# Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing

arXiv: https://arxiv.org/abs/2004.06896 · HTML: https://mlsanity.com/paper/2004.06896/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2020.

## Built on by (most cited first, 5 of 5)

- ["X of Information'' Continuum: A Survey on AI-Driven Multi-dimensional Metrics for Next-Generation Networked Systems](https://mlsanity.com/paper/2507.19657/) (2025)
- [Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach](https://mlsanity.com/paper/2108.03872/) (2021)
- [Dynamic Decision Tree Ensembles for Energy-Efficient Inference on IoT Edge Nodes](https://mlsanity.com/paper/2306.09789/) (2023)
- [HD-CB: The First Exploration of Hyperdimensional Computing for Contextual Bandits Problems](https://mlsanity.com/paper/2501.16863/) (2025)
- [Contextual Bandits for Resource-Constrained Devices using Probabilistic Learning](https://mlsanity.com/paper/2605.13346/) (2026)

## Abstract (arXiv)

Advances in deep neural networks (DNN) greatly bolster real-time detection of anomalous IoT data. However, IoT devices can hardly afford complex DNN models, and offloading anomaly detection tasks to the cloud incurs long delay. In this paper, we propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems to solve this problem, for both univariate and multivariate IoT data. First, we construct multiple anomaly detection DNN models with increasing complexity, and associate each model with a layer in HEC from bottom to top. Then, we design an adaptive scheme to select one of these models on the fly, based on the contextual information extracted from each input data. The model selection is formulated as a contextual bandit problem characterized by a single-step Markov decision process, and is solved using a reinforcement learning policy network. We build an HEC testbed, implement our proposed approach, and evaluate it using real IoT datasets. The demo shows that our proposed approach significantly reduces detection delay (e.g., by 71.4% for univariate dataset) without sacrificing accuracy, as compared to offloading detection tasks to the cloud. We also compare it with other baseline schemes and demonstrate that it achieves the best accuracy-delay tradeoff. Our demo is also available online: https://rebrand.ly/91a71