Context-Aware Service Ranking in Wireless Sensor Networks

  • Authors:

  • Wenjia Niu;Jun Lei;Endong Tong;Gang Li;Liang Chang;Zhongzhi Shi;Song Ci

  • Affiliations:

  • Institute of Acoustics, Chinese Academy of Sciences, Beijing, People's Republic of China 100190;Institute of Microelectronics, Chinese Academy of Sciences, Beijing, People's Republic of China 100029;Institute of Acoustics, Chinese Academy of Sciences, Beijing, People's Republic of China 100190;School of Information Technology, Deakin University, Melbourne, Australia 3125;Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, People's Republic of China 541004;Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, People's Republic of China 100190;Institute of Acoustics, Chinese Academy of Sciences, Beijing, People's Republic of China 100190

  • Venue:
  • Journal of Network and Systems Management
  • Year:
  • 2014

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Abstract

Wireless sensor networks (WSNs) are widely used in practice for comprehensively monitoring and gathering physical information via a multitude of sensors. As the development of WSNs, the integration of them with the external Internet is a urgent need. By wrapping the sensor functionality as a WSN service, the Web service is considered as the most promising technology to incorporate WSNs into the Internet. The quest for selecting the service with the best performance promotes service ranking technology. However, due to the dynamic WSN environment, traditional quality of service (QoS) based ranking approaches for general Web services are no longer suitable for the WSN service. In this article, in order to fit the characteristics of the WSN environment, we propose a context-aware WSN service ranking approach by aggregating the user rating and WSN service context. First, the User QoS Assessment(UQA) and Context QoS Assessment(CQA) are proposed, respectively. Then, through the performance influence on the WSN service by the variations in their context, a Fuzzy mechanism is further developed to aggregate the UQA and the CQA. Finally, the experiments are presented to confirm the validity of the proposed approach.