Wireless Sensor Networks for Effective Environmental Tracking System Using IoT and Sensors

Punniamoorthy, D.; Kamadal, Vikram S.; Yadav, Bhumika; Reddy, V.Sriram

doi:10.1109/i-smac.2018.8653672

Cited by 8 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…|| (11) g i z is the corresponding observation values， R is noise variance ③ The golden section grey wolf algorithm is used to perform the resampling process of particle filtering.   2 25 ( 1) ( ) 0.5 ( 1) 8cos[1.2( 1)] ( ) 1 ( 1)…”

Section: Algorithm and Particle Filtering Algorithmmentioning

confidence: 99%

Application of Grey Wolf Particle Filter Algorithm based on Golden Section in WSN Mobile Target Tracking

Peng,

Xie,

Liu

et al. 2023

Preprint

View full text Add to dashboard Cite

A WSN (Wireless Sensor Network) mobile target tracking algorithm based on the golden section grey wolf particle filter (PF) is proposed to address the problem of low tracking accuracy caused by sample impoverishment. This algorithm guides the particles as a whole to move towards randomly selected regions with higher weights, effectively adjusting the global exploration and local exploration capabilities of the particle swarm, and improving the problems of sample impoverishment and local extreme values. Additionally, this method increases the diversity of the particle swarm, further improving the tracking performance. The filtering tracking is performed using a constant velocity circular motion model (CM). The Extended Kalman Filter (EKF) algorithm and PF algorithm are compared with this algorithm using the root mean square error as the evaluation metric. The results of the simulation experiments show that the proposed algorithm reduces the root mean square error by 57% and 37% compared to the above-mentioned algorithms, respectively.

show abstract

Section: Algorithm and Particle Filtering Algorithmmentioning

confidence: 99%

Application of Grey Wolf Particle Filter Algorithm based on Golden Section in WSN Mobile Target Tracking

Peng,

Xie,

Liu

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Some works have applied SMAC technologies to smart home, smart agriculture and other fields, which achieved impressive results. The authors [6] use Internet of Things(IoT) and sensors to implement a long-term environmental monitoring system. The detailed design and components of the platform take into account the characteristics of low application cost, large number of sensors, fast deployment, long life, low maintenance, and good service quality.…”

Section: Smac (Social Mobile Analytics and Cloudmentioning

confidence: 99%

Indoor temperature monitoring using wireless sensor networks: A SMAC application in smart cities

Chen

Zhang

Tang

et al. 2020

Sustainable Cities and Society

View full text Add to dashboard Cite

“…Internet connection of monitoring systems can bring several improvements to SEMS. For example, there have been implementations where users can be notified of potential hazardous situations in their environment by a Short Message Service (SMS) text directly to their phones [7], by a specialized app [8] or via a website where several users can even do a continuous monitoring of the different sensors [9]. On a larger scale, several SEMS can cooperate for the monitoring of a wider area.…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Smart Living Environment Monitoring Systems from a Metrological Point of View

Jimenez¹,

Carnì

Trojman³

et al. 2022

2022 IEEE International Workshop on Metrology for Living Environment (MetroLivEn)

View full text Add to dashboard Cite

The constant pursuit of low-cost Smart Environmental Monitoring Systems (SEMS) leads to a constant use of off the shelf sensors and components. However, with a lack of validation and calibration of the sensors, the accuracy of the measurements should be question. Especially when it comes to the monitoring of living environments such as homes, offices, and classrooms, where individuals remain for extended periods of time, and where prolonged exposure to hazardous environments can lead to health problems and discomfort. The objective of this research is to shine the light on a constantly overlooked parameter, the accuracy of the measurements taken by SEMS. In particular, the presented research focuses on the measurement science point of view giving a comparison of the most widely utilized sensors for living environment monitoring and the accuracy that can be expected from them. The final aim is, in on one end, to stimulate the research in the field to define the desired accuracy in the different applicative sectors, and on the other end, to increase the awareness of both producers and consumers about the metrological aspects of the sensors they produce/use and of the overall monitoring systems.

show abstract

Wireless Sensor Networks for Effective Environmental Tracking System Using IoT and Sensors

Cited by 8 publications

References 5 publications

Application of Grey Wolf Particle Filter Algorithm based on Golden Section in WSN Mobile Target Tracking

Application of Grey Wolf Particle Filter Algorithm based on Golden Section in WSN Mobile Target Tracking

Indoor temperature monitoring using wireless sensor networks: A SMAC application in smart cities

Low-Cost Smart Living Environment Monitoring Systems from a Metrological Point of View

Contact Info

Product

Resources

About