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.
Highlights
Detection for Smart Hospital IoTRecently, the deployment of the Internet of Things (IoT) has become highly recommended in many applications in different fields
This paper focuses on the e-health field [2] and on smart hospital infrastructures
The second part deals with the adopted simulation process to implement different attacks with multiple nodes
Summary
Detection for Smart Hospital IoTRecently, the deployment of the Internet of Things (IoT) has become highly recommended in many applications in different fields. IoT relies on sensors that collect data from the environment in order to ensure tasks, such as surveillance or monitoring for wide areas [1]. This paper focuses on the e-health field [2] and on smart hospital infrastructures In such infrastructures, sensors are collecting patients’ and their environment data and sending them to intermediate gateways. Edge computing is executed on nodes close to IoT sensors, typically on gateways This is required when data collection, transformation and analysis must be achieved in minimal latency. In some IoT systems, sensors and gateways are deployed in harsh environments and can hardly conserve their energy for long periods In such systems, edge-processing has to be minimal to save node energy. The communication edge-cloud has to be secure since critical patients’ data is transferred
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