@article{guimaraesBenchmarkingSelfSupervisedAudio2025,

title = {Benchmarking Self-Supervised Audio Representations for IoT-Enabled Acoustic Beehive Monitoring},

author = {H. R. Guimaraes and M. Abdollahi and Y. Zhu and S. Maucourt and N. Coallier and P. Giovenazzo and T. H. Falk},

url = {https://www.scopus.com/pages/publications/105013592730?origin=resultslist},

doi = {10.1109/JIOT.2025.3599483},

issn = {23274662 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Internet of Things Journal},

volume = {12},

number = {21},

pages = {45000–45010},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Self-supervised learning (SSL) has enabled the development of universal feature extractors that have redefined the performance envelope of computer vision and speech applications. Recent works have started to explore SSL in other domains, including bioacoustics, which could have significant societal benefits. Honeybees (Apis mellifera), for example, are crucial pollinators contributing to one-third of global food production. However, massive colony losses in recent years have raised concerns. Traditional hive monitoring methods rely on intrusive visual inspections by beekeepers, which can further disrupt colony dynamics. As such, Internet of Things (IoT)-based automated monitoring systems have emerged, integrating environmental and bioacoustic sensing to enable real-time, noninvasive hive assessment. In this work, we introduce a comprehensive evaluation and benchmarking of general-purpose and bioacoustic audio representations that generalize across various tasks in IoT-enabled acoustic beehive monitoring, even with limited labeled data. Herein, fourteen models are evaluated across four critical tasks: beehive state detection, beehive strength assessment, buzzing identification, and beekeeper voice activity detection. Reported results demonstrate the strong generalizability of existing representations, paving the way for advanced, scalable honeybee colony monitoring and preservation. © 2014 IEEE.},

keywords = {Acoustics, Audio acoustics, Audio representation, Beehive monitoring, Benchmarking, Bioacoustics, Computer vision applications, Deep learning, Honeybee, honeybees, Internet of Things (IoT), IoT, Labeled data, Performance, Real time systems, Self-supervised learning, self-supervised learning (SSL), Societal benefits, Speech applications, Speech recognition, Supervised learning, Universal feature extractors},

pubstate = {published},

tppubtype = {article}

}

@article{khosrojerdi_integrating_2022,

title = {Integrating artificial intelligence and analytics in smart grids: a systematic literature review},

author = {F. Khosrojerdi and O. Akhigbe and S. Gagnon and A. Ramirez and G. Richards},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112559126&doi=10.1108%2fIJESM-06-2020-0011&partnerID=40&md5=7052f94c993368405955c1d33d87043c},

doi = {10.1108/IJESM-06-2020-0011},

year  = {2022},

date = {2022-01-01},

journal = {International Journal of Energy Sector Management},

volume = {16},

number = {2},

pages = {318–338},

abstract = {Purpose: The purpose of this study is to explore the latest approaches in integrating artificial intelligence and analytics (AIA) in energy smart grid projects. Empirical results are synthesized to highlight their relevance from a technology and project management standpoint, identifying several lessons learned that can be used for planning highly integrated and automated smart grid projects. Design/methodology/approach: A systematic literature review leads to selecting 108 research articles dealing with smart grids and AIA applications. Keywords are based on the following research questions: What is the growth trend in Smart Grid projects using intelligent systems and data analytics? What business value is offered when AI-based methods are applied? How do applications of intelligent systems combine with data analytics? What lessons can be learned for Smart Grid and AIA projects? Findings: The 108 selected articles are classified according to the following four research issues in smart grids project management: AIA integrated applications; AI-focused technologies; analytics-focused technologies; architecture and design methods. A broad set of smart grid functionality is reviewed, seeking to find commonality among several applications, including as follows: dynamic energy management; automation of extract, transform and load for Supervisory Control And Data Acquisition (SCADA) systems data; multi-level representations of data; the relationship between the standard three-phase transforms and modern data analytics; real-time or short-time voltage stability assessment; smart city architecture; home energy management system; building energy consumption; automated fault and disturbance analysis; and power quality control. Originality/value: Given the diversity of issues reviewed, a more capability-focused research agenda is needed to further synthesize empirical findings for AI-based smart grids. Research may converge toward more focus on business rules systems, that may best support smart grid design, proof development, governance and effectiveness. These AIA technologies must be further integrated with smart grid project management methodologies and enable a greater diversity of renewable and non-renewable production sources. © 2021, Emerald Publishing Limited.},

keywords = {Advanced Analytics, Automation, Building energy consumption, Data Analytics, Design/methodology/approach, Dynamic energy managements, Electric power system control, Electric power transmission networks, Energy management, Energy management systems, Energy utilization, Extract, Home energy management systems, Information management, Intelligent systems, Project management, quality control, Real time systems, SCADA systems, Smart power grids, Solar buildings, Supervisory control and dataacquisition systems (SCADA), System stability, Systematic literature review, transform and loads, Voltage stability assessment},

pubstate = {published},

tppubtype = {article}

}