ANALYSIS RESOURCE AWARE FRAMEWORK BY COMBINING SUNSPOT AND IMOTE2 PLATFORM WIRELESS SENSOR NETWORKS USING DISTANCE VECTOR ALGORITHM

Efficiency energy and stream data mining on Wireless Sensor Networks (WSNs) are a very interesting issue to be discussed. Routing protocols technology and resource-aware can be done to improve energy efficiency. In this paper we try to merge routing protocol technology using routing Distance Vector and Resource-Aware (RA) framework on heterogeneity wireless sensor networks by combining sun-SPOT and Imote2 platform wireless sensor networks. RA perform resource monitoring process of the battery, memory and CPU load more optimally and efficiently. The process uses Light-Weight Clustering (LWC) and Light Weight Frequent Item (LWF). The results obtained that by adapting Resource-Aware in wireless sensor networks, the lifetime of wireless sensor improve up to ± 16.62%. Efisiensi energi dan stream data mining pada Wireless Sensor Networks (WSN) adalah masalah yang sangat menarik untuk dibahas. Teknologi Routing Protocol dan Resource-Aware dapat dilakukan untuk meningkatkan efisiensi energi. Dalam penelitian ini peneliti mencoba untuk menggabungkan teknologi Routing Protocol menggunakan routing Distance Vector dan Resource-Aware (RA) framework pada Wireless Sensor Networks heterogen dengan menggabungkan sun-SPOT dan platform Imote2 Wireless Sensor Networks. RA melakukan proses pemantauan sumber daya dari memori, baterai, dan beban CPU lebih optimal dan efisien. Proses ini menggunakan Light-Weight Clustering (LWC) dan Light Weight Frequent Item (LWF). Hasil yang diperoleh bahwa dengan mengadaptasi Resource-Aware dalam Wireless Sensor Networks, masa pakai wireless sensor meningkatkan sampai ± 16,62%

A Study of Limited Resources and Security Adaptation in Wireless Sensor Network

13301甲第4825号博士（工学）金沢大学博士論文本文Full 以下に掲載：sensors 18(1594) pp.1-15. 2018. MDPI. 共著者：Jumadi Mabe Parenreng, Akio Kitagaw

A wireless data stream mining model

The sensor networks, web click stream and astronomical applications generate a continuous flow of data streams. Most likely data streams are generated in a wireless environment. These data streams challenge our ability to store and process them in real-time with limited computing capabilities of the wireless environment. Querying and mining data streams have attracted attention in the past two years. The main idea behind the proposed techniques in mining data streams in to develop efficient approximate algorithms with an acceptable accuracy. Recently, we have proposed algorithm output granularity as an approach in mining data streams. This approach has the advantage of being resource-aware in addition to its generality. In this paper, a model for mining data streams in a wireless environment has been proposed. The model contains two novel contributions; a ubiquitous data mining system architecture and algorithm output granularity approach in mining data streams.Upprättat; 2004; 20080213 (ysko