Service-oriented node scheduling scheme for wireless sensor networks using Markov random field model.
Future wireless sensor networks are expected to provide various sensing services and energy efficiency is one of the most important criterions. The node scheduling strategy aims to increase network lifetime by selecting a set of sensor nodes to provide the required sensing services in a periodic manner. In this paper, we are concerned with the service-oriented node scheduling problem to provide multiple sensing services while maximizing the network lifetime. We firstly introduce how to model the data correlation for different services by using Markov Random Field (MRF) model. Secondly, we formulate the service-oriented node scheduling issue into three different problems, namely, the multi-service data denoising problem which aims at minimizing the noise level of sensed data, the representative node selection problem concerning with selecting a number of active nodes while determining the services they provide, and the multi-service node scheduling problem which aims at maximizing the network lifetime. Thirdly, we propose a Multi-service Data Denoising (MDD) algorithm, a novel multi-service Representative node Selection and service Determination (RSD) algorithm, and a novel MRF-based Multi-service Node Scheduling (MMNS) scheme to solve the above three problems respectively. Finally, extensive experiments demonstrate that the proposed scheme efficiently extends the network lifetime.
College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China. cscheng@fzu.edu.cn.
Hongju
H
Cheng
College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China. zhihuangsu@126.com.
Zhihuang
Z
Su
Instituto de Investigación Para la Gestión Integrada de Zonas Costeras, Universidad Politécnica de Valencia, C/ Paranimf nº 1, Grao de Gandia, Gandia, Valencia 46730, Spain. jlloret@dcom.upv.es.
Jaime
J
Lloret
College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China. fzucgl@163.com.
Guolong
G
Chen