A Context-Aware Model for Dynamic Adaptability of Software for Embedded Systems
Notice bibliographique
Résumé
The scientific contributions of this thesis are three-fold. Firstly, a novel specialized embedded systems software architecture for contextawareness is presented. This architecture is developed for use on a resource constrained hardware platform and is low latency. For firmware applications with many sources of context, a specialized architecture is important to achieve code readability, modularity, extensibility and maintainability. Context in embedded systems firmware development is defined as changeable and characterizing information such as sensor data (IR - infrared, GPS, accelerometer) or profile attributes (user, vehicle, device, etc.). A second focus was on dynamic architecture adaptability in the form of a cognitive engine which processes real-time updates to its user-configurable module. Dynamic adaptability improves the application software's flexibility and responsiveness according to different user requirements or varying operational conditions. Adaptability is defined as system changes according to changes in context and in terms of the four W's - why are there changes, what remains unchanged, when do the changes occur and who manages these changes. Thirdly, the concept of context-aware map logic (CAML) is introduced. Cognitive engine updates are performed using these logic maps which are derived from/inspired by fuzzy cognitive maps (FCM) and GPS (global positioning system) coverage maps. The logic maps feature phi, delta, timer, complement, latched and momentary operands. The logic maps were specifically designed for resource constrained hardware. No previous work has been done on the use of fuzzy cognitive maps specifically with linguistic weights for enabling dynamic, resource constrained firmware adaptability. Fuzzy cognitive maps are at the intersection of fuzzy logic and neural networks. A resource constrained hardware platform is defined as a single-processor microcontroller with low processing power and limited memory space as compared against large memory, multi-core, multi-media processors e.g. cell-phones. The targeted hardware platform could be a legacy processor or a low power processor typically found in wireless sensor networks or energy-aware or cost-aware solutions. Context-awareness is an important topic in the wireless sensor networks research field. Wireless sensor networks comprise wirelessly enabled embedded systems for data acquisition and control for a wide array of applications. In this thesis context is defined as changeable and characterizing information such as sensor data, profile attributes or explicitly provided user information. The embedded systems software architecture is a layered model with context and cognitive planes which focus on dynamic adaptability. The context plane features a microarchitecture, which includes context collectors, context controllers and a context task based coordinator. The cognitive plane is responsible for dynamic adaptable logic reconfiguration inspired by fuzzy cognitive maps. Proof-of-concept firmware was developed for a wireless physiological sensor showing context collector implementation. An ATE (automatic test equipment) test architecture was also developed for the sensor highlighting architecture development and providing the groundwork for the context controller development. The lead-up to the cognitive engine is explored in an introduction to fuzzy cognitive maps, its implementations and applications to current research. An industrial application, Novax's Accessible Pedestrian System (APS) and simulations using the Rapita suite of tools are presented.
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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».