Affordable Access

deepdyve-link
Publisher Website

Efficient Anomaly Detection for Smart Hospital IoT Systems.

Authors
  • Said, Abdel Mlak1
  • Yahyaoui, Aymen1, 2
  • Abdellatif, Takoua1
  • 1 SERCOM Lab, University of Carthage, Carthage 1054, Tunisia. , (Tunisia)
  • 2 Military Academy of Fondouk Jedid, Nabeul 8012, Tunisia. , (Tunisia)
Type
Published Article
Journal
Sensors
Publisher
MDPI AG
Publication Date
Feb 03, 2021
Volume
21
Issue
4
Identifiers
DOI: 10.3390/s21041026
PMID: 33546169
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

In critical Internet of Things (IoT) application domains, such as the Defense Industry and Healthcare, false alerts have many negative effects, such as fear, disruption of emergency services, and waste of resources. Therefore, an alert must only be sent if triggered by a correct event. Nevertheless, IoT networks are exposed to intrusions, which affects event detection accuracy. In this paper, an Anomaly Detection System (ADS) is proposed in a smart hospital IoT system for detecting events of interest about patients' health and environment and, at the same time, for network intrusions. Providing a single system for network infrastructure supervision and e-health monitoring has been shown to optimize resources and enforce the system reliability. Consequently, decisions regarding patients' care and their environments' adaptation are more accurate. The low latency is ensured, thanks to a deployment on the edge to allow for a processing close to data sources. The proposed ADS is implemented and evaluated while using Contiki Cooja simulator and the e-health event detection is based on a realistic data-set analysis. The results show a high detection accuracy for both e-health related events and IoT network intrusions.

Report this publication

Statistics

Seen <100 times