Open AccessGoal-Programming-Driven Genetic Algorithm Model for Wireless Access Point Deployment Optimization
Author(s) -
Chen-Shu Wang,
ChingTer Chang
Publication year - 2012
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2012/780637
Subject(s) - software deployment , wireless , computer science , genetic algorithm , point (geometry) , interference (communication) , computer network , wireless network , dynamic programming , optimization problem , stability (learning theory) , distributed computing , telecommunications , channel (broadcasting) , algorithm , geometry , mathematics , machine learning , operating system
Appropriate wireless access point deployment (APD) is essential for ensuring seamless user communication. Optimal APD enables good telecommunication quality, balanced capacity loading, and optimal deployment costs. APD is a typical NP-complex problem because improving wireless networking infrastructure has multiple objectives (MOs). This paper proposes a method that integrates a goal-programming-driven model (PM) and a genetic algorithm (GA) to resolve the MO-APD problem. The PM identifies the target deployment subject of four constraints: budget, coverage, capacity, and interference. The PM also calculates dynamic capacity requirements to replicate real wireless communication. Three experiments validate the feasibility of the PM. The results demonstrate the utility and stability of the proposed method. Decision makers can easily refer to the PM-identified target deployment before allocating APs
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