Improving Observability of Stochastic Complex Networks under the Supervision of Cognitive Dynamic Systems

TL;DR

This paper introduces a novel supervisory approach using cognitive dynamic systems to enhance the degree of observability in stochastic complex networks, addressing challenges posed by complexity and randomness.

Contribution

It proposes a goal-seeking supervisory system that dynamically reconfigures observations to improve network observability in stochastic environments.

Findings

Supervisory system effectively improves observability degree.

Dynamic reconfiguration enhances network monitoring.

Experimental results validate the approach's potential.

Abstract

Much has been said about observability in system theory and control; however, it has been recently that observability in complex networks has seriously attracted the attention of researchers. This paper examines the state-of-the-art and discusses some issues raised due to "complexity" and "stochasticity". These unresolved issues call for a new practical methodology. For stochastic systems, a degree of observability may be defined and the observability problem is not a binary (i.e., yes-no) question anymore. Here, we propose to employ a goal-seeking system to play a supervisory role in the network. Hence, improving the degree of observability would be a valid objective for the supervisory system. Towards this goal, the supervisor dynamically optimizes the observation process by reconfiguring the sensory parts in the network. A cognitive dynamic system is suggested as a proper choice for the supervisory system. In this framework, the network itself is viewed as the environment with which the cognitive dynamic system interacts. Computer experiments confirm the potential of the proposed approach for addressing some of the issues raised in networks due to complexity and stochasticity.