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© 2024 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0, Traditional dynamic energy management methods optimize the energy usage in wireless sensor nodes adjusting their behavior to the operating conditions. However, this comes at the cost of losing the predictability in the operation of the sensor nodes. This loss of predictability is particularly problematic for the battery life, as it determines when the nodes need to be serviced. In this paper, we propose an energy and relevance-aware monitoring method, which leverages the principles of self-awareness to address this challenge. On one hand, the relevance-aware behavior optimizes how the monitoring efforts are allocated to maximize the monitoring accuracy; while on the other hand, the power-aware behavior adjusts the overall energy consumption of the node to achieve the target battery life. The proposed method is able to balance both behaviors so as to achieve the target battery life, at the same time is able to exploit variations in the collected data to maximize the monitoring accuracy. Furthermore, the proposed method coordinates two different adaptive schemes, a dynamic sampling period scheme, and a dual prediction scheme, to adjust the behavior of the sensor node. The evaluation results show that the proposed method consistently meets its battery lifetime goal, even when the operating conditions are artificially changed, and is able to improve the mean square error of the collected signal by up to 20% with respect to the same method with the relevance-aware behavior disabled, and of up to 16% with respect the same algorithm with just the adaptive sampling period or the dual prediction scheme enabled. Consequently showing the ability of the proposed method of making appropriate decisions to balance the competing interest of its two behaviors and coordinate the two adaptive schemes to improve their performance., This study was supported by the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR 2019 DI 075 to David Arnaiz). The founder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript., Peer Reviewed, Postprint (published version)

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, The aim of this paper is to present and discuss an analysis approach that can be deployed independently of the modelling tool capable of evaluating the samples of an assembled machine. By including an analysis of the results given the context of the input variables and the training performance, the method could be applied independently to the changes in modelling or the modelling approach. A real case study based on a water-cooling device has been used to illustrate the proposed approach., Peer Reviewed, Postprint (author's final draft)

The Internet of Things (IoT) represents a powerful new paradigm for connecting and communicating with the world around us. It has the potential to transform the way we live, work, and interact with our surroundings. IoT devices are transmitting information over the Internet, most of them with different data formats, despite they may be communicating similar concepts. This often leads to data incompatibilities and makes it difficult to extract the knowledge underlying that data. Because of the heterogeneity of IoT devices and data, interoperability is a challenge, and efforts are underway to overcome this through research and standardization. While data collection and monitoring in IoT systems are becoming more prevalent, contextualizing the data and taking appropriate actions to address issues in the monitored environment is still an ongoing concern. Context Awareness is a highly relevant topic in IoT, as it aims to provide a deeper understanding of the data collected and enable more informed decision-making. In this paper, we propose a semantic ontology designed to monitor global entities in the IoT. By leveraging semantic definitions, it enables end-users to model the entire process from detection to action, including context-aware rules for taking appropriate actions. The advantages of using semantic definitions include more accurate and consistent data interpretation, which improves the overall monitoring process and enables more effective decision-making based on the collected insights. Our proposal includes semantic models for defining the entities responsible for monitoring and executing actions, as well as the elements that need to be considered for an effective monitoring process. Additionally, we provide a new definition for the components known as gateways, which enable the connection and communication between devices and the Internet. Finally, we show the benefits of our ontology by applying it to a critical infrastructure domain where a rapid response i, This work is partially funded by Industrial Doctorates from Generalitat de Catalunya (2019 DI 001), the SUDOQU project, PID2021-126436OB-C21 from MCIN/AEI, 10.13039/501100011033, FEDER, UE, and the Grup de Recerca Consolidat IMP, 2021 SGR-01252., Peer Reviewed, Postprint (published version)

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, Practical implementation of data analytics in industrial environments has always been a problematic area because of data availability and quality. In this paper, a Gaussian sampling methodology is proposed to address the problem of imbalanced telemetry datasets that is one of the root causes that make modelling less reliable. By generating subsets that achieve homogeneous density distributions this problem is addressed. By comparing the impact of this method with the baseline case of random sampling, this paper aims to address this problem and propose a practical solution. A case study based on an industrial cooling device is used to assess and illustrate the proposed approach., This work has been co-financed by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project SaCoAV (ref. MINECO PID2020-114244RB-I00), Peer Reviewed, Postprint (author's final draft)

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., Self-awareness has been successfully utilized to create adaptive behaviors in wireless sensor nodes. However, its adoption can be daunting in scenarios, such as structural health monitoring, where the monitored environment is too complex for it to be accurately modeled by a sensor node. This article addresses this challenge by proposing a novel and lightweight anomaly-aware monitoring method for structural health monitoring that can be directly executed by a sensor node. Instead of modeling the complete structure, the proposed anomaly-aware monitoring method uses the vibration measurements of the sensor node to identify local deviations in the dynamic response of the monitored structure. The self-awareness module can then use this information to guide the dynamic behavior of the sensor node, replacing more resource-intensive structural models. We use data from multiple public benchmark structures to evaluate different features and propose an unsupervised feature selection method. Additionally, we evaluate different anomaly detection algorithms comparing their ability to detect local structural damages, also taking into account their memory and energy cost. The proposed method has been implemented in a commercial sensor node, and deployed in a scaled structure where various damage scenarios were simulated to validate the proposed method, where it was able to successfully detect the presence of damages in over 88% of the cases. Finally, we showcase how the proposed method can enhance self-awareness through the use of a simulation, where the proposed monitoring method was able to extend the battery life of the sensor node by over 59%, without impacting the node’s ability to swiftly detect damages in the structure., This work was supported in part by the Industrial Doctorate Plan of the Department of Research and Universities of the Generalitat de Catalunya. The work of David Arnaiz was supported by Agència de Gestió d’Ajuts Universitaris de Recerca under Grant AGAUR 2019 DI 075., Peer Reviewed, Postprint (published version)

There are a large number of Internet of Things (IoT) devices that transmit information over the Internet, each with a different data format to denote the same semantic concept. This often leads to data incompatibilities and makes it difficult to extract the knowledge underlying that data. The only way to close this gap is to establish a common vocabulary in order to achieve interoperability between different sources and semantic data integration. This is the main goal of our proposal: to specify a general semantics for IoT sensing that allows the management of data between gateways, nodes, and sensors in a homogeneous way. Our proposal builds upon the joint Semantic Sensor Network and Sensor-Observation-Sample-Actuator (SSN/SOSA) ontology., This work is partially funded by Industrial Doctorates DI- 2019 from Generalitat de Catalunya and Grup de Recerca Consolidat IMP, 2017-SGR- 1749. Also with the support of inLab FIB at UPC, Worldsensing and the REMEDiAL project (TIN2017-87610-R)., Peer Reviewed, Postprint (author's final draft)

This article presents a dataset obtained from the deployment of an IEEE 802.15.4g SUN (Smart Utility Network) single-hop network (11 nodes) in a large industrial scenario (110,044 m 2 ) for a long period of time (99 days). The dataset contains ~11 M entries with RSSI (Received Signal Strength Indicator), CCA (Clear Channel Assessment), and PDR (Packet Delivery Ratio) values. The analyzed results show a high variability in the average RSSI (i.e., between -82.1 dBm and -101.7 dBm) and CCA (i.e., between -111.2 dBm and -119.9 dBm) values, which is caused by the effects of multi-path propagation and external interference. Despite being above the sensitivity limit for each modulation, these values result in poor average PDR values (i.e., from 65.9% to 87.4%), indicating that additional schemes are needed to meet the link reliability requirements of industrial applications. Hence, the presented dataset will allow researchers and practitioners to propose novel mechanisms and evaluate their performance using realistic conditions, enabling the dependability vision of the RAW (Reliable and Available Wireless) WG (Working Group) at the IETF (Internet Engineering Task Force)., Peer Reviewed, Postprint (published version)