WSN Multidimensional Complex Query Processing Analysis And Verification

Wireless Sensor Network is a new information accessing and processing technology,which is widely used in military and civil fields.An tiny sensor node contains various perceptive components,it can generate multi-dimensional attributes continuously.On one hand,with the depth of applications,user's queries become more complex and diverse,traditional simple queries can't meet the needs of users.So the multi-dimensional complex query can better support data analysis and user decision-making.On the other hand,hardware improvement makes the increase of sensor's computing power,communications power and storage capacity.It provide the precondition for the multi-dimensional complex queries.However,due to the characteristics of wireless sensor networks with limited resources and complex semantics,multi-dimensional complex query in WSNs is challenging.The paper focuses on multi-dimensional query processing in Wireless Sensor Networks and summarizes the research status of this area.We select two types of multi-dimensional complex queries,which are multi-dimensional top-k query based user's preference and k-dominated skyline query,and study energy-efficient algorithms of them.There are many users in the large-scale wireless sensor applications,different user has different preferences on each attribute,so that each users define distinct top-k scoring functions according to their preferences.When there are many different top-k queries in the network,processing each query as a separate query will cause duplicate data transmission,and consume network resources.We introduce multi-dimensional top-k query based user's preference into wireless sensor networks,which can returns k nodes with highest value of different users' preferences in the entire networks.In order to save the energy consumption of networks,we deeply analyze the query semantic,explore the in-network filtering condition and propose an in-network query processing algorithm based on grid index and a continuous query algorithm by global information.The experimental results on synthetic data and real data sets demonstrate the algorithm can significantly reduce the data transmission and extend the network life.Skyline query can be used for multi-objective decision and attract widespread attentions.But with the increase of data dimensions the skyline results size grows exponentially,it is not suitable for the sensor networks with limited resources.We study a kind of skyline query with the relax dominating condition,which is called k-dominating skyline query.It can return the data object not dominated by other points in any K dimensions.K-dominating skyline can not only greatly reduce the size of the result set,but also return more important and meaningful decision-making information.Based on the in-network filtering,we propose two continuous query algorithms based space partition and hierarchical filter.Two algorithms inhibit the forwarding of network data from different aspects to save energy.Through the network simulation experiments on three different distributions of data sets,we validate the effectiveness of the proposed algorithms.