Near optimal citywide WiFi network deployment using a hybrid grouping genetic algorithm

Agustin-Blas, L.E.; Salcedo‐Sanz, Sancho; Vidales, Pablo; Urueta, G.; Portilla-Figueras, Antonio

doi:10.1016/j.eswa.2011.01.141

Cited by 30 publications

(12 citation statements)

References 20 publications

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“…• The city is composed by eight 30 meters-wide streets that have been randomly constructed. For the sake of fairness in the comparison, we use the same scenario as that used in [7], [8].…”

Section: Proposed Grouping Algorithm For the Wifidpmentioning

confidence: 99%

“…COMPUTATIONAL EXPERIMENTS In order to test the performance of the GHS approach and compare the obtained results with the ones presented in [8], different WIFIDP instances are generated with the following parameters:…”

Section: Proposed Grouping Algorithm For the Wifidpmentioning

confidence: 99%

“…Let us mathematically pose the considered WIFIDP optimization problem by following the notation in [6], [8]. All WiFi oAPs are assumed to cover a circular area of identical radio R. We further define a binary coverage matrix E whose entry e ij equals 1 if user i is inside the covered area of the oAP j (e ij = 0 otherwise).…”

Section: Problem Formulationmentioning

confidence: 99%

“…Likewise, in [7] a Genetic Algorithm (GA) is designed to deal with the WiFi access deployment (WIFIDP) problem with budget constraints based on two different synthetically-modeled scenarios. More recently, an extension of their previous work considering interference between routers has been presented in [8].…”

Section: Introductionmentioning

confidence: 97%

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A Grouping Harmony Search approach for the Citywide WiFi deployment problem

Landa-Torres

Gil-López

Ser

et al. 2011

2011 11th International Conference on Intelligent Systems Design and Applications

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This paper presents a novel Grouping Harmony Search (GHS) algorithm for the Citywide Ubiquitous WiFi Network Design problem (WIFIDP). The WIFIDP is a NP-hard problem where private customers owning wireless access points connected to Internet share bandwidth with third parties. Aspects such as allocated budget and router capacities (coverage radius, capacity, price, etc) are taken into account in order to obtain the optimal network deployment (in terms of costeffectiveness) when applying the GHS algorithm. The approach to tackle the aforementioned WIFIDP problem consists of a hybrid Grouping Harmony Search (GHS) algorithm with a local search method and a technique for repairing unfeasible solutions. Furthermore, the presented GHS algorithm is differential, since each proposed harmony is produced (improvised) based on the same harmony in the previous iteration. This differential scheme employs the grouping concept based on the connectivity between nomadic users and routers, which increases significantly its searching capability. Preliminary Monte Carlo simulations show that this proposed technique statistically outperforms genetically-inspired algorithms previously presented for the WIFIDP, with an emphasis in scenarios with stringent capacity and budget constraints. This first approach paves the way for future research aimed at applying the proposed algorithm to real scenarios.

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Section: Proposed Grouping Algorithm For the Wifidpmentioning

confidence: 99%

Section: Proposed Grouping Algorithm For the Wifidpmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

See 2 more Smart Citations

A Grouping Harmony Search approach for the Citywide WiFi deployment problem

Landa-Torres

Gil-López

Ser

et al. 2011

2011 11th International Conference on Intelligent Systems Design and Applications

View full text Add to dashboard Cite

show abstract

“…In the GGA, the encoding, crossover and mutation operators of traditional GAs are modified to be applied in grouping problems with a high performance. The GGA has been successfully applied to a number of problems in different fields such as telecommunications [18], manufacturing [19], and industrial engineering [20,21,22,23].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Grouping Genetic Algorithm for Data Clustering and Big Data Analysis

Razavi¹,

Ebadati

Asadi

et al. 2015

Adaptation, Learning, and Optimization

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Abstract. Clustering as a formal, systematic subject in dissertations can be considered the most influential unsupervised learning problem; so, as every other problem of this kind, it deals with finding the structure in a collection of unlabeled data. One of the matters associated with this subject is undoubtedly determination of the number of clusters. In this chapter, an efficient grouping genetic algorithm is proposed under the circumstances of an anonymous number of clusters. Concurrent clustering with different number of clusters is implemented on the same data in each chromosome of grouping genetic algorithm in order to discern the accurate number of clusters. In subsequent iterations of the algorithm, new solutions with different clusters number or distinct accuracy of clustering are produced by application of efficient crossover and mutation operators that led to significant improvement of clustering. Furthermore, a local search by a special probability is applied in each chromosome of each new population in order to increase the accuracy of clustering.These special operators will lead to the successful application of the proposed method in the big data analysis. To prove the accuracy and the efficiency of the algorithm, its tested on various artificial and real data sets in a comparable manner. Most of the datasets consisted of overlapping clusters, but the algorithm could detect the proper number of all data sets with high accuracy of clustering. The consequences make the best evidence of the algorithms successful performance of finding an appropriate number of clusters and accomplishment of the best clusterings quality in comparison with others.

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Enhancing Micro-location Accuracy for Asset Tracking: An Evaluation of Two Fingerprinting Approaches Using Three Machine Learning Algorithms

Sykes

Mustafa

2021

Lecture Notes in Networks and Systems

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Near optimal citywide WiFi network deployment using a hybrid grouping genetic algorithm

Cited by 30 publications

References 20 publications

A Grouping Harmony Search approach for the Citywide WiFi deployment problem

A Grouping Harmony Search approach for the Citywide WiFi deployment problem

An Efficient Grouping Genetic Algorithm for Data Clustering and Big Data Analysis

Enhancing Micro-location Accuracy for Asset Tracking: An Evaluation of Two Fingerprinting Approaches Using Three Machine Learning Algorithms

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