Luigi Vigneri
email: luigi.vigneri@eurecom.fr

Thrasyvoulos Spyropoulos
email: thrasyvoulos.spyropoulos@eurecom.fr

Chadi Barakat
email: chadi.barakat@inria.fr

Storage on Wheels: Offloading Popular Contents Through a Vehicular Cloud

The increasing demand for mobile data is overloading the cellular infrastructure. Small cells and edge caching is being explored as an alternative, but installation and maintenance costs for sufficient coverage are significant. In this work, we perform a preliminary study of an alternative architecture based on two main ideas: (i) using vehicles as mobile caches that can be accessed by user devices; compared to small cells, vehicles are more widespread and require lower costs; (ii) combining the mobility of vehicles with delayed content access to increase the number of cache hits (and reduce the load on the infrastructure). Contrary to standard DTN-type approaches, in our system max delays are guaranteed to be kept to a few minutes (beyond this deadline, the content is fetched from the infrastructure). We first propose an analytical framework to compute the optimal number of content replicas that one should cache, in order to minimize the infrastructure load. We then investigate how to optimally refresh these caches to introduce new contents, as well as to react to the temporal variability in content popularity. Simulations suggest that our vehicular cloud considerably reduces the infrastructure load in urban settings, assuming modest penetration rates and tolerable content access delays.

I. INTRODUCTION

The rapidly increasing demand for mobile data is menacing cellular operators. Upgrading the architecture to 4G is expensive, making operators reluctant. Densification through small cells is proving to be a bigger hassle and investment than initially expected [START_REF]NGMN 5G White Paper[END_REF]. What is more, introducing small cells requires significant upgrades to the backhaul network, which is predicted to become the new bottleneck [START_REF]Backhaul technologies for small cells: Use cases, requirements and solutions[END_REF].

Caching (popular) contents at the edge of the network (e.g., in small cells) has been proposed to alleviate the load on the backhaul and core network [START_REF] Bao | Dataspotting: Exploiting naturally clustered mobile devices to offload cellular traffic[END_REF], [START_REF] Golrezaei | Femtocaching: Wireless video content delivery through distributed caching helpers[END_REF], [START_REF] Poularakis | Video delivery over heterogeneous cellular networks: Optimizing cost and performance[END_REF]. The utility of such caching systems is supported by recent studies on content popularity, demonstrating a significant overlap between user demands. Nevertheless, to ensure a high enough amount of requests are served by small cells, and thus significantly reduce the load on the main infrastructure, extensive coverage by small cells is necessary [START_REF] Robson | Small cell deployment strategies and best practice backhaul[END_REF], which raises again concerns for CAPEX/OPEX costs.

To this end, in this paper we propose and study an alternative architecture, based on two main ideas. First, we propose to use vehicles as mobile small cells and data caches. These caches are controlled by the ISP (e.g., over a cellular interface [START_REF]Your Car Is About To Get Smarter Than You[END_REF]) and can be accessed directly by mobile devices (e.g. using WiFi or 802.11p [START_REF] Mir | LTE and IEEE 802.11p for vehicular networking: a performance evaluation[END_REF]). In urban environments, the number of vehicles (private or public) is expected to be considerably higher than any envisioned small cell deployment.

Furthermore, the related CAPEX/OPEX costs are expected to be lower, as many future vehicles will already be equipped with wireless communication equipment and storage [START_REF]Your Car Is About To Get Smarter Than You[END_REF], and some of the involved costs can be delegated, as in the femtocell model. While there exist other works that have considered content storage in vehicles [START_REF] Zhang | Roadcast: A popularity aware content sharing scheme in vanets[END_REF], [START_REF] Zhao | VADD: Vehicle-Assisted Data Delivery in Vehicular Ad Hoc Networks[END_REF], this is the first to consider such vehicles as a common cloud maintained by an ISP.

Second, we propose to exploit the mobility of vehicles to serve more content requests, locally, from vehicular caches. Specifically, in our architecture, a user requests a content initially from the infrastructure (which redirects the user to the vehicular cloud) or queries nearby vehicles using WiFi. However, a key component in our system is that, if the content is not immediately available at a nearby vehicle, the user agrees to wait few minutes until any car with the content moves within range. Such delayed content access can increase the number of cache hits and reduce the load on the infrastructure, compared to the case of static, small cell caches.

Delayed content access has been widely investigated in the context of delay tolerant and opportunistic networking [START_REF] Cai | Exploiting double opportunities for deadline based content propagation in wireless networks[END_REF], [START_REF] Han | Mobile data offloading through opportunistic communications and social participation[END_REF] as well as for WiFi-based offloading [START_REF] Balasubramanian | Augmenting mobile 3g using wifi[END_REF], [START_REF] Mehmeti | Is it worth to be patient? Analysis and optimization of delayed mobile data offloading[END_REF]. However, contrary to most such "DTN-type" approaches, in our system maximum delays are guaranteed, and kept to a few minutes: beyond a Time-To-Live (TTL) agreed between the ISP and the user, the content is fetched from the infrastructure. Such additional waiting delays could be easily amortized for large content transmissions (e.g., videos, software downloads), or be acceptable based on user subscription level (e.g. some users might be willing to pay cheaper plans and live with the occasional longer delays [START_REF] Ha | Tube: Timedependent pricing for mobile data[END_REF]) and context (e.g. roaming users might be more willing to wait for a low cost access).

While deadlines or TTLs are also considered in some WiFibased offloading proposals, the sparseness of WiFi coverage implies that considerably larger deadlines, e.g. in the order of hours, are required to achieve significant offloading gains [START_REF] Lee | Mobile data offloading: How much can wifi deliver?[END_REF]. Furthermore, device-based offloading systems [START_REF] Sermpezis | Not all content is created equal: Effect of popularity and availability for content-centric opportunistic networking[END_REF], while comprising a comparable number of mobile caches, face major resource constraints (battery lifetime, storage space, etc.) that vehicles do not face, raising significant concerns about the likelihood of such approaches being adopted in the near future.

The basic communication protocol for our "Storage on Wheels" system is defined by six steps (Fig. 1): (1) base stations or macro-cells push popular contents in vehicles; we refer to this action as seeding; (2) a user requests a content to nearby vehicle: (3) if the content is found (cache hit), the user can immediately download it; (4) otherwise he waits for new vehicles. ( 5) When TTL expires and the content has not been found in the vehicles (cache miss), the user contacts the cellular network [START_REF]NGMN 5G White Paper[END_REF] to get the content directly from the infrastructure. In this work, we study how to optimally allocate contents in order to minimize the load on the cellular infrastructure. Our main contributions in this paper are the following:

• We study analytically the problem of optimal content allocation, and derive the optimal number of copies of each content to be allocated to vehicles in the cloud, assuming a stable catalogue and average content popularity; • We extend our analysis to more practical settings where connectivity can be lost while retrieving the content (due to mobility, interference, etc.) or (large) content is retrieved from multiple vehicles (e.g. in differently sized chunks); • We study how to refresh the vehicles caches dynamically, to react to new content and changing content popularity; • We use real traces for content popularity and mobility to study the feasibility of our system, and show that considerable offloading gains can be achieved even with modest technology penetration (< 1% of vehicles participating in the cloud) and reasonable max delays (1-5min).

Summarizing, this work is structured as follows: in Section II, we present the model and the general formulation of the content allocation problem; next, in Section III, we provide closed form expressions for the optimal allocation when vehicles caches are initially empty; then, we present an algorithm to periodically update the caches according to the variability of the content popularity in Section IV; after that, we validate our results through simulations in Section V; finally, we review related work in Section VI and conclude our paper with a summary and future work in Section VII.

II. PROBLEM FORMULATION

In this section, we first describe the proposed architecture and the system model. Then, we add some implementation details of the system. Finally, we define the cost function to minimize in order to optimally allocate contents in the vehicles. The main notation used in the paper is summarized in Table I.

System model. We have a network with three types of nodes:

• Infrastructure nodes (I), i.e. base stations or macro-cells.

Their role is to serve users with the contents requested and to fill the vehicles buffers; • Mobile nodes (M), i.e. the users asking for online contents through their mobile devices, such as smartphones, tablets or netbooks. These nodes do not store any content, rather they ask for them to C or I nodes. Specifically, a communication between C and M can take place when their distance is less than a certain communication range (contact). Finally, we assume that the amount of time a user is willing to wait is smaller than the time window over which we are optimizing.1

Let K denote a set of contents available, where |K| = k. We refer to it as catalogue. Each content is assumed to have a known popularity rate ϕ (i) during a given time window ∆t, calculated as ϕ (i) 1/∆t ∆t 0 ϕ

t ∂t. To keep analysis tractable, we assume that all contents have equal size, e.g. equal to the mean file size in the catalogue. While this assumption is not true in reality, our model trivially allows to absorb the heterogeneity in the contents size: in fact, large contents can be split in chunks of equal smaller size, where chunks for the same content have the same popularity (and thus obtain the same number of replicas).

Finally, we assume that the mobility between C and M nodes leads to a contact process between them exhibiting exponential pairwise inter-contact times with rate ν. While inter-contact time distributions are still a subject of debate, several studies suggest that this is a reasonable assumption, especially in the tail of the distribution [START_REF] Conan | Characterizing pairwise intercontact patterns in delay tolerant networks[END_REF] [29] [START_REF] Karagiannis | Power law and exponential decay of intercontact times between mobile devices[END_REF]. Architecture. ISP providers often offer low cost data plans with limited bandwidth, because of the increasing demand for data. However, the bandwidth offered is usually not enough to satisfy the users' needs. Thus, we propose our infrastructure as an additional feature to boost these cheap data plans. Basically, a user can browse the Internet contacting directly his cellular provider as usual. The cellular provider might decide to redirect the request to the vehicles for the popular contents as it happens in the CDN context, if the user has subscribed for the "Storage on Wheels" additional feature. Recall that the ISP is aware of what contents are popular or not. This setup might be really interesting for roaming users as well, that do not want to use expensive data plans abroad.

An alternative use case is to consider querying directly vehicles through WiFi. Because of the immense size and of the variety of the catalogue, we think that is more profitable in terms of cache hits to limit the user's choice to a smaller number of contents. For instance, the user can ask contents based on a generic list of popular contents updated by the I nodes. This list could be directly downloaded from the I or C nodes or in alternative ways (e.g., NFC tags or QR codes at bus stops). Finally, our architecture can naturally provide additional services such as traffic information, advertisement, events, news, P2P applications, etc. However, the definition of a complete and detailed architecture is beyond the scope of this paper as we focus on performance optimization.

Problem formulation. In our model, both pushing a copy in the vehicular cloud and having a cache miss cost a transmission. The final goal is to minimize the load on the infrastructure, i.e. the number of transmissions. To this end, we choose the cost function to be equal to the sum of the number of copies to be seeded (I -→ C) and the cache misses that occur in the considered time window ∆t (I -→ M). Both of these require resources (e.g. bandwidth) from I to be expended, and thus should be minimized. Yet, reducing the one often leads to increasing the other. Hence, we can write the corresponding optimization problem in order to minimize the number of accesses to the I nodes: min

N (i) k i=1 ∆tϕ (i) P[X (i) > T T L] + max N (i) -N (i) 0 , 0 s.t.: 0 ≤ N (i) ≤ α, i = 1, . . . , k, k i=1 N (i) ≤ αβ, (1)
where N (i) is the number of copies stored in the vehicles for the content i, N (i) 0

is the initial number of copies and X (i) is a random variable corresponding to the time needed to successfully download content i.

The first term of the objective function takes into account the number of cache misses occurring in ∆t: ∆tϕ (i) is the expected number of requests for content i during ∆t. Every time that a requested content is not provided by the vehicles within TTL, we have a cache miss that increases the cost. P[X (i) > T T L] is the probability not to download the whole content within TTL. Hence, the product of these three terms is equal to the total number of missed requests in the time window. The second term corresponds to the incremental seeding cost for the communication I -→ C, i.e. the cost of adding new copies when buffers are not empty (we assume that removing a copy does not have a cost). The objective function is subject to the constraints:

• the number of vehicles having the content i cannot be negative: N (i) ≥ 0, • the number of vehicles having the content i cannot be higher than the cardinality of C (|C| = α): N (i) ≤ α, • each vehicle has storage constraints and cannot store more than β contents2 : k i=1 N (i) ≤ αβ. In the following section, we discuss the particular case when the vehicles caches are empty (N

(i) 0 = 0); then, in

III. OPTIMAL CONTENT ALLOCATION FOR LARGE ∆t

In this section, we consider how to optimally allocate contents in the vehicular cloud, when the caches are initially empty. This scenario can be considered as a bootstrap phase for the infrastructure, where the buffers need to be filled. Furthermore, when the operator defines a large ∆t, we assume that the new allocation does not depend on the initial set of contents stored in the vehicles (i.e. N (i) 0 = 0, ∀i ∈ K), since content popularity substantially changes in large intervals. Thus, the operator needs to reboot all its caches. This scenario provides useful insights and methodology for subsequent results.

A. Baseline scenario

We first consider the baseline scenario, where we assume that if a M node encounters a C node, it will be able to receive the whole requested content with probability 1. Thus, we can derive the following lemma: Lemma 1. In the baseline scenario, the probability not to download the whole content within TTL is P[

X (i) > T T L] = e -νT T L•N (i) .
Proof: The inter-meeting time between two contacts is assumed to be exponentially distributed. In this scenario, X (i) corresponds to the time needed to meet the first vehicle having the requested content, therefore

X (i) ∼ exp(ν • N (i)
). The result in Lemma 1 directly follows from elementary properties of the exponential distribution [START_REF] Durrett | Probability: Theory and Examples[END_REF].

By using Lemma 1, we obtain the following result: Result 1. Consider the problem described by Eq. (1). The optimal number of copies to allocate is given by:

N (i) =      0, if ϕ (i) < L 1 νT T L ln νT T Lϕ (i) ∆t γ (i) , if L ≤ ϕ (i) ≤ U α, if ϕ (i) > U where L 1+ρ νT T L∆t , U (1+ρ)e ανT T L
νT T L∆t , γ (i) 1 -µ (i) + λ (i) + ρ and µ (i) , λ (i) and ρ are appropriate Lagrangian multipliers (non-negative since the constraints are formulated as inequalities).

Proof: The problem in Eq. ( 1) is convex since the objective is the sum of convex functions and the constraints are all linear. Thus, it can be solved with the method of Lagrangian multipliers [START_REF] Antoniou | Practical optimization -algorithms and engineering applications[END_REF]. We convert Eq. (1) to the standard form (from minimization to maximization) and we write the Lagrangian function of the problem:

L = -∆t k i=1 ϕ (i) e -νT T L•N (i) - k i=1 N (i) + k i=1 µ (i) N (i) + k i=1 λ (i) α -N (i) + ρ αβ - k i=1 N (i) , (2)
where λ (i) , µ (i) and ρ are the Lagrangian multipliers. The corresponding KKT conditions are: The differentiation of Eq. ( 2) with respect to N (i) gives:

     µ (i) N (i) = 0 λ (i) (α -N (i) ) = 0 ρ(αβ - k i=1 N (i) ) = 0 (3)
∂L ∂N (i) =νT T Lϕ (i) ∆te -νN (i) T T L -1 + µ (i) -λ (i) -ρ = 0 ⇒ N (i) = 1 νT T L ln νT T Lϕ (i) ∆t γ (i) . (4)
Eq. ( 4) is subject to the constraints described by Eq. ( 3). Specifically:

• Low popularity contents (µ > 0): since the allocation cannot be negative, when the popularity is too low the number of copies to allocate is 0 (left side of Fig. 2). We are in this case when:

1 νT T L ln νT T Lϕ (i) ∆t γ (i) < 0 ⇒ ϕ (i) < 1 + ρ νT T L∆t L (5) and µ (i) = 1 + ρ -νT T Lϕ (i) ∆t. (6)
• High popularity contents (λ > 0): on the other hand, the number of copies in the cars is limited, upper-bounded by the number of cars available (N (i) = α) as we can see from the right side of Fig. 2. We are in this case when:

ϕ (i) > (1 + ρ)e ανT T L νT LL∆t U (7)
and

λ (i) = νT T Lϕ (i) ∆t • e -ανT T L -1 -ρ. (8)
As we can see from Eqs. ( 6) and ( 8), µ (i) and λ (i) directly depend on the value of ρ. For this reason, a last step in deriving the exact allocation is just to infer the value of the Lagrangian multiplier ρ. Unluckily, since the value of ρ depends on the popularity of the entire set of contents K, it is not possible to determine a closed form expression for it. However, it can be proved that the cost function is monotonically increasing according to ρ (the proof is omitted due to space limitations and can be found in [1]), i.e. the cost is minimum for the minimum value of ρ that satisfies the capacity constraint. Moreover, if the capacity constraint is satisfied, ρ is trivially equal to 0. Thanks to these considerations, we can find this value of ρ through a simple minimum search algorithm.

According to the value of ρ, we now elaborate on the different solution regimes for the Medium popularity contents: All constraints inactive (γ = 1): when all the constraints are inactive, the optimal allocation depends on the logarithm of the popularity (Fig. 2). Since here the buffer space is not an issue, the content allocation is decoupled, i.e. the optimal number of copies per content is independent of the popularity of the other contents. Finally, from Eq. ( 4), we can note that the optimal number of copies is attenuated by νT T L. Capacity constraint active (γ = 1 + ρ): increasing the popularity or reducing the buffer space can violate the storage constraint. Even in this case, the allocation is still logarithmic on popularity. When the capacity is limited, the multiplier ρ reduces the optimal allocation as the number of contents increases or the total capacity decreases by increasing the thresholds L and U .

B. Connectivity loss scenario

When an M node is inside the communication range of a C node, the connectivity might be lost. This can be due to many reasons, such as network failures, vehicle mobility and interferences. In detail, a success or a failure in downloading a content depends on few key factors: (i) the contact duration, that is the amount of time during which M and C nodes can exchange data inside the communication range; (ii) the throughput that depends on the distance between the nodes: in fact, WiFi protocols are defined to use dynamic rate scaling, and the throughput will automatically decrease as the signal strength decreases, i.e. as the distance between the nodes increases; (iii) the size of the content; (iv) interferences and variability in the urban radio environment [START_REF] Mahajan | Understanding wifi-based connectivity from moving vehicles[END_REF]. In this subsection, we propose two policies to face the problem of connectivity loss:

1) Repeat policy: In reality, it might be difficult to give an accurate estimation of the success/failure probability per contact. For this reason, we define the average probability p to successfully download a content during a meeting. In the repeat policy, the content download will restart from the beginning when an M node loses the connection with a vehicle (e.g., TCP session expires) and meets a new one. Result 2. The optimal content allocation for the repeat policy is given by:

N (i) =      0, if ϕ (i) < L 1 pνT T L ln pνT T Lϕ (i) ∆t γ (i) , if L ≤ ϕ (i) ≤ U α, if ϕ (i) > U
where L 1+ρ pνT T L∆t and U (1+ρ)e pανT T L pνT T L∆t . Proof: Given the Poisson process with rate ν counting the contacts between C and I nodes, suppose that each event corresponds to a successful download with probability p. Then successful downloads form a Poisson process with rate pν. For this reason, we have:

P[X (i) > T T L] = e -pνT T L•N (i)
.

The rest of the proof proceeds as in the proof of Result 1.

2) Resume policy: Due to the limited contact duration and to the large size of the contents, downloading a content in one shot might be hard, leading to a small value of p. Alternatively, if we consider large contents, like videos, chunking is a popular way to break down the file into smaller pieces. Hence, during a contact a node could download one or more chunks. Moreover, new technologies allow easily to stop and resume the download at any time (e.g., latest versions of browsers, online music players, etc.). In the resume policy, when a M node loses the connection, the download will resume from the point of interruption during the following cache hit. Specifically, we suppose that for each meeting j, we can download r j bytes of the content, where r j is a continuous random variable with values ∈ [0, +∞). Furthermore, we define s 0 as the content size in bytes (recall that s 0 is assumed equal for any content).

Lemma 2. Let Y (i)

M (i)
j=1 r j be the number of bytes downloaded within TTL for content i, where M (i) indicates the number of cache hits in that interval. In the resume scenario, the probability not to download the whole content within TTL is equal to:

P[X (i) > T T L] ≡ P[Y (i) < s 0 ] = L -1 e [R * (s)-1]νT T L•N (i) s (s 0 ). (9)
where L -1 {F (s)}(t) is the inverse Laplace transform of F (s).

Proof: Since the inter-meeting time between two contacts is assumed to be exponentially distributed, M (i) follows a Poisson distribution. Furthermore, a random sum of identically distributed random variables has a Laplace transform that is related to the transform of the summed random variable and of the number of terms in the sum:

Y * (i) (s) = M * (i) (R * (s)),
where Y * (i) (resp. R * ) is the Laplace transform of Y (i) (resp. r j ) and M * (i) is the Z-transform of M (i) . The number of meetings within TTL is Poisson distributed, therefore we can write Y * (i) (s) as follows:

Y * (i) (s) = e [R * (s)-1]νT T L•N (i)
.

In the resume policy, the probability not to download the whole content within TTL is equal to the probability that the sum of the bytes downloaded within TTL (Y (i) ) is less than s 0 . Since the cdf of f X is given by F X (x) = L -1 L{f X }} s (s 0 ), we can compute P[X (i) > T T L] as in Eq. ( 9). While the above formula is generic, it requires knowledge of the distribution of r, the "effective capacity" of contacts, which might be difficult to obtain. Furthermore, calculating the inverse transform is not trivial, except for very specific r distributions. To proceed and obtain a closed form estimate of this probability, observe that Y (i) is a compound Poisson process, because the number of meetings are Poisson distributed and the reward (bytes downloaded) in each contact is independent of the inter-contact times. According to this definition, we can derive the following result: Result 3. For large values of M (i) , the optimal content allocation corresponding to the resume policy can be approximated using the solution of the following equation:

N (i) = ω (i) e -ω (i)2 • ϕ (i) ∆t/(γ (i) √ 8π),
where

ω (i) (s 0 -E[Y (i) ])/ Var(Y (i) ). Proof: Let E[Y (i) ] = rνT T L • N (i)
and Var(Y (i) ) = (r 2 + σ 2 )νT T L • N (i) be the mean and variance of the compound Poisson process Y (i) , where r E[r j ] and σ 2

Var(r j ) are respectively the mean and the variance of r j . Especially in urban environments, the number of contacts is expected to be considerably large. When M (i) corresponds to large values, we could use a normal approximation for Y (i) using only the first two moments [START_REF] Schmidli | Lecture Notes on Risk Theory[END_REF], i.e. Y (i) ∼ N (E[Y (i) ], Var(Y (i) )); in this case, the probability not to download the whole content within TTL is:

P[Y (i) < s 0 ] ≈ Φ s 0 -rνT T L • N (i) (r 2 + σ 2 )νT T L • N (i) (10)
Finally, from Lemma 2 we know that P[X (i) > T T L] ≡ P[Y (i) < s 0 ]. Thus, we can replace Eq. ( 10) in Eq. (1) and solve with the method of the Lagrangian multipliers as in the proof of Result 1. For this approximation to hold, the "stopping" M (i) , i.e. the number of contacts until the whole content is retrieved, needs to be sufficiently large. However, if Y (i) is highly skewed, then this approximation might also fail. In this case, it is possible to use other approximations (e.g., gamma, Edgeworth).

IV. DYNAMIC ADAPTATION TO CHANGING POPULARITY

In this section, we consider a more practical setup, where caches are updated dynamically, as new contents are introduced, and/or existing contents exhibit a significant change in popularity. Adapting to changing content popularity is not only important to introduce new contents and delete obsolete ones, but also to increase the potential performance gains.

Specifically, suppose that video A and video B have the same number of views per day. Moreover, suppose that video A is very popular during the day, while video B is more popular during the night. We would like to capture this behaviour by allocating more copies for A during the day and for B during the night. Since they have the same average popularity over one day, if ∆t is too large (e.g. one day) the two videos will be allocated the same number of copies. On the other hand, for a small ∆t, the cache hits due to the additional copies allocated during the interval, are not large enough to amortize the cost of seeding these new copies, being perhaps removed before a newer allocation is selected in the next time window.

In such a system, seeding is incremental, taking into account the existing allocation, and adjusting it where necessary, depending on the potential gains predicted for a shorter time window (i.e., until the next update).

Result 4. Consider that the initial allocation N (i) 0 is given and not null. Then, given a ∆t, the optimal number of copies to add (or remove) is:

∆N (i) = 1 νT T L ln νT T Lϕ (i) ∆t γ(i) -N (i) 0 , (11)
where

∆N (i) N (i) -N (i) 0 , γ(i) 1 A (∆N (i) )-µ (i) +λ (i) +ρ and A = (0, +∞).
Proof: The proof proceeds as in the proof of Result 1, by using the method of the Lagrangian multipliers. However, the derivative of the max function in Eq. ( 1) is equal to:

∂ max(∆N (i) , 0) ∂∆N (i) = 0, if ∆N (i) ≤ 0 1, if ∆N (i) > 0
i.e., the solution is different depending whether we add or remove copies.

As we can see from Result 4, the incremental seeding makes the problem of updating the caches non trivial, because ∆N (i) appears also in γ(i) . Thus, we now propose a simple algorithm computing the number of copies to add or remove every ∆t for each content. This heuristic allows to have an easy implementation in practice. Specifically, every time window, we make the decision if it is convenient adding more copies for a given content. Basically, seeding one more copy provides a gain equal to the number of cache misses saved by the additional copy. The gain is given by:

gain (i) ϕ (i) ∆t • e -νT T L•N (i)
(1 -e -νT T L ).

Then, we sort the contents according to the gain that can provide and, if this is higher than the seeding cost, we add a copy (or more copies) in the cloud until the buffer is full or there no other contents to seed. On the other hand, if all the caches are full, storing new copies must follow the deletion of the less popular ones; removing one copy leads to a loss equal to the additional cache misses:

loss (i) ϕ (i) ∆t • e -νT T L•N (i)
(e νT T L -1).

Then, we select the content with the highest gain and the one with the lowest loss. If max (gain) -min (loss) > 1, then the switching is advantageous. We call switching the action taken by the I nodes to remove a content and replace it with another one. We recompute the gain and the loss for the contents switched and we iterate until the condition is satisfied, i.e. there is at least one advantageous switching. We add/switch the contents every time window.

V. SIMULATION RESULTS We validate our model through MATLAB simulations. We simulate the load on the infrastructure and we compare it with other caching systems. In Section V-A, we consider the optimal allocation policies described in Section III and we analyse the impact of different parameters in the proposed cache system. Then, in Section V-B we test the performance of the contents replacement algorithm seen in Section IV. Finally, in Section V-C we perform simulations based on a real mobility trace to inspect the cost savings in a real world scenario.

In our analysis we consider the content popularity of YouTube videos, since a large percentage of mobile data traffic is represented by video files. We download from [START_REF]YouStatAnalyzer database[END_REF] a database generated with YouStatAnalyzer [START_REF] Zeni | Youstatanalyzer: A tool for analysing the dynamics of youtube content popularity[END_REF] that collects statistics for 100.000 YouTube videos. The database includes static (title, description, author, duration, related videos, etc.) and dynamic information (daily and cumulative views, shares, comments, etc.). In our simulations, we only take into account the number of views related to the last 360 days. However, these values are equal to the total number of views per day in the world, then we scale them properly 3 . We have also created synthetic traces based on the work in [START_REF] Crane | Robust dynamic classes revealed by measuring the response function of a social system[END_REF]. Simulations based on these synthetic trace confirm the observations made using the real trace. We therefore focus on the former.

Furthermore, we consider a square area 5000m x 5000m in the center of San Francisco. We use the Cabspotting 4 trace to compute the average meeting rate ν: we randomly place the M nodes and, considering a communication range of 200m, we calculate the meeting rate with each C node. We find ν = 4 contacts/day. According to the density of the city and to the number of vehicles per capita, we estimate to 100.000 the 3 We scale them linearly taking into account the number YouTube users and the population of San Francisco 4 GPS coordinates from 536 taxis in San Francisco over 23 days [START_REF] Piorkowski | CRAW-DAD data set epfl/mobility (v. 2009-02-24[END_REF].

number of vehicles in the area considered. However, in order to be realistic about initial technology penetration we assume that only 1% of these vehicles is participating in the cloud. We assume that each car can store 100 contents (0, 1% of the catalogue). We set TTL to 3 minutes. Unless otherwise stated, we will use these parameters summarized in Table II Finally, we compare our allocation policies with:

• No cache: no contents are stored in the vehicles; the probability of miss is equal to 1, therefore the cost corresponds to the total demand: cost = ∆t K i=1 ϕ (i) ; • Random: contents are allocated randomly in the vehicles;

• Square root: this policy behaves similarly to our optimal allocation, but it replaces the logarithm with the square root, after an appropriate normalization to satisfy the storage constraint.

A. Optimal Content Allocation for Large Time Windows

We perform numerical simulations considering constant content popularity during a time window ∆t, which is set to 1 week. Specifically, we compare the effect of buffer size, TTL and other parameters on the final gain comparing different policies. We show that the allocation policies presented in Section III clearly decrease the load on the infrastructure. Fig. 3 depicts the cost in terms of total accesses during the period ∆t, broken down into seeding cost and cache misses, for the various policies. Our policy reduces the total cost by around 65%, more than any other policy. What is more, it improves twice the performance compared to the square root policy, which is known to achieve optimal results in conventional peerto-peer networks [START_REF] Cohen | Replication strategies in unstructured peerto-peer networks[END_REF]. In Fig. 4, we plot how TTL, buffer size and number of cars affect the final cost. Specifically, Fig. 4a shows the cost according to the value of TTL for the different policies. It is very important to note that considerable gains can be achieved with very small TTL values (in the order of a few minutes) and small number of vehicles participating in the cloud (1%). This provides some evidence on the advantages of offloading based on a vehicular cloud, compared to offloading using small cells or WiFi, as for example in [START_REF] Balasubramanian | Augmenting mobile 3g using wifi[END_REF] or [START_REF] Lee | Mobile data offloading: How much can wifi deliver?[END_REF]. E.g, [START_REF] Balasubramanian | Augmenting mobile 3g using wifi[END_REF] reports minor gains for similar small deadlines, while [START_REF] Lee | Mobile data offloading: How much can wifi deliver?[END_REF] requires a much longer TTL (order of 1-2 hours) to achieve similar gains. In addition, increasing the TTL further has diminishing returns. This implies that even users not willing to wait too long could participate in such a system (benefiting themselves and the operator). An efficient cache system should store as many popular contents as possible. However, in reality the catalogue of online contents is really large and only a small percentage of them can be stored. Fig. 4b shows the cost according to the buffer size. From the plot we can observe that storing 100 contents/car (only 0,1% of the total number of contents) provides a gain of almost 60%. In a scenario with a larger catalogue (e.g. 100 millions), it seems doable to store 0,1% of the contents (e.g. 100.000 contents/car) needed to achieve good savings. What is more, due to the intrinsic characteristics of the popularity distribution, the system might require an even smaller number of storage in order to achieve similar gains.

In an urban environment, the great availability of vehicles leads to large gains for the proposed infrastructure. However, an operator will probably keep using our framework even if the number of vehicles available decreases: in Fig. 4c we depict the cost savings according to the number of C nodes participating in the cloud and the gain observed is not less than 50% when more than 250 vehicles are part of the cloud. So far we have assumed that the probability of downloading a content during a meeting is equal to 1. However, because of external factors, a user might not be able to get the requested content during a meeting. According to the repeat policy discussed in Section III-B1, in Fig. 5 we plot the percentage of savings for different values of p. We can note that, even when p = 0.5, i.e. a M node loses the connection during half of the downloads, we can have a gain of almost 60% in terms of total number of accesses to the core infrastructure. Clearly, this will be at the expense of some larger delay compared to the case of no disconnections. Furthermore, we plot the gain provided by the original allocation policy (calculated with p = 1) in a scenario with losses: the plot shows that it is important trying to estimate the value of p and tune the allocation accordingly, since this can bring up to the 20% of additional savings.

Finally, it has been long observed in many contexts, including Internet contents, that popularity exhibits strong skewedness. To evaluate the effect of such popularity differences, in Fig. 6 we do not take into account the real dataset, rather we consider bounded Pareto distributions (minimum value = 1 request/day, maximum value = 100.000 requests/day and ζ as shape parameter). We can observe that when the variance increases (ζ low), the optimal allocation brings a considerable gain up to the 70% in terms of number of accesses to the backbone. This is due to the fact that, if ζ is low, some contents have very high popularity, and caching them leads to a large number of cache hits. On the other hand, the gain goes to 0 when ζ increases, i.e. the differences in the content popularity are negligible making it hard to create enough cache hits with any subset of them that can fit in the cloud.

B. Dynamic Adaptation to Changing Content Popularity

In this subsection, we analyse how refreshing the caches affects the performance of the vehicular cloud. We still use the parameters described in Table II. Moreover, here we increase the realism of the simulations by considering that the popularity of the contents is not known in advance, but it requires to be estimated. Several studies have been done on content popularity prediction [START_REF] Gill | Youtube traffic characterization: A view from the edge[END_REF] [START_REF] Dezsö | Dynamics of information access on the web[END_REF]. In our simulator, we build a simple predictor to estimate the future popularity according to the previous samples, by using an Exponential Weighted Moving Average based on the latest 10 time windows. Building the best predictor goes beyond the scope of this paper. Here, we just want to show how an error in the prediction affects the system and if the considerations done are still valid.

The content popularity provided by the database used [START_REF]YouStatAnalyzer database[END_REF] has daily granularity; however, several studies have shown a clear sinusoidal behaviour on a daily basis [START_REF] Gill | Youtube traffic characterization: A view from the edge[END_REF] [START_REF] Abrahamsson | Program popularity and viewer behaviour in a large tv-on-demand system[END_REF]. We exploit these studies to estimate the content popularity on a hourly basis. Fig. 7 shows the final cost when caches are updated with a varying refresh time over a long time period to ensure capturing the variations in the content popularity (in the simulator, we set this long time period to 1 year). In this scenario, the vehicular cloud still provides a considerable number of savings (from 50% to 70% depending on ∆t) in terms of accesses to the infrastructure. Moreover, we can have gains similar to the case with perfect knowledge (line "optimal") even by using a simple predictor.

In Fig. 8 we can observe a snapshot of the number of copies allocated per content according to its content popularity. We capture the snapshot at a random day of our trace. The plot is in semilog scale and ∆t = 2 days. In this case, even if the shape globally follows the expected logarithmic behaviour, we can see a significant number of outliers. This is due to the fact that the number of copies does not change instantaneously when the popularity varies, due to the seeding cost. It is interesting to note that, in Fig. 8b, since the number of cars is limited, we observe a single-threshold behaviour, where the system will end up either caching contents everywhere (case of popular contents) or not caching them at all (case of non popular ones). Indeed, from Eqs. ( 5) and [START_REF] Antoniou | Practical optimization -algorithms and engineering applications[END_REF], when vehicles buffers cannot satisfy the large user demand, L and U get closer.

C. Validation through real mobility traces

In this subsection, we present the results of MATLAB simulations based on real mobility traces. We use the Cabspotting database [START_REF] Piorkowski | CRAW-DAD data set epfl/mobility (v. 2009-02-24[END_REF], where ∆t is equal to 23 days and the number of vehicles α is 536. In these simulations, we first generate a set of requests per content according to the popularity obtained by the YouTube database [START_REF]YouStatAnalyzer database[END_REF]. Then, we associate a timestamp and a location to each request. We assume that the number of requests in an area is proportional to the density of vehicles in that area and that the requests are concentrated during daytime. Once a request appears, we check if one of the vehicles in the communication range of the request within TTL has the required content. In this scenario, we set TTL to 5 minutes. However, despite we consider small buffer capacity per vehicle (0,1% of the total number of contents), we will show that, on average, contents are delivered much earlier than TTL. Furthermore, we assume that, when vehicles are closer to the location of the request, the contact duration and the throughput are larger, increasing the probability p to successfully deliver the content. For this reason, we consider p as inversely proportional to this distance, with mean equal to 0,7. We perform trace-based simulations comparing (i) repeat policy (called "optimal" in the figure), (ii) dynamic adaptation to the changing popularity ("optimal(var)"), where caches are updated on a daily basis, and non optimal policies. Fig. 9 depicts the cost in terms of number of accesses to the infrastructure during the period ∆t when the Cabspotting trace for mobility is used. The plot shows that repeat policy can decrease the load on the infrastructure until 40% even if only few hundreds of vehicles (with small buffer capacity) are participating into the cloud. Similar cost savings are provided when we allow to vary the content allocation on daily basis. What is more, users usually wait much less than TTL in order to download the requested content: simulations revealed that, in more than 50% of cases, a user waits less than 60 seconds when the content is eventually delivered.

VI. RELATED WORK

The high skewness in the content popularity and the rapidly increasing demand for mobile data has led to a number of recent papers related to mobile data offloading through opportunistic communications [START_REF] Bao | Dataspotting: Exploiting naturally clustered mobile devices to offload cellular traffic[END_REF] [21] and distributed storage [START_REF] Dandapat | Sprinkler: Distributed content storage for just-in-time streaming[END_REF] [START_REF] Golrezaei | Femtocaching: Wireless video content delivery through distributed caching helpers[END_REF]. For instance, in [START_REF] Bao | Dataspotting: Exploiting naturally clustered mobile devices to offload cellular traffic[END_REF] the authors find a way to serve user requests from other mobile devices located geographically close to the user. They achieve this by clustering crowded places (dataspots), where a user can ask for contents directly to the close smartphones. [START_REF] Han | Mobile data offloading through opportunistic communications and social participation[END_REF] uses opportunistic local connections and remote connections through cellular networks. The solution proposed in [START_REF] Dandapat | Sprinkler: Distributed content storage for just-in-time streaming[END_REF] disseminates contents for Justin-Time streaming (Netflix, Hulu or YouTube) in the access points distributed city-wide. [START_REF] Golrezaei | Femtocaching: Wireless video content delivery through distributed caching helpers[END_REF] uses small cell access points (helpers) to cache contents. In their work, the authors allocate files in the helpers according to the network topology and the file popularity distribution. Files not available from helpers are transmitted by the cellular base station. In the context of vehicular cloud, some preliminary attempts to build an infrastructure similar to our proposal have been done by [START_REF] Zhang | Roadcast: A popularity aware content sharing scheme in vanets[END_REF] and [START_REF] Zhao | VADD: Vehicle-Assisted Data Delivery in Vehicular Ad Hoc Networks[END_REF]. In these works, authors model an architecture to carry and forward contents making use of the predictable vehicle mobility [START_REF] Zhao | VADD: Vehicle-Assisted Data Delivery in Vehicular Ad Hoc Networks[END_REF] or propose a P2P content sharing scheme based on popularity [START_REF] Zhang | Roadcast: A popularity aware content sharing scheme in vanets[END_REF], using a sub-optimal policy to store contents.

In this paper, we propose a new alternative caching infrastructure allowing to store popular contents in a vehicular cloud overlapping the existing cellular network; compared to other solutions, we limit resources constraints (e.g. caching in mobile devices) or we decrease installation and maintenance costs (e.g. caching in small cells). Moreover, in this work we use the high mobility of the vehicles in order to provide better performances and high availability of the popular contents. Finally, refreshing the caches allows to further exploit the variability of the content popularity to decrease the number of the accesses to the infrastructure.

VII. SUMMARY AND FUTURE WORK

Offloading contents is considered a solution to the rapid increase of mobile data demand and we firmly believe that vehicular networks will play an important role in this field. This is confirmed by the interest of research and companies in exploiting vehicles to carry contents or to give connectivity to users [START_REF] Veniam | Internet of Moving Things[END_REF]. In this paper, we provide an analytical framework to allocate popular contents in a vehicular cloud. Based on our model, we suggest the periodicity of refreshing the caches in order to face the variability in the content popularity.

As a part of future work, we plan to extend our model including heterogeneity in content size, V2V communications and location based popularity (e.g. football videos are more likely to be downloaded near a stadium), using the recent studies on floating contents [START_REF] Thompson | Locus: A locationbased data overlay for disruption-tolerant networks[END_REF] [START_REF] Hyytia | When does content float? characterizing availability of anchored information in opportunistic content sharing[END_REF]. We also plan to investigate the impact of different TTL per content to decrease the total load and/or to improve the end user Quality of Experience (e.g., analysing the slowdown metric).

Fig. 1 :

 1 Fig. 1: Basic communication protocol of the offloading mechanism for the proposed infrastructure.

Fig. 2 :

 2 Fig. 2: Optimal allocation in semi-log scale (α = 100).

Fig. 3 :

 3 Fig. 3: Cost savings with different policies (∆t = 1 week) with numerical simulations.

Fig. 5 :

 5 Fig. 5: Cost savings with different values of p.

Fig. 6 :

 6 Fig. 6: Sensitivity analysis vs. popularity distributions.

Fig. 7 :

 7 Fig. 7: Sensitivity analysis vs. ∆t.

  (a) α = 1000.

  (b) α = 20.

Fig. 8 :

 8 Fig. 8: Contents allocation (∆t = 2 days).

  Cost comparison according to number of vehicles.

Fig. 4 :

 4 Fig. 4: Sensitivity analysis vs. TTL, buffer size and number of vehicles.

Fig. 9 :

 9 Fig. 9: Cost savings for different policies (∆t = 23 days).

TABLE I :

 I Notation used in the paper.

α Number of vehicles β Buffer size per vehicle ν Meeting rate TTL Time that the user is willing to wait before getting the content ϕ (i) t Popularity rate for content i at time t N (i) Number of copies stored in the vehicles for content i k Number of contents in the catalogue ∆t Refresh time and time window considered in the optimization p Probability to successfully download a content during a meeting vehicles do not exchange contents with each other; intervehicular communication (V2V) is left as future work;

  .

	DESCRIPTION	PARAM	VALUE
	Number of vehicles	α	1000 cars
	Buffer size	β	100 contents/car
	Meeting rate	ν	4 contacts/day
	Time-To-Live	TTL	3 minutes
	Number of contents	K	100.000 contents
	Communication range		200m

TABLE II :

 II Parameters of the scenarios considered.

In fact, in our system TTL << ∆t, since we only consider TTLs in the order of a few minutes. This is in contrast with some related work which use much longer TTLs to obtain similar gains[START_REF] Lee | Mobile data offloading: How much can wifi deliver?[END_REF].

We do not need to put extra constraints for individual car capacities (i.e. no car having more than β contents), because we assume that all the vehicles are statistically identical.Section IV, we solve the optimization problem in Eq. (1) considering also the incremental seeding.


E Le Coarer

M Barthelemy

A Vialatte

M Prugniaux

G Bourdarot

T Sequies

P Monsinjon

R Puget

N Guerineau

ATISE: A MINIATURE FOURIER-TRANSFORM SPECTRO-IMAGING CONCEPT FOR SURVEYING AURORAS AND AIRGLOW MONITORING FROM A 6/12U CUBESAT

I. INTRODUCTION

The nanosatellite ATISE is a mission dedicated to the observation of the emission spectra of the upper atmosphere (i.e. Airglow and Auroras) mainly related to both the solar UV flux and the precipitation of suprathermal particles coming from the solar wind through the magnetosphere. ATISE will measure specifically the auroral emissions, and the airglow (day-and night) in the spectral range between 380 and 900 nm at altitudes between 100 and 350 km. The exposure time will be 1 second in auroral region and 20 s at low latitude regions. The 5 year expected lifetime of this mission should cover almost a half of solar cycle (2 years nominal). This instrument concept is based on an innovative miniaturized Fourier-transform spectrometer (FTS) allowing simultaneous 1 Rayleigh sensitivity detection along six 1.5°x1° limb lines of sight. This 1-2kg payload instrument is hosted in a 12U cubeSat where 6U are allocated to the payload and 6U to the plateform subsystems. This represents a miniaturisation by a factor of 500 on weight and volume compared to previous Arizona-GLO instrument for equivalent performances in the visible. The instrument is based on microSPOC concept developed by ONERA and IPAG using one Fizeau interferometer per line of sight directly glued on top of the half of a very sensitive CMOS Pyxalis HDPYX detector. Three detectors are necessary with a total electrical consumption compatible with a 6U nanoSat. Each interferometer occupies a 1.4 M pixel part of detector, each is placed on an image of the entrance pupil corresponding to a unique direction of the six lines of sight, this in order to have a uniform illumination permitting good spectral Fourier reconstruction from fringes created between the Fizeau plate and the detector itself. Despite a limited 8x6 cm telescope, this configuration takes advantage of FTS multiplex effect and permits us to maximize the throughput and to integrate very faint emission lines over a wide field of view even if the 1 second integrated signal is comparable to the detector noise.

II. SCIENTIFIC GOALS AND MISSION REQUIEREMENTS

The science Requirements are :

-producing 6 contiguous 1.5°x1° field of view -with a spectral domain between [350,900nm]. This allow to get the main part of the Vegard Kaplan N2 band in the lower part of the spectra and to get the O-844 nm line which is important in auroral region. -The spectral resolution at 600nm must be lower than 1nm. Hence : R=λ/δλ=600 at 600nm. This allow to disentangle the vibrational line of the molecular bands. The rotational structure is not reachable at these spectral resolution. -The detection threshold of each line must be 5 Rayleigh (1 R = 1.10 6 photons.cm-2.s-1) and the sensitivity 1 R. Contrarily to a classic optical instrument, the goal of ATISE is not to make an image of each field of view, but to obtain an area which is uniformly illumined on each detector (those area on the detector are called "plage de Fabry" [START_REF] Coarer | SWIFTS-LA : An Unprecedently Small Static Imaging FT Spectrometer[END_REF]). To this end, the optical instrument can be viewed in two parts : first, a system focuses the entire field of view on the focal plane. In those 6 areas, we Micro lenses divided then the input light in 6 contiguous field. These micro lenses are positioned to project an image of the entrance pupil on the detectors. Thus, we obtain on each detector uniformly illumined area of each field, which are perfectly delimited, due to the pupil imaging through micro-lenses. As we will see further, the aperture (which is characterized by the f-number : N=focal/diameter) of the micro-lenses is the fundamental parameter of the design of this instrument. The aperture of the beam is yet strongly related to the nature of the spectrometer and to the expected spectral resolution. A µ-SPOC Fourier transform spectrometer is selected, in accordance with the mission specifications. This miniaturised Fizeau interferometer use the principle of Fourier Transform spectroscopy to produce spectra, that we are going to explain and illustrate in this paragraph.

ICSO 2016

Biarritz, France International Conference on Space Optics 18 -21 October 2016 Fig. 2. ATISE will observe aurorae on 70° inclined orbit at an altitude around 650km

II. INSTRUMENT PRINCIPLE AND DESCRIPTION

ATISE instrument is based on the MICROSPOC principle that was described in [START_REF] Rommeluère | Infrared focal plane array with a built-in stationary Fourier-transform spectrometer: basic concepts[END_REF] and [START_REF] Coarer | SWIFTS-LA : An Unprecedently Small Static Imaging FT Spectrometer[END_REF] It is made of a two-wave interferometer glued in front of the detector. Rays coming from infinite are partly reflected by the detector face and the substrate face as illustrated in fig. 3a make interferences detected by the active layer of the detector. Contrarily to a classic optical instrument, the goal of ATISE is not to make an image of each field of view, but to obtain an area which is uniformly illumined on each detector (those area on the detector are called "plage de Fabry"). To this end, the optical instrument can be viewed in two parts : first, a system focuses the 6 contiguous field of view on the focal plane. In those 6 areas, we put micro-lenses which make the image of the entrance pupil (and not of the field of view) on the detector. Thus, we obtain on each detector uniformly illumined area of each field, which are perfectly delimited, due to the pupil imaging through micro-lenses. As we will see further, the aperture (which is characterized by the f-number : N=focal/diameter) of the micro-lenses is the fundamental parameter of the design of this instrument. The aperture of the beam is yet strongly related to the nature of the spectrometer and to the expected spectral resolution. A µ-SPOC Fourier transform spectrometer is selected, in accordance with the specifications of the mission. This miniaturised Fizeau interferometer use the principle of Fourier Transform spectroscopy to produce spectra.

A. Optical design

Main entrance lens is 81x69 mm wide with 650mm focal lens focalizing 6°x1.5° field of view focal plane after 3 reflections on folding mirrors. In the focal plane, a line of six 19x12mm lenses integrates flux on each 1x1.5° sub field of view, each of them making an uniform image of entrance pupil with flux integrated in each field. Fig 4b shows these illuminated zones on the three detectors assembled contiguously. The instrument strength is to maximize optical acceptance of spectrometer up to the spectral resolution limit. For this geometry, an auroral 1 Rayleigh emission gives 1500 photons integrated on 1400x1088 detector zone. We will show further that it is compatible with the goal of 5 Rayleigh sensitivity for 3e RMS of detector including 1s dark current (total RMS 3e).

B. Mechanical design

C. Spectrometer design

The µ-SPOC spectrometer consists of a two-wave interferometer (a Fizeau interferometer) a semi-transparent glass plate assembled on top of a detector. The hypothesis that only two waves interferes depending of the reflectivity of the prism and the natural reflexion of the uncoated detector, as we will see further (contrast of fringes). A monochromatic beam coming from the infinite on a substrate which has the height e reflects off the detector and interferes with itself with an optical path difference (OPD) , where n is the optical index of the substrate (Fig 5a). The reflectivity coefficient maximizing two waves detection is set between 25% and 50% thus, the Fizeau finesse is comprised between 2 and 4. For glass plate part, a quaterwave single layer Ti02 gives 42% of reflexion as it has been design in [START_REF] Coarer | SWIFTS-LA : An Unprecedently Small Static Imaging FT Spectrometer[END_REF]. The second face of interferometer is assumed by the natural reflectivity of detector which is also 40%. Both reflectivity giving a fringe contrast of 66%. The 2808x1088 pixel detectors are divided in two zones of 1400*1088 pixels. On each zone, the fringes will be sampled along the 1400 pixels fast axis giving possibility to reach R=700 spectral resolution at 365nm wavelength. The detector pixel size is 10x10µm. the size of one Fizeau plate is 14x10mm. in front of each interferometer the entrance pupil is imaged with 15x12mm microlens with 100mm focal then the interferometer is illuminated by f/8 beam which is compatible with the R=600 spectral resolution at 600nm.

II. PERFORMANCES VERIFICATIONS

A. Detector characterization

Two versions of electronics has been tested, the first one version is very compact but not yet optimized regarding performances demanded by ATISE mission but this first prototype gives us these first encouraging images with 24e readout noise. A second electronics used by detector manufacturer gives 2.5 e readout noise which is the nominal readout noise without external perturbations and well stabilized alimentations. This electronic cannot be used yet for these optical tests but will be available later. The functioning temperature has been set to 20°C to avoid condensation. Some tests are under progress to determine the right functioning temperature in order to have 3e in 1second exposure time i.e. less than 2e of dark current. The temperature of the Fizeau and detector assembly will be set close to 0°C in satellite to fulfill the 3e noise specification in 1 second.

Fig. 9. left) HDPYX dark image in High gain configuration showing 4 ADU RMS corresponding to attended 2.5e read out noise. In ATISE, the detector will be essentially used in high gain configuration with 10ke sample on 14bits ADC. The full well capacity of pixel is 100ke can provide 120dB dynamics compatible with observation of very strong solar events.

B. Spectra reconstruction

Mainly, three parameters are necessary to reconstruct the spectrum:

-Exact depth between Fizeau plate and detector reflection face principally the detector silicon interface with metallic deposition for pixel's electronic. This depth depends of wavelength because of TiO2 layer and passivation layer. -Chromatic Pixel Photometric response depending of instrument transmission, Fizeau plate reflection coefficient and detector quantum efficiency. -Chromatic Fringe contrast which depends also of reflectivity of Fizeau Plate and detector reflectivity. The calibration of these values for each pixel is obtained from two measurements: -Observation of simple emission spectra such Neon lamp or high altitude night glow dominated by OI 630nm emission unresolved triplet at ATISE spectral resolution. -Observation of broadband flat-field on integrating sphere, Moon or solar light reflecting on desert or ocean surface After this calibration, the 1 dimensional interferogram is obtained by averaging every pixel contribution on a equidistant OPD distribution between optical contact (OPD=0) and maximum OPD.

C. Spectral resolution

The spectral resolution of Fourier Transform spectrometer depends only of the maximum sampled OPD. In our case, the Fizeau sampling starts at minimum optical distance at mechanical contact between detector surface and Fizeau plate surface. This minimum distance is around 1 micron according to passivation layer depth of detector. This mean that we don't measure the first fringe but this is not necessary for emission line spectra such aurorae. The maximum OPD distance is given by the depth of spacer set on the opposite side i.e. the angle of interferometer is 0.5°. This angle corresponds to a Shannon sampling of 700 fringes at 365nm which is the shorter wavelength.. If this medium is vacuum, this depth is 125µm if this medium is optical glue, n=1.5 and the depth is 82µm. SPOC sample one side interferogram, the full spectral resolution is attained when the interferogram is symmetrized before Fourier transform or to use directly the Cosine transforms. In two cases, the knowledge of absolute value of OPD is required. The pattern of neon's fringes of fig. 6. is sufficient to determine absolute OPD values. The spectral resolution is 1.5nm at 703 nm corresponds to the ATISE goal.

D. Low illumination Sensitivity

ATISE will observe at limb integrating aurora oval from the side as shown in the Fig. 2. The expected brightness is estimated to be 10kR i.e. 10^8 photons/s on each line of sight. This represents 5e6 photon on 1s exposure on detector. A very first laboratory experiment has been made observing an argon and neon discharged lamp which represents similar emission lines. The readout electronic is not yet optimized and the readout noise has been actually measured at 25e , this means that we has obtain similar results introducing 5e7 photons in the systems. Fainter distinguishable lines represent 50R that is equivalent to the ATISE's 5R requirement according the actual experimental noise excess that will be corrected during next study phases.

IV. CONCLUSIONS

ATISE is an innovative spectrometer taking advantage of detector progress. It permits us to design small and powerful instruments to surveying aurorae and upper atmosphere airglows. Strengths of this concept if its ability to integrate very faint diffuse emissions under a large solid angle contrary to slit spectrometers. The new HDPYX CMOS detector has an exceptional dynamics especially optimized for Fourier spectrometry with low readout noise. It is well adapted to ATISE to follow large dynamic events without saturations. The uncoated detector is well adapted to play a role of semi-transparent mirror , we exploit this feature to win a 40% of

ICSO 2016

Biarritz, France International Conference on Space Optics 18 -21 October 2016 transmission. The measurements accuracy don't depend of optics stability but only of the stability of the assembly of Fizeau interferometer to detector which not requires 0.1° thermal regulation on a very small volume and weight.

Fig. 1 .

 1 Fig. 1. Spectrum of the auroral simulator planeterrella (ref papier Jean) between 520 and 700 nm. The resolution is around 1 nm. The spectrum of the aurora is somehow different since forbidden transitions also appear in aurora and airglow mainly O 1 S at 557 nm and O 1 D triplet at 630, 639 and 639 nm

Fig. 3 .Fig. 3 .

 33 Fig.3. shows a sketch of 6U ATISE payload with 2U dedicated to field imager, on board electronic. During next study phase, all optical part support and baffles will be designed. Detectors need to be regulated at 0°C +/-0.1.

Fig. 5 .

 5 Fig. 5. a) SPOC principle : A semi-transparent plate is set on top of detector. Light coming from observed field of view reflects both on semi-transparent plate and detector itself. Fringe pattern formed by interference is measured by detector. Fourier transforms of this image gives the spectra corresponding to whole field of view concern by this way. b) An HDPYX detector is divided in two zone with one interferometer by zone. 3 detectors permits us to select 6 lines of sight

Fig. 6 .Fig. 8 .

 68 Fig. 6. left) Pyxalis HDPYX Sensor in BGA, Ceramic Ring configuration; right) tested SPOC configuration , a 60nm TiO2 layer has been deposited on plane of plano-convex lens. An optical fiber is set at focal plane of the lens to obtain flat illumination.

Fig. 10 .

 10 Fig. 10. left) Under low flux illumination corresponding to flux receive in 1s exposure of aurora .Fringes visible on fig. 7 are not visible but are sufficient to build a usable spectra after Fourier transforms.

AKNOWLEDGMENTS

We would acknowledge the FOCUS labex which funds first prototype and supports the SPOC project. ATISE project is developed in collaboration with Zelenograd university and the University Space Center in Toulouse. 50 students has worked for ATISE project in Grenoble University Space Centre which is funded by Grenoble INP and UGA and Rhone-Alpes Région. Many thanks to CNES-JANUS programs and Michel Moulin.


Rui Pedro Lourenço

Leila Serra
email: leila.maria@ufma.br

An Online Transparency for Accountability Maturity Model

Keywords: Transparency, accountability, assessment, maturity model

Online transparency for accountability assessment exercises reported in the literature rely solely on the analysis of public entities' individual web sites, measuring the data disclosed and the way it is disclosed, and not taking into consideration the context in which these 'target' entities operate. This paper aims at identifying key contextual elements that may influence the way data is disclosed by public entities in their individual web sites, and therefore should be taken into consideration when designing the assessment models and exercises. The contextual elements identified were organized into an online transparency for accountability maturity model that may be used on its own to assess the overall level of sophistication of a country or region ('context'), or it may be used in a stage-gate approach to define the appropriate type of entities assessment model. Researchers wanting to assess a set of 'target' entities should therefore begin by analyzing the context in which they operate (using the proposed maturity model) and then define their assessment model according to the recommendations proposed in this paper for the corresponding maturity level.

Introduction

The subject of Open Government has been emerging as a top topic of interest in Electronic Government Research over the last few years [START_REF] Scholl | Electronic Government Research: Topical Directions and Preferences[END_REF]. Meijer et al. [START_REF] Meijer | Open government: connecting vision and voice[END_REF] emphasize that openness includes both the possibility of citizens to monitor governmental action ("vision": transparency), but also to influence government processes through access to decision-making arenas ("voice"). As one of the Open Government objectives [START_REF] Linders | What is Open Government? One Year after the Directive[END_REF], transparency, in particular, has also received attention from both academics and practitioners, the later confirmed by the emergence of Open Government Data Portals (such as Data.Gov) worldwide 1 .

With respect to transparency in the open government context, Linders and Wilson [START_REF] Linders | What is Open Government? One Year after the Directive[END_REF] further distinguish between the disclosure of government data aiming to promote its reuse for social or economic value, and data openness with the intent to support accountability of public officials. While the former is more closely associated with the creation of high-profile Open Government Data Portals, this work aims at addressing the latter (transparency for accountability).

The concept of accountability is very complex and may be understood from many different perspectives. Bovens [START_REF] Bovens | Analysing and Assessing Accountability: A Conceptual Framework[END_REF] not only advances a very synthetic description for accountability ("the obligation to explain and justify conduct"), but also proposes several perspectives from which the concept of accountability may be analyzed. From the "To Whom is Account to be Rendered" perspective emerges the concept of political accountability, whereby citizens (among others) are the recipients of governmental disclosure efforts as a counterpart for the power delegation which characterizes representative political systems [START_REF] Bovens | Analysing and Assessing Accountability: A Conceptual Framework[END_REF]. Open Government in general, and transparency in particular, may be considered an important prerequisite for political accountability because they allow citizens to access the information they need to assess the conduct of public officials responsible for managing the resources at their disposal [START_REF] Bovens | Analysing and Assessing Accountability: A Conceptual Framework[END_REF].

While transparency has been associated for a long time with 'traditional' (paper based) freedom of information, the technological transformations of the last decades, the Internet in particular, have impacted profoundly the disclosure processes and access possibilities to government information [START_REF] Jaeger | Transparency and technological change: Ensuring equal and sustained public access to government information[END_REF]. The relevance of online transparency has led to several assessment exercises reported in the literature (see, for instance, [START_REF] Lourenço | Data disclosure and transparency for accountability: A strategy and case analysis[END_REF] for a list of examples). In these exercises, researchers usually select a set of 'target' public entities, define a set of transparency requirements (assessment model) and analyze those individual entities' web sites to assess in what extend they meet the requirements. The result is usually expressed as a disclosure index, a "single-figure summary indicator" [START_REF] Coy | The public accountability index: crafting a parametric disclosure index for annual reports[END_REF] which is considered as a proxy for the entity transparency level.

However, such online assessment models and exercises, by analyzing the individual entities web sites in isolation, do not consider the context in which the entities operate. Such context (country, federal state/region, …) might condition the assessment models applicable to them. The importance of the context may be illustrated by the emergence of open government dataset portals, and their impact on the way transparency is 'traditionally' assessed: data concerning a particular public entity (under assessment) may no longer be disclosed solely at the entity web site. This, and other characteristics of the entities context, creates new challenges to online transparency assessment exercises and 'traditional' assessment models may no longer be totally adequate for the purpose. In sum, online transparency assessment exercises can no longer rely solely on entities' individual web sites analysis and need also to consider the context in which they operate.

Although some maturity models have been proposed in the context of open government (see following section) they do not establish any connection with 'traditional' individual entities online transparency assessment models. The goal of this paper is then to close this gap. It starts by identify key contextual elements which may influence the way 'target' entities assessing models are defined and applied, and then proposes an online transparency for accountability maturity model based upon them ('context maturity model'). Instead of just computing a single-figure summary indicator (index) for each entity, with a one-size fits all assessment model that disregards the context in which the entities operate, we propose to use a stage-gate approach: in a first step, the model is used to assess the context maturity level from an internet-enabled transparency perspective (common to all individual entities under assessment); then, depending on the maturity of the context, a specific (more detailed) assessment model ('entities assessment model') is used to provide an index value for each entity. In the end, both indicators (context level and entity index) will form a global assessment of each entity efforts concerning online transparency for accountability. Alternatively, the proposed context maturity model may be used in a standalone basis to assess and compare the development of countries, federal states, regions or any other contextual entities.

The remainder of this paper is organized as follows. The next section will reflect upon some of the maturity models already proposed in open government related literature. This will be followed by a section where a set of key context characteristics relevant to individual public entities online transparency assessment exercises is presented. Then, section 4 presents and characterized the proposed maturity model. The paper will end with some conclusions and reflections about further developments.

Previous research on maturity models

Maturity models are commonly used to describe or represent the "anticipated, desired, or typical evolution path" [START_REF] Becker | Developing Maturity Models for IT Management[END_REF] of an entity (such as an organization or country) or class of objects (such as processes) over time. In this context, maturity is considered as a synonym of "competency, capability, or level of sophistication" on a particular domain [START_REF] De Bruin | Understanding the Main Phases of Developing a Maturity Assessment Model[END_REF]. Although such development path may be represented by a continuous index, usually it is modelled by a discrete staged maturity model [START_REF] Klimko | Knowledge Management and Maturity Models: Building Common Understanding In: Second European Conference on Knowledge Management[END_REF] and a set of criteria is used to assign each entity to a particular stage at a particular moment ("a snap-shot of the organization regarding the given criteria" [START_REF] Becker | Developing Maturity Models for IT Management[END_REF]). Stages in maturity models are also commonly considered as cumulative, that is, "higher stages build on the requirements of lower stages" [START_REF] De Bruin | Understanding the Main Phases of Developing a Maturity Assessment Model[END_REF] as entities progress from the lower stages to top ones. This one-dimensional linear approach to maturity assessment, although simpler, may not be fully adequate to the complexity of the relevant domain. An alternative is to use a stage-gate approach where separate assessment models are used for each of the 'main' maturity model stages [START_REF] De Bruin | Understanding the Main Phases of Developing a Maturity Assessment Model[END_REF]. Maturity (stage) models have been proposed for a long time both in the field of eGovernment [START_REF] Andersen | The forgotten promise of e-government maturity: Assessing responsiveness in the digital public sector[END_REF][START_REF] Andersen | E-government maturity models: Extension of the Layne and Lee model[END_REF][START_REF] Layne | Developing fully functional E-government: A four stage model[END_REF][START_REF] Lee | 10 year retrospect on stage models of e-Government: A qualitative metasynthesis[END_REF][START_REF] Siau | Synthesizing E-government Stage Models -a Meta-synthesis Based on Meta-ethnography Approach[END_REF], eDemocracy/eParticipation [START_REF] Tambouris | A Framework for Assessing eParticipation Projects and Tools[END_REF][START_REF] Macintosh | Using information and communication technologies to enhance citizen engagement in the policy process[END_REF], and eGovernance [START_REF] Lourenço | From e-Government and e-Democracy to e-Governance: a unified view[END_REF]. However, these models focused mainly on the sophistication of online service provision and/or citizens' engagement and participation, reserving a secondary role for information provision and were therefore not aligned with the current Open Government initiatives who put transparency and participation at the heart of eGovernment and eDemocracy.

Recently, an Open Government Data stage model [START_REF] Kalampokis | Open Government Data: A Stage Model[END_REF] was proposed to address the shortcomings of the previous eGovernment maturity models with respect to online information provision. This model focus specifically on data integration and consists of four stages ranging from 'Aggregation of Government Data' to 'Integration of Government Data with Non-Gov Formal data and Social data'.

In the same context, Lee and Kwak [START_REF] Lee | An Open Government Maturity Model for social mediabased public engagement[END_REF] proposed an Open Government Maturity Model to assess and guide the development of open government initiatives with a special attention to the way social media may contribute to increase public engagement. The model follows closely the three main open government objectives (transparency, participation and collaboration [START_REF] Linders | What is Open Government? One Year after the Directive[END_REF]) by making them correspond to the three intermediary stages between 'initial conditions' (level 1) and the more sophisticated 'ubiquitous engagement' (level 5).

Despite these efforts, none of the two previous models specifically address the global aspects of online transparency for accountability assessment, but rather focus on particular aspects of data provision. The proposed maturity model will not only address contextual online transparency (when used in a 'standalone mode'), but will also serve as a bridge to 'traditional' individual entities online transparency assessment models by adopting a stage-gate approach: in the first step the maturity model will analyze the context in which these entities operate and, depending on this first assessment, will then serve as a guidance to develop and apply the appropriate entities assessment procedure. This approach will result in an index value for each entity within the maturity level of the overall context. This way it is possible to avoid unnecessary analysis concerning 'advanced aspects' when the overall context is still characterized by a low maturity level.

The next section will present the key context characteristics considered to develop the proposed model.

Key context characteristics

The proposed maturity model is based on the identification of key characteristics associated with the context in which public entities operate which were derived from literature analysis. These characteristics concern both technological and organizational aspects, and the way they influence and relate to each other.

Technological infra-structure

The first major contextual characteristic which is deemed relevant to the type of online transparency assessment model applicable to individual public entities is a technological one: technical infra-structure. Since online transparency assessment exercises tend to be performed in developed (or, at least, developing) countries, it is almost always taken for granted that a technical infra-structure exists, namely the Internet, over which public entities disclose their data. Moreover, it is usually assumed that most (if not all) entities addressed have a web presence of some sort and that they use such presence to disclose (more or less) relevant accountability data. Beside the infra-structure itself, other aspects that might be used characterize the context include broadband internet adoption rate by citizens (the ultimate recipients of accountability data), for instance.

3.2

Dataset portals and web sites An important element that may influence the way individual entities disclose accountability information is related to the existence of external (global) dataset portals and web sites, corresponding to what Kalampokis et al. [START_REF] Kalampokis | A classification scheme for open government data: towards linking decentralized data[END_REF] refer to as "direct data provision"2 . As part of Open Government initiatives, many generic open data portals, such as Data.gov, were created as aggregators of data that is usually reported by entities on a voluntary basis.

A different type of thematic (more specific) portals has also emerged, such as Recovery.gov [START_REF] Huijboom | Open data: an international comparison of strategies[END_REF] or European national sites disclosing data concerning the projects and beneficiaries of the European Regional Development Fund (ERDF) and the European Social Fund (ESF) [START_REF] Reggi | Information Strategies for Open Government in Europe: EU Regions Opening Up the Data on Structural Funds[END_REF].

Still in this category, another type of portals may be considered, stemming from the way public entities in modern States are organized into sectors: clusters of entities grouped together according to their similar legal status (regime), type of services provided, goals pursued, or administrative autonomy. Sometimes these sectors have dedicated portals, curated by a particular supervising entity that collects, processes and discloses data concerning all public entities from that sector. In sum, data portals, either generic, thematic or sectorial, changed the relevant context for accountability data disclosure and should be taken into consideration when performing assessment exercises.

Accountability networks

In the last decades the structure and organization of modern States changed profoundly as a result, among others, of privatizations and New Public Management inspired reforms [START_REF] Heald | Why is transparency about public expenditure so elusive?[END_REF]. This resulted in a more complex and fragmented State, with a blurred frontier among private and public entities, therefore making it more difficult for ordinary citizens to "comprehend, map and record" the resulting constellations of public entities [START_REF] Heald | Why is transparency about public expenditure so elusive?[END_REF]. These structural changes had also an impact on existing accountability regimes ("the sum of a series of interconnected accountability arrangements and relationships regarding a particular actor") [START_REF] Bovens | Towards a More Accountable EU: Retrospective and Roadmap[END_REF], thus leading to dense and complex "networks of accountability" [START_REF] Scott | Accountability in the Regulatory State[END_REF]. Under these accountability regimes, individual public entities are subjected to administrative and financial supervision and control from auditors, inspectors, controllers and other supervising entities. The existence of such internal (not public), administrative accountability networks changed the relevant context for accountability data disclosure since entities are now subjected to mandatory data internal disclosure regimes.

This new reality should also be taken into consideration when performing assessment exercises, namely in what concerns the type and amount of data expected to be disclosed publicly on entities individual web sites (at least a subset of that reported through the accountability network, for instance).

3.4

Overall structure and organization of information An important element of context characterization is related to the existence of overall structures that increase the visibility and access to information, facilitate the organization of the disclosed data by individual entities, and provide the necessary framework and guidelines to such disclosure procedures.

An example of such structures would be Public Sector Information (PSI) catalogues [START_REF] Shadbolt | Linked Open Government Data: Lessons from Data.gov.uk[END_REF], which may include the identification and characterization of public sectors in which the State entities are clustered, and the identification and characterization of all entities belonging to each sector. Other catalogues may exist to list and describe information resources (including open government data portals and individual entities web sites), thus increasing their visibility and facilitating the access to accountability related data.

From a more technical perspective, an example of an overall structure would be the existence of common ontologies which individual entities may use to describe the disclosed datasets (metadata), thus facilitating their search, retrieval and analysis. Yet another contextual element would be the existence of a global Linked Data framework designed to facilitate publishing data on the Web "in such a way that it is machinereadable, its meaning is explicitly defined, it is linked to other external data sets, and can in turn be linked to from external data sets" [START_REF] Bizer | Linked datathe story so far[END_REF]. With such a framework in place, individual entities would then be able to use it in a more effective way.

In sum, these technological transformations have also changed the relevant context for accountability data disclosure and should be taken into consideration when performing assessment exercises.

The Online Transparency Maturity Model

According to De Bruin et al. [START_REF] De Bruin | Understanding the Main Phases of Developing a Maturity Assessment Model[END_REF], maturity models may be applied for descriptive, prescriptive or comparative purposes. The proposed maturity model aims to be descriptive in the sense that it could be used to assess (describe) the as-is situation of online transparency development of a particular context for a set of public entities, such as a country or federal state. Similarly, the model may be used to provide a benchmarking baseline among countries (a typical 'context', for instance), and as a prescriptive model in the sense that it may be used to provide a framework to develop and implement an online transparency policy. The model assumes that the public entities operate in a political democratic context, whereby a legal and Constitutional framework exist that protects freedom of information and general access to administrative documents. Rather than simply using a one-dimensional standalone maturity model, we propose to adopt a global stage-gate assessment approach to structure online transparency assessment exercises in two steps:  In the first step we take advantage of the simplicity of a maturity model by using it in a preliminary evaluation of the context in which 'target' public entities operate;

 Then, a specific assessment model is used to complete the analysis of the 'target' entities. Such model considers the potential and limitations of the context, as expressed by the evaluation resulting from the maturity model. Each stage will be characterized below using the contextual elements identified in the previous section. Also, some of the major implications for online transparency assessment models appropriate for each context maturity level will be discussed.

4.1

Maturity Level 0 -No technical infra-structure According to Becker et al. [START_REF] Becker | Developing Maturity Models for IT Management[END_REF], "the bottom stage [of a maturity model] stands for an initial state that can be, for instance, characterized by an organization having little capabilities in the domain under consideration." From the perspective of this maturity model, this means that the context in which the 'target' entities operate is characterized by a poorly developed internet infra-structure with few public entities having its own web site. If so, it is perhaps meaningless to conduct online (internet-enabled) transparency exercises and therefore to define and apply any online transparency assessment model. If, however, such assessment is to be performed anyway, the model and procedure used should focus on simple characteristics of online disclosure.

Maturity Level 1 -Initial conditions

Once the internet infra-structure is in place and entities (both public and private) generally have their own web site, we may consider that the context in which public entities operate has reached its 'initial conditions'. Other elements to consider when assigning 'contexts' to this stage may include the level of broadband access rate by citizens and entities, and other similar indicators.

At this context maturity level, online transparency assessment models should consider solely the data disclosed in each entity web site as each public entity independently discloses accountability related data. 'Traditional' assessment models, as described earlier in this paper, fall into this category and therefore should be considered adequate to assess entities functioning in this level of context maturity. Furthermore, such models should not expect individual entities to adopt sophisticated technological approaches, such as the ones associated with Linked Data [START_REF] Bizer | Linked datathe story so far[END_REF], for instance, to disclose data. Rather, data might be disclosed in a simpler spreadsheet format. Other dimensions of the entities assessment models should also adopt a conservative perspective in what concerns technological and organizational sophistication.

Maturity Level 2 -Data portals

The existence of open government data portals in the context in which 'target' entities operate is a pre-condition for the maturity of such a context to be considered in this level. The Open Government movement has contributed to the emergence of both generic (such as Data.gov) and thematic (such as Recovery.gov) portals. Sector portals have also been created in some countries, but they depend much more on the internal organization of States. Generic data portals usually depend on individual entities voluntary disclosure of data, while thematic and sector dedicated portals are usually associated with some kind of supervising entity (theme or sector) to which individual public entities are obliged to report accountability data. In this case it is up to the entities responsible for these thematic or sector dedicated portals to publish some (or all) of the reported data. From a technological point of view, establishing such data portals does not present a major technical challenge since many open source platforms (such as the widely used CKAN3 ) are currently available. At this maturity level, online transparency assessment models must take into consideration the existence of these external/contextual data portals, and that entities may use them to disclose some (or all) relevant datasets (either voluntarily or not). Therefore, some of the characteristics of the portals themselves (dataset format, downloading possibilities, …) must be considered in the individual assessment models corresponding to this stage. In particular, such models should consider how is the data published in such portals visible and referred to (linked) from the entities web sites (that is, the way individual web sites relate to external portals) and how do they deal with the possibility of duplicated data (inconsistency).

Maturity Level 3 -Accountability networks

This stage presupposes not only that an internal (administrative) accountability network exists for the different types of public entities (even stretching beyond the public sector 'supervising' entities) but also that such network is explicit and visible: it is well known exactly what entities are part of the network and what is their role in it. It should also be clear what type of accountability relevant data is reported by each type of public entities through their accountability network. At this maturity level, online transparency assessment models must take into consideration the existence of such networks and the way they might influence how data is disclosed. In general, such models should consider the accountability network of a particular entity as a reference to what should be available and how by:  Defining a minimal set of data individual entities should disclose to the public (political accountability), considering that such data is already being produced for internal (network) reporting purposes (administrative accountability);  Considering that part (or all) of that data is disclosed by the 'supervising' entities of the network and not directly in the target entities individual web site.

Maturity Level 4 -Overall structure and organization

For a certain context to be considered at this top level maturity, the requirements of all previous levels should be fulfilled. However, to reach this level, there needs also to exist an overall structure that gives coherence and connects all the individual elements that characterize the levels so far. This may include a catalogue of all resources relevant for online transparency assessment purposes (according to previous levels), a global ontology and a Linked Data infra-structure. At this maturity level, online transparency assessment models must take into consideration whether or not individual entities take advantage of these technological elements provided by the context. For instance, each entity, its web site, and relevant data resources disclosed in it, should be visible in the global catalogues mentioned (almost like a Google search engine visibility). Furthermore, individual entities should use the global transparency for accountability ontology to provide standardized metadata for the datasets disclosed which, in conjunction with the adoption of Linked Data principles, should facilitate data search, retrieve and processing.

Conclusions

In the last few years the context in which public entities function has suffered the impact of both organizational and technological transformations. The complexity of accountability networks in which entities are inserted, and the emergence of Open Government Data portals, for instance, pose new challenges to the way individual entities may be assessed concerning how they use the Internet to disclose accountability related data. This means 'traditional' online transparency assessment methods that focus solely on the characteristics of individual entities web sites, thus ignoring such transformations in the context, may no longer give an accurate picture of the transparency panorama. Also, since different entities might operate in different contexts (with diverse technological and organizational characteristics), it is not adequate to use a one-size fits all assessment model. This paper proposes an online transparency maturity model, based on some of the most prominent context characteristics (from an internet-enabled perspective on transparency), which may be used in a standalone manner to assess the context in which entities operate or, in a stage-gate approach, as a first step to define an adequate assessment model for the 'target' entities (depending on the maturity of their context).

Like any other maturity model, the advantage of its simplicity may be subjected to criticism. The model steps sequence and cumulativeness represent a certain desired evolution path of sophistication that may not correspond entirely to the reality of some contexts. For instance, a particular country being assessed might exhibit characteristics of several stages or even develop the elements of a top level before the developing the ones in the levels below. Nevertheless, the sequence in which the levels are proposed took into consideration the complexity, (global) scope and impact of the technological and organizational characteristics considered in each level.

The proposed model is intended not only to provide a macro assessment tool applicable to contexts such as countries or federal and regional states, but also to close the gap between this and other micro level (entity level) 'traditional' assessment exercises. Therefore, those wanting to initiate individual entities assessment exercises should consider first the level of maturity of the surrounding context, and then adapt the appropriate assessment model according to the suggestions made. In the end, the overall analysis should help both academics and public officials to develop better online transparency for accountability systems both at macro and micro level.

As this maturity model focus on internet-enabled transparency, further research might consider the possible impacts of proactive open government legal frameworks on online transparency maturity. Such proactive policy and legal structures extend beyond the general protection of freedom of information that constitute the hallmark of modern western-like democracies.

Figure 1 Level 4 -Fig. 1 .

 141 Figure 1 presents the cumulative stages of the proposed online transparency maturity model. Level 4 -Overall structure and organization

See https://www.data.gov/open-gov/ for a list of such initiatives (last consulted in13-03-2014)

From the perspective of the entities to which the data belongs, publishing it in a global portal rather than on their own web sites would perhaps better qualify as an "indirect data provision".

www.ckan.org

Acknowledgements. This work has been partially supported by the Portuguese Foundation for Science and Technology under project grant PEst-OE/ EEI/UI308/2014.