Reliability Analysis of IoT Networks with Community Structures

• Published in: IEEE transactions on network science and engineering Vol. 7; no. 1; pp. 304 - 315
• Main Authors: Mo, Yuchang, Xing, Liudong, Guo, Wenzhong, Cai, Shaobin, Zhang, Zhao, Jiang, Jianhui
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: binary decision diagram; Binary decision diagrams; community structure; Complex networks; Complexity; Computer network reliability; Decision analysis; Electronic devices; Heuristic; Internet of Things; Internet of Things (IoT); Network reliability; Network theory (graphs); Networks; ordering heuristic; Reliability analysis; Reliability engineering; Smart grid; terminal-pair reliability
• ISSN: 2327-4697; 2334-329X
• DOI: 10.1109/TNSE.2018.2869167

Network infrastructure and connectivity in the Internet of Things (IoT) applications are becoming increasingly complex and heterogeneous, opening up many challenges including reliability. Many real-world networks exhibit community structure, where the networked devices can be easily grouped into sets with dense internal connections but sparse connections between different sets. Examples of such community-structured networks can be found in diverse IoT applications such as smart grids, smart cities, and military systems. Due to these critical applications, reliability analysis is of great significance for robust and safe design and operation of IoT networks. In this paper, we present an efficient binary decision diagram (BDD)-based approach to analyze the reliability of an IoT network with community structure and subject to random link failures. As efficiency of the BDD-based approach heavily depends on the ordering of input variables, we make novel contributions by proposing efficient ordering heuristics for individual communities and the whole IoT network composed of multiple communities. Performance of the proposed ordering heuristics for IoT networks with either linear interconnection pattern or random interconnection pattern is investigated. As demonstrated through comprehensive experiments, the proposed ordering heuristics provide significantly better performance in model complexity than the traditional ordering heuristics.