Amani Khecharem

Carlos Gomez

Julien Deantoni

Frédéric Mallet
email: frederic.mallet@unice.fr

Robert De Simone
email: robert.desimone@inria.fr

Execution of Heterogeneous Models for Thermal Analysis with a Multi-view Approach

Keywords: Multi-View, Heterogeneous Models, UML

To deal with the high complexity of embedded systems, engineers rely on high-level heterogeneous models that combine functional and non-functional aspects, hardware/software artifacts, structural and behavioral descriptions. PRISMSYS is a system-level multi-view modeling framework, which provides a means to specify functional and non-functional aspects in interrelated views. Each concern/view is addressed separately with a dedicated set of models and correspondence rules, maintaining the semantic consistency between those different views. The behavioral specification mixes UML state machines with equational models defined as SYSML parametric diagrams. To supply a complete non-functional property-aware simulation environment, it is mandatory to formalize 1) the execution semantics of the UML state machines, 2) the SYSML parametric diagrams and 3) the coordination between them. This is achieved by using CCSL, the Clock Constraint Specification Language, to provide an event-based semantics for each model and their coordination. The proposed co-simulation framework combines TIMESQUARE, a discrete event simulator for CCSL, and Scilab, a tool for numerical computation. The framework is illustrated on a CPU thermal manager case study with a joint simulation of both its functional and non-functional models.

I. INTRODUCTION

The design of embedded systems requires a strict conformance to the non-functional requirements, such as heat dissipation, energy consumption, safety or time performance. These properties are usually addressed (i.e., modeled and analyzed) by experts of different domains. Experts have their own languages and tools to describe the system modeled from their own point of view. Nevertheless, these expert's models are strongly connected; the behavior of a model could impact the behavior of the other ones. For instance, powering a component off in a power management model affects the functional behavior and the performances of the component in a functional description model.

The recent IEEE 42010 standard [START_REF]Systems and software engineering -architecture description[END_REF] proposes a common vocabulary to capture the different views of heterogeneous models. Correspondences are used to associate domain elements from different views. For instance, in a thermal view, if the temperature of a CPU is reaching the maximum acceptable temperature, the controller may reduce the clock frequency and voltage level (or turn the CPU off) to avoid damaging the CPU. This syntactic link between a non-functional property in a specific view and its impact on the other view should also be considered from a semantic point of view.

More generally, the behavior specification of an embedded system should explicitly take the non-functional properties into account to provide a correct representation of the system behavior. While it may eventually be implemented by using specific sensors, there is a need for early modeling and co-simulation of two types of behavior: one based on a discrete (logical) time representing the functional behavior of the system; and another one based on continuous time representing the evolution of non-functional properties and/or other physical phenomenon.

PRISMSYS [START_REF] Gomez | Multi-view Power Modeling Based on UML, MARTE and SysML[END_REF] proposes to tack the semantics of the MoCs on top of traditional engineering models based on the UML (Unified Modeling Language [START_REF] Omg | OMG Unified Modeling Language[END_REF]) or one of its specializations like SYSML [START_REF]Omg. systems modeling language (sysml)[END_REF] or MARTE [START_REF]UML Profile for MARTE[END_REF]. PRISMSYS [START_REF] Gomez | Multi-view Power Modeling Based on UML, MARTE and SysML[END_REF] also proposes to keep the semantics of the MoC explicit and separate from the functional model to ease its extraction, modification and analysis. PRISMSYS implements the IEEE 42010 standard. It relies on UML and SYSML to capture both functional and non-functional properties [START_REF] Gomez | Power Consumption Analysis Using Multi-View Modeling[END_REF].

In this paper, we propose an operational framework for the joint simulation of both the discrete and continuous parts of the PRISMSYS model. The semantics of PRISMSYS models is described using the Clock Constraint Specification Language (CCSL [START_REF] André | The Time Model of Logical Clocks available in the OMG MARTE profile[END_REF], [START_REF] André | Syntax and Semantics of the Clock Constraint Specification Language (CCSL)[END_REF]). CCSL is a formal declarative language for discrete logical time specification amenable to analysis and simulation in the associated tool TIMESQUARE [START_REF] Deantoni | Timesquare: treat your models with logical time[END_REF]. We have built a dedicated backend for TIMESQUARE to drive, not only the execution of the discrete part of the model, but also of the continuous part. This particular aspect is delegated to Scilab tool [START_REF]Scilab Consortium[END_REF]. We illustrate the approach on a thermal manager and we present the model, explain the way the backend is built and give some execution results.

The paper starts with discussing related works (Section II) on both heterogeneous modeling and modeling of thermal aspects. Then, Section III briefly recalls the fundamentals of PRISMSYS. Section IV describes the execution semantic of PRISMSYS for heterogeneous execution based on CCSL. CCSL is introduced on-the-fly, with only the minimum material required. Finally, the implementation of TIMESQUARE's backend for Scilab is described in Section V and illustrated on a case study in Section VI. Section VII concludes with possible extensions.

II. BACKGROUND AND RELATED WORK A. Heterogeneous Modeling

The joint use of different Models of Computation (MoCs) [START_REF] Edwards | Design of embedded systems: formal models, validation, and synthesis[END_REF], [START_REF] Jantsch | Modeling embedded systems and SoC's: concurrency and time in models of computation[END_REF], [START_REF] Jantsch | Models of computation and languages for embedded system design[END_REF] for a single system is known as Heterogeneous Modeling. A MoC (e.g., Synchronous Data Flow, Finite State Machine or Continuous Time) makes explicit the execution semantics applied to a specific syntactic model. Several approaches and tools address the problem of modeling and simulation of heterogeneous models.

Hybrid automata [START_REF] Henzinger | The theory of hybrid automata[END_REF] offer a theoretical framework to combine discrete and continuous phenomena. An attempt to mix Hybrid Automata and CCSL has been conducted [START_REF] Liu | Hybrid marte statecharts[END_REF]. However, hybrid automata do not offer any specific support for combining heterogeneous syntactic models (dataflow, equations, control) or for a clean separation of preoccupations or views. This is addressed here.

Ptolemy II [START_REF] Eker | Taming Heterogeneity -The Ptolemy Approach[END_REF] relies on a generic actor model to capture hierarchical heterogeneous models. The model of computation (MoC) and its interactions with other MoCs are described in a so-called director that conforms to a predefined Java API. Ptolemy II is widely used and has inspired many derived products or evolutions. There are two main differences with our proposal. First, we rely on UML and SYSML to capture the structural information instead of a pure actor model. Second, the synchronization and scheduling constraints are given explicitly in CCSL rather than being hidden inside the Java code and mixed with the functional description. Making the MoC explicit and separate allows for reasoning on its properties.

BIP [START_REF] Basu | Modeling heterogeneous real-time components in bip[END_REF] is an interesting alternative that supports explicit heterogeneous interaction models. The behavior is described using timed automata, then several interaction schemes can be used. Contrary to BIP, PRISMSYS does not require to use statebased representation and may also rely on data flow models or equational models to capture continuous aspects. This increase of expressiveness comes at the cost of decidability results.

There are several works that focus on the interactions between discrete and continuous time, like MatLab/Simulink or Scilab/Scicos. Amongst the solutions that support synchronous models of computation, Zelus [START_REF] Bourke | Zelus: A Synchronous Language with ODEs[END_REF] provides a nice integration with Lustre. However, in all these solutions, the functional and interaction models are intertwined and hidden as design choices inside the tool. Our proposal is to make the choices explicit in the model (like using a fixed-step solver with a given step) and use the model to drive the tools.

On the engineering side, SYSML [START_REF]Omg. systems modeling language (sysml)[END_REF] introduces the notion of parametrics to capture acausal models. This is adequate to describe continuous functions or relations and PRISMSYS heavily rely on this new construct to capture the thermal and power-related information. Our proposal is to define an execution framework to execute such models.

B. Non-Functional (Thermal) Modeling

Non-functional properties are more and more important in many embedded systems, they must be consequently taken into account during the design process.

Most of the time, non-functional properties are modeled (and analyzed) by using specific tools. For instance, to analyze the impact of temporal properties on the system schedulability, experts in time performance use tools such as Cheddar [START_REF] Singhoff | Cheddar: a flexible real time scheduling framework[END_REF] where the focus is made on specific temporal properties while the other aspects of the application and its architecture are largely abstract away. Additionally, to study the thermal aspects of a system, temperature experts employ tools like Hotspot [START_REF] Huang | Hotspot: a compact thermal modeling methodology for early-stage vlsi design[END_REF] or Aceplorer [START_REF] Power | Aceplorer[END_REF]. Once again, the tools give many details on the properties that deal with their specificity (in this case, the thermal equations), but the other parts of the system remains largely abstract.

In consequence, the study of several non-functional properties requires the duplication of some parts of the system model in these dedicated tools. Due to the strong abstraction of specific parts, these tools cannot be used standalone. For instance, since Aceplorer does not model the system functional behavior, it needs an execution scenario, generated from a functional system simulation, to activate the elements of its power model and consequently evaluate the power consumption of the system. In the same way, Hotspot is based on the Compact Thermal Model [START_REF] Vassighi | Thermal and power management of integrated circuits[END_REF], which defines the model of a the thermal features of the system component and their thermal influence over their neighbors. This model does not represent the system activity needed by Hotspot to generate the temperature layout of the system.

One current problem comes from the use of dedicated controller (e.g., the power manager of an OMAP platform [START_REF]OMAP35x Applications Processor Technical Reference Manual[END_REF]) whose goal is to change the functional behavior in reaction to non-functional property evolution. For instance, because the power consumption of a chip is exponentially increasing with its temperature, a power controller can choose to slow down (or to stop) a chip when its temperature is exceeding a specific threshold. In this case, the functional simulation sets the chip activity according to the chip temperature, but the chip temperature in tools like Aceplorer or Hotspot requires the chip activity. Therefore, there is a cyclic dependency between the execution of the tools that can be solved by a co-simulation of the domain specific models (or tools).

As a consequence, it is important to define a central (multiview) model where the behavioral dependencies are made explicit. The semantics of these dependencies can thus be taken into account to coordinate the domain specific models. To specify these dependencies, we define a system-level framework where the behavioral semantics of each part takes into account its links to the other parts of the system. We focused here on the co-simulation of these models to show the benefits of the approach. This framework is named PRISMSYS.

III. PRISMSYS: A MODELING MULTI-VIEW FRAMEWORK FOR SYSTEMS

PRISMSYS is a multi-view framework for modeling systems, focused on the description and analysis of functional and non-functional aspects. In this framework, a view characterizes the aspects of a specific domain relevant for the system. For instance, time performance model, power model and temperature model are different views of the same system, and they characterize aspects such as time, power consumption and temperature evolution.

Figure 1 depicts the main elements of PRISMSYS. A PRISMSYS view contains up to three sub-views: Control, Structural and Equational. The structural sub-view states domain specific elements, which can possibly be an abstraction of elements that exist in another view. These elements define the non-functional properties relevant from the domain point of view. The behavior of these elements is described by Finite State Machine (FSM). The FSM abstracts the operation modes of the element in a specific domain and the transitions are sensitive to events generated from the control sub-view. We use UML components to represent these elements and MARTE Non-Functional Properties (NFP) to type the properties defined in the sub-view elements. The equational sub-view specifies the equations that characterize the properties stated in the structural sub-view. We use SYSML Parametric Diagram to represent the equational sub-view. The control sub-view commands the execution of the elements specified in the structural sub-view. A way to specify these views is to use controllers, elements which behavior is stated as a FSM. In contrast to the structural sub-view FSM, the transitions of the controller FSM are sensitive to the evaluation of a condition, i.e., to a guard. Once the transition is fired, an effect is produced, sending an event to the corresponding FSM in the structural sub-view. To distinguish between both FSMs, we employ the term mode-FSM to reference the FSM specified in structural sub-view. The consistence between different views is defined through correspondences. A correspondence is a syntactic association between elements of two different views, e.g., the abstraction of an element from a first view characterizes this element from another point of view. Besides specifying associations between views, sub-views also own relationships between them. We named them sub-correspondences. For instance, PRISMSYS provides a state-equation association through the characterization sub-correspondence. This sub-correspondence was defined since it is not possible to specify when a SYSML equation is active or not by activating a state in a FSM. Equivalence is another sub-correspondence used to bind the structural sub-views properties to the value calculated on the equational sub-view. To address the control events of the mode-FSMs explicitly, a control sub-correspondence must be specified. Values tested in the FSM guards are extracted from the structural sub-view properties by using the data sub-correspondence.

A PRISMSYS view combines the execution of two kinds of MoCs: FSMs based on a discrete (logical) time, and the equations based on a Continuous Time. This MoC combination could be homogeneous (FSM and Mode-FSM) or heterogeneous (Mode-FSM and Continuous Time). To give an execution semantics to FSM, Mode-FSM, Continuous Time and the coordination between them, PRISMSYS defines the relevant events of the models together with constraints that specify the causal relations and synchronizations. Each relevant action in a MoC is expressed as an event and we use CCSL [START_REF] Andrade | A Methodology for Mapping SysML Activity Diagram to Time Petri Net for Requirement Validation of Embedded Real-Time Systems with Energy Constraints[END_REF], an event constraint language, to represent the allowed sequences of actions conforming to the selected MoC.

IV. PRISMSYS EXECUTION SEMANTICS

PRISMSYS behaviors, which initially are described by a static definition (meta-model), need a formal way to express their actions associated with their static elements. CCSL provides the needed concepts to express the actions of these behaviors allowing the association action-element and the behavioral analysis by simulation. First, we define the FSM execution semantics. Second, we state the execution semantics in Continuous Time. Finally, we describe the coordination between FSMs and Continuous Time.

A. Constraint Clock Specification Language

Execution Semantics of PRISMSYS relies on the CCSL formalism. We recall briefly the basic facts on CCSL, which is presented at large in [START_REF] André | The Time Model of Logical Clocks available in the OMG MARTE profile[END_REF], [START_REF] André | Syntax and Semantics of the Clock Constraint Specification Language (CCSL)[END_REF].

In CCSL, it is possible to define Clocks, which are possibly infinite ordered sets of instants. These clocks represent relevant changes in a system, on which constraints can be specified. A clock can be either chronometric or logical. The former is employed to specify a constraint associated with a physical dimension like the physical time or distance. The later defines a terminology referring to events (if events are sequences of event occurrences, as clocks are sequences of clock ticks). Distinct clocks can be independent (fully asynchronous), or partially ordered. The goal is that, after completion of design the specification represents a set of partially ordered clocks and eventually, at runtime, all clocks are mapped onto a single, most fundamental and totally ordered master clock representation simulation/execution step, but before that, designing with independent logical clocks is usually highly beneficial.

Clocks (and clock ticks) may enjoy two basic types of ordering relations: a clock is faster than another one (based on tick precedence), or a clock may be subclock of another one (based on tick simultaneity). The former relation is denoted a ≺ b, the latter a ⊂ b, where a and b are clocks. Relations, a = b (all ticks are simultaneous) or a # b (no ticks are common), can also be stated.

Based on those types of relations, more sophisticated constraints may be introduced. A sample is given:

• a ∼ b: a and b tick once in turn, • a ❉ b: defines a new clock that ticks with b if there was a tick of a after the preceding tick of b,

• a mask: defines a new clock that is subclock of a but only ticks in a place where mask, a periodic binary word, reads 1. We employ these CCSL relations and expressions to define the execution semantics of FSM, the evaluation of equations (Continuous Time) and the coordination between them. The execution semantics of finite state machine are defined on the UML metamodel by an ECL (Event Constraint Language [START_REF] Deantoni | ECL: the Event Constraint Language, an Extension of OCL with Events[END_REF]) specification. Consequently, the CCSL specification can be automatically generated for a specific FSM model.

B. Finite State Machine Semantics

In a FSM, there are various relevant events that occur during its execution. Most of the FSM concepts are associated with one or more events that describe a particular FSM execution change, e.g., the entering in a state or the firing of a transition. These events are represented in CCSL by clocks. We give a precision about the FSM model, we constrain that a transition t ij is sensitive to either a guard ij or a trigger ij , however not both of them. If a guard is defined, then guard ij clock is stated, else trigger ij is specified.

Once the FSM clocks are defined, we identify the constraints on these clocks to describe the FSM execution semantics. We start defining the activation of a specific state. A state is active between the corresponding entering and leaving occurrences. In other words, the state s is active when the k th occurrence of s enter clock ticks and it stops being active when the k th occurrence of s leave ticks. A state cannot be transitory, i.e., the s enter and s leave ticks cannot be simultaneous. Moreover, a state cannot be activated if it is already active. Consequently, we use an Alternate relation between s enter and s leave in CCSL for all the states of FSM as follows: detailed in this paper, all the CCSL constraints were also specified in ECL in order to automate the generation of the CCSL specification for any FSM model. According to the execution semantics of FSM [START_REF] Cassandras | Introduction to Discrete Event Systems[END_REF], a transition t ij from a state s i to a state s j is fired if two conditions are achieved: s i is active, and either the t ij guard evaluation is true or trigger ij ticks. However, the instant when the guard is evaluated is not specified. Because it affects the timing properties, it is of primary importance for a state machine of an embedded system to define when the guard is evaluated. In PRISMSYS, we explicitly define a chronometric clock named eval that specifies the period when the guard is evaluated. This clock is the same for all the guards in the system and its period must be chosen according the nonfunctional property dynamic (see section IV-D). Hence, when the evaluation of the t ij guard condition returns true, guard ij occurs. Considering that s i is active and guard ij ticks, then the t ij transition is fired. In contrast, if a transition is sensitive to a trigger event, the t ij transition is fired when trigger ij ticks. We specify the relationship of these clocks by using CCSL expressions. We present the CCSL constraints that define when to fire the t ij transition by guard ij :

s enter ∼ s leave ( 1
)
f ire t ij = [(s ienter ❉ guard ij ) s i leave ] • f ire t ij
this expression can be read as: a transition t ij is fired if guard ij ticks while the state is active (i.e., after a s ienter occurrence and up to an occurrence of s i leave ).

The state s i stops being active (i.e., s i leave ticks) when one of its outgoing transitions occurs. The relationship between the leaving of a state and the firing of the outgoing transitions is specified as follows:

s i leave = t∈tout s i f ire t
where t outs i is the set of outgoing transitions from s i .

When a transition is fired, the targeted state is entered simultaneously, i.e.,entering state s j coincides with the firing of all the incoming transitions. This is specified as follows:

s jenter = t∈tin s j f ire t
where t ins j is the set of incoming transitions to s j .

If the transition t ij has an action, the ef f ect ij clock occurs simultaneously with the transition firing. This relationship is specified by:

ef f ect ij = f ire t ij
The UML state machines must have at least one initial state. We constrain them to have one and only one initial state. The clock init is specified to start the FSM execution by activating the initial state. We only need one tick in init to activate the initial state. Thus, we state init in CCSL as follows: init = init 1(0) w this equation forces init to tick only once.

The init clock must be associated with the initial state. Considering that s init is the initial state of the FSM, we define its activation as follows:

s initenter = init
By using these rules, we defined the event based semantics of the UML state machine. It is then possible to reason about a specific state machine and to simulate it in TIMESQUARE, obtaining a timing diagram and/or a diagram animation. It is now mandatory to define the continuous time execution semantics.

C. Continuous Time Execution Semantics

In the considered systems, non-functional properties evolution often depend on physical time. There are two main families of continuous time solvers, the fixed-step solvers and the variable step solvers. A fixed-step solver evaluates the equation of the modeled process periodically with a predefined step size. The disadvantage of such solvers is that they can hide some discontinuities in the modeled process if the step size is too big. The variable step solvers adapt the step size to the dynamic of the modeled process in order to avoid hiding such discontinuities. This is of great importance when the goal is to provide an accurate representation of the modeled process.

In our case, we model by equations the evolution of nonfunctional properties according to time. One important goal of this work is to allow the prototyping of non-functional property managers. Eventually, these managers will retrieve the value of the corresponding non-functional properties by using (periodic) sensors. Consequently, we do not try to obtain the best representation of the non-functional property evolution, but rather a representative view of the process at specific points in time, as seen by the managers. Therefore, we can use either fixed-step or variable-step solvers but we do not use zero crossing functionality and we explicitly specify in the UML model the point in time when the value from the process are retrieved. To ease the implementation we used for now only fixed-step solvers so that the points in time when the value from the process are retrieved are periodic and correspond to the solver fix step.

In the model, to specify the step size used by the solver, we employ a chronometric clock from MARTE. In CCSL, this is encoded by discretizing a dense clock (i.e., a clock in which whatever pair of two instants you choose, there exists always one instant between these two instants). We use the physical time dense clock defined in CCSL and we name the discretized clock step. At each occurrence of step, the active equations specified in the model are evaluated by the continuous time solver. In CCSL, the step clock is defined by:

step physicalT ime discretizedBy ∆t ( 2
)
where ∆t is the step size as defined in the UML model and used by the fixed-step solver.

Once the FSM and Continuous Time execution semantics are defined, we must coordinate the actions of both semantics to achieve a complete execution of a PRISMSYS view.

D. FSM and Continuous Time Coordination

We provided the event based semantics for the UML state machine (FSM) and for the SYSML parametric diagram (Continuous Time). Control and structural sub-views follow the FSM semantics and the equational sub-view follows a Continuous Time semantics. Instead of providing a tailored semantics for a new model mixing both semantics, such as in an hybrid automata [START_REF] Cassandras | Introduction to Discrete Event Systems[END_REF], we propose to define the semantics of the syntactic association between the different sub-views (named sub-correspondences in PRISMSYS). The coordination semantics is also specified in CCSL.

In the structural sub-view, the FSM are representing some mode automata. The transition between two modes is caused by the reception of an event sent from the control subview. Therefore, there is a correspondence between the output events generated from the control sub-view and the events associated with the mode transitions. This correspondence is syntactically represented by control connectors between control and structural sub-views.

From the behavioral semantic perspective, a control event generated from the FSM of the control sub-view can cause a mode transition in the mode automata of an element in the structural sub-view. This relation can be tailored to be synchronous, purely causal or even timed. Here we chose that the sending and the reception of an event occurrence is simultaneous. By using CCSL, if an event occurrence is transferred by a control connector from a transition t ij to the trigger of a mode automata (trigger mode automata ) we enforce the following coincidence relation:

ef f ect ij = trigger mode automata
we read this expression as a control event, produced during the transition in a control FSM (ef f ect ij ), coincides with the trigger of a transition in the mode automata (trigger mode automata ).

In PRISMSYS, the mode automata are used to specify the modes under which there are different distributions that govern the evolution of a non-functional property. For instance the instantaneous consumption of a CPU depends on its activity (e.g.,sleep, idle or active). These evolutions are specified by different equations that are enabled or not according to the active state of a mode-FSM (see section III). To represent this semantics, a mode-FSM and an equation are syntactically linked by a characterization sub-correspondence. When a mode becomes active (i.e., clock s enter ticks), the associated equation is enabled. When the mode is left, (i.e., clock s leave ticks), the corresponding equation is disabled. Of course, for the model to be consistent, a non-functional property should be at least defined by one equation during a run.

We have two chronometric clocks in the system. The one used for the evaluation of the guard and the one that defines the fix step of the solver. To avoid the evaluation of the guards without a new value computed by the equation, the chronometric clock used for the solver step must be the same or faster than the one used to evaluate the guards. We express this coordination in CCSL as:

step eval (3)
where eval is the clock whose instants command the evaluation of the guards and step is the fix step of the solver. If these two clocks are simultaneous, it is mandatory to evaluate the equations before the evaluation of the guards to respect the causality (even if instantaneous in this case). It guarantees that the non-functional properties used in the guards have the latest value resulting from equation evaluation. Now that the execution semantics of PRISMSYS is specified, the remainder of the paper presents the associated implementation.

V. PRISMSYS MODEL CO-SIMULATION

TIMESQUARE is an Eclipse and model-based environment for the specification, analysis and verification of causal and temporal constraints. Particularly, TIMESQUARE allows defining, analyzing and verifying a CCSL specification. Nevertheless, this tool is not adapted to evaluate equations (e.g., power and temperature equations). Therefore, we use Scilab [START_REF]Scilab Consortium[END_REF], an open source tool for numerical computation, as a fixed-step solver for the evaluation of the equations. To coordinate the TIMESQUARE solver with the Scilab solver, we implemented a TIMESQUARE backend that acts like a "connector" between these tools. We named it T2 BACKEND FOR SCILAB.

Figure 3 depicts an overview of this implementation. It shows three dependent domains: the one on top is the PRISM-SYS model, detailed in Section III. The one at bottom describes the TIMESQUARE and Scilab co-simulation and the one in the middle defines the PRISMSYS semantics to execute and to coordinate the behavior of the PRISMSYS model. We focus in this section on the block at the bottom, the T2 BACKEND FOR SCILAB.

TIMESQUARE offers the possibility to add user-defined backends that trigger specific actions on selected event occurrences or relations. These new backends can be added to the backend manager by using an eclipse extension point. During a simulation, the backend manager receives the status of each clock (it ticks or not) at each simulation step. It also receives the status of relations (causality and coincidence) as well as the status of the assertions (violated or not). A developer can create its own backend that subscribes to some of these events. TIMESQUARE is already distributed with some backends like the VCD backend that draws the timing diagram of the clocks during the simulation and the Papyrus Animator that animates the elements of an UML model specified in Papyrus1 . Alike, by using the backend manager and connecting to the specific extension point, we implement T2 BACKEND FOR SCILAB as an Eclipse Plug-in to bind the TIMESQUARE solver module with the Scilab solver. This back-end receives the status of the clock (it ticks or not) at each simulation step and consequently configures the Scilab solver according to the information in the UML PRISMSYS model and drives the Scilab solver to evaluate the equations during the simulation. The user can visualize the value of the non-functional properties thanks to the Scilab graphic window called by the Scilab solver.

The solver configuration consists in the extraction of different equations from the model as well as the extraction of the chronometric clock that defines the solver step size. It is mandatory for the backend to register to the entering and leaving clock of all the mode-FSM states linked to an equation in order to add or remove it from the Scilab solver accordingly. This backend also commands the evaluation of the guards, therefore such pieces of information must also be extracted from the PRISMSYS model. During the simulation, the T2 BACKEND FOR SCILAB receives the status of each relevant clocks. When an entering state (s ienter ) clock ticks, T2 BACKEND FOR SCILAB adds the equation(s) associated with the active state. When the step clock ticks, the active equations are evaluated by the Scilab solver. The result of the evaluation is plotted in a Scilab plot window and stored in the backend. For each step occurrence, the guards are evaluated. If a guard is evaluated to true, T2 BACKEND FOR SCILAB forces the associated guard clock occurrence in the TIMESQUARE solver (see guard ij in Section IV). Such occurrences can cause a change of the controller state, and then a mode change. We can note that the guard evaluation is done for each step occurrence and not necessarily immediately when the parameter exceeds the thresholds. It emphasizes the importance to select the appropriated solver step size in order to react soon enough to the evolution of the non-functional properties.

VI. CASE STUDY: CPU THERMAL CONTROLLER

The case study shows how a thermal controller can be prototyped by using PRISMSYS. The temperature, a nonfunctional property, must be monitored in order to reduce the power consumption, extending the battery charge and fulfilling the temperature requirements. The CPU is the component on which we focus in this case study. We define a CPU mutiview model by using PRISMSYS. In Figure 4, we present a simplified version of the thermal view model. The figure depicts the three PRISMSYS behaviors (control, modes and equations).

The structural sub-view of this figure represents the abstraction of a CPU from a thermal point of view. It describes a non-functional property representing the temperature (T). The equational sub-view defines the equations that characterize the evolution of the CPU temperature. The Control sub-view specifies a thermal controller, which changes the activity of the CPU according to its temperature to avoid damages.

The CPU thermal behavior is represented by two modes: COOLING and HEATING. Each mode corresponds to a different temperature evolution. COOLING indicates the CPU The change between two modes occurs when the transition between them is fired. Transitions are sensitive to associated events sent from the control sub-view (point 1 in Figure 4). The control sub-view contains a thermal controller whose task is to manage the temperature evolution of the CPU. The controller behavior is another state machine of two states: LOW and HIGH. LOW represents that the CPU temperature is below the recommended temperature. HIGH expresses the temperature exceeds the maximum recommended value. In contrast to the CPU thermal FSM, the transition of the controller is sensitive to the evaluation of guards. In this case study, guards evaluate if the temperature surpasses 85 • C or is lower than 80 • C (point 3 in Figure 4). Once a transition is fired, a specific event is sent to the CPU thermal FSM in order to change its mode and thus activate the equation to be evaluated.

Figure 5 depicts the evolution (plotted in Scilab) of the temperature T according to the time t. When the simulation starts, i.e., at t=0, T2 BACKEND FOR SCILAB extracts the clocks associated with modes COOLING and HEATING and the step clock. T2 BACKEND FOR SCILAB also identifies and extracts the thermal equations. In our thermal model, we assume that the ambient temperature is 25 In Figure 5, we note that the temperature actually exceeds some how the threshold defined in the guard. This is due to the periodic monitoring of the temperature and the periodic evaluation of the guard. To limit such phenomenon, one could decrease the step size. However, this phenomenon has to be taken into account during the development of the system.

VII. CONCLUSION AND FUTURE WORK

In this paper, we have presented a simulation framework based on PRISMSYS to execute heterogeneous models that combine functional and non-functional views. We have focused on the execution semantics of the PRISMSYS discrete (logical) time (FSM) and its coordination with the Continuous Time model of the non-functional property equations. We have also described the implementation, T2 BACKEND FOR SCILAB, a connector between TIMESQUARE and Scilab to enable the co-simulation of discrete (logical) time and Continuous Time. A simple thermal view model is used to illustrate PRISMSYS and the execution framework. As a future work, we intend to explore further the different correspondence rules between views. Some correspondence rules are mere importation of elements from one view to another one. Other ones are closer to coordination/synchronization rules.

VIII. ACKNOWLEDGEMENT

Fig. 1 .

 1 Fig. 1. PRISMSYS View

Fig. 3 .

 3 Fig. 3. T2 BACKEND FOR SCILAB Overview on a PRISMSYS thermal view case study.

Fig. 4 .

 4 Fig. 4. Case Study: CPU Thermal Controller

  • C. Thus, the TIMESQUARE simulation starts at 25 • C and the activating LOW and HEAT-ING states (which are the initial states) are activated. When the temperature exceeds 85 • C, the corresponding transition is fired, the state changes to HIGH and the eCool event is sent to the CPU Thermal Modes (other events, which set the CPU activity to idle in other views, are also sent but not represented here). Such an event causes the leaving of the HEATING mode to enter in the COOLING mode, consequently deactivating the equation dT /dt = -0.6 * (T -100) and activating the equation dT /dt = -0.6 * (T -30). In the next simulation steps, the associated COOLING equation is evaluated and the temperature decreases. When the temperature is lower than 80 • C, the controller state machine changes to the LOW state and the cycle is repeated.

Fig. 5 .

 5 Fig. 5. PRISMSYS ThermalView simulation in Scilab by using T2 BACKEND FOR SCILAB.

  Table I summarizes the clocks defined to represent the relevant actions of a FSM. Firing transition t ij from s i to s j guard ij Ticking once the t ij guard evaluation is true trigger ij Receiving a trigger event on t ij ef f ect ij Generating an Event once t ij is fired

	Clock	Action
	init	Initializing the execution of FSM
	senter	Entering state s
	s leave	Leaving state s
	f ire t ij

TABLE I CLOCKS

 I REPRESENTING THE RELEVANT ACTIONS IN A FINITE STATE MACHINE.

  Equation 1 is specified in ECL by an event invariant on the concepts of UML State Machine as illustrated in Figure 2. This ECL constraint defines two properties whose type is Event (enter and leave) specified in the context of the State UML concept. The relation between both events is Alternate, i.e., this ECL specification represents s enter , s leave and their relation for all the State instances in a model. While not c o n t e x t S t a t e d e f : e n t e r : Event d e f : l e a v e : Event i n v e n t e r O n c e B e f o r e T o L e a v e : R e l a t i o n A l t e r n a t e s ( s e l f . e n t e r , s e l f . l e a v e ) Fig. 2. ECL specification of the UML State activation.

http://www.eclipse.org/papyrus/

This work is partially supported by the ANR INS Projects HOPE (ANR-12-INSE-0003) and GEMOC (ANR-12-INSE-0011).


Mohammad Alamgir Hossain

Mohammed Quaddus

1 AN ADOPTION DIFFUSION MODEL OF RFID-BASED LIVESTOCK MANAGEMENT SYSTEM IN AUSTRALIA

Keywords: RFID, adoption, diffusion, extended use, livestock industry, Australia

Many countries, like Australia, have introduced a radio frequency identification (RFID) based livestock identification and management system,which can be used for condition monitoring and fault prognosis during an outbreak situation. This paper examines the adoption process and its subsequent diffusion and extended usage of RFID in Australian livestock management practices, and proposes a research model. The model is primarily built on Rogers' innovation-diffusion theory and Oliver's expectation-confirmation theory, with some logical modifications. It posits that while adoption of RFID may be the result of legislative pressure, its further diffusion is an evaluative process, which is judged against "satisfaction" and "performance" derived from RFID systems. The implications of these and other related concepts are also discussed. Hypotheses are developed which can be tested via empirical study. The proposed model has both theoretical and practical implications. Although it is developed on the basis of the Australian livestock industry, it can be used in other countries and also in other applications with some industryspecific modifications.

INTRODUCTION

Radio frequency identification (RFID) technology is one of the most effective enabling technologies in identifying an object uniquely and keeping the desired data as long as required, which can later be retrieved as information. Along with tens of other applications, RFID is a very efficient method of identifying animals and collecting data more quickly, and is potentially a revolutionary innovation for the food industry. For livestock markets, the data and information serve as the "certificate" of the product and the international livestock market is increasingly demanding an RFID-generated data and information system from the livestock and livestock products supplying countries that can be used for condition monitoring and fault prognosis in the event of an outbreak situation [START_REF] Sullivan | RFID Technology Could Be Used to Build a National Livestock-Tracking System[END_REF]). The pressure intensified with the scrutiny of various government agencies. To this end, some countries such as Canada, Australia, and Uruguay, as well as numerous European countries, passed legislation on mandatory use of autoidentification, while other countries such as the United States, Malaysia, and Japan are implementing the system on a volunteer basis [START_REF] Patent | Awareness and Intended Compliance of Beef Cattle Exhibitors in the National Animal Identification System[END_REF]). Australia has the world's first and largest RFID-enabled national livestock identification system, called NLIS [START_REF] Tonsor | Livestock Identification: Lessons for the U.S. Beef Industry from the Australian System[END_REF]. When integrated with post-slaughter tracking systems, the NLIS database allows for rapid and accurate tracing of cattle in the event of a disease outbreak or residue incident and saves the industry from more losses than would otherwise be the case. For example, in July 2008, Russia, Australia's fourth largest beef export market, banned three kangaroo processing plants and a beef facility because of microbial contamination (ABC 2008). If NLIS could not track the sources of the contamination quickly, the entire Australian meat industry would have suffered from this ban.

The Australian meat and livestock industry is regarded as one of the largest in the world and is renowned for its "clean and green" beef. Australia is the second-largest exporter of beef, mutton, and lamb in the world, exporting to more than 100 countries [START_REF] Tonsor | Livestock Identification: Lessons for the U.S. Beef Industry from the Australian System[END_REF], and world's largest exporter of beef (ABARE 2008). During 2007During -2008, the gross value of livestock production was $41.5 billion, contributing 63 percent of the gross value of agricultural product ($65.4 billion). A significant portion of this products was exported, contributing 53 percent of the agribusiness's export in 2007-2008, $14.5 billion of the total $27.5 billion (ABARE 2008).

Most studies on RFID have dealt with technology issues (frequency standardization, tag orientation), security, privacy, and implementation issues primarily in supply chain and logistics management, although several studies have been undertaken in the RFID adoption diffusion area [START_REF] Chang | The Determinants of RFID Adoption in the Logistics Industry: A Supply Chain Management Perspective[END_REF][START_REF] Cheng | Investigating Key Factors of Deciding RFID's Adoption in Logistics Service Providers[END_REF][START_REF] Huyskens | RFID Adoption: Theoretical Concepts and Their Practical Application in Fashion[END_REF][START_REF] Krasnova | Drivers of RFID Adoption in the Automotive Industry[END_REF][START_REF] Lee | An Exploratory Study of Radio Frequency Identification (RFID) Adoption in the Healthcare Industry[END_REF]Matta andMoberg 2006, 2007;[START_REF] Ranganathan | Adoption of RFID Technology: An Exploratory Examination from Supplier's Perspective[END_REF][START_REF] Schmitt | Why RFID Adoption and Diffusion Takes Time: The Role of Standards in the Automotive Industry[END_REF][START_REF] Schmitt | Adoption and Diffusion of RFID Technology in the Automotive Industry[END_REF][START_REF] Sharma | Radio Frequency Identification (RFID) Adoption Drivers: A Radical Innovation Adoption Process[END_REF]. The adoption of RFID in the Australian livestock industry is, however, a special type of adoption as the government imposed the legal requirement of using this technology for cattle identification. However, farmers may also use RFID voluntarily for identifying other animals or in other livestock applications. Therefore, it would seem to be interesting to examine how farmers embraced this technology and whether they got what they expected. It is likely that if they are satisfied they would diffuse this tech-nology and possibly use it for other applications. Alternatively, if they are not satisfied, they are likely to use this technology only for mandated purposes. However, unfortunately, no significant work has been performed to explain the farmers' adoption and diffusion behavior in RFID-based livestock identification and management field. This paper addresses and attempts to close this research gap. Equipped with a background of adoption and diffusion of innovation, this paper develops a research model to examine and identify the factors affecting the adoption-diffusion and extended use of RFID in the livestock industry. The two dominant research questions of this study are 1. What are the factors that influence the farmers' adoption decision of RFID technology in their livestock identification operation? 2. What are the factors influencing the diffusion and extended use decision of RFID in livestock management operations?

BACKGROUND

Adoption diffusion of an innovation has been studied primarily with three theories: innovation-diffusion theory (IDT), theory of reasoned action (TRA), and technology acceptance model (TAM). While IDT focuses on the diffusion processes of an innovation, the TRA and TAM models explain the relationship between actual use of a technology and user attitudes, perceptions, and beliefs [START_REF] Xu | Exploring the Factors Influencing End Users' Acceptance of Knowledge Management Systems: Development of a Research Model of Adoption and Continued Use[END_REF]. RFID adoption is more or less imposed rather than voluntary, which is based on the attitude of the users or perceived usefulness, while RFID diffusion is believed to be more dependent on performance evaluation rather than belief or attitude. [START_REF] Bhattacherjee | Understanding Information systems Continuance: An Expectation-Confirmation Model[END_REF] argues that not only adoption but continued use is necessary for an innovation's ultimate success and has used expectation confirmation theory (ECT) in explaining IS continuance, although ECT is primarily used in consumer behavior literature. This study, based on IDT and ECT, develops a theoretically grounded, comprehensive framework to investigate the antecedents of RFID adoption, diffusion, and extended use, which is further refined using theoretical and empirical findings from prior literature. The following subsections discuss the two underlying theories as they relate to adoption diffusion and continued use of an innovation.

Innovation Diffusion Theory (IDT)

An "innovation is the adoption of a change which is new to an organization and to the relevant environment" (Knight 1967, p. 467). Diffusion is "the process by which an innovation is communicated through certain channels over time among the members of a social system" (Rogers 1995, p. 5). Therefore, RFID can certainly be examined by IDT.

IDT proposes that potential adopters of an innovation must gain some knowledge about the innovation, then be persuaded of its value, decide to adopt and implement it, and confirm the decision to adopt the innovation. For pursuing the maximum benefits, however, mere adoption of RFID is not sufficient; rather, it is necessary to institutionalize this technology into routine operations and practices and extend its use to different applications, where possible, suitable, or profitable. The continued and extended use behavior is different from and possibly more important than its initial adoption because many RFID adopters are initially driven by mandatory pressure and later choose different level of use depending on their internal judgment. IDT is silent, not explaining how adopters go to the next stage of diffusion and to further innovative applications. Moreover, IDT is primarily based on individual-level adoption decisions [START_REF] Eveland | The Deployment of Technology[END_REF]), but RFID adoption needs to be examined from government and organizational perspectives because it involves many national policies and requires modification of organizational structures and operations.

Expectation-Confirmation Theory

Expectation-confirmation theory (ECT) is widely used in the consumer behavior literature to study consumer satisfaction and post-purchase behavior. Post-purchase behavior is somewhat related to the continued and extended use characteristics of an innovation. The process by which consumers reach repurchase intentions in an ECT framework is as follows (Oliver 1980, p. 462): First, buyers form an initial expectation of a specific product or service prior to purchase. Second, consumption reveals a perceived quality level, which is influenced by expectations. Third, they determine the extent to which their expectation is confirmed. Fourth, they form a satisfaction based on their confirmation level and expectation. Finally, satisfied consumers form a repurchase intention, while dissatisfied consumers discontinue its subsequent use.

Expectation

In ECT, expectation acts as the frame-of-reference to measure confirmation and satisfaction. ECT, however, does not explain the basis of expectations and ignores the driving forces behind adopting a product, service, or innovation. It ignores the mandatory introduction of a product or service; rather, it is based on voluntary decisions. In a mandatory system, the expectations are shaped and biased by the imposing authority and, therefore, not an independent variable as proposed in ECT.

Conceptual Framework

This study suggests that farmers, driven by the market and/or legislative requirements, gather and utilize all available information (from internal and external sources), share information, and develop and adjust their expectations in developing rational expectations [START_REF] Au | What Do You Know? Rational Expectations in Information Technology Adoption and Investment[END_REF] before they adopt RFID. Adoption drives RFID to further diffusion. Meanwhile, the expectations are modified. Eventually the satisfied farmers diffuse RFID into their routine operations while dissatisfied farmers may use it for limited operation(s). Farmers in Australia do not have the choice to reject RFID use as it is mandatory for cattle identification. Moreover, some satisfied farmers may voluntarily use RFID for extended use. Satisfaction, however, is measured against the level of confirmation of the expectations, and the performance of RFID. Thus the framework is presented in two parts: External factors 6 Knowledge 6 Adoption; and Expectations 6 Satisfaction 6 Diffusion and Extended Use. We use this conceptual framework for the development of the RFID adoption-diffusion + model, which is discussed in the following section.

THE RESEARCH MODEL AND HYPOTHESES DEVELOPMENT

Figure 2 depicts the proposed model. The following section describes the constructs of the research model.

RFID Knowledge

RFID knowledge describes the amount of knowledge in the field of RFID that a farmer accumulates in theory and practice. In this study, the RFID knowledge construct consists of basic, technical, and technological RFID knowledge. Basic RFID knowledge refers to knowledge about the existence and potential benefits of RFID. Technical RFID knowledge is knowledge about the state-of-the-art of RFID technology. Technological RFID knowledge is about technological laws (scientific and IT mathematics), functional rules (RFID standard and frequency use), structural rules (depth of integration), and socio-technical understanding (such as ethics, privacy, etc.) of RFID. RFID knowledge itself is dependant of external information sources and the organization's internal information sources [START_REF] Tellkamp | The Adoption of RFID-Based Self-Check-Out-Systems at the Point-of-Sale: An Empirical Investigation[END_REF]. Furthermore, the amount of RFID knowl-

Hypothesis 1a: RFID knowledge positively influences the speed and extent of RFID adoption.

Hypothesis 1b: RFID knowledge positively influences RFID expectations in livestock farms.

External Environment

The external environment is the global control where the organization operates and it is beyond the organization's control but is important in decision-making behavior [START_REF] Quaddus | An Investigation into the Factors Influencing the Adoption of B2B Trading Exchanges in Small Business[END_REF]. External environment has been recognized as playing a very significant role in adoption-diffusion research, including RFID adoption [START_REF] Sharma | Radio Frequency Identification (RFID) Adoption Drivers: A Radical Innovation Adoption Process[END_REF]. Related to RFID adoption, the external environment consists of external pressure [START_REF] Matta | Defining the Antecedents for Adoption of RFID in the Supply Chain[END_REF], government [START_REF] Matta | Defining the Antecedents for Adoption of RFID in the Supply Chain[END_REF][START_REF] Zhu | Electronic Business Adoption by European Firms: A Cross-Country Assessment of the Facilitators and Inhibitors[END_REF], external information sources [START_REF] Jeyaraj | A Review of the Predictors, Linkages, and Biases in IT Innovation Adoption Research[END_REF][START_REF] Matta | Defining the Antecedents for Adoption of RFID in the Supply Chain[END_REF], and industry-wide standardization and cost [START_REF] Brown | Radio Frequency Identification Technology: An Exploratory Study on Adoption in the South African Retail Sector[END_REF][START_REF] Cheng | Investigating Key Factors of Deciding RFID's Adoption in Logistics Service Providers[END_REF][START_REF] Juban | Would You Like Chips With That? Consumer Perspectives of RFID[END_REF]. External pressure: External pressure consists of government pressure [START_REF] Kuan | A Perception-Based Model of EDI Adoption in Small Businesses Using a Technology-Organizational-Environmental Framework[END_REF] and market pressure [START_REF] Chang | The Determinants of RFID Adoption in the Logistics Industry: A Supply Chain Management Perspective[END_REF][START_REF] Ranganathan | Adoption of RFID Technology: An Exploratory Examination from Supplier's Perspective[END_REF]. A practical reason for livestock farmers to adopt RFID is government policy and regulations. Like Australia, a growing number of animal exporting countries have introduced legislation on compulsory RFID-based livestock management systems. Therefore, farmers do not have any choice but to adopt RFID technology. Another reason for livestock farmers to adopt RFID is because of the increasing market pressure for RFIDbased animal tracking systems [START_REF] Li | Radio Frequency Identification: Supply Chain Impact and Implementation Challenges[END_REF].

Government: Government can play an important role in RFID adoption and diffusion through information provision, research and development policies and facilities, incentives for the adopters, and building and enhancing the infrastructure [START_REF] Scupola | The Adoption of Internet Commerce by SMEs in the South of Italy: An Environmental, Technological and Organizational Perspective[END_REF].

External information sources: Farmers acquire RFID information from external information sources, including farm magazines, government agencies [START_REF] Kettinger | National Infrastructure Diffusion and the US Information Super Highway[END_REF], and through interpersonal channels such as other adopters who are similar in socioeconomic status [START_REF] Ghadim | A Conceptual Framework of Adoption of an Agricultural Innovation[END_REF]. External information sources contribute to livestock farmers acquiring RFID knowledge and also motivates the farmers to adopt RFID.

Industry-wide standardization and cost: One of the main inhibitors of RFID adoption is the lack of standardization and the cost of RFID tags [START_REF] Schmitt | Why RFID Adoption and Diffusion Takes Time: The Role of Standards in the Automotive Industry[END_REF]. Although the RFID standards (ISO11784/11785) are defined by the International Standards Organization (ISO) to use LF 134.2 KHz, farmers are easily confused when they find too many providers with many different types of tags and readers. They are afraid of buying tags and readers from different providers because they think that the tags might not be read by readers from a different company (readability). That brings the fear of not being able to read the tags in different countries and hinders the process of a global system of livestock identification (interoperability). Therefore, adopters are looking for an industry-wide standard system [START_REF] Brown | Radio Frequency Identification Technology: An Exploratory Study on Adoption in the South African Retail Sector[END_REF][START_REF] Cheng | Investigating Key Factors of Deciding RFID's Adoption in Logistics Service Providers[END_REF][START_REF] Juban | Would You Like Chips With That? Consumer Perspectives of RFID[END_REF][START_REF] Sharma | Radio Frequency Identification (RFID) Adoption Drivers: A Radical Innovation Adoption Process[END_REF] and the tags need be cheaper [START_REF] Schmitt | Why RFID Adoption and Diffusion Takes Time: The Role of Standards in the Automotive Industry[END_REF][START_REF] Sharma | Radio Frequency Identification (RFID) Adoption Drivers: A Radical Innovation Adoption Process[END_REF][START_REF] Sharma | Strategic and Institutional Perspectives in the Adoption and Early Integration of Radio Frequency Identification (RFID)[END_REF]). As a whole,

Hypothesis 2a: External environment positively influences RFID adoption.

Hypothesis 2b: External environment positively influences RFID knowledge. [START_REF] Tornatzky | Process of Technological Innovation[END_REF] argued that organizational factors must be considered in any organizational innovation adoption research. Organizational variables such as (top) management attitude towards adoption of RFID with adequate funding and expertise ensures RFID adoption [START_REF] Brown | Radio Frequency Identification Technology: An Exploratory Study on Adoption in the South African Retail Sector[END_REF][START_REF] Huyskens | RFID Adoption: Theoretical Concepts and Their Practical Application in Fashion[END_REF][START_REF] Krasnova | Drivers of RFID Adoption in the Automotive Industry[END_REF][START_REF] Ranganathan | Adoption of RFID Technology: An Exploratory Examination from Supplier's Perspective[END_REF][START_REF] Schmitt | Adoption and Diffusion of RFID Technology in the Automotive Industry[END_REF][START_REF] Sharma | Radio Frequency Identification (RFID) Adoption Drivers: A Radical Innovation Adoption Process[END_REF][START_REF] Sharma | Strategic and Institutional Perspectives in the Adoption and Early Integration of Radio Frequency Identification (RFID)[END_REF]. Organizational size also is an important factor as the RFID integration-depth comes with the physical, financial, and technological resources of the farm [START_REF] Brown | Radio Frequency Identification Technology: An Exploratory Study on Adoption in the South African Retail Sector[END_REF][START_REF] Huyskens | RFID Adoption: Theoretical Concepts and Their Practical Application in Fashion[END_REF]Matta andMoberg 2006, 2007). Organizational size facilitates knowledge acquisition (and/or knowledge generation). Infrastructure-related facilities (IS/IT/ICT) available in the organization influence RFID adoption [START_REF] Ranganathan | Adoption of RFID Technology: An Exploratory Examination from Supplier's Perspective[END_REF][START_REF] Sharma | Radio Frequency Identification (RFID) Adoption Drivers: A Radical Innovation Adoption Process[END_REF][START_REF] Sharma | Strategic and Institutional Perspectives in the Adoption and Early Integration of Radio Frequency Identification (RFID)[END_REF]). As a whole, Hypothesis 3a: Organizational factors positively influence RFID knowledge for its adoption and diffusion among livestock farmers. Hypothesis 3b: Organizational factors positively influence RFID adoption in livestock farms.

Organizational Factors

RFID Expectations

RFID expectations are the expected attributes of RFID that are derived during the knowledge gathering phase, and are similar to the perceived characteristics of an innovation in IDT. This study differentiates expectation from attributes as some expectations are developed during the knowledge gathering phase, while some expectations are formed, or past expectations are modified, after the initial adoption, which means expectation is a continuous process rather than an initial and complete stage. Among the main attributes of an innovation in IDT (e.g., trialability, observability, relative advantage, complexity, and compatibility), empirical research and theoretical analysis found that relative advantage [START_REF] Brown | Radio Frequency Identification Technology: An Exploratory Study on Adoption in the South African Retail Sector[END_REF][START_REF] Krasnova | Drivers of RFID Adoption in the Automotive Industry[END_REF], complexity [START_REF] Chang | The Determinants of RFID Adoption in the Logistics Industry: A Supply Chain Management Perspective[END_REF][START_REF] Schmitt | Adoption and Diffusion of RFID Technology in the Automotive Industry[END_REF], and compatibility [START_REF] Schmitt | Adoption and Diffusion of RFID Technology in the Automotive Industry[END_REF]) are consistently used attributes. Some other attributes, including profitability [START_REF] Lindner | Adoption and Diffusion of Technology: An Overview[END_REF], cost [START_REF] Sharma | Radio Frequency Identification (RFID) Adoption Drivers: A Radical Innovation Adoption Process[END_REF], market penetration, and risk and uncertainty [START_REF] Ghadim | Risk, Uncertainty, and Learning in Adoption of a Crop Innovation[END_REF] are also important for RFID study. However, adopters' expectations have a direct negative influence on satisfaction [START_REF] Bearden | Selected Determinants of Consumer Satisfaction and Complaint Reports[END_REF][START_REF] Churchill | An Investigation into the Determinants of Customer Satisfaction[END_REF][START_REF] Oliver | A Cognitive Model for the Antecedents and Consequences of Satisfaction Decisions[END_REF][START_REF] Westbrook | Developing Better Measures of Consumer Satisfaction: Some Preliminary Results[END_REF] as satisfaction is the difference between performance and expectations.

Hypothesis 4: RFID expectations negatively influence the level of satisfaction.

RFID Adoption

Mandatory RFID adoption drives farmers to identify their cattle with RFID tags. Successful RFID adoption motivates farmers to further diffuse the technology.

Hypothesis 5: RFID adoption positively influences RFID diffusion and extended use in livestock industry.

Performance

Performance is the manner of functioning or operating. Once the system is implemented, the performance of the RFID system is measured, including general (works as designed), financial (on-budget, return-on-investment, etc.), and technical (complexity, maintenance, data recovery) performance of the system. [START_REF] Churchill | An Investigation into the Determinants of Customer Satisfaction[END_REF] and [START_REF] Oliver | Response Determinants in Satisfaction Judgements[END_REF] showed that performance has a direct and significant effect on satisfaction. However, [START_REF] Bhattacherjee | Understanding Information systems Continuance: An Expectation-Confirmation Model[END_REF] and [START_REF] Schmitt | Adoption and Diffusion of RFID Technology in the Automotive Industry[END_REF] found that performance influences RFID diffusion.

Hypothesis 5a: RFID performance positively influences satisfaction of RFID use.

Hypothesis 5b: RFID performance positively influences diffusion and extended use of RFID system.

Satisfaction

Satisfaction is defined as "the summary of psychological state resulting when the emotion surrounding disconfirmation expectations is coupled with the consumer's prior feelings about the consumption experience" (Oliver 1981, p. 27). It is an evaluative response concerning the perceived outcomes of experience from that product or service. The satisfaction construct is composed of the presence of affect and the notion of satisfaction. Presence of affect is central to satisfaction, which is the evaluation of outcomes such as happy or regretful and so on. The notion of satisfaction is the degree of affect that the adopter is more or less inclined to continue use of the product. ECT holds that satisfaction has a direct effect on consumers' intention to repurchase a product or continue use [START_REF] Anderson | The Antecedents and Consequences of Customer Satisfaction for Firms[END_REF][START_REF] Oliver | A Cognitive Model for the Antecedents and Consequences of Satisfaction Decisions[END_REF].

Hypothesis 6: Satisfaction positively influences diffusion and extended use of the RFID system.

RFID Diffusion and Extended Use

Diffusion and extended use is the only dependent variable of this study. This variable refers to the degree of use of the RFID systems in livestock management. Here, farmers do not have the luxury to decide not to continue RFID use as its use is mandatory in Australia (for cattle identification). However, farmers can decide the degree or depth of its use. Voluntarily, farmers may identify other animals and/or apply RFID for livestock operations including herd management and dairy management.

Farmers' Voluntariness

Farmers' RFID voluntariness is the degree to which farmers decide to implement RFID in livestock management operations of their own free will. It is the willingness to take initiative to try out RFID in livestock applications other than identification. Voluntariness is considered as one of the perceived characteristics for the diffusion of an innovation [START_REF] Agarwal | The Role of Innovation Characteristics and Perceived Voluntariness in the Acceptance of Information Technologies[END_REF]. Voluntariness may be varied with required investment and slack resources. However, this study postulates that farmers with more voluntariness would show interest and implement RFID in extended livestockmanagement application(s) than other farmers who are not volunteers.

Hypothesis 7: Farmers' voluntariness positively influences RFID diffusion and extended use in livestock management processes.

Measurement Scale

The variables of this study will be measured by adapting measurement scales from previous studies: organizational factors will be adapted from Patterson et al.'s (2003) organizational characteristics scale; satisfaction measure will be adapted from [START_REF] Westbrook | Developing Better Measures of Consumer Satisfaction: Some Preliminary Results[END_REF] satisfaction scale; competition measure will be adapted from [START_REF] Kuan | A Perception-Based Model of EDI Adoption in Small Businesses Using a Technology-Organizational-Environmental Framework[END_REF]. The measure for attributes of RFID expectations will be adapted from the innovation-diffusion literature. Adapted measurement scales will be subjected to rigorous reliability and validity tests.

FUTURE DIRECTIONS AND CONCLUSION

This article presents a research model for investigating the factors influencing the adoption, diffusion, and extended use of RFID technology-a subject that has not been well explored in the literature. Future research could test the entire research model. Parts of the model could also be extracted and investigated in detail. As the model is divided into two parts, future study could be conducted using different types of adopters. The model, including both of its main constructs and subfactors, can be taken as-is or finetuned for a comprehensive survey. Organizations embarking on RFID use can use the constructs and factors of the study to perform an internal audit to find out how they vary. The proposed model also provides guidelines for RFID adoption-diffusion practitioners and consultants. Therefore, this study will make two main contributions. First, it will test the theories in a new setting. Second, it will give practitioners new insights at the operational level as well assist with strategic decisions about effective diffusion policies and further RFID investments in the Australian livestock industry.

Figure 1

 1 Figure 1 Expectation-Confirmation Theory (Adapted from Figure 1 in A. Bhattacherjee, "Understanding Information Systems Continuance: An Expectation-Confirmation Model," MIS Quarterly (25:3), pp. 351-370. Copyright © 2001, Regents of the University of Minnesota. Used with permission.)

Figure 2

 2 Figure 2 The Proposed Research Model of RFID Adoption-Diffusion +