Design and Analysis of a Persistent, Efficient, and Self-Contained WSN Data Collection System

Abstract

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 Multiple Access Network With a Sectional Configured Data Collection System and Codeword Filtering Mechanism

“…The protocols proposed to improve the intermediate performance of the LT codes [15], [16] need to utilize stable feedback channels, which is difficult to be guaranteed in disaster scenarios. The Growth codes protocol with good data collection efficiency performs poorly in sparse networks [20]. Moreover, in a disaster scenario, excessive design of topology or a centralized control of the network can result in too much network overhead.…”

“…As the number of recovered symbols increases, the degree of codeword also needs to be increased in order to maintain the high decodable probability. In the later period of data collection, there will be a large amount of redundant data in the network [20], which will affect the efficiency of data collection.…”

“…Zhang et al [20] proposed the Random feedback digestion (RFDG) model, which uses feedbacks to eliminate redundant codewords. In order to control the feedback messages, three different feedback mechanisms are proposed, and the degree distribution is also modified, which improves the data collection efficiency.…”

A spanning tree construction algorithm for industrial wireless sensor networks based on quantum artificial bee colony

“…Field equipment production process data and network status data are collected periodically [18]. IWSN analyzes and processes this data to monitor factory equipment and networks.…”

Energy-efficient filtering algorithm for a class of industrial sensor network systems with packet dropouts, time-varying delay, and multiplicative noises

“…It is well known that the limited network channel bandwidth and limited power are significant factors constraining the performance of industrial sensor network systems [14][15][16][17][18][19]. In traditional time-triggered communication mechanism, the signal of sensor is transmitted to the filter or controller at every time, which does not consider the limited bandwidth of communication channel and therefore increases the burden of industrial sensor network channel.…”