Abstract
Ontologies have been widely used to facilitate semantic interoperability and serve as a common information model in many applications or domains. The Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project, aiming to facilitate coordination and cooperation between heterogeneous underwater vehicles, also adopts ontologies to formalize information that is necessarily exchanged between vehicles. However, how to derive more useful contexts based on ontologies still remains a challenge. In particular, the extreme nature of the underwater environment introduces uncertainties in context data, thus imposing more difficulties in context reasoning. None of the existing context reasoning methods could individually deal with all intricacies in the underwater robot field. To this end, this paper presents the first proposal applying a hybrid context reasoning mechanism that includes ontological, rule-based, and Multi-Entity Bayesian Network (MEBN) reasoning methods to reason about contexts and their uncertainties in the underwater robot field. The theoretical foundation of applying this reasoning mechanism in underwater robots is given by a case study on the oil spill monitoring. The simulated reasoning results are useful for further decision-making by operators or robots and they show that the consolidation of different reasoning methods is a promising approach for context reasoning in underwater robots.
Highlights
Context awareness [1] is important in many research domains, such as Smart Cities [2], Smart Homes [3], Ambient Assisted Living [4], and Smart Grids [5]
Oil spill detection is one of the use cases that are defined in the SWARMs project
This paper has presented a conceptual proposal of a context-aware framework for underwater robots
Summary
Context awareness [1] is important in many research domains, such as Smart Cities [2], Smart Homes [3], Ambient Assisted Living [4], and Smart Grids [5]. As a key enabler for entities to understand their environment and make adaptations context awareness implies an effective exploitation of contexts. To make the most of the available contexts is key to achieving context awareness. Three conventional approaches have been used to achieve context awareness [6]: (1) each application or domain acquires, processes, and employs contexts of its interest in its own manner; (2) some libraries that provide methods to process contexts are used in context-aware applications or domains; and (3) a context-aware framework/middleware/intermediation architecture is adopted to provide common functionalities to manage contexts and deliver context awareness. According to Li et al [7], the third approach is regarded as the best solution due to its ability to decrease the complexity of building context-aware applications
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