Wireless mesh network planning: A multi-objective optimization approach

2008 ◽  
Author(s):  
Djohara Benyamina ◽  
Abdelhakim Hafid ◽  
Michel Gendreau
Keyword(s):  
Wireless Mesh Network ◽  
Network Planning ◽  
Mesh Network ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Wireless Mesh

Wireless Mesh Network Planning and Optimization through Genetic Algorithms

2009 ◽  
Author(s):  
Rastin Pries ◽  
Dirk Staehle ◽  
Marieta Stoykova ◽  
Barbara Staehle ◽  
Phuoc Tran-Gia
Keyword(s):  
Genetic Algorithms ◽  
Wireless Mesh Network ◽  
Network Planning ◽  
Mesh Network ◽  
Wireless Mesh

Robust multi-objective optimization for solving the RFID network planning problem

2021 ◽  
Vol 8 (4) ◽  
pp. 616-626
Author(s):  
S. Ait Lhadj Lamin ◽  
◽  
A. Raghib ◽  
B. Abou El Majd ◽  
◽  
...  
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
New Technology ◽  
Network Planning ◽  
Environmental Parameters ◽  
Planning Problem ◽  
Optimization Approach ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Multi Objective

Radio-frequency identification (RFID) is a new technology used for identifying and tracking objects or people by radio-frequency waves to facilitate automated traceability and data collection. The RFID system consists of an electronic tag attached to an object, readers, and a middleware. In the latest real applications based on the RFID technology, the deployment of readers has become a central issue for RFID network planning by means of optimizing several objectives such as the coverage of tags, the number of readers, and the readers/tags interferences. In practice, the system is affected by uncertainty and uncontrollable environmental parameters. Therefore, the optimal solutions to the RFID network planning problem can be significantly reduced with uncontrollable variations in some parameters, such as the reader's transmitted power. In this work, we propose a robust multi-objective optimization approach to solve the deployment of RFID readers. In this way, we achieve robust optimal solutions that are insensitive to uncertainties in the optimization parameters.

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