Neuromorphic Computing Pathways for Low-Power Environmental Monitoring

Nathaniel J. Brooks

Neuromorphic Computing Pathways for Low-Power Environmental Monitoring

Author(s)	Dr. Nathaniel J. Brooks
Country	United States
Abstract	Continuous environmental monitoring increasingly relies on distributed sensors for air quality, soil moisture, water conditions, ecological acoustics, wildfire indicators, chemical emissions, and other environmental variables. Conventional monitoring architectures often acquire and transmit measurements continuously even when environmental conditions remain unchanged, creating substantial processing, communication, and battery requirements for remote sensor nodes. Neuromorphic computing offers an alternative pathway through event-driven sensing, sparse spiking neural networks, near-sensor computation, and asynchronous processing. Rather than processing every sampled value at uniform computational intensity, neuromorphic systems can allocate computation primarily when environmentally meaningful changes occur. This characteristic is particularly relevant to environmental monitoring, where events of interest may be infrequent but operationally important. Recent research explicitly identifies neuromorphic edge AI as a promising approach for remote and rural environmental monitoring, while experimental studies have demonstrated spike-driven soil-moisture sensing, environmental gas recognition, and extremely low-power asynchronous neuromorphic hardware. The present study develops a simulation-based pathway for evaluating neuromorphic environmental-monitoring architectures. Four computational pathways were compared: cloud-streaming convolutional inference, conventional edge CNN processing, spiking neural-network inference on a microcontroller, and an integrated neuromorphic sensor–SNN architecture. Performance was evaluated through event-detection accuracy, average decision latency, communication reduction, processing-and-transmission energy, and projected autonomy. The simulated neuromorphic sensor–SNN pathway achieved an event-detection accuracy of 93.1%, reduced raw-data communication by 92.8%, and required 2.7 kJ of cumulative processing-and-transmission energy over the modeled 30-day period compared with 37.5 kJ for the cloud-streaming architecture. The paper concludes that neuromorphic environmental monitoring has considerable potential for long-duration, battery- or energy-harvested deployments, but practical adoption requires stronger environmental benchmarks, hardware–sensor integration, training-tool maturity, robustness testing, and field-scale validation.
Keywords	neuromorphic computing, environmental monitoring, spiking neural networks, low-power sensing, edge AI, event-driven sensors, environmental IoT, TinyML, sensor networks, sustainable computing
Field	Engineering
Published In	Volume 3, Issue 2, March-April 2022
Published On	2022-03-28

View / Download PDF File

About AJAMIR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+1-872-302-3851	editor@ajamir.com

American Journal of Advanced Multidisciplinary Innovation and Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Neuromorphic Computing Pathways for Low-Power Environmental Monitoring

Share this