PERVASIVE computing 75 80 PERVASIVE computing Presentation Input sensor Coordinator Model Figure 4. Application framework infrastructure. The coordinator oversees the composition of the model, presentation, and controller components.82 PERVASIVE computing PERVASIVE computing 83 the AUTHORS Manuel Román is a PhD candidate at the University of Illinois at Urbana-Champaign. His research interests include ubiquitous computing, middleware, operating systems, and interactive and programmable active spaces. He received his BS and MS in computer science from the La Salle School of Engineering (Ramon Llull Univ.).

Abstract-Providing efficient data aggregation while preserving data privacy is a challenging problem in wireless sensor networks research. In this paper, we present two privacy-preserving data aggregation schemes for additive aggregation functions. The first scheme -Cluster-based Private Data Aggregation (CPDA)-leverages clustering protocol and algebraic properties of polynomials. It has the advantage of incurring less communication overhead. The second scheme -Slice-Mix-AggRegaTe (SMART)-builds on slicing techniques and the associative property of addition. It has the advantage of incurring less computation overhead. The goal of our work is to bridge the gap between collaborative data collection by wireless sensor networks and data privacy. We assess the two schemes by privacy-preservation efficacy, communication overhead, and data aggregation accuracy. We present simulation results of our schemes and compare their performance to a typical data aggregation scheme -TAG, where no data privacy protection is provided. Results show the efficacy and efficiency of our schemes. To the best of our knowledge, this paper is among the first on privacy-preserving data aggregation in wireless sensor networks.

The realization of end-to-end quality of service (QoS) guarantees in emerging network-based applications requires mechanisms that support first dynamic discovery and then advance or immediate reservation of resources that will often be heterogeneous in type and implementation and independently controlled and administered. We propose the Globus Architecture for Reservation and Allocation (GARA) to address these four issues. GARA treats both reservations and computational elements such as processes, network flows, and memory blocks as first class entities, allowing them to be created, monitored, and managed independently and uniformly. It simplifies management of heterogeneous resource types by defining uniform mechanisms for computers, networks, disk, memory, and other resources. Layering on these standard mechanisms, GARA enables the construction of application-level co-reservation and coallocation libraries that applications can use to dynamically assemble collections of resources, guided by both application QoS requirements and the local administration policy of individual resources. We describe a prototype GARA implementation that supports three different resource typesparallel computers, individual CPUs under control of the Dynamic Soft Real-Time scheduler, and Integrated Services networks-and provide performance results that quantify the costs of our techniques.

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