Distributed fermat-point location estimation for wireless sensor network applications

2007 ◽  
Author(s):  
Jiann-Liang Chen ◽  
Ming-Chiao Chen ◽  
Tsui-Lien Chiang ◽  
Yao-Chung Chang ◽  
Frank Y. Shih
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Location Estimation ◽  
Wireless Sensor ◽  
Point Location ◽  
Network Applications ◽  
Fermat Point

Measurement of a compact Boomarang-shaped microstrip antenna for wireless sensor network applications

2021 ◽  
Vol 68 ◽  
pp. 105-110
Author(s):  
M.N. Shakib ◽  
M. Moghavvemi ◽  
M.A. Rahman ◽  
R.M. Ramli ◽  
H.S. Lim ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Microstrip Antenna ◽  
Wireless Sensor ◽  
Network Applications

scholarly journals CrimeSPOT : A language and runtime for developing active wireless sensor network applications

2013 ◽  
Vol 78 (10) ◽  
pp. 1951-1970 ◽  
Author(s):  
Coen De Roover ◽  
Christophe Scholliers ◽  
Wouter Amerijckx ◽  
Theo D’Hondt ◽  
Wolfgang De Meuter
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Network Applications

Wireless Sensor Network Applications

2019 ◽  
pp. 13-24 ◽  
Author(s):  
Ankur Dumka ◽  
Sandip K. Chaurasiya ◽  
Arindam Biswas ◽  
Hardwari Lal Mandoria
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Network Applications

scholarly journals A Research Cluster based Scheme for Node Positioning in Indoor Environment

2019 ◽  
Vol 8 (6S) ◽  
pp. 79-83
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Indoor Environment ◽  
Cluster Head ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Location Estimation ◽  
Wireless Sensor ◽  
Localization Error ◽  
Network Efficiency

Location estimation in Wireless Sensor Network (WSN) is mandatory to achieve high network efficiency. Identifying the positions of sensors is an uphill task as monitoring nodes are involved in estimation and localization. Clustered Positioning for Indoor Environment (CPIE) is proposed for estimating the position of the sensors using a Cluster Head (CH) based mechanism. The CH estimates the number of neighbor nodes in each floor of the indoor environment. It sends the requests to the cluster members and the positions are estimated based on the Received Signal Strength Indicators (RSSIs) from the members of the cluster. The performance of the proposed scheme is analyzed for both stable and mobile conditions by varying the number of floors. Experimental results show that the propounded scheme offers better network efficiency and reduces delay and localization error

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