Franc ¸ois Pacull Maxime Louvel

Maria Isabel Vergara-Gallego

Reliable Control through Wireless Networks

Reliability is one of the biggest challenges when using Wireless Sensor-Actuator Networks in control systems. This paper exploits the transactional guarantees offered by the LINC coordination environment to provide reliability and robustness in wireless control systems.

First, LINC transactions were embedded in the mircocontrollers to deal with possible communication errors, faulty devices, and concurrent access to the devices. Then, an active replication mechanism was provided so that the system can be correctly recovered from hardware and communication failures.

A case study of a ball and plate problem is detailed. The plate is lift up and down by three motors. Each motor is controlled by a micro-controller communicating in wireless with the system controller.

I. INTRODUCTION

Wireless Sensor-Actuator Networks (WSANs) are being seriously considered to improve classical control applications such as process and building automation in the so called industrial IoT or Industry 4.0. Indeed, compared to classical wired technologies, wireless technologies are cheaper and easier to deploy and maintain. However, the wireless communication channel has an unreliable nature. Data will be lost or corrupted at some point, for instance, due to interference, obstacles, or other wireless communications.

Despite the efforts to improve communication error resilience [START_REF] Mahmood | Reliability in wireless sensor networks: A survey and challenges ahead[END_REF] in WSANs, end-to-end data delivery is not guaranteed. In addition, variation of channel condition in industrial environments may further reduce the reliability of communication. In such applications, if data is lost (i.e. a command to an actuator), the system could go to an unsafe state. Let's consider the example of a room ventilated with a Heating Ventilating and Air Conditioning (HVAC) system. To save energy a window may be opened under some conditions. If the window fails to open and the HVAC is stopped the room will not be ventilated anymore.

Failures and errors in WSANs cannot be removed. Adding WSAN in industrial control systems requires to handle such errors properly. However, this error management must not add extra complexity in the system. Two types of failures can be distinguished: failure of the controlled system and failure of the controller.

This paper describes how the coordination environment LINC [START_REF] Louvel | Linc: A compact yet powerful coordination environment[END_REF] is used to answer these challenges. LINC provides a high level rule based language to manage the complexity of WSANs. In addition LINC relies on a transactional engine to ensure the system consistency. In this work LINC has been extended to provide transactional guarantees up to the sensor/actuator level to handle failures in the controlled systems or the communication. This paper also details how LINC has been extended to provide active replication of the controllers.

This paper is organized as follows. After introducing LINC in Section II, Section III describes the proposed solution for reliability. Then, Section IV describes a demonstrator of a ball-and-plate control problem implemented with wireless communicating devices (ZigBee and Wi-Fi). Finally, Section V summarizes some related works and Section VI concludes the paper.

II. LINC OVERVIEW

This section briefly describes the coordination environment LINC to make the paper self-contained. Further details can be found in [START_REF] Louvel | Linc: A compact yet powerful coordination environment[END_REF]. Inspired by Linda [START_REF] Carriero | Linda in context[END_REF], the abstraction layer of LINC is an associative memory implemented as a distributed set of bags. Each bag contains resources shaped as tuples. Bags are accessed through three operations:

• rd(): takes a partially instantiated tuple as input parameter and returns from the bag a stream of fully instantiated tuples matching the given input pattern; • put(): takes a fully instantiated tuple as input parameter and inserts it in the bag; • get(): takes a fully instantiated tuple as input parameter, verifies if a matching resource exists in the bag and consumes it in an atomic way.

A. Coordination rules

The three operations rd(), get() and put() are used within production rules [START_REF] Cooper | Rule-based Programming with OPS5[END_REF]. A production rule is composed of a precondition phase and a performance phase.

Precondition phase: The precondition phase is a sequence of rd() operations which detect or wait for the presence of resources bags. In the precondition phase:

• the output fields of a rd() operation can be used to define input fields of subsequent rd() operations; • a rd() is blocked until at least one resource corresponding to the input pattern is available. Performance phase: The performance phase of a LINC rule combines the three rd(), get() and put() operations to respectively verify that some resources are present (e.g. sensor values), consume some resources (e.g. events that have been handled), and insert new resources (e.g. to command actuators).

In this phase, the operations are embedded in one or multiple distributed transactions [START_REF] Bernstein | Concurrency control and recovery in database systems[END_REF] executed sequentially. Every transaction executes a set operations in an atomic manner, i.e. all the operations are successfully done or none is executed. Thus LINC guarantees that:

• conditions responsible for firing the rule (precondition) are still valid at the time of the performance phase completion (e.g. a sensor value that has changed or an event that has been consumed by another rule); • all bags involved in the transaction are accessible (i.e. no communication error occurred); • all commands have been processed successfully. Transactions are implemented using a two-phase commit. The first phase checks that all the actions involved in the transactions can be executed (e.g. resources are available, bags are accessible, and actuator commands are valid). In this phase, all the resources are locked for the transaction. Any other transaction trying to access a resource that has been reserved will be canceled and retried later. If no action failed in the first phase, in the second phase the actions are actually performed (i.e. resources are consumed and actuator commands are executed). If one action failed in the first phase the second phase is triggered to release the resources locked in the first phase.

LINC rules are executed by dedicated components called coordinators. A rule can be executed by several coordinators which may run in different machines or different networks to provide redundancy. Thanks to the transactions, the same rule may safely be executed by several coordinators. If, for instance, the same resource or the same actuator are accessed by multiple coordinators, only the fist coordinator that locks the resources will perform the transaction. The other coordinators will fail when accessing the resources and the transaction will be canceled and retried later. If the first coordinator successfully executes the transaction, the other ones will fail because resources have been removed from bags. If the first coordinator fails (e.g. power or network failure in the machine hosting the coordinator), the other ones will try to execute rule.

III. RELIABLE WIRELESS CONTROL USING LINC

This section describes an approach that, based on LINC, provides reliability on top of a WSAN. First, it describes how the LINC transactional protocol was integrated with the wireless devices; then, it describes how to introduce redundancy to the system by implementing an active replication mechanism.

A. Embedding Transactions in Sensors/Actuators

LINC relies on distributed transactions to enforce consistency of the system. Transactions guarantee that all (or none) actions of a group of actions are correctly executed. Therefore, at the end of a transaction it is possible to guarantee that no communication or hardware errors happened. In a scenario integrating wireless sensors and actuators, to ensure the correct execution of a transaction, these wireless devices must be able to behave in a transactional way. With this purpose, the LINC transactional protocol was embedded in the wireless devices; therefore, these devices can be accessed using LINC rules as if they were a standard LINC bag. The introduction of protocolaware sensors/actuators was already proposed and partially implemented in a previous work [START_REF] Iris | Smart sensors and actuators: A question of discipline[END_REF]. This papers goes a step further, and implements full two-phase commit transactions.

To make WSAN nodes transactional, the main objective is to enable the coordinators to access the WSAN nodes through LINC rules. For that, a set of primitives at both the coordinator side and the WSAN nodes side must be implemented. Following, the proposed solution and its implementation are described; the most important extensions introduced by this work are highlighted.

The LINC protocol is provided by two components: the stub on the coordinator side and the skeleton on the WSAN nodes side. Once both sides have been implemented for a WSAN, the sensors and actuators can be manipulated through LINC rules as any other element in the application.

1) The Stub: To communicate with the WSAN nodes, coordinators must know how to access the WSAN nodes. For that, a piece of code called the Stub is available. The Stub provides all the primitives, invoked by LINC coordinators, to access resources in a bag. In a LINC application, a special object, called the NameServer, keeps information to construct a Stub on any bag belonging to the application.

When a coordinator wants to access a WSAN node, it asks to the NameServer information regarding the Stub associated to the WSAN node. Then, the coordinator can call the Stub primitives to communicate with the nodes with its network protocol. The Stub executes the proper methods to encode/decode frames to/from the WSAN and receive/transmit them from/to the desired nodes in the network.

Transmitted frames contain information such as the operation to be executed, the coordinator identifier, the transaction identifier, the bag to be accessed, the resource to be manipulated, and so on. Received frames contain the result of the operation. For each operation, the Stub encodes and sends the frame, and decodes the response from the node, to be used by the coordinator.

2) The Skeleton: The skeleton are all the primitives, implemented at the WSAN node side, to get the protocol working. The nodes primitives are called either in the precondition phase or in the performance phase.

Precondition Phase: The precondition phase detects the presence of resources. Implementing the precondition of a rule, requires implementing three basic operations: matches or they all have been returned for this stream).

When a new resource is added in the bag, if it matches the stream, the coordinator is unblocked. In the non blocking mode, the read returns a no more resource response to inform the coordinator that it is pointless to wait for new resources. The blocking mode is normally implemented by the skeleton. In this implementation, to save computation time in the nodes, the blocking read is implemented as a busy waiting loop by the stub. When the same resource is returned by the WSAN node, the stub waits for a configurable time and asks again to the WSAN node. Thus, from the coordinator point of view, the read is blocking. The notion of blocking/non-blocking read operations is an extension of [START_REF] Iris | Smart sensors and actuators: A question of discipline[END_REF], where only the non-blocking read operations were possible.

• Closestream: this operation closes an existing stream meaning no more read will be done for this stream. Performance Phase: The performance phase corresponds to the execution of the two-phase commit transaction. Thus, it is formed of two phases: the pre and the commit/abort phases.

For the first phase, the following operations are provided:

• pre_rd and pre_get: these operations execute equivalent tasks. During these pre * operations, the resource, if existing, is locked. Then, other rule instances will not have access to this locked resource. If the resource is locked, this operation returns busy, telling the coordinator to retry the operation later. If the resource does not exist, the operation fails and the transaction is aborted by the coordinator.

• pre_put: when a pre_put operation is executed, the peripheral being accessed (i.e. the gpio_1) is locked. Thus, the value of the peripheral cannot be accessed or modified by any other rule instance. If the peripheral was locked by a previous pre * operation, the pre_put operation returns busy, telling the coordinator to retry the operation later. If the peripheral does not exist the operation fails and the transaction is aborted. Some resources may be read (rd) and modified (put) in the same transaction. For instance when reading and modifying the state of a gpio in a transaction. In this case, the pre_put operation returns busy because the resource is locked. To prevent this, the lock is ignored if both actions are executed in the same transaction (identified with the transaction identifier). This behavior is different from [START_REF] Iris | Smart sensors and actuators: A question of discipline[END_REF], where a pre_put operation does not lock the peripheral and no notion of transaction identifier is kept. For the second phase of the transaction, the following operations are provided:

• commit: this operation is executed after a pre_put or a pre_get operation. In the first case, the operation (i.e. sending a value to a peripheral) is actually performed. In the second case, the desired resource is actually consumed (i.e. a local value in the node is consumed). • release: this operation is executed when one action in the transaction fails. The release operation unlocks previously locked resources or peripherals (to rollback the transaction). The release operation is also used after a pre_rd operation. Indeed the pre_rd does not remove the resource from the bag, it is used only to validate the presence of the resource. 3) WSAN in LINC rules: Resources in the nodes correspond to values of any peripheral of the node microcontroller (e.g. GPIOs, ADCs, Timers, or an I2C/SPI device), a memory or local variable value, or any external device connected to the microcontroller. An application controlling devices in WSAN then consists of several LINC rules. The transactional protocol and reliability guarantees are completely transparent to the application. The application developer does not care about communication or hardware errors, as they are handled by the transaction.

Listing 1, shows an example of rule which accesses two different WSAN devices (Arduino1 and OpenPicus1). In the example, line 1 reads the value of the variable1 declared in the OpenPicus1 device. Then, in the performance phase, line 4 determines if the value of variable1 is still valid and, if this is the case, line 5 puts the value in the spi_0 peripheral of the Arduino1. In the example, the action of sending a value through the spi_0 is executed only if all the operations in the transaction succeed. All the operations that verify that the action can be performed are hidden to the application; the application developer only has to write three lines of code.

B. Active replication

Active replication consists in replicating the control of the system. In LINC, the control is done with LINC rules. Hence, rules replication offers redundancy and fault tolerance in case one coordinator fails. Thanks to the transactions, the possibility of replicating a rule comes for free: it simply needs to be executed by several coordinators. LINC ensures that if several instances of a rule want to consume or access the same resource, only one rule succeeds. Indeed, the other instances will fail, since resources are consumed by the first rule to succeed.

LINC transactions are executed in a two-phase commit approach. If a coordinator fails outside of the execution of a transaction, nothing need to be done. Another coordinator will do the job. However, if a coordinator fails during the execution of a transaction, resources may remain locked, preventing other coordinators to execute properly. Two situations might block the system:

• The coordinator is in the first phase of the transaction: the coordinator has locked some resources in the nodes. These resources cannot be accessed by another coordinator. Thus, the locked resources must be released for the system to continue working.

• The coordinator is in the second phase of the transaction: the coordinator has locked all the resources, and it has confirmed (or released) some of the actions. In this case, the transaction must be finished.

The coordinator may be stopped or disconnected at any moment, so, it is not possible to guarantee that these two situations will not happen. To recover from these situations, coordinators can inform other coordinators about the rules they are executing. It is possible to inform only about a subset of rules (i.e. the most critical ones). Coordinators that communicate between them are called here Tandem Coordinators. Each coordinator informs its tandems when:

• it starts a transaction (i.e. the coordinator is in the first phase); • it finishes the first phase with the status of the first phase (i.e. success or failed) (i.e. the coordinator is in the second phase); • it finishes the second phase of a transaction (i.e. the coordinator has finished the transaction).

This information is exchanged by adding resources in a special bag of the tandem coordinator. When a resource is added in this bag, it triggers a rule in the tandem coordinator. This rule waits for a configurable time. This time is different regarding the type of application or system. After this configurable time, a transaction is triggered to read the same resource and finish the transaction of the first coordinator. Hence, if the first coordinator continued its transaction, the rule executed by the tandem fails (e.g. the resource phase_1_started has been replace by done). If the resource is still there, it means the first coordinator has failed. The transaction is thus finished by the tandem.

If the first coordinator fails in the first phase, the tandem has to release all the resources reserved by the transaction. To do this, the tandem asks all the bags involved to cancel locks associated to the transaction identifier of the failed transaction. If the first coordinator fails in the second phase, the tandem has to confirm (or release) all the operations according to the status of the first phase. The tandem can not know which operations have already been confirmed (or released). Thus it confirms (or releases) all of the operations.

A potential drawback of rules replication is that, as multiple coordinators try to communicate with the WSAN nodes simultaneously, contention, delays, and the probability of loosing information are increased. Thus, the communication reliability decreases as the number of coordinators increases. To overcome this channel contention problem, only one coordinator is kept active at a time. The other coordinators execute a (configurable) time-out mechanism to determine whether the active coordinator has failed. This time-out mechanism is implemented by a rule. First, the active coordinator periodically adds its time-stamp value in the time_out bag of its tandems. Adding this resource triggers a rule in the tandems, which waits for a configurable time. After this time passes, the rule checks if the same time-stamp resource still exists.

If it has changed, this means that the active coordinator is still alive, otherwise it means that the active coordinator has stopped working. In the case of failure of an active coordinator, a second coordinator is activated. The new active coordinator may finish transactions started by the faulty coordinator and it will continue to control the system.

Making a coordinator the active one, is done by the presence of a resource in a dedicated bag of the coordinator object. In the precondition phase of the rule, the presence of the resource is tested. If the resource is not there, the rule does not execute until the resource is added. Listing 2 shows how the resource is read, from the bag State of the actual rule is executed. This section presents a demonstrator of a ball and plate control problem implemented with LINC. Three motors are used to lift up and down a plate with a ball on top of it. The motors are moved step by step. To lift the plate, a command is sent to each motor, via a wireless link, to make it turn one step in the same direction. Due to delays in communication, it is not possible to guarantee that the three step movements are performed at the same time. Therefore, there is a small hole in the middle of the plate, to keep the ball in place when the motors do not move concurrently. This demonstrator can handle one step difference between the motors. However, with more than one step, the ball will fall from the plate.

This demonstration shows the ability of LINC to reliably control a system with unreliable devices and communication links. Despite no real-time guarantees are provided here, the consistency of the system is ensured.

Figure 1 shows the components and the architecture of the demonstrator. There are two Tandem coordinators, running on different devices for redundancy: a Beaglebone Black, and a RaspberryPi B. The coordinators execute the same rule that moves the three motors step-by-step, inside a transaction. Each motor is controlled by a wireless communicating device. The first motor is controlled by an ArduinoUno, which communicates through ZigBee. The other two motors are controlled by two Openpicus communicating through Wi-Fi. Both, Ar-duinoUno and Openpicus communicate using a predefined protocol stack (ZigBee and Http over Wi-Fi respectively). The LINC transactional protocol was implemented on top of these protocol stacks.

The coordinators can access the WSAN nodes using the special objects object_arduino and object_openpicus_n. The latter objects implement special Stubs to access nodes in the specific WSAN.

In addition, the demonstrator provides a lightweight interface used to monitor the state of the system. This interface is accessible from a web browser (e.g. from a tablet or a mobile phone). The interface application is also synchronized with the system. It is seen as another equipment and, if absent, the system stops working. This demonstrates that software and hardware can be safely coordinated in a wireless and distributed system. The case study supports the following errors:

• One coordinator failure (hardware, software or communication): If the faulty coordinator is active, the second coordinator will detect the error, and it will continue executing the rule that moves the motors. • One or several nodes communication failure: If a node becomes unreachable when used in a transaction, an error is raised to the coordinator by the stub. The coordinator will stop trying to execute this transaction for a configurable time and retry until it succeeds. The resources locked stay locked preventing any transactions to move the motors. When the node becomes available again (i.e. communication is working again), the transaction is finished by the coordinator. • One node hardware or software failure: If a hardware or software fault occurs in a node, the transactions will stop as for a communication failure. However, in this case, the node needs to be restarted. As soon as the faulty node is reconnected, its state is forced to the current motor state, which can be retrieved from the other two nodes. • User interface failure (hardware, software or communication): The user interface is provided by a LINC object. [ " I n t e r f a c e " , " s t e p s " ] . put( " s t e p s " , new step) ; } .

Listing 3: Rule to control the movement of the three motors Listing 3 shows the rule used to control the system. The precondition phase reads (line 1) the current step of one of the motors (i.e. the one connected to the Openpicus2). As the step values of all the devices are the same, reading the value of one of them is sufficient. According to the current step value, the new step value is computed (line 2). Then, the performance phase (after ::) sends the commands to move the three motors (line 5 to 7). In the example, NStep is a customized peripheral that keeps a variable with the number of performed steps and that moves the motor when this variable is modified. Finally, the interface is updated (line 8).

During the first phase of the performance of Listing 3, all the pre_put operations are executed to lock the NStep resource in each node. If the communication with one node fails, or the motor is not accessible, the transaction is rolled-back (all locked resources are released) and the position of the three motors does not change. If the first phase is successful, the second phase of the performance executes the commit operations and the actions are actually executed. If the first phase failed, the other operations are released and nothing happens. The put operations are sent in a sequential way. If the confirm operation of one of the motors does not work, the transaction will be blocked waiting for the device to be accessible. In this case it is possible that only one or two motors move, and the plate will not be completely horizontal. Therefore, the system must tolerate one step difference between the motors.

V. RELATED WORK

The increasing interest in wireless technologies for industrial applications has motivated the development of algorithms and solutions to support this type of applications [START_REF] Zhao | Wireless sensor networks for industrial process monitoring and control: A survey[END_REF]. These solutions, implemented at the network and lower layers, aim at improving communication reliability, security, and delay. The work in this paper proposes an application-level solution for reliability, that can be integrated with lower level solutions, to further improve the reliability and enforce the consistence of the system. Similarly, Feng et al. [START_REF] Xia | Wireless sensor/actuator network design for mobile control applications[END_REF] propose an applicationlevel solution that relies on packet loss knowledge to keep controlling the system even when data is lost. Nevertheless, given the distributed nature of WSANs, guaranteeing a consistent state of the global system in this case is challenging.

When performing rules replication, the problem of resource locking when a coordinator fails must be addressed. This problem has been tackled in database access applications where transactions are common use. For instance, three-phase commit protocols [START_REF] Skeen | Nonblocking commit protocols[END_REF] remove the blocking problem at the price of data exchange overhead and high complexity. For this reason, two-phase protocols are still preferred over three-phase protocols. Other solutions [START_REF] Lampson | A new presumed commit optimization for two phase commit[END_REF] propose the participants (in our case the WSAN nodes) to participate in the failure recovery procedure. Due to the highly constrained computational resources in WSANs, such approach is not a feasible solution for embedded devices. Manikandan et al. [START_REF] Manikandan | An efficient non blocking two phase commit protocol for distributed transactions[END_REF] propose the use of a backup coordinator and a connection manager. If the main coordinator fails, the connection manager detects the failure and transfers all transactions to the backup coordinator. However, if the connection manager fails, or it cannot access the faulty coordinator, the application will stop working. The use of Tandem coordinators do not require the intervention of a third-part entity; in this case, the coordinators decide in a distributed way if they should finish an ongoing transaction.

VI. CONCLUSIONS

This paper proposed a solution to build reliable control systems using Wireless Sensor-Actuator Networks (WSANs). The proposed approach provides reliability even under communication errors and hardware failures. The work relies on the coordination environment LINC. LINC provides distributed rule engines and transactional guarantees.

LINC primitives were embedded into the WSAN nodes, so that transactions can be performed when accessing sensors and actuators in the network. Besides, thanks to the use of transactions, LINC permits introducing replication of rules. An active replication mechanism was proposed to recover the system when there is a failure in one LINC component.

A case study of a ball and a plate is presented. The case study is composed of three step motors lifting up and down a plate with a ball. The motors are controlled by two types of embedded platforms: Openpicus and ArduinoUno. The first uses a WiFi communication and the latter Zigbee communication. The three motors are controlled by LINC components running in a Raspberry PI and a Beaglebone Black with active replication. The objective of keeping the ball on the plate is achieved even with communication and hardware failures. In addition a software user interface is also synchronized with the three motors. Thanks to the proposed approach, only 6 lines of application code have been written.

1 { 3 {Listing 1 :

 131 * , ! } [ " OpenPicus1" , " Sensors" ] . r d ( " v a r i a b l e 1 " , v a l u e ) & : : [ " OpenPicus1" , " Sensors" ] . r d( " v a r i a b l e 1 " , v a l u e ) ; [ " A rd u i n o1 " , " A c t u a t o r s " ] . put( " s p i 0 " , v a l u e ) ; } . Example of rule accessing WSAN devices

{ 4 . . . Listing 2 :

 42 * , ! } [ " C o o r d i n a t o r A " , " S t a t e " ] . r d ( " a c t i v e " ) & 2 . . . C o o r d i n a t i o n r u l e Test if a coordinator is active IV. CASE STUDY: THE BALL AND PLATE EXAMPLE

Fig. 1 :

 1 Fig. 1: Components of the demonstration

Fig. 2 : 4 { 6 [

 246 Fig. 2: Picture of the demonstration

8

 8

• Openstream: this operation opens a stream, associated with a coordinator, that matches a given pattern. For instance, an openstream with the pattern ("gpio_1", " * "), will open a stream associated to the current state of the gpio_1 of the microcontroller. • Read: after opening a stream, the coordinator can invoke the read operation, to actually retrieve the desired value (i.e. the state of the gpio_1). This read operation on a bag can be either blocking or non-blocking. In blocking mode, the coordinator is blocked when there is no matching resource in the bag (i.e. either no resource

ACKNOWLEDGMENT

This work has funded by the Artemis ARROWHEAD (grant 332987) and the H2020 TOPas project (grant 676760).


M Meftah

• A Hauchecorne

• A Irbah

• G Cessateur

• S Bekki

• L Damé

• D Bolsée

• N Pereira

Solar Spectral Irradiance at 782 nm as Measured by the SES Sensor Onboard PICARD

Keywords: Solar Irradiance, Solar Cycle, Instrumentation and Data Management, Instrumental Effects

Picard is a satellite dedicated to the simultaneous measurement of the total and solar spectral irradiance, the solar diameter, the solar shape, and to the Sun's interior probing by the helioseismology method. The satellite was launched on June 15, 2010, and pursued its acquisitions until March 2014. A Sun Ecartometry Sensor (SES) was developed to provide the stringent pointing requirements of the satellite. The SES sensor produced an image of the Sun at 782 ±2.5 nm. From the SES data, we obtained a new time series of the solar spectral irradiance at 782 nm from 2010 to 2014. During this period of solar cycle 24, the amplitude of the changes has been on the order of ±0.08 %, corresponding to a range of about 2×10 -3 W m -2 nm -1 . SES observations provided a qualitatively consistent evolution of the solar spectral irradiance variability at 782 nm. SES data show similar amplitude variations with Spectral And Total Irradiance REconstruction for the Satellite era (SATIRE-S) semi-empirical model whereas the Spectral Irradiance Monitor instrument (SIM) onboard the Solar Radiation and Climate Experiment satellite (SORCE) highlights higher amplitudes.

Introduction

The measurement of the solar spectral irradiance (SSI) is vital for understanding how solar variability impacts climate. In addition, it allows a validation of climate model sensitivity with a spectral variation of the solar forcing. The question of how the Earth's climate is influenced by solar variability remains central to the understanding of anthropogenic climate change. But today, while the total solar irradiance (TSI) and the smoothed sunspot number (SSN) retain paramount roles in feeding climate investigations toward the past, the SSI is considered as even more pertinent when the issue becomes the fine control of the Earth atmosphere dynamics. The spectral distribution of the Sun's radiation at a mean Earth-Sun distance of one astronomical unit is shown in Figure 1. These curves are obtained with data of the Spectral And Total Irradiance REconstruction for the Satellite era (SATIRE-S) semi-empirical model. SATIRE-S [START_REF] Yeo | Reconstruction of total and spectral solar irradiance from 1974 to 2013 based on KPVT, SoHO/MDI, and SDO/HMI observations[END_REF] is a semi-empirical model, which provides reconstructions of TSI and SSI spanning the entire period of satellite observation at daily cadence. SATIRE-S uses full-disc magnetograms and continuum images of the Sun to quantify the fractional disc area coverage by different surface components (quiet Sun, sunspot umbrae, sunspot penumbrae, faculae, and network) as well as their spatial distribution. Other semi-empirical models exist in the literature (COde for Solar Irradiance (COSI, [START_REF] Shapiro | NLTE solar irradiance modeling with the COSI code[END_REF]), Solar Radiation Physical Modelling (SRPM, [START_REF] Fontenla | Physical modeling of spectral irradiance variations[END_REF][START_REF] Fontenla | High-resolution solar spectral irradiance from extreme ultraviolet to far infrared[END_REF]), Naval Research Laboratory Solar Spectral Irradiance (NRLSSI, [START_REF] Lean | Evolution of the Sun's Spectral Irradiance Since the Maunder Minimum[END_REF]), Osservatorio Astronomico di Roma (OAR, (Ermolli et al., 2013)), etc.). Near-infrared (NIR) observations (700 -1400 nm) have been sporadic. The level of scientific understanding of NIR irradiance variations is poor [START_REF] Pagaran | Intercomparison of SCIAMACHY and SIM vis-IR irradiance over several solar rotational timescales[END_REF]. Derivation of reliable irradiance change estimates over an extended period like a solar cycle remains a challenging task. Thus, new observations are required to advance our understanding of the SSI variability in the NIR region. Indeed, NIR irradiance observations allow quantitative comparisons to TSI variability of continuum emission for the deepest layers of the photosphere. Moreover, it allows to clearly identifying the solar rotational modulation for a NIR wavelength. These new measurements may well be compared to different models as mentioned above. However, the SSI variability over the long-term (i.e. 11-year change) is highly uncertain because of the accumulated uncertainty on the instruments.

Several instruments have been developed to perform SSI measurements in the visible and near-infrared. Regular long-term observations of the visible and nearinfrared bands started in 1996 with Variability of solar IRradiance and Gravity Oscillations Sun PhotoMeter (VIRGO-SPM) onboard Solar and Heliospheric Observatory (SoHO), which monitor spectral irradiance at 402, 500, and 862 nm [START_REF] Fröhlich | VIRGO: Experiment for Helioseismology and Solar Irradiance Monitoring[END_REF]. Ermolli et al. (2013) gave an overview of the different space instruments devoted to these measurements:

• The SCanning Imaging Absorption spectroMeter for Atmospheric CHar-tographY (SCIAMACHY) onboard ENVIronment SATellite (ENVISAT) [START_REF] Bovensmann | Recent variability of the solar spectral irradiance and its impact on climate modelling[END_REF][START_REF] Pagaran | Solar Variability from 240 to 1750 nm in Terms of Faculae Brightening and Sunspot Darkening from SCIAMACHY[END_REF][START_REF] Pagaran | Intercomparison of SCIAMACHY and SIM vis-IR irradiance over several solar rotational timescales[END_REF], • The Spectral Irradiance Monitor (SIM) onboard the SOlar Radiation and Climate Experiment satellite (SORCE) [START_REF] Harder | The Spectral Irradiance Monitor: Scientific Requirements, Instrument Design, and Operation Modes[END_REF], • The SOLar SPECtrum (SOLSPEC) instrument onboard the International Space Station (ISS) [START_REF] Thuillier | SOLAR/SOLSPEC: Scientific Objectives, Instrument Performance and Its Absolute Calibration Using a Blackbody as Primary Standard Source[END_REF], • and the PREcision Monitor Sensor (PREMOS) onboard Picard [START_REF] Schmutz | The PREMOS/PICARD instrument calibration[END_REF].

Thus, a new instrument provides an evolution of the SSI over time. It is the Sun Ecartometry Sensor (SES), which is an instrument of the Solar Diameter Imager and Surface Mapper (SODISM) telescope onboard Picard [START_REF] Meftah | Picard SODISM, a Space Telescope to Study the Sun from the Middle Ultraviolet to the Near Infrared[END_REF]. The SES sensor (Figure 2) was developed along with a fine pointing mode to provide the stringent pointing requirements of the Picard satellite. Thus, the SES sensor data were used to provide an evolution over time of the SSI in the NIR region (5 nm bandwidth centred at 782 nm). Nowadays, the SORCE/SIM SSI data (Version 22, May 2015) are available. Moreover this instrument is still acquiring daily spectra from 240 nm to 2412 nm since April 2003. Thus, SIM SSI at 782 nm will be used to perform a direct comparison with the SES data. Like the SES sensor, the PREMOS and VIRGO photometers provide important information in the NIR region, and a comparative study would be valuable in a future work. Therefore, we will focus on SIM observations where there are conflicting trends with semi-empirical models such as SATIRE-S over the whole solar spectrum [START_REF] Yeo | Reconstruction of total and spectral solar irradiance from 1974 to 2013 based on KPVT, SoHO/MDI, and SDO/HMI observations[END_REF]. Thus, in our analysis, we will also make comparisons between the SES SSI evolution and the SATIRE-S SSI evolution at 782 nm. These comparisons are useful from the moment we know that space instrumentation suffers significantly from degradation, which is particularly severe for instruments devoted to SSI observations. Indeed, the degradation characteristics of all these filters instruments are very different in behaviour. In this article, we report on the SSI measurements of the SES sensor at 782 nm that correspond to preliminary results. These measurements were carried out between July 2010 and March 2014.

Description of the SES Sensor

The Picard satellite was successfully launched into a Sun synchronous orbit at an altitude of ∼735 km, allowing us to observe the Sun non-stop, except for short periods during a November-January 'eclipse season' when the Earth comes within SODISM-SES line-of-sight once per orbit. The satellite attitude and orbit control subsystem (AOCS) is continuously operated to maintain the solar rotation axis in a constant direction, i.e. ordinarily the solar poles are in a direction parallel to the vertical axis of SODISM images. With respect to the generic AOCS satellite, a dedicated fine pointing mode has been introduced in addition to the normal mode. This fine pointing is based on a SES sensor developed specifically for that purpose and installed within the payload (SODISM telescope). The 2-axes information of the SES are fused with the output of a 'classical' autonomous star tracker for providing a 3-axes attitude introduced in the control algorithms. The SES sensor is composed of two parts: an optical front part (FP) and an optoelectronic back part (BP) as shown in the Figure 3. The two parts of the SES sensor are directly mounted on the SODISM telescope in order to minimise the misalignments. The AOCS and the SES sensor achieved a pointing within ±36 arcsec with a maximum drift of 5 arcsec per second. The SES field of view is close to 2000 arcsec. The resolution of the SES is equal to 1 arcsec. A detailed description of the SES is given by [START_REF] Joannes | Solar Ecartometry Sensor with 1 arcsec Resolution for the Picard Satellite[END_REF]. The front part consists of a front window, which comprises a front filter (transmission of 14% at 782 nm) and a wide band interference filter (transmission ∼ 90%). All these filters are manufactured with BK7-G18 radiation resistant glass. The clear aperture of the system is equal to 13.3 mm, where the solar irradiance at 782 nm is close to 1.19 W m -2 nm -1 at the SES pupil. The back part includes the lenses that are in BK7-G18 and SF6-G05 glasses (transmission ∼ 90%), the narrow band interference filter at 782 ±2.5 nm (transmission ∼ 75% and rejection ∼ 10 -4 ) in BK7-G18 glass, the detector (quad-cell silicon photodiode API SD-085-23-21-021 with a responsivity of ∼ 0.55 A W -1 at 782 nm), and the electronic unit. The doublet lens produces an image of the Sun on the detector. Considering the Sun half field angle of 960 arcsec, the focal length of the doublet lens (107.6 mm) is such that the image diameter is close to 1 mm. The filtered solar image is projected on the four quadrants detector (quad-cell silicon photodiode with a sensitive area of 4×1.5 mm×1.5 mm) of the electronic back part. Each cell of the detector provides a voltage output proportional to the received light intensity. All the optical elements in the path attenuate the solar radiation to a power density of 7.5 mW cm -2 into the spot of 1 mm diameter, meaning a total power of 58.9 µW. This produces a photocurrent of 31.45 µA, i.e. 7.86 µA per quadrant for a centred spot. In our application, we will use the four signals (mean value) of the detector to obtain the evolution of the SES over time (SES signal evolution) with a time acquisition of 30 seconds. The absolute calibration of the SES sensor has not been performed with a blackbody. Thus, the stated solar spectral irradiance at 782 nm is taken from the ATLAS 3 value [START_REF] Thuillier | The Solar Spectral Irradiance from 200 to 2400 nm as Measured by the SOLSPEC Spectrometer from the Atlas and Eureca Missions[END_REF] without a further ground-based validation.

Observational Results and Corrections

SES Signal and Temperatures Observational Results

The SES sensor was exposed to sunlight for the first time on July 20, 2010. The integrated intensity in the SES signal at 782 nm has been computed during the entire mission of the Picard satellite and its temporal evolution is displayed in Figure 4 (left). The black curve (SES signal) corresponds to this evolution and gives an indication of flight operations (payload switch off, stellar pointing, satellite roll, etc.). The cyan curve (Seq. 1) corresponds to the first measurement sequence (July 2010 to January 2011). The Picard satellite entered safe SOLA: 2016_3_07_SSI_PICARD.tex; 8 March 2016; 0:40; p. 5 mode on January 9, 2011 due to an anomaly with a stellar sensor. All nonessential systems such as the SES sensor were shut down. This date corresponds to the end of the sequence 1. The blue curve (Seq. 2) corresponds to the second measurement sequence (January 24, 2011 to October 2013), which is associated with a nominal operation. From October 26, 2013, the Picard satellite pursued acquisitions to the end of March 2014, during technological operations of the satellite (closing and opening of the SODISM telescope door, satellite rotations, etc.). The magenta curve corresponds to the third sequence of measurements (Seq. 3). The technological operations of the satellite during this sequence affect the measurements made by the SES sensor and its temperatures (Figure 4, right). The SES back part (BP) temperature is slightly affected, unlike the front part (FP) temperature of the sensor. The SES FP temperature has significantly varied during the mission. Fluctuations result from a large number of factors. The thermal effects caused by these factors have a range in duration from hours to a long-term of months or years. There are short-term factors (SES switch OFF/ON, orbital variation, technological operations of the satellite). There are also intermediate term factors (Earth eclipses, mean distance to the Sun). During Earth eclipse seasons (between November and February), the overall temperature of the SES is reduced. We can see that when the SES temperatures change, the SES sensor performance is affected (Figure 4, left).

Orbital Variation of the SES Signal

The Picard satellite was placed in a ∼735 km Sun synchronous orbit (06h00-18h00) with inclination of 98.28 degrees. The selection of a Sun synchronous orbit with a local time of ascending node of 06h00 permits to observe the Sun as long as possible and to limit the eclipses. The Picard satellite dedicated to study the Sun takes advantage of Sun synchronous orbit near polar inclination. The period of the satellite is close to 99.4 mn. During an orbit, the whole platform is further subject to thermal inputs from the Sun and the Earth. They vary along the orbit because the platform maintains a fixed attitude with respect to the Sun and therefore is not fixed with respect to the Earth, and because the terrestrial surface is not homogeneous (lands and seas, etc.). Thus, there is a variation of the outgoing long-wave radiation (infrared emission) and of the reflected shortwave radiation (albedo) of the Earth during an orbit. It has consequences on the temperature stability of the SES sensor. Therefore, we have to face orbital effects that affect the radiometric signal of the SES sensor. The black curve (SES signal) as shown in the Figure 5 (left) corresponds to this effect. During an orbit, the SES sensor could be sensitive to solar variations of few minutes due to solar convection and oscillations. However, it is especially sensitive to the indirect effects of the outgoing long-wave radiation and the reflected short-wave radiation variations that affect the temperature of the SODISM telescope structure. At 782 nm, red enhanced quad-cell silicon photodiode have a small but significant temperature coefficient of radiant sensitivity of about the magnitude of the excursions seen in the Figure 5 (left). However, there is no adequate temperature housekeeping (HK) telemetry near the SES quad-cell silicon photodiode, where a correction could have been built with the manufacturer's temperature coefficients of the SES detector. Thus, by making daily average or 99.4 mn period of the SES sensor signal, we avoid this problem.

Dark Current of the SES Signal

The dark current level of the SES sensor is obtained during space calibration (stellar calibration, Earth eclipse seasons, closing and opening of the SODISM door) where the sensor was not exposed to sunlight. The dark current level of the SES sensor is displayed in Figure 5 (right). The corrected signal of the SES sensor in order to obtain the SSI at 782 nm should consider this effect.

Transit of Venus and Sensitivity of the SES

Figure 6 (left) displays the high cadence solar spectral irradiance at 782 nm as measured by the SES sensor, where a decrease of about 1000 parts per million (ppm) is caused by the Venus transit, which has been observed with the Picard satellite on June 5-6, 2012. The Venus transit was therefore unmistakable in the plotted SES measurements. A complete revolution of the satellite around the Earth disturbs the SES sensor measurements. This revolution generates a variation of the SES measurements with a period of 99.4 mn that is clearly visible in the Figure 6 (left). A sine fit is calculated to correct the signal from this disturbance. The corrected signal of the SES sensor during the transit of Venus is shown in Figure 6 (right). In good agreement with predictions based on the positions of the Picard satellite, Venus, and the Sun, the incident sunlight as seen by the SES sensor at 782 nm decreased by 970 ±40 ppm during the transit. The SES sensor has a precision of approximately 40 ppm, so it can detect very small fluctuations in the solar spectral irradiance at 782 nm as needed for monitoring long-term SSI variability. By making daily average or 99.4 mn period of the SES sensor measurements, the precision is even better. With this extremely good level of sensitivity, normal solar fluctuations of a few dozen ppm due to solar convection and oscillations are readily apparent, as are larger (∼ 1000 ppm), longer-duration changes due to sunspots and faculae. The SES sensor took measurements every 30 seconds. (right) Sine fit corrected signal of the SES sensor during the transit of Venus to take into account the orbital variation.

Ageing Correction of the SES Measurements

The SES sensor was continuously exposed to the Sun except during short periods (closing and opening of the SODISM telescope door). Under the influence of the harsh space environment, the general behaviour of the SES signal was not nominal (see SES sequences evolution). Thus, a degradation of the SES sensor was observed (Figure 4, left). Investigation and analysis of the degradation of space instruments are crucial parts of achieving the scientific goals of all such instruments. The degradation of space instruments can be complex and their causes are difficult to understand. In our case, the SES sensor degradation could be the result of the combination of several independent degradation processes. We can cite at least different processes of degradation. The degradation seen by the SES sensor can be due to the build-up of a contamination layer on the front filter of the SES FP (transmission decrease observed). In addition, there may be a deviation of internal transmittance of the SES front filter due to irradiation (gamma, electron, proton, and neutrons). There may also be an evolution of the temperature gradient of the SES FP, which causes a change in the focal length of the SES such as the SODISM front window [START_REF] Meftah | On the Constancy of the Diameter of the Sun during the Rising Phase of Solar Cycle 24[END_REF]. Moreover, the transmission of the narrow band interference filter may have also changed with the evolution of the SES FP temperature (wavelength shift). Finally, the quadcell silicon photodiode may have suffered of a drop in gain owing to the exposure of radiation. The first measurement sequence (July 2010 to January 2011) of the SES sensor corresponds to a period of low solar activity that highlights a fast process of degradation. Indeed, the SES sensor showed steady degradation of the first-order signal over the first six months of operation as shown in the Figure 4 (left). Then, the degradation processes were decreased during the second measurement sequence to become very low. The degradation process (contamination) in nature may have exponential dependencies, where the filters and the lenses used in the SES probably suffered a loss in the signal that varies exponentially according to the thickness and characteristics of the contaminant. Thus, an exponential fit corresponds to the evolution of the SES sensor measurements. This exponential fit is used to correct the SES data. Therefore, the non-solar features should be extracted from the SES sensor data. Ideally, the use of a redundant SES sensor would have provided an episodic measurement in order to correct the degradation of the operational SES sensor. However, the SES sensor was not originally designed to achieve these measurements. Nevertheless, the data obtained with the SES sensor are relevant.

Discussion about the Potential Mechanisms of the SES Sensor Degradation

We have listed a number of potential mechanisms for degradation of the SES sensor, but there is no quantitative analysis to determine the magnitude, slope, and relative contributions from the different mechanisms listed in the previous section. All the mechanisms discussed in the Section 3.5 are not necessarily exponential in character and evolve with different time constants. In particular, narrow band interference filters do not degrade as simple exponential functions and their characteristics vary widely. The curves shown in Figure 4 (left) are probably not consistent with only a build-up of organic material with an increasing opacity as discussed in this section. As further example, the red enhanced quad-cell silicon photodiode used in this sensor is a p-n photodiode that experiences about 10-100 times faster degradation for electron bombardment and roughly a factor of three faster for protons than its n-p counterpart. The shape of the curves in Figure 4 (left) are suggestive a different sort of degradation process in the latter part of the mission. For there to be meaningful comparison of the long-term trends, a viable measurement equation for the degradation process and a time dependent evaluation of the terms in this equation (with their associated errors) would be necessary. Nevertheless, this process is not possible for this sensor because there is no on-board standard to identify long-term contributions (i.e. a lamp system or a comparison radiometer) and no pre-flight calibration to identify critical instrument performance parameters. Therefore, we fit the time series and remove it as longer term trend.

SES SSI Time Series, Results, and Discussion

The SES sensor has made continuous measurements of SSI at 782 nm from July 2010 to March 2014. These measurements are interesting compared to SSI existing data. Indeed, new observations may help to resolve some of the differences in existing long-term time series of measurements. Thus, comparisons were carried out between the Level 1 SES data at 782 nm and SIM measurements at 781.3 nm. Moreover, a comparison between measurements and SATIRE-S semi-empirical model was performed. In the near-infrared, different models give similar results. Thus, SATIRE-S was taken as a reference model. Figure 7 solar spectral irradiance as seen by the SES sensor, SIM, and SATIRE-S model from July 2010 to March 2014. There are conflicting trends between SIM observations and SES results, whereas the SATIRE-S semi-empirical model and the SES results show similar evolutions. As a reminder, claimed SIM results on solar cycle variability [START_REF] Harder | Trends in solar spectral irradiance variability in the visible and infrared[END_REF] indicate out-of-phase variations for visible and near-infrared variations. SIM out-of-phase variations for near-infrared variations are in debate as they do not agree with prior measurements and most solar irradiance models. Figure 7 (right) displays the SSI differences between SATIRE-S, SIM, and SES results. The conflict between SES and SIM results is clearly visible for the SSI variability over the long-term. During the entire mission of Picard, the correlation coefficient (R 2 ) between SES and SATIRE-S SSI variations is close to 0.75. There is a good correlation between these two sets of data based on the Pearson definition. The correlation coefficient between SES and SIM observations is less good (Table 1). The excellent correlation between the SATIRE-S empirical model and the TSI raises question. Indeed, the correlation coefficient between the SES sensor and the the TSI observations is close to 0.74 very near to SIM/TSI correlation. All correlation coefficients are given in the Table 1. For a better comparison of the results, a spectral analysis for each set of data was carried out. For space measurements, it is common to have incomplete or unevenly sampled time series for a given variable. Determining cycles in such series is not directly possible with methods such as Fast Fourier Transform (FFT) and may require some degree of interpolation to fill the gaps. An alternative is the Lomb-Scargle method [START_REF] Lomb | Least-squares frequency analysis of unequally spaced data[END_REF][START_REF] Scargle | Studies in astronomical time series analysis. II -Statistical aspects of spectral analysis of unevenly spaced data[END_REF], which estimates a frequency spectrum based on a least squares fit of sinusoid. Figure 8 displays the Lomb-Scargle periodograms of three time-series (daily mean), which show the link between the SATIRE-S semi-empirical model, the SES sensor, and SIM. SIM data show no periodicities with statistical significance level over 99% (Figure 8). On short-term rotational basis, the SES data appears to be just fine but in a comparison with SIM there is no way to ascertain that the long-term trends that are in the SIM data are not plausible. For proper comparison with SIM, such as done in the Lomb-Scargle comparison the slowly varying trend contributions in SIM increase the variance in the data and thus decrease the power spectral density (PSD) relative to the false alarm probability (fap) or statistical significance level. A simple way to show this is to high pass filter on SIM data and then compare the PSD as a function of the period as done in Figure 8. Figure 9 (top) displays the Lomb-Scargle periodograms of the SIM data high pass filtered. We see that this data set surpasses the fap estimate at the periodicities called out in Figure 8. Finally, we highlight that SIM has not made continuous measurements from July 2010 to March 2014. Indeed, all SORCE instruments ceased operations on July 30, 2013 due to an anomaly of the satellite (battery degradation). The SIM instrument was again operational on December 22, 2013. Thus, the discontinuity in the SIM data may influence the results of the spectral analysis especially for long periods. So, the comparison between the SATIRE-S semi-empirical model, the SES sensor, and the corrected SIM data is conceivable. Comparison of short-term variations show a rather good similitude between these three time-series (Figure 8 and Figure 9). Similar solar periodicities were highlighted (14.6, 20.3, 28.3, and ∼ 32 days). SES data show a significant solar periodicity around 59.5 days, which is also observed with SODISM solar radius data [START_REF] Meftah | On the Constancy of the Diameter of the Sun during the Rising Phase of Solar Cycle 24[END_REF] and sunspots number data (Figure 9 (bottom)). The 59.5-day component that appears in the SES data are not present in SIM or SATIRE-S with statistical significance levels. It's unlikely that this suggests an uncorrected SES instrumental artefact. Indeed, this is probably an actual solar response, since it is also observed in the sunspots number. As the periodicities in sunspot and SES time series vary strongly with time, we use the wavelet transform coherence (WTC) to follow the temporal evolution of the coherence. WTC is a method to estimate the mean squared coherence and phase lag between time series as a function of both time and period/frequency. Figure 10 shows the WTC results between sunspot and SES. The coherence field looks patchy, partly because of the intermittent character of the periodicities in the time series and of the relatively small temporal interval considered here (corresponding to the temporal resolution of the WTC), resulting in large uncertainties in the spectral analysis. The most outstanding feature is an area of strong significative coherence centred around 59.5 days but extending vertically to about 45 and 80 days, and lasting about 7 months (from the end of 2011 to middle of 2012). Smaller patches of strong coherence can also be found over the interval of the 16 to 35 days period, most likely related to the solar rotational periodicity.

Finally, the SATIRE-S semi-empirical model and the SIM data highlight a significant period around 70 days that is not found significant with the SES data as with sunspots number data. It might be worth checking the PREMOS 782 nm feature for confirmation.

Thus, there may be existing differences in these time series at 782 nm that highlight the importance of having a large number of measurements to improve the models. Indeed, the time history of SSI variability in the near-infrared part of the spectral distribution of solar irradiance is not nearly reliable. Figure 11 (top) displays daily time series of the solar spectral irradiance at 782 nm as seen by the SATIRE-S semi-empirical model and the SES sensor. The median standard deviation (σ = 1) of the SES SSI daily time series after the piecewise exponential fit was removed is close to ±2.5×10 -5 W m -2 nm -1 (∼ ±20 ppm). SES SSI standard deviation (Figure 12) is greatest during 'eclipse season' where a temperature effect perturbs the measurements. Indeed, the temperature response of the SES quad-cell silicon photodiode may be greatly varying during this typical period of high temperature variations. Our observations require further studies to conclude. Finally, the spectral irradiance of the SES is close to 1.19 W m -2 nm -1

Conclusion

Measurements of SSI are scattered and inhomogeneous. Instrument stability and measurement continuity are needed for discerning long-term solar variability. Indeed, possible degradation trends for space missions exist and introduce errors in SSI measurements. Known artefacts remain in some reported measurements. Thus, new observations are required to advance our understanding of the solar spectral irradiance variability that is predominantly caused by magnetic features on the solar surface. The SES sensor of the Picard mission provides a new time series of the solar spectral irradiance at 782 nm from 2010 to 2014. During this period, the amplitude of the SSI changes has been on the order of ±0.08 %, corresponding to a range of about 2×10 -3 W m -2 nm -1 . The median uncertainty of the SSI time series is close to ±2.5×10 -5 W m -2 nm -1 . The SES trends diverge significantly from those shown by SORCE/SIM while they are compatible with the SATIRE-S semi-empirical model. The finding of too strong SORCE/SIM trends is consistent with the conclusion of a recent study that the ozone response to solar variability simulated using the SORCE spectral solar irradiance data was inconsistent with the ozone observations [START_REF] Ball | High solar cycle spectral variations inconsistent with stratospheric ozone observations[END_REF].

Similar solar periodicities between the SES and SATIRE-S were highlighted (14.6, 20.3, 28.3, and ∼ 32 days). SES data show a significant solar periodicity around 59.5 days, which is also observed with SODISM solar radius data and sunspots number data. This period is not found to be significant with the SATIRE-S semi-empirical model and with the SIM data. In contrast, they highlight a significant period around 70 days that is not found to be significant in both the SES and sunspots number data. Thus, it exists differences in these time series that highlight the importance of having a large number of measurements to improve the models.

Figure 1 .

 1 Figure 1. (top) Spectral distribution of solar irradiance obtained with SATIRE-S above the Earth's atmosphere(July 26, 2010 and March 6, 2014). The range shown (115.5 -2500 nm) includes 96.6% of the total solar irradiance with most of the remaining at longer wavelengths. (bottom) SSI difference obtained between July 2010 (beginning of the SES acquisition) and March 2014 (Picard end of life). This paper is highly specific and covers a single wavelength at 782 nm where the irradiance difference between July 2010 SSI and March 2014 SSI is close to 0.05% (500 ppm).

Figure 2 .

 2 Figure 2. Artist's view of the Picard satellite and SES location that highlights the front part of the sensor with its front filter.

Figure 3 .

 3 Figure 3. SES sensor optical path consists of a front window that comprises a front filter and a wide band interference filter, a doublet lens, a narrow band interference filter, and quad-cell silicon photodiode. There is a front part (FP) and a back part (BP).

Figure 4 .

 4 Figure 4. (left) SES signal evolution over time (black curve) obtained with the mean of the four signals of the detector. The cyan curve corresponds to the first sequence of measurements (Seq. 1). The blue curve corresponds to the second sequence of measurements (Seq. 2). The magenta curve corresponds to the third sequence of measurements (Seq. 3). (right) Temperature evolution of the SES FP (blue curve) and of the SES BP (red curve).

Figure 5 .

 5 Figure 5. (left) SES signal evolution within four orbits (black curve), which highlights an orbital signature in the data linked with the temperature variation. The blue curve corresponds to the sine fit of the signal (short-term instrumental variations). (right) The red curve corresponds to the dark current level of the SES signal during space calibrations (long-term instrumental trend).

Figure 6 .

 6 Figure 6. (left) The 5-6 June 2012 Venus transit as measured with the SES sensor at 782 nm. The SES sensor took measurements every 30 seconds. (right) Sine fit corrected signal of the SES sensor during the transit of Venus to take into account the orbital variation.

Figure 7 .

 7 Figure 7. (left) Relative solar spectral irradiance change as observed by the SES sensor (black curve) and SIM (blue curve). The red curve corresponds to the solar spectral irradiance at 782 nm obtained with SATIRE-S model. (right) Differences between SATIRE-S, SIM, and SES results. SIM has not made continuous measurements from July 2010 to March 2014. Indeed, the SORCE satellite entered safe mode during the year 2013 due to an anomaly.

Figure 8 .

 8 Figure 8. Lomb-Scargle periodogram of three time-series (SATIRE-S, SES, and SIM daily mean) with statistical significance levels (0.01 corresponds to 99% significance level).

PeriodFigure 9 .

 9 Figure 9. Lomb-Scargle periodogram of two time-series (SIM daily mean corrected data and sunspots number data) with statistical significance levels. Sunspot data source: WDC-SILSO, Royal Observatory of Belgium, Brussels.

Figure 10 .

 10 Figure 10. Wavelet Transform Coherence (WTC) between sunspot and SES. Solid contour lines represent the 95% confidence level. Arrows represent the relative phase between sunspot and SES time series. The cone-of-influences is represented by the pale areas on the vertical edges of the Figure.

Figure 12 .

 12 Figure 12. Evolution of the SES SSI standard deviation over time.

  (left) displays the

		1500
			SATIRE-S (782 nm)
			SES (782 nm)
			SIM (781.3 nm)
		1000
	Relative SSI change [ppm]	0 500				Difference [ppm]
		-500
		-1000	01/01/11	01/01/12	01/01/13	01/01/14	01/01/11	01/01/12	01/01/13	01/01/14

Table 1 .

 1 Pearson's linear correlation coefficient (R 2 ) between the SES sensor, SATIRE-S, SIM, and the TSI.

	SES/SATIRE-S	SES/SIM	SES/TSI
	0.75	0.52	0.74
	SATIRE-S/SIM SATIRE-S/TSI SIM/TSI
	0.62	0.98	0.70

  Figure 11. (top) SSI variability time series of the SES sensor at 782 nm and of the SATIRE-S model at 782 nm. (bottom) Differences between SATIRE-S and SES SSI variability at 782 nm.There may be up to 500-ppm difference in the solar spectral irradiance level between SES and SATIRE-S.(782 nm) at the beginning of the mission (see Section 2). SATIRE-S and SES time series highlights a good agreement. Differences between SATIRE-S and SES SSI variability at 782 nm are lower than few hundred ppm (Figure11, bottom). The SATIRE-S trends diverge very weakly from those shown by the SES sensor. The slope of the difference between SATIRE-S and SES SSI variability is close to 6 ppm per year. This confirms the good agreement between SES measurements and SATIRE-S model.

				2	×10 -4
						SES (782 nm)
				1.8
			]
			.nm -1	1.6
			SSI standard deviation [W.m -2	0.4 0.6 0.8 1 1.2 1.4
				0.2
				0	01/01/11	01/01/12	01/01/13	01/01/14
			1.192
		1.1915	SATIRE-S (782 nm) SES (782 nm)
	]		1.191
	.nm -1	1.1905
	SSI [W.m -2	1.1895 1.19 1.189	1000 ppm
		1.1885
			1.188	01/01/11	01/01/12	01/01/13	01/01/14
	(SATIRE-S -SES) [ppm]	-400 0 200 400 600 -200		Slope=5.8 ppm/yr
			-600	01/01/11	01/01/12	01/01/13	01/01/14

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Acknowledgements Picard is a mission supported by the French national centre for scientific research (CNRS/INSU), by the French space agency (CNES), by the French Atomic Energy and Alternative Energies Commission (CEA), by the Belgian Space Policy (BELSPO), by the Swiss Space Office (SSO), and by the European Space Agency (ESA). The authors would like to thank the Laboratory for Atmospheric and Space Physics (LASP, United States) for providing the SORCE/SIM data and the Max Planck Institute for Solar System Research (Germany) for providing the SATIRE-S data. In addition, we wish to thank the CNES project managers (François Buisson and Michel Rouzé) of the Picard mission. Finally, the authors thank the anonymous referee for the very good improvements, remarks, and suggestions.


Mickaël Royer

Alain Pirovano

Fabien Garcia

Content Based Publish / Subscribe systems for AANETs

AANET (Aeronautical Ad hoc NETworks) are a subclass of Mobile Ad-Hoc Networks (MANET). They are a very promising solution in order to answer to new air-ground communication needs. The Publish / Subscribe communication paradigm enjoys wide applicability in AANET where resources are limited. Many Publish / Subscribe systems exist for wired networks, however, these solutions are not fully adapted to mobile networks like AANET. In this article, we present the interest of a Content Based Publish / Subscribe system in a AANET. We argue that some extensions are needed to fulfill the need of aeronautical applications interested by this service. Then we present some existing solutions proposed for MANET and explain why they are not suitable for AANET. Finally, we propose a new P/S system adapted to AANET, based on an overlay built thanks to a 1-hop clustering algorithm.

INTRODUCTION

Publish/Subscribe scheme is a communication paradigm which differs in many ways from classical communication schemes where addresses are used to identify the recipients. These addresses can target a single receiver (unicast communication), multiple receivers (multicast communication) or all the users (broadcast communication). On the contrary, Publish/Subscribe systems allow event distribution from publisher (event producer) to subscriber (event consumer) without the use of any explicit address. Instead, the event distribution is based on declared subscribers interests. Those interests can be expressed in different ways depending on the subscription model used.

In the topic based subscription model, clients subscribe to topics characterized by a name. Events are then published on topics and the P/S system is in charge of the forwarding of events to clients who sub-scribed to this topic. The topic based subscription is quite simple but it's also limited since we can only filter events from a single attribute, i.e.: the topic name.

On the other side, in the content based subscription model, clients base their subscriptions on the content of events. So, when an event is published, the P/S system has to forward it to the clients whose subscriptions match the event content. The filter syntax "attribute -operator -pattern" can be used to define this subscription as described in [START_REF] Aguilera | Matching events in a content-based subscription system[END_REF].

For example, persons holding stock of a company are generally interested in the evolution of its price. With to a topic based P/S system, we can easily think of a solution where these clients subscribe to a 'stock topic' and receive notifications each time the price of the stock has changed. With the use of a content based P/S system, we can be much more efficient in filtering events. For example, a subscription can allow to receive notification only when price pass through a threshold.

P/S systems offer many advantages over traditional communication. In particular, they offer communications decoupled in space (subscribers do not need to know publishers and vice-versa) and potentially in time if the P/S system is able to store events for clients which are temporally disconnected (as in delay tolerant networks [START_REF] Zhang | Routing in intermittently connected mobile ad hoc networks and delay tolerant networks: overview and challenges[END_REF]).

In the past decade, many solutions (see for example [START_REF] Antony | SCRIBE: The Design of a Large-Scale Event Notification Infrastructure[END_REF] and [START_REF] Pietzuch | Hermes: a distributed event-based middleware architecture[END_REF]) have been proposed by the research community and industrials to build P/S system over wired networks. However, as emphasized in [START_REF] Farooq | Performance of publish/subscribe middleware in mobile wireless networks[END_REF], those solutions are not adapted to the emerging category of networks called Mobile Ad hoc Networks (MANET) which are self-configuring networks of mobile nodes connected by wireless links without a fixed infrastructure. However, this paradigm should profit to AANET (Aeronautical Ad hoc NETworks, which consist in aircraft acting as node), which is a subclass of MANET. Indeed, in a context of increasing air traffic and new needs in term of air ground communication systems, AANET represents a relevant solution as explained in [START_REF] Besse | Wireless Ad Hoc Networks Access For Aeronautical Communications[END_REF] and content-based information dissemination enjoys wide applicability in this kind of networks where resources (like bandwidth) are limited. The aim of this article is to propose a new approach to build a P/S system over AANET.

This paper is organized as follow, in the next section we present possible applications of P/S systems over AANET. Then, we use those applications to explain the expected features of P/S systems in order to be adapted to an AANET context. After, we describe existing solutions of P/S systems proposed for MANET with these features and explain why they are not suitable to our purpose and constraints. Based on this analysis, the chapter 5 details our proposition to build a P/S system adapted to AANET. Finally, the last section concludes this article and introduces further work on this topic.

APPLICATION EXAMPLES IN AANET

To better understand the interest of P/S systems in an AANET context, this section introduces some examples of aeronautical applications which will be more efficient with the use of a P/S service.

Weather situation update

For safety reasons, weather information are crucial during a flight. Some phenomena like storm or wind shear are very dangerous for aircraft and so must be transmitted from ground weather stations or from an aircraft discovering a new situation to all aircraft that might encounter it. Today, most of these communications, in particular in oceanic area, use satellite links which are more expensive than classical air-group communication infrastructure like VDL (VHF Data Link) or HFDL (HF Data Link) network, as illustrated in [START_REF] Gallagher | Communication system architecture for air traffic management and weather information dissemination[END_REF].

A new solution for those communications can be found with the use of a P/S system over an AANET composed of commercial aircraft and ground stations. In this case, publishers would be weather stations or aircraft which have knowledge about dangerous weather situations and subscribers would be aircraft whose trajectory will get close to the concerned geographical area during the period of the weather phenomena. In order to cover this communication need, events must contain at least the following attributes: type of phenomenon, geographic area of weather phenomena and lifetime (since the weather informations are necessarily associated to a time period).

Similarly, in order to subscribe an aircraft need to know its geographical position (for example with a GPS receiver) and, if possible, its short term trajectory. Indeed, an aircraft will be only interested in weather information near its actual or short term position. A sub-Figure 1: Example of an aircraft sending a message about a dangerous storm scription will consequently follow the following principle : "I want to receive all weather informations regarding the geographical area <position> which will happen in the time period <time> (deduced from current position and trajectory)."

Consequently, as illustrated in figure 1, an event will be disseminated to subscribers located near the geographical area of the event, or converging to that point in the near future.

Message to all aircraft of an airline

Another important class of communication in civil aviation consists in the communication between airlines operational center and their aircraft (this type of communication is called Airline Operational Communication or AOC). During a flight, many information must be exchanged between these two actors (e.g.: maintenance information, crew communications, etc.). Nowadays, this kind of communication mainly uses ACARS (Aircraft Communication Addressing and Reporting System). ACARS is a network deployed in the 80s by airlines to allow data communication with their aircraft. Depending on the aircraft position, it can use VDL (VHF Data Link) network or SATCOM means. Although robust, this network has very limited resources (bandwidth in the amount of tens of Kbits/s, [START_REF] Kitaori | A performance comparison between VDL mode 2 and VHF ACARS by protocol simulator[END_REF]). As in the previous example, a new solution can be proposed with the use of a P/S system over an AANET (which offers better performance than ACARS, as explained in [START_REF] Besse | Wireless Ad Hoc Networks Access For Aeronautical Communications[END_REF]) composed of aircraft and airline operational center. In this example, as illustrated in figure 2, publishers will be airline operational center and subscribers will be aircraft. To be properly filtered, events and subscriptions must contain at least the airline name as attribute. Furthermore, the advantage of a P/S system over a classical multicast communication would be in the filtering capabilities it offers. Indeed, more focused communications can easily be achieved with events being transmitted only to a specific type of aircraft or to aircraft bound for a given airport.

Wind networking

In addition to the knowledge of dangerous phenomena, other weather information is useful for aircraft during flight. This is the case in particular of the wind which has an influence on the speed of the aircraft and hence its travel time. Now, as part of major research programs SESAR, NextGen and CARAT which aims to improve the efficiency and safety of air traffic in the world, a new concept emerged: 4D trajectory (adding the dimension time for 3-dimensional space). The main idea is to be able to use these 4D trajectories to provide air control in the near future. For a given flight, the trajectory would be negotiated between the air traffic controller and the pilot. It would become mandatory to ensure separation from other aircraft. Currently the weather data used by an aircraft in flight to calculate the time of arrival at the crossing points, and therefore the 4D trajectory are derived from forecasts provided by the weather service before departure. On long-range flights, these data can therefore be older than 12 hours, which results in significant differences between actual and forecast encountered winds and then errors in the calculation of the 4D trajectory. To solve this problem, studies are being conducted on the concept of "Wind Networking" [START_REF] Legrand | Wind networking applied to aircraft trajectory prediction[END_REF].

The principle, illustrated in Figure 3, uses the fact that aircraft are capable of measuring the surrounding weather data such as wind. Based on this assumption, each flight announces its measured wind, allowing everyone to create a "real" wind map and so update the data used to calculate its 4D trajectory. In order to implement this promising concept, the use of a P/S service over an AANET seems appropriate. Indeed, each aircraft at regular intervals, sends an event with its geographical location as context and the measured wind value as content. On the other side, the aircraft subscribe to content on their short-term trajectory, the P/S system then linking the events and the interested

EXPECTED FEATURES OF P/S SYS-TEMS FOR AANET

The examples of the previous section show that traditional P/S system can not provide an optimal answer to the AANET content-based communication research question. In the first example, with a classical P/S solution, planes can subscribe to 'dangerous weather situation'. However, this subscription will be too large and will lead to the reception of useless events since planes are interested specifically by 'dangerous weather situation close to their position or route '. To accommodate this kind of subscription, a new concept is needed: the node context. Furthermore, one of the AANET characteristics is the high probability of network partitioning leading to frequent disconnections. A consequence of these disconnections is that some events (potentially critical) will not be received. To avoid this situation, a P/S system designed for AANET needs to allow the persistence of events in the network. These two features are detailed in this section.

Node context

A node context is a list of attributes which complements subscriptions, and is used to more precisely filter received events. This better filtering allows a reduction in the use of network resources which are scarce in AANET. Several definitions exist for node context. Simple definitions reduce context to the node location ("location context", see [START_REF] Eugster | Pervaho: A specialized middleware for mobile context-aware applications[END_REF], [START_REF] Meier | STEAM: event-based middleware for wireless ad hoc networks[END_REF] or [START_REF] Holzer | ALPS -Adaptive Location-based Publish/Subscribe[END_REF]). Some other definitions like [START_REF] Frey | Contextaware publish subscribe in mobile ad hoc networks[END_REF] are more generic and define the node context has a combination of static attributes and dynamic attributes, either defined in an absolute way or defined in relation with other nodes. For example, the velocity of a node can be considered as a dynamic context attribute. An absolute definition of this attribute will be 'velocity = 50 mph' while a relative definition will be 'velocity superior to the velocity of the node in front of me'. In relation to the examples discussed above, a node context would be defined as the node location, changing over time. We think that static attributes are not mandatory as they can easily be included in the subscriptions.

For the second example introduced above, the company name of an aircraft can be seen as a static context attribute. In a content-based P/S system, this context attribute could easily be replaced by an 'airline name' attribute correctly filled in subscriptions and published events.

Node context should not be mandatory to dispatch events. Indeed, in the example in section 2.2 the node location is not relevant for the communication (i.e.: an airline want to send a message to all aircraft, regardless of their position).

Persistent events

As explain in [START_REF] Th | The many faces of publish/subscribe[END_REF], the persistence of events is the ability for a P/S system to store events during its lifetime. Thus, subscribers interested by an event but not connected when this event has been sent are able to retrieve it later.

Traditional P/S systems for fixed infrastructure network do not necessarily implement this feature. However, this feature is essential for AANET where disconnections of node are frequent. To save network resources, the persistence can be handled with the introduction of a lifetime associated to each event. Indeed, most of the time, an event is of interest only for a given time period and can be deleted afterwards. Some solutions, like [START_REF] Frey | Contextaware publish subscribe in mobile ad hoc networks[END_REF], also use a lifetime associated to each subscription. We do not think this is mandatory since P/S API generally include an UNSUBSCRIBE primitive to remove a subscription from the system. To add persistence to a P/S system, two family of solutions exist. Events can be retransmitted periodically by publishers, though this is costly in network bandwidth, especially for a AANET. Another solution would be to store events in the network during its lifetime. Events can be hold by publishers (the simplest way) or by any node in the network (such as a broker for a P/S system based on overlay network, see next section).

CONTEXT-AWARE P/S SOLUTIONS IN MANET

Some P/S systems, with the specific features introduced in the previous section have been proposed for MANET or VANET (Vehicular Ad Hoc Network, a subcategory of MANET whose nodes are vehicles). They can be classified in three categories: geographic routing, proximity routing and overlay network based solutions.

Geographic routing based solutions

Geographic routing based solutions like [START_REF] Leontiadis | Persistent content-based information dissemination in hybrid vehicular networks[END_REF] or [START_REF] Frey | Contextaware publish subscribe in mobile ad hoc networks[END_REF] are based on the assumption that each event / subscription is linked to a geographical area. The publish / subscribe system uses a geographic routing protocol to dispatch events from the publisher to the subscribers. Actually, an event is sent to the nodes located in the geographical area defined as the context of the event.

In the same way, a subscription is sent to the geographical area defined in the context. Then, as described in figure 4, when a subscription matches with an event, nodes located at the intersection can act as middleman between publisher and subscribers.

Proximity routing based solutions

Proximity routing based solutions like [START_REF] Meier | STEAM: event-based middleware for wireless ad hoc networks[END_REF] or [START_REF] Eugster | Design and Implementation of the Pervaho Middleware for Mobile Context-Aware Applications[END_REF] provide many likeness with the previous solution. The main difference relates to the area where events and subscriptions are sent. This kind of solutions disseminate events/subscriptions only within a radius close to the sender (see figure 5).

Overlay based solutions

Overlay network based solutions introduce a new component in P/S architecture, the broker. Brokers are responsible for dispatching events from publishers to subscribers. They are interconnected and thus form an overlay network. In most of these solutions, like [START_REF] Cugola | REDS: a reconfigurable dispatching system[END_REF], brokers form an acyclic graph. Subscriptions are thus sent to all brokers of the graph (or just a part of it if context is used to filter recipient brokers). Based on these subscriptions, they can populate the table used to forward events to interested subscribers.

Performances in AANET context

In order to assess the relevance of the solutions introduced in the previous paragraphs, some performance evaluations have been conducted using simulation models developed on Omnet++ software. The mobility model used for the simulations is based on real traffic traces from the French civil aviation authority and Eurocontrol based on either radar data or position report from the aircraft itself. As AANET is especially interesting for area where no ground infrastructures are available, as over oceans, we choose to reduce the scope of our simulations to the transatlantic traffic. That is why our simulations take into account aircraft whose latitude belongs to the interval [23,5 • -70 • ] and the longitude belongs to the interval [-90

• -10 • ].
Two applications has been modeled for this evaluation : the announce of dangerous weather phenomenon and the communications from an airline to its fleet. Three P/S systems has been compared during these tests :

• The first one is a simple heuristic which is sending events to all nodes of the network. This solution is used as reference since, intuitively, we know that it will offer best performance in terms of delivery ratio of events even if the use of the network capacity will not be optimal ;

• The second one is a geographic routing based solution. LBM (Location Based Multicast) [START_REF] Ko | Geocasting in mobile ad hoc networks: Location-based multicast algotithms[END_REF] is the geographical routing protocol used to dispatch events and subscriptions. LBM allows to send a packet to all nodes within a target geographic area (called a multicast region). In this way, the sender of the message defines a forwarding area according to his position and the multicast region. Only nodes within the forwarding area retransmit the message. This family of Publish / Subscribe solutions also requires a unicast protocol in order to send an event from the node used as an intermediary to subscribers interested by the event. In our case, we choose AODV [START_REF] Perkins | Ad hoc On-Demand Distance Vector (AODV) Routing[END_REF] (Ad hoc On-Demand . This choice was motivated by the nature of the network (hundreds of highly mobile nodes) and the quality of service expected by the application.

• The third one is a proximity routing based solution. For each event/subscription, a proximity perimeter is defined (parameter of the simulation).

The message is sent to all nodes in this area. As with previous solution, when a node receives an event from which it knows subscribers, AODV is used to send this event to recipients.

The table 1 shows the results obtained with the traffic of the December 12 2012. For each P/S system, two results have been observed. The first one is the Delivery ratio. for each event Ratio = Number of subscribers that received the event Total number of subscribers

The second one is the Network load, which is the total number of bytes sent in the network (including control message) to dispatch events to subscribers. It is calculated as the sum of sent bytes (at the level of MAC layer) for all nodes of the network. These results show that interesting approaches for MANET or VANET like the use of geographical routing protocol or proximity based approach are not suitable in this situation and lead to worse results than a basic approach based on the broadcast of the event in the network. However, we have to keep in mind that this approach is a very bandwidth consuming solution and can not be deploy in an AANET.

A NEW P/S SYSTEM ADAPTED TO AANET

Since the evaluated solutions in the previous section are not totally efficient in a AANET context, our proposition is to build a P/S system over a hierarchical structure such as a clustered structure. This proposition is detailed in this section.

Clustering algorithm

The aim of a clustering algorithm is to organize the mobile nodes in a number of virtual groups called clusters. In a group there are usually three types of nodes as shown in figure 7.

Figure 7: Roles in a cluster

• The cluster-head in charge for the management of the group. There is only one cluster-head per group.

• The gateways which are able to communicate with nodes located in neighbor groups.

• The other nodes are simply member of the group.

In MANET, the clustering algorithms have been developed mainly to improve the efficiency of dynamic routing by building a hierarchical structure. Indeed, the existence of a hierarchical architecture allows to differentiate the behavior of the protocol between intergroup and intragroup communications. In our proposition, regarding this idea, we adapt it to build a scalable P/S system for AANET.

Working principle of P/S system

The main idea of this P/S system is to used clusterheads as brokers. They have two main functions. The first one is to collect events and subscriptions of cluster members. The second one is to dispatch events in the group if at least one member is interested by the content of this event.

The other choice, done for the development of this system, is to use unicast and multicast routing service offered by the network layer (IP layer in our case). For example, when a cluster-head wants to send a message to all other cluster-heads, this message is encapsulated in an IP packet whose destination address is a multicast address on which all cluster-heads are subscribed. Precisely, an approach proposed in this P/S system is to transmit events to all the groups available in the network. The working principle is then:

• All cluster members send their subscriptions to their cluster-head. It saves them in a table containing the identity of the source, the identifier (unique in all the network) and constraints related to this subscription.

• When an event is generated, it is transmitted to all cluster-heads, using the multicast routing service.

• For each received event, cluster-heads consult their subscriptions table and if one of them matches, this event is dispatch in the cluster.

This principle is illustrated with the figure 8.

Performance evaluations

In order to evaluate the efficiency of our P/S system, we compare it with the solutions introduced in section 4. The results are detailed in the table 2. For these simulations, we use the traffic of the June 28 2013. These results confirm the relevance of our approach. Indeed, the delivery ratio obtained with our proposition is close to the one obtained with the simple heuristic. Furthermore, the network load is considerably lower than all the other solutions (around six times lower than the simple approach for example).

CONCLUSION

In this article, we introduced a proposition for content based P/S system adapted to AANET. We first presented the concept of P/S system. Then, to highlight the interest of this paradigm in an AANET, we detailed three examples of aeronautical application which will be more efficient with the use of a P/S service. Using these applications, we have extracted two mandatory features of P/S systems for AANET, namely the node context filtering and the persistence of events in the network. Since some solutions with these features have already been proposed for MANET or VANET, a brief state of the art is presented in this article. The existing solutions can be classified in three categories: geographic routing, proximity routing and overlay network based solutions. However, using a simulation approach, we demonstrated that these solutions are not fully adapted to AANET. That's why we proposed a new P/S system suitable to AANET. This solution uses a hierarchical structure, built thanks to a clustering algorithm, to dispatch events to interested subscribers. Further work will concentrate on the optimization of our solution and the enhancement of our simulation model.

Figure 2 :

 2 Figure 2: Example of an airline sending a message to a part of its fleet

Figure 3 :

 3 Figure 3: Principle of wind networking

Figure 4 :Figure 5 :

 45 Figure 4: principle of geographic routing based P/S

Figure 6 :

 6 Figure 6: principle of overlay network based P/S

Figure 8 :

 8 Figure 8: Principle of overlay network based P/S

Table 1 :

 1 Performance of P/S systems Distance Vector)

	P/S System	Simple	Geographic	Proximity
	Delivery ratio	63.17 ± 0.2% 59.9 ± 0.4% 50.7 ± 0.2%
	Network load (in Gbits)	23.3 ± 0.57	59.8 ± 6.4	19 ± 0.26

Table 2 :

 2 Performance of our cluster based P/S systems regarding the other solutions