A Context-Aware Model for Dynamic Adaptability of Software for Embedded Systems

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.