An LSTM-Based Outlier Detection Approach for IoT Sensor Data in Hierarchical Edge Computing

Somia Bibi (1) , Chafiq Titouna (2) , Faiza Titouna (3) , Farid Naït-Abdesselam (4)

1 University of Batna 2
2 LIGM - Laboratoire d'Informatique Gaspard-Monge
3 University of Batna2
4 University of Kansas [Kansas City]

Outlier detection in sensor data has recently gained significant recognition, particularly with the proliferation of wireless sensor networks (WSNs) and the Internet of Things (IoT). Several challenges face outlier detection in WSNs and IoTs, including sensor nodes' limited energy and processing capabilities and high communication costs. This paper presents a novel deep learning-based outlier detection approach for IoT sensor data in hierarchical edge computing. First, we proposed a hierarchical edge computing framework to save energy, provide load balance, and low latency data processing at sensor ends. Then, we designed an outlier detection algorithm that resides on each edge server. The proposed algorithm consists of two modules: a predictor model and an outlier detector. The predictor module uses Long Short-Term Memory (LSTM) networks to predict the subsequent data measurements of sensor nodes. The predicted values are then passed to an outlier detector module, which decides whether a data point is an outlier.

Mots clés: Fault tolerance; Internet of Things; Wireless Sensor Networks; Edge Computing; outlier detection; Deep learning; Short-Term Memory (LSTM)

Domaines: Informatique [cs]

Somia Bibi, Chafiq Titouna, Faiza Titouna, Farid Naït-Abdesselam. An LSTM-Based Outlier Detection Approach for IoT Sensor Data in Hierarchical Edge Computing. 2023 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Sep 2023, Split, Croatia. pp.1-6, ⟨10.23919/SoftCOM58365.2023.10271607⟩. ⟨hal-04487532⟩