The performance of mobile ad hoc networks (MANETs) depends upon a number of dynamic factors that ultimately influence protocol and overall system performance. Adaptive protocols have been proposed that adjust their operation based on the values of factors, such as traffic load, node mobility, and link quality. In this work, however, we are investigating the feasibility of an adaptive model-based selfcontroller that can manage the values of controllable factors in MANETs. In general, the proposed self-controller should determine a set of factor values that will maximize system performance or satisfy specific performance requirements. The model-based controller adapts or reconfigures system-wide parameters or protocol operation as a function of the dynamically changing network state. In this paper, we describe the proposed self-controller, its design issues, and provide a preliminary case study to demonstrate the effectiveness and tradeoffs of two potential empirical-modeling techniques: regression and artificial neural networks.

The performance of mobile ad hoc network is influenced by a number of factors, such as protocol selections, parameter settings, wireless channel conditions, network size, transmission ranges, and traffic loads. In this work, we present an Autonomic Network Performance Management (ANPM) framework for ad hoc networks. The proposed framework follows a control-theoretic approach and is designed to monitor, model, optimize, and configure the controllable node and systemlevel factors to satisfy global performance goals. The functional components of the ANPM framework are presented along with a case study of establishing a call center in a disaster recovery operation using MANET. In the case study, tuning the performance of the overall network performance is shown through: (1) changing whole network protocols, and (2) changing settings within the network protocols.

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