Route Selection for Cabling Considering Cost Minimization and Earthquake Survivability Via a Semi-Supervised Probabilistic Model

• Published in: IEEE transactions on industrial informatics Vol. 13; no. 2; pp. 502 - 511
• Main Authors: Mingbo Zhao, Chow, Tommy W. S., Peng Tang, Zengfu Wang, Jun Guo, Zukerman, Moshe
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cable route planning; Communication cables; Computer simulation; Data points; Earthquakes; Estimation; Hazards; Informatics; kernel density estimation; Optimization; Planning; Probabilistic models; Route selection; semi-supervised learning; Statistical analysis; Surfaces; Survivability; telecommunication cabling; Topography; Topology; Undersea; Taiwan Strait
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2016.2593664

This paper focuses on an important and fundamental problem of connecting two points by a cable, subject to a tradeoff between cost and earthquake survivability. In particular, we address the problem of selecting a route for laying a cable under arbitrary topography, based on earthquake data. First, we derive a semi-supervised probability density estimation model for the likelihood of earthquake disaster. Based on this probabilistic model, we generate a nearest neighbor graph. The graph represents each data point with a four-dimensional space formed by the three-dimensional undersea coordinates and the one-dimensional data of earthquake disaster level. It then forms the weight on graph between any positions. The data used in this study are all real data of undersea topography and earthquake information of the Taiwan Strait. As a result, both the undersea topology and the earthquake level can be transferred into a distance for shortest route finding. Finally, Dijkstra's algorithm is used for finding the optimal shortest route for cabling between the two given points on the graph. Extensive simulations based on a synthetic dataset and the Taiwan Strait real-world dataset corroborate the effectiveness of the proposed method.