Today, sensing resources (both devices and processes) play a crucial role in the success of critical tasks such as border monitoring and surveillance. Although there are various types of resources available, each with different capabilities, only a subset of these resources is useful for a specific task. This is due to the dynamism in tasks' environment and the heterogeneity of the resources. Thus, an effective mechanism to select resources for tasks is needed so that the selected resources cater for the needs of the tasks whilst respecting the context of operation. When we started our research a few years back, there was a critical gap between the state-of-the-art and the need to perform context-aware resource selection for tasks.
In this paper, we summaries our knowledge-based approach which introduces the context of operation to the resource selection process. First, we present a formalism to represent sensor domain. We then introduce sound and complete mechanisms through which effective resource solutions for tasks are discovered. An extension to the representation is then proposed so that the agility in resource selection is increased. Finally, we present an architecture whereby a multitude of such knowledge bases are exposed as services so that a coalition can fully benefit from its networked resources. Our approach is general in that, it can be applied in many other domains—especially in service sciences; we have provided some evidence towards this.
Geeth R. de Mel and Tien Pham, "Intelligent resource selection for sensor-task assignment: the story so far (Conference Presentation)," Proc. SPIE 10643, Autonomous Systems: Sensors, Vehicles, Security, and the Internet of Everything, 106430O (Presented at SPIE Defense + Security: April 17, 2018; Published: 14 May 2018); https://doi.org/10.1117/12.2305140.5783303739001.
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