On the Observability and Controllability of Large-Scale IoT Networks: Reducing Number of Unmatched Nodes via Link Addition

TL;DR

This paper analyzes the observability and controllability of large-scale IoT networks, comparing different network models and proposing a link addition algorithm to reduce unmatched nodes, thereby lowering monitoring and control costs.

Contribution

It introduces a new algorithm for reducing unmatched nodes in scale-free networks through link addition, enhancing network controllability and observability.

Findings

Link addition significantly reduces unmatched nodes.

Clustering coefficient correlates with unmatched nodes.

Proposed method decreases sensor/actuator requirements.

Abstract

In this paper, we study large-scale networks in terms of observability and controllability. In particular, we compare the number of unmatched nodes in two main types of Scale-Free (SF) networks: the Barab{\'a}si-Albert (BA) model and the Holme-Kim (HK) model. Comparing the two models based on theory and simulation, we discuss the possible relation between clustering coefficient and the number of unmatched nodes. In this direction, we propose a new algorithm to reduce the number of unmatched nodes via link addition. The results are significant as one can reduce the number of unmatched nodes and therefore number of embedded sensors/actuators in, for example, an IoT network. This may significantly reduce the cost of controlling devices or monitoring cost in large-scale systems.