Dormancy strategy for temporal-spatial correlated nodes in wireless sensor networks

Reducing network power consumption plays an important role in the research of wireless sensor networks. This paper focuses on energy efficiency in environmental data collection scenarios. The data collected in this scenario are usually redundant due to spatial and temporal correlation. Consequently, selecting some nodes for dormancy can reduce power consumption. It is the key issue that how to select dormant nodes, and existing research mainly focuses on uniform clustering and optimal routing algorithms. However, the algorithms cannot guide the selection of dormant nodes because of their less consideration of attribute characteristics. Therefore, this paper proposes a node dormancy strategy for temporal-spatial correlated nodes in wireless sensor networks. The temporal-spatial correlation of the data is firstly verified; then the attributes combined with the location information are provided for FCM clustering; after, dormant node selection and head node selection are performed according to the clustering. Experiments on real temperature datasets demonstrate that using this paper's strategy, data accuracy can still be maintained at more than 95% of what no dormant node perform when 50% of nodes are dormant and around 90% when 80% of nodes are dormant. The improvement even reaches at most 80% against the traditional strategy with the same percentage of dormancy.