Distributed multisensor processing, decision making, and control under constrained resources for remote health and environmental monitoring

Ashit Talukder; Tanwir Sheikh; Lavanya Chandramouli

doi:10.1117/12.548080

12 April 2004 Distributed multisensor processing, decision making, and control under constrained resources for remote health and environmental monitoring

Ashit Talukder, Tanwir Sheikh, Lavanya Chandramouli

Author Affiliations +

Proceedings Volume 5437, Optical Pattern Recognition XV; (2004) https://doi.org/10.1117/12.548080
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

Previous field-deployable distributed sensing systems for health/biomedical applications and environmental sensing have been designed for data collection and data transmission at pre-set intervals, rather than for on-board processing These previous sensing systems lack autonomous capabilities, and have limited lifespans. We propose the use of an integrated machine learning architecture, with automated planning-scheduling and resource management capabilities that can be used for a variety of autonomous sensing applications with very limited computing, power, and bandwidth resources. We lay out general solutions for efficient processing in a multi-tiered (three-tier) machine learning framework that is suited for remote, mobile sensing systems. Novel dimensionality reduction techniques that are designed for classification are used to compress each individual sensor data and pass only relevant information to the mobile multisensor fusion module (second-tier). Statistical classifiers that are capable of handling missing/partial sensory data due to sensor failure or power loss are used to detect critical events and pass the information to the third tier (central server) for manual analysis and/or analysis by advanced pattern recognition techniques. Genetic optimisation algorithms are used to control the system in the presence of dynamic events, and also ensure that system requirements (i.e. minimum life of the system) are met. This tight integration of control optimisation and machine learning algorithms results in a highly efficient sensor network with intelligent decision making capabilities. The applicability of our technology in remote health monitoring and environmental monitoring is shown. Other uses of our solution are also discussed.

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Ashit Talukder, Tanwir Sheikh, and Lavanya Chandramouli "Distributed multisensor processing, decision making, and control under constrained resources for remote health and environmental monitoring", Proc. SPIE 5437, Optical Pattern Recognition XV, (12 April 2004); https://doi.org/10.1117/12.548080

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KEYWORDS

Sensors

Control systems

Sensor networks

Data processing

Environmental monitoring

Evolutionary algorithms

Machine learning

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