Energy limitation is a crucial problem in wireless sensor networks. The researches show that the energy is mainly consumed during data transmission. However, data aggregation can make an optimization of the apperceiving data to constrain the redundancy data for avoiding unnecessary energy consumption. Generally, a sensor node is combined with diversified sensors, hence, it is unreasonable to collect and transmit the sensing data in same frequency. This paper proposed a kind of adaptive classified data aggregation arithmetic which can classify the different sensing data, control the transmission according to the dynamically threshold set, and complete the data aggregation by adjusting data quantity along the transmission path. Different sets of parameters were conducted in these experiments to test the property of sensor network adopting the proposed arithmetic. The experiment results have verified the efficient energy utilization.
The rate-monotonic (RM) algorithm is a classic fixed priority real-time scheduling algorithm. However, in most embedded real-time systems where the workload is composed of many tasks of high frequency and short execution time, the overheads from preemptions of the real-time operating system will lead to a low resource utilization rate if the RM algorithm is directly used. By studying the preemption relationship of the tasks scheduled by RM algorithm, a model of preemption overheads is established with task attributes, based on which the run-time preemption overheads of RM algorithm are reduced by optimizing the start time of the tasks in embedded real-time systems. Finally, the experimental results show the validity of the proposed strategy.
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