]. In contrast, in the bay of Brest, the recover of spits and barriers after the 10 March 2008 storm event was not so obvious. For instance, Loc'h spit showed a tendency of erosion for the whole period of measurements, especially since the 10 March 2008 Johanna storm (Stéphan and Laforge 2013) (fig 6).

MANAGMENT STRATEGIES OF GRAVEL SPITS

After several decades of coastal erosion management strategy mainly based on hard defense structures, the Sillon de Talbert has experienced since 2001 a new management policy provided by the 'Conservatoire du Littoral'. The principal goal is not let the spit retreating naturally while attempting to prevent its breaching. In October 2004, a large part of the frontal armor located on the Units 2 and 3 was removed (fig. 2G and2H). The riprap was crushed and the material was deposited on the rear of the spit, forming three embankments in a way to slow down the landward retreat barrier. After the major morphological changes caused by the Johanna storm, human interventions were only restricted to plug breaches in the dune, and to set up new fences in a way to recover dune morphology. It denotes a radical change in terms of coastal management strategy facing erosion in France. The 'Conservatoire du Littoral' is a national organisation in charge of the acquisition of coastal lands for there preservation. Since the 2000's, the 'Conservatoire du Littoral' initiated a policy of acquisition of low-lying coastal lands threatened to flooding, situated on the rear of the Sillon de Talbert spit. This policy promotes the strategic withdrawal of human settlements. In the bay of Brest, a managment stragegy of gravel spits and backbarrier salt-marshes is establishing by local authorities [START_REF] Stéphan | Long-term morphodynamic evolution of the Sillon de Talbert gravel barrier spit, Brittany, France[END_REF]) in order to protect barriers affected by erosion and restore ecological value of marsh environments. Gravel spits are also considered as geological heritage that must be protected from any human forcing using management plans.

FIGURESFig. 1 Fig. 2 aFig. 3 Fig. 5

 1235 FIGURES


Meriem Chakroun
email: chakroun@latmos.ipsl.fr

Sophie Bastin

Marjolaine Chiriaco

Hélène Chepfer

Hélène Chepfer Coupling Caliop

Bastin

Coupling CALIOP observations and

come

This work aims to study the clouds' role on regional climate variability. At first order, European climate is driven by large scale circulations. However, clouds are known to have two major radiative effects impacting the surface temperature: the greenhouse effect and the albedo effect. These effects are strongly dependent on macrophysical and microphysical properties of clouds. It is then necessary to consider the vertical distribution of clouds to better understand their impact on regional climate.

Since June 2006, A-train observations are available and allow the description of this vertical distribution and of other microphysical properties. However, the sampling is limited when considering small scale variability. To complete these observations, we use a regional climate model which may allow to extend the period of study and to better understand the link between clouds and surface temperature.

In this study we address the ability of our tools to study impact of clouds on European climate at a resolution suitable to take into account the complex terrain of this area. Seasonal and inter annual clouds variability is presented for observations and simulations. We also evaluate the amplitude of clouds variability in the simulations and the uncertainties linked to the satellite sampling.

B. Effect of satellite under sampling GCM Oriented Calipso Cloud Product-GOCCP (Chepfer et al., 2010)

Using the microphysical properties of the simulated clouds, we compute the SR that would be observed by the CALIOP lidar. We can then use the same cloud diagnostics for both observations and simulations

WRF-MedCordex simulations Adaptation of COSP Lidar Simulator

References and acknowledgements

Fig5:

  Fig5: SR simulated histograms, satellite sampling(a); WRF sampling (b); Sat. sampling -WRF sampling (c)

  Fig3: Instant SR 19/01/2009 night: (a) Observations, (b) WRF simulations and (c) WRF+lidar simulator simulations

Fig10:

  Fig10: Envelope of inter annual CF anomaly compared to mean 2006-2011 of horizontally averaged over Europe (only continent) and over the Mediterranean sea (only sea) cloud fraction (observations; simulations with sat. sampling; simulations with WRF sampling) for winter and summer


Rossano Gaeta

Marco Grangetto

Riccardo Loti

SIEVE: a distributed, accurate, and robust technique to identify malicious nodes in data dissemination on MANET

Keywords: MANET, data dissemination, malicious node identification, statistical inference

In this paper we consider the following problem: nodes in a MANET must disseminate data chunks using rateless codes but some nodes are assumed to be malicious, i.e., before transmitting a coded packet they may modify its payload. Nodes receiving corrupted coded packets are prevented from correctly decoding the original chunk. We propose SIEVE, a fully distributed technique to identify malicious nodes. SIEVE is based on special messages called checks that nodes periodically transmit. A check contains the list of nodes identifiers that provided coded packets of a chunk as well as a flag to signal if the chunk has been corrupted. SIEVE operates on top of an otherwise reliable architecture and it is based on the construction of a factor graph obtained from the collected checks on which an incremental belief propagation algorithm is run to compute the probability of a node being malicious. Analysis is carried out by detailed simulations using ns-3. We show that SIEVE is very accurate and discuss how nodes speed impacts on its accuracy. We also show SIEVE robustness under several attack scenarios and deceiving actions.

I. INTRODUCTION

Due to their open, fully distributed and dynamic architecture, shared wireless channel, and resource limited participants, MANET are vulnerable to attacks at any layer of the Internet model [START_REF] Yang | Security in mobile ad hoc networks: challenges and solutions[END_REF].

We consider a particular type of active, non-cryptography related attack: data corruption at the application level introduced by insider nodes. In this paper we deem as a use case a data dissemination application over a MANET. Nodes generate data chunks to be disseminated to all participants using rateless codes; some malicious nodes deliberately modify coded packets of a chunk before relaying them to prevent honest nodes from obtaining the original information. In this paper we propose SIEVE a distributed, accurate and robust technique to identify malicious nodes on top of an otherwise reliable and attacker-free architecture. Each node in SIEVE dynamically creates a bipartite graph (factor graph) whose vertices are checks. A check is a report created by a node upon decoding a data chunk; a check contains a variable length list of nodes identifiers that provided parts of the data as well as a flag to signal if the data chunk has been corrupted. Detection of the compromised chunks is achieved exploiting the constraints imposed by linear channel coding. The factor graph is periodically and independently analyzed by each node running an incremental version of the Belief Propagation (BP) algorithm [START_REF] Yedidia | Constructing freeenergy approximations and generalized belief propagation algorithms[END_REF]. The proposed algorithm allows each node to compute the probability of any other node being malicious; these latter probabilities are used to derive a suspect ranking of nodes in the MANET. The major contributions of the paper are the recasting of the problem of malicious nodes identification in terms of the estimation of the marginal probabilities on a bipartite graph and the proposal of a distributed and accurate solution based on the BP algorithm. Paper contributions are completed by a comprehensive experimental investigation of SIEVE capabilities. It is worth pointing out that the selected data dissemination application is just a quite popular use case [START_REF] Park | Codecast: a network-coding-based ad hoc multicast protocol[END_REF], [START_REF] Nguyen | Wireless broadcast using network coding[END_REF], whereas the proposed approach is by no means constrained to such particular scenario. In particular, SIEVE can be used in any application that uses multi-party download or collaboration, provided that is possible to detect that a given set of collaborating entities is compromised by at least one malicious node.

SIEVE fits well two key MANET features that must be accounted for when devising any security solution: it is fully distributed and does not rely on any infrastructure (as opposed to some solutions in the area of peer-to-peer streaming where special well known nodes are necessary, e.g., [START_REF] Wang | MIS: Malicious nodes identification scheme in network-coding-based peer-to-peer streaming[END_REF]) and does not have a high computational cost (as opposed to on-the-fly verification techniques in the area of network coding, e.g., [START_REF] Krohn | On-thefly verification of rateless erasure codes for efficient content distribution[END_REF], [START_REF] Gkantsidis | Cooperative security for network coding file distribution[END_REF], [START_REF] Yu | An efficient signature-based scheme for securing network coding against pollution attacks[END_REF], [START_REF] Kehdi | Null keys: Limiting malicious attacks via null space properties of network coding[END_REF], [START_REF] Yu | An efficient scheme for securing xor network coding against pollution attacks[END_REF]).

The paper is organized as follows: Section II describes the system model we consider, Section III presents the SIEVE technique, Section IV discusses the simulation methodology and the accuracy, reactivity, and robustness results we obtained, Section V summarizes other works related to SIEVE, finally Section VI draws conclusions and outlines directions for future developments.

II. A USE CASE FOR SIEVE

In this paper we consider a MANET composed of N wireless nodes on a square area whose side length is l meters. Nodes move using different average speeds: N f ast nodes moving at V f ast m/s (e.g., cars, buses, motorcycles), N slow nodes at V slow m/s (e.g., pedestrian, bikes), and N still fixed nodes (e.g., sensors, relay stations, shops). As a consequence we have N = N f ast + N slow + N still .

Nodes periodically produce a new data chunk to be disseminated to all others once every h seconds; to this end, all nodes cooperate by running a common application that assigns each node an unforgeable unique identifier, e.g., an integer value. When nodes produce or successfully obtain a chunk they turn into data sources for that chunk and help in spreading it. Data is transmitted by source nodes using rateless codes [START_REF] Luby | LT codes[END_REF]: a chunk (whose size in bytes is fixed and is denoted as S) is divided in K equally sized blocks. The source node then creates and forward coded packets using rateless LT codes [START_REF] Luby | LT codes[END_REF], combining random subsets of the K blocks; the size of each coded packet is S cb = S K . According to [START_REF] Luby | LT codes[END_REF] the original chunk can be obtained by any node able to collect any set of K • (1 + ) coded packets ( is known as the code overhead). Coded packets are produced only by source nodes for a chunk and consist in simple binary XOR operations among a random set of d original data packets, provided that d is selected according to the Robust Soliton Distribution (RSD) with parameters c and δ [START_REF] Luby | LT codes[END_REF]. A coded packet conveys the XORed payloads of the corresponding original packets as well as a header signalling the indexes of the combined packets. Decoding is carried out by solving a system of linear equations. In [START_REF] Luby | LT codes[END_REF] it is shown that the most simple decoder based the simplification of the equations of degree 1 exhibits asymptotically optimal overhead, provided that the RSD distribution is used to encode packets.

Each node can simultaneously collect coded packets for different chunks; to this end a window based mechanism is used where the w r most recent chunks can be concurrently downloaded. Similarly, each node can serve as data source for the w t most recent received chunks. A simple round robin scheduling policy is used when transmitting coded packets of chunks in the upload window; coded packets are transmitted every T tx msec using UDP over an 802.11g wireless communication interface yielding an average transmission range of r meters.

A subset of P < N nodes (where P = P f ast + P slow + P still ) is composed of malicious nodes, i.e., they deliberately modify the payload of coded packets they produce before transmission to prevent honest nodes from correctly decoding the original chunk. In presence of coding even a single corrupted coded packet can prevent an honest node from decoding the original chunk.

III. THE SIEVE PROTOCOL

SIEVE is based on the concept of checks that are reports created by nodes upon decoding a chunk. A check contains the list of the identifiers of nodes that provided coded blocks of a chunk and a flag to label such chunk as corrupted or not.

A node is able to detect such condition as soon as an inconsistency is found in the solution of the underlying system of linear equations. In particular, according to the procedure [START_REF] Luby | LT codes[END_REF] the decoder keeps cancelling out all the already known data packets. This is achieved by observing that a coded packet with a degree 1 equation represents a data packet in the clear. Such data packet can be simplified from all the incoming equations. Since LT codes have a certain overhead some coded packets that are linearly dependent on the ones received previously are always collected before successful decoding; this amount to the reception of some equations whose terms are all already known. As soon as this condition is met the LT decoder can check the consistency of the payload carried by the coded packet; in other words, the same linear combination must be obtained combining a set of already known packets. If this constraint is violated the whole chunk is recognized as corrupted. Please note that the receiver node is not able to identify the corrupted block(s) but only that at least one of them has been maliciously manipulated. A check describing a corrupted chunk is called a positive check while it is termed a negative check otherwise. Each nodes n maintains a list of all checks created that is denoted as L n .

Each node, besides accumulating the checks from its local decoding operations, gossips them in the neighborhood. Each node n in the MANET transmits its checks in two cases:

• as soon as n decodes a chunk it inserts it in L n and broadcasts it; • once every T s seconds n randomly selects Q checks in L n and transmits them. The checks in L n and all checks received by n are used to build a factor graph G n =(U, C, E). G n is a bipartite graph where the vertex set U is the set of uploader nodes, the vertex set C is the set of checks, and an undirected edge {i, I} ∈ E exists if and only if check I ∈ C depends on uploader i ∈ U.

In the following we will refer to the set of uploaders involved in check I as U I and the set of checks that node i contributes as an uploader as C i . An example of factor graph with four uploaders (circles) and two checks (squares) is show in Fig. 1. Each uploader i can be in one of two states x i = 1 or x i = 0, depending on whether uploader i is or is not a malicious node. Each check can report one of two observations c I = 0 or c I = 1 in case of negative or positive pollution detection, respectively. Coming back to the example of Fig. 1 the filled circle is used to represent a polluter that in turn causes a positive check (filled square).

The problem of identifying the malicious nodes from a given number of checks can be recast as an inference problem. The goal of the inference is the estimation of the hidden state of the nodes, i.e. being malicious or not, given a set of observations corresponding to the checks. Each check can be interpreted as an accusation raised against a set of uploader nodes by a witness node. In this paper we adopt the Belief Propagation (BP) algorithm [START_REF] Yedidia | Constructing freeenergy approximations and generalized belief propagation algorithms[END_REF].

The BP algorithm can be used to estimate from the factor graph the so called variable marginals (P (x i )) i∈U , i.e. the probability of node i being malicious. BP is an iterative algorithm based on the exchange of probability messages, the belief, along the edges of the bipartite graph G n . In case of a Bayesian network BP represents a close form solution for the marginals. The same algorithm can be applied iteratively to a general factor graph that, as opposed to a Bayesian network, includes loops; in this case BP has proven to be a robust estimator for the variable marginals [START_REF] Yedidia | Constructing freeenergy approximations and generalized belief propagation algorithms[END_REF].

In our setting it is convenient to distinguish between two classes of messages: message from uploader i to check I, m x iI , that is meant to be the probability that uploader i is in state x, given the information collected via checks other than check I (C i \ I); message from check I to uploader i, m x Ii is defined as the probability of check I having value c I if uploader i is considered in state x and all the other uploaders states have a separable distribution given by the probabilities {m x i I : i ∈ U I \ i}. The messages are initialized to the values m 0 iI = m 1 iI = 0.5, i.e. all nodes are equally likely to be malicious in the first run. Then, the probabilities m x Ii are estimated using:

m x Ii = {x i :i ∈U I \i} P (c I |x i = x, {x i : i ∈ U I \ i}) i ∈U I \i m x i i I
(1) Equation ( 1) depends on the probability of observing a certain check value c I , given the states of the uploaders of such check. Given that a check turns out to be positive as soon as at least one of the uploaders is a malicious node we can write:

P (c I = 1|{x i : i = 1, . . . , k}) = 0, if x i = 0, ∀i 1, otherwise (2)
Analogously, observing that a check can be negative if and only if all the uploaders are not malicious we get

P (c I = 0|{x i : i = 1, . . . , k}) = 1, if x i = 0, ∀i 0, otherwise (3)
Plugging the last two expressions into Equation ( 1) we can simplify it as shown in Table I for the four possible combinations of c I and x. The next step is constituted by Table I: Definition of m x Ii as a function of x and c I .

c I \x 0 1 0 i ∈U I \i m 0 i I 0 1 1 -i ∈U I \i m 0 i I 1
the updating of the probabilities m x iI , using information from previous computation in the checks: m x iI = I ∈Ci\I m x iI . Finally, the marginal P (x i = x) can be computed as

P (x i = x) = I ∈Ci m x iI .
To conclude the BP algorithm initializes the values of m iI , then keeps updates messages from checks to nodes and from nodes to checks. A reliable estimate of P (x i = x) is computed after a certain number of iterations. In all the experiments reported in this paper 3 iterations have been used.

A. Incremental BP estimation

In the previous description of the BP we have assumed that the factor graph G n is known in advance and kept fixed for all the iterations. In practice this assumption is not met in the proposed scenario. Nonetheless, the proposed algorithm can be implemented using an incremental (or sliding window) approach as follows.

Each node n keeps receiving checks from the other ones and it is allowed to run the BP on G n every T seconds considering only the checks received and created in a time window of the past w seconds. At time t, depending on the checks stored during a time window w, an updated factor graph G nt,w is obtained by removing the old checks and adding the new ones; then, the corresponding estimates P t,w (x i = x) are computed through BP. It is worth pointing out that the belief values are initialized only once, as soon as a node shows up as an uploader for the first time in a check; then, the partial estimates of m x iI and m x Ii are propagated to all the computation windows where such values are needed.

After every BP algorithm ran a list of suspect nodes is obtained by setting a threshold on the probability P t,w (x i = 1) ≥ η. Each node n keeps a counter for each of its uploader nodes i: the nodes in the list of suspects after the BP run have their counter increased by 1. Finally, a suspects ranking R n over uploader nodes is defined by sorting their counters in decreasing order. As an example, the first node in the suspects ranking at time t is the uploader that more often has been included in the list of suspects after all the BP runs performed up to time t.

IV. RESULTS

In this section we present results on the accuracy and robustness of SIEVE. The performance of SIEVE have been investigated by simulations using ns-3 version 3.12 on a Under these settings we derived the maximal transmission range r as 175 meters.

We conducted terminating simulations (not steady-state) runs that lasted for 1 hour of simulated time. We used the independent replication method to obtain N EXP = 30 realizations and computed 95% confidence intervals. Each realization is obtained with different streams of the random number generator provided by ns-3. The simulation output is made of a log file containing all checks received by each node that is post-processed by our analysis software to run the SIEVE protocol to detect malicious nodes.

All results have been obtained by setting as data sources only the still nodes; all N nodes contribute to the spreading of the information according to the policy described in Section II. All the system parameter values of the reference scenario considered in the following are summarized in Table II.

For each node n in the system we define the following performance indexes: the first node in R n is not malicious then r n (t) = 0.

• p(n) an
• t n,trigger the time node n received the very first positive check that triggered the SIEVE activation; • t n,hit (x) = min t {t : r n (t) ≥ x}, that is, the minimum time to identify at least x malicious nodes in the top x positions of R n . In each independent replication trial we compute the following averages:

• c (i) (t) = N n=1 p(n)cn(t)
N n=1 p(n) , i.e., the fraction of honest nodes that detected the attack and have a non empty R;

• a (i) (t) = 1 P N n=1 p(n)cn(t)an(t) N n=1 p(n)cn(t) , i.e., the average accuracy of honest nodes with non empty R. When a (i) (t) = 1 it means that all honest nodes with non empty R have correctly identified all malicious nodes.

• tsi (i) (x) = N n=1 p(n)(t n,hit (x)-tn,trigger) N n=1 p(n)
, i.e., the average time honest nodes require to unambiguously identify x malicious nodes. Finally, we computed averages and 95% confidence intervals over N EXP realizations and considered c(t), a(t), and tsi(x). In all computations we set η = 0.99 (η is the threshold on the probability P t,w (x i = 1) for suspect identification) and iterated each run of the BP algorithm on a specific factor graph three times.

A. Sensitivity results

The first analysis we conducted is on the sensitivity of c(t), a(t), and tsi(x) to parameters w, T , T s , and Q used by SIEVE as defined in Section III. Due to space limitations, in the following we omit graphs for tsi(x). To this end we considered integer multiples of 10s values for w ranging from 10s to 90s, T = {5, 10}s, T s = {5, 10, 15}s, and Q = {5, 10}. We analyzed all 108 combinations and observed that SIEVE performance is most sensitive to the value w. Figure 2 show the completeness c(t) for w = 60s when T = T s = 10s, and Q = 10 (all values of w yield very similar results so we chose a representative case to avoid cluttering the figures). It can be noted that c(t) approaches 1 after about 4 minutes; the behavior of c(t) clearly depends on the delay after which a node has collected a sufficient amount of checks to start its own suspect ranking. All experiments worked out in the following exhibit a very similar behavior for c(t) so we avoid repeating similar figures. Furthermore, all following graphs compare different scenarios so we plot average results without confidence intervals for better readability.

Figure 3 shows the performance index a(t) with the same settings (all 108 configurations yielded similar results so we selected a representative scenario). It can be noted that too small or too large values for w yield the worst performance. Indeed, small window sizes do not allow the factor graph to include enough checks to accurately infer the node status; on the other hand, large values of w provide more checks in G n but increase the number of short loops in the factor graph which in turn impact on the accuracy of the probability estimates yielded by the BP algorithm [START_REF] Luby | Improved low-density parity-check codes using irregular graphs[END_REF]. As for SIEVE reactivity, we observe that w = 60s yields the lowest values for tsi(x) (we obtain tsi(1) = 33s).

We selected w = 60s as the value for all successive evaluation. Performance of SIEVE are less dependent on the values of other parameters. Actually, small values for T improve reactivity by decreasing tsi(x) but increase the frequency of BP computation; this may cause concerns for battery operated nodes whose lifetime could be shortened. Battery lifetime can be prolonged by using large values for T s and small values of Q. Nevertheless, performance of SIEVE deteriorates for the same reasons observed for small values of w. We selected a compromise between performance and energy consumption awareness using the following setting for the rest of the paper: w = 60s,

B. Mobility and SIEVE performance

The first interesting observations we made is that nodes mobility affects SIEVE performance. Figure 4 shows the overall performance of SIEVE in the reference scenario and in scenarios with different mixes of fast and slow nodes. The system where all moving nodes are fast yields much higher accuracy and much lower reaction times with respect to other extreme case where all nodes move slowly. Nevertheless, SIEVE accuracy is 0.83 at the end of the one hour long experiments and approaches 1 for longer runs.

Figure 5 shows results in the reference scenario and in scenarios where all malicious nodes are either fast or slow. The best way for malicious nodes to delay identification by honest nodes is to move as slow as possible; indeed, if all malicious nodes move fast all of them are quickly identified.

Although SIEVE is able to identify all malicious nodes in the long run in any setting, in all three cases we can conclude that high speed is key to obtain both high accuracy and low reaction delays by honest nodes. Why is high speed beneficial for SIEVE performance of honest nodes but detrimental for malicious nodes? Consider the case where different mixes of fast and slow nodes (Figure 4) are compared and assume an extreme scenario composed of all still nodes. Since nodes do not move it is possible to define a static geometric graph O describing connections among nodes where vertices are nodes and an undirected edge between two vertices exist if the corresponding nodes are within the transmission radio range r. For any node n ∈ O let N (n) denote the set of nodes connected to n (the neighborhood of n).

Consider one data source s and a chunk c; clearly, when s has transmitted K • (1 + ) coded packets, c is decoded by all nodes in N (s); in turn, these nodes start transmitting fresh coded packets for c and, if O is connected, all nodes will be able to decode c after some time. It can be noted that all coded packets produced by s will follow the same paths in O. This means that for any node n ∈ O chunks originating from s will be provided to n by the same set of uploaders U s (n) that is a subset of N (n). Of course, the same reasoning is valid for all checks describing chunks produced by any data source other than s. The final effect is that the set of checks created by each node n upon decoding chunks (L n ) is such that strong intersections exist among the uploaders of different checks (given that O is static and N (n) does not change over time); this translates into a high number of short loops in the factor graph used by SIEVE that is a well known cause of poor performance of the BP algorithm [START_REF] Luby | Improved low-density parity-check codes using irregular graphs[END_REF]. The shortest length of a loop in a bipartite graph with at most one edge between any two nodes is 4 and is due to the presence of at least two common uploaders in a pair of checks, i.e. the cardinality of the intersection between the uploaders of a pair of checks is at least 2. When the cardinality of the intersection is x > 2 then each of the x 2 pairs defines a length 4 loop. When nodes move the geometric graph O becomes dynamic, i.e., for any node n its N (n) varies with time. Higher speed translates into higher rate of changing in N (n) that, in turn, lowers the number of short loops in G n increasing accuracy of the BP algorithm. For the reference scenario we computed the average number of checks in G n for fast, slow, and still nodes: we obtained 435, 423, and 742, respectively. We also computed the average total number of length 4 loops obtaining 29978, 41574, and 76563, respectively. It can be noted that still nodes must work on a larger G n (the average neighborhood size is 7.37, 7.69, and 12.73, respectively so still nodes receive more random selections of Q checks from their neighbors) that results in a much higher number of short loops. Furthermore, the average number of checks for a node (the degree of a node) is higher for still nodes (24.75) with respect to fast and slow nodes (16.49 and 16.56, respectively). Of course, the higher the presence of a node in different checks, the higher the probability of short loops forming in G n because of intersection of the set of uploaders.

C. Deceiving actions and SIEVE robustness

Besides following the data dissemination and SIEVE protocols, malicious nodes may also implement disturbing actions hoping to avoid or delay identification by honest nodes. Malicious nodes may deceive honest nodes by:

• reducing pollution intensity: a coded packet is modified before transmission only with probability p poll by flipping a coin. Figure 6 shows how SIEVE performs when nodes lower their pollution intensity by 50% (p poll = 0.5) in the attempt of making their identification harder.

It can be noted that SIEVE is able to quickly identify all malicious nodes also for p poll = 0.5; indeed, malicious nodes that reduce their pollution intensity are less effective in damaging honest nodes and do not succeed in substantially delay identification. • lying on the status of the check: they flip a coin and with probability p lie a positive check is sent out as negative and viceversa. Figure 7 shows that this trick is almost ineffective. able to correctly identify all malicious nodes anyway at the end of the simulation experiments. The last stress test for SIEVE is to consider its performance for an increasing number of malicious nodes that coordinate to launch the colluding attack. In Figure 9 we show results for up to P = 40 malicious nodes in the system (in all cases, P f ast = P slow = P 2 ). It can be noted that SIEVE accuracy reaches 0.9 at the end of the simulation experiments.

D. Computational cost

One of the main requirements for MANET is to have a low computational and memory cost. We measured the average CPU time experienced to run SIEVE on a single factor graph in our C++ implementation on an Intel(R) Core i5 2.80GHz CPU to be 25ms. Of course, MANET nodes do not have the computational power of a PC but newer CPUs equipping tablets and smart phones are reducing the gap, e.g., the ARM Cortex-A9 has up to 4 cores, 2 GHz clock, and 10,000 DMIPS. Furthermore, storage requirements are very low: the average number of checks in G n for fast, V. RELATED WORKS MANET are vulnerable to attacks at any layer of the Internet model [START_REF] Yang | Security in mobile ad hoc networks: challenges and solutions[END_REF]. In network coding several efforts have been devoted to devise on-the-fly verification techniques by participants [START_REF] Krohn | On-thefly verification of rateless erasure codes for efficient content distribution[END_REF], [START_REF] Gkantsidis | Cooperative security for network coding file distribution[END_REF], [START_REF] Yu | An efficient signature-based scheme for securing network coding against pollution attacks[END_REF], [START_REF] Kehdi | Null keys: Limiting malicious attacks via null space properties of network coding[END_REF], [START_REF] Yu | An efficient scheme for securing xor network coding against pollution attacks[END_REF] to identify the sources of corrupted data. Major drawbacks of these elegant methods are the high computational verification costs and communication overhead due to pre-distribution of verification information. Error correction is another approach to deal with data corruption attacks in network coding [START_REF] Ho | Byzantine modification detection in multicast networks with random network coding[END_REF], [START_REF] Jaggi | Resilient network coding in the presence of byzantine adversaries[END_REF], [START_REF] Koetter | Coding for errors and erasures in random network coding[END_REF]; these methods introduce redundancy to allow receivers to correct errors but their effectiveness depends on the amount of corrupted information. The data corruption attack we consider in this paper is a well-known plague in peer-to-peer streaming systems. Unfortunately, all solutions developed in that field are not easily adoptable in MANET. The work by Wang et al [START_REF] Wang | MIS: Malicious nodes identification scheme in network-coding-based peer-to-peer streaming[END_REF] proposes a detection scheme where each peer is able to detect receipt of corrupted blocks by checking the decoded chunk to the specific formats of the video stream. Peers detecting polluted chunks send alerts to the video server and the tracker. Upon receipt of an alert the server computes a checksum of the original chunk, used by peers to identify which uploader actually sent a corrupted block, and disseminates it to all peers in the overlay. Peers report their suspects to the server and true polluters cannot lie (the authors develop a non repudiation protocol to ensure that peers cannot lie when reporting suspects to the servers). Sequence numbers are used to tag alerts to deal with cycles in the overlay. Lack of a centralized monitoring and management point makes it hard to adapt this solution to MANET.

The work by Li and Lui [START_REF] Li | Stochastic analysis of a randomized detection algorithm for pollution attack in P2P live streaming systems[END_REF] presents a distributed detection algorithm and analyzes its performance based on simple intersection operations by peers: each starts with a suspects set equal to the entire neighborhood that shrinks as long as chunks are downloaded from a random subset of uploaders independently chosen from the entire set of neighbors. The scheme allows malicious nodes to send corrupted blocks using a pollution probability. The technique is analyzed with a known number of malicious nodes in the neighborhood and an approximation is proposed when this quantity is unknown, it is attractive due to its simplicity and fully distributed nature. Performance deteriorates when multiple polluter exist and it is not clear if the technique can work when the neighborhood of a peer varies with time.

The work by Jin et al [START_REF] Jin | Detecting malicious nodes in peerto-peer streaming by peer-based monitoring[END_REF] proposed a monitoring reputation system that peers use to select neighbors. The focus of the paper is on reputation computation, storage and load balancing among monitoring nodes. Results show the system is able to detect malicious nodes up to a certain degree of lies. Nevertheless, the technique relies on assumption that each peer is able to compute the amount of corrupted blocks received by each uploader during a monitoring period. This appears to be a rather unrealistic assumption.

VI. CONCLUSIONS

In this paper we proposed the novel SIEVE technique for identification of malicious nodes performing an active, non-cryptography attack within a MANET. SIEVE is a fully distributed technique that, using statistical inference based on the belief propagation algorithm, allows each node to independently analyze local snapshots of the bipartite graph of the collected checks to estimate the probability of nodes being malicious and to perform a progressive ranking of the suspect nodes. Our results, worked out using detailed ns-3 simulations, show that SIEVE is accurate in letting each honest node identify all malicious nodes under several scenarios. We analyzed the sensitivity of SIEVE performance to the nodes mobility; we discovered that SIEVE is very robust to several deceiving actions, colluding attacks by malicious nodes, and amount of malicious nodes inside the network.

Figure 1 :

 1 Figure 1: Example of factor graph.

Figure 2 :

 2 Figure 2: Completeness for w = 60s with 95% confidence intervals.

Figure 3 :

 3 Figure 3: Accuracy as a function of w.

Figure 4 :

 4 Figure 4: Accuracy for moving nodes ranging from all fast to all slow.

Figure 5 :

 5 Figure 5: Accuracy for malicious nodes that are either all fast or all slow.

Figure 6 :

 6 Figure 6: Accuracy for different pollution intensities.

Figure 8 :

 8 Figure 8: Accuracy for different disparaging attacks.

Table II :

 II Parameter values for the reference scenario. Red Hat 4.4.6-3 machine using standard variable settings. In particular, we developed a node object implementing the sender and receiver functionalities according to the protocol described in Sections II and III. All non-fixed nodes movements are described by the same mobility model and moving nodes are randomly distributed across the simulation area at the beginning of the experiment. Still nodes are distributed on a grid to avoid clustering in a particular region. We used the ns-3 default transmission model composed of a propagation delay model (a constant value model) and a propagation loss model (based on a log distance model with a reference loss of 46.677 dB at a distance of 1 meter).

	N f ast , N slow	100	K	100
	N still	50	S cb	500 Bytes
	P f ast , P slow	5	Ttx	100 ms
	P still	0	c, δ	0.01
	r	175 m	l	2000 m
	V f ast	uniform [10-40] m/s	h	600s
	V slow	uniform [1-5] m/s	wr	50
	Mobility model	2D random walk	wt	6

  indicator function whose value is equal to 1 if node n is not malicious and 0 otherwise; • c n (t) an indicator function whose value is equal to 1 if R n is non empty at time t and 0 otherwise; • a n (t) the number of actual malicious nodes correctly identified by node n at time t. More precisely, a n (t) = x if x nodes in the top P positions of R n are truly malicious;

			Completeness c(t)
	1.2
	1
	0.8
	0.6
	0.4
	0.2
						w=60
	0
	0	50	100	150	200	250	300
				Time (sec)

• r n (t) the number of actual malicious nodes correctly identified in the top positions of R n at time t. More precisely, r n (t) = x if top x nodes in R n are all actually malicious . If SIEVE at time t has identified P -1 out of P malicious nodes (a n (t) = P -1) but

ACKNOWLEDGMENT

This work was partially supported by Università degli Studi di Torino and Compagnia di San Paolo under project AMALFI (ORTO119W8J).