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%

An Investigation in Efficient Spatial Patterns Mining

The technical progress in computerized spatial data acquisition and storage results in the growth of vast spatial databases. Faced with large amounts of increasing spatial data, a terminal user has more difficulty in understanding them without the helpful knowledge from spatial databases. Thus, spatial data mining has been brought under the umbrella of data mining and is attracting more attention. Spatial data mining presents challenges. Differing from usual data, spatial data includes not only positional data and attribute data, but also spatial relationships among spatial events. Further, the instances of spatial events are embedded in a continuous space and share a variety of spatial relationships, so the mining of spatial patterns demands new techniques. In this thesis, several contributions were made. Some new techniques were proposed, i.e., fuzzy co-location mining, CPI-tree (Co-location Pattern Instance Tree), maximal co-location patterns mining, AOI-ags (Attribute-Oriented Induction based on Attributes’ Generalization Sequences), and fuzzy association prediction. Three algorithms were put forward on co-location patterns mining: the fuzzy co-location mining algorithm, the CPI-tree based co-location mining algorithm (CPI-tree algorithm) and the orderclique- based maximal prevalence co-location mining algorithm (order-clique-based algorithm). An attribute-oriented induction algorithm based on attributes’ generalization sequences (AOI-ags algorithm) is further given, which unified the attribute thresholds and the tuple thresholds. On the two real-world databases with time-series data, a fuzzy association prediction algorithm is designed. Also a cell-based spatial object fusion algorithm is proposed. Two fuzzy clustering methods using domain knowledge were proposed: Natural Method and Graph-Based Method, both of which were controlled by a threshold. The threshold was confirmed by polynomial regression. Finally, a prototype system on spatial co-location patterns’ mining was developed, and shows the relative efficiencies of the co-location techniques proposed The techniques presented in the thesis focus on improving the feasibility, usefulness, effectiveness, and scalability of related algorithm. In the design of fuzzy co-location Abstract mining algorithm, a new data structure, the binary partition tree, used to improve the process of fuzzy equivalence partitioning, was proposed. A prefix-based approach to partition the prevalent event set search space into subsets, where each sub-problem can be solved in main-memory, was also presented. The scalability of CPI-tree algorithm is guaranteed since it does not require expensive spatial joins or instance joins for identifying co-location table instances. In the order-clique-based algorithm, the co-location table instances do not need be stored after computing the Pi value of corresponding colocation, which dramatically reduces the executive time and space of mining maximal colocations. Some technologies, for example, partitions, equivalence partition trees, prune optimization strategies and interestingness, were used to improve the efficiency of the AOI-ags algorithm. To implement the fuzzy association prediction algorithm, the “growing window” and the proximity computation pruning were introduced to reduce both I/O and CPU costs in computing the fuzzy semantic proximity between time-series. For new techniques and algorithms, theoretical analysis and experimental results on synthetic data sets and real-world datasets were presented and discussed in the thesis

An investigation in efficient spatial patterns mining

The technical progress in computerized spatial data acquisition and storage results in the growth of vast spatial databases. Faced with large amounts of increasing spatial data, a terminal user has more difficulty in understanding them without the helpful knowledge from spatial databases. Thus, spatial data mining has been brought under the umbrella of data mining and is attracting more attention. Spatial data mining presents challenges. Differing from usual data, spatial data includes not only positional data and attribute data, but also spatial relationships among spatial events. Further, the instances of spatial events are embedded in a continuous space and share a variety of spatial relationships, so the mining of spatial patterns demands new techniques. In this thesis, several contributions were made. Some new techniques were proposed, i.e., fuzzy co-location mining, CPI-tree (Co-location Pattern Instance Tree), maximal co-location patterns mining, AOI-ags (Attribute-Oriented Induction based on Attributes’ Generalization Sequences), and fuzzy association prediction. Three algorithms were put forward on co-location patterns mining: the fuzzy co-location mining algorithm, the CPI-tree based co-location mining algorithm (CPI-tree algorithm) and the orderclique- based maximal prevalence co-location mining algorithm (order-clique-based algorithm). An attribute-oriented induction algorithm based on attributes’ generalization sequences (AOI-ags algorithm) is further given, which unified the attribute thresholds and the tuple thresholds. On the two real-world databases with time-series data, a fuzzy association prediction algorithm is designed. Also a cell-based spatial object fusion algorithm is proposed. Two fuzzy clustering methods using domain knowledge were proposed: Natural Method and Graph-Based Method, both of which were controlled by a threshold. The threshold was confirmed by polynomial regression. Finally, a prototype system on spatial co-location patterns’ mining was developed, and shows the relative efficiencies of the co-location techniques proposed The techniques presented in the thesis focus on improving the feasibility, usefulness, effectiveness, and scalability of related algorithm. In the design of fuzzy co-location Abstract mining algorithm, a new data structure, the binary partition tree, used to improve the process of fuzzy equivalence partitioning, was proposed. A prefix-based approach to partition the prevalent event set search space into subsets, where each sub-problem can be solved in main-memory, was also presented. The scalability of CPI-tree algorithm is guaranteed since it does not require expensive spatial joins or instance joins for identifying co-location table instances. In the order-clique-based algorithm, the co-location table instances do not need be stored after computing the Pi value of corresponding colocation, which dramatically reduces the executive time and space of mining maximal colocations. Some technologies, for example, partitions, equivalence partition trees, prune optimization strategies and interestingness, were used to improve the efficiency of the AOI-ags algorithm. To implement the fuzzy association prediction algorithm, the “growing window” and the proximity computation pruning were introduced to reduce both I/O and CPU costs in computing the fuzzy semantic proximity between time-series. For new techniques and algorithms, theoretical analysis and experimental results on synthetic data sets and real-world datasets were presented and discussed in the thesis.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

An investigation in efficient spatial patterns mining

The technical progress in computerized spatial data acquisition and storage results in the growth of vast spatial databases. Faced with large amounts of increasing spatial data, a terminal user has more difficulty in understanding them without the helpful knowledge from spatial databases. Thus, spatial data mining has been brought under the umbrella of data mining and is attracting more attention. Spatial data mining presents challenges. Differing from usual data, spatial data includes not only positional data and attribute data, but also spatial relationships among spatial events. Further, the instances of spatial events are embedded in a continuous space and share a variety of spatial relationships, so the mining of spatial patterns demands new techniques. In this thesis, several contributions were made. Some new techniques were proposed, i.e., fuzzy co-location mining, CPI-tree (Co-location Pattern Instance Tree), maximal co-location patterns mining, AOI-ags (Attribute-Oriented Induction based on Attributes’ Generalization Sequences), and fuzzy association prediction. Three algorithms were put forward on co-location patterns mining: the fuzzy co-location mining algorithm, the CPI-tree based co-location mining algorithm (CPI-tree algorithm) and the orderclique- based maximal prevalence co-location mining algorithm (order-clique-based algorithm). An attribute-oriented induction algorithm based on attributes’ generalization sequences (AOI-ags algorithm) is further given, which unified the attribute thresholds and the tuple thresholds. On the two real-world databases with time-series data, a fuzzy association prediction algorithm is designed. Also a cell-based spatial object fusion algorithm is proposed. Two fuzzy clustering methods using domain knowledge were proposed: Natural Method and Graph-Based Method, both of which were controlled by a threshold. The threshold was confirmed by polynomial regression. Finally, a prototype system on spatial co-location patterns’ mining was developed, and shows the relative efficiencies of the co-location techniques proposed The techniques presented in the thesis focus on improving the feasibility, usefulness, effectiveness, and scalability of related algorithm. In the design of fuzzy co-location Abstract mining algorithm, a new data structure, the binary partition tree, used to improve the process of fuzzy equivalence partitioning, was proposed. A prefix-based approach to partition the prevalent event set search space into subsets, where each sub-problem can be solved in main-memory, was also presented. The scalability of CPI-tree algorithm is guaranteed since it does not require expensive spatial joins or instance joins for identifying co-location table instances. In the order-clique-based algorithm, the co-location table instances do not need be stored after computing the Pi value of corresponding colocation, which dramatically reduces the executive time and space of mining maximal colocations. Some technologies, for example, partitions, equivalence partition trees, prune optimization strategies and interestingness, were used to improve the efficiency of the AOI-ags algorithm. To implement the fuzzy association prediction algorithm, the “growing window” and the proximity computation pruning were introduced to reduce both I/O and CPU costs in computing the fuzzy semantic proximity between time-series. For new techniques and algorithms, theoretical analysis and experimental results on synthetic data sets and real-world datasets were presented and discussed in the thesis.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A general framework for IoT-ready geotechnical objects

The project focuses on the relationship between current IoT trends and geotechnics within the civil engineering context. A simple framework for geotechnical data exchange tailored to IoT applications, potentially suitable for both small to large scale structural and infrastructural scenarios, has been prototyped. Within such scenarios further a special case of data generation from sensor-enabled geosynthetics has been explored. Additionally, different modelling and analytics strategies, based both on FEA procedures and Machine Learning algorithms, have been developed to provide the necessary data-to-knowledge tools to complement the framework