This deals with the framework implementation including the testing project which was used during development. It includes programmer documentation for the test project and the framework. Framework structure and usage description was also covered in order to help other adopters of the framework.

Even when the framework and the test environment were developed with standards in mind it is not always possible to maintain compatibility with wide range of tools. In some places experimental features such as C++14 were used in order to produce simpler and more readable code. In order to allow reproduction of the outputs of this thesis the following text provides versions and settings for tools used during the development.

A Toolchain is the core of every development process. It consists of basic utilities and libraries such as compiler, linker and debugger. It is even more important for embedded projects as those require cross-compiler setup. The toolchain used for testing and development of the framework on the STM32F4 board was created using crossdev application using these commands:

$ crossdev --target armv7m-hardfloat-eabi --ex-gcc --ex-gdb

Unfortunately this great tool is Gentoo linux specific as it uses the systems package installer to build the toolchain. As a replacement toolchain provided by ST company or custom build toolchain with the armv7m-hardfloat-eabi target triplet can be used. Hardware floating point is essential for framework code to work as FreeRTOS requires it. These versions of software were included in the toolchain used for development of the framework and testing application.

• binutils version: 2.24-r3 with C++ support
• gcc version: 4.9.0 with C++ support
• gdb version: 7.7.1 with XML support
• newlib version: 2.1.0

Aside from newlib library which is considered to be part of the toolchain the framework uses FreeRTOS operating system and STM32F4 peripheral library. FreeRTOS is a simple operating system which comes in form of a library. It contains scheduler and basic synchronization primitives. Due to nature of this library the sources and other needed files found in FreeRTOS were integrated directly into the project. The STM32F4 peripheral library was also included in the project as its nature requires some files to be modified in order to suit the application usage.

• FreeRTOS version: 8.0.0
• STM32F4xx DSP and Standard Peripherals Library version: 1.3.0

In general many very different tools ranging from hex editor to multimeter were used in development of the framework. Brands and versions of most are not important enough to be noted here. The two tools where version can be important are Eclipse development environment and On-Chip debugger software which was used to debug and flash the application on the STM32F4 development board.

• Eclipse SDK version 4.3.2 (Kepler Service Release 2)
• Eclipse CDT plug-in version: 8.3.0
• Eclipse CDT plug-in C/C++ GDB Hardware Debugging version: 8.3.0
• OpenOCD version 0.8.0 with USB support

In order to speedup development a project called beeclickarm was used as a base for the implementation. The beeclikcarm project focuses on exposing STM32F4 sensors to a application running on general computer via serial link. Slightly modified versions of some drivers and build script were taken from this project and incorporated into the test application. The original project can be found at Github.

The CDEECo++ application is expected to follow template described in usage example. In general the user is expected to implement CDEECO::Radio interface and create instance of it. Then the user should instantiate CDEECO::System with the provided radio implementation. Once the system object is created then the DEECo components inheriting from CDEECO::Component can be instantiated. The CDEECO::KnowledgeChache instances may also be instantiated and passed to the system object in order to provide caching of remote knowledge. Once the caches and components are in place the ensemble implementations inheriting from CDEECO::Ensemble can be instantiated as their constructor requires reference to caches and components. As the last step user program is expected to start FreeRTOS scheduler. Starting the scheduler will start the tasks defined by CDEECo++ system.

The deployment of application and the framework is the same as common deployment of embedded C/C++ application. It consists from compiling the application and framework sources using cross-compiler for target architecture. Linking objects together and creating single application image. Once the binary image is ready it is flashed into the embedded hardware memory. The deployment of the example project on the STM32F4 board is performed using make program. The configuration Makefile is placed in the project root. The makefile may need to adjust tools names and paths. The defaults are set to:

• OPENOCD=openocd
• CC=armv7m-hardfloat-eabi-gcc
• CXX=armv7m-hardfloat-eabi-g++
• OBJCOPY=armv7m-hardfloat-eabi-objcopy
• SIZE=armv7m-hardfloat-eabi-size

Once the paths are set the project can be complied, linked and flashed to the connected STM32F4 board by issuing these commands in the project's root directory:

• $ make clean
• $ make
• $ make flash

In order to speedup development the project can be built in parallel. The parallel build was tested with 9 tasks and produced significantly faster builds on 8 core CPU. The AMD FX8350 CPU was able to build the project in 2 seconds when building in parallel but it took over 12 seconds to build the project using single thread. In order to run 9 parallel build threads these commands may be used:

• $ make clean
• $ make -9
• $ make flash

Structure of the CDEECo++ application is important for understanding the background of the user defined components and ensembles. The key part of the CDEECo++ application is the system class. Its instance is used as glue between components and radio interface. The system object is created with provided instance of CDEECO::Radio implementation which provides the system with communication capabilities. The system object allows components to broadcast their knowledge and processes knowledge fragments received from other nodes. The system object also handles rebroadcasting of received knowledge fragments. In order to do that the system has internal rebroadcast storage of the size defined by template argument. The system also has several slots for knowledge caches. The exact number is also defined by system template argument. Knowledge caches are similar to fragment caches, but they do not store knowledge fragments, but instead try to reconstruct complete knowledge data from remote nodes. Each cache reconstructs knowledge of selected type which is together with cache size set by template argument. Knowledge caches are used as remote knowledge sources for ensembles. Thus every node has knowledge caches for knowledge types that are used by local ensembles. System stores knowledge data into knowledge caches using references to caches which are set using the system's registerCache method. Components are provided with reference to system which they use to broadcast knowledge changes. Ensembles are instantiated with component and knowledge cache references as they use component for knowledge update and knowledge cache as source of remote knowledge.

One of the things that the framework cannot do for the user is the low level communication. As there are various drivers and communication media the framework cannot come with the complete implementation of driver and communication handling. The CDEECo++ framework deals with packet generation, rebroadcast and processing of received data using the provided radio implementation. User defined radio implementation is a class that inherits from CDEECO::Radio. The CDEECO::Radio is a simple class that provides radio wrapper. It manages registering a receiver object which will process incoming knowledge fragments. The implementation is expected to call base class receiveFragment method when new data is received and implement broadcastFragment virtual method in order to allow system broadcasting new data.

Setting up system object and caches do not require implementation of any classes. Instead the needed objects are created using the templates. As the whole application should not rely on dynamic allocation, sizes of internal data needs to be set using templates. The system object has to be provided with two sizes. The first one is the maximum number of knowledge caches that can be plugged in. These are used when the knowledge fragments received by radio in order to store the fragment. The second one is the size of rebroadcast storage. The rebroadcast storage is used to store all received knowledge fragments and rebroadcast them later. In order to do so the rebroadcast storage in the system object runs a FreeRTOS thread. The cache needs to be registered with the system in order to receive data. It makes no point to create cache and not to register it. It is advised to create and register all caches before starting the system by running the FreeRTOS scheduler.

Component implementation includes definition of component knowledge type, knowledge trait, component class and classes of component processes. The component is just normal class as well as knowledge is a plain structure and processes are also normal classes. Thus the implementation can vary a lot based on the user demands. The following text should be taken as recommendation and source of examples.

It is recommended to wrap a component related classes in the name-space. Let say a component named submarine is being defined. The component knowledge type may be named SumbmarineKnowledge and the component class may be named SubmarineComponent. Normally this would not be a problem, but the CDEECo++ framework classes are almost all templates and the knowledge type will be repeated many times. Thus it is wise to create name-space Submarine and define knowledge named Knowledge and the component named Component inside it. Also the processes can be defined inside the name-space to simplify things.

The knowledge is just an ordinary C++ structure, but it is handled in special way. The knowledge data are broadcast as contained in the memory. Thus keeping pointers and references in the knowledge makes no sense. Instead all data stored in the knowledge should be direct parts of the knowledge. Thus the knowledge memory region contains all knowledge information and has constant size. Moreover when the knowledge is broadcast it has to be split into a knowledge fragments as it may not fit into a packet. In order to keep the system robust and decentralized the fragments created by single knowledge broadcast are not combined into a knowledge on the receiver. Instead they are considered to be binary patches that can be applied on the old knowledge with the same type and id. This leads to possible inconsistencies as some of the packets may be lost or delayed. This may result in partial update of the old data. In order to face the inconsistency the user has two options. The first one is to keep knowledge definition simple and handle the inconsistencies manually. The second one is to define a knowledge trait which tells how the knowledge is broken in the fragments. This approach do not solve consistency problems completely, but allows the user to keep small portions of the knowledge which fits into the packet consistent. For instance this can be used to keep position components consistent with each. In other words to be sure that the longitude is consistent with the latitude. The control on the breaking knowledge into packets is provided by definition of the allowed packet offsets. The user is supposed to define a array which contains allowed offsets in the knowledge where packets can start. When the array is empty then the packet creation is not restricted. On the other side when the array is not empty then the user is responsible for definition of enough offsets to allow broadcasting of the complete knowledge. If the user fails to do so runtime errors may emerge. The definition of the offsets array is performed by specialization of the CDEECO::KnowledgeTrait template for the target knowledge type. Unfortunately doing so is quite a lot of code and the specialization must be part of the CDEECO name-space. When the template is not specialized a general version of the template is used. It contains empty array which means that the packets can start everywhere. As the processes and ensembles take many template arguments and the knowledge member types are among them it is wise to typedef knowledge member types in the knowledge structure. For example when an ensemble which outputs the depth member of the submarine knowledge is defined as an int it will be used as one of ensemble's template arguments. Among other arguments it may not be clear what the int means. When a typedef is used for the depth (as in the example) then the int can be replaced by Submarine::Knowledge::Depth which tells clearly what it is. When the knowledge needs to be initialized with certain startup values or the knowledge needs to be filled with zeros at startup then this should be done in the component constructor.

A periodic task is implemented by inheriting from CDEECO::PeriodicTask which is a template. The PeriodicTask template has two template arguments. The first one is the component's knowledge type and the second is the output knowledge member type. These arguments are needed in order to provide type safety for the run method. The run method is virtual in the base class and needs to be implemented by user. The run method receives constant copy of the knowledge as parameter and is expected to return output knowledge. When the base class is constructed it needs to be provided with task period, component reference and output knowledge reference. The period will be in most cases provided as constant value. Component reference is reference to the task's component. It is expected to be passed to user defined task constructor when the system is being setup. The output knowledge reference is just reference to knowledge member in the component's knowledge. Under some conditions it may come handy to create periodic task (or triggered task) which has no output. The run method would return void. This is possible by setting void as template argument that stands for the output knowledge type. Then the run method will be declared as void and the base class constructor will not take output knowledge reference as parameter. It may seem that it makes no sense to have tasks with no output, but it may come handy when the task performs hardware control instead of pure knowledge processing.

Definition of a triggered task is very similar to the periodic task. User defined triggered task implementation inherits from the CDEECO::TriggeredTask. The base class is also a template and besides a trigger knowledge the arguments are the same as for the periodic task. The triggered task differs from periodic task by extra template argument that specifies type of the knowledge member used to trigger task execution. Also the triggered task constructor do not require period, but instead a reference to the trigger knowledge member must be provided.

The current implementation provides only periodic and triggered task. Moreover the triggered tasks can react only on the knowledge change. The framework itself implement the base task in a separate class CDEECO::Task. It is possible to inherit from this class and create customized versions of periodic and triggered tasks or introduce a whole new task concept.

When a task is executed the knowledge access lock is acquired, the knowledge is copied and the lock is released again. Then the task is executed with the copied knowledge as constant input. When task finishes the knowledge access lock is acquired again, the knowledge is updated with the task output and the lock is released. Thus the task should be guaranteed not to work with partially updated knowledge. Periodic and triggered task base class constructors have two more parameters that have default values set and are not discussed in triggered task nor in periodic task description. These are used to set execution priority and stack size of the task. Default values are set to FreeRTOS wrapper default stack size and priority which are priority level 1 and 1024 bytes for stack. Overriding these default constructor values can be used to set different priority or another stack size.

Similar to components and tasks an ensemble is implemented by inheriting from its base class template. In case of an ensemble it is CDEECO::Ensemble template. Unfortunately ensemble template arguments are quite many. Ensemble works with two components of different type so it needs to know types of their knowledge. It also has two mapping functions so it needs to have two output types specified. Thus ensemble template has four template arguments. First pair is formed by coordinator knowledge type and coordinator output type. The second pair is formed by member knowledge type and member output type. Similarly to tasks the output type can be specified as void which disables the output. This can be used to achieve one way only mapping. As there are many template arguments and the type is frequently used when inheriting from template, it is recommended to typedef custom ensemble type. The ensemble base class as well as the implemented ensemble class has two constructors. One is used on the coordinator node in order to provide mapping from member to coordinator and the other one is used on the member node where the mapping is from coordinator to member. In both cases four parameters are provided to the base class constructor. The first one is pointer to the component. The second one is pointer to the output member of the component's knowledge. The third one is pointer to the library of the remote knowledge which is periodically scanned for possible knowledge exchange candidates. The last one is the exchange execution period. As well as in case of processes an ensemble is free to store some user defined values in the ensemble class. These will not be affected by the framework.

Beside the classes and methods discussed in the previous parts there are some methods that can extend usage of the framework. Tasks and ensembles are restricted to output only one value. It is possible to contain more output values to substructure and then output that structure. This is how position is returned as structure containing longitude and latitude in the examples. Unfortunately sometimes the knowledge cannot be restructured in this way. In such cases component's methods lockReadKnoweldge and especially lockWriteKnowledge may be handy. These two are used by the framework when a task is executed. The first one is used to copy the task input and the second is sued to write task output. Using the second one the user can output more values in the task at the cost of losing synchronization between writes. It may happen that the two subsequent calls to lockWriteKnowledge will be split by some other call to the same method in another task or ensemble. The component class also contains methods getId and getType which may also be handy in some situations.

This part reveals framework internal structures and principles. It should be consulted when the framework is about to be extended or functionality modified. It also discusses collection of drivers that were included in order to provide hardware access for the example code. The drivers are implemented as C++ classes. They are designed to be used as global objects due to need of calling their methods from interrupt servicing routines.

Beside console driver the drivers are not used by the framework. Their purpose is to accompany example usage of the framework. The drivers reside in the src/drivers directory.

This driver was implemented from scratch in order to provide sensor access in the example application code. It provides control over SHT1x temperature and humidity sensor. The SHT1x chip has many features, but this driver aims to provide just basic access. It allows user to read temperature and humidity using the highest precision without CRC control.

The GMD1602 driver provides interface to 16x2 character alphanumerical display using 4 data lines. It was implemented as practice in the low level hardware control and used in early project stages. Unfortunately the nature of the device and lack of configuration in the driver prevent usage of the device with this driver once the shield with additional hardware is connected to the STM32F4 board.

Console is not a true driver. In fact it is front-end to the UART driver and provides logging of messages to the serial port connected via USB to the PC. The serial link can be used as both great debugging tool and communication channel to reveal DEECo information to other parts of the application. The console is the only driver used directly by the framework. The current implementation of the framework to console binding requires quite a bit of user cooperation when including header files. Fortunately the console usage in the framework is limited just to logging so the console usage can be easily removed from the framework.

The StopWatch driver is not intended for general usage. Instead it is designed to be used for execution time measurements at the microsecond level. It has very short maximum measurement period but when used with enabled interrupts, it should detect underlaying timer overruns.

Various drivers were included in the project that were provided as a base for implementation of this thesis. These drivers include Button driver, GPS driver, LED drivers, MRF24J40 radio driver, Timer and UART driver.

The FreeRTOS is a C library so it has C language API. In order to simplify usage of the FreeRTOS features in the framework a set of C++ wrapping classes were added to the project. These are placed in the src/wrappers directory and include wrappers for FreeRTOS mutex, FreeRTOS semaphore and FreeRTOS task. Wrappers are simple classes that hold the wrapped object handle and provide methods that wrap the related FreeRTOS function calls. Some less frequent FreeRTOS functions are called directly from the framework without the wrappers. As the FreeRTOS allocates memory when an task or semaphore is created it needs a memory allocator. FreeRTOS is quite flexible with allocator selection. There is option to use own allocator or to use some implementation provided by FreeRTOS itself. It was decided to use provided allocator that internally uses malloc and free provided by C runtime. This approach has been tested to work the best of provided implementations. There are concerns about the real-time properties of the system as the malloc and free calls may block execution in critical sections. Fortunately the whole system can work such way that it allocates only at startup and never call free. Thus it will never allocate from fragmented heap. Features provided by FreeRTOS are used for creation of processes/threads found in components and ensembles as well as for internal tasks inside the framework. Beside task management functions, synchronization primitives provided by FreeRTOS, are heavily used by the framework.

There are two subsystems that handle the remote knowledge in the framework. The first one is responsible for rebroadcasting of received knowledge fragments. The second one is used to store fragments of defined types and combine them into a complete knowledge. Complete knowledge records are used as source of remote component knowledge for ensembles.

Rebroadcasting of received fragment is performed by RebroadcastStorage class. It is a template the only argument of which is the size of the rebroadcast storage. The class contains static array of RebroadcastRecord type. Each record contains knowledge fragment data, received time-stamp, scheduled rebroadcast time-stamp and used flag. When new knowledge fragment is received it is added to the storage with certain probability in order to implement stochastic time-to-live. If there is no free slot in the storage the oldest record is rebroadcast and replaced by new one. When new record is added a rebroadcast time-stamp is calculated based on the received link quality. The calculation is quite simple. Rebroadcast interval is linear function of receiver link quality. Both stochastic time-to-live and rebroadcast interval calculation may need further tunning to adapt real environment with more deployed nodes. RebroadcastStorage class has internal thread which is used to periodically check storage for records to be rebroadcast. Access to the storage is protected by mutex. Possible delay causes by waiting on the mutex is limited by rebroadcast storage size and generally do not cause problems.

Unlike rebroadcast cache the knowledge cache is a bit more complicated. The actual storage is implemented by class KnowledgeCache which is also template. It takes three template arguments. The first one specifies component type magic number. The second one is the knowledge type. The last one is size of the cache. Each type of knowledge is handled by custom instance of KnowledgeCache class template. The cache is also formed by fixed array of records. Each record holds: knowledge data, mask (valid regions of the data), time-stamp and complete flag. Each time new knowledge fragment of matching cache type and record id is processed its data are added to the record and the availability mask is updated. When the mask covers whole knowledge than the complete flag is set to true. If the cache is full then the oldest record is replaced. The KnowledgeCache class inherits from two helper classes. The first one is the KnowledgeStorage class which is an interface for storing fragments in the cache. It is not a template thus its type can be used to store array of caches in the CDEECO::System class. Instances of this type can be used to store received fragments. The second one is the KnowledgeLibrary template. It has the only template argument which specifies knowledge type. The library can iterate over the complete records in the cache. Thus it allows ensembles to query complete cache records for membership and possible knowledge exchange. The library interface simplifies cache handling as the access to the library is possible without knowing cache size and knowledge magic, but still the library has the knowledge type so it can return properly typed data.

Component is represented by class template Component the only template argument of which is the knowledge type. The component class is responsible for knowledge storage, access and running triggered tasks. Knowledge is public member of component class and the class provides methods to safely read and write knowledge. These are lockReadKnowledge which is used to obtain consistent copy of the knowledge and lockWriteKnowledge which is used to consistently write part of the knowledge. Triggered tasks are added using the component's method addTriggeredTask. It stores triggered tasks in the linked list using members provided via ListedTriggerTask interface which is implemented by TriggeredTask class. The root of the linked list is stored in the Component class itself. When the lockWriteKnowledge method is executed and new knowledge is different from the old one then the listed tasks are consulted and those affected are executed. The component also owns a thread which is set to periodically broadcast complete component knowledge. Doing so is important as broadcasting changed parts is not enough. Remote nodes can miss some rare updates of several knowledge areas. Thus their cached knowledge will remain incomplete and unusable.

A DEECo process is called task in the CDEECo++ context. Task class templates form a hierarchy where the responsibilities are split. The top level templates called TriggeredTask and PeriodicTask are responsible for scheduling while they inherit ability to execute the task code from base Task. The base task itself is composed of the Task and its base TaskBase in order to reduce code duplication. All the task related classes are templates and take knowledge type and output knowledge type as template arguments. The triggered task also takes a trigger knowledge type as template argument. As the output knowledge type may be void and creating references to void is not allowed the Task template has to be specialized for the void output knowledge. The specialized implementation do not contain output knowledge reference, thus it do not write output knowledge and avoids creating references to void. The common parts of the specialized and normal implementation were moved to TaskBase in order not to duplicate the code. Constructors of the task related classes take several parameters. The TriggeredTask and the PeriodicTask are the only instantiated by user. Those take all parameters and pass some of them to base class constructors. The period and trigger knowledge reference are used directly by PeriodicTask respective TriggeredTask. Component reference and output knowledge reference (if used) are passed to the base classes. In order to allow user not to pass output knowledge reference when the output type is void the top-level classes has two constructors. One passes output knowledge while the other one do not. The output knowledge reference is passed from constructor to base constructor as auto type reference which allows the code to be valid even when the output knowledge type is void. Doing so requires usage of C++1y features. The TaskBase class which is at the base of the task class hierarchy has the virtual method run. The method takes constant copy of the knowledge as parameter and returns output knowledge type. The run method is not implemented inside the framework as it is expected to be implemented by the user and contain task code to be executed when the task runs.

Periodic task is quite simple compared to triggered task. Its only parameter is the period. The PeriodicTask class also inherits from FreeRTOSThread and uses the thread to perform periodic execution of the execute method provided by base task.

On the other side the triggered task is more complicated. It also has internal thread which runs the base class execute method. The thread lowers the semaphore and execute the task in infinite cycle. The semaphore has initial value of zero. So each rise of the semaphore value causes task to execute. In order to perform check whenever the watched knowledge has been changed the TriggeredTask inherits from ListedTriggerTask. Listed trigger task members are used to form a linked list of triggered tasks. The list is rooted in the component and check methods are executed on the whole list when the knowledge is changed. As the template function cannot be virtual the checked region is passed in form of two pointers that mark the area in the knowledge structure.

The Ensemble design is similar to two combined periodic tasks. Class Ensemble is a template which takes two pairs of knowledge and knowledge output types as template arguments. The first pair is used for coordinator and the second one for the member. The user implements virtual ensemble methods in order to provide membership decision method, member to coordinator mapping method and coordinator to member mapping method. Once the ensemble is implemented it has to be able to be used in two different ways. They can be instantiated on the node where coordinator resides and map from member to coordinator. It also can be instantiated on the node where member resides and thus provide mapping from coordinator to member. In order to accomplish this the ensemble base type has pointers to both member and coordinator knowledge output, but just one pair is used by every instance. The Ensemble has two constructors one takes coordinator component and member library, the second one takes member component and coordinator library. Where the KnowledgeLibrary is interface to KnowledgeCache that provides iterating over remote knowledge of specified type. Similarly to the tasks the output knowledge type for either coordinator output knowledge or member output knowledge may be defined as void. In case of ensembles it makes very good sense to do so as it may be desired to provide just one-way mapping. Unfortunately when the template argument is set to void an illegal code occurs in the ensemble template. This is caused mainly by execution of mapping functions as the code stores return in the variable and the the variable cannot be declared to have void type. In order to avoid compilation errors SFIANE feature is used to handle the cases where the error can occur. The resulting template code looks complicated, but the only point is to mask methods that makes no sense when the particular template argument is set to void. In order to run the membership tests and the knowledge exchange an ensemble inherits from the FreeRTOSTask thus it contains a thread. It uses periodic scheduling to execute membership tests and possibly run the knowledge exchange.

The system provides binding between radio and other parts of the system. It is quite simple class template. Template arguments specify maximum number of caches and size of rebroadcast storage which is also hosted in the system class. The system is just a proxy through which the components broadcast their knowledge fragments. It is also responsible for processing received data. The received data is stored in the rebroadcast cache which is included in the CDEECO::System and the received data are also passed to registered knowledge caches. The caches are registered using the CDEECO::KnowledgeStorage interface. The interface hides template arguments of the CDEECO::KnowledgeCache, thus it simplifies storage of pointer pointing to the registered caches. As many classes in the system has template arguments which complicate their usage as those needs to be passed to every other class that will use those templates it was decided to implement interfaces which hide those template arguments. As the system is used to broadcast and receive the knowledge fragments it inherits from Receiver and Broadcaster. These are simple interfaces which take no template arguments and can be used easily without complicated template constructs. Thanks to those interfaces component template do not have to have size of rebroadcast storage as argument.

Embedded hardware differs quite a lot when dealing with different models, brands and kinds of hardware. As the output of this thesis is not a single application but a framework that is expected to be used on wide range of hardware the framework was designed not to rely on particular hardware features. For instance the framework do not use floating point variables, nor it talks to hardware directly. Instead it moves application specific hardware access implementation to the user who has to implement sensor drivers and communication interface. The rest of the hardware specific code, namely the scheduling and synchronization, was implemented using the widely used real-time operating system called FreeRTOS. It was ported to 34 architectures at the time of writing. Thus porting the framework to one of those architectures should be as easy as changing the configuration. Detailed FreeRTOS hardware support can be found at FreeRTOS.