## 4 Experiments
### Results and Discussion
Figure 10 compares the recognition accuracy, number of transmissions and network lifetime for the three methods of transmission reduction. The chart in (Figure 10a) shows the recognition accuracy, which was achieved when the network lifetime was equal for the compared methods (equal to 101 h and 50 min). It should be noted that the method based on neural prediction did not reach this value of network lifetime, thus it was not considered in the chart. The recognition accuracy for the proposed approach is significantly higher than for the naive prediction method. It should be noted that the columns in Figure 10 represent average results, while error bars correspond to minimal and maximal values of the determined parameters for 10 runs of the experiment. Figure 8: Accuracy of human activity recognition for different number of trees. Figure 7: Accuracy of human activity recognition for compared machine learning algorithms. The results shown in (Figure 10b,c) relates to the situation when the accuracy of activity recognition is at the same level (average of 0.95%) for the three compared methods. Number of data transmissions from sensor nodes to cluster head per hour is analysed in Figure 10b) and network lifetime in Figure 10c). Note that the maximum number of data transmissions from two sensor nodes to cluster head during one hour equals 7200. It can be observed that the proposed method is more effective in reducing data transmission and extending network lifetime than the state-of-the-art methods. Dependency between recognition accuracy and network lifetime for the compared methods is analysed in Figure 11. The numbers presented near data points in this chart describe threshold values for the state-of-the-art methods. By using the state-of-the-art methods with higher threshold value, the lifetime of sensor network can be extended. Figure 10: Comparison of accuracy (**a**), transmission number (**b**), and network lifetime (**c**). Figure 9: Comparison of activity recognition accuracy for sensor network and separate sensor nodes. However, the recognition accuracy is significantly decreased when extending the lifetime. In contrast, the proposed method enables a significant network lifetime extension and maintains the high recognition accuracy of human activities. The decrease of accuracy and reduction of data transmissions are analysed in Figure 12. In these results it can be observed that the proposed approach decreases the recognition accuracy by only 1.8%, while reduces 75% of data transmissions. The compared algorithms achieve a significantly worse trade-off between the accuracy and data reduction. The reason behind the superior effectiveness of the proposed approach is that the selection of transmitted data is well fitted to the needs of the recognition algorithm. This fitting is achieved by appropriate training of the classifiers that control the selection of transmitted data. It should be noted that only one data point is presented for the proposed method in Figures 11 and 12 because this method does not use a threshold parameter. The compared prediction-based methods assume that some error of the collected data is acceptable. This acceptable error level is expressed by the threshold parameter. In contrast, the main objective of the proposed method is to maintain the high recognition accuracy when reducing the data transmission. Thus, a parameter (threshold) that would allow us to intentionally decrease the accuracy is not introduced. When comparing the naive and neural prediction methods, it can be observed that the first one provides better results. The reason is that the naive algorithm more accurately predicts the chaotic time series of sensor readings. The chaotic character of the sensor readings collected by wearable devices during walk is illustrated by the example of light sensor data in Figure 13.  
Figure 6:0 Accuracy of activity recognition vs. lifetime of sensor network. Figure 6:1 Reduction of transmissions vs. accuracy decrease of activity recognition.