Gilles Marck
email: gilles.marck@ljll.math.upmc.fr

Yannick Privat
email: yannick.privat@ljll.math.upmc.fr

ON SOME SHAPE AND TOPOLOGY OPTIMIZATION PROBLEMS IN CONDUCTIVE AND CONVECTIVE HEAT TRANSFERS

Keywords: topology optimization, heat transfer, conduction, convection, laminar flow

The topology optimization of systems subject to heat and mass transfers shows a wide potential for designing optimal and innovative structures in energy engineering. The present works apply the concepts of the homogenization method to two configurations subject to conductive and convective heat transfers. After recalling some basic principles, a special attention is given to the numerical approach dealing with multi-objective optimization problems that naturally occurs in several practical case studies. The two last sections are dedicated to some numerical investigations and provide a physical interpretation of the structural features reached by the optimization process. The main conclusion deals with the possibility of finding an acceptable trade-off between different objective functions, for both conductive and convective shape optimization problems, provided that the Pareto frontier is convex.

INTRODUCTION

Heat and mass transfers are two physical phenomena at the root of several thermal systems. Shape and topology optimization problems in connection with this field are investigated through this proceeding, mainly from a numerical point of view, and a special attention is given to two heat transfer modes, respectively conduction and convection. They are both addressed by means of the same numerical approach, the so-called homogenization method [START_REF] Allaire | Shape Optimization by the Homogenization Method[END_REF], also referred as the Solid Isotropic Material with Penalization method (SIMP) [START_REF] Bendsoe | Topology Optimization: Theory, Methods and Applications[END_REF]. Succinctly, the different shape optimization problems are transformed into topological ones by conveniently adapting the coefficients of the Partial Differential Equations (PDE) involved in their physical modelling. This allows solving only one set of equations even if the problems deal with subdomains characterized by different physical properties.

An overview of the main principles, such as the algorithm and some mathematical background, is provided in first section. The strategy used to properly handle the multi-objective nature of heat transfer problems is also detailed. Indeed, from a engineering point of view, most of industrial problems may be tackled by optimizing several objective functions at the same time, leading to a set of optimal solutions, the so-called Pareto set. However, establishing these optimal sets is computationally expensive and requires a suitable method to accurately capture the trade-offs between each solution.

The second and third sections are dedicated to the physical analysis of several numerical results. First, we consider a topology optimization problem in conduction: it aims at determining the best shape of a high conductivity layer located above a solid-state finite-size volume generating heat. This academic case has been introduced by Bejan [START_REF] Bejan | Constructal-theory network of conducting paths for cooling a heat generating volume[END_REF] and has been studied with several different methods, ranging from physical approaches to genetic algorithms [START_REF] Marck | A new perspective of constructal networks cooling a finite-size volume generating heat[END_REF][START_REF] Boichot | Tree-network structure generation for heat conduction by cellular automaton[END_REF][START_REF] Gosselin | Review of utilization of genetic algorithms in heat transfer problems[END_REF]. It has also been investigated in several articles using methods coming from the topology or shape optimization theory [START_REF] Zhuang | Topology optimization of multi-material for the heat conduction problem based on the level set method[END_REF][START_REF] Zhuang | Topology optimization of the transient heat conduction problem on a triangular mesh[END_REF][START_REF] Gersborg-Hansen | Topology optimization of heat conduction problems using the finite volume method[END_REF][START_REF] Marck | Topology optimization using the SIMP method for multiobjective conductive problems[END_REF], but without dealing with the multi-objective nature of the problem.

Secondly, we consider a similar topology optimization problem, but by expanding the physical modelling to take into account the influence of fluid motions on heat transfer. Consequently, the problem becomes more complex, since it attempts to establish the distribution of solid and fluid subdomains optimizing several objective functions subject to the Navier-Stokes and energy equations. On the one hand, a classical objective aims at minimizing the viscous dissipation occurring through the fluid phase, in order to minimize the power required to set it into motion. On the other hand, an objective relying on the thermal aspects of the problem pursue to maximize the heat transfer taking place inside the solid domain.

Minimizing the viscous dissipation of a fluid flow thanks to a topology optimization approach has initially been investigated by Borrvall and Petersson, in the frame of Stokes flow [START_REF] Borrvall | Topology optimization of fluids in Stokes flow[END_REF]. This first work has been followed by several studies using the same optimization strategy but with different physical models, such as Navier-Stokes [START_REF] Gersborg-Hansen | Topology optimization of flow problems[END_REF], Darcy-Stokes [START_REF] Guest | Topology optimization of creeping fluid flows using a Darcy-Stokes finite element[END_REF] or unsteady Navier-Stokes [START_REF] Kreissl | Topology optimization for unsteady flow[END_REF]. More recently, several studies have studied the coupling of Navier-Stokes with a transport equation, such as the energy equation, in order to increase the performances of thermal systems [START_REF] Dede | Multiphysics topology optimization of heat transfer and fluid flow systems[END_REF][START_REF] Papoutsis-Kiachagias | Constrained topology optimization for laminar and turbulent flows, including heat transfer[END_REF][START_REF] Kontoleontos | Adjoint-based constrained topology optimization for viscous flows, including heat transfer[END_REF]. However, only [START_REF] Marck | Optimisation Topologique des Transferts de Chaleur et de Masse -Application aux Échangeurs de Chaleur[END_REF][START_REF] Marck | Topology Optimization of Heat and Mass Transfer Problems: Laminar Flow[END_REF] looks at establishing the set of optimal solutions underlining the trade-off taking place in the design of each structure.

ALGORITHM AND BASIC PRINCIPLES

Topology optimization problem

Fig. 1 pictures two common problems that occur in the enhancements of heat transfer systems. These both cases are considered under steady-state regime for this whole proceeding. On the left-hand side, the layout of two solid-state sub-domains, called Ω 0 and Ω 1 , is optimized for a given functional. Both solid subdomains are characterized by two different thermal conductivities, respectively k 0 and k 1 , and two different heat generation rate, respectively q 0 and q 1 . Since no transport mechanism takes place inside the whole optimization domain, denoted Ω = Ω 0 ∪ Ω 1 , the stationary heat equation for conduction may accurately describe the temperature behaviour.

By extension, the right-hand side of Fig. 1 illustrates a configuration where a convection phenomenon occurs in presence of a fluid flow. In this arrangement, one of the previous solidstate subdomain, for instance Ω 0 , is replaced by a fluid phase flowing through the subdomain denoted Ω f . On the other hand, the complementary subdomain remains in a solid state and is called Ω s . Therefore, it means that the heat flux is conducted trough Ω s , then convected by the flow motion inside Ω f , leading to consider a conducto-convection phenomenon over the whole domain For both problems, the thermal boundary conditions applied along ∂Ω, also denoted Γ for the sake of clarity, are the following:

Ω = Ω f ∪ Ω s . !=0,T d ! i ,T i ' f α f , k f ! o , ∂T ∂n =0 ∂T ∂n =0 !=0, Γ o Γ i Γ a Γ d ' s α s , k s Γ d T d ∂T ∂n =0 ' 1 k 1 , q 1 ' 0 k 0 , q 0 !"#$%!&'"# !"#$%&'($% !"#()!&'"# !"#$%&!"#$%
• the Dirichlet boundary condition, setting a constant temperature along the edge Γ d ;

• the Neumann boundary condition, setting a zero normal flux along the edge Γ a . It is also referred as the adiabatic boundary condition in thermal engineering. By definition, it means that ∇T • n ≡ ∂T /∂n = 0 where n is the normal vector to the boundary Γ pointing outwards the domain Ω.

In addition, the fluid flow boundary conditions are of two kinds:

• the walls, such as Γ a or Γ d are subject to a non-slip boundary conditions, assuming that u = 0;

• the inlet and outlet flows are set up with a parabolic Poiseuille profile, fulfilling the law of conservation over the whole domain Ω.

Without giving details of the physical modelling leading to the different sets of PDE (see [START_REF] Marck | Topology optimization using the SIMP method for multiobjective conductive problems[END_REF][START_REF] Marck | Topology Optimization of Heat and Mass Transfer Problems: Laminar Flow[END_REF] for further explanations), the different topology optimization problems may be generically written as inf

χω∈D ϕ J(χ ω ) = Ω f (ζ, χ ω , x)dx
subject to PDE with respect to variables ζ.

(

) 1
and where

D ϕ = {χ ω ∈ L ∞ (Ω, {0, 1}), |ω| ≤ ϕ|Ω|}.
In this definition, ω is the subdomain whose is subject to the topology optimization process, that is Ω 1 or Ω f , and χ ω is its characteristic function. In addition, the feasible region D ϕ is restricted by a volume constraint on |ω|, expressed as a fraction ϕ ∈ [0, 1] of the whole volume domain |Ω|. Indeed, for most optimization problems, ω = Ω is the best trivial solution for several classes of objective functions J(χ ω ), but is either unrealistic from an engineering point view or too expensive due to economic aspects.

Algorithm for homogenization method

As pointed out in the introduction, the topology optimization approach aims at conveniently adapting the coefficients of PDE to solve only one set of equations over the whole domain Ω. For instance, in the case of the conduction optimization problem, the thermal conductivity over Ω is given by k

(χ Ω 1 ) = k 0 + (k 1 -k 0 )χ Ω 1 . (2)
This allows solving only one equation without explicitly taking into account the differences between the subdomains Ω 0 and Ω 1 , apart from the thermal conductivity computation step. This idea is also applied to the heat generation rate q or Navier-Stokes equation, as detailed in the sections 3 and 4.

It is well known that the solutions of such infinite dimensional parametric problem do not necessarily exist (see [START_REF] Henrot | Variation et optimisation de formes: Une analyse géométrique[END_REF][START_REF] Allaire | Conception optimale de structures[END_REF]), especially in the frame of heat conduction. To overcome this difficulty, a relaxation procedure is carried out: it mainly aims at extending the admissible set of solutions by replacing χ ω ∈ L ∞ (Ω, {0, 1}) by η ∈ L ∞ (Ω, [0, 1]). From now on, any physical scalar parameter depending on χ ω continuously depends on the function η. This feature can be exploited to cleverly control the convergence process by adding a penalization parameter p (see [START_REF] Bendsoe | Topology Optimization: Theory, Methods and Applications[END_REF]). For example, the same thermal conductivity k now writes

k(η) = k 0 + (k 1 -k 0 )η p with 0 ≤ η ≤ 1 and p ≥ 1. (3)
The penalization parameter p masters the convexity of function k(η) and allows tuning the convergence of the optimization problem. Other functions may be considered, while the penalization parameter p impacts on their convexity (see [START_REF] Marck | Topology Optimization of Heat and Mass Transfer Problems: Laminar Flow[END_REF] for further details). Combined with other numerical tools such as filtering, it allows speeding up or slowing down the convergence and escaping from some local optima. Note that from this point, since the design parameters η are continuous, the domain Ω includes "composite" materials, with physical values ranging between their bounds defined in each subdomains Ω 0 and Ω 1 . Consequently, one main role of the penalization parameter is to ensure that the optimization process converges toward a black and white design: it progressively suppresses the gray level by penalizing the objective function.

Behind the non-existence issue, the second advantage of the relaxation step is to allow computing the topology derivative from the parametric formulation. Let denote dJ(η), θ , the differential of J with respect to η in direction θ. This topology derivative, also referred as sensitivity, is computed thanks to an adjoint state, which may be formulated from either the continuous or discrete forms of the EDP used as a constraint in problem [START_REF] Allaire | Shape Optimization by the Homogenization Method[END_REF]. Such computations are expensive either from a mathematical or programming point of view: the corresponding numerical strategies are detailed in [START_REF] Marck | Topology optimization using the SIMP method for multiobjective conductive problems[END_REF][START_REF] Marck | Topology Optimization of Heat and Mass Transfer Problems: Laminar Flow[END_REF]and are beyond the scope of this proceeding for the purpose of brevity.

The main sequence optimizing the objective function J(η) is detailed in Alg. 1 [START_REF] Bendsoe | Topology Optimization: Theory, Methods and Applications[END_REF]. The computation of the objective function is not detailed here: the instruction J ← J(η, p) means that a finite volume solver computes the solution of the corresponding EDP in order to evaluate the objective functions. Note that the penalization parameter is a vector from now on: indeed, different penalizations may be used in agreement with the physical modelling.

The algorithm itself is based on an inner loop, which is nested within an outer loop. The inner loop aims at solving the optimization problem for a given state of the penalization parameters, whereas the outer loop controls them and is responsible for the convergence test leading to exit the whole algorithm.

J i ← Θ ; J ← J(η, p 0 ) ; for j ← 0 to ℓ do while |J -J i | ≥ ǫ i,j do J i ← J ;
compute dJ(η), θ ; compute dJ(η ı ), θ with r j ; compute ϕ(η) and dϕ(η), θ ; create MMA subproblem ; while r IP ≥ ǫ IP do interior-point method iteration ; end J ← J(η, p j ) ; end end Algorithm 1: Main algorithm for the homogenization method with an inner loop (indexed with i) nested inside an outer loop (indexed with j).

The inner loop sequentially computes the shape derivative dJ(η), θ , its numerically filtered form dJ(η ı ), θ , as well as the problem constraints and their gradient, respectively denoted ϕ(η) and dϕ(η), θ . These variables becomes the inputs of the Method of Moving Asymptotes (MMA), which belongs to a comprehensive class of gradient optimization methods based on conservative convex approximations. MMA has been shown to be particularly efficient for solving inequality-constrained non-linear programming problems, especially in the frame of structural and parametric optimization [START_REF] Svanberg | The Method of Moving Asymptotes -A new method for structural optimization[END_REF][START_REF] Svanberg | A class of globally convergent optimization methods based on conservative convex separable approximations[END_REF]. Once a new state η have been generated, the set of EDP is solved again and the algorithm loops until the convergence criterion ǫ i,j is satisfied.

The outer loop is indexed by means of an integer j ranging from 0 to a fixed number of iterations ℓ. This index drives three inner different parameters: the residual ǫ i,j , the penalization parameter p j , and the radius r j of the filter applied to the shape derivative. This numerical filter is a convolution product involving the field of discrete design parameter η ı and writes [START_REF] Bendsoe | Topology Optimization: Theory, Methods and Applications[END_REF],

dJ(η ı ), θ = N ℓ=1 η ℓ H ℓ dJ(η ℓ ), θ η ı N ℓ=1 H ℓ (4)
where N is the number of discrete elements satisfying

|x ı -x ℓ | ≤ r and H ℓ = r -|x ı -x ℓ |.
This step aims at avoiding the so-called checkerboard problems leading to unrealistic optimal configurations due to the discretization process.

Strategy for bi-objective optimization

For many real optimization problems, the challenge is to conjointly optimize at least two or more objective functions in order to reach a configuration being a trade-off between these several objectives. A trade-off means that further optimization of one objective function decreases the performances of the other ones. This strategy leads to multiple solutions, referred to as the Pareto front. For instance, if the optimization problem is bi-objective, it can be written as

inf χ∈D ϕ J 1 , inf χ∈D ϕ J 2 (5)
subject to the EDP of problem [START_REF] Allaire | Shape Optimization by the Homogenization Method[END_REF]. A suitable strategy allowing reaching the Pareto front lies on a linear combination of both objectives, which is also known as the aggregated objective function method. This approach can only generates the convex part of the Pareto frontier: consequently, this feature is assumed during the computational phase and will be checked afterwards. However, before linearly combining the different objective functions, they must be rescaled in order that the solutions reach an homogenous distribution along the Pareto frontier. This rescaling writes

Ĵ = J -J J -J (6)
where J and J respectively stands for the inferior and superior bounds of J functions. Consequently, before computing an optimal design for an aggregated objective function made of n objectives, it is required to solve 2n optimization problems. Then, for example if n = 2, the Pareto front may be outlined by minimizing J with several different values of w ∈ [0, 1], where J writes

J(η) = w Ĵ1 (η) + (1 -w) Ĵ2 (η). (7)

OPTIMAL SHAPE FOR CONDUCTIVE HEAT TRANSFER

Recalling the notation of Fig. 1, the non-relaxed topology optimization problem in conduction writes inf

χ Ω 1 ∈D ϕ J(χ Ω 1 ) subject to -∇ • (k(χ Ω 1 )∇T ) = q(χ Ω 1 ) (8)
where

D ϕ = {χ Ω 1 ∈ L ∞ (Ω, {0, 1}), |Ω 1 | ≤ ϕ|Ω|}
and two objective functions J(χ Ω 1 ) are defined below. The heat equation should be completed with the boundary conditions introduced in section 1. This problem aims at finding the optimal shape of a high-conductivity layer made of expensive raw material, such as copper for instance. Without the volume constraint ϕ, the best trivial solution for most of objective functions is to cover the whole domain Ω with such material.

Objective functions

This problem has been mainly studied to address the efficient cooling of small electronic devices. Such components require high-conductivity networks cleverly allocated over their printed circuit board, since the more they are miniaturized, the more their heat generation rate increases. One obvious objective function deals with the minimization of the mean temperature over the domain Ω, in order to prevent damaging the electronic components due to overheating phenomena. This objective function simply writes

J 1 (χ Ω 1 ) = 1 |Ω| Ω T dΩ. (9)
A less straightforward goal aims at ensuring that the temperature field over the components remains as homogeneous as possible. This requirement comes from the necessity of the electronic device to communicate with its different sections. Since the computational frequency is directly linked with the silicon temperature, it is required to keep it as constant as possible in order to avoid any overload within the communication process. Consequently the second objective functional corresponds to the minimization of the temperature variance over the whole domain Ω and writes

J 2 (χ Ω 1 ) = 1 |Ω| Ω (T -J 1 (χ Ω 1 )) 2 dx = 1 |Ω| Ω T 2 dx -J 2 1 (χ Ω 1 ). (10)
Even though both expressions are strictly equivalent, the last one is more efficient to work with, especially for computing the adjoint expression and the topology derivative dJ 2 (χ Ω ), θ .

Numerical results

The numerical case investigated is well-known as the volume-to-point heat conduction problem [START_REF] Bejan | Constructal-theory network of conducting paths for cooling a heat generating volume[END_REF]. It has been initially disclosed in 1997 and has been defined as: Consider a finite-size volume in which heat is being generated at every point, and which is cooled through a small patch (heat sink) located on its boundary. A finite amount of high conductivity (k p ) material is available. Determine the optimal distribution of k p material through the given volume such that the highest temperature is minimized. Since all calculations are run under steady-state conditions, it means that the whole heat produced by the volume is evacuated through the small heat sink. It is worth noting that the material generating heat (Ω 0 ) and high conductivity material (Ω 1 ) are viewed as homogenous and isotropic, without temperature effect on their respective conductivity. The geometrical and physical parameters are displayed on Fig. 2 for an arbitrary distribution of Ω 1 and are set as follow:

• the thermal conductivities are constant, with k 0 = 1 W/(m 2 .K) and k 1 = 100 W/(m 2 .K);

• the heat generation rates are assumed to be equal and constant with q 0 = q 1 = 10 kW/m 2 ;

• all structures have a square aspect ratio (L = H = 0.1 m), with a heat sink located in the middle of their left-hand side;

• with the exception of the heat sink which is characterized by a Dirichlet boundary condition (T d = 0 • C), all the other boundaries are adiabatic (∇T • n = 0). The results of the bi-objective optimization problem, as well as the influence of the volume constraint, are pictured on Fig. 3. For each volume constraint, ϕ = 0.1 and ϕ = 0.125, multi-objective optimizations have been run by discretizing w ∈ [0, 1] with 101 elements, after rescaling each objective function according to Eq. ( 6). Fig. 3 displays a few results, sorted by volume constraint and weighting:

! 1 k 0 , q 0 L H T d k 1 , q 1 ! 0 D
• Each column displays the structures reached from the two different volume constraints with a constant weighting w. Concretely, adding more high-conductivity material k 1 leads to strengthen the main V-shape, while adding new smaller scales to the existing high conductivity paths.

• Each row shows the evolution of solutions for w = 0, 0.5 and 1, i.e. from minimizing the temperature variance to minimizing the mean temperature over Ω. The main point is structural: the discontinuities existing for the structures reached from the temperature variance minimization are quickly reattached to the main tree, as clearly underlined by Figs. 3b and3e.

The main structural difference between mean and variance temperature minimization lies on the discontinuity of the variance configuration displayed on Figs. 3a and3d, compared to the uninterrupted connection of k 1 elements in Figs. 3c and3f. Fig. 4 shows a deeper analysis of this specific feature from a physical point of view, by manually degrading the optimal solution (Fig. 4a). In the absence of discontinuous structures, two vertical temperature gradients take place between the heat sink and the northern and southern areas 4b. Therefore, the two discontinuous high conductivity strips allow draining the heat flux towards the heat sink, without evacuating it. Indeed, if those were linked to the main Ω 1 structure, such as done in Fig. 4c, both temperature gradients would appear again because the cold area next to the heat sink would spread through the domain Ω 0 . Consequently, it would not contribute to make homogeneous the temperature field. The Pareto Fronts, reached from the 101 different weightings of J 1 (η) and J 2 (η) objective functions, are displayed in Fig. 5. Those are plotted for three different volume constraints, respectively ϕ = 0.1 (•), ϕ = 0.125 (+) and ϕ = 0.15 (•). From a thermal point of view, it underlines the impact of adding k 1 material, decreasing both objective functions J 1 (η) and J 2 (η) and shifting the Pareto frontiers towards its origin. This transformation takes place inside the white area in Fig. 5, since both limits of Pareto frontiers stand for the single objective minimization of J 1 (η) or J 2 (η): these two values decrease when enlarging the volume constraint, constricting the front within a smaller space.

Another point highlighted by Fig. 5 is the optimality of a few solutions, thanks to the convex shape of the Pareto frontiers. For each front, the weightings around w = 0.5 provide structures having both J 1 (η) and J 2 (η) minima close to their single objective minimization. This behaviour is obvious for ϕ = 0.15 (•), where J 1 = 4.0 • C and J 2 = 0.97 • C 2 , compared

OPTIMAL SHAPE FOR CONVECTIVE HEAT TRANSFER

From now on, the domain Ω is made of a fluid and solid subdomains, respectively denoted Ω f and Ω s . Fluid is assumed to be Newtonian and incompressible, under laminar regime. It is characterized by its dynamic viscosity µ, its thermal heat capacity C p , its density ρ and its thermal conductivity k f . Conversely, solid subdomain are only characterized with a thermal conductivity k s . The approach chosen here locally adds an internal friction force between the fluid and a small obstacle in the same way as the Darcy's law within a porous media [START_REF] Borrvall | Topology optimization of fluids in Stokes flow[END_REF]. This new inverse permeability coefficient, denoted α, ranges from 0 to a sufficiently large value to create a momentum sink modelling the behaviour of solid subdomains. As previously explained, this additional term allows solving only one set of equations for both subdomains, if the α and k coefficients of Navier-Stokes and energy transport equations are properly fitted. Following the assumptions made in this section, the optimization problem writes

inf χ Ω f ∈D ϕ J(u, P, T ) subject to ∇ • u = 0 ρ(u • ∇)u + α(χ ω )u = -∇P + µ∇ 2 u ρC p (u • ∇)T = ∇ • (k(χ ω )∇T ) (11)
where

D ϕ = {χ Ω f ∈ L ∞ (Ω, {0, 1}), |Ω f | ≤ ϕ|Ω|} and α(χ Ω f ) and k(χ Ω f ) are domain depen- dent.
The boundary conditions detailed in section 1 have to be added to the constraint definition as well. The functions α and k behaves in the following way:

if x ∈ Ω f , then α(1) = 0 and k(1) = k f . Conversely, if x ∈ Ω s , then α(0) → ∞ and k(0) = k s .
Further details about the definition of α and k can be found in [START_REF] Marck | Topology Optimization of Heat and Mass Transfer Problems: Laminar Flow[END_REF]. Also note that different parameters p are also used for penalizing these both parameters. The Navier-Stokes and energy equations are discretized with the finite volume method, and computed with the so-called SIMPLER algorithm, which allows solving the pressure-velocity coupling [START_REF] Versteeg | An introduction to computational fluid dynamics -The finite volume method[END_REF]. Furthermore, an additional correction is included to properly take into account the viscous dissipation caused by the wall shear stress along the solid domain (see [START_REF] Marck | Topology Optimization of Heat and Mass Transfer Problems: Laminar Flow[END_REF] for details).

Objective functions

The heat and mass transfer optimization problem introduced as so far is function of a generic objective function depending explicitly on u, P and T , and implicitly on χ Ω f . This section aims at defining the physical goals pursue by the optimization process and gives them a mathematical formulation.

First, the objective function J 1 (η) is relative to minimizing the mechanic power dissipated by the fluid through the domain Ω and can be computed thanks to the total pressure losses as

J 1 (u, P ) = - Γ P + 1 2 ρ|u| 2 (u • n) dx, ( 12
)
where Γ is the boundary of Ω domain. This objective is similar to minimizing the mechanical power spent to set the fluid into motion. Secondly, the objective function J 2 aims at maximizing the thermal power recovered from the domain Ω, by means of the inlet and outlet flow boundary conditions. This net thermal power is given by

J 2 (u, T ) = ρC p Γ T (u • n) dx (13)
Therefore, the challenge is to conjointly optimize J 1 and J 2 in order to reach a configuration being a trade-off between both objectives: this means that further maximization of the thermal power extracted by the fluid involves increasing the mechanical losses, and conversely. This problematic is really similar to the one encounters in the optimization of thermal heat exchangers, where manufacturers seek for the highest thermal transfer rates, while spending the lowest pumping power as possible.

For such bi-objective problem, the aggregated objective function J(η) now writes J(u, P, T ) = (1 -w) Ĵ1 (u, P ) -w Ĵ2 (u, T ).

Note that the thermal objective Ĵ2 is negatively weighted because it has to be maximized.

Numerical results

The study case is a square-shaped domain of side ℓ = 0.1 m, displayed in Fig. 6. The inlet and outlet flows are horizontally lined up in front of each other, with the same Poiseuille u-velocity profiles. The inlet flow temperature is set to T i = 0 • C and the vertical walls are assumed to be adiabatic. The horizontal walls closing the domain are subject to a constant temperature T w = 10 • C. The Reynolds number of this system, computed on the basis of the characteristic dimension ℓ/5 and the average inlet velocity, is Re = 3. The thermal parameters are the following: k s = 10 W/(m.K), k f = 1.0 W/(m.K), and C p = 5.0 kJ/(kg.K). The domain is discretized with 100 × 100 design elements and the volume constraint is ϕ = 0.4.

Figure 7 displays the bi-objective optimization results that are reached by gradually increasing the weighting w. Four main classes of solution can be distinguished from their respective topology:

• For w = 0, the fluid is transported through a direct pipe, as expected from a physical point of view.

• For 0.06 ≤ w ≤ 0.12, a solid core takes place at the center of the domain, splitting the fluid subdomain into a lower and an upper flows, as shown from Figures 7b to 7d. This moves both flows towards the lower and upper walls at constant temperature T w , heating up the fluid without the temperature losses induced by the heat flux conduction through the solid domain. The more the w weighting increases, the more the central core width increases.

• For 0.26 ≤ w ≤ 0.60, the central core is vertically split into several sub-cores, as shown from Figures 7e to 7g. This behavior breaks the horizontal temperature gradient through the solid core, by inserting one or more strips of fluid acting like a heat insulation material. Further details about this phenomenon are provided below.

• For w = 0.75, the surface of solid and fluid interfaces closed to the boundary conditions T w are artificially increased by small solid inserts. This feature increases the main surfaces taking part into the heat transfer process.

Figure 8 displays the results for w = 0, minimizing the power lost by viscous dissipation within the fluid domain. As underlined by Figure 8a, the flow straightforwardly joins the inlet and outlet boundary conditions, with the largest fluid domain as possible. This aims at letting Ω f being as wide as allowed by the constraint ϕ(η) ≤ ϕ in order to minimize the shears inside the fluid flow. Figure 8b gives details about the pressure field within the Ω f domain, which mainly follows an horizontal gradient. Figure 8c highlights both physical configurations for the heat transfer: • conduction within the solid domain, which mainly remains at temperatures close to T w ;

T w ∂T ∂x =0 ! !! " ! " !! " ' s ' f α f ,k f , φ#$#%&' T w ! i T i ! o α s ,k s
• conducto-convection within the fluid flow, which mainly transports the energy in its lower and upper boundary layers.

Figure 9 demonstrates the interest of splitting the central core in several different parts. Since k f < k s , the fluid areas having a zero-velocity field can be seen as an insulation material from the heat flux point of view. From a conductive point of view, the fluid strips act as large thermal resistances for the heat flowing through the solid core. The central field temperature is deeply affected by these discontinuities, as underlined by Figs. 9a and9b. The temperature profile along the x-axis also shows the temperature gaps resulting from the "insulating fluid material" between each subcore, as plotted on Fig. 9e.

Without the strips of fluid, an horizontal temperature gradient would take place through the core, driving the heat flux from the near outlet flow area to the near inlet one (see Figs. 9c and9d). Consequently, the outlet flow would be cooled by conduction through the central core, penalizing the objective function J 2 . In other words, dividing the central part allows the temperature homogenization for each of its subset by restricting heat conduction, and ensures that both extreme parts are mainly under the influence of their closest thermal boundary condition.

One other interesting point underlined by Figure 9a is the presence of homogenized areas along the fluid/solid interfaces. Indeed, even if this solution seems to be fully converged, the solid boundaries remain rough: they are made of an artificial porous media. The split core may also be seen as a numerical manifestation of the homogenization process, since a core made of an infinite succession of vertical solid and fluid layers would perform better than any other one, for the same reasons as those aforementioned.

CONCLUSION

In this proceeding, we show that topology optimization may be successfully applied for the optimization of heat and mass transfers, mainly for the conductive and convective modes. It also underlines that multi-objective optimization is a reasonable method to tackle such problems and that interesting structural trade-offs between the different objectives could be reached. The numerical investigations reveal that most optimized structures are not trivial from a structural point of view, especially when they reached non-connected patterns.

The convective test case illustrates the main problematic of heat and mass transfers, consisting in increasing the thermal heat exchange with the fluid, while reducing as far as possible the power required to set it in motion. In this case, the aggregated objective function is proven to be a suitable approach, since the Pareto front of the problem is almost fully convex. However, the topology optimization method, that transforms a shape optimization problem into a parametric one, raises several theoretical and numerical issues, such as the well-known homogenization phenomenon detailed in [START_REF] Allaire | Shape Optimization by the Homogenization Method[END_REF]. The numerical observation of this anomaly is made possible thanks to the problem relaxation, but should also be investigated from a theoretical point of view. It is worth nothing that the numerical experiments tend to show that the more the weighting of the thermal objective is larger, the more the homogenization process become prominent. Further works have to be done in order to propose adequate remedies for this problem.

Figure 1 :

 1 Figure 1: Conductive (left-hand side) and convective (right-hand side) shape and topology optimization problem in the frame of heat transfers.

Figure 2 :

 2 Figure 2: Academic study case aiming at cooling down a finite-size volume generating heat, closed by adiabatic boundary conditions with the exception of a small heat sink. This one is characterized by a Dirichlet boundary condition, at temperature T d , located in the middle of the left-hand side boundary condition with a length D.

  (a) w = 0 and ϕ = 0.1 (b) w = 0.5 and ϕ = 0.1 (c) w = 1 and ϕ = 0.1 (d) w = 0 and ϕ = 0.125 (e) w = 0.5 and ϕ = 0.125 (f) w = 1 and ϕ = 0.125

Figure 3 :

 3 Figure 3: Numerical results for the topology optimization of two subdomains Ω 0 and Ω 1 made of different thermal conductivities k 0 and k 1 for the finite-size volume generating heat case. The solutions on each row correspond to two different volume constraints, respectively ϕ = 0.1 and ϕ = 0.125. The solutions on each column are computed with different weighting when linearly combining the functionals J = w Ĵ1 + (1 -w) Ĵ2 .

  (a) Optimized solution (J 2 = 2, 33 • C 2 ). (b) Optimized solution with manually deleted branches (J 2 = 2, 68 • C 2 ). (c) Optimized solution with manually connected branches(J 2 = 2, 76 • C 2 ).

Figure 4 :

 4 Figure 4: Optimality analysis of non-connected patterns reached in structures minimizing the functional J 2 .

Figure 5 :

 5 Figure 5: Pareto frontiers of J 1 (x-axis) and J 2 (y-axis) minimization problem established for 3 different volume constraints: ϕ = 10% (•), ϕ = 12.5% (+) and ϕ = 15% (•) for the finite-size volume generating heat case.

Figure 6 :

 6 Figure 6: Academic study case aiming at maximizing the fluid outlet temperature, while minimizing the viscous dissipation through the fluid flow. Inlet and outlet flow boundary conditions face each other and are assumed to be driven by a Poiseuille flow. In addition, the vertical walls are adiabatic (∇T • n = 0) and the upper and lower walls are set to a constant temperature T w . Solid domain is pictured in black and indexed with s, whereas the fluid phase is pictured in white and indexed with f .

  (a) w = 0.0 (b) w = 0.03 (c) w = 0.06 (d) w = 0.12 (e) w = 0.26 (f) w = 0.28 (g) w = 0.60 (h) w = 0.75

Figure 7 :

 7 Figure 7: Optimal structures depending on w weighting.

(a) 0 Figure 8 :

 08 Figure 8: Minimization of the viscous dissipation in Ω for w = 0. The different state variables are pictured from left to right: velocity magnitude (a), pressure (b) and temperature (c).

  (T -T i )/(T w -T i ) (T -T i )/(T w -T i )Horizontal temperature profile for y = 0.5ℓ.

Figure 9 :

 9 Figure 9: Analysis of the core fragmentation from a thermal point of view.

5 )Figure 10 :

 510 Figure 10: Pareto front corresponding to the minimization of J 1 (x-axis) and the maximization of J 2 (y-axis) established for the volume constraint ϕ = 0.4.


Jarrod Trevathan

Ian Atkinson

Wayne Read

Ron Johnstone

Nigel Bajema

James Mcgeachin

Establishing Low Cost Aquatic Monitoring Networks for Developing Countries

Keywords: Sensor networks, aquaculture, enabling technologies, middleware integration platform, communications infrastructure

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The commodity of the natural environment is a precious reserve and means of income for many developing countries (DCs). Conserving the natural environment is vital to ensure the sustainability and economic progress of such countries. However, it is all too common that environmental degradation is the first thing to occur when these economies move along a path towards industrialization. For many South-East Asian countries, the harnessing of the natural environment to support aquaculture is an important economic resource and provides much needed protein for their populations. As such, these ventures are extremely sensitive to pollution and human activity.

The main reason for environmental damage in DCs is the lack of government policies, limited means to monitor resource usage, and the inability to act on such information if it was available. First world countries (FWC) are now starting to employ sophisticated sensor network (SN) systems to augment traditional environmental monitoring programs used to monitor and analyze environmental phenomena. However, there are many issues facing DCs which presently has made these potentially more effective approaches unviable. Significant obstacles are associated with the costs related with setting up a monitoring system, and also the availability of IT and engineering expertise. Many examples of systems in FWCs have been overly expensive, and for the most part ineffective. Furthermore, generally DCs have very limited ICT infrastructure. Such infrastructure is required to collect, collate and communicate the information to, and perform analysis on the behalf of policy makers/end-users. Furthermore, whatever infrastructure policy makers have access to can be disparate and tends not to be designed to smoothly integrate.

In recent years, middleware software solutions have emerged that can be used to integrate heterogeneous IT [START_REF] Gigan | Towards a uniform software interface to heterogeneous hardware[END_REF][START_REF] Gigan | Sensor Abstraction Layer: a unique software interface to effectively manage SNs[END_REF][START_REF] Handziski | Flexible Hardware Abstraction for Wireless Sensor Networks[END_REF][START_REF] Heinzelman | Middleware to support Sensor Network applications[END_REF][START_REF] Rmer | Middleware challenges for wireless sensor networks[END_REF]. The effect of this middleware approach is that the existing IT infrastructure in a DC, no matter how basic or incompatible can be used to establish basic SNs for environmental monitoring. Despite only possibly having access to older and/or obsolete hardware/software platforms, SN middleware technology can bridge the gaps between hardware systems and allow DCs to use low cost and pervasive sensors. When combined with emerging standards such as Sensor Web Enablement [START_REF] Aloisio | Globus Monitoring and Discovery Service and SensorML for Grid Sensor Networks[END_REF][START_REF] Botts | OGC Sensor Web Enablement: Overview and High Level Architecture[END_REF], data collected from SNs can be archived so that external monitoring agencies can view and analyze the data. Such end-to-end monitoring systems will be of great value for remotely monitoring aquatic environments and assist, for example, in managing the sustainability of the aquaculture industry.

This paper examines the issues facing DCs in deploying SNs for remote aquatic monitoring, and contrasts the approaches taken in more developed countries. This analysis allows us to define the level of quality that would be sufficient for providing DCs with valuable data for the purposes of environmental monitoring and management. We describe how software mechanisms can be used to integrate disparate hardware and communications technologies existing in DCs. The software middleware we have developed (referred to as SAL [START_REF] Gigan | Towards a uniform software interface to heterogeneous hardware[END_REF][START_REF] Gigan | Sensor Abstraction Layer: a unique software interface to effectively manage SNs[END_REF]) is scalable and can support a SN of almost any complexity. Finally, we present real world examples of how the proposed software is being used for environmental monitoring, and how it is being tailored specifically for aquatic settings.

This paper is organized as follows: Section 2 discusses the issues facing DCs and draws comparisons with FWCs in terms of establishing viable and sustainable SN systems. Section 3 presents the software technologies we are developing in order to address some of these issues. Section 4 gives a series of test deployments to illustrate its scalability and resourcefulness to handle SNs containing dissimilar technologies. Section 5 examines the SEMAT project and our contribution to the development of its technologies. Section 6 provides some concluding remarks.

Issues Facing Developing Countries

This section describes the issues facing DCs for the deployment and management of SN systems. We contrast these issues with the ICT requirements for SNs and the lessons learned from projects in FWCs. This allows us to define the quality of service and the minimal technological infrastructure needed to deploy a network that will at the very least provide valuable data for end-users.

The underlying core issues for deploying environmental monitoring systems in DCs are basically the same problems as in FWCs. However, there are several features that make key differences. Firstly, FWCs generally have access to some pervasive broadband infrastructure. That is, communications technology is usually high speed, reliable and has wide coverage. Secondly, FWCs have access to an abundant amount of current generation computing hardware and software platforms for collecting, archiving and analyzing data. Finally, most FWCs have established policy making/endorsing bodies that use the collected data to ensure that phenomena under study conforms to the established policies and legal practices.

In contrast, DCs often only have access to basic ICTs. However, there is a very large range -between the ultimate extreme of having absolutely nothing, or being relatively in league with FWCs. In any case, we have to allow for the fact that there will be heterogeneous technologies which might be as basic as ordinary radio communication, or it might be equivalent to FWC broadband and cellular network standards. Computing and software platforms within DCs are often older, or in many cases obsolete compared to the latest technology in use by FWCs. Furthermore, there may be no established policy making mechanism, or this may be primitive with only a mediocre level of effectiveness. The resounding question this raises is, "what is the minimum technological infrastructure required to establish an environmental monitoring SN system in a DC"? To answer this question it is prudent to examine similar systems for FWCs.

An example of a system is not particularly suited for use in a DC is being established by the Australian Institute of Marine Science (AIMS) 1 as part of the Australian Integrated Marine Observing System (IMOS). This project involves the deployment of remote sensing buoys at strategic locations around the Great Barrier Reef2 [START_REF] Bainbridge | Sensor Networks on the Great Barrier Reef -managing marine sensor data[END_REF]. Their purpose is to detect oceanographic features such as salinity, temperature, light, etc. However, the solution falls short for use in by a DC in multiple ways. The first major shortcoming is cost. Each buoy costs an estimated $50,000 AUD, with deployment and maintenance also requiring significant technical infrastructure and expertise. Furthermore, it is not pervasive and gives only localized answers. That is, there are only four sampling points across a reef that spans 344,400 square kilometers. Therefore, the effectiveness of the data for any sort of modeling is limited. As such, this sort of approach is unsuitable for a DC.

With this example in mind, let us examine the features of cheaper alternatives. Regardless of the cost, the system must be able to deploy "enough" sensors to measure with "sufficient" accuracy to present useful information. (Note that less precise assumes sufficient accuracy.) Four million cheap sensors with limited precision are arguably of much greater value than four expensive sensors of extremely high precision. Furthermore, four million sensors will cover a larger geographical area thereby maximizing the coverage for the network and environments sensed. In terms of an aquatic setting, the geographic dimensions are important. Sensor readings are not just required horizontally between locations, but also need to be at multiple points vertically between the surface and the bottom of the water body. This makes the cost of the sensors extremely important in order to take the most measurements. Figure 1 illustrates the cost spectrum for SN systems. As the design choice moves between the extremes, the features of either extreme will become more pronounced. A cheap system will allow for a pervasive network of low-cost sensors and gateways to be deployed on a potentially massive scale. Whereas expensive systems (as in the AIMS example [START_REF] Bainbridge | Sensor Networks on the Great Barrier Reef -managing marine sensor data[END_REF]) can only result in a few sensor nodes thereby giving localized readings. Low-cost systems potentially have lower power requirements and the components are physically smaller than in an expensive infrastructure. However, cheap systems typically do not have the same endurance and life span as more expensive setups. Although the pace of improvement in technology means that sensors can be outdated within two/three years in any case. In general, cheap sensor nodes can be easily replaced which means their lifespan may not be a significant limiting factor. Furthermore, expensive systems that are damaged, lost or stolen are quite difficult to replace making less expensive systems more attractive. While expensive systems are highly proficient (and precise) due to their sophistication, cheap systems are usually capable albeit with less precision.

In light of the cost structure and requirements, we define the parameters for a suitable/feasible solution for remote SNs in DCs as follows:

• The system must be cost effective, to provide the most coverage and is affordable for a DC. (Sensors embedded in packaging can be as cheap as $10-$15 each.) • It must use existing hardware and communication infrastructure. Even though solutions may interconnect to high technology front ends when they are available, there must be basic, low cost solutions that do not depend on any advanced technology. For example, a SN deployment using second hand/obsolete computers with very low cost sensors communicating via early technology mobile networks. There is also scope to capitalize on initiatives that telecommunications companies are offering DCs. For example, Ericsson in collaboration with the Swedish Program of ICTs in Developing Regions (SPIDER) has initiated investigation of wireless SNs in developing regions, including the allowance for bandwidth on GSM mobile phone networks to be used for environmental monitoring. 3• The data collected must be valuable to the end users and accessible to all interested parties. External bodies such as a government or a United Nations agency can in theory access the collected data. This would aid in the establishment of sound, or in the very least accountable environmental management policies.

It is interesting to note that low technology solutions may also be viable in some locations and not others. For example, deploying and manually cleaning sensors on a daily basis (as would be required with some very low-cost infrastructure) would be cost prohibitive for most FWCs. However, this solution may in fact fit very well with the structure of a DC where labor can be both effective and relatively inexpensive. This may be a consideration that affects any SN deployment in a DC.

Much of the hardware that is present in a DC typically will come at least in part via aide from FWCs. It might be in the form of money to purchase the equipment, but it is more likely to come in the form of donated second hand equipment. Whatever, the method for acquiring the hardware, the underlying point is that there will be a menagerie of heterogeneous and incompatible technologies to work with.

To achieve a system that satisfies the aforementioned constraints requires the use of a software stack to handle the range of communication and hardware technologies. That is, a middleware solution to bring together disparate technologies which range in computational power and cost.

Scalable Software Solutions for Abstracting Hardware and Communications Technologies

Lack of standards makes it difficult to integrate heterogeneous sensors in a single SN. Therefore it is important to have middleware which is able to manage all types of sensors. This middleware is often implemented specifically for one SN, which makes it very hardware dependent. Changes in the network, such as adding a new sensor, lead to the need for manipulating the code of the middleware. This section describes the Sensor Abstraction Layer (SAL) which is designed to address these issues. Architectural overview of a system stack using SAL SAL is a middleware which provides a plug-in-based model where support for new types of sensors can be loaded to the running system via plug-in. The system automatically detects and configures new sensors, if permitted by the hardware and underlying OS. Furthermore, it provides a unified interface to all sensors by abstracting the sensor specific features. With this it aims at simplifying the access to a SN as well as the management and control of its sensors [START_REF] Gigan | Towards a uniform software interface to heterogeneous hardware[END_REF][START_REF] Gigan | Sensor Abstraction Layer: a unique software interface to effectively manage SNs[END_REF].

SAL can be seen as a low-level software layer as it bridges a network of sensors with high-level applications or further middleware technologies, e.g., a ring buffer. Figure 2 presents an architectural overview of a system stack using SAL.

SAL is implemented as a client-server application and thus consists of two components, the SAL client and the SAL agent. The SAL client represents an interface for SAL to either the user via a user interface or to other applications. It implements the SAL agent Application Programming Interface (API) in order to provide the functionality of the SAL agent. The API is grouped into the following categories:

Sensor Management -Methods to manage the pool of sensors and including operations for enumerating, adding and removing sensors. The SAL agent implements the various features of SAL. It runs on a platform which is connected to the sensors and therefore is regarded as a sensor gateway. The connection between the platform and the sensors can be either direct using platform specific I/O ports like USB or indirect by using wireless technology. The SAL agent manages the sensors which are directly connected and are found by the agent and the indirectly connected sensors it has been told of. The SAL agent consists of three layers: Agent Layer, Communication Layer, and EndPoint Layer (see Figure 3).

The Agent Layer is responsible for the communication with the SAL client. It receives messages, parses and forwards them to the underlying Communication Layer and sends the response back to the client.

The Communication Layer provides methods for managing and controlling sensors. The managing methods are used to configure and set up hardware while the controlling methods translate a generic command into a sensor native command, which then can be transmitted to the sensor. The generic commands are provided in the SAL API. For translating the generic commands the two sub-layers Abstraction Layer and Protocol Layer are used. The Abstraction Layer is an adapter layer where the generic commands are implemented. From here the sensor-specific methods are called, which are implemented in the Protocol Layer.

The EndPoint Layer is tightly coupled to the I/O ports available on the sensor gateway. It is responsible for transmitting native sensor commands produced by the sub-layer Protocol to the sensor. In the other direction, data from the sensor is transmitted to the SAL agent. The software code for this layer is normally included in the operating system. SAL ensures it is available and configured correctly.

The two sub-layers Abstraction and Protocol and the EndPoint Layer together form a Logical Port. Each sensor is connected to a Logical Port. SAL allows a specific client to control multiple agents (horizontal scalability). Furthermore, the logical port allows SAL to scale vertically (vertical scalability) in that an agent can control other agents in a hierarchical manner. This is due to the logical port treating the attached device as a data source, independent of whether it is a sensor or another agent. The ability to scale allows SNs of almost any topology or configuration.

SAL is also integrated with the RBNB Data Turbine developed by the San Diego Super Computing Group [START_REF] Tilak | The Ring Buffer Network Bus (RBNB) Data Turbine Streaming Data Middleware for Environmental Observing Systems[END_REF]. The Data Turbine allows for near real-time streaming of data from multiple sources (as is common in a SN). All incoming data is temporarily stored in a time-stamped buffer prior to being consumed by the end-user. The enduser can essentially fast-forward and rewind the buffered data which is extremely useful for time series analysis and presentation.

SAL has been trialed on the Davies Reef SN project [START_REF] Huddleston-Homes | Infrastructure for a Sensor Network on Davies Reef, Great Barrier Reef[END_REF] whereby it controls a sensor array via a low cost/power computer in a remote reef. Of greater importance to this paper's objectives is SAL's role in the SEMAT project [START_REF] Johnstone | Smart Environmental Measurement & Analysis Technologies (SEMAT): Wireless Sensor Networks in the marine environment[END_REF] which aims to develop low cost pervasive aquatic SN systems (see Section 5).

A Test Implementation

To show SAL's feasibility for deployment in a low cost heterogeneous environment, we created a series of sample SNs of varying scales. This section describes the test setup and SAL's performance at increasing levels of complexity.

One Client, One Agent -The first test involved using one agent that had multiple heterogeneous sensors attached. There was a single client controlling the agent. The aim for this test was to illustrate SAL's ability to connect, manage and collect data from numerous heterogeneous sensor types simultaneously from different vendors.

The sensors included a one wire dongle sensing temperature and humidity, a Web camera streaming live data, fifteen Java sun SPOTs4 (which contains a package of multiple sensor types), and a one wire bus containing ten independent thermistors.

For this test the SAL agent and client were run on separate single board computers (WAFER-945GSE SBC from ICP Electronics5 ) which communicated wirelessly. Each computer had a processor speed of 1.6 Ghz and 2 GB of RAM. The price for each computer was $427 AUD. Once registered, each sensor type could be plugged and unplugged without affecting other devices. The number of devices we could attach and the amount of data transferred essentially was only limited by number of USB connections and the network bandwidth respectively.

The tests were then repeated with a Gumstix6 device. These are miniature computers (approximately the size of a stick of chewing gum) designed to run in purpose built applications. These devices are indicative of what would be deployed in the field for a wireless SN. The processor speed is 400 MHz with 128 MB RAM. Our tests used the Vertex series package which retails for approximately $300 AUD. These tests were designed to examine how SAL performs on lower powered devices as what might occur in a DC.

There was no notable deviation in performance from the tests run on the single board computers. The entire price for the test SN was approximately $3,000 AUD depending on the computers used and the number of desired sensors.

Multiple Agents at the Same Level (Horizontal Scalability) -

The second test involved testing SAL's ability to scale horizontally. There was one client controlling multiple agents. Each agent was independent to and peer with each of the other agents. Each agent had a varying array of sensors attached.

This test involved the single board computers and also some relatively old desktop computers (which ranged in processing power and memory capacity). This test was used to examine SALs performance by combining some equipment that might be representative of what is available to DCs.

As per the previous tests, the system performed well. We theorized that SAL can be used on any computer that has the processing power equivalent of the early Pentium range (i.e., 1.6 MHz). The memory requirements are only constrained by the ability to support the Linux operating system and the Java run-time environment.

Multiple Agents at Differing Levels (Vertical Scalability) -The final test determined how well SAL scales vertically where one client that controls one agent. This agent then controlled two agents directly below it, each of which had differing arrays of sensors (refer to the discussion of a logical port in Section 3). The results showed that this worked seamlessly in line with how SAL handles data sources.

In conclusion, regardless of the technology employed, SAL was able to seamlessly integrate the entire SN. Almost any computer from the mid 90s onwards can run SAL. The price for an entire SN can be as little as $500 -which only increases with the number of agents/clients used and the number of and costs of the sensors desired.

Pervasive, Low Cost and Intelligent Aquatic SNs

This section describes the Smart Environment Monitoring and Analysis Technologies (SEMAT) project as an example of an end system that could be used in the context of a DC [START_REF] Johnstone | Smart Environmental Measurement & Analysis Technologies (SEMAT): Wireless Sensor Networks in the marine environment[END_REF]. SEMAT is largely driven by the need to create a low cost intelligent SN system for monitoring aquatic and coastal environments, and importantly the analysis of that data into information which can be used for management and planning. The specific goals for SEMAT are as follows:

1) Underwater wireless communications -As aquatic environments are remote and vast, it is not economically viable or practical to have sensors wired together. Often in such an environment the positioning of the cables represents a significant practical problem. The cable itself is also vulnerable to breakage or degradation over time.

2) Short-range wireless and power transmission -Interconnecting cables for data communications and/or power in-situ is complex in a marine environment. SEMAT aims to develop technologies whereby neighboring cables can be connected underwater and inductive methods used transfer power and data between nodes.

3) Plug and Play -A major problem facing the deployment of wireless SNs is the disparate technologies used as equipment must be combined from different manufacturers. Even the simple case of adding a new type of sensor usually involves reconfiguring the entire system so that the end user can view the sensor's output. SEMAT will use SAL to allow new equipment to be added to the network such that it is instantly recognized and configured for use. This is analogous to plugging in a new peripheral device for a computer such as a printer or mouse, which the operating system automatically detects and allows instant use. Making a wireless SN plug and play removes much of the technical overhead for managing the network by novices.

4) Minimal deployment expertise -SEMAT will offer end users a complete package. The end user will only need to choose what sensors they require and SEMAT will auto-configure the necessary parameters. Essentially once deployed, the user could take a laptop down to the beach (for example) and can begin to view the sensed data.

5)

Near real time analysis tools -SEMAT will provide software tools that allow data to be streamed in near real-time from sensors. Users will have the ability to buffer large amounts of data and sift through the data at hand using the RBNB Data Turbine [START_REF] Johnstone | Smart Environmental Measurement & Analysis Technologies (SEMAT): Wireless Sensor Networks in the marine environment[END_REF]. Data collected is put into a format that is recognized by standards bodies (i.e., Sensor Web Enablement [START_REF] Botts | OGC Sensor Web Enablement: Overview and High Level Architecture[END_REF]) and therefore can be imported into sophisticated data modeling and visualization tools.

6) Intelligent sensors -Sensor nodes in SEMAT will have a level of intelligence in that they have two way communications with each other. This will allow sensors to have a degree of autonomy from the end user such that if there is a sudden change in a condition which affects the phenomena under study, then sensor nodes can communicate with each other to change their parameters to better study the changes in the environment. For example, if one set of sensors detects that significant rainfall is occurring, it might communicate with the salinity sensor to increase its sensing rate from daily to hourly. There are almost limitless uses for such intelligence.

SEMAT is an ambitious and large project which draws on expertise from multiple disciplines including marine biology, engineering, mathematics, and computer science. There are several parties involved with the development of SEMAT including: DHI Group7 (Denmark); University of Queensland (UQ) 8 (Australia); James Cook University (JCU) 9 (Australia); and the Torino Foundation (Italy).

DHI and UQ are responsible for the marine science and high level modeling of marine phenomena. For the purposes of SEMAT, this study is related to the prevalence of Lymbia algae blooms at Deception Bay in Queensland Australia.

DHI have developed hydrodynamic modeling software that is being used for the initial studies SEMAT will be conducting. The Torino Foundation specializes in developing and commercializing sensor technology. They are tasked with designing purpose built intelligent sensors for the SEMAT system. The initial sensors under development will be geared towards the studies being undertaken by DHI and UQ.

The Information Technology and Electrical Engineering school at UQ is designing the wireless power transfer and communications systems. They are working in conjunction with the Torino Foundation to decide on the electrical standards for the power transfer. UQ is also working in conjunction with JCU on the communications protocols that will be used between sensor nodes and how the information will be relayed back to the gateway devices.

The eResearch Centre at JCU is responsible for providing the middleware (i.e., SAL) and end user interface for the data acquisition, storage and presentation from the SEMAT system. This is perhaps one of the most challenging components of the SEMAT project. JCU is essentially tasked with making the system plug and play and allowing for near real time analysis of the collected data.

Lymbia Algae Study at Deception Bay

Lymbia Algae is a particularly virulent form of algae that attaches itself to sea grass. As the algae grows, it absorbs nutrients from the sea floor and surrounding areas and physically covers sea grasses preventing light reaching the sea grass -essentially killing it off. Once the algal has finished growing (blooming), it breaks off and typically washes up on the beach/shore line. It leaves scaring along the sea bed where it was present and it usually prevents future sea grass from growing.

While it is uncertain what causes the algae to bloom, it is suspected that it forms mainly in warm conditions, shallow and calm water, and human activity (possibly in the form of nutrient run off) may affect its life cycle. The initial case study for SEMAT will be to study the influences that contribute to the spread of Lymbia algae. Ideally the result will be to suggest a plan for environmental management to control its spread in aquatic environments.

In recent years Deception Bay in Queensland Australia has experienced significant Lymbia algae blooms. The bay is largely protected from the greater ocean and doesn't contain any significant wave action. It is shallow with high tide only raising the water depth to approximately three meters at its peak. Deception Bay is situated between the mainland and the southern region of Bribie Island therefore it is heavily influenced by human activity.

As the initial application for SEMAT is for monitoring aquatic environments for marine science studies, UQ and DHI selected Deception Bay as the premiere site for testing the initial SEMAT system. There were several factors influencing this decision including: proximity to Brisbane; prevalence of algal blooms; subtropical environment; shallow water; and calm conditions Ultimately, the SEMAT development environment will use six different sensors to examine different potential influences and their interaction on Lymbia algae blooms. The types of sensors include: temperature, total light, photo synthetically active radiation (PAR), salinity/conductivity, turbidity, pH, and dissolved oxygen. Several data sinks will be deployed at strategic locations around Deception Bay. Each sink will host the aforementioned sensors.

Conclusions

DCs are heavily reliant on their aquaculture resources for food and exports. Effective management of this vital resource is critical to ensuring the future sustainability and economic progress of a DC. SN technology can be used to actively monitor resource use to provide end-users, policy makers and external third parties with the information they need in order make more informed decisions. However, excessive cost, lack of infrastructure, and limited expertise/experience, hinders the adoption of SN technology in DCs.

This paper examined these issues and provided a framework for how a DC can use low cost sensors in combination with a country's existing infrastructure to deploy a capable SN for aquatic monitoring. We propose that SAL can be used as a viable middleware solution to establish a SN in a DC regardless of conflicting technologies. Preliminary test results and real world deployments indicate that SAL is both scalable and capable of suiting any network configuration and sensor types. The cost is only constrained by the computers used (which can be as old as a Pentium), and the number and expense of the desired sensors. The proposed solution is being integrated into the SEMAT project as the nexus for a fully plug and play system capable of being easily deployed in any aquatic environment.

Future work involves increasing the magnitude and scale of the tests conducted with SAL. That is, we are aiming to test SAL on a large-scale horizontal and vertical level incorporating sensors from numerous vendors. We are also going to perform a series of 'wet tests' whereby SAL is used with submersible sensors and low cost computing devices as a proof of concept. Finally, SAL's versatility will be further developed and tested in conjunction with the aims of the SEMAT project.

Fig. 1 .

 1 Fig. 1. The cost spectrum for SN systems

Fig 2 .

 2 Fig 2. Architectural overview of a system stack using SAL

  Sensor Control -Methods to report on a sensor's capabilities and control the streaming of the data. Platform Configuration -Methods to adjust the platform, e.g., add support for a new sensor type.Each category uses a different markup language. The Sensor Management methods use SensorML[START_REF] Aloisio | Globus Monitoring and Discovery Service and SensorML for Grid Sensor Networks[END_REF], which describes a sensor's configuration. The methods in the category Sensor Control use CommandML. The CommandML documents contain a list of commands which are supported by a sensor. The last category, Platform Configuration, uses Platform Capabilities and Configuration Markup Language (PCML). Documents of the type PCML contain information on how the platform is to be configured in order to support a certain type of sensor technology.

Fig 3 .

 3 Fig 3. SAL components and software layers

www.aims.gov.au

http://imos.org.au/faimms.html

http://www.spidercenter.org/project/national-research-and-development-center-wireless-adhoc-and-sensor-networks-wasn

http://www.sunspotworld.com/

http://www.icp-australia.com.au/

http://www.gumstix.com/

www.dhigroup.com.au

www.uq.edu.au

www.jcu.edu.au

Acknowledgments. This work was supported in part by the Queensland Government National and International Research Alliances Program.