Spike-Driven Neuromorphic Sensing for Energy-Proportional Indoor Air Quality Monitoring in Multi-Zone IoT-Enabled Smart Building Environments.

Luigi Carlo M De Jesus, Aaron Don M Africa, Ana Antoniette C Illahi, Reggie C Gustilo, Stanley Glenn E Brucal

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In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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5 authors.

Luigi Carlo M De JesusDepartment of Electronics, Computer, and Electrical Engineering, De La Salle University, 2401 Taft Avenue, Manila 0922, Philippines.ORCID 0000-0001-7889-6044

Aaron Don M AfricaDepartment of Electronics, Computer, and Electrical Engineering, De La Salle University, 2401 Taft Avenue, Manila 0922, Philippines.ORCID 0000-0003-4572-251X

Ana Antoniette C IllahiDepartment of Electronics, Computer, and Electrical Engineering, De La Salle University, 2401 Taft Avenue, Manila 0922, Philippines.ORCID 0000-0002-8858-7795

Reggie C GustiloDepartment of Electronics, Computer, and Electrical Engineering, De La Salle University, 2401 Taft Avenue, Manila 0922, Philippines.ORCID 0000-0002-2115-1364

Stanley Glenn E BrucalDepartment of Electronics, Computer, and Electrical Engineering, De La Salle University, 2401 Taft Avenue, Manila 0922, Philippines.ORCID 0000-0001-5481-2803

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Indoor Air Quality (IAQ) monitoring, especially in multi-zone smart buildings, is typically limited by the high computational and energy requirements of continuous sensor processing, which makes event-driven methods desirable for efficiency. Energy proportionality, in this context, refers to a system whose computational cost scales with the significance of detected environmental changes rather than with the fixed sampling rate. This paper presents a spike-driven neuromorphic sensing framework for decentralized IAQ monitoring that combines adaptive Kalman filter preprocessing, dynamic threshold-based asynchronous spike encoding, and a Leaky Integrate-and-Fire neural network with Spike-Timing-Dependent Plasticity (STDP) learning. Multiple-parameter IAQ data including PM

Indexed as

Air Pollution, IndoorEnvironmental MonitoringInternet of ThingsNeural Networks, ComputerAlgorithmsadaptive Kalman filtercross-paradigm evaluationenergy-efficient sensingevent-driven processingIoT sensor networksleaky integrate and fireneuromorphic computingsparse codingspike-timing-dependent plasticityspiking neural networks

Identifiers

PMID42451237

DOI10.3390/s26133992

PMCPMC13364322

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.