Flexible Resource Assignment in Sensor Networks: A Hybrid Reasoning Approach

Today, sensing resources are the most valuable assets of critical tasks (e.g., border monitoring). Although, there are various types of assets available, each with dierent capabilities, only a subset of these assets is useful for a specific task. This is due to the varying information needs of tasks. This gives rise to assigning useful assets to tasks such that the assets fully cover the information requirements of the individual tasks. The importance of this is amplified in the intelligence, surveillance, and reconnaissance (ISR) domain, especially in a coalition context. This is due to a variety of reasons such as the dynamic nature
of the environment, scarcity of assets, high demand placed on available assets, sharing of assets among coalition parties, and so on. A significant amount of research been done by different communities to efficiently assign assets to tasks and deliver information to the end user. However, there is little work done to infer sound alternative means to satisfy the information requirements of tasks so that the satisfiable tasks are increased. In this paper, we propose a hybrid reasoning approach (viz., a combination of rule-based and ontology-based reasoning) based on current Semantic Web4 technologies to infer assets types that are necessary and sufficient to satisfy the requirements of tasks in a flexible manner.