Network Resilience Architecture and Analysis for Smart Homes

Abstract

The Internet of Things (IoT) is evolving rapidly to every aspect of human life including, healthcare, homes, cities, and driverless vehicles that makes humans more dependent on the Internet and related infrastructure. While many researchers have studied the structure of the Internet that is resilient as a whole, new studies are required to investigate the resilience of the edge networks in which people and “things” connect to the Internet. Since the range of service requirements varies at the edge of the network, a wide variety of technologies with different topologies are involved. Though the heterogeneity of the technologies at the edge networks can improve the robustness through the diversity of mechanisms, other issues such as connectivity among the utilized technologies and cascade of failures would not have the same effect as a simple network. Therefore, regardless of the size of networks at the edge, the structure of these networks is complicated and requires appropriate study. In this dissertation, we propose an abstract model for smart homes, as part of one of the fast-growing networks at the edge, to illustrate the heterogeneity and complexity of the network structure. As the next step, we make two instances of the abstract smart home model and perform a graph-theoretic analysis to recognize the fundamental behavior of the network to improve its robustness. During the process, we introduce a formal multilayer graph model to highlight the structures, topologies, and connectivity of various technologies at the edge networks and their connections to the Internet core. Furthermore, we propose another graph model, technology interdependence graph, to represent the connectivity of technologies. This representation shows the degree of connectivity among technologies and illustrates which technologies are more vulnerable to link and node failures. Moreover, the dominant topologies at the edge change the node and link vulnerability, which can be used to apply worst-case scenario attacks. Restructuring of the network by adding new links associated with various protocols to maximize the robustness of a given network can have distinctive outcomes for different robustness metrics. However, typical centrality metrics usually fail to identify important nodes in multi-technology networks such as smart homes. We propose four new centrality metrics to improve the process of identifying important nodes in multi-technology networks and recognize vulnerable nodes. We perform the process of improvement through modifying topology, adding extra nodes, and links when necessary. The improvement process would be verified by calculation of the proper graph metrics and introducing new metrics when it is appropriate. Finally, we study over 1000 different smart home topologies to examine the resilience of the networks with typical and the proposed centrality metrics.