Anomaly Detection for IoT Global Connectivity

TL;DR

This paper introduces ANCHOR, an unsupervised anomaly detection system designed to proactively identify IoT connectivity issues in a global roaming platform, improving service reliability and reducing reactive troubleshooting.

Contribution

The paper presents the design, implementation, and operational evaluation of ANCHOR, a novel unsupervised anomaly detection solution tailored for IoT connectivity in complex roaming networks.

Findings

ANCHOR effectively filters large data volumes to identify problematic clients.

The system enables proactive detection of connectivity issues before service degradation.

Operational deployment demonstrates improved issue resolution times.

Abstract

Internet of Things (IoT) application providers rely on Mobile Network Operators (MNOs) and roaming infrastructures to deliver their services globally. In this complex ecosystem, where the end-to-end communication path traverses multiple entities, it has become increasingly challenging to guarantee communication availability and reliability. Further, most platform operators use a reactive approach to communication issues, responding to user complaints only after incidents have become severe, compromising service quality. This paper presents our experience in the design and deployment of ANCHOR -- an unsupervised anomaly detection solution for the IoT connectivity service of a large global roaming platform. ANCHOR assists engineers by filtering vast amounts of data to identify potential problematic clients (i.e., those with connectivity issues affecting several of their IoT devices), enabling proactive issue resolution before the service is critically impacted. We first describe the IoT service, infrastructure, and network visibility of the IoT connectivity provider we operate. Second, we describe the main challenges and operational requirements for designing an unsupervised anomaly detection solution on this platform. Following these guidelines, we propose different statistical rules, and machine- and deep-learning models for IoT verticals anomaly detection based on passive signaling traffic. We describe the steps we followed working with the operational teams on the design and evaluation of our solution on the operational platform, and report an evaluation on operational IoT customers.