Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023)

Sensor networks are at the front line of advanced analytic systems. Sensor networks are getting smarter through deployment of machine learning empowered sensors in many application areas including tactical systems, intelligent transportation, health care, environmental monitoring, oil drilling and smart power distribution. Application systems (i.e. infrastructure, hardware and software) based on smart sensor networks are usually open ended and developed incrementally in several iterations. Being open implies that the system must cope with superfluous/conflicting requirements. Theoretical basis of designing such systems has been studied in the distributed systems (DS) research in which functionality and/or control are distributed. DS related theories and their practical implementations are discussed in detail in software engineering and artificial intelligence, e.g. distributed software systems (DSS) and multi-agent systems (MAS). In DS the way components (e.g. sensors, agents) interact is usually described by scenarios (e.g. sequence diagrams). In a large scale system, thousands of such scenarios may exist. Maintaining consistency among scenarios in multiple iteration of design and development is a complex and expensive task. For example, in a commercial unmanned aerial vehicle (UAV) fleet, there are several sensors in each UAV and a fleet of heterogeneous UAVs may have different motion scenarios and task allocations. The full communication between the UAVs enables coordination and dynamic task allocation. The UAVs are assembled as a fleet, incrementally. Due to lack of centralized control and multiplicity of scenarios, the overall system may exhibit unintended/unexpected behavior, commonly known as “emergent behavior” (EB) at the component level (e.g. within each UAV) and “implied scenario” (IS) at the system level (e.g. UAVs in a fleet). EB/IS may lead to costly and/or irreversible damage to the users, environment, and the business.
Our goal in this research is to manage (i.e. model, analyze, detect and resolve) unwanted behavior (i.e. EB and IS) by identifying design flaws that may lead to unwanted behavior as early as possible in the system development process.
Unique points in this research are: (1) Modeling both interactions of components and their internal states together that saves the components’ states and preserves the interaction information among the components; (2) Using interaction information to detect potential problems; (3) Ability to investigate whether new behavior can exist between components based on identified interactions; (4) Ability to suggest solutions based on the cause of the detected problem; (5) Ability to investigate interactions of the same-type components, which are common in sensor networks.
Typical applications areas - extremely important to Canada - are intelligent transportation, energy, health and robotics, among the others.