Signal processing applied to chemically inspired communication protocols

A challenging problem when engineering network and communication protocols is to predict and formally describe the dynamic behavior of a protocol. Chemical Networking Protocols (CNPs) offer the opportunity to transpose the good and well-understood analyzability of chemical reactions to networking protocols, not only at the design stage of protocols, but also for the study of their dynamic behavior. In this paper we focus on this latter aspect and we demonstrate how signal processing techniques from classical signal and control theory can be employed in the study of the average flow properties of CNPs. Our analysis methods include a model linearization as proposed in Metabolic Control Analysis, a statespace description classically used in control theory, and the system's characterization in the frequency domain, which is central to signal theory. We demonstrate the feasibility of our method by applying it to an actual congestion control protocol that models TCP's behavior. With our contribution, CNP designers can easily determine the key parameters of their protocols and understand how to calibrate them in order to obtain the desired behavior.