Jinfeng Ni

Efficient key establishment for group-based wireless sensor deployments

Establishing pairwise keys for each pair of neighboring sensors is the first concern in securing communication in sensor networks. This task is challenging because resources are limited. Several random key predistribution schemes have been proposed, but they are appropriate only when sensors are uniformly distributed with high density. These schemes also suffer from a dramatic degradation of security when the number of compromised sensors exceeds a threshold. In this paper, we present a group-based key predistribution scheme, GKE, which enables any pair of neighboring sensors to establish a unique pairwise key, regardless of sensor density or distribution. Since pairwise keys are unique, security in GKE degrades gracefully as the number of compromised nodes increases. In addition, GKE is very efficient since it requires only localized communication to establish pairwise keys, thus significantly reducing the communication overhead. Our security analysis and performance evaluation illustrate the superiority of GKE in terms of resilience, connectivity, communication overhead and memory requirement.

Indexing Spatio-Temporal Trajectories with Efficient Polynomial Approximations

Complex queries on trajectory data are increasingly common in applications involving moving objects. MBR or grid-cell approximations on trajectories perform suboptimally since they do not capture the smoothness and lack of internal area of trajectories. We describe a parametric space indexing method for historical trajectory data, approximating a sequence of movement functions with single continuous polynomial. Our approach works well, yielding much finer approximation quality than MBRs. We present the PA-tree, a parametric index that uses this method, and show through extensive experiments that PA-trees have excellent performance for offline and online spatio-temporal range queries. Compared to MVR-trees, PA-trees are an order of magnitude faster to construct and incur I/O cost for spatio-temporal range queries lower by a factor of 2-4. SETI is faster than our method for index construction and timestamp queries, but incurs twice the I/O cost for time interval queries, which are much more expensive and are the bottleneck in online processing. Therefore, the PA-tree is an excellent choice for both offline and online processing of historical trajectories

Supporting Secure Communication and Data Collection in Mobile Sensor Networks

Sensor deployments may be static, but researchers have recently been making a case for mobile collector nodes to enhance data acquisition. Since mobile nodes are often more privileged, their compromise can give the adversary a significant advantage. Hence, security mechanisms for such networks must tolerate mobile node compromises. Unlike static sensors, which communicate mostly with their neighbors, mobile nodes may communicate with nodes all over the network. Hence, key establishment is a much harder challenge with mobile nodes. We first analyze the impact of mobile collector compromises on the reliability of data received by the base station, and the circumstances under which reliability can be guaranteed. Second, we present mGKE, a key predistribution scheme for very general group-based sensor deployments. mGKE allows any pair of neighboring sensors to establish a unique pairwise key, regardless of sensor density or distribution. It is also usable by mobile collectors. Our analysis and evaluation show the superiority of mGKE over current methods in terms of resilience, connectivity, communication overhead, and memory requirements.

Uncertain spatial data handling: Modeling, indexing and query

Managing and manipulating uncertainty in spatial databases are important problems for various practical applications of geographic information systems. Unlike the traditional fuzzy approaches in relational databases, in this paper a probability-based method to model and index uncertain spatial data is proposed. In this scheme, each object is represented by a probability density function (PDF) and a general measure is proposed for measuring similarity between the objects. To index objects, an optimized Gaussian mixture hierarchy (OGMH) is designed to support both certain/uncertain data and certain/uncertain queries. An uncertain R-tree is designed with two query filtering schemes, UR1 and UR2, for the special case when the query is certain. By performing a comprehensive comparison among OGMH, UR1, UR2 and a standard R-tree on US Census Bureau TIGER/Line^(R) Southern California landmark point dataset, it is found that UR1 is the best for certain queries. As an example of uncertain query support OGMH is applied to the Mojave Desert endangered species protection real dataset. It is found that OGMH provides more selective, efficient and flexible search than the results provided by the existing trial and error approach for endangered species habitat search. Details of the experiments are given and discussed.

Probabilistic Spatial Database Operations

Spatial databases typically assume that the positional attributes of spatial objects are precisely known. In practice, however, they are known only approximately, with the error depending on the nature of the measurement and the source of data. In this paper, we address the problem how to perform spatial database operations in the presence of uncertainty. We first discuss a probabilistic spatial data model to represent the positional uncertainty. We then present a method for performing the probabilistic spatial join operations, which, given two uncertain data sets, find all pairs of polygons whose probability of overlap is larger than a given threshold. This method uses an R-tree based probabilistic index structure (PrR-tree) to support probabilistic filtering, and an efficient algorithm to compute the intersection probability between two uncertain polygons for the refinement step. Our experiments show that our method achieves higher accuracy than methods based on traditional spatial joins, while reducing overall cost by a factor of more than two.

Pointwise-Dense Region Queries in Spatio-temporal Databases

Applications such as traffic management and resource scheduling for location-based services commonly need to identify regions with high concentrations of moving objects. Such queries are called dense region queries in spatio-temporal databases, and desire regions in which the density of moving objects exceeds a given threshold. Current methods for addressing this important class of queries suffer from several drawbacks. For example, they may fail to find all dense regions, provide ambiguous answers, impose restrictions on size, or lack a notion of local density. We address these issues in this paper, starting with a new definition of dense regions. We show that we are able to answer dense region queries completely and uniquely using this definition. Dense regions in our approach may have arbitrary shape and size, as well as local density guarantees. We present two methods, the first, an exact method, and the second, an approximate method. We demonstrate through extensive experiments that our exact method is efficient and is superior to current approaches. Our approximate method runs orders of magnitude faster than our exact method, at the cost of a tolerable loss of accuracy.

PA-tree: a parametric indexing scheme for spatio-temporal trajectories

Many new applications involving moving objects require the collection and querying of trajectory data, so efficient indexing methods are needed to support complex spatio-temporal queries on such data. Current work in this domain has used MBRs to approximate trajectories, which fail to capture some basic properties of trajectories, including smoothness and lack of internal area. This mismatch leads to poor pruning when such indices are used. In this work, we revisit the issue of using parametric space indexing for historical trajectory data. We approximate a sequence of movement functions with single continuous polynomial. Since trajectories tend to be smooth, our approximations work well and yield much finer approximation quality than MBRs. We present the PA-tree, a parametric index that uses this new approximation method. Experiments show that PA-tree construction costs are orders of magnitude lower than that of competing methods. Further, for spatio-temporal range queries, MBR-based methods require 20%–60% more I/O than PA-trees with clustered indicies, and 300%–400% more I/O than PA-trees with non-clustered indicies.

Dealing with random and selective attacks in wireless sensor systems

We present a framework for analyzing the effects of random and selective compromises (using order statistics) in sensor networks. We discuss the problem of ensuring data integrity at the source and during transit in sensor networks, and present an analysis of the reliability of reports from mobile collectors. No analysis has appeared in the literature of source integrity for mobile nodes, or of selective attacks in sensor networks. We address transit data integrity by presenting mGKE, a key establishment scheme for general group-based sensor deployments, and present a detailed analytical and experimental comparison of mGKE with current schemes. mGKE outperforms current methods in terms of resilience, connectivity, and memory and communication overhead.

Short Paper: GKE: Efficient Group-based Key Establishment for Large Sensor Networks

We present a group-based key predistribution scheme, GKE, which enables all pairs of neighboring sensors to establish a unique pairwise key, regardless of sensor density or distribution. Since pairwise keys are unique, security in GKE degrades gracefully as the number of compromised nodes increases. In addition, GKE is very efficient since it requires only localized communication to establish pairwise keys, significantly reducing communication overheads. Our security analysis and performance evaluation show that GKE performs very well in terms of resilience, connectivity, communication overhead and memory requirements

Efficient data dissemination using locale covers

Location-dependent data are central to many emerging applications, ranging from traffic information services to sensor networks. The standard pull- and push-based data dissemination models become unworkable since the data volumes and number of clients are high. We address this problem using locale covers, a subset of the original set of locations of interest, chosen to include at least one location in a suitably defined neighborhood of any client. Since location-dependent values are highly correlated with location, a query can be answered using a location close to the query point. Typical closeness measures might be Euclidean distance, or a k-nearest neighbor criterion. We show that location-dependent queries may be answered satisfactorily using locale covers. Our approach is independent of locations and speeds of clients, and is applicable to mobile clients. We also introduce a nested locale cover scheme that ensures fair access latencies, and allows clients to refine the accuracy of their information over time. We also prove two important results: one regarding the greedy algorithm for sensor covers and the other pertaining to randomized locale covers for k-nearest neighbor queries.