On modeling of cognitive interrogator-sensor network: Layered discrete memoryless channel and finite-state Markov channel

Abstract

This paper looks into the modeling of information transmission over cognitive interrogator-sensor networks (CISNs), which represent an import class of sensor networks deployed for surveillance, tracking, and imaging applications. The crux of the problem is to develop a channel model that allows for evaluation of the sensing channel capacity and error rate performance, where the sensing link is overlaid by the communication link. First, the layered discrete memoryless channel (DMC) and finite-state Markov channel (FSMC) models are identified as a useful tool to capture the essence of information transfer over the communication and sensing links in a CISN. Subsequently, a study case considering a typical CISN for environmental monitoring subject to various wireless propagation and sensing conditions is presented to demonstrate how the model parameters may be derived. Finally, the applications of the proposed analytical framework including cognitive sensing, network performance assessment and simulation are discussed.