Characterization of Interconnections in Smart-X Applications

Abstract

Smart-X applications are realized by interconnecting several objects to achieve real-time capability by improving safety, reliability, and efficiency. These interconnections can be broadly classified as physical connections and logical network connections. A malfunction or damage in a physical connection may be catastrophic, leading to system downtime and sometimes fatal accidents. Thus, monitoring these connections is crucial to identify the onset of damage or a malfunction. Logical network connections are used in several applications to track and monitor a mesh-connected group of things called the Mesh of Things (MoT). In our research work, we focus on the characterization of physical and logical network connections. We apply these characterizations to: (a) reliably detect insulation damage in signaling and indoor power cables, (b) reliably detect and report a detangled MoT network, and (c) develop a suitable testbed to demonstrate measures and algorithms for anomaly reasoning in Smart-X connectivity. \\

In our first use case, we implement Power Line Communication (PLC) based measurement methods and propose algorithms to detect, classify and localize cable insulation faults. The solution considers factors such as the type and length of the cable, the width, and length of the fault, the structure of the network wiring, and source and load variations on the cable. In order to increase the accuracy of cable fault detection and localization, we use a conglomerate diagnostic solution. We perform extensive measurements using an in-situ non-invasive Software Defined Radio (SDR) based composite diagnostic kit. We develop a Bayesian inversion framework to estimate the Health Index (HI) of the cable. The HI assists in determining the cable state in real time and thus can suggest the need for preventive maintenance. Secondly, we develop an anomaly reasoning framework for the cable insulation fault by utilizing our experimental data collected from the testbed. Since source and load variations can contribute to an anomaly, we derive insights using measures such as SNR, S parameter, and Reflectogram. We identify the most probable root cause for an anomaly in a cable section. In particular, we are interested in estimating the belief for the severity of a cable insulation fault.\\

In our second use case, we are interested in the real-time detection of a detangled MoT. Our solution uses Bluetooth Low Energy (BLE) based mesh and PLC backbone network. Measures such as latency and Packet Delivery Ratio (PDR) are characterized for this heterogeneous network which is subjected to interference and impulse noise. Our use case is related to air cargo monitoring and tracking within an airport terminal. Finally, we analyze the performance of the integrated BLE-PLC network through numerical simulations. We use the generalized Noisy-OR model for mesh reliability and Dijkstra’s shortest path model for latency analysis. For the backbone network, we use a measurement-aided channel frequency response model for latency and reliability analysis. We validate the simulation results with our empirical setup.