# Towards a Hybrid Approach to Context Reasoning for Underwater Robots
## 1 Introduction  
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 accordingly, context awareness implies an effective exploitation of contexts. With advances in sensing, pervasive computing, and communication technologies, more and more contexts can be obtained and promisingly used. To make the most of the available contexts is key to achieving context awareness. In general, 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 [PERSON] 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. The European Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) [8] project includes introducing context awareness into the field of underwater robotics as one of its objectives. This project aims to expand the use of unmanned underwater vehicles (e.g., AUVs orROVs) in maritime and offshore operations in a collaborative, cooperative, and context-aware manner. The SWARMs approach is underpinned by designing a semantic and distributed middleware. The middleware layer has been designed to facilitate communication between heterogeneous vehicles and a Command & Control Station (C & CS) and provide a set of common services. A context-aware framework, dedicated to delivering context awareness in underwater robots, is designed as part of the middleware. The context-aware framework aims to provide different context treatments that comply with the general lifecycle of context awareness. As depicted in Figure 1, the context awareness lifecycle is summarized into four essential phases [9], namely context acquisition (obtaining necessary context data), context modeling (representing contexts in a machine-readable and processable form), context reasoning (deriving high-level contexts from available contexts), and context dissemination (distributing useful contexts). Ontologies, which are considered the most promising modeling technique in terms of expressiveness and interoperability [10], have been adopted in the context-aware framework to formally represent heterogeneous contexts that are exchanged between vehicles, such as the battery level of vehicles, capabilities of vehicles, turbidity, position, speed, salinity, and wind direction [11]. It is worth noting that operators or vehicles are more willing to use high-level context information instead of raw context to conceive and offer context-aware services [12]. For instance, a piece of high-level context, _vehicle A might collide with vehicle B soon_, is more meaningful for operators to make decisions than the basic context information _vehicle A is out of trajectory and heading in the direction of vehicle B_. Therefore, context reasoning based on information formatted in ontologies is important for realizing context awareness in underwater robots. In particular, the harsh nature of the underwater environment and the limitations of sensors attached to vehicles impose more challenges in making effective context reasoning in underwater robots. In the underwater environment, the majority of contexts, namely sensory data, are prone to be uncertain [12]. Uncertainties, as an inherent characteristic of context, should be handled in the reasoning phase. Therefore, there is a need for context-aware framework to provide suitable accommodation for the inclusion of uncertainties.