• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.360-374
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• 2016

Since WSNs (Wireless Sensor Networks) applied to their application areas such as smart home, smart factory, environment monitoring, etc., depend on sensor data, the sensor data is the most important among WSN components. The resources of each node consisting of WSN are extremely limited in energy, hardware and so on. Due to these limitation, communication failure probabilities become much higher and the communication failure causes data loss to occur. For this reason, this paper proposes 2MC (Maximum/Minimum Compression) that is a method to compress sensor data by selecting circular queue-based maximum/minimum sensor data values. Our proposed method reduces sensor data losses and value errors when they are recovered. Experimental results of 2MC method show the maximum/minimum 35% reduction efficiency in average sensor data accumulation error rate after the 3 times compression, comparing with CQP (Circular Queue Compression based on Period) after the compressed data recovering.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.301-304
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• 2015

센서네트워크는 기본적으로 센서 데이터를 활용하고 있기 때문에 센서 데이터는 센서네트워크의 구성요소 중 가장 중요하다 해도 과언이 아니다. 하지만 통신장애 발생 시 다양한 특징을 갖는 센서 데이터의 손실이 발생할 수 있다. 본 논문에서는 이를 위해 최대/최소값 중심으로 센서 데이터를 메모리에 저장 및 압축하는 2MC 방법을 제안하고, 보간법을 사용하여 데이터 복구 후 실제 데이터와 비교하여 그 성능을 검증하였다. 실험결과, 기존 방법과 비교하여 최대 8배까지 압축한 복구 데이터를 실제로 사용할 수 있는 정도이며, 평균 오차율은 8배 압축에서 기존 압축 방법 대비 최대 35% 감소하였다.

• Journal of the Korea Society for Simulation
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• v.25 no.2
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• pp.31-40
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• 2016

Wireless sensor networks have been used in many applications such as marine environment, army installation, etc. The sensor data is very important, because all these applications depend on sensor data. The possibility of communication failures becomes high since the surrounding environment of a wireless sense network has an sensitive effect on its communications. In particular, communication failures in underwater communications occur more frequently because of a narrow bandwidth, slow transmission speed, noises from the surrounding environments and so on. In cases of communication failures, the sensor data can be lost in the sensor data delivery process and these kinds of sensor data losses can make critical huge physical damages on human or environments in applications such as fire surveillance systems. For this reason, although a few of studies for storing and compressing sensor data have been proposed, there are lots of difficulties in actual realization of the studies due to none-existence of the framework using network communications. In this paper, we propose a framework for reducing loss of the sensor data and analyze its performance. The our analyzed results in non-framework application show a decreasing data recovery rate, T/t, as t time passes after a network failure, where T is a time period to fill the storage with sensor data after the network failure. Moreover, all the sensor data generated after a network failure are the errors impossible to recover. But, on the other hand, the analyzed results in framework application show 100% data recovery rate with 2~6% data error rate after data recovery.