Sensing resources play a crucial role in the success of critical tasks such as surveillance. Therefore, it is important to assigning appropriate sensing resources to tasks such that the selected resources fully cater the needs of the tasks. However, selecting the right resources to tasks is a computationally hard problem to solve. Most of the existing approaches address the efficiency aspect of the resource selection by considering the physical aspects of the sensor network (e.g., range, power, etc.) but have ignored important domain related properties such as capabilities of assets, environmental conditions, policies and so on which makes the selection effective. In this paper we present a knowledge rich mechanism to intelligently select resources for tasks such that the selected resources sufficiently cover the needs of the tasks. Ontologies are used to capture the crucial domain knowledge and semantic matchmaking is used to perform sensor-task matching. A combination of ontological and first-order-logic reasoning is considered for the solution architecture.
|Title of host publication||International Conference on Advanced Topics in Artificial Intelligence|
|Publisher||Global Science and Technology Forum|
|Publication status||Published - Nov 2010|
|Event||International Conference on Advanced Topics in Artificial Intelligence - Phuket, Thailand|
Duration: 29 Nov 2010 → 30 Nov 2010
|Conference||International Conference on Advanced Topics in Artificial Intelligence|
|Period||29/11/10 → 30/11/10|
- knowledge representation
- semantic matchmaking
- resource assignment
De Mel, G. R., Vasconcelos, W. W., & Norman, T. J. (2010). Intelligent Resource Selection For Sensor-Task Assignment: A Knowledge Based Approach. In International Conference on Advanced Topics in Artificial Intelligence Global Science and Technology Forum.