Design and Implementation of Cadastral Geo-spatial IoT Network Gateway Analyzer for Urban Scale Infrastructure Health Monitoring
Institute of Electrical and Electronics Engineers Inc.
Urban and global scale health monitoring systems have gained significance in digital ecosystems. Every stakeholder is striving for efficiency through performance analysis of digital infrastructure health monitoring (IHM) solutions. In this work, a geospatial network analyzer (GNA) is designed and implemented in python using the synergic strengths of Plotly, NetworkX, Scipy, Numpy, MatplotLib, Network2tikz, Pysocks, and PyPing. The GNA uses as a case study a utility computing model (UCM) to make structural health monitoring (SHM) that is based on analysis of geographical area network (GAN). A geo-distributed SHM deployment is assessed from a network performance perspective and verified from geo-spatial packet processing in GNA. The results have shown that this work can lead to standardizing the future of global-scale IoT networks analytics. © 2020 IEEE.
This conference paper is not available at CUD collection. The version of scholarly record of this conference paper is published in 2020 10th Annual Computing and Communication Workshop and Conference (CCWC) (2020), available online at: https://doi.org/10.1109/CCWC47524.2020.9031188
Cloud and Big Data, geo-distributed analysis, Industry 4.0, infrastructural health sensor nodes, Internet of Things, network analyzer, packets, utility computing, Gateways (computer networks), Structural health monitoring, Design and implementations, Digital ecosystem, Digital infrastructures, Health monitoring system, Infrastructure health monitoring, Packet processing, Performance analysis, Structural health monitoring (SHM)
Tariq, H., Abdaoui, A., Touati, F., Al-Hitmi, M. A. E., Crescini, D., & Mnaouer, A. B. (2020, January). Design and Implementation of Cadastral Geo-spatial IoT Network Gateway Analyzer for Urban Scale Infrastructure Health Monitoring. In 2020 10th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0858-0863). IEEE. https://doi.org/10.1109/CCWC47524.2020.9031188