A large-scale resource sharing system (e.g., collaborative cloud computing and grid computing) creates a virtual supercomputer by providing an infrastructure for sharing tremendous amounts of resources (e.g., computing, storage, and data) distributed over the Internet. A resource information service, which collects resource data and provides resource search functionality for locating desired resources, is a crucial component of the resource sharing system. In addition to resource discovery speed and cost (i.e., efficiency), the ability to accurately locate all satisfying resources (i.e., fidelity) is also an important metric for evaluating service quality. Previously, a number of resource information service systems have been proposed based on Distributed Hash Tables (DHTs) that offer scalable key-based lookup functions. However, these systems either achieve high fidelity at low efficiency, or high efficiency at low fidelity. Moreover, some systems have limited flexibility by only providing exact-matching services or by describing a resource using a pre-defined list of attributes. This paper presents a resource information service that offers high efficiency and fidelity without restricting resource expressiveness, while also providing a similar-matching service. Extensive simulation and PlanetLab experimental results show that the proposed service outperforms other services in terms of efficiency, fidelity, and flexibility; it dramatically reduces overhead and yields significant enhancements in efficiency and fidelity.

Wireless sensor networks can monitor physical and environmental conditions inexpensively, and the networks have been widely used in military and civilian applications. The sensor nodes in the networks not only have the ability to sense, but also can supply data storage, computation and communicate with each other. However, energy efficiency is the major concern in wireless sensor networks, which usually rely on battery for energy source. Centralized data storage and query processing schemes lead to high energy consumption. This paper proposes a distributed data storage scheme (DDS) that disseminates related event data in a neighborhood in wireless sensor networks. Besides offering data query according to event names, DDS also provides distributed querying. Experimental results show that the DDS scheme yields higher efficiency and effectiveness than the centralized scheme.

Since centralized data storage and search schemes often lead to high overhead and latency, distributed data centric storage becomes a preferable approach in large-scale WSNs. However, most of existing methods lack optimization for spatial- temporal search and similarity search for multi-attribute data. Some methods are optimized under circumstances where nodes are equipped with locating systems (e.g., GPS) which consumes high energy. This paper proposes a distributed spatial-temporal similarity data storage scheme (SDS). It disseminates event data in such a way that the distance between WSN neighborhoods represents the similarity of data stored in them. In addition, SDS carpooling routing algorithm efficiently routes messages without the aid of a locating system. SDS provides efficient spatial- temporal and similarity data searching service. Experimental results show that SDS yields significant improvements on the efficiency of data querying compared with existing approaches.

Internet-based distributed systems enable globally scattered resources to be collectively pooled and used in a cooperative manner to achieve unprecedented petascale super computing capabilities. Numerous resource discovery approaches have been proposed to help achieve this goal. To report or discover a multi-attribute resource, most approaches use multiple messages with each message for an attribute, leading to high overhead. Anther approach can reduce multi-attribute to one index, but it is not practically effective in an environment with a large number of different resource attributes. Furthermore, few approaches are able to locate resources geographically close to the requesters, which is critical to system performance. This paper presents a P2P-based intelligent resource discovery (PIRD) mechanism that weaves all attributes into a set of indices using locality sensitive hashing, and then maps the indices to a structured P2P. It further incorporates Lempel-Ziv-Welch algorithm to compress attribute information for higher efficiency. In addition, it helps to search resources geographically close to requesters by relying on a hierarchical P2P structure. PIRD significantly reduces overhead and improves the efficiency and effectiveness of resource discovery. Theoretical analysis and simulation results demonstrate the efficiency of PIRD in comparison with other approaches. It dramatically reduces overhead and yields significant improvements on the efficiency of resource discovery.