Probabilistic sensor network design

Conference item

Sensor networks are designed to detect events and their applicability is dependent on the likelihood of a correct detection. A network that can't detect events with a high enough probability becomes ineffective. Therefore, it can be very valuable to be able to establish which network design might yield he best detection rate. The endless possibilities in terms of sensor network designs make it difficult to apply a pure experimental method. Computational modelling using statistical techniques can provide a useful tool to explore the sensor network design space. The concept of a probabilistic sensor network (PSN) model is introduced in this paper. A framework is established and examples are given of the PSN model. The PSN model is tested in a hypothetical scenario by computing Root Mean Square Errors (RMSEs) and Absolute Errors between simulation outcomes and the results of the PSN model. The RMSEs between the simulation and the model were approximately 0.02 indicating a close comparison between the simulation and the model. The proposed probabilistic sensor network method provides an intuitive and promising tool to test sensor network designs virtually.

Authors: Bergmann, J; Noble, J; Thompson, M

• Publisher: Institute of Electrical and Electronics Engineers
• Host title: The 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN2016)
• Journal: 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN2016) More from this journal
• Publication date: 2016-07-21
• Acceptance date: 2016-05-05
• DOI: 10.1109/BSN.2016.7516234