MI-FIWARE: A web component development method for FIWARE using microservices

Energy consumption has been a big challenge for electronic devices, particularly, for the battery-powered Internet of Things (IoT) equipment. To address such a challenge, on one hand, low-power electronic design methodologies and novel power management techniques have been proposed, such as nonvolatile memories and instantly on/off systems; on the other hand, the energy harvesting technology by collecting signals from human activity or the environment has attracted widespread attention in the IoT area. However, the system with self-powered energy harvesting may suffer frequent energy failures or fluctuating energy conditions, which degrade system reliability and user experience. Therefore, how to make the system under unreliable power inputs operate correctly and efficiently is one of the most critical issues for the energy harvesting technology. In this paper, we built an instantly on/off system based on nonvolatile STT-MRAM for IoT applications, which can instantly power on/off under different conditions of the harvested energy. The system powers on and operates normally when the harvested energy is enough (over the preset threshold); otherwise, the system powers off and stores the operational data back to the nonvolatile STT-MRAM. We described implementations of the hardware/software co-designed architecture (with image acquisition as an example) based on the commercialized 32MB STT-MRAM, and experimentally demonstrated the system functionality and efficiency under five typical energy harvesting scenarios, including radio-frequency (RF), thermal, solar, piezoelectric and WIFI. Our experimental results show that the power consumption and data restore time were reduced by 15.1 \(\% \) and 714 times respectively in comparison with the DRAM-based counterpart.

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Securing Microservices‐Based IoT Networks: Real‐Time Anomaly Detection Using Machine Learning

Plazas Olaya,

Vergara Tejada,

Aedo Cobo

2024

Journal of Computer Networks and Communications

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Increased attention is being given to Internet of things (IoT) network security due to attempts to exploit vulnerabilities. Security techniques protecting availability, confidentiality, and information integrity have intensified as IoT devices are viewed as gateways to larger networks by malicious actors. As an additional factor, the microservices‐based platforms have overtaken the deployment of applications that support smart cities; however, the distributed nature of these architectures heightens susceptibility to malicious network infrastructure use. These risks can result in disruptions to system functioning or data compromise. Proposed strategies to mitigate these risks include developing intrusion detection systems and utilizing machine learning to differentiate between normal and anomalous network traffic, indicating potential attacks. This article outlines the development and implementation of an intrusion detection system (IDS) using machine learning to detect online anomalies in network traffic. Comprising a traffic extractor and anomaly detector, the system employs supervised learning with various datasets to train models. The results demonstrate the effectiveness of the decision tree model in detecting traditional denial of service (DoS) attacks, achieving high scores across multiple metrics: an F1‐score of 98.08%, precision of 99.25%, recall of 96.96%, and accuracy of 99.62%. The random forest model excels in identifying slow‐rate DoS attacks, attaining an F1‐score of 99.85%, precision of 99.91%, recall of 99.80%, and accuracy of 99.88%.

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MI-FIWARE: A web component development method for FIWARE using microservices

Cited by 3 publications

References 19 publications

Machine learning based models for detecting attacks in IoT systems

Machine learning based models for detecting attacks in IoT systems

Experimental Demonstration of STT-MRAM-based Nonvolatile Instantly On/Off System for IoT Applications: Case Studies

Securing Microservices‐Based IoT Networks: Real‐Time Anomaly Detection Using Machine Learning

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