Optimized location-based service selection for QoS improvement in wireless networks. (November 2017)

• Record Type: Journal Article
• Authors: Rajasekar, S.S.; Palanisamy, C.
• Is Part Of: Computers & electrical engineering. Volume 64(2017)
• Page Start: 277
• Page End: 287
• Publication Date: 2017-11
• Subjects: Location based services -- MADE -- Wireless networks -- QoS -- Service selection -- SAM -- Service pool -- Data availability; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.08.013
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: The development of information and communication technology has improved the stability in wireless network services at various levels. The services provided can be accessed by the network users through a variety of devices like Mobile phones, PDA (Personal Digital Assistant) and communication media. The quality of service in wireless network highly depends on various factors like service availability, data availability, throughput and latency. Many approaches were introduced earlier for location-based services, but still, there are hurdles in data management, service selection, and other QoS metrics. It is necessary to develop an optimized location based service selection approach using multi attribute data estimation (MADE) technique. In that intention, the service requested by the user is identified with similar service. Upon identifying the service, the set of supporting services which are necessary to complete the request are recognized. Based on the services' necessity, user location and service availability, the service availability measure (SAM) is computed based on the selected service point. By selecting the service according to their SAM value, the service selection efficiency can be improved. The selection of service point is performed as per the data availability and multi attribute data estimation technique. The MADE approach enables to collect the data requested and the data sensor which possess the data. Further, it facilitates the data depth of each sensor. Based on these contents, a service support score is computed by SAM, MADE to select a single service point. The proposed algorithm improves the performance in service selection and increases the quality of service parameters.