초록

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Abstract ▼ AI-Helper

Recently, the advances on sensor technology increases the study on data processing oriented middleware. Usually, most of middleware uses the naive and delta method for queue management to process data effectively. But such a queue management method it is difficult to support guaranteeing of the requ...

Recently, the advances on sensor technology increases the study on data processing oriented middleware. Usually, most of middleware uses the naive and delta method for queue management to process data effectively. But such a queue management method it is difficult to support guaranteeing of the requested quality of services because it simply discards the data in a queue when the overflow is occurred. To handle this problem, some methods for minimizing data volumes in a wireless sensor network have been studied, but most of them cause another problem that needs the huge processing time additionally with higher complexity of the proposed algorithm. In this paper, we propose a new method of delta-average queue management by applying the mean value to the exiting delta queue management method using the data differences to handle the problem of queue overflow. The proposed method has lower complexity than the others and increases the QoS of a WSN application by using mean value instead of using data discarding policy. In addition, it is designed to manage a queue effectively by controlling the range of data differences adaptively to the target sensor network environment.