2015/09/14 by Juan Augusto Maya, Maya, Juan Augusto, Cecilia G. Galarza +3
Computer Science · Mathematics · #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Information Theory (cs.IT) #Statistical Methods and Inference #Target Tracking and Data Fusion in Sensor Networks
paper · pdf · doi:10.48550/arxiv.1509.04119
openalex publication_date 2015/09/14 · openalex created_date 2022/10/02 · openalex updated_date 2026/07/28
We consider the detection of a correlated random process immersed in noise in\na wireless sensor network. Each node has an individual energy constraint and\nthe communication with the processing central units are affected by the path\nloss propagation effect. Guided by energy efficiency concerns, we consider the\npartition of the whole network into clusters, each one with a coordination node\nor \cluster head. Thus, the nodes transmit their measurements to the\ncorresponding cluster heads, which after some processing, communicate a summary\nof the received information to the fusion center, which takes the final\ndecision about the state of the nature. As the network has a fixed size,\ncommunication within smaller clusters will be less affected by the path loss\neffect, reducing energy consumption in the information exchange process between\nnodes and cluster heads. However, this limits the capability of the network of\nbeneficially exploiting the spatial correlation of the process, specially when\nthe spatial correlation coherence of the process is of the same scale as the\nclusters size. Therefore, a trade-off is established between the energy\nefficiency and the beneficial use of spatial correlation. The study of this\ntrade-off is the main goal of this paper. We derive tight approximations of the\nfalse alarm and miss-detection error probabilities under the Neyman-Pearson\nframework for the above scenario. We also consider the application of these\nresults to a particular network and correlation model obtaining closed form\nexpressions. Finally, we validate the results for more general network and\ncorrelation models through numerical simulations.\n