Wireless sensor networks are usually deployed in harsh and emergency scenarios, such as floods, fires, or earthquakes, where human participation to monitor and collect environmental data may be too dangerous. It can be also used for healthcare in extreme and remote environments. In such an environment, sensor nodes are faced with the risk of failure and the loss of valuable healthcare data. Therefore, fast collection and reliable storage of data becomes the two important basic topics for reliable data collection. Traditional distributed data collection protocols based on the network, such as Growth Codes proposed by Karma et al., have improved the persistence of data and the efficiency of reliable data collection in disaster scenarios. However, there are still some problems that reduce the overall efficiency. In this paper, we analyze the factors that affect the collection efficiency from a new perspective, the ratio of redundant symbols. Random feedback digestion (RFDG) model is proposed to digest the redundant symbols, similiar to our stomach digesting food, to remove redundant symbols and reduce resource consumption by using the feedback information of the already decoded code words sent by the sink node. This model can increase the valid information ratio in the network and finally increase data decoding efficiency. Three protocols are proposed in this paper according to different feedback mechanisms based on RFDG. It is shown that protocols based on RFDG outperform the growth codes protocol in data collection efficiency and reduce the delayed effect. INDEX TERMS Wireless sensor network, network coding, growth codes, data collection, feedback digestion.

Wireless sensor network are widely used in various types of environmental monitoring and there are also many applications in the industrial field, we usually deploy sensor nodes in the corresponding areas to monitor and collect relevant data, waiting for a sink node to collect the stored data for further analysis and decision. But in some extremely harsh scenarios, the living environment of the sensor node is very bad, hence the data on the nodes will always lose. Generally, we use LT Codes to improve data persistence. However, some difficult problems with regard to the efficiency of coding and decoding of data can not be resolved by traditional solutions. In terms of the above problems, this paper improves the traditional coding method by reducing the number of encoded packets, so that the network congestion can be alleviated under the premise of successful data coding. What's more, we propose an edge layered collection model and a new coding strategy Edge Layering Fountain Codes (ELFC) based on the model, each sensor node inside the network sends its data to the edge nodes which are relatively safe to ensure the secure collection of data and allocate the resource consumption of the sensor nodes around the sink node. On this basis, the Ordered Edge Layering Fountain Codes (OELFC) is further proposed. The OELFC can collect data packets in a low degree-to-high degree manner, which greatly improves the efficiency of data decoding. INDEX TERMS Wireless sensor network, network coding, LT codes, cliff effect, coding complexity, random walk, load balance decoding. NEAL N. XIONG received both the Ph.D. degrees from Wuhan University (about sensor system engineering), and the Japan Advanced Institute of Science and Technology (about dependable sensor networks), respectively. He is currently a Professor with the