Keywords: Réseaux radio cognitifs multi-sauts, routage opportuniste, sélection dynamique de fréquence, dissémination de données

Dissémination fiable de données dans les réseaux radio cognitifs multi-sauts Résumé : Nous présentons dans cet article une nouvelle stratégie de sélection de fréquences pour la dissémination fiable des données dans le réseau radio cognitifs multi-sauts. Le principal défi ici est de sélectionner des fréquences offrant un bon compromis entre la connectivité et les contentions. En d'autres termes, il s'agit de sélectionner les fréquences offrant de bonnes opportunités de communication en raison de (1) la faible activité des noeuds radio primaire (PR), et (2) la contention limitée entre les noeuds ratios cognitifs (CR) opérant sur ces fréquence. Ainsi, en explorant dynamiquement les ressources résiduelles sur les fréquences des primaires et en contrôlant le nombre de CR sur une fréquence particulière, notre stratégie SURF permet de construire un réseau connecté à contention limitée où des communications fiables peuvent être effectuées. Grâce à une large étude basées sur des simulations, nous étudions les performances de SURF par rapport à trois autres approches. Les résultats de simulation confirment que notre stratégie permet la sélection des meilleures fréquences adaptées à une dissémination fiable et efficace des données dans un réseau radio cognitifs multisauts.

Introduction

Data dissemination is a classical and a fundamental function in any kind of network. In wireless networks, the characteristics and problems intrinsic to the wireless links bring several challenges in data dissemination in the shape of message losses, collisions, and broadcast storm problem, just to name a few. However, in the context of Cognitive Radio Network (CRN) [START_REF] Akyildiz | Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey[END_REF], reliable data dissemination is much more challenging than traditional wireless networks. First, in addition to the already known issues of wireless environments, the diversity in the number of channels each cognitive node can use adds another challenge by limiting node's accessibility to its neighbors. Second, cognitive radio nodes have to compete with the Primary Radio (PR) nodes for the residual resources on many channels and use them opportunistically. Besides, during communication CR nodes should communicate in such a way that it should not degrade the reception quality of PR nodes by causing CR-to-PR interference. In addition, CR nodes should immediately interrupt its transmission whenever a neighboring PR activity is detected [START_REF] Khalife | Probabilistic path selection in opportunistic cognitive radio networks[END_REF]. In multi-hop cognitive radio ad hoc networks, where coordination between CRs is hard to achieve and no central entity for regulating the access over channels is to be envisaged, reliable data dissemination is even more complex. In this perspective, the first step in having efficient data dissemination is to know how to select best channels. Thus, differently from most works in the literature dealing with single-hop communication [START_REF] Yang | Traffic-aware dynamic spectrum access[END_REF][START_REF] Cordeiro | Ieee 802.22: An introduction to the first wireless standard based on cognitive radios[END_REF], we go a step further here and build up a channel selection strategy for multi-hop communication in CRN. The objective of every cognitive radio node is to select the best channel ensuring a maximum connectivity and consequently, allowing the largest data dissemination in network. This corresponds to the use of channels having not only low primary radio nodes (PRs) activities, nevertheless the reliability of the dissemination process is achieved by limiting the contention of cognitive ratio nodes (CRs) acceding selected channels.

The effect of CR contentions on dissemination is highlighted in Fig. 1 that shows the evolution of the average success delivery ratio at receivers of a single source, with the number of competing CRs. It is clear that the performance of a channel with low PR activity decreases with the number of CR competing for the available resource. Nevertheless, a channel with higher PR activity can be a good choice if CR contention is low. The challenge here is then how to find a good tradeoff between connectivity and contention.

In this paper, we propose a channel selection strategy, named SURF. The goal of SURF is to ensure reliable contention-aware data dissemination and is specifically designed for multi-hop cognitive radio ad hoc networks. Usually channel selection strategies provide a way to nodes to select channels for transmission. On the contrary, SURF endue CR nodes to select best channels not only for transmission but also for overhearing. As a result, both sender and receiver tuned to the right channel for effective and reliable data dissemination. Additionally, by dynamically exploring residual resources on channels and by monitoring the number of CRs on a particular channel, SURF allows building a connected network with limited contention where reliable communication can take place. To counter this issue, we define the "Tenancy Factor β", which enables SURF algorithm to avoid channels with high CR contention.

Through simulations, we show that SURF builds, as expected, a highly connected network suitable for reliable dissemination. Moreover, SURF outperforms existing algorithms. In fact, we compared our solution with two variants of Selective Broadcasting (SB) strategy [START_REF] Kondareddy | Selective broadcasting in multi-hop cognitive radio networks[END_REF], the closest technique to SURF available today. The simplicity and decentralized nature of our solution makes it usable in ad hoc CRNs deployed to convey emergency messages and alerts. It can also be employed in commercial applications to disseminate short publicity messages.

The remainder of this paper is organized as follows: we discuss connectivity vs. contention trade-off in Section 2. We give general overview of SURF in Section 3. Section 4 deals with detailed description of SURF. Section 5 provides comprehensive analysis of SURF. Performance analysis is done in section 6, then the major advantages of SURF are highlighted in section 7. Section 8 discuss related work and finally, section 9 concludes the paper.

Cognitive radio ad hoc networks: connectivity VS. contention trade-off

In a highly dynamic/opportunistic cognitive radio network, cognitive users compete for residual resources (a.k.a spectrum holes) left by the activity of the legacy users more formally called primary radio users. Every cognitive node, using an intelligent selection strategy, selects the appropriate channel for transmitting with the major constraint of not degrading the service of ongoing primary radio communications. Indeed, primary radios have the absolute priority over the communication channels. In an opportunistic multi-hop cognitive radio network where coordination between CRs is hard to achieve and no central entity for regulating the access over channels is to be envisaged, the objective of every cognitive radio is to select the channel ensuring a maximum connectivity. Such spectrum band has the highest number of active cognitive radios hence allows quick and effective data dissemination in the network. Intuitively, one may think that the best strategy for all CRs is to dynamically switch to the less occupied channel (by PRs). Thus, satisfying the objective of verifying priority constraints imposed by PRs. Nevertheless, such strategy leads to many classical problems already well known in wireless networking. First, forcing all CRs in a geographic area to be active over the same channel makes all nodes compete for the same resource thus generating contention and collision problems. Second, such approach wastes the valuable additional capacity on different channels that the cognitive radio concept offers. Indeed, it was already shown in traditional wireless networking that networks with high contention, where repetitive collisions are frequent, suffer from close to zero throughput [START_REF] Li | Capacity of ad hoc wireless networks[END_REF]. A typical example is described in Fig. 2. Initially, channel 1 has more primary radio activities and should be avoided by the CR transmitters. However, if enough CRs switch to channel 2 to communicate, channel 1 quickly becomes less occupied and able to carry higher throughputs than channel 2. Therefore, taking into account contention issues due to CR transmissions is necessary when selecting spectrum bands for CR communications.

Any proposed strategy for channel selection in CRN has to optimize the connectivity vs. contention trade-off. We propose hereafter a channel selection strategy that monitors the number of active CR nodes on a particular channel. As a result, we are able to build a well connected network while dynamically exploiting residual resources on many channels. We detail how our proposed strategy named SURF handles this trade-off in the following sections.

SURF: General overview

SURF channel selection strategy is specifically designed for ad hoc cognitive radio networks. The general goal of our strategy is to ensure a reliable data dissemination over a multi-hop CRN. Such technique can be used to convey emergency alarms and alerts or to deliver low priority data such as advertisement messages in a cognitive radio multi-hop context. Recall that in order to achieve our goal and ensure coverage and reliability, the connectivity vs. contention trade-off should be optimized.

SURF strategy is exclusively implemented by every CR node and is used for transmission and/or overhearing. As detailled hereafter, using the decentralized algorithm proposed by SURF, every CR sender judiciously selects the best frequency band for sending messages and every CR receiver tunes to the right channel (selected by the sender) to retrieve the sent data.

With SURF, each CR node looks first for the less PR-occupied channel to help deciding autonomously which channel to use. In addition to PR occupancy, we also consider CR neighbors competing for the same channel resource. More precisely, every CR node classifies available channels based on the observed PR-occupancy over these channels. This classification is then refined by identifying the number of active CRs over each band. The best channel for transmission is the channel that has the lowest PR activity and a reasonable ongoing CR activity. Indeed, choosing a channel with few CRs yields to a disconnected network. The challenge in our strategy is in finding the number of active CRs on every channel that gives the best connectivity with limited contention. Practically, every CR after classifying available channels, switches dynamically to the best one and broadcasts the stored message.

Additionally, CRs with no messages to transmit implement the SURF strategy in order to tune to the best channel for data reception. Clearly, using the same strategy implemented by the sender allows nodes in the close geographic areas to select the same channel as sender for overhearing with high probability. Intuitively, it is likely that CRs in the sender's vicinity have the same PR occupancy, hence channels available to a CR sender is also available to its neighbors with high probability [START_REF] Zhao | Decentralized cognitive mac for opportunistic spectrum access in ad hoc networks: A pomdp framework[END_REF]. Therefore, SURF controls the number of CR receivers, thus a connected topology with low contention is created. Once a packet is received, every CR receiver undergoes again the same procedure to choose the appropriate channel for conveying the message for its neighbor.

Detailed description of SURF

Considered Scenario

We consider an infrastructureless multi-hop cognitive radio ad hoc network in which only CR nodes collaborate. In the assumed configuration, no cooperation or feedback from primary nodes is expected. Consequently, CR nodes can only rely on information obtained or inferred locally to undergo transmission or reception decisions.

Moreover, we consider that CR nodes are capable to switch over the available channels easily. For the sake of simplicity, we assume that every channel is divided into equal time slots. Each slot can be exclusively used either by a primary radio or by a cognitive radio when no primary activity is present over that slot. Hence, every channel is composed of τ t = τ o + τ a time slots, where τ o and τ a are the slots occupied by PR nodes and the available slots, respectively. A total of C frequency channels are available in the network. These channels can either be used by PR or CR nodes. Due to PR nodes' localized and timely activity, the total frequency channels vary with time and location, which results in a non-uniform, scattered and diverse set of channels available to CR nodes.

We consider CR nodes equipped with a single transceiver, where a single channel can be selected at a time and used exclusively for transmission or overhearing. It is worth noting here that, contrarily to other approaches in the literature where the costly assumption of having CR nodes equipped with multi-transceivers is used, our assumption is highly realistic today and is already considered in some cognitive radio devices and prototypes [START_REF] Harada | A small-size software defined cognitive radio prototype[END_REF]. In these devices, physical constraints limit the access of CR nodes to a limited set of available channels. Indeed, covering all the spectrum bands is a highly costly process, thus we assume that every cognitive radio device can handle a predefined number of channels. We denote the set of spectrum channels each CR can exploit by Acs such that Acs ∈ C and |Acs| < |C|. We shall investigate the impact of the size of Acs on the performance of our strategy later in the paper (Section 5.3).

INRIA

In the considered scenario described above, SURF strategy provides every CR with a way to select the appropriate channel for transmission and reception.

PR and CR Occupancy

We consider that the spectrum sensing block provides the spectrum opportunity map as described in [START_REF] Yucek | A survey of spectrum sensing algorithms for cognitive radio applications[END_REF], which is then used by CR nodes to calculate the PR and CR occupancy. The spectrum sensing block is responsible for obtaining awareness about the spectrum usage and presence of primary users; whereas spectrum opportunity map identifies whether primary users have been detected or not for each channel. PR occupancy is thus, denoted here by P R

(i)
o and is defined as the time slots percentage of the channel i occupied by PR nodes, i.e. the ratio between the number of PR nodes and the total number of time slots τ t .

Practically, our estimation of the PR occupancy follows a conservative approach. In fact, computing the PR occupancy based on the total number of primary radios assumes these nodes are permanently active. Intuitively, given the higher priority of PRs in accessing spectrum bands, considering that PR are always active gives them additional guarantees at the price of a lesser space for CRs activity. The remaining available percentage of the channel i, i.e. 1 -P R (i) o , gives then the space available for channel sharing among CR nodes, named

CR (i) as . The CR occupancy CR (i)
o is then obtained from the available space for CR activities on a particular channel i, CR (i) as , as described hereafter.

Since we are considering non-cooperative infrastructureless architecture, there is no centralized authority that helps CR nodes for their channel selection. Therefore, there is no way to prevent collision and message losses when the number of CR nodes competing for the same channel increases. Additionally, CR nodes have to rely on locally inferred information in a distributed manner to select channels with a higher number of 1-hop CR receivers. To allow nodes to select channel having a good compromise between the number of CR receivers and the number of competing CR transmitters, we use the Tenancy Factor, named β, to compute the CR occupancy CR (i) o of each channel i. β provides the upper bound in terms of number of CR neighbors on a particular channel, where the communication is still performed with a good probability of success. The goal here is then to maximize the chances of selecting channels that have a a good number of CR neighbors (close to Tenancy Factor β).

Figure 3 shows algorithm how CR nodes calculate CR occupancy according to the tenancy factor β and the number of CR neighbors competing for the channel. When the number of CR neighbors, i.e. CR (i) n , on a particular channel i is lower than β, the chances of the channel with number of neighbors close to β to be selected increases. The best channel in terms of channel availability is the one with CR neighbors equal to β. When CR neighbors are higher than β, the higher number of neighbors decreases the chances of the channel to be selected.

if CR (i) n < β then CR (i) o ← CR (i) as (β-CR (i) n ) else if CR (i) n = β then CR (i) o ← CR (i) as else CR (i) n > β then CR (i) o ← CR (i) as CR (i) n end if

Channel Selection

SU RF strategy classifies channels by assigning a weight P (i) w to each observed channel i in the Acs set. Thus, every cognitive radio running SURF, locally computes the P (i) w using the following equation:

∀i ∈ C : P (i) w = e -P R (i) o × CR (i) o (1)
P (i)
w describes the availability level of a channel (i) and is calculated based on the occupancy of PR (i.e. P R o , then the CR node randomly selects one channel among the channels with identical and higher values of P w .

Practically, the computed availability in Eq. 1 exponentially decreases with the PR occupancy and linearly increases with the available space for CR activities. These two behaviors are directly related to the two objectives the SURF strategy needs to satisfy. The major objective of protecting the ongoing PR activity is mapped into an exponential decrease of a channel weight as a function of the PR occupancy. The higher the PR occupancy over a spectrum band the exponentially lower the weight will be. Thus, SURF gives high importance to not degrading the service of ongoing primary communications. The second objective of ensuring a maximum connectivity is implemented in the second term of Eq.1. More precisely, the CR (i) o obtained through Algorithm (cf. Figure3) increases with available space for CRs activity, while carefully considering the connectivity versus contention trade-off.

SURF Comprehensive Analysis

In this section, we investigate how SURF reacts to different CRN conditions (i.e. PR activity, available channels, etc) when different values of β are used. The goal is thus, INRIA to well understand the β effect and to be able to select the good one for the SURF performance's evaluation (cf. Section 6).

Methodology

We study the SURF behavior for different values of β when varying PR activity, set of available channels Acs, and total number of channels C in the network. We then evaluate three performance issues: (i) the average number of CR neighbors per hop, which is the potential 1-hop receivers at each channel of the transmitter's Acs set; (ii) the average number of CR receivers per hop, which is the CRs that correctly received the sent packet (i.e., the average number of CR nodes that have selected for overhearing the same channel than the transmitter selected for sending and, even if unreliable links are considered, have correctly received the packet), and (iii) the average loss ratio per hop, which is the number of lost packets over the total number of transmitted packets. Since, the system we explore is highly complex and many parameters can be modified, for results tractability and clarity, we modify each of a single parameter while fixing all the others. The influence of β over those performance issues is monitored by first varying the PR occupancy (cf. Section 5.2), second by varying the available channel set (cf. Section 5.3), and finally, under a dynamic environment where both the available channel set and PR occupancy vary, simultaneously (cf. Section 5.4).

We run simulations over a cognitive radio specific simulator written in C++. Within the network, we consider that two nodes can communicate if they use at least one common channel and if they are within the transmission range of each other. In order to simulate message losses, a probability P (i) s of successfully sending a message is assigned to each channel i and equals to:

P (i) s = τ (i) a CR (i) n ∀i ∈ C (2)
while loss ratio equals to 1 -

P (i)
s . In fact, equation [START_REF] Cordeiro | Ieee 802.22: An introduction to the first wireless standard based on cognitive radios[END_REF] states that the probability of sending a message is dependent on the available slots τ a and the number of CR nodes competing for the channel i.e. CR (i) n . We consider 1% of message losses, if CR (i) n < τ a . It is worth noting here that no retransmission in implemented in our simulations.

Results are generated from an average of 1000 simulations, along with 95% of confidence intervals. For all results, unless otherwise specified, we consider 30 PR nodes and τ t =6 total time slots for each channel. To ensure total network connectivity, the transmission range is set to R = 250m and the average CR neighbor density d avg before Acs computation, is set to 20. We consider Acs size of [START_REF] Khalife | Probabilistic path selection in opportunistic cognitive radio networks[END_REF][START_REF] Harada | A small-size software defined cognitive radio prototype[END_REF][START_REF] Gupta | Critical power for asymptotic connectivity in wireless networks[END_REF] for 5 total number of channels (Ch = 5), and [START_REF] Yucek | A survey of spectrum sensing algorithms for cognitive radio applications[END_REF][START_REF] Shiang | Distributed resource management in multihop cognitive radio networks for delay-sensitive transmission[END_REF][START_REF] Li | Capacity of ad hoc wireless networks[END_REF] for 15 total channels (Ch = 15). The number of CR nodes is fixed to N=70 and randomly deployed within a square area of a 2 =707x707m 2 [START_REF] Gupta | Critical power for asymptotic connectivity in wireless networks[END_REF]. Because the total number of CRs is fixed, it is straightforward to notice that the number of average CR neighbors per channel decrease as the Acs size decrease. TTL is introduced to disseminate the message in the whole network. It is the maximum number of hops required for a packet to traverse the whole network and is set to 2a R , i.e. T T L = 6 in our simulation scenario. The total number of PRs are uniformly distributed among the existing channels. Higher (cf. lower) PR occupied channels give lower (cf. higher) space for CR communication. Thus, to investigate the impact of PR occupancy over the analysed performance issues, we varied the PR occupancy from 0%, 33%, and 50% by changing the slots occupied by PR nodes to 0, 2, and 3, respectively. We then fixed the total number of channels Ch to 5 and the size of the Acs set per node to 3, which results in an average density of approximately 8 CR neighbors per channel of the Acs set.

Impact of PR Occupancy

Fig. 4 and Fig. 5 shows then the average number of CR neighbors and CR receivers per hop (left axes), and the loss ratio per hop (right axes) for varying values of β. In particular, those figures show how the PR occupancy impacts the CR contention and consequently, the loss ratio when different values of β are used. The number of neighbors (cf. Fig. 4) remains constant for values of β lower than 8, i.e. when CR

(i) n ≥ β.
In this case and according to the SURF algorithm (cf. Figure 3), low values of β do not play any role in limiting network contention among CR nodes. Thus, channels are being weighted based only on the total number of available slots (i.e. CR (i) as ) or CR neighbors competing for the channels (i.e.

CR (i) o = CR (i) as CR (i) n
). On the other hand, the increase of β for values higher than CR (i) n , increases the chances of selecting channels that have higher number of neighbors, and consequently, higher number of receivers. This happens until a certain threshold, when the channel contention is increased and higher loss ratio is detected, affecting the number of receivers. This can be perceived for values of β higher than 8, as shown in Fig. 5. Note that, since no retransmission of lost messages is implemented, the contention among nodes is limited to their first try for transmitting a message. The same happens for loss ratio measurement, which is also limited to losses at the first transmission of each message only. This can explain why the increase in loss ratio doe not significantly decrease the number of receivers in Fig. 5. Additionally, as expected, with the increase of PR occupancy, the average number of CR receivers per hop decreases and the average loss ratio per hop increases. This is primarily because of the lower available slots CR (i) as to CR nodes communicating, which leads to more contention and collisions. Particularly, when there is no PR occupancy, i.e. PR occupancy=0%, the average number of receivers per hop is the highest and the average loss ratio per hop is the lowest (cf. Fig. 5), for any value of β. This is due to the fact that 0% of PR occupancy yields to higher available channel slots τ a , which results in less CR contention. On the other hand, as the number of available channel slots τ a decreases (e.g. PR occupancy=50%), CR nodes finds less opportunity to communicate, resulting in higher loss ratio due to higher contention.

Impact of Available Channel Set Acs

The size of the Acs set and the diversity in number of channels each cognitive node can use, limit node's accessibility to its neighbors. Thus, if the Acs set is too small compared to total channels C and diverse for each CR, the number of receivers is reduced. In Fig. 6 and Fig. 7, we evaluate this effect for total number of channels equal to Ch = 5 and Ch = 15, respectively, and, for clarity reasons, consider that all channels are unoccupied by PR nodes (i.e. PR occupancy=0%). We vary the size of the Acs set to 4 and 3 for Ch = 5 and to 12 and 8 for Ch = 15.

INRIA

It can be verified that when a node is accessible by its neighbors on all the channels, Ch = Acs (i.e. all CRs can overhear all channels), β does not impact on the average number of receivers and loss ratio. This is due to the fact that all the channels will have the same computed weight P (i) w : the same PR occupancy (PR occupancy=0%) and number of CR neighbors CR (i) n is perceived on all channels. Thus, CR nodes randomly select the channel for transmission and/or overhearing. Indeed, a random selection of a channel for transmitting and overhearing reduces the chances of message reception by neighboring CR. Moreover, this effect is even aggravated by the increase of the number of available channels for communication, note the decrease from 4 receivers to 1 for Ch = 5 and Ch = 15, respectively (cf. Fig. 6 and Fig. 7).

The decrease of the Acs set's size to 4 and 3 for Ch = 5 and to 12 and 8 for Ch = 15, imposes some diversity at the number of neighbors per channel, resulting in different weights P (i) w being assigned to channels. In particular, the average density of CR nodes per channel is: 12 for Acs = 4 and for Acs = 12 and 8 for Acs = 3 and for Acs = 8. That neighborhood diversity implies different levels of contention per channel, which consequently, makes the use of β impacting the average number of receivers and loss ratio, since channels will be assigned to varying weight values. It can be seen from Fig. 6 and Fig. 7 that for lower values of β (i.e. when CR (i) n ≥ β), CR nodes try to select those channels that have number of neighbors close to β. In this case, the average number of receivers and the average loss ratio is lower due to less contending nodes for same channel resource. Whereas with the increase of β to values higher than CR (i) n (i.e. higher than 12 for Acs = 4 and Acs = 12 or 8 for Acs = 3 and Acs = 8), we notice an increase in the average number of receivers as well as the average loss ratio. As previously mentioned, this happens due to the fact that message retransmission is not implemented in our simulations. Thus, the contention increase caused by the increase of β does not significantly affects the average number of receivers.

Choosing the correct value of Tenancy Factor β

In real environments, channels can be available in some parts of the network and occupied in others. Thus, to incorporate this notion, we consider varying PR occupancy and limited available channels. We then set PR occupancy to the range of [20%-80%] for each PR node over each channel and Acs size to 3 and 8 for Ch = 5 and Ch = 15, respectively. Here, we investigate how SURF adapts under this dynamic environment with varying PR occupancy and limited Acs sets. We perform experiments in order to determine appropriate value of β to be used in SURF performance analysis presented in the next section.

In Fig. 8 and Fig. 9, we investigate the impact of β on average number of neighbors, number of receivers, and loss ratio per hop. Clearly, the best value of β is the one that provides a good tradeoff between number of receivers and loss ratio. In this case, for Ch = 5, the best value of β is β = 10 (cf. Fig. 9). For Ch = 15, the low average number of 5 neighbors on channels (cf. Fig. 8) is not enough to cause high contention and consequently, to decrease the receiver number when high values of β are used, as shown in Fig. 9. Therefore, it is better to use the channels with higher number of neighbors, and thus, a higher value of beta. For this reason, we select β = 18 for Ch = 15, as it provides a good tradeoff between receivers and loss ratio.

Note that, in our simulations, we consider that the same number of PRs is spread over the available channels. Thus, using more channels reduces the number of PRs over INRIA each channel and consequently reduces the PR occupancy, resulting in higher available space for CR nodes for communicating. Therefore, the average loss ratio per hop for Ch = 15 is much lower than Ch = 5. This is due to the fact that CR nodes find more space and hence, causes less contention for the same channel resource. On the other hand, the increase of the number of channels increases the probability of having two or more channels assigned to the same

P (i)
w value, increasing consequently the probability of having a random selection of channels.

Performance Evaluation

Simulation Environment

In order to evaluate the performance of SURF, we compare it with an intuitive random strategy (RD) and the two variants of selective broadcasting protocol [START_REF] Kondareddy | Selective broadcasting in multi-hop cognitive radio networks[END_REF], i.e. selective broadcasting strategy (SB) [START_REF] Kondareddy | Selective broadcasting in multi-hop cognitive radio networks[END_REF] without any centralized authority, and selective broadcasting with centralized authority (CA).

In RD strategy, channels are randomly selected to be used by CR nodes for transmission and/or overhearing, i.e. without any consideration to the ongoing PR and CR activity over these channels. In selective broadcasting SB, each CR node selects a minimum set of channels i.e. Essential Channel Set (ECS) for transmission, that covers all its geographic neighbors, without considering the PR occupancy. In our simulations, we consider an implementation of SB with a single transceiver. Thus, transmissions over multiple channels in the presence of single transceiver is done sequentially with incurred delay, i.e with a round robin process over the channels of the ECS. Regarding message reception, each neighbor sequentially overhears on the channels present in the ECS list. Clearly, selecting channels from the ECS one after the other for overhearing reduces the probability of reception on each of them. Selective broadcasting with centralized authority CA, i.e. the third algorithm we compare SURF with, works on the same principle for transmission as SB, except that each neighbor node simultaneously overhears on all the channels present in the ECS list.

It is worth noting that selective broadcasting with centralized authority (CA) can be used as a theoretical upper bound in message dissemination comparison; since it maximizes the number of receptions by performing overhearing over multiple channels, simultaneously. The main difference between SB, CA, RD, and SURF is the number of transmissions generated by the first two strategies: 2.5 times more than RD and SURF. Additionally, multiple transmissions of the same message over multiple channels may cause multiple receptions of the same message at neighbor nodes, decreasing the transmission opportunity perceived by CRs. Otherwise, in SURF, nodes switch to a single channel based on its occupancy and receivers availability, being no multiple transmissions performed which results in less message overhead. Therefore, SURF has an added advantage in this case, as there is no need for a central entity or any other control message to switch overhearing nodes to the same channel on which the neighboring node is transmitting.

We assume that the spectrum opportunity map of PR is available for cognitive radios. We further consider in our simulations that PR nodes over every channel switch evenly between ON/OFF states with probability in range [20%-80%]. At each CR transmission, the PR occupancy per channel i, (P R (i) o ), is calculated according to the number of PR nodes provided by the opportunity map. Additionally, each CR node locally computes the CR occupancy (CR (i) o ) and the availability level (P (i) w ) of each channel i. The channel with the highest weight is then selected for transmission and/or overhearing (cf. Section 4). The message dissemination phase then starts, in which a randomly selected CR node disseminates the message on the selected channel by setting the TTL. CR neighbor nodes that are on the same selected channel will overhear the message, decrease TTL, redo the spectrum sensing, select the best available channel, and disseminate the message to the next-hop neighbors until TTL=0.

Blocking Ratio

We say that a message is blocked if it is lost because no CR is overhearing over the same channel (while the TTL value is still >0). The Blocking Ratio is then defined as the number of blocked messages over the total number of sent packets.

For effective data dissemination, not only the sender should select the best channel but also the receiver should be tuned to the right channel (selected by the sender) in order to receive the sent information. Thus, a good channel selection strategy is the one that tunes both the sender and the receiver to the right channel in the multi-hop context. Fig. 10 shows, for example, that if receivers nodes are not tuned to the right channel, the dissemination will be stopped before reaching the highest distant nodes in the network. In Fig. 11, we compare the blocking ratio obtained with the four strategies detailed above for multi-hop CRN. Over 1000 sent packets, we compute the blocking ratio caused by receiver not overhearing the appropriate channel.

In a network where 15 channels are available for CR use (Ch = 15), RD and SB strategies have higher blocking ratio than SURF and CA. Such results are highly predictable due to (1) the naive selection approach of the random strategy and (2) the availability of single transceiver and the lack of any central entity of the SB strategy.

INRIA

In particular, in SB, the probability of a node overhearing the same channel used for transmission is 1 |ECS| . In fact, in both cases when Ch = 5 and Ch = 15, the blocking probability of SB is higher than RD. Practically, the round robin process used at the transmitter and at the receiver, requires that both of them are tuned to the same frequency in the same time to correctly receive sent messages. This strict synchronization process is hard to achieve first because the sender/receiver do not necessarily have the same set of ECS on which they sequentially transmit/receive, and second, a light dephasing on the transmitter or the receiver side may yield to errors in messages reception. Additionally, since SB try to use channels with higher number of neighbors, there is also a chance of having higher contention and consequently, higher loss ratio than in a random selected channel. The same happens for CA, where the higher number of CR neighbors increases the contention in each channel, increasing then the number of blocked messages. Moreover, the blocking ratio of RD increases with the increase in the total number of channels. This is because less nodes overhear on the same channel selected by the transmitting node, since CRs are spread over different channels. On the contrary, an increase in the number of channels has a minor effect on SB. This is because the increase of total number of channels results in lower sizes of ECS, composed by channels that can potentially reach more neighbors. Therefore, more nodes have the probability to overhear over the same channel.

SURF has lower blocking probability because our decentralized channel selection makes more nodes overhear over the same channel. In fact, this happens since during channel selection SURF considers both the PR occupancy and number of CR neighbor receivers. More surprisingly, SURF has a decrease in blocking ratio as the number of channels increases. This is mainly due to the fact of having nodes selecting best channels for transmission and reception.

Reliability in Data Dissemination

To assess the performance of SURF with RD, SB, and CA in term of reliable data dissemination, two performance metrics are evaluated with different total number of channels: (i) the average delivery ratio, which is the ratio of packet received by a particular CR node over total packets sent in the network and (ii) the average number of accumulative CR receivers at each transmission, until TTL=0. Recall that higher number of channels yields to lower PR occupancy. In addition, it is worth mentioning here that even the centralized approach CA could not get a 100% of data dissemination because of the performed randomly assignment of Acs set to CR nodes. This may generate topology disconnections caused by physical close nodes being assigned to disjoint channels. In this way, as previously stated, we consider the CA approach gets the theoretical upper bound results in terms of message dissemination.

Fig. 12(a) compares the number of accumulative CR receivers at each hop of communication until TTL=0, for the four strategies. When Ch = 15, SURF allows the message dissemination to 55% of nodes in the network (i.e. 38 out of 70 CR nodes), while CA allows 78% (i.e. 54 over 70 CR nodes). Additionally, due to its central control and multiple transmissions, the CA strategy reaches this upper bound of receivers percentage at the TTL=4. It can be clearly seen that SURF outperforms RD and SB and compared to CA, only provides a decrease of 25% in performance. The gain achieved with CA is at the price of more transmissions, more energy consumption, and more expensive and sophisticated devices.

Fig. 12(b) compares delivery ratio of RD, SB, CA and SURF, as a function of the CR nodes' ID. SURF outperforms RD and SB in terms of delivery ratio, when number of channels are high. Compared to the CA strategy, SURF has only 20% of performance reduction. In particular, for Ch=5 and Ch=15, SURF guarantees the delivery of approximately 60% of messages (with a single transmission), contrarily to less than 20% for the RD and SB strategies (with single and multiple transmissions, respectively) and 80% for the CA strategy (with multiple transmissions).

Advantages of SURF

SURF, by exploiting information regarding PR and CR occupancy, brings several advantages. Some of them are highlighted below:

• Less interference with PR nodes: Through our channel selection strategy, CR nodes are bound to select those channels which are less utilized by PR nodes. Therefore they cause less interference to PR nodes thus, satisfying the major constraint of CRN.

• Autonomy and decentralization: CR nodes make local and distributed decision for channel selection. Therefore SURF makes CR nodes autonomous in their channel selection decision.

• No control messages exchange: Implementing the same strategy at the sender and receiver helps both of them tune to the appropriate channel for undergoing transmissions or reception without the need of any prior information exchange or synchronization. • Less overhead: As SURF is based on single transmission, it generates less overhead compared to channel selection strategies based on multiple transmissions (e.g. [START_REF] Kondareddy | Selective broadcasting in multi-hop cognitive radio networks[END_REF]).

• Practical feasibility and low cost: A key characteristic of our channel selection strategy is that it assumes the availability of single transceiver, which is used for both transmission and/or overhearing. It reduces thus, the operational cost of the network. Besides reducing transmissions overhead, transmitting over a single channel cuts down energy consumption and increases the battery lifetime of CRs.

• Information relaying: Another key advantage of our multi-hop channel selection strategy is that the same strategy can be reused and reconfigured to relay information from one or more users to others receiving users located in various locations in the network.

• Network coverage improvement: Improved network performance in terms of CR network' perspective is achieved by making CR nodes to switch to highly reliable channels, which as a consequence, increase network coverage.

Related Work

Recently, a lot of work has been carried out for dynamic channel management in cognitive radio networks [START_REF] Yang | Traffic-aware dynamic spectrum access[END_REF][START_REF] Zhao | Decentralized cognitive mac for opportunistic spectrum access in ad hoc networks: A pomdp framework[END_REF][START_REF] Niyato | Competitive spectrum sharing in cognitive radio networks: A dynamic game approach[END_REF][START_REF] Rahul | Learning to share: Narrowband-friendly wideband wireless networks[END_REF]. However, all these approaches focuses on single-hop cognitive radio networks and either requires the presence of any central entity or coordination with primary radio nodes in their channel selection decision. For instance, [START_REF] Yang | Traffic-aware dynamic spectrum access[END_REF] proposed an efficient spectrum allocation architecture that adapts to dynamic traffic demands but they considered a single-hop scenario of Access Points (APs) in Wi-Fi networks. [START_REF] Husain Rehmani | Adaptive and occupancy-based channel selection for unreliable cognitive radio networks[END_REF] proposed a channel selection strategy based on the primary user's occupancy but specifically designed for single-hop architecture.

In this paper, we focus on channel selection in the context of multi-hop cognitive radio ad hoc networks, where no cooperation or feedback is expected from primary nodes and the network operates in the absence of any centralized authority. In addition, an adaptive channel selection strategy is required at both the sender and receiver node, so that the receiver node tuned to the right channel to receive sent information. Moreover, the holding time and the granularity of wireless spectrum bands also affects on multi-hop CR communications [START_REF] Khalife | Multihop cognitive radio networks: to route or not to route[END_REF]. All these factors makes channel selection in these networks extremely challenging, having very few works been done so far [START_REF] Kondareddy | Selective broadcasting in multi-hop cognitive radio networks[END_REF][START_REF] Shiang | Distributed resource management in multihop cognitive radio networks for delay-sensitive transmission[END_REF]. In [START_REF] Shiang | Distributed resource management in multihop cognitive radio networks for delay-sensitive transmission[END_REF], the authors proposed a dynamic resource management scheme for multi-hop cognitive radio networks. But their approach is based on periodic control information exchange among nodes, which is not the case in SURF.

In selective broadcasting (SB) [START_REF] Kondareddy | Selective broadcasting in multi-hop cognitive radio networks[END_REF], each cognitive node selects a minimum set of channels (ECS) covering all of its geographic neighbors to disseminate data in multihop cognitive radio networks. There are however, several challenges in the practicality of SB. Indeed, from the communication perspective, simultaneous transmission over a ECS requires more than one transceiver, which means having bigger and more complex devices, as it is done for military applications [START_REF] Younis | Cognitive manet design for mission-critical networks[END_REF].

Conclusion and Future Work

In this paper we have proposed SURF, a channel selection strategy for reliable contentionaware data dissemination in multi-hop cognitive radio network. SURF selects a single channel for every message transmission allowing the best opportunities for CR-to-CR communications due to (1) low primary radio nodes activities, and (2) limited contention of cognitive radio nodes acceding that channel. The result is a connected network with limited contention, where reliable communication can take place. SURF strategy is simple, completely decentralized, and is based on practical assumptions. Thus, it can be applied today to many possible cognitive radio networks deployments to INRIA deliver emergency alerts and advertisement messages. We have demonstrated through simulations the performance of our channel selection strategy when compared with three other related approaches. Simulation results confirmed that our approach is effective in selecting the best channels for efficient and reliable multi-hop data dissemination.

As plan of our future work, we intend to investigate the time needed to disseminate messages in the network. This delay will surely depend on the size of the network and on the number of available channels at CR nodes. Besides, empowering SURF with channel history components that assist the channel selection process and mainly that tie-breaks when two or more channels have the same weight is also a future research direction.

  Delivery Ratio (%) No. of CR Competing Nodes Channel # 1, 10% PR occupancy Channel # 2, 30% PR occupancy

Figure 1 :

 1 Figure 1: CR nodes competing for the same channel.

Figure 2 :

 2 Figure 2: PR and CR Nodes occupancy over channels

Figure 3 :

 3 Figure 3: Algorithm for CR occupancy's computation

  (i) o ) and CR (i.e. CR (i) o ) nodes over this channel (c.f. Section 4.2). If a CR node finds two or more channels having identical higher values of P w , it firstly tries to select the one that has lower P R (i) o among them. If they also have identical values of P R (i)

Figure 4 :

 4 Figure 4: Tenancy factor β, average number of neighbors per hop, and average loss ratio per hop, in a CRN with 70 CR nodes for varying PR occupancy (i.e. fixed time slots occupied by PR nodes) and fixed Acs, for channels=5.

Figure 5 :

 5 Figure 5: Tenancy factor β, average number of receivers per hop and average loss ratio per hop, in a CRN with 70 CR nodes for varying PR occupancy (i.e. fixed time slots occupied by PR nodes) and fixed Acs, for channels=5.

Figure 7 :

 7 Figure 7: Tenancy factor β, average number of receivers per hop and average loss ratio per hop, in a CRN with 70 CR nodes for varying number of Acs and fixed PR occupancy, for channels=15.

  . of Neighbors per Hop Avg. Loss Ratio per Hop Tenancy Factor β Neighbors: Ch=5, Acs=3, PR=[20%-80%] Neighbors: Ch=15, Acs=8, PR=[20%-80%] Loss Ratio: Ch=5, Acs=3, PR=[20%-80%] Loss Ratio: Ch=15, Acs=8, PR=[20%-80%]

Figure 8 :

 8 Figure 8: Tenancy factor β, average number of neighbors per hop, and average loss ratio per hop, in a CRN with 70 CR nodes for varying PR occupancy in the range [20%-80%] by PR nodes.

  . of Receivers per Hop Avg. Loss Ratio per Hop Tenancy Factor β Receivers: Ch=5, Acs=3, PR=[20%-80%] Receivers: Ch=15, Acs=8, PR=[20%-80%] Loss Ratio: Ch=5, Acs=3, PR=[20%-80%] Loss Ratio: Ch=15, Acs=8, PR=[20%-80%]

Figure 9 :

 9 Figure 9: Tenancy factor β, average number of receivers per hop and average loss ratio per hop, in a CRN with 70 CR nodes for varying PR occupancy in the range [20%-80%] by PR nodes.

Figure 10 :

 10 Figure 10: Message blocked after T T L = 4 in a multi-hop CRN.

  = 5 SURF: Ch = 5, β=10 SB: Radio = 1, Ch = 5 RD: Ch = 15 CA: Ch = 15 SURF: Ch = 15, β=18 SB: Radio = 1, Ch = 15

Figure 11 :

 11 Figure 11: Blocking Ratio in a CRN with 70 CR nodes, for varying number of channels.

Figure 12 :

 12 Figure 12: Average number of accumulative receivers per hop and average delivery ratio in a 70-node CRN, for random (RD), selective broadcasting (SB), centralized approach (CA), and our strategy (SURF).

RR n°7288


Hugues Chevassus
email: h-chevassus@chu-montpellier.fr

Jean-Baptiste Gaillard

Anne Farret

Françoise Costa

Isabelle Gabillaud

Emilie Mas

Anne-Marie Dupuy

Françoise Michel

Caroline Cantié

Eric Renard

Florence Galtier

Pierre Petit

A fenugreek seed extract selectively reduces spontaneous fat

Keywords: eating behaviour, energy intake, fat, fenugreek seed, overweight subjects, human

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

Introduction

Fenugreek (Trigonella foenum-graecum L.) is an herbaceous annual plant from the family of Leguminosae, cultivated in Mediterranean countries and India. Its seeds have been known for a long time as herbal medicine for various pathological conditions. A number of pharmacological and clinical studies have shown glucose and lipid lowering properties of either the seed itself, seed extracts or purified components (see [START_REF] Sauvaire | Chemistry and pharmacology of fenugreek[END_REF] for review). In addition, antioxidant properties have been observed in animal studies [START_REF] Ravikumar | Effect of fenugreek seeds on blood lipid peroxidation and antioxidants in diabetic rats[END_REF][START_REF] Choudhary | Modulation of glyoxalase, glutathione Stransferase and antioxidant enzymes in the liver, spleen and erythrocytes of mice by dietary administration of fenugreek seeds[END_REF].

Fenugreek-based preparations are used in humans to stimulate appetite and promote weight gain [START_REF] Rguibi | Body size preferences and sociocultural influences on attitudes towards obesity among Moroccan Sahraoui women[END_REF]. However, these properties are supported only by animal data, which show some inconsistencies. We have previously demonstrated that a repeated administration of a hydro-alcoholic seed extract enhanced motivation to eat and food consumption in rats, with a slight weight increase [START_REF] Petit | Effects of a fenugreek seed extract on feeding behaviour in the rat: metabolic-endocrine correlates[END_REF]. Appetite and food intake were also increased by a subchronic treatment with a purified steroid saponin fraction from the seed, with a modified circadian rhythm of feeding behaviour [START_REF] Petit | Steroid saponins from fenugreek seeds: extraction, purification, and pharmacological investigation on feeding behavior and plasma cholesterol[END_REF]. In contrast, in a safety study using a dietary supplement with fenugreek seeds in animals [START_REF] Rao | Short term nutritional and safety evaluation of fenugreek[END_REF], no effect was observed on food consumption. More recently, it was found that a fenugreek seed extract reduced the body weight gain induced by a high-fat diet in obese mice [START_REF] Handa | Effects of fenugreek seed extract in obese mice fed a high-fat diet[END_REF]. However, no clinical data on body weight and food intake was available since our recent report showing a reduction of fat consumption in freeliving conditions in healthy normal weight volunteers, after a 14-day repeated administration of a fenugreek seed extract [START_REF] Chevassus | Human energy expenditure in affluent societies: an analysis of 574 doubly-labelled water measurements[END_REF].

As the latter property could be of interest in overweight subjects, we undertook the present study to investigate the effects of this fenugreek seed extract on fat intake in overweight patients. Secondary objectives included weight, glycaemia, plasma insulin, lipid profile, as well as oxidative stress and antioxidant capacities, which improvements with fenugreek seeds have been demonstrated in animals, but never shown in humans.

Materials and methods

Study design

The study was designed as a 6-week double-blind randomized placebo-controlled parallel trial.

Subjects

Thirty-nine healthy overweight male volunteers, aged 18-59 years (mean: 38 years), completed this study. All were of stable weight (mean weight: 85.4 kg; range: 75.2-105.5; mean body mass index: 27.3 kg.m -2 ; range: 24.9-29.4). One subject of the 40 included was withdrawn of the study before the first experimental drug administration because of the consumption of a non-authorized treatment.

Ethical aspects

This study was approved by the Ethics Committee "Comité de Protection des Personnes Montpellier-Saint-Eloi" and conducted in accordance with the Helsinki declaration and the ICH guideline for Good Clinical Practice. All the subjects gave written informed consent to participate.

Test compound

The test compound was a marketed dry hydro-alcoholic fenugreek seed extract administered three times daily as oral coated tablets coming from the batch used in our previous study conducted in healthy normal weight volunteers [START_REF] Chevassus | Human energy expenditure in affluent societies: an analysis of 574 doubly-labelled water measurements[END_REF]. The total daily dose of 1176 mg (approximately 14 mg.kg -1 ) is the double of the extract's daily dose commonly prescribed in human. It was selected because it appeared to be the active dose in healthy volunteers [START_REF] Chevassus | Human energy expenditure in affluent societies: an analysis of 574 doubly-labelled water measurements[END_REF]. This dose is also in accordance with the two active doses used in the animal study testing the same extract [START_REF] Petit | Effects of a fenugreek seed extract on feeding behaviour in the rat: metabolic-endocrine correlates[END_REF], corresponding to 5 and 53 mg.kg -1 human equivalent doses. The extract contained diosgenine, steroid saponins, 1.38% trigonelline and 1.50% 4-hydroxyisoleucine, as characterized by thin-layer chromatography and high performance liquid chromatography. Placebo tablets were manufactured using the same excipients, the same process and the same packaging as for active tablets making them indistinguishable.

The similarity between the seed extract and the placebo tablets was verified on 5 criteria (aspect, size, weight, colour and smell).

Investigations

Diet and physical activity were assessed in free-living conditions before and at the end of the ambulatory treatment period, using a 7-day record, accurately reviewed by a trained dietician and a physician. Reported energy intake (REI) was determined with Enkal-Pro ® software (Loging Software, Lille, France). Total energy expenditure (TEE) was calculated as basal metabolic rate (BMR) multiplied by physical activity level (PAL) according to Black et al [10]. Energy intake was expressed as the ratio REI/TEE. Underreporting was defined as a ratio REI/BMR < 1.1 [START_REF] Goldberg | Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording[END_REF].

The day following the first 7-day record completion, subjects attended the study centre for a baseline visit in fasting condition. Body weight was recorded while the subjects were dressed in underwear and after they had urinated, using the same balance Seca ® 861 (Seca, Hamburg, Germany) with an accuracy of ± 0.1 g. Body composition was measured by a multi-frequency bioelectrical impedance analysis. Blood was drawn and urine was collected for determination of plasma glucose, serum insulin and lipid profile, oxidative stress and antioxidant capacities. They were then discharged and treatment was taken ambulatory. During the last week of treatment, subjects completed a second 7-day record, and then attended the study centre after an overnight fast for their final visit, which comprised all the baseline evaluations. In addition, they had to consume completely in a maximum of 30 min a standardized breakfast (13% protein, 35% fat, 52% carbohydrate) providing 20% of their daily energy requirements, and to describe their appetite, satiety and palatability feelings on visual analogue scales [START_REF] Hill | Hunger and palatability: tracking ratings of subjective experience before, during and after the consumption of preferred and less preferred food[END_REF][START_REF] Raben | The reproducibility of subjective appetite scores[END_REF] before and every 30 min for 270 min after starting eating breakfast. At 12:30 a.m., they were served an ad libitum mixed meal consisting of 13% protein, 35 % fat, 52 % carbohydrate, and the amount of food ingested was weighed with an accuracy of ± 0.1 g.

Analytical methods

Plasma glucose was assayed by the glucose oxidase method. Serum insulin was measured by RIA (BI-INSULIN IRMA, CIS Bio-international, Gif-sur-Yvette, France). Serum triglycerides, total cholesterol and high-density lipoprotein (HDL)-cholesterol were assayed with an enzymatic method (lipase/glycerokinase, cholesterol oxidase and cholesterol oxidase after separation respectively).

Antioxidant parameters were total plasma antioxidant capacity (quantitative colorimetric technique), low density lipoproteines (LDL) susceptibility to oxidation, serum and LDL vitamin E (HPLC), and plasma vitamin C (HPLC). LDL were obtained by plasma sequential ultracentrifugation with a potassium bromide-adjusted density of 1.063. LDL oxidation was initiated with Copper [Cu (II), 5 µM] and continuously monitored during 6 hours by measuring the increase in absorbance at 234 nm due to conjugated diene formation. LDL susceptibility to oxidation was expressed by the reaction lag phase time, the maximal propagation rate, the time of half oxidation and the slope of the propagation phase. Oxidative stress was assessed by plasma malonyldialdéhyde [14], plasma advanced oxidation protein products [START_REF] Witko-Sarsat | AOPP-induced activation of human neutrophil and monocyte oxidative metabolism: a potential target for Nacetylcysteine treatment in dialysis patients[END_REF], plasma advanced glycation end products (fluorescence spectroscopy) and the urinary ratio free 8-iso PGF2 (LC-MS/MS)/creatinine.

Data analysis and statistics

The sample size (40 subjects to be enrolled in 2 groups of 20) was calculated using data obtained in our previous study [START_REF] Chevassus | Human energy expenditure in affluent societies: an analysis of 574 doubly-labelled water measurements[END_REF], with an expected mean difference for energy consumption (main outcome) between fenugreek seed extract and placebo of 216 kcal per day, a common SD of 238 kcal par day, a two-sided alpha of 0.05 and a statistical power of 80%.

Results are expressed as the mean ± standard error of the mean (SEM). 95% Confidence intervals for the differences (95% CI) are given where necessary.

Means were compared using analysis of covariance (ANCOVA) with baseline data as covariate, after logarithmic transformation if necessary. Non parametric data were compared using the Mann-Whitney test.

Calculations and statistical analysis were performed using the Systat ® 10.0 software for Windows ® (SPSS, Chicago, IL). The level of significance was set at P < 0.05.

Results

Underreporting was similar between fenugreek and placebo groups (38.9% (7/18) vs 40% (8/20), NS). As a result, total-REI was lower than daily TEE in both groups (basal ratio total-REI/TEE: 0.74 ± 0.05 (n = 18), and 0.73 ± 0.03 (n = 20) respectively, NS). At baseline, macronutrient intake was similar between fenugreek and placebo groups (Figure 1, panel A). After 6 weeks of treatment with the fenugreek seed extract, a significant decrease in TEE-adjusted reported fat intake was observed (post treatment fat-REI/TEE: 0.26 ± 0.02 (n = 18) vs 0.30 ± 0.01 (n = 20), P = 0.032), while protein and carbohydrate intakes were not significantly modified (Figure 1, panel B). Total-REI was not significantly modified by the treatment (post treatment total-REI/TEE: 0.68 ± 0.05 (n = 18) vs 0.73 ± 0.03 (n = 20), NS). Spontaneous food intake during the ad libitum meal test, weight, body composition, appetite and satiety scores did not differ between treated and untreated subjects.

The main biological parameters are described in tables I and II. The ratio of fasting serum insulin/plasma glucose was significantly decreased in subjects treated with fenugreek seed extract (0.89 ± 0.09 (n = 19) vs 1.06 ± 0.10 (n = 19) mUI.mmol -1 , P = 0.044) (Table I). No effect on plasma lipid profile (Table I), antioxidant parameters and oxidative stress markers (Table II) could be observed.

No serious adverse events occurred. Among the 35 non serious adverse events reported (17 with fenugreek, 18 with placebo), only 5 might be related to the experimental treatment: 4 cases of mild gastro-intestinal symptoms and 1 case of specific urine and sweat smell.

Discussion

The present report shows for the first time that a treatment with a fenugreek seed extract selectively reduces spontaneous fat consumption in overweight male subjects. We also show that the fenugreek seed extract does not increase food consumption. This latter result differs from the traditional use of fenugreek-based preparations for appetite stimulation and weight gain [START_REF] Rguibi | Body size preferences and sociocultural influences on attitudes towards obesity among Moroccan Sahraoui women[END_REF]. It also contrasts with our previous observations of enhanced food consumption in animals treated by a hydro-alcoholic crude extract or a purified steroid saponin fraction [START_REF] Petit | Effects of a fenugreek seed extract on feeding behaviour in the rat: metabolic-endocrine correlates[END_REF][START_REF] Petit | Steroid saponins from fenugreek seeds: extraction, purification, and pharmacological investigation on feeding behavior and plasma cholesterol[END_REF].

However, this observation is in accordance with the results of our previous investigation in normal weight healthy subjects [START_REF] Chevassus | Human energy expenditure in affluent societies: an analysis of 574 doubly-labelled water measurements[END_REF]. This effect has been observed with a reliable evaluation of energy intake in free-living conditions i.e. baseline and post-treatment 7-day records, adjustment on physical activity, systematic dietician review. While selfreported dietary records may underreport energy intake, they remain an important tool for nutrition researchers and a record of 7 consecutive days is considered reliable to quantify diet for outpatients [START_REF] Jackson | Methods to assess relative reliability of diet records: minimum records for monitoring lipid and caloric intake[END_REF]. As expected in an overweight population [START_REF] Johansson | Under-and overreporting of energy intake related to weight status and lifestyle in a nationwide sample[END_REF], underreporting of energy intake was frequent (≈ 40%), but similar in treated and control groups, which were initially comparable.

The effect on fat consumption could not appear in standardized conditions because the meal test used was composed of a single course that didn't allow any dietary choice. Such a specific effect, without any significant change in the intake of other nutrients, and without any alteration of appetite or palatability, has already been observed in pharmacological studies undertaken in rats by activation of the hypothalamic serotonin pathway [START_REF] Smith | Activation of hypothalamic serotonin receptors reduced intake of dietary fat and protein but not carbohydrate[END_REF], and in human after a methylphenidate challenge activating the brain dopamine pathway by blocking its reuptake [START_REF] Goldfield | Methylphenidate reduces energy intake and dietary fat intake in adults: a mechanism of reduced reinforcing value of food?[END_REF].

The moderate magnitude of this effect (-10%) made it unlikely to induce a decrease in weight over short periods, and indeed we did not observe any modifications in body weight or body composition. It is also worthy to notice that no significant change in body weight was observed by Sharma et al. [START_REF] Sharma | Toxicological evaluation of fenugreek seeds: a long term feeding experiment in diabetic patients[END_REF] in a clinical study evaluating the safety of fenugreek seeds consumed in diabetic patients, although their study was not designed to assess eating behaviour or weight modifications. Nevertheless, it can be hypothesized that fenugreek seed extract may favour a weight reduction in the long term, particularly in overweight or obese patients for whom a low-fat diet is recommended, and thus improve the metabolic status of these subjects.

The lower ratio of fasting serum insulin/plasma glucose may reflect an improved insulin sensitivity, as previously described with fenugreek seed preparations in type 2 diabetes [START_REF] Madar | Glucose-lowering effect of fenugreek in non-insulin dependent diabetics[END_REF]. This effect may be related to 4hydroxyisoleucine, an amino acid present in fenugreek seeds, known for its insulin-sensitizing effect [START_REF] Broca | Insulinotropic agent ID-1101 (4-hydroxyisoleucine) activates insulin signaling in rat[END_REF].

However, even if this biological mechanism would support the hypothesis of an improved insulin sensitivity in our sample, fasting glucose and insulin values (and related calculated indexes) are insufficient to draw such a conclusion, and specific investigations such as clamp studies would be required. As fenugreek is traditionally used to treat type 2 diabetic patients in Mediterranean and Asian countries, our results provide a rationale for conducting further research on the potential usefulness of fenugreek in disorders of carbohydrate metabolism.

In spite of an extensive exploration, we observed no significant effect on plasma antioxidant capacity or oxidative stress, which were ancillary biological objectives. This result is in contrast with previously reported data in rats [START_REF] Ravikumar | Effect of fenugreek seeds on blood lipid peroxidation and antioxidants in diabetic rats[END_REF] and mice [START_REF] Choudhary | Modulation of glyoxalase, glutathione Stransferase and antioxidant enzymes in the liver, spleen and erythrocytes of mice by dietary administration of fenugreek seeds[END_REF]. This might be related to our experimental conditions. First, our population was characterized by a normal baseline oxidative/antioxidant profile, and antioxidant properties are easier to detect in conditions of increased oxidative stress. Second, our sample size may have been insufficient to show a significant difference on these parameters. Alternatively, it is possible that the antioxidant properties shown in animal studies are not present in human.

Finally, it has to be noticed that the treatment with the fenugreek seed extract did not modify the subjects' lipid profile, which also may be explained by the healthy status of the subjects.

Although fenugreek seed extract is coloured and displays a specific smell, active and placebo tablets could not be identified by the investigators and the subjects, thanks to the coating process making them indistinguishable.

Nevertheless, one subject detected an unusual smell in his urine and sweat during the treatment period without having expressly been interpreted as treatment-related. This unique case of possible identification of the treatment received by the subject did not conduct to any de-blinding for the rest of the volunteers. Considering this problem, the occurrence of de-blinding has to be systematically tested in clinical studies investigating fenugreek seed preparations.

Conclusion

The repeated administration of a fenugreek seed extract slightly but significantly decreased dietary fat consumption in human in this short-term study; this new property, together with a potential favourable effect on insulin sensitivity, may be helpful in subjects who need to decrease their fat intake, and may improve the metabolic status of overweight subjects. The fenugreek seed's component(s) involved and the mechanism(s) of this effect remain to be elucidated.

Table I

Comparison of fasting data of plasma glucose, serum insulin and lipid profile between fenugreek seed extract 1176 mg/day and placebo in overweight subjects

Table II

Comparison of antioxidant status between fenugreek seed extract 1176 mg/day and placebo in overweight subjects

Figure 1 A 1

 11 Figure 1

Table I

 I

	Table II
	Main metabolic parameters Antioxidant status	Fenugreek Fenugreek	Placebo Placebo	P P
	Fasting plasma glucose (mmol.L -1 ) -Baseline -Post-treatment Total plasma antioxidant capacity (mmol.L -1 ) -Baseline -Post-treatment Fasting serum insulin (mU.L -1 ) -Baseline -Post-treatment Fasting insulin/glucose ratio (mU.mmol -1 ) -Baseline -Post-treatment Total cholesterol (mmol.L -1 ) -Baseline -Post-treatment LDL susceptibility to oxidation : lag phase time (min) 4.61 ± 0.21 5.38 ± 0.10 1.48 ± 0.03 1.51 ± 0.04 5.10 ± 0.41 4.73 ± 0.43 1.17 ± 0.13 0.89 ± 0.09 4.82 ± 0.26 4.88 ± 0.25 -Baseline 16.2 ± 1.7 -Post-treatment 15.3 ± 0.6 LDL susceptibility to oxidation : slope of the propagation phase (OD.min -1 ) 4.87 ± 0.19 5.26 ± 0.16 1.51 ± 0.03 1.49 ± 0.04 5.02 ± 0.31 5.38 ± 0.36 1.07 ± 0.08 1.06 ± 0.10 5.19 ± 0.19 5.06 ± 0.20 15.5 ± 0.8 14.8 ± 0.7 -Baseline 0.017 ± 0.001 0.018 ± 0.001 -Post-treatment 0.018 ± 0.001 0.020 ± 0.001 LDL susceptibility to oxidation : maximal propagation rate (OD.min -1 ) -Baseline 0.019 ± 0.001 0.020 ± 0.001 -Post-treatment 0.020 ± 0.001 0.021 ± 0.001 HDL-cholesterol (mmol.L -1 ) -Baseline 1.27 ± 0.05 1.22 ± 0.08 -Post-treatment 1.30 ± 0.05 1.17 ± 0.07 Triglycerides (mmol.L -1 ) -Baseline 1.25 ± 0.15 1.15 ± 0.11 -Post-treatment 1.27 ± 0.16 1.41 ± 0.14 LDL susceptibility to oxidation : half-time of maximum diene formation (min) -Baseline 35.4 ± 2.6 34.9 ± 1.2 -Post-treatment 33.9 ± 1.0 34.1 ± 1.4 Serum vitamin E (µmol.L -1 ) -Baseline 27.9 ± 1.6 27.3 ± 0.9 -Post-treatment 28.1 ± 1.8 27.7 ± 1.2 LDL vitamin E (mg.L -1 of LDL) -Baseline 10.9 ± 0.9 12.3 ± 0.6 -Post-treatment 12.4 ± 1.1 11.8 ± 0.77 Plasma vitamin C (µmol.L -1 ) -Baseline 42.2 ± 4.0 52.7 ± 4.1 -Post-treatment 43.9 ± 4.0 52.1 ± 4.3 Plasma malonyldialdehyde (µmol.L -1 ) -Baseline 0.89 ± 0.07 0.85 ± 0.05 -Post-treatment 0.85 ± 0.06 0.82 ± 0.05 Urine F2-isoprostanes/creatinine (pmol.mmol -1 ) -Baseline 307 ± 31 478 ± 55 -Post-treatment 357 ± 54 510 ± 82	0.355 0.545 0.572 0.138 0.887 0.057 0.708 0.044 0.254 0.207 0.826 0.546 0.419 0.264 0.339 0.647 0.555 0.067 0.732 0.148 0.788 0.877 0.752 0.870 0.210 0.220 0.071 0.762 0.758 0.841 0.004 0.704
	Plasma advanced glycation end products -Baseline -Post-treatment Plasma advanced oxidation protein products(µmol.L -1 ) 13887 ± 982 12737 ± 697 -Baseline 57.9 ± 5.5 -Post-treatment 54.0 ± 6.9	12302 ± 445 12204 ± 478 61.4 ± 3.4 61.6 ± 5.3	0.261 0.804 0.592 0.504

Acknowledgements

This study was sponsored by the University Hospital of Montpellier (UF7698) with the financial support of French Ministry of Health (PHRC régional 2001) and Laboratoires Legras (Cachan, France).

We specially thank B Bories-Azeau (administrative assistant) for her extensive work concerning volunteers recruitment and study management.

This study was also successfully carried out thank to the expert scientific, medical, technical and logistic assistance of JL Allaz, AM Barbanel, R Blanc, A Bonardet, A Cadène, JP Cristol, M Escoffier, J Foppolo, P Jonquet, C Montaudié, A Mouraret, C Piat, MC Picot, G Pouly, AM Puech-Cathala, I Roch-Torreilles, MH Rocolle and H Tsiambakaïna.