In wireless sensor networks, sensor nodes collect local data and transfer to the base station often relayed by other nodes. If deploying sensor nodes evenly, sensor nodes nearer to the base station will consume more e...In wireless sensor networks, sensor nodes collect local data and transfer to the base station often relayed by other nodes. If deploying sensor nodes evenly, sensor nodes nearer to the base station will consume more energy and use up their energy faster that reduces system lifetime. By analyzing energy consumption, a density formula of deploying nodes is proposed. The ratio of whole energy of sensor nodes to energy consumption speed of sensor nodes in every area can get consistent if deploying nodes by the density formula, therefore system lifetime is prolonged. Analysis and simulation results show that when communication dominates whole energy consumption and the monitored region is big compared with radio range of sensor node, system lifetime under this scheme can be 3R/(2t) times of that under deploying nodes evenly, where R is radius of the monitored region and t is radio range of sensor node.展开更多
Abstract Big data has received great attention in research and application. However, most of the current efforts focus on system and application to handle the challenges of ＂volume＂ and ＂velocity＂, and not much ha...Abstract Big data has received great attention in research and application. However, most of the current efforts focus on system and application to handle the challenges of ＂volume＂ and ＂velocity＂, and not much has been done on the theoreti- cal foundation and to handle the challenge of ＂variety＂. Based on metric-space indexing and computationalcomplexity the- ory, we propose a parallel computing framework for big data. This framework consists of three components, i.e., universal representation of big data by abstracting various data types into metric space, partitioning of big data based on pair-wise distances in metric space, and parallel computing of big data with the NC-class computing theory.展开更多
文摘In wireless sensor networks, sensor nodes collect local data and transfer to the base station often relayed by other nodes. If deploying sensor nodes evenly, sensor nodes nearer to the base station will consume more energy and use up their energy faster that reduces system lifetime. By analyzing energy consumption, a density formula of deploying nodes is proposed. The ratio of whole energy of sensor nodes to energy consumption speed of sensor nodes in every area can get consistent if deploying nodes by the density formula, therefore system lifetime is prolonged. Analysis and simulation results show that when communication dominates whole energy consumption and the monitored region is big compared with radio range of sensor node, system lifetime under this scheme can be 3R/(2t) times of that under deploying nodes evenly, where R is radius of the monitored region and t is radio range of sensor node.
文摘Abstract Big data has received great attention in research and application. However, most of the current efforts focus on system and application to handle the challenges of ＂volume＂ and ＂velocity＂, and not much has been done on the theoreti- cal foundation and to handle the challenge of ＂variety＂. Based on metric-space indexing and computationalcomplexity the- ory, we propose a parallel computing framework for big data. This framework consists of three components, i.e., universal representation of big data by abstracting various data types into metric space, partitioning of big data based on pair-wise distances in metric space, and parallel computing of big data with the NC-class computing theory.
