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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., 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)