Techniques are presented herein that support the derivation, leveraging machine learning (ML) algorithms, of a cross correlation index of Key Performance Indicators (KPIs) – specific to wireless technologies such as, for example, Internet Protocol version 6 (IPv6) over Networks of Resource-constrained Nodes (6Lo), Wi-Fi, and Institute of Electrical and Electronics Engineers (IEEE) technical standard 802.15.4 variants – across multiple tenants within a cloud native multi-tenanted environment to proactively optimize routing performance and identify performance optimizations so that the network may be automatically self-healed. Aspects of the presented techniques provide intelligent fault management capabilities for a customer (having, for example, outdoor large-scale wireless sensor network (WSN) devices) thus reducing the difficulty and the cost of deployed device maintenance, support the generation of a working scheme for the field staff to repair faulty devices, provide a Vulnerability Scan Service (VSS) for connected mesh endpoints, and support the use of multi-tenant functionality for each vendor.
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Zhang, Lele; Zhao, Li; Sheriff, Akram; Li, Chuanwei; and Tiwari, Arvind, "EDGE INTELLIGENCE-BASED SELF-HEALING NETWORK FOR LARGE-SCALE LOW POWER AND LOSSY NETWORK DEVICES WITH IOC", Technical Disclosure Commons, (November 12, 2020)