Data Compression Method for Reducing Sensor Data Loss and Error in Wireless Sensor Networks

A Formal Approach to the Selection by Minimum Error and Pattern Method for Sensor Data Loss Reduction in Unstable Wireless Sensor Network Communications

“…Specifically, most of them are based on the premise that transmissions are in real-time and stable in communications. Therefore, in the cases of the absence of their recovery methods in these mechanisms, the sensor data sampled during a communication failure period are lost and the lost sensor data cannot be recovered [ 20 , 21 , 22 , 24 ].…”

“…For longer communication failure durations, in many applications it is more important for us to know some or all of the above. However, as the failure duration is longer, networks or nodes have higher probabilities of sensor data loss in the course of transmission [ 19 , 20 ]. Accordingly, long-period communication failures cause a large amount of sensor data loss on sensor networks and such sensor data loss may cause disadvantages for human beings.…”

“…For example, long-term sensor data loss in an ocean-area contamination disaster impedes not only the cause and origin identification of the contamination but also the analysis of its dissemination progress and pollution area. Successively, such blockings prevent humankind from the prediction and prevention of the future disasters and their movements [ 19 , 20 , 21 , 22 ]. Ultimately, the sensor data lossless preservation for long-term communication failure in the previous disaster is certainly helpful for us to predict and prevent the same or similar future disasters, even though we fail in the real-time monitoring and controlling for the current disaster.…”

“…In other words, we need a method to reduce the sensor data loss caused by the memory capacity limit of the sensor node during communication failure. To date, only a few such methods, including winavg [ 23 ], delta [ 23 ], CQP [ 24 ], and 2 MC [ 19 , 20 ] have been proposed. These methods use compression to replace two or more consecutive sensor data with an average value as a representative or to select a representative between two or three consecutive sensor data.…”

“…The remainder of this paper is organized as follows. In Section 2 , we introduce related works on technologies for sensor data compression, including winavg [ 23 ], delta [ 23 ], CQP [ 24 ], and 2 MC [ 19 , 20 ], which are mostly applicable for sensor data loss reduction in sensor networks. Section 3 proposes a communication framework for the reduction of sensor data loss during communication failures.…”