Energy-efficient node scheduling algorithms for wireless sensor networks using Markov Random Field model

One important way to extend the lifetime of wireless sensor networks is to deploy the sensors in a dense manner. The redundancy among the sensed data demonstrates that it is not efficient to collect raw data from all nodes in the network if we further consider that the data is generally spatial-correlated. The node scheduling strategy aims at selecting a set of representative nodes to provide the required data service in a periodic manner with accuracy guarantee. This strategy can effectively reduce the number of active nodes and the amount of messages in the network, and extend the network lifetime accordingly. In this paper, we firstly introduce how to model the spatial correlation among sensed data by Markov Random Field (MRF) model. Secondly, we formulate the problem definitions, namely, the data amendment problem which maximizes the data coverage range for a given node by amending the raw noise-corrupted data from the neighbors, while the representative nodes selection problem focuses on reducing the number of representative nodes and covering all nodes in the network, and the node scheduling problem aims at maximizing the network lifetime. Thirdly, we propose a novel Data Amendment Procedure (DAP), Representative node Selection Procedure (RSP) and energy-efficient Node Scheduling Algorithm (NSA) respectively for these above problems. Finally, extensive experiments demonstrate that the proposed node scheduling algorithm can significantly improve the network lifetime compared with related works with an average increment of about 80%.