Self-Healing of Semantically Interoperable Smart and Prescriptive Edge Devices in IoT

Dimara, Asimina; Vasilopoulos, Vasileios Georgios; Papaioannou, Alexios; Angelis, Sotiris; Kotis, Konstantinos; Anagnostopoulos, Christos‐Nikolaos; Krinidis, Stelios; Ioannidis, Dimosthenis; Tzovaras, Dimitrios

doi:10.3390/app122211650

Cited by 9 publications

(4 citation statements)

References 46 publications

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“…In the evaluation of AE models (Dense and LSTM), three different confidence values (k) were employed, leading to different threshold values as presented in Equation (11). In the case of OC-SVM, different values of the nu parameter were employed, where nu serves as an upper bound on the fraction of margin errors and a lower bound on the fraction of support vectors relative to the total number of training examples.…”

Section: Malfunction Detection Resultsmentioning

confidence: 99%

“…A prescriptive edge-to-edge IoT network management architecture with micro-services was proposed in [11] for multiple smart edge devices. The proposed architecture utilized: the collection and processing of sensors' raw data by edge artificial-intelligence-(AI)-based lightweight services; the monitoring of the edge and IoT network performance while considering various automated self-healing actions; and the detection of potential and fatal errors on edge devices as well as IoT entities, including sensors, actuators, and devices, using long short-term memory (LSTM) and autoencoder (AE) machine learning (ML) networks.…”

Section: Related Workmentioning

confidence: 99%

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LP-OPTIMA: A Framework for Prescriptive Maintenance and Optimization of IoT Resources for Low-Power Embedded Systems

Papaioannou,

Dimara,

Kouzinopoulos

et al. 2024

Sensors

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Low-power embedded systems have been widely used in a variety of applications, allowing devices to efficiently collect and exchange data while minimizing energy consumption. However, the lack of extensive maintenance procedures designed specifically for low-power systems, coupled with constraints on anticipating faults and monitoring capacities, presents notable difficulties and intricacies in identifying failures and customized reaction mechanisms. The proposed approach seeks to address the gaps in current resource management frameworks and maintenance protocols for low-power embedded systems. Furthermore, this paper offers a trilateral framework that provides periodic prescriptions to stakeholders, a periodic control mechanism for automated actions and messages to prevent breakdowns, and a backup AI malfunction detection module to prevent the system from accessing any stress points. To evaluate the AI malfunction detection module approach, three novel autonomous embedded systems based on different ARM Cortex cores have been specifically designed and developed. Real-life results obtained from the testing of the proposed AI malfunction detection module in the developed embedded systems demonstrated outstanding performance, with metrics consistently exceeding 98%. This affirms the efficacy and reliability of the developed approach in enhancing the fault tolerance and maintenance capabilities of low-power embedded systems.

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Section: Malfunction Detection Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

LP-OPTIMA: A Framework for Prescriptive Maintenance and Optimization of IoT Resources for Low-Power Embedded Systems

Papaioannou,

Dimara,

Kouzinopoulos

et al. 2024

Sensors

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“…Once the data is collected, the data handling service proceeds to format it into a predefined JSON structure. This formatting ensures consistency and standardization of the data for further processing [32]. The structured data is then posted to a dedicated database, specifically designed to store and organize the system's data.…”

Section: Data Layermentioning

confidence: 99%

An Applied Framework for Smarter Buildings Exploiting a Self-Adapted Advantage Weighted Actor-Critic

Papaioannou,

Dimara,

Korkas

et al. 2024

Energies

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Smart buildings are rapidly becoming more prevalent, aiming to create energy-efficient and comfortable living spaces. Nevertheless, the design of a smart building is a multifaceted approach that faces numerous challenges, with the primary one being the algorithm needed for energy management. In this paper, the design of a smart building, with a particular emphasis on the algorithm for controlling the indoor environment, is addressed. The implementation and evaluation of the Advantage-Weighted Actor-Critic algorithm is examined in a four-unit residential simulated building. Moreover, a novel self-adapted Advantage-Weighted Actor-Critic algorithm is proposed, tested, and evaluated in both the simulated and real building. The results underscore the effectiveness of the proposed control strategy compared to Rule-Based Controllers, Deep Deterministic Policy Gradient, and Advantage-Weighted Actor-Critic. Experimental results demonstrate a 34.91% improvement compared to the Deep Deterministic Policy Gradient and a 2.50% increase compared to the best Advantage-Weighted Actor-Critic method in the first epoch during a real-life scenario. These findings solidify the Self-Adapted Advantage-Weighted Actor-Critic algorithm’s efficacy, positioning it as a promising and advanced solution in the realm of smart building optimization.

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“…There are a few research works focused on erasing urban services silos. Most of them aim at the use of semantic interoperability in different application fields such as energy [21], agriculture [22], or building management [23]. Other approaches concentrate on the integration of BIM and IoT, both for building [24], public facilities [25], and cities [26].…”

Section: Introductionmentioning

confidence: 99%

Virtual Reality and Internet of Things Based Digital Twin for Smart City Cross-Domain Interoperability

del Campo,

Saavedra,

Piovano

et al. 2024

Applied Sciences

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The fusion of Internet of Things (IoT), Digital Twins, and Virtual Reality (VR) technologies marks a pivotal advancement in urban development, offering new services to citizens and municipalities in urban environments. This integration promises enhanced urban planning, management, and engagement by providing a comprehensive, real-time digital reflection of the city, enriched with immersive experiences and interactive capabilities. It enables smarter decision-making, efficient resource management, and personalized citizen services, transforming the urban landscape into a more sustainable, livable, and responsive environment. The research presented herein focuses on the practical implementation of a DT concept for managing cross-domain smart city services, leveraging VR technology to create a virtual replica of the urban environment and IoT devices. Imperative for cross-domain city services is interoperability, which is crucial not only for the seamless operation of these advanced tools but also for unlocking the potential of cross-service applications. Through the deployment of our model at the IoTMADLab facilities, we showcase the integration of IoT devices within varied urban infrastructures. The outcomes demonstrate the efficacy of VR interfaces in simplifying complex interactions, offering pivotal insights into device functionality, and enabling informed decision-making processes.

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Self-Healing of Semantically Interoperable Smart and Prescriptive Edge Devices in IoT

Cited by 9 publications

References 46 publications

LP-OPTIMA: A Framework for Prescriptive Maintenance and Optimization of IoT Resources for Low-Power Embedded Systems

LP-OPTIMA: A Framework for Prescriptive Maintenance and Optimization of IoT Resources for Low-Power Embedded Systems

An Applied Framework for Smarter Buildings Exploiting a Self-Adapted Advantage Weighted Actor-Critic

Virtual Reality and Internet of Things Based Digital Twin for Smart City Cross-Domain Interoperability

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