In the past research, smart sensor devices have become mature enough for large, distributed networks of such sensors to start to be deployed. Such networks can include tens or hundreds of independent nodes that can perform their functions without human interactions such as recharging of batteries, the configuration of network routes and others. Each of the sensors in the wireless sensor network is considered as microsystem, which consists of memory, processor, transducers and low bandwidth as well as a low range radio transceiver. This study investigates an adaptive sampling strategy for WSS aimed at reducing the number of data samples by sensing data only when a significant change in these processes is detected. This detection strategy is based on an extension to Holt's Method and statistical model. To investigate this strategy, the water consumption in a household is used as a case study. A query distribution approach is proposed, which is presented in detail in chapter 5. Our developed wireless sensor query engine is programmed on Sensinode testbed cc2430. The implemented model used on the wireless sensor platform and the architecture of the model is presented in chapters six, seven, and eight. This thesis presents a contribution by designing the experimental simulation setup and by developing the required database interface GUI sensing system, which enables the end user to send the inquiries to the sensor s network whenever needed, the On-Demand Query Sensing system ODQS is enhanced with a probabilistic model for the purpose of sensing only when the system is insufficient to answer the user queries. Moreover, a dynamic aggregation methodology is integrated so as to make the system more adaptive to query message costs. Dynamic on-demand approach for aggregated queries is implemented, based in a wireless sensor network by integrating the dynamic programming technique for the most optimal query decision, the optimality factor in our experiment is the query cost. In-network query processing of wireless sensor networks is discussed in detail in order to develop a more energy efficient approach to query processing. Initially, a survey of the research on existing WSN query processing approaches is presented. Building on this background, novel primary achievements includes an adaptive sampling mechanism and a dynamic query optimiser. These new approaches are extremely helpful when existing statistics are not sufficient to generate an optimal plan. There are two distinct aspects in query processing optimisation; query dynamic adaptive plans, which focus on improving the initial execution of a query, and dynamic adaptive statistics, which provide the best query execution plan to improve subsequent executions of the aggregation of on-demand queries requested by multiple end-users. In-network query processing is attractive to researchers developing user-friendly sensing systems. Since the sensors are a limited resource and battery powered devices, more robust features are recommended to limit the communication access to the sensor nodes in order to maximise the sensor lifetime. For this reason, a new architecture that combines a probability modelling technique with dynamic programming (DP) query processing to optimise the communication cost of queries is proposed. In this thesis, a dynamic technique to enhance the query engine for the interactive sensing system interface is developed. The probability technique is responsible for reducing communication costs for each query executed outside the wireless sensor networks. As remote sensors have limited resources and rely on battery power, control strategies should limit communication access to sensor nodes to maximise battery life. We propose an energy-efficient data acquisition system to extend the battery life of nodes in wireless sensor networks. The system considers a graph-based network structure, evaluates multiple query execution plans, and selects the best plan with the lowest cost obtained from an energy consumption model. Also, a genetic algorithm is used to analyse the performance of the approach. Experimental testing are provided to demonstrate the proposed on-demand sensing system capabilities to successfully predict the query answer injected by the on-demand sensing system end-user based-on a sensor network architecture and input query statement attributes and the query engine ability to determine the best and close to the optimal execution plan, given specific constraints of these query attributes . As a result of the above, the thesis contributes to the state-of-art in a network distributed wireless sensor network query design, implementation, analysis, evaluation, performance and optimisation.

XML (eXtensible Markup Language)是今日新興在網路上所使用的延伸性的標記語言。它具有豐富的語意表達及與展現方式獨立的資料獨立性。由於這些特性，使得XML成為新的資料交換標準並且其應用在資料庫中產生了許多新的研究議題在資料儲存和查詢處理上。在本篇研究中，將研究XML資料庫中的績效評估的議題並且發展一個可適用於不同應用領域及各種平台上的使用者導向且開放型工作量模式以評估XML資料庫績效。此XML開放型的工作量模型包含三個子模型─XML資料模型、查詢模型以及控制模型。XML資料模型將模式化XML文件中階層式的結構概念；查詢模型包含了一連串查詢模組以供測試XML資料庫的處理查詢能力以及一個開放型的查詢輸入介面以供使用者依照需求設定所需的測試查詢；控制模型中定義了一連串變數以供設定績效評估系統中的執行環境。我們發展此系統化且具開放型的工作量方法可以幫助各種不同應用領域的使用者預測及展現XML資料庫系統的績效。 / XML (eXtensible Markup Language) is the emerging data format for data processing on the Internet. XML provides a rich data semantics and data independence from the presentation. Thanks to these features, XML becomes a new data exchange standard and leads new storage and query processing issues on database research communities. In this paper, the performance evaluation issues on XML databases have been studied and a generic and requirement-driven XML workload model that is applicable to any application scenario or movable on various platforms is developed. There are three sub-models in this generic workload model, the XML data model, the query model, and the control model. The XML data model formulates the generic hierarchy structure of XML documents and supports a flexible document structure of the test database. The XML query model contains a flexible classical query module selector and an open query input to define the requirement-driven test query model to challenge the XML query processing ability of the XML database. The control model defines variables that are used to set up the implementation of a benchmark. This open, flexible, and systematic workload method permits users in various application domains to predicate or profile the performance of the XML database systems.