Optimal motion sensor placement in smart homes and intelligent environments using a hybrid WOA-PSO algorithm

2021 ◽  
Author(s):  
Samineh Nasrollahzadeh ◽  
Mohsen Maadani ◽  
Mohammad Ali Pourmina
Keyword(s):  
Sensor Placement ◽  
Smart Homes ◽  
Pso Algorithm ◽  
Motion Sensor ◽  
Intelligent Environments ◽  
Optimal Motion

scholarly journals Optimal Water Quality Sensor Placement by Accounting for Possible Contamination Events in Water Distribution Networks

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 1999
Author(s):  
Malvin S. Marlim ◽  
Doosun Kang
Keyword(s):  
Water Quality ◽  
Water Distribution ◽  
Sensor Placement ◽  
Pso Algorithm ◽  
Distribution Networks ◽  
Fair Value ◽  
Water Distribution Networks ◽  
Optimization Approach ◽  
Protection Measure ◽  
Contamination Events

Contamination in water distribution networks (WDNs) can occur at any time and location. One protection measure in WDNs is the placement of water quality sensors (WQSs) to detect contamination and provide information for locating the potential contamination source. The placement of WQSs in WDNs must be optimally planned. Therefore, a robust sensor-placement strategy (SPS) is vital. The SPS should have clear objectives regarding what needs to be achieved by the sensor configuration. Here, the objectives of the SPS were set to cover the contamination event stages of detection, consumption, and source localization. As contamination events occur in any form of intrusion, at any location and time, the objectives had to be tested against many possible scenarios, and they needed to reach a fair value considering all scenarios. In this study, the particle swarm optimization (PSO) algorithm was selected as the optimizer. The SPS was further reinforced using a databasing method to improve its computational efficiency. The performance of the proposed method was examined by comparing it with a benchmark SPS example and applying it to DMA-sized, real WDNs. The proposed optimization approach improved the overall fitness of the configuration by 23.1% and showed a stable placement behavior with the increase in sensors.

Optimal Damage Detection Sensor Placement Using PSO

2011 ◽  
Vol 110-116 ◽  
pp. 5336-5341 ◽  
Author(s):  
M. O. Abdalla ◽  
E. Al-Khawaldeh
Keyword(s):  
Damage Detection ◽  
Fitness Function ◽  
Sensor Placement ◽  
Pso Algorithm ◽  
Linear Matrix ◽  
Matrix Inequalities ◽  
Swarm Optimization ◽  
Detection Algorithms ◽  
Cantilevered Beam ◽  
Detection Schemes

An optimal damage detection sensor placement methodology is presented. The techniques utilize a Particle Swarm Optimization (PSO) algorithm. The proposed method is iterative in nature and it permits the use of incomplete measurements. Also, it allows diversity of damage detection algorithms to be used to generate the PSO required fitness function. However, in this work Linear Matrix Inequalities are used as the damage detection schemes. Computer simulations of a cantilevered beam will be used to demonstrate the effectiveness of the methodology.

Model-based identification of motion sensor placement for tracking retraction and elongation of the tongue

2012 ◽  
Vol 12 (2) ◽  
pp. 383-399 ◽  
Author(s):  
Yikun K. Wang ◽  
Martyn P. Nash ◽  
Andrew J. Pullan ◽  
Jules A. Kieser ◽  
Oliver Röhrle
Keyword(s):  
Sensor Placement ◽  
Motion Sensor ◽  
Model Based

scholarly journals A Genetic Algorithm approach to motion sensor placement in smart environments

2015 ◽  
Vol 2 (1) ◽  
pp. 3-16 ◽  
Author(s):  
Brian L. Thomas ◽  
Aaron S. Crandall ◽  
Diane J. Cook
Keyword(s):  
Genetic Algorithm ◽  
Sensor Placement ◽  
Smart Environments ◽  
Motion Sensor ◽  
Genetic Algorithm Approach

scholarly journals Environment Optimization Scheme Based on Edge Computing Using PSO for Efficient Thermal Comfort Control in Resident Space

Actuators ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 241
Author(s):  
Rongxu Xu ◽  
Wenquan Jin ◽  
Dohyeun Kim
Keyword(s):  
Thermal Comfort ◽  
Home Environment ◽  
Smart Home ◽  
Smart Homes ◽  
Pso Algorithm ◽  
Edge Computing ◽  
Comfort Level ◽  
Optimization Scheme ◽  
Ongoing Research ◽  
Smart Home Environment

With the fast development of infrastructure and communication technology, the Internet of Things (IoT) has become a promising field. Ongoing research is looking at the smart home environment as the most promising sector that adopts IoT and cloud computing to improve resident live experiences. The IoT and cloud-dependent smart home services related to recent researches have security, bandwidth issues, and a lack of concerning thermal comfort of residents. In this paper, we propose an environment optimization scheme based on edge computing using Particle Swarm Optimization (PSO) for efficient thermal comfort control in resident space to overcome the aforementioned limitations of researches on smart homes. The comfort level of a resident in a smart home is evaluated by Predicted Mean Vote (PMV) that represents the thermal response of occupants. The PSO algorithm combined with PMV to improve the accuracy of the optimization results for efficient thermal comfort control in a smart home environment. We integrate IoT with edge computing to upgrade the capabilities of IoT nodes in computing power, storage space, and reliable connectivity. We use EdgeX as an edge computing platform to develop a thermal comfort considering PMV-based optimization engine with a PSO algorithm to generate the resident’s friendly environment parameters and rules engine to detects the environmental change of the smart home in real-time to maintain the indoor environment thermal comfortable. For evaluating our proposed system that maintenance resident environment with thermal comfort index based on PSO optimization scheme in smart homes, we conduct the comparison between the real data with optimized data, and measure the execution times of optimization function. From the experimental results, when our proposed system is applied, it satisfies thermal comfort and consumes energy more stably.

Evaluation for Effectiveness of Individual Lighting Control using Image-based Motion Sensor

2018 ◽  
Vol 138 (11) ◽  
pp. 1362-1374 ◽  
Author(s):  
Takuya Futagami ◽  
Toru Yano ◽  
Chingchun Huang ◽  
Takaaki Enohara
Keyword(s):  
Motion Sensor ◽  
Lighting Control

Sensor placement in arbitrarily restricted region for field estimation based on Gaussian process

2021 ◽  
Author(s):  
Tomoya Nishida ◽  
Natsuki Ueno ◽  
Shoichi Koyama ◽  
Hiroshi Saruwatari
Keyword(s):  
Gaussian Process ◽  
Sensor Placement ◽  
Field Estimation ◽  
Restricted Region

Sensor placement and structural damage identification from minimal sensor information

AIAA Journal ◽  
1997 ◽  
Vol 35 ◽  
pp. 369-374 ◽  
Author(s):  
Richard G. Cobb ◽  
Brad S. Liebst
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Sensor Placement ◽  
Structural Damage Identification ◽  
Sensor Information
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