Keywords: Distributed Data Storage, Heterogeneous Sensor Networks Stockage distribué de données, réseaux de capteurs hétérogènes

This paper presents ProFlex, a proactive data distribution protocol for heterogeneous wireless sensor networks (HWSNs). ProFlex guarantees robustness in data retrieval by intelligently managing data replication among selected storage nodes in the network. Contrarily to related protocols in the literature, ProFlex considers the resource constraints of sensor nodes and constructs multiple data replication structures, which are managed by more powerful nodes. Additionally, ProFlex takes profit of the higher communication range of such powerful nodes in the network and use the long link to improve data distribution. When compared with Supple -a related protocol, we show by simulation that Proflex increases the network resilience under failures circumstances, decreases the overhead of transmitted messages, and decreases the number of hops to find a specific data in the network.

Introduction

The deployment of new Wireless Sensor Network (WSN) applications (e.g., health monitoring of patients, environment sensing, and factory monitoring systems), which operate unattended for a long period of time and generate a considerable amount of data, poses several challenges. In such applications, one of the major challenge is where and how to store and retrieve the sensed data.

For this, different distributed data storage protocols have been proposed in the literature as a way to increase resilience to failures and, hence, decrease possible losses of sensed data [START_REF] Sheng | Data storage placement in sensor networks[END_REF][START_REF] Sheng | An approximation algorithm for data storage placement in sensor networks[END_REF][START_REF] Vincze | Adaptive sink mobility in event-driven multi-hop wireless sensor networks[END_REF][START_REF] Ben | A line-based data dissemination protocol for wireless sensor networks with mobile sink[END_REF][START_REF] Vecchio | Deep: Densitybased proactive data dissemination protocol for wireless sensor networks with uncontrolled sink mobility[END_REF]4]. A distributed approach has the advantage of not having a single point of failure, since the sensed data is distributed among the network nodes. Nevertheless, several problems arise in this context, such as how to efficiently store the sensed data to be later retrieved and how much data a sensor node should store. The first problem is related to resilience and corresponds to the distribution of the sensed data over the network, whereas the second one is related to resource management and corresponds to how much data from other nodes a node should store.

To overcome the aforementioned problems, some studies proposed the use of more powerful nodes to perform data storage [START_REF] Sheng | Data storage placement in sensor networks[END_REF][START_REF] Anastasi | Data collection in sensor networks with data mules: An integrated simulation analysis[END_REF]. In this scenario, only these powerful nodes are responsible for storing all sensed data, since the assumption is that those nodes have no memory limitation. Nevertheless, the use of more powerful nodes does not overcome the problem of data losses, since these nodes still can fail. To increase the network resilience to failures, a possible approach is to replicate a given data and keep it at different nodes. Moreover, in the presence of a mobile sink, a good setup on the number of replicas at nodes might enable the sink to get a representative view of the entire network data by only visiting a small percentage of nodes [START_REF] Vecchio | Deep: Densitybased proactive data dissemination protocol for wireless sensor networks with uncontrolled sink mobility[END_REF]4]. Hence, the use of more powerful nodes as data storage nodes also requires replication mechanisms to deal with resilience.

In this work, we propose the use of powerful nodes to perform distributed data storage in Heterogeneous Sensor Networks (HSNs). However, instead of using the extra memory features of these nodes, we take profit of their powerful communication range and use the long link to improve data distribution. The proposed protocol, called ProFlex, was designed to be aware of the heterogeneous topology. ProFlex is divided into three phases (Section 3). The first phase is a tree construction initiated by all powerful nodes. These trees will be used to route the collected packets from sensor nodes to their selected storage nodes. The second phase performs the importance factor distribution, which will be used in the last phase to determine the probability of a node to become a storing node. The last phase is the data distribution process, which uses the tree topology and the importance factor to decide whether a sensor node should store the data. Due to their similarities, we evaluate and compare ProFlex with the Supple [4] protocol (Section 4). Simulation results show that using a heterogeneous network topology, ProFlex increases the network resilience under failures circumstances, decreases the overhead of transmitted messages, and decreases the number of hops to find a specific data in the network. In addition, we discuss the related work and present the system model in Section 2, and conclude the paper and discuss the future work in Section 5.

Background

Data Storage Protocols

In the literature, there are some proposals for data storage in WSNs. We discuss some of them in the following.

Sheng et al. [START_REF] Sheng | Data storage placement in sensor networks[END_REF][START_REF] Sheng | An approximation algorithm for data storage placement in sensor networks[END_REF] study the data storage placement in WSNs to deal with the traditional problem of traffic overhead and, consequently, high energy consumption of nodes closer to the sink. To overcome this problem, they propose two network models. The first one considers a tree topology rooted at the sink and a subset of afterward selected storage nodes, storing data collected by their descendants in the tree. In the second model, a tree topology is constructed after the deployment of the storage nodes, whose positioning is obtained from a linear programming optimization. Nevertheless, node failures are not considered in both models, which might result in the loss of all data collected by storage nodes' descendants.

Anastasi et al. [START_REF] Anastasi | Data collection in sensor networks with data mules: An integrated simulation analysis[END_REF] and Tseng et al. [START_REF] Tseng | Using mobile mules for collecting data from an isolated wireless sensor network[END_REF] investigate the use of data mules (mobile nodes with higher hardware capabilities) to collect and store data in wireless sensor networks. The drawback of this approach is the problem of data mule failures, in which case all collected data might be lost. Also, this approach has high latency to collect the sensed data, since the mule node must be close enough to the sensor node to collect the data.

To overcome the problem of storage node failures, proactive data replication strategies have been proposed in the literature. The goal of such approaches is to replicate sensed data to a selected subset of nodes [START_REF] Vincze | Adaptive sink mobility in event-driven multi-hop wireless sensor networks[END_REF][START_REF] Ben | A line-based data dissemination protocol for wireless sensor networks with mobile sink[END_REF][START_REF] Vecchio | Deep: Densitybased proactive data dissemination protocol for wireless sensor networks with uncontrolled sink mobility[END_REF]4], which can be later retrieved by mobile sinks. Such approaches consider predictable [START_REF] Vincze | Adaptive sink mobility in event-driven multi-hop wireless sensor networks[END_REF][START_REF] Ben | A line-based data dissemination protocol for wireless sensor networks with mobile sink[END_REF] or uncontrolled [START_REF] Vecchio | Deep: Densitybased proactive data dissemination protocol for wireless sensor networks with uncontrolled sink mobility[END_REF]4] mobility of the sink nodes.

More related to our approach is the Supple protocol [4] -a flexible probabilistic data dissemination protocol for WSNs that considers static or mobile sink nodes. The Supple protocol has three phases: tree construction, weight distribution, and data replication. The first phase relies on a binary tree construction by a central sensor node of the sensing area (e.g., the sink node). The central sensor node is responsible for receiving and replicating the collected data in the network. The second phase assigns weights to nodes, which represent the probability of a node storing a data. Supple uses the hop distance of a node to the central node to calculate this probability. In the last phase, the sensor nodes send their data to the central node and this node replicates each data r(v) times using the tree infrastructure and according to its storage probability. The number of replicas depends on the weights and the amount of data each node is allowed to store. The authors claim that a mobile sink node visiting a small fraction of nodes, i.e., about 2.3 √ n, for a total of n nodes, can retrieve all the generated data in the network. Moreover, thanks to r(v), the failure of a small number of nodes will not cause data losses. However, the Supple protocol does not consider the problem of finding a good positioned central node as well as the problem of energy consumption and traffic overload through the nodes closer to the central node.

Our protocol ProFlex deals with proactive data distribution in WSNs and with the Supple's drawbacks. ProFlex guarantees robustness in data retrieval by intelligently managing data replication among selected storage nodes and considers sensor nodes' resource constraints by using multiple replication struc-tures. Although the described drawbacks, we evaluate ProFlex's performance compared Supple since it is the most similar protocol to ours.

System Model

The main adopted assumptions are detailed hereafter.

Nodes. We consider that there is a large number (n) of sensor nodes scattered on a given geographic area for collecting data or monitoring events. All sensors are uniquely identified and can be of two types. The first one, named L-sensor for low-end sensor, is a node with limited resources, including processor, storage, communication and power resources. The second type, named H-sensor for high-end sensor, is a node with more sophisticated resources. Thus, H-sensor nodes have improved processing, storage, battery and communication power when compared to L-sensor nodes. A question that might arise is: Why not design a sensor network comprised of just H-sensor nodes? Whereas H-sensor nodes are more powerful when compared with L-sensor nodes, the latter are much less expensive. Hence, it is assumed the network is composed of n l Lsensor nodes, and n h H-sensor nodes, where n = n l + n h and n l ≫ n h . It is worth noting that in this work, we assume that only L-sensor nodes are data producers, so H-sensor nodes do not perform data sensing. Moreover, nodes later selected as a storage node are provided with a partial view v regarding some other nodes, including itself. We define S as the storing nodes set. Therefore, each node may act as a storage node for some other nodes, but not for all of them. Due to the limited buffer of L-sensor nodes, power-aware compression [START_REF] Marcelloni | A simple algorithm for data compression in wireless sensor networks[END_REF] and reduction algorithms [START_REF] Luiz | Data centric sensor stream reduction for real-time applications in wireless sensor networks[END_REF] may be employed.

Communication. We consider a connected network topology along the time. Given the expected network lifetime, we can estimate the amount of sensor nodes to achieve this goal. A L-sensor node i can communicate with another node j (L-sensor or H-sensor ) that is inside its communication radius r 1 , i.e., the distance between i and j should be less than or equal to r 1 (d(i, j) ≤ r 1 ). Hsensor nodes are equipped with two radios, each one with a different frequency and a different communication radius (r 1 and r 2 , r 2 ≫ r 1 ). It is also assumed that radio frequencies do not interfere with each other. A H-sensor node can communicate with both L-sensor and H-sensor nodes inside communication radius r 1 and r 2 , respectively. Finally, the network is modeled as a graph G = (V, E) where V = V L + V H is the set of L-sensor and H-sensor nodes, and E = E L + E H models their neighboring links. We assume the long range links r 2 , among H-sensor nodes, are selected in such a way to yield a network with a small average path length, as presented in [START_REF] Daniel Ludovico Guidoni | On the design of resilient heterogeneous wireless sensor networks based on small world concepts[END_REF]. Therefore, a message in a network with this characteristic needs less hops to reach its destination. Initial knowledge. Initially, a node i ∈ V only knows its identity, which is unique, its sensor type (L-sensor or H-sensor ), and a parameter I(i) that defines its importance factor in the network (I : S → {0, 1}, S ⊆ V , called the importance factor function). Importance factors are initially assigned to nodes based on an external criterion. It determines the nodes in the network responsible for storing data. If the criterion is the sensor location, only nodes at a specified location will be used as storing nodes and will have I(i) = 1. It may also be desired to choose as storing nodes only H-sensor nodes, since they are more powerful than L-sensor nodes. In this scenario, H-sensor nodes will have I(i) = 1 and L-sensor nodes will have I(i) = 0. On the other hand, if all nodes can be uniformly selected as storing nodes, then all nodes in the network will have I(i) = 1. In this work, we choose as storing nodes only L-sensors, hence for all L-sensor nodes I(i) = 1 and for all H-sensor nodes, I(i) = 0. In summary, the distribution of the importance factors among nodes will define the storing nodes set S.

Proposed Protocol

In this section, we present ProFlex, a Probabilistic and Flexible Data Storage Protocol for Heterogeneous Wireless Sensor Networks. ProFlex provides the capability for each node in a network to independently determine its own group of storage. Although requiring a predefined selection criterion, this storing selection procedure enables its combination with data gathering strategies based both on static and mobile sinks. Initially, we introduce the major steps of the protocol and how it allows a flexible data distribution to a previously specified subset of storing nodes in a WSN. Then, we present a general discussion about the main characteristics and possible drawbacks of the algorithm when compared with Supple [4].

ProFlex Algorithm

The ProFlex algorithm is comprised of three phases. The first one builds a tree-based routing structure starting with the H-sensor nodes. The second phase distributes the importance factor, in which the L-sensor nodes forward their importance factor value towards the H-sensor nodes operating as root of their tree. Finally, in the third data L-sensor nodes forward data to their Hsensor root nodes to be later replicated among nodes in the storing nodes set S. Algorithm 1 presents a general overview of ProFlex.

Algorithm 1: General principle of ProFlex

Input: Graph G = (V, E) Input: Storing nodes set S ⊆ V Input: Importance factor function I : S -→ N Input: Storing function X : V -→ {0, 1} 1. Construction of the trees TH (G) from G 2.
Propagation of the importance factor and number of storing nodes in each tree 3. foreach node i ∈ VL do Send data(i) to the root of TH (G) 4. The root propagates each data(i), r(v) times according to the probabilities induced by the importance factors over the storing nodes set INRIA

Tree Construction

The first step of ProFlex is a tree construction initiated by all H-sensor nodes in the network. Let G = (V, E) be the graph representing the network, T H (G) the trees that aggregate the shortest paths from each L-sensor node to the closest H-sensor node. In this work, a shortest path means the number of hops, but any other metric can be used, i.e., delay, capacity, etc. It is worth mentioning that although each H-sensor node builds a tree rooted at itself, each L-sensor node will belong only to the tree rooted at the closest H-sensor node. Hence, during the tree construction, when a L-sensor node receives several H-sensor 's messages, it will update its local information and forward the message further, only if the message is from a closer H-sensor node. Otherwise, the node will simply discard the message.

For comparison reasons with the Supple protocol (Section 4), we consider the use of a binary tree as a routing structure. We also use in our performance analysis the PeerNet [START_REF] Eriksson | Scalable ad hoc routing: the case for dynamic addressing[END_REF] protocol to build the binary tree. ProFlex supports, however, any other routing structure.

Importance Factor Distribution

In ProFlex, all nodes of the storing nodes set have an importance factor assigned by the function I : S → {0, 1}. The importance factor assigned to a particular node i dictates if node i will play the rule of storing data for other nodes (I(i) = 1) or not (I(i) = 0) and will be later used at the computation of node i's storing probability.

The importance factor function of nodes is defined by a selection criterion. Although ProFlex allows the use of any selection criterion, for comparison reasons with the Supple protocol, we use the uniform selection criterion. Thus, S = V L and for all nodes i ∈ S, I(i) = 1; if i ̸ ∈ S, I(i) = 0. This gives to all nodes in S equal chance of being selected as storing nodes. Note that the storing nodes set S is only composed by L-sensor nodes, therefore no H-sensor node will store any packet. It is straightforward to notice that due to their better resource capabilities, H-sensor nodes could also be used as storage nodes and have a greater importance factor than L-sensor nodes. Thus, the former would store more data than the latter, but we choose not to do so. Such a decision is backed by the fact that in this work, we only intend to leverage the characteristics inherent to heterogeneous WSNs and not the power of H-sensor nodes themselves.

In existing protocols [4,[START_REF] Bar-Yossef | Rawms -random walk based lightweight membership service for wireless ad hoc networks[END_REF][START_REF] Vecchio | Deep: Densitybased proactive data dissemination protocol for wireless sensor networks with uncontrolled sink mobility[END_REF], the partial view size |v| (i.e., maximum number of allowed stored packets at a given node) is a statically configured parameter. It is configured considering a uniform distribution of the data and based on the size of the storing nodes set in the considered replication structure. By doing this, they ensure that there will be enough space to store the data generated by the network. In particular, in Supple, a unique tree-based replication structure is considered and, if uniform selection criterion is used, the set size of the storing nodes will be equivalent to the number of nodes in the tree, i.e., n nodes. Instead, ProFlex uses several replication structures at the data distribution phase, which are given by the multiple constructed trees. Thus, each tree defines different storing nodes and S sizes. This requires a dynamic configuration of the partial view size of nodes per replication structure.

foreach node i ∈ TH (G) do create (I l (i), I(i), Ir(i), X l (i), X(i), Xr(i)) foreach node i ∈ TH (G) in a breadth-first search starting from the leaves do if j = left child of i then I l (i) = I l (j) + I(j) + Ir(j) X l (i) = X l (j) + X(j) + Xr(j) if k = right child of i then I l (i) = I l (k) + I(k) + Ir(k) X l (i) = X l (k) + X(k) + Xr(k) foreach node i ∈ VH do |Si| = X l (i) + X(i) + Xr(i) Send |Si| to H-sensor neighbors foreach node i ∈ VH do |S i aggr | = |Si| + ∑ j∈N (i) |Sj| foreach node i ∈ VH do |v| = √ |S i aggr |
Viana et al. [4] show that for a partial view of size |v| and a network with n data producers, the storing nodes set S must contain at least Θ( n v ln n) nodes in order to guarantee with high probability a good storage of all n collected data. On the other way, the partial view size must be v ≥ n |S| ln n. Viana et al. also show that a partial view size |v| = √ n provides a good compromise between resilience and sensors' resource consumption when |S| = n. Notice that here, the partial view size depends on the set size |S| of the storing nodes and the number of data producers n in the replication structure. Since ProFlex uses several replication structures rather, the partial view size |v| of storing nodes will be different for each tree t. As discussed in Section3.1.3, a H-sensor node h stores in its tree t h all data produced by nodes belonging to its tree and by nodes belonging to the neighboring trees. Neighboring trees are trees rooted at H-sensor neighbors of the H-sensor root node h, denoted by N (h).

For the special case where n = |V L |, a H-sensor node only needs to know the set size of the storing nodes of its tree and of the neighboring trees. Consider the storing function X : V → {0, 1}, where X(i) = 1 if I(i) ̸ = 0; or X(i) = 0 if I(i) = 0. Algorithm 2 defines the set size of the storing nodes and the importance factor. The main idea behind this algorithm is to initialize each node i ∈ V with (I l (i), I(i), I r (i), X l (i), X(i), X r (i)), where I l (i) and X l (i) (similarly to third component I r (i) and sixth component X r (i)) are the importance factor and number of storing nodes of the left (similarly to right) subtree of i, and I(i) and X(i) are the importance factor and the value of the storing function of the node i in the set of storing nodes, respectively. Note that I l (i) (similarly to

INRIA I r (i))
is the sum of all importance factors of nodes in the left (right) subtree of node i.

When the H-sensor node h knows the set size of the storing nodes |S h | = X l (h) + X r (h) in its tree, it will forward this value to its H-sensor neighbors. Eventually, a H-sensor node h will also receive the set size of the storing nodes from its neighboring H-sensor nodes. Finally, a H-sensor node h calculates the set size of the aggregated storing nodes |S h aggr |, i.e., its own set size of the storing nodes plus the set sizes of the storing nodes at its neighboring trees:

|S h aggr | = |S h | + ∑ j∈N (h) |S j |.
Based on this information, a H-sensor node h calculates the partial view size |v| =

√

|S h aggr | for storing nodes in its tree.

Data Distribution

Algorithm 3: Data distribution algorithm The data dissemination phase is at the heart of ProFlex, responsible for propagating properly data to the storing nodes. In the case of a uniform selection criterion, ProFlex ensures a uniform distribution of node's data among the storing nodes set. In fact, the partial view of nodes is constructed due to the distribution on each tree of a number of replicas r(v) of each data. More specifically, the transmission of r(v) replicas by the root i of each tree t i will guarantee the storage of v =

Input: Tree T (G) Input: (I l (i), I(i), Ir(i), X l (i), X(i), Xr(i)) for each i ∈ V //

√

|S i aggr | data at each storing node of this tree. Algorithm 3 shows the main actions a node must perform when it produces or receives a data packet. Initially, when a L-sensor node produces a data packet, it sends its packet to the H-sensor node that is the root of the tree the L-sensor node belongs to.

When a H-sensor node receives a data packet, three actions take place. First, it forwards the received data packet to every other H-sensor node that is within its communication radius (only if the received packet was not from another Hsensor node). Note that, this is done using the long range communication links. By forwarding packets to other trees in the network, we intend to increase the algorithm resiliency to local failures and catastrophic events on a given region.

Second, a H-sensor node calls NumberOfReplicas (Algorithm 4) to determine how many replicas it must produce in order to forward to its children. Such computation should ensure that nodes belonging to the set of storing nodes receive with high probability |v| distinct data from other nodes. As for the partial view size, the number of replicas is computed based on the set size of the storing nodes. As shown in [4], when |S| = n, then r(v) = n ln ( n n-v ); and when |S| ̸ = n, then r(v) = n ln n. In particular, as described in [4] Priority policies and r(v) computation. One of the foremost contributions proposed in this work is the use of priority policies to determine how many replicas a given data should have. The main idea behind this choice is that a higher priority packet certainly deserves a higher number of replicas when compared with a packet with a lower priority, i.e., a lower priority packet should not consume as many resources of the network as one with a higher priority.

To accomplish this, we adopt a priority policy based on three priority levels: high, normal and low. Packets of each priority are then replicated r(v) times, where r(v) is defined according to (i) the formalization given by Supple (as described above); and (ii) the partial view sizes previously computed to each tree . Note that, although a priority policy based on three levels was adopted, any other policy may be employed. It is only required that for a different priority value, the procedure NumberOfReplicas returns a different value for r(v).

t i : v = √ |S i aggr |.
Finally, after determining the number of replicas, the H-sensor node calls ForwardData (Algorithm 5) r(v) times. The propagation by the H-sensor INRIA node is done according to the importance factor of its left and right subtree and also to its own importance factor. Moreover, when a L-sensor node receives a data packet from its parent, it also calls ForwardData to determine whether it will forward or store the packet. It must be noted that messages are forwarded asynchronously, i.e., a H-sensor node does not need to finish the r(v) sequential data transmission of a node to start transmitting data of another one. The algorithm naturally stops when the message is received by a leaf node in the set of storing nodes. The partial view size of a node, i.e., the maximum number of packets a node can store, is piggybacked on every data packet. After the H-sensor node knows the set size of the aggregated storing nodes |S aggr |, it calculates the partial view size |v| (Algorithm 2) and embeds this value in every replicated data packet. Using this information, a L-sensor node knows when its buffer reached its allowed capacity.

At the end of the data dissemination, all nodes of the set of the storing nodes will have, with high probability, a partial view size v corresponding to its tree with uniformly disseminated data.

Algorithm 5: ForwardData(data) procedure

Input: Data packet Pick at random uniformly x ∈ [0, I l (i) + I(i) + Ir(i)] if x < I l (i) then Send data to left child if I l (i) ≤ x ≤ I(i) then
Store data in own view if I(i) + I l (i) < x then Send data to right child

Discussion

Existing protocols, in special Supple, suffer from well-known problems like hot spots closer to the sink (root), huge volume of traffic, and reliability issues.

As discussed in Section 4, the use of a heterogeneous network and a protocol specially tailored to operate on it yields a series of benefits. First, its small average path length means that during the gathering phase, the information can be timely retrieved. Moreover, the overhead on the nodes closer to the root of the tree is drastically reduced. Finally, although the use of more powerful nodes (even a small number) may result in an increase on the network's cost design, heterogeneous networks provide a level of resilience that is difficult to achieve with homogeneous ones. The use of data priority has also its benefits. Basically, by treating the data produced by the network according to its importance means that a data packet will not consume as many resources as every other data packet in the network. As shown below, reducing the number of replicas of a given packet does not lead to a great impact on ProFlex's efficiency in data gathering.

Performance Analysis

In order to assess the behavior of our proposed protocol, a series of simulations was performed. The simulator used was Sinalgo [START_REF]Sinalgo -simulator for network algorithms[END_REF], version 0.75.3. All the results are the arithmetic mean of 33 simulations with a confidence interval of 95%. Each simulation comprises a dissemination, where each node sends its sensed data in a single data packet, and a gathering phase. In the first phase, each node executes either ProFlex or Supple for data dissemination. Then, in the gathering phase, a mobile or a static sink node will collect the data from the storing nodes. In particular, the sink will perform as many visits (in the mobile case) or send as many queries (in the static case) as necessary to get a representative amount of data of the network, i.e., getting n l different entries of storing node's views.

Experimental Setup

In our experiment, four scenarios have been considered. In the first one, ProFlex or Supple performs the dissemination phase, and then a mobile sink visits all the nodes in the network by using a random trajectory. It is assumed a perfect transmission medium, without loss or interference. The second scenario adds to the previous one the loss of messages in the network, after the importance factor distribution phase ends. By evaluating this scenario, we show how our proposed protocol surpass Supple resilience to failures. In the third scenario, again ProFlex or Supple performs the dissemination phase, but no mobile sink performs the gathering phase. Here, after the dissemination, a static sink starts querying all data in the network. Finally, the fourth scenario also adds to the previous one the loss of messages in the network, after the end of the importance factor distribution phase 3.1.2. It is worth mentioning that in all scenarios, 33 different random choices are performed at the ForwardData 3.1.3 under 33 different topologies.

We evaluate the following metrics: (i) the message overhead as a function of the node's depth in the binary tree; (ii) data gathering efficiency, which is the accumulated amount of collected information after a node is visited by the mobile sink; and (iii) search query efficiency, which is the number of hops to reach a given information. In all figures, ProFlex represents our proposed protocol without the employment of data priority policies, i.e., all data packets are treated equally; ProFlex-Pr represents our proposed protocol with data priority enabled as stated in Section 3.1.3; and Supple represents the Supple protocol.

Simulation Parameters

To conceive the simulation, a fixed number of n = 1010 nodes was adopted, where n l = 1000 L-sensor nodes and n h = 10 H-sensor nodes, and only Lsensor nodes store data. In the case of Supple, a H-sensor node positioned at the center of the sensor field makes the role of a central node responsible for starting the algorithm. The nodes are randomly placed in a squared area following a uniform distribution. The average number of neighbors of a given node was fixed in 20. Nevertheless, these parameters are dynamically configured in ProFlex and depend on the number of storing nodes (e.g., the number of L-sensors here) in each tree 3.1. A size-based policy for buffer management was employed, so the oldest entry is removed to make room for new information in the view. Moreover, when adding a new entry to the view, it is first checked if it is already present, hence no entry is added twice.

Simulated Results

Communication Overhead

A well-known problem in WSNs is the energy hole problem [START_REF] Li | Analytical modeling and mitigation techniques for the energy hole problem in sensor networks[END_REF][START_REF] Liu | Research on the energy hole problem based on unequal cluster-radius for wireless sensor networks[END_REF][START_REF] Wu | Avoiding energy holes in wireless sensor networks with nonuniform node distribution[END_REF] in which nodes closer to the sink tend to consume its energy resources faster than other nodes, since they have to route packets from all other nodes in the network. In ProFlex and Supple, when a node has data to distribute to the network, it first sends the data to the root of the tree, and only then the root node will be responsible for distributing the data to the network. Hence, nodes closer to the root node tend to route more packets than other nodes as the case of the energy hole problem. Therefore, we evaluated the impact of the node's depth in the binary tree regarding the number of data messages transmitted. Fig. 4.3.1 shows the average number of data messages sent by a node as a function of its depth in the binary tree.

As expected, the closer the node is to the root, the more messages it has to send. However, in ProFlex, each H-sensor node is also a root node, and, consequently, more trees for data distribution are created in the network. The Under this scheme, for a network with S = 1000, after the data replication phase, there are 32000 replicated data packets in the entire network (i.e., each node stores 32 data regarding other distinct nodes). Meanwhile, in ProFlex-Pr, not all packets are equally replicated. Only packets with the greatest priority will be replicated r(v) = √ |S| times. Clearly, the reduction factor depends on the priority policy adopted. In the scenario showed in Fig. 4 packets have a priority uniformly randomly chosen between high priority, normal priority, and lower priority. Compared with Supple, the adopted priority policy resulted in a reduction of approximately 54% in the number of replicated data packets in the network. Note that, even when operating without data priority, ProFlex reduces in 24% the number of replicated data packets. Hereafter, we show the impact of this reduction on the data gathering and searching phase.

Efficiency in Data Gathering

After the dissemination of data to the network, a mobile sink placed at a random position visits the node at this position and then chooses the next position to visit, as described in [START_REF] Friedman | Probabilistic quorum systems in wireless ad hoc networks[END_REF]. When visiting a node, the sink gathers all data stored at this node. This procedure continues until all nodes have been visited. Fig. 4.3.1 shows the aggregated number of collected IDs (representing the data spread in the network) as a function of the number of visited nodes. As can be observed, Supple still presents better efficiency in data gathering, despite being closely followed by our proposed approaches. The explanation for such a behavior is that Supple still replicates a greater number of data in the network when compared to ProFlex, due to its use of a static number of replicas. For instance, Supple needs to visit about 200 nodes to get a complete view of the network, while ProFlex-Pr needs to visit about 300 nodes, a 10% increase. For a reduction in 54% in the amount of data replicated with an increase of just 10% on the number of nodes that needs to be visited, we argue that this is a good tradeoff.

To assess the resilience of our proposed protocol when compared with Supple, the former scenario was reused, but now with the introduction of message losses during the data distribution phase. Fig. 4.3.1 shows the amount of collected data after the mobile sink has visited 300 nodes as a function of the probability INRIA of message loss. As can be observed, the use of a heterogeneous infrastructure greatly increases the resilience of our proposed approaches in the presence of message losses when compared with Supple. For instance, under a probability of message loss of 10%, Supple is able to recover about 33% of the entire data produced, whereas ProFlex is able to recover about 55% of the entire data, almost 22% more data. Thus, even requiring more visits under a reliable scenario, ProFlex and ProFlex-Pr behave much better under an unreliable scenario than Supple, making the tradeoff acceptable.

Efficiency in Search Query

In this scenario, there is no mobile sink for data gathering, but a static sink positioned at the center of the sensor field. After the data distribution phase, the static sink starts issuing queries for all data produced, and the number of hops to reach the data is recorded. It is worth noticing that the sink always tries to reach the data that is closer to him. Fig. 4.3.1 shows the average number of hops to reach all the data in the network. The existence of multiple trees in the ProFlex protocols, in which each tree is rooted at each H-sensor node, results in trees of a smaller height than a single tree as in the case of Supple. Therefore, ProFlex and ProFlex-Pr need less hops to reach a given data. Moreover, as discussed in Section 2.2, the existence of long range links reduces the average path length, and, therefore, the number of hops to reach a given node. For instance, ProFlex needs on average 2.4 hops to reach a given data, ProFlex-Pr needs 2.8 hops and Supple needs 4.1 hops. The small increase in the number of hops of ProFlex-Pr when compared with ProFlex is that the latter replicates much more packets than the former.

Here, again we assess the behavior of the proposed protocol under the presence of message losses, in the scenario with a static sink. During the data distribution phase, message losses are introduced in the network. Then, the sink starts issuing queries for all data in the network. It is also assumed that the queries issued by the sink are not lost. Fig. 4.3.1 shows the average number of hops to reach all data in the network for a probability of message loss equal to 10%. Again, ProFlex and ProFlex-Pr need less hops to reach all data in the network. For instance, ProFlex needs on average 2.5 hops to reach a given data. This represents an increase of just 4% on the average number of hops when compared with a reliable scenario. Finally, Supple needs on average 4.7 hops to reach a given data, an increase of about 12% on the average number of hops when compared with a reliable scenario.

Conclusion and Future Work

In this work, we presented ProFlex, a distributed data storage protocol for heterogeneous WSNs. We showed the use of a heterogeneous infrastructure greatly increases ProFlex's resilience to message losses. For instance, under the presence of a probability of message loss of 10%, ProFlex recovers about 22% more data than Supple. Additionally, ProFlex requires about 41% less hops to gather information from a static sink. Thus, ProFlex poses a significant improvement over Supple with an acceptable tradeoff. Moreover, simulation results revealed that the adoption of a new replication policy that does not treat all packets equally, as the case of ProFlex-Pr, drastically reduces the amount of replicated packets in the network, yet without hurting efficiency. For instance, with a reduction of about 54% in the number of replicated data in the network, ProFlex-Pr needs about 10% more visits than Supple to gather all data in the network. As future work, we intend to assess the data distribution efficiency under different topologies and investigate the minimum number of powerful nodes required to achieve good results regarding collection and search efficiency.

Algorithm 2 :

 2 Importance factor and number of storing nodes distribution algorithm Input: Trees TH (G) Input: Storing nodes set S ⊆ V Input: Importance factor function I : S -→ N Input: Storing Function X : V -→ {0, 1}

Figure 1 :

 1 Figure 1: Average number of messages sent by a node as a function of its depth in the tree. .

Figure 4 :

 4 Figure 4: Amount of collected data as a function of probability of message loss.

Figure 5 :

 5 Figure 5: Average number of hops to reach a specific data.

Figure 6 :

 6 Figure 6: Average number of hops to reach a specific data in the presence of message loss equal to 10%.

  Higher priorities packets are replicated r(v) =

	√
	|S i aggr |
	times (|S i aggr | is calculated in Algorithm 2 for each tree). Packets with normal priorities are replicated r(v) = √ |S i aggr | 2 times, while low priority packets are √ |S i aggr | replicated r(v) = 3

RR n°7695

http://www.inria.fr ISSN 0249-6399

Scenario

overhead is, thus, distributed among the trees, alleviating the impact of the energy hole problem. It is worth mentioning that although Supple is also operating under a heterogeneous infrastructure, it is not tailored to take advantage of the features provided by this kind of network, as opposed to ProFlex. For instance, when the node's depth is 1, ProFlex and ProFlex-Pr send 91% and 94% less messages than Supple, respectively. Using a data priority scheme also decreases the overhead over the nodes closer to the root. This is due to the amount of data packets being replicated in


Dr Pascal Chanez
email: pascal.chanez@univmed.fr

P S Burge

R Dahl

J Creemers

A Chuchalin

R Lamarca

E Garcia Gil

Garcia Gil

Aclidinium bromide provides long-acting bronchodilation in patients

Keywords: Aclidinium, COPD, Bronchodilation, Phase II, FEV 1, LAMA

Aclidinium bromide is a novel, long-acting, muscarinic antagonist in phase III development for the maintenance treatment of COPD. This phase IIb study investigated the efficacy and safety of aclidinium for the treatment of moderate to severe COPD to establish the optimal dose for phase III studies. A total of 464

patients with moderate to severe stable COPD were randomised to double-blind, once-daily treatment with aclidinium (25, 50, 100, 200, or 400 μg), placebo, or openlabel tiotropium (18 μg) for 4 weeks. Spirometric measurements were performed at 22-24 h after the first dose and then at weekly intervals, and from 0.5-6 h post-dose on day 1 and day 29. Compared with placebo, aclidinium 200 μg and 400 μg significantly increased trough FEV 1 on day 29 versus baseline. During the first 6 h post-dose, the bronchodilatory effect of aclidinium (all doses) on day 1 was comparable to that on day 29. Time to peak FEV 1 was 3 h for aclidinium 100-400 μg. Aclidinium was well tolerated, with no dose-dependent effect on ECG, laboratory parameters, or adverse events. The incidence of AEs was generally comparable to placebo. Aclidinium produced sustained bronchodilation over 24 h and was well tolerated during this short-term study. Based on these data, aclidinium 200 μg was selected as the investigational dose for future clinical trials in COPD.

Introduction

Chronic obstructive pulmonary disease (COPD) is a preventable and treatable lung disease that is progressive and characterised by airflow limitation that is not fully reversible; it is associated with an abnormal inflammatory response of the lung to noxious particles or gases, particularly cigarette smoke [START_REF] Rabe | Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease: GOLD Executive Summary[END_REF]. COPD is highly prevalent [START_REF] Murray | Alternative projections of mortality and disability by cause 1990-2020: Global Burden of Disease Study[END_REF], responsible for 2.7 million deaths globally in 2000 [START_REF] Lopez | Chronic obstructive pulmonary disease: current burden and future projections[END_REF], and associated with increased morbidity and mortality as well as a reduced quality of life [START_REF] Engstrom | Functional status and well being in chronic obstructive pulmonary disease with regard to clinical parameters and smoking: a descriptive and comparative study[END_REF][START_REF] Domingo-Salvany | Health-related quality of life and mortality in male patients with chronic obstructive pulmonary disease[END_REF]. The 1997 Global Burden of Disease Study report has projected that COPD will become the third leading cause of death worldwide by 2020 [START_REF] Murray | Alternative projections of mortality and disability by cause 1990-2020: Global Burden of Disease Study[END_REF].

Cholinergic mechanisms are involved in the pathophysiology of obstructive airways disease and, therefore, anticholinergic agents are considered to play a significant role in its treatment. There are currently only two approved anticholinergic treatment options for COPD; a short-acting agent, ipratropium, and a long-acting agent, tiotropium. The Global Initiative for Chronic Obstructive Lung Disease [START_REF] Rabe | Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease: GOLD Executive Summary[END_REF] recommends daily management of COPD with long-acting bronchodilators, such as anticholinergics, which offer more effective and convenient treatment than shortacting bronchodilators. Currently, there is only one approved long-acting anticholinergic bronchodilator, to which patients have different responses both in terms of efficacy and tolerability. Consequently, development of additional longacting anticholinergic treatment options is warranted.

Aclidinium bromide is a novel agent with specificity for muscarinic receptors currently in clinical development for the treatment of COPD. Preclinical studies have shown aclidinium to be a potent muscarinic receptor antagonist [START_REF] Gavalda | Aclidinium bromide, a novel muscarinic receptor combining long residence at M3 receptors and rapid plasma clearance[END_REF] that is rapidly hydrolysed in human plasma to two major inactive metabolites [START_REF] Gavalda | Aclidinium bromide, a novel muscarinic receptor combining long residence at M3 receptors and rapid plasma clearance[END_REF]. These pharmacologic properties give aclidinium a unique profile that could offer significant advantages to the COPD patient providing sustained bronchodilation and a reduced potential for systemic side effects.

The pharmacokinetics and pharmacodynamics of aclidinium have been investigated in phase I (doses of 50, 300, and 600 μg) [START_REF] Schelfhout | Bronchodilator/bronchoprotective effects of aclidinium bromide, a novel longacting anticholinergic: a Phase I study[END_REF] and phase IIa (doses of 100, 300, and 900 μg) [START_REF] Joos | Bronchodilator effects of aclidinium bromide, a novel long-acting anticholinergic, in COPD patients: a Phase II study[END_REF] studies. The results from the phase I study showed that, in healthy

M A N U S C R I P T A C C E P T E D
subjects, aclidinium produced significant, long-lasting (≥24 h) protection against methacholine-induced bronchoconstriction. The phase IIa study in patients with COPD showed that aclidinium provided sustained increases in forced expiratory volume in one second (FEV 1 ), which suggests suitability for once-daily dosing. In both studies, aclidinium was not detected in the plasma, which suggests a limited potential for systemic adverse events. This phase IIb study was designed to assess the efficacy, safety, and tolerability of aclidinium compared with placebo, and to establish the optimal dose of aclidinium for use in phase III studies.

Materials and methods

Centres

This was a phase IIb study conducted in 42 centres in Europe and Russia. The trial was conducted in accordance with the amended Declaration of Helsinki (2002) and followed the International Conference on Harmonisation Good Clinical Practice Guidelines.

Study subjects

Male and female patients with a diagnosis of stable moderate to severe COPD according to American Thoracic Society criteria [START_REF] Thoracic | Standards for the diagnosis and care of patients with chronic obstructive pulmonary disease (COPD) and asthma[END_REF] were included in the study.

Patients had to be ≥40 years of age, have a smoking history of ≥10 pack-years, and FEV 1 in the range of 30-65% of predicted normal (Quanjer predicted normal value [START_REF] Greguletz | A novel multidose dry powder inhaler for the delivery of various types of inhalation powder[END_REF]) at the screening visit. At visit 1, the randomisation visit, pre-dose FEV 1 had to be within 20% of the screening visit value. At both the screening visit and visit 1, the ratio between FEV 1 and forced vital capacity (FVC) was required to be ≤70%.

Patients were excluded for any of the following reasons: history of or current asthma, allergic rhinitis, or atopy; reversibility to inhaled salbutamol 400 µg >20% of predose value; blood eosinophil count >400 cells/mm 3 ; respiratory tract infection or COPD exacerbation within 1 month (or hospitalisation with 3 months) of the Concomitant use of inhaled corticosteroids, oral sustained-release theophyllines (dosing suspended at least 48 h before each study visit), antihistamines, nedocromil, and ketotifen was permitted, provided a stable dose was administered prior to randomisation. The morning dose of these medications was delayed until after completion of the spirometry measurements at each visit. Salbutamol 100 μg/puff was allowed as rescue medication, although use was not permitted for 8 h prior to any visit. Any other medication used for the treatment of COPD was withdrawn prior to study start, and medications with pro-arrhythmic effects or that affected heart rate or QTc were not permitted.

Study design

This was a randomised, parallel-group, multicentre, double-blind (open-label for patients randomised to tiotropium), dose-finding study. After the screening visit, patients fulfilling the inclusion/exclusion criteria underwent a 14-day run-in period to ensure disease stability and washout of prohibited medications, followed by a 4-week treatment period during which they were randomised to one of seven treatments: aclidinium 25 μg, 50 μg, 100 μg, 200 μg, or 400 μg; matching placebo; or tiotropium 18 μg. All treatments were taken once-daily in the morning between 08.00 and 12.00.

There were five study visits: day -14 (screening), days 1-2, day 8, day 15, and day 29. Aclidinium and placebo were administered via a novel multi-dose dry-powder inhaler (Genuair ® , Almirall Sofotec, Germany) developed from the Novolizer ® which is approved and established in Europe. The new inhaler is a breath actuated, multidose, dry powder inhaler with a cyclone system for rapid particle dispersion, which generates a high fine particle dose (drug mass of particles <5 μm expressed as the percentage of the nominal dose that is available for deposition in the lungs) of 29-50% across a range of different drug types [START_REF] Greguletz | A novel multidose dry powder inhaler for the delivery of various types of inhalation powder[END_REF]. Tiotropium was administered via HandiHaler ® . Aclidinium was supplied by Laboratorios Almirall, Spain, and tiotropium (Boehringer Ingelheim, Germany) was purchased commercially.

M A N U S C R I P T A C C E P T E D

Measurements

Two spirometry measurements were taken at 1-hour intervals at the screening visit and before randomisation on day 1, and the averaged values provided the screening and baseline spirometry measurements. Efficacy spirometry measurements were taken at 0.5, 1, 2, 3, 4, 5, and 6 h after the first and last dose (days 1 and 29, respectively) in addition to 22, 23, and 24 h after the first dose and at each study visit (i.e. after the 7 th , 14 th , and 28 th day of drug administration). Three acceptable readings were taken for each measurement at each time point according to the ATS/ERS recommendations on spirometric assessments [START_REF] Quanjer | Lung volumes and forced ventilatory flows. Report Working Party Standardization of Lung Function Tests, European Community for Steel and Coal. Official Statement of the European Respiratory Society[END_REF].

The primary efficacy end point was trough FEV 1 on day 29 for aclidinium (all doses) versus placebo. Trough FEV 1 was defined as the mean value of the three highest readings assessed at 22, 23, and 24 h following the trial drug administration. Exploratory sub-group analyses of trough FEV 1 at day 29 were performed by gender, age (<65 years, ≥65 years), COPD severity (FEV 1 ≥50% predicted, ≥35-49% predicted, <35% predicted), smoking status, responder or non-responder after first dose (responder was defined as increase in FEV 1 ≥12% and ≥200 ml at 0-6 hours), and response to salbutamol (increase over pre-dose <12%, ≥12%).

Secondary end points included trough

Adverse events were recorded throughout the study. Other safety investigations included a 12-lead ECG at screening, day 1 and day 29, and laboratory tests for haematology, biochemistry and urinalysis at screening and day 29.

M A N U S C R I P T A C C E P T E D

Statistical analysis

The planned sample size was 413 evaluable patients (59 per treatment group). This number of patients provided a power of 80% to detect a difference of 120 ml in trough FEV 1 on day 29 between one of the aclidinium doses and placebo with a type I error rate of 0.05 (two-sided significance level). To allow for a dropout rate of 10% postrandomisation, it was planned to randomise a total of 462 patients (66 patients per treatment group).

Efficacy data are reported for the intent-to-treat (ITT) population, defined as all randomised patients who received at least one dose of study medication and who had a baseline and at least one post-baseline efficacy assessment. The safety population was comprised of all randomised patients who received at least one dose of study medication.

FEV 1 and FVC variables, PEFR, SGRQ total score, TDI and use of rescue medication were evaluated using an analysis of covariance (ANCOVA) with treatment, country, and treatment-by-country interaction as factors, and baseline value as covariate. The interaction term was omitted from the model if it was not statistically significant. The Missing spirometry data were handled as follows: for discontinuations because of lack of efficacy, the least favourable value prior to discontinuation was imputed; for other discontinuations, the last value carried forward approach was used. When only some values were missing from a test day, linear interpolation was used to estimate a value missing between two valid measurements. If values were missing for an entire visit or at the beginning or the end of an assessment period (i.e. 0.5, 6, 22, or 24 h), timematched values of the previous available visit were used.

M A N U S C R I P T A C C E P T E D

Results

Subject demographics

A total of 464 patients were randomised to treatment, and 441 patients completed the study (Fig. 1). The safety population comprised all 464 patients, and the ITT population consisted of 460 patients. Baseline characteristics of the ITT population by treatment group are shown in Table 1. The demographic and baseline characteristics for the ITT population were comparable across treatment groups.

Trough FEV 1 on day 29

Aclidinium increased trough FEV 1 values on day 29 in a dose-dependent manner (primary efficacy variable; Fig. 2). Aclidinium at doses of 200 μg and 400 μg and tiotropium were statistically significantly more effective than placebo in increasing trough FEV 1 at day 29. Adjusted mean differences in trough FEV 1 for aclidinium compared with placebo were 39 ml (25 μg; P>0.05), 36 ml (50 μg; P>0.05), 83 ml (100 μg; P>0.05), 148 ml (200 μg; P = 0.006), and 128 ml (400 μg; P = 0.018); and for tiotropium compared with placebo the adjusted mean difference in FEV 1 was 161 ml (18 μg; P = 0.003).

Trough FEV 1 at other time points

After the first dose on day 1, a statistically significant increase in trough FEV 1 compared with placebo was observed for aclidinium 200 μg (P = 0.044) and tiotropium 18 μg (P = 0.005). Across the four study visits, improvements in trough FEV 1 were sustained in the active treatment groups, although with some variability observed on days 8 and 15 in the aclidinium 200 μg group (Fig. 3).

FEV 1 improvements 0-6 h

A dose-dependent increase in FEV 1 was also observed following the first administration of aclidinium on day 1, with statistically significant increases from baseline in FEV 1 versus placebo seen at the first assessment (30 min after dosing) and maintained through the first 6 h post-administration for the aclidinium 100 μg

M A N U S C R I P T A C C E P T E D

(P = 0.014), 200 μg (P<0.001), and 400 μg (P<0.001) and tiotropium 18 μg (P = 0.02) (Fig. 4a). These changes were clinically significant at all time points for aclidinium 200 μg and 400 μg and tiotropium 18 μg.

On day 29, there were statistically significant increases in mean FEV 1 over placebo throughout the 6-h assessment for aclidinium 100 μg, 200 μg, and 400 μg, as well as for tiotropium 18 μg (P<0.001 in all cases except for the aclidinium 100 μg dose: P = 0.006) (Fig. 4b).

Changes in peak FEV 1

In line with the increases in mean FEV 1 , there were also dose-dependent increases in change from baseline in peak FEV 1 for aclidinium 25-200 μg (Table 2). These changes were statistically significant and similar on day 1 and day 29 for aclidinium 100 μg, 200 μg, and 400 μg compared with placebo.

Median time to peak FEV 1 on day 1 was 3 h for aclidinium 200 μg and 400 μg and 4 h for tiotropium 18 μg (Table 2). On day 29, median time to peak FEV 1 was 2 h for aclidinium 200 μg, and 3 h for aclidinium 400 μg and tiotropium 18 μg (Table 2).

Changes in FVC

Over the first 6 h following treatment on day 1, statistically significant improvements in FVC over baseline (P≤0.028) were observed from 30 min post administration for aclidinium 200 μg (0.194 l) and 400 μg (0.111 l), and tiotropium 18 μg (0.108 l), and from 1 h post administration for aclidinium 50 μg (0.120 l) and 100 μg (0.150 l).

Statistically significant improvements from baseline in FVC (P≤0.024) were maintained throughout the 6 h post-dose period; at 6 h aclidinium 50 μg (0.160 l), 200 μg (0.224 l), and 400 μg (0.140 l), and tiotropium 18 μg (0.186 l). On day 29, all treatments showed statistically significant increases over baseline in FVC (P<0.035) at all time points assessed from 30 min-6 h.

Other secondary end points

During the 4-week treatment period, the SGRQ total score decreased (improved) from baseline with all doses of aclidinium. The percentage of patients with a clinically meaningful improvement in SGRQ total score of ≥4 points from baseline ranged from 52.6% in the 400 μg group to 64.4% in the 100 μg group. The improvements seen in the SGRQ were mainly due to changes in the symptoms and impacts components.

There was a trend towards an increase in TDI total score compared with placebo for all doses of aclidinium. Statistically significant increases in the TDI component scores functional impairment, magnitude of task, and transition focal component were seen with aclidinium 100 μg and 400 μg compared with placebo; there was no significant difference compared with placebo for any treatment group in the magnitude of effort component score.

Overall, there were no differences between any active treatment group and placebo in the mean number of days when COPD symptoms of breathlessness, cough, wheeze, or sputum were reported or the number of daily puffs of salbutamol taken as rescue medication compared with baseline.

Safety evaluation

Aclidinium was well tolerated; most AEs were mild to moderate in intensity and considered by the investigators to be unrelated to treatment. AEs reported by ≥2 patients are shown in Table 3. The incidence of specific AEs was low across all active treatment groups and comparable with placebo, with the exception of headache, which occurred with an incidence of >5% in the aclidinium 50 μg and 100 μg groups (6.2% and 10.0%, respectively), and dry mouth, which occurred with an incidence of 6.2% in the tiotropium group. There was no relationship between the dose of aclidinium and the number of AEs.

There was one non-fatal serious AE of severe COPD exacerbation after 1 day of treatment with aclidinium 100 μg that resulted in the patient discontinuing the study. This AE was considered by the investigator as unlikely to be related to study drug.

Six other patients withdrew from the study because of AEs: one in the placebo group, two in each of the aclidinium 50 μg and 200 μg groups, and one in the tiotropium 18 μg group. The only AEs leading to withdrawal of more than one patient were cough, hoarseness, and dry mouth.

M A N U S C R I P T A C C E P T E D

There were no clinically relevant changes in laboratory parameters or vital signs in any of the treatment groups. The ECG results showed no dose-related changes in cardiovascular function associated with aclidinium treatment, and QTc changes from baseline were similar to placebo.

Discussion

The results from this randomised, parallel-group, dose-finding study showed that aclidinium provided a sustained 24-h bronchodilation effect in patients with moderate to severe COPD that was significantly superior to placebo. The effect was maintained over the 4 weeks of the study, which indicated an absence of any treatment-associated tachyphylaxis. The aclidinium doses selected in this study ranged from an expected non-effective dose (25 µg) to a known safe and effective dose (400 µg). The trough FEV 1 results showed a maximal effect with aclidinium 200 μg daily. The minimum effective dose, defined as the lowest dose of aclidinium which significantly improved FEV 1 over placebo on day 29, was 100 μg in this COPD population.

Spirometric results observed for aclidinium 200 μg and 400 μg administered oncedaily were similar to those observed with tiotropium 18 μg over the 4 weeks of the study. However, a direct comparison between aclidinium and tiotropium cannot be made as the tiotropium arm was included in the study for exploratory purposes and the drug was administered in an open-label fashion, which may have resulted in the introduction of bias to the tiotropium estimates. The mean increase in trough FEV 1 for aclidinium 200 μg, aclidinium 400 μg and tiotropium suggested clinically meaningful and significant bronchodilation (≥200 ml) [START_REF] Pellegrino | Interpretative strategies for lung function tests[END_REF].

The variations in mean trough FEV 1 seen at day 8 and day 15 with the aclidinium 200 μg dose (Fig. 3) were unexpected observations. However, the differences between mean FEV 1 on days 8 and 15 versus day 29 (the maximum value) were ≤65 ml. This variability is considered to be well within the acceptable FEV 1 repeatability criteria of 150 ml, according to the latest ATS/ERS Task Force on Standardisation of Lung Function Testing [START_REF] Miller | Standardisation of spirometry[END_REF]. As similar fluctuations in the trough 100 μg and 400 μg doses, and consistent results were seen across all aclidinium doses for FVC, a parameter which is not likely to be affected by patient expiratory effort, it is considered that the FEV 1 results for the 200 μg dose are most likely due to random variability.

Although there was some evidence of a trend to a faster median time to peak FEV 1 with aclidinium 200 μg and 400 μg compared with tiotropium, no conclusions can be drawn from these data due to the variability seen in each of the treatment groups.

Additional studies will be required to determine whether these observations are substantiated.

During this study there was a trend towards improvements in the TDI total score and the SGRQ total score, although there was no apparent relationship with aclidinium dose. Whilst this was a short-term study, improvements in these variables over a longer time period could result in substantial improvements in patient quality of life and, therefore, these data are encouraging.

The type of AEs reported during the use of anticholinergic agents is important because muscarinic receptors are widely distributed outside of the airways and lungs. Therefore, it is important that such therapies have minimal systemic exposure to reduce the risk of unwanted systemic effects [START_REF] Abrams | Muscarinic receptors: their distribution and function in body systems, and the implications for treating overactive bladder[END_REF]. Previous studies have shown low and transient concentrations of aclidinium in plasma [START_REF] Joos | Bronchodilator effects of aclidinium bromide, a novel long-acting anticholinergic, in COPD patients: a Phase II study[END_REF], suggesting that the incidence of systemic anticholinergic AEs would be very low [START_REF] Gavalda | Aclidinium bromide, a novel muscarinic receptor combining long residence at M3 receptors and rapid plasma clearance[END_REF]. The safety results from this study confirm a very low incidence of reported AEs with all doses of aclidinium.

Patients were not allowed in this study if they had a reversibility to inhaled salbutamol 400 µg ≥20% of pre-dose value in order to exclude highly reversible patients. Results from the recent studies indicate that patients with COPD demonstrate substantial acute bronchodilator reversibility using three different published criteria [START_REF] Tashkin | Bronchodilator responsiveness in patients with COPD[END_REF], suggesting that partial reversibility (FEV 1 change ≥12% after maximal bronchodilation) should no longer be an exclusion criterion in clinical trials.

In our study, the mean reversibility to salbutamol of the study population at baseline was 7.8% (SD = 7.7), however, the allowance of patients with a greater bronchodilator reversibility than 12% may have contributed to better discrimination between different doses of aclidinium. In conclusion, aclidinium is effective at providing sustained bronchodilation over 24 h without any evidence of treatment-associated tachyphylaxis with a minimum effective dose in patients with moderate to severe chronic COPD of 100 μg. The safety results confirmed the positive safety profile of aclidinium, particularly in terms of a very low propensity to cause anticholinergic AEs. Based on the efficacy, safety, and tolerability results from this study, aclidinium 200 μg once daily was selected for evaluation in phase III studies.

Acknowledgements

We thank Dr. Mark Hughes from Complete Medical Communications who provided medical writing support funded by Almirall, Barcelona, Spain. Median (range) time to peak, hours Day 1 2.0 (0.5-6.0) 4.0 (0.5-6.0) 4.0 (0.5-6.0) 2.0 (0.5-6.0) 3.0 (0.5-6.0) 3.0 (0.5-6.0) 4.0 (0.5-6.0) Day 29 2.0 (0.5-6.0) 2.0 (0.5-6.0) 2.0 (0.5-6.0) 2.0 (0.5-6.0) 2.0 (0.5-6.0) 3.0 (0.5-6.0) 3.0 (0.5-6.0)

Figure footnotes

Definition of abbreviation: NA: Not applicable.

  E P T E Dscreening visit; clinically significant or relevant cardiovascular conditions, laboratory tests, or electrocardiogram (ECG) parameters; QTcB >450 ms; history of narrowangle glaucoma, symptomatic prostatic hypertrophy, or bladder neck obstruction.

  FEV 1 on days 2, 8, and 15; trough FVC (defined in the same way as FEV 1 ) on days 2, 8, 15, and 29; change from baseline in trough and peak FEV 1 and FVC; change from baseline in total and component (symptoms, activity, and impact) scores of the St. George's Respiratory Questionnaire (SGRQ); improvement in Transition Dyspnoea Index (TDI); number of days with COPD symptoms; change from baseline in average morning and evening peak expiratory flow rate (PEFR); and use of rescue medication throughout the study. Data on COPD symptoms, PEFR, and rescue medication were as recorded in the patient's diary.

  treatment effect was initially assessed in an overall test with treatment comparisons undertaken only if this first test was statistically significant. Pairwise comparisons were based on Fisher's protected Least Significance Difference method to control for multiple tests. Treatment effects were estimated by least square means (LS Means), their standard errors (SE), and 95% confidence intervals (CI). Data were analysed using the SAS ® System version 8.2. Number of days with COPD symptoms, time to peak values, sub-group populations, and safety parameters were analysed descriptively.

FEV 1

 1 for aclidinium 200 μg on days 8 and 15 were not observed with the aclidinium M A N U S C R I P T A C C E P T E D ARTICLE IN PRESS Page 12

Figure 1 .

 1 Figure legends

Figure 2 .

 2 Figure 2. Trough FEV 1 values on day 29 (intent-to-treat population).

Figure 3 .

 3 Figure 3. Trough FEV 1 values throughout the study (intent-to-treat population).

Figure 4 .

 4 Figure 4. Change from baseline in FEV 1 0-6 h post-administration (intent-to-treat population). A. Day 1. B. Day 29.

Figure 3 :

 3 Figure 3: Statistically significant increase compared with placebo for aclidinium 200 μg at Day 1 (P = 0.044) and Day 29 (P = 0.006); aclidinium 400 μg at Day 15 (P = 0.035) and Day 29 (P = 0.018); and tiotropium 18 μg at all time points (P ≤ 0.005)

  Figure 2 Least squares mean trough FEV 1

Table 1

 1 Demographic and baseline characteristics of the intent-to-treat population

	Placebo

Table 3

 3 Adverse events reported by ≥2 patients in any treatment group (safety population)

	Number (%) of patients reporting adverse events