queryprocessing methods have been studied extensively in traditional database systems. But few of them can be directly applied into sensor database systems due to the characteristics of sensor networks: decentralized...
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ISBN:
(纸本)9781424402694;9781424402700
queryprocessing methods have been studied extensively in traditional database systems. But few of them can be directly applied into sensor database systems due to the characteristics of sensor networks: decentralized nature of sensor networks, limited computational power, imperfect information recorded, and energy scarcity of individual sensor nodes. In this paper, we propose a quality-guaranteed and energy-efficient algorithm (QGEE) for sensor database systems. We employ an in-network query processing method to task sensor networks through declarative queries. Given a query, our QGEE will adaptively form an optimal query plan in terms of energy efficiency and query quality. The goal of our approach is to reduce interference coming from measurements with extreme errors and to minimize energy consumption by providing service that is considerably necessary and sufficient for the requirement of applications. Moreover, we employ probabilistic method to formulate the distribution of imperfect information sources in terms of probability distribution function (PDF), and acquire probabilistic query answers on uncertain data. The probability to an answer allows users to place appropriate confidence in it. Simulation results demonstrate that our algorithm can reduce resource usage and supply quality satisfied query answers to users.
queryprocessing methods have been studied extensively in traditional database systems. But few of them can be directly applied into sensor database systems due to the characteristics of sensor networks: decentralized...
详细信息
queryprocessing methods have been studied extensively in traditional database systems. But few of them can be directly applied into sensor database systems due to the characteristics of sensor networks: decentralized nature of sensor networks, limited computational power, imperfect information recorded, and energy scarcity of individual sensor nodes. In this paper, we propose a quality-guaranteed and energy-efficient algorithm (QGEE) for sensor database systems. We employ an in-network query processing method to task sensor networks through declarative queries. Given a query, our QGEE will adaptively form an optimal query plan in terms of energy efficiency and query quality. The goal of our approach is to reduce interference coming from measurements with extreme errors and to minimize energy consumption by providing service that is considerably necessary and sufficient for the requirement of applications. Moreover, we employ probabilistic method to formulate the distribution of imperfect information sources in terms of probability distribution function (PDF), and acquire probabilistic query answers on uncertain data. The probability to an answer allows users to place appropriate confidence in it. Simulation results demonstrate that our algorithm can reduce resource usage and supply quality satisfied query answers to users.
In-network aggregation has been proposed as one method for reducing energy consumption in sensor networks. In this paper, we explore two ideas related to further reducing energy consumption in the context of in-networ...
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In-network aggregation has been proposed as one method for reducing energy consumption in sensor networks. In this paper, we explore two ideas related to further reducing energy consumption in the context of in-network aggregation. The first is by influencing the construction of the routing trees for sensor networks with the goal of reducing the size of transmitted data. To this end, we propose a group-aware network configuration method that "clusters" along the same path sensor nodes that belong to the same group. The second idea involves imposing a hierarchy of output filters on the sensor network with the goal of both reducing the size of transmitted data and minimizing the number of transmitted messages. More specifically, we propose a framework to use temporal coherency tolerances in conjunction with in-network aggregation to save energy at the sensor nodes while maintaining specified quality of data. These tolerances are based on user preferences or can be dictated by the network in cases where the network cannot support the current tolerance level. Our framework, called TiNA, works on top of existing in-network aggregation schemes. We evaluate experimentally our proposed schemes in the context of existing in-network aggregation schemes. We present experimental results measuring energy consumption, response time, and quality of data for Group-By queries. Overall, our schemes provide significant energy savings with respect to communication and a negligible drop in quality of data.
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