Wireless LAN access point placement using a multi-objective genetic algorithm

Maksuriwong, K.; Varavithya, Vara; Chaiyaratana, Nachol

doi:10.1109/icsmc.2003.1244696

Cited by 34 publications

(19 citation statements)

References 9 publications

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“…They just consider the number of users and types of services, such as mail, video-conferencing, VoIP, etc. Since their parameters are more complete than the others [4,5,8], our proposed approach takes benefit of their terminologies in order to make it easier to understand.…”

Section: Related Workmentioning

confidence: 99%

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Wireless LAN Access Point Placement Based on User Mobility

Taufiq

Abdullah

Choi

2011

Wireless Pers Commun

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In a wide area campus, a university provides Wireless Local Area Network (WLAN) for users to connect to the Internet. Most users take advantage of this WLAN benefit by using their laptops. However, the number of smart phone users is growing fast. Since a smart phone is able to get an Internet connection using WLAN, users can use their smart phones without having to pay for a cellular operator. Users tend to use their smart phones more, due to their higher mobility compared to a laptop. This capability enables new services in the market, such as Fixed-Mobile Convergence (FMC), which integrates a fixed network (traditional telephony, WLAN) and a mobile network (cellular) to provide seamless voice communications anytime, anywhere. These new applications require a WLAN connection availability nearly everywhere. However, due to limited budgets, a university can only install APs in places with a high connection demand. We propose a novel WLAN AP placement technique that takes user mobility into consideration. This new approach is more complete than previous approaches, which mainly focus on coverage area and throughput data. Our technique has been implemented in our university. The results show the suitability of the WLAN access point locations in our university campus based on user mobility and activities.

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Section: Related Workmentioning

confidence: 99%

“…Maksuriwong et al [8] take advantage of he Multi-Objective Genetic Algorithm (MOGA) to find the best combination of access point locations. Instead of using (BER), their aim is to maximize the signal coverage in the services area.…”

Section: Related Workmentioning

confidence: 99%

Wireless LAN Access Point Placement Based on User Mobility

Taufiq

Abdullah

Choi

2011

Wireless Pers Commun

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“…We have decided to consider two placements of reference points (sensor patterns) belonging to three different technologies (i.e., Bluetooth (BT), WiFi and RFID), which are illustrated in Fig. 12a, and drawn from [44]: Saw-tooth Partitioning, which places the sensors according to a saw-tooth configuration, and Equal Partitioning, which decomposes the overall environment into four portions and puts a sensor of a technology at the 2.4 frequency (e.g., Bluetooth or WiFi) in the center of each partition and place an RFID sensor at the external walls. These two placements are the ones that someone would come up with if signal coverage and interference avoidance would be the only matters, without making use of any particular automated planning tool.…”

Section: Pattern Selection Evaluationmentioning

confidence: 99%

Deployment of RSS-Based Indoor Positioning Systems

Esposito

Ficco

2011

Int J Wireless Inf Networks

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Location estimation based on Received Signal Strength (RSS) is the prevalent method in indoor positioning. For such positioning systems, a massive collection of training samples is needed for their calibration. The accuracy of these methods is directly related to the placement of the reference points and the radio map used to compute the device location. Traditionally, deploying the reference points and building the radio map require human intervention and are extremely time-consuming. In this paper we present an approach to reduce the manual calibration efforts needed to deploy an RSS-based localization system, both when using only one RF technology or when using a combination of RF technologies. It is an automatic approach both to build a radio map in a given workspace by means of a signal propagation model, and to assess the system calibration that best fits the required accuracy by using a multi-objective genetic algorithm.

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“…Thanks to their versatility, they can be used in different fi elds and fi nd a lot of applications in wireless network optimization problems [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30].…”

Section: Genetic Algorithmsmentioning

confidence: 99%

The safety/security/communication wireless lan of the underground gran sasso mountain national laboratories of the italian institute of nuclear physics

Garzia¹,

Cusani²

2012

Int. J. SAFE

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Wireless networks are very useful in mobile services. In particular environments, such as the underground Gran Sasso mountain National Laboratories of the Italian Institute of Nuclear Physics they represent a vital resource for safety/security/communication. The purpose of this paper is to illustrate the design procedure, based on genetic algorithm, which has been used to design the mentioned wireless network, guaranteeing a high level of performances and reliability.

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Wireless LAN access point placement using a multi-objective genetic algorithm

Cited by 34 publications

References 9 publications

Wireless LAN Access Point Placement Based on User Mobility

Wireless LAN Access Point Placement Based on User Mobility

Deployment of RSS-Based Indoor Positioning Systems

The safety/security/communication wireless lan of the underground gran sasso mountain national laboratories of the italian institute of nuclear physics

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