A Descend-Based Evolutionary Approach to Enhance Position Estimation in Wireless Sensor Networks

2006 ◽  
Author(s):  
Vincent Tam ◽  
King-yip Cheng ◽  
King-shan Lui
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Position Estimation ◽  
Wireless Sensor ◽  
Evolutionary Approach

scholarly journals Considerations about the Signal Level Measurement in Wireless Sensor Networks for Node Position Estimation

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4179 ◽  
Author(s):  
Stelian Dolha ◽  
Paul Negirla ◽  
Florin Alexa ◽  
Ioan Silea
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Signal Strength ◽  
Data Access ◽  
Received Signal Strength ◽  
Position Estimation ◽  
Wireless Sensor ◽  
Node Localization ◽  
Received Signal Strength Indicator ◽  
Starting Point

Wireless Sensor Networks (WSN) are widely used in different monitoring systems. Given the distributed nature of WSN, a constantly increasing number of research studies are concentrated on some important aspects: maximizing network autonomy, node localization, and data access security. The node localization and distance estimation algorithms have, as their starting points, different information provided by the nodes. The level of signal strength is often such a starting point. A system for Received Signal Strength Indicator (RSSI) acquisition has been designed, implemented, and tested. In this paper, experiments in different operating environments have been conducted to show the variation of Received Signal Strength Indicator (RSSI) metric related to distance and geometrical orientation of the nodes and environment, both indoor and outdoor. Energy aware data transmission algorithms adjust the power consumed by the nodes according to the relative distance between the nodes. Experiments have been conducted to measure the current consumed by the node depending on the adjusted transmission power. In order to use the RSSI values as input for distance or location detection algorithms, the RSSI values can’t be used without intermediate processing steps to mitigate with the non-linearity of the measured values. The results of the measurements confirmed that the RSSI level varies with distance, geometrical orientation of the sensors, and environment characteristics.

A Hybrid Multiobjective Evolutionary Approach for Improving the Performance of Wireless Sensor Networks

2011 ◽  
Vol 11 (3) ◽  
pp. 545-554 ◽  
Author(s):  
Flávio V. C. Martins ◽  
Eduardo G. Carrano ◽  
Elizabeth F. Wanner ◽  
Ricardo H. C. Takahashi ◽  
Geraldo R. Mateus
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Evolutionary Approach
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