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Wireless sensor networks (WSNs) are envisioned to consist of hundreds to thousands of wireless sensor nodes. The operator doesn't interest in the data sensed by a specific sensor node generally, on the contrary, he pays more attention to the data gathered from a specific area in granted time. One crucial problem is how to process the great deal of data and respond to the query request. We propose a novel query processing based data attributes, called Spatio-Temporal Attributes R-tree based Query (STARQ). Consider the similarity of data collected by a sensor node and its neighboring nodes, partial clustering algorithm is used to form a storage cluster. Partial clustering algorithm is implemented in two phases. First phase is the beginning of partial clustering, in which an object occurs. In second phase, a certain node (e.g., resumes from failure) senses an existed object. The method provided in this paper aims to obtain the neighboring nodes firstly, and judges whether existing a storage cluster that conforms to metadata of the sensor node or not. If the relevant storage cluster does not exist, first phase works, otherwise second phase. If failed in first phase, partial clustering algorithm is called again after a random time. If there are more than one relevant storage cluster in second phase, exercises a sort algorithm which is in descending order according to the storage node's capability weight, and tries to join a storage cluster in turn. R-tree [ 1 ] is an approximately balanced search tree that is widely used to handle spatial data in traditional database systems. Motivated by the unique characteristic of R-tree, a Saptio-Temporal Attributes R-tree (STAR) is built on the top of storage clusters. Objects in STAR are not restricted to the geographical rectangles and could be any abstract ranges of arbitrary attributes. A top-down approach that achieves energy efficiency is adopted to locate the corresponding storage nodes, which transmit the relevant data back to the operator. We compare STARQ with Directed Diffusion [ 2 ] and GHT [ 3 ] in NS-2. To measure the performance of these protocols, we consider two metrics: interval of query and the size of network. The simulation results show that STARQ has better performance with different query intervals and network size.

In this paper, a Reliable-Multicast (RM) based static deployment system for cluster is designed which can be functionally divided into three parts: image capture, image transference, and node configuration. Image capture takes the snapshot of the operating system in the source node. Image transference distributes the captured image to target nodes. Node configuration mainly finishes the configuration of some identified information to target nodes. Applying the technology of Image-Based Installation [ 1 ] and an idea of separating the operating system image from service software, this system solves the tedious problems involved when the large-scale cluster is divided into a number of functional pools; using the network transmit model of RM, this system reduces the cost of network bandwidth and significantly improves the efficiency in deployment; combining WOL [ 2 ] and PXE [ 3 ], the system provides the function of single control point and lightens the workload of the system administrator. So this system can solve the problems of current cluster deployment mechanisms, such as inefficiency, imperfection of automated node configuration, and only supporting the Linux-based operating system deployment, etc. The deployment system consists of the deployment server, image servers, and target nodes. The deployment server is the core of the entire deployment system, which provides DHCP and TFTP service to ensure that the deployment control program can finish the deployment of target nodes after the cluster nodes start up from the NIC. Image servers mainly store the images and applications which can be accessed by the deployment server or target nodes by the NFS service. Target nodes are the nodes which need to be deployed in the cluster. Before the deployment process, the image of the source node and the service environment of the deployment server and image server should be prepared. During the deployment, the target node communicates with the deployment server by using PXE and WOL, and then the deployment server deploys the corresponding image and application service to the target nodes through the RM method. Finally, the target nodes finish their configurations. In the same experimental environment, some experiments respectively for this system, clonesys, and a deployment system identified by X have been built. As proved in experiments, this system can build a cluster more quickly and efficiently.