Exploring spatial correlation for link quality estimation in wireless sensor networks
The irregularity in quality of wireless communication links poses significant research challenges in wireless sensor network design. Dynamic network conditions and environmental factors make an online, self-adapted link quality estimation mechanism within sensor nodes a necessity for making routing decisions and improving network performance. In this paper, we present a weighted regression algorithm for efficient and accurate estimation of link quality in wireless sensor networks. This algorithm captures the spatial correlation in quality of links between a sensor node and its neighbor nodes, such that the quality of a link to a neighbor node can be estimated based on the quality of links to other nodes geographically close. We evaluate the proposed algorithm using a trace-based simulator which takes into account the variances of link quality over time and spatial locations. The experimental results show that the weighted regression algorithm is able to achieve more accurate estimates than WMEWMA, a state-of-the-art link quality estimator, at a much lower communication cost

title: The D-tree: an index structure for planar point queries in location-based wireless services
abstract: Location-based services (LBSs), considered as a killer application in the wireless data market, provide information based on locations specified in the queries. In this paper, we examine the indexing issue for querying location-dependent data in wireless LBSs; in particular, we focus on an important class of queries, planar point queries. To address the issues of responsiveness, energy consumption, and bandwidth contention in wireless communications, an index has to minimize the search time and maintain a small storage overhead. It is shown that the traditional point-location algorithms and spatial index structures fail to achieve either objective or both. This paper proposes a new index structure, called D-tree, which indexes spatial regions based on the divisions that form the boundaries of the regions. We describe how to construct a binary D-tree index, how to process queries based on the D-tree, and how to page the binary D-tree. Moreover, two parameterized methods for partitioning the original space, called fixed grid assignment (FGA) and adaptive grid assignment (AGA), are proposed to enhance the D-tree. The performance of the D-tree is evaluated using both synthetic and real data sets. Experimental results show that the proposed D-tree outperforms the well-known indexes such as the R/sup */-tree, and that both the FGA and AGA approaches can achieve different performance trade-offs between the index search time and storage overhead by fine-tuning their algorithmic parameters.

title: Search K Nearest Neighbors on Air
abstract: While the K-Nearest-Neighbor (KNN) problem is well studied in the traditional wired, disk-based client-server environment, it has not been tackled in a wireless broadcast environment. In this paper, the problem of organizing location dependent data and answering KNN queries on air are investigated. The linear property of wireless broadcast media and power conserving requirement of mobile devices make this problem particularly interesting and challenging. An efficient data organization, called sorted list, and the corresponding search algorithm are proposed and compared with the well-known spatial index, R-Tree. In addition, we develop an approximate search scope to guide the search at the very beginning of the search process and a learning algorithm to adapt the search scope during the search to improve energy and access efficiency. Simulation based performance evaluation is conducted to compare sorted list and R-Tree. The results show that the utilization of search scope and learning algorithm improves search efficiency of both index mechanisms significantly. While R-Tree is more power efficient when a large number of nearest neighbors is requested, the sorted list has better access efficiency and less power consumption when the number of nearest neighbors is small.

title: Semantic small world: an overlay network for peer-to-peer search
abstract: For a peer-to-peer (P2P) system holding massive amount of data, efficient semantic based search for resources (such as data or services) is a key determinant to its scalability. This work presents the design of an overlay network, namely semantic small world (SSW), that facilitates efficient semantic based search in P2P systems. SSW is based on three innovative ideas: 1) small world network; 2) semantic clustering; 3) dimension reduction. Peers in SSW are clustered according to the semantics of their local data and self-organized as a small world overlay network. To address the maintenance issue of high dimensional overlay networks, a dynamic dimension reduction method, called adaptive space linearization, is used to construct a one-dimensional SSW that supports operations in the high dimensional semantic space. SSW achieves a very competitive trade-off between the search latencies/traffic and maintenance overheads. Through extensive simulations, we show that SSW is much more scalable to very large network sizes and very large numbers of data objects compared to pSearch, the state-of-the-art semantic-based search technique for P2P systems. In addition, SSW is adaptive to distribution of data and locality of interest; is very resilient to failures; and has good load balancing property.

title: Caching Complementary Space for Location-Based Services
abstract: In this paper, we propose a novel client-side, multi-granularity caching scheme, called “Complementary Space Caching” (CS caching), for location-based services in mobile environments. Different from conventional data caching schemes that only cache a portion of dataset, CS caching maintains a global view of the whole dataset. Different portions of this view are cached in varied granularity based on the probabilities of being accessed in the future queries. The data objects with very high access probabilities are cached in the finest granularity, i.e., the data objects themselves. The data objects which are less likely to be accessed in the near future are abstracted and logically cached in the form of complementary regions (CRs) in a coarse granularity. CS caching naturally supports all types of location-based queries. In this paper, we explore several design and system issues of CS caching, including cache memory allocation between objects and CRs, and CR coalescence. We develop algorithms for location-based queries and a cache replacement mechanism. Through an extensive performance evaluation, we show that CS caching is superior to existing caching schemes for location-based services.

title: Energy-Conserving Air Indexes for Nearest Neighbor Search
abstract: A location-based service (LBS) provides information based on the location information specified in a query. Nearest-neighbor (NN) search is an important class of queries supported in LBSs. This paper studies energy-conserving air indexes for NN search in a wireless broadcast environment. Linear access requirement of wireless broadcast weakens the performance of existing search algorithms designed for traditional spatial database. In this paper, we propose a new energy- conserving index, called grid-partition index, which enables a single linear scan of the index for any NN queries. The idea is to partition the search space for NN queries into grid cells and index all the objects that are potential nearest neighbors of a query point in each grid cell. Three grid partition schemes are proposed for the grid-partition index. Performance of the proposed grid-partition indexes and two representative traditional indexes (enhanced for wireless broadcast) is evaluated using both synthetic and real data. The result shows that the grid-partition index substantially outperforms the traditional indexes.

title: A fully distributed spatial index for wireless data broadcast
abstract: To support location-based services in wireless data broadcast systems, a distributed spatial index (called DSI) is proposed in this paper. DSI is highly efficient because it has a linear yet fully distributed structure that naturally facilitates multiple replications of the index by sharing links in different search trees. Search algorithms for point queries, window queries, and kNN queries, based on DSI are presented. Empirical evaluation of DSI are conducted. Result shows that DSI significantly out-performs R-tree and Hilbert curve index, two state-of-the-art spatial indexing techniques for wireless data broadcast.

title: Scalar operand networks
abstract: The bypass paths and multiported register files in microprocessors serve as an implicit interconnect to communicate operand values among pipeline stages and multiple ALUs. Previous superscalar designs implemented this interconnect using centralized structures that do not scale with increasing ILP demands. In search of scalability, recent microprocessor designs in industry and academia exhibit a trend toward distributed resources such as partitioned register files, banked caches, multiple independent compute pipelines, and even multiple program counters. Some of these partitioned microprocessor designs have begun to implement bypassing and operand transport using point-to-point interconnects. We call interconnects optimized for scalar data transport, whether centralized or distributed, scalar operand networks. Although these networks share many of the challenges of multiprocessor networks such as scalability and deadlock avoidance, they have many unique requirements, including ultra-low latency (a few cycles versus tens of cycles) and ultra-fast operation-operand matching. This work discusses the unique properties of scalar operand networks (SONs), examines alternative ways of implementing them, and introduces the AsTrO taxonomy to distinguish between them. It discusses the design of two alternative networks in the context of the Raw microprocessor, and presents timing, area, and energy statistics for a real implementation. The paper also presents a 5-tuple performance model for SONs and analyzes their performance sensitivity to network properties for ILP workloads.

title: Genetic Algorithmic Topology Control for Two-Tiered Wireless Sensor Networks
abstract: This paper proposes an optimized topology control scheme (GA-OTC) based on a genetic algorithm for clustering-based hierarchical wireless sensor networks (WSNs). By using a genetic algorithm, we can obtain optimal solutions to multiple objective functions according to the two criteria of both balanced energy consumption and minimized total energy consumption of cluster heads while the entire WSNs field is covered by clusters. Through performance evaluation studies, we show that GA-OTCachieves desirable properties.

title: A Slicing-based Approach to Enhance Petri Net Reachability Analysis
abstract: Journal of Research and Practice in Information Technology, Vol. 32, No. 2, May 2000 131  General permission to republish, but not for profit, all or part of this material is granted, provided that the JRPIT copyright notice is given and that reference is made to the publication, to its date of issue, and to the fact that reprinting privileges were granted by permission of the Australian Computer Society Inc.

title: Lower bounds on the VC-dimension of smoothly parametrized function classes
abstract: We examine the relationship between the VC-dimension and the number of parameters of a smoothly parametrized function class. We show that the VC-dimension of such a function class is at least k if there exists a k-dimensional differentiable manifold in the parameter space such that each member of the manifold corresponds to a different decision boundary. Using this result, we are able to obtain lower bounds on the VC-dimension proportional to the number of parameters for several function classes including two-layer neural networks with certain smooth activation functions and radial basis functions with a gaussian basis. These lower bounds hold even if the magnitudes of the parameters are restricted to be arbitarily small. In Valiant's probably approximately correct learning framework, this implies that the number of example necessary for learning these function classes is at least linear in the number of parameters.