Real world wireless mesh sensor network solutions

2006 ◽  
Author(s):  
N. Baker
Keyword(s):  
Sensor Network ◽  
Real World ◽  
Wireless Mesh

Real-World Sensor Network for Long-Term Volcano Monitoring: Design and Findings

2012 ◽  
Vol 23 (2) ◽  
pp. 321-329 ◽  
Author(s):  
Renjie Huang ◽  
Wen-Zhan Song ◽  
Mingsen Xu ◽  
Nina Peterson ◽  
Behrooz Shirazi ◽  
...  
Keyword(s):  
Sensor Network ◽  
Real World ◽  
Volcano Monitoring ◽  
Monitoring Design

A Real-World Event Search System in Sensor Network Environments

2006 ◽  
Author(s):  
T. Okadome ◽  
T. Hattori ◽  
K. Hiramatsu ◽  
Y. Yanagisawa
Keyword(s):  
Sensor Network ◽  
Real World ◽  
Search System ◽  
Network Environments

UTILIZING ACTIVE RFID ON WIRELESS SENSOR NETWORK PLATFORM FOR PRODUCTION MONITORING

2016 ◽  
Vol 78 (2) ◽  
Author(s):  
Che Zalina Zulkifli ◽  
Nursyahida Mohd Noor ◽  
Ahmad Zamzuri Mohamad Ali ◽  
Siti Norainah Semunab
Keyword(s):  
Embedded System ◽  
Sensor Network ◽  
Production Plant ◽  
Rfid Technology ◽  
Web Based ◽  
Output Performance ◽  
Wireless Mesh ◽  
Efficient Communication ◽  
Network Platform ◽  
Point To Point

In today’s manufacturing environment, delay in monitoring output performance might cause production mess in the industry. Therefore, an efficient communication and real time feedback to maximize uptime and improve productivity seems vital. Adapting the capabilities of RFID technology and Wireless Mesh Sensor Network (WMSN) through web-based monitoring system might solve this problem. Thus, an embedded system namely ERFIDC was developed as a potential solution in addressing this demand. This paper reports the proposed embedded system architecture and evaluation of its reading range in point-to-point and WMSN setup at the selected production plant.  

Automated Production Line Monitoring Base on Wireless Mesh Sensor Network

2020 ◽  
Author(s):  
Lihui Wang ◽  
Yonghui Zhang ◽  
Zhenjia Chen
Keyword(s):  
Sensor Network ◽  
Production Line ◽  
Automated Production ◽  
Wireless Mesh

scholarly journals Wireless Sensor Network Operating System Design Rules Based on Real-World Deployment Survey

2013 ◽  
Vol 2 (3) ◽  
pp. 509-556 ◽  
Author(s):  
Girts Strazdins ◽  
Atis Elsts ◽  
Krisjanis Nesenbergs ◽  
Leo Selavo
Keyword(s):  
Operating System ◽  
Wireless Sensor Network ◽  
System Design ◽  
Sensor Network ◽  
Real World ◽  
Wireless Sensor ◽  
Design Rules ◽  
Operating System Design

A wireless mesh sensor network for hazard and safety monitoring at the Port of Brisbane

2012 ◽  
Author(s):  
Amin Ahmadi ◽  
Abbas Bigdeli ◽  
Mahsa Baktashmotlagh ◽  
Brian C. Lovell
Keyword(s):  
Sensor Network ◽  
Safety Monitoring ◽  
Wireless Mesh

scholarly journals Mobile sensor network noise reduction and re-calibration using Bayesian network

2015 ◽  
Vol 8 (8) ◽  
pp. 8971-9008 ◽  
Author(s):  
Y. Xiang ◽  
Y. Tang ◽  
W. Zhu
Keyword(s):  
Bayesian Network ◽  
Sensor Network ◽  
Real World ◽  
Bayesian Belief Network ◽  
Sensor Calibration ◽  
Mobile Sensor ◽  
Real World Data ◽  
Mobile Sensor Network ◽  
Belief Network ◽  
The Real

Abstract. People are becoming increasingly interested in mobile air quality sensor network applications. By eliminating the inaccuracies caused by spatial and temporal heterogeneity of pollutant distributions, this method shows great potentials in atmosphere researches. However, such system usually suffers from the problem of sensor noises and drift. For the sensing systems to operate stably and reliably in the real-world applications, those problems must be addressed. In this work, we exploit the correlation of different types of sensors caused by cross sensitivity to help identify and correct the outlier readings. By employing a Bayesian network based system, we are able to recover the erroneous readings and re-calibrate the drifted sensors simultaneously. Specifically, we have (1) designed a Bayesian belief network based system to detect and recover the abnormal readings; (2) developed methods to update the sensor calibration functions in-field without requirement of ground truth; and (3) deployed a real-world mobile sensor network using the custom-built M-Pods to verify our assumptions and technique. Compared with the existing Bayesian belief network technique, the experiment results on the real-world data demonstrate that our system can reduce error by 34.1 % and recover 4 times more data on average.

Real-World Energy Measurements of a Wireless Mesh Network

2013 ◽  
pp. 218-232 ◽  
Author(s):  
A. Jamakovic ◽  
D. C. Dimitrova ◽  
M. Anwander ◽  
T. Macicas ◽  
T. Braun ◽  
...  
Keyword(s):  
Real World ◽  
Wireless Mesh Network ◽  
Mesh Network ◽  
Wireless Mesh ◽  
World Energy
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