Applying parachutes-deployed Wireless Sensor Network (WSN) in monitoring the\nhigh-altitude space is a promising solution for its effectiveness and cost. However, both the\nhigh deviation of data and the rapid change of various environment factors (air pressure, temperature,\nwind speed, etc.) pose a great challenge. To this end, we solve this challenge with data compensation\nin dynamic stress measurements of parachutes during the working stage. Specifically, we construct a\ndata compensation model to correct the deviation based on neural network by taking into account a\nvariety of environmental parameters, and name it as Data Compensation based on Back Propagation\nNeural Network (DC-BPNN). Then, for improving the speed and accuracy of training the DC-BPNN,\nwe propose a novel Adaptive Artificial Bee Colony (AABC) algorithm. We also address its stability of\nsolution by deriving a stability bound. Finally, to verify the real performance, we conduct a set of real\nimplemented experiments of airdropped WSN.
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