Wireless Ad Hoc and Sensor Networks

3Congestion Control in ATM Networksand the InternetIn the last chapter, background information on dynamic systems andLyapunov stability was introduced. In the remainder of the book, we willapply these concepts from control theory to the design and developmentof protocols for wired and wireless networks. It can be observed that byusing the control theory concepts, one can demonstrate not only stabilitybut also performance of these protocols, in terms of quality of service(QoS) metrics such as throughput, packet/cell losses, end-to-end delay,jitter, energy efficiency, etc. In particular, we will focus on the developmentof congestion and admission control, distributed power control and rateadaptation, distributed fair scheduling, and routing protocols for wiredand wireless networks. First, we will apply the control system conceptsto the design of congestion control protocol for wired networks.This chapter proposes an adaptive methodology for the available bitrate (ABR) service class of an asynchronous transfer mode network (ATM)first, and then an end-to-end congestion control scheme for the Internet,both being high-speed networks. In this methodology, the transmissionrates of the sources are controlled, in response to the feedback informationfrom the network nodes, to prevent congestion. The network is modeledas a nonlinear discrete-time system. As the traffic behavior on a networkis self-similar, and its behavior is not typically known a priori, an adaptivescheme using a neural network (NN)-based controller is designed toprevent congestion, where the NN is used to estimate the traffic accumulationin the buffers at the given switch/destination. Tuning methods areprovided for the NN, based on the delta rule, to estimate the unknowntraffic. Mathematical analysis is presented to demonstrate the stability ofthe closed-loop error in a buffer occupancy system, so that a desired QoScan be guaranteed. The QoS for an ATM network is defined in terms ofcell loss ratio (CLR), transmission or transfer delay (latency), and fairness.No learning phase is required for the NN, and initialization of the networkweights is straightforward. However, by adding an initial learning phase,the QoS is shown to improve in terms of cell losses during transientconditions.79