Year: 2023
Type: article
Abstract: In the past decade, different sensing mechanisms and algorithms have been developed to detect or estimate indoor occupancy. One of the most recent advancements is using networked sensor nodes to create a more comprehensive occupancy detection system where multiple sensors can identify human presence within more expansive areas while delivering enhanced accuracy compared to a system that relies on stand-alone sensor nodes. The present work reviews the studies from 2012 to 2022 that use networked sensor nodes to detect indoor occupancy, focusing on PIR-based sensors. Methods are compared based on pivotal ADPs that play a significant role in selecting an occupancy detection system for applications such as Health and Safety or occupant comfort. These parameters include accuracy, information requirement, maximum sensor failure and minimum observation rate, and feasible detection area. We briefly describe the overview of occupancy detection criteria used by each study and introduce a metric called “sensor node deployment density” through our analysis. This metric captures the strength of network-level data filtering and fusion algorithms found in the literature. It is hinged on the fact that a robust occupancy estimation algorithm requires a minimal number of nodes to estimate occupancy. This review only focuses on the occupancy estimation models for networked sensor nodes. It thus provides a standardized insight into networked nodes’ occupancy sensing pipelines, which employ data fusion strategies, network-level machine learning algorithms, and occupancy estimation algorithms. This review thus helps determine the suitability of the reviewed methods to a standard set of application areas by analyzing their gaps.
Source: Future Internet
DOI: 10.3390/fi15030116
Authors: Muhammad Emad-Ud-Din, Ya Wang
Institution: Texas A&M University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,779
Awards: CAREER: Understanding Dynamics of Ultra-small Magnetic Nanoparticles in the Brain for Neuron Regeneration Therapies, TEXAS A&M UNIVERSITY: NEW SENSOR AWARD.
CONTROL NUMBER: 1737-1539
TITLE: ''SLEEPIR–SYNCHRONIZED LOW-ENERGY ELECTRONICALLY-CHOPPED PIR SENSOR FOR OCCUPANCY DETECTION''
TEXAS A&M UNIVERSITY WILL DESIGN SLEEPIR, AN ADVANCED, LOW-COST OCCUPANCY ENERGY SOLUTION FOR RESIDENTIAL HOMES. AN INDUSTRY-STANDARD PYROELECTRIC INFRARED SENSOR (PIR) IS MODIFIED WITH AN OPTICAL CHOPPER TO ALLOW THE DETECTION OF BOTH STATIONARY AND MOVING INDIVIDUALS. THE PROJECT’S GOAL IS TO OBTAIN A 30% ENERGY SAVINGS BY A COMBINED PROGRAM OF OCCUPANCY DETECTION, NIGHTTIME SETBACK, AND OCCUPANT DAILY PATTERN RECOGNITION.
----------, GCR: Programmable Nanorobots Integration with Magnetically-Driven Neuron and Brain Tissue Regeneration, G7811849951, G8404313022
OpenAlex ID: W4353073774