Alexandru Coman

A framework for spatio-temporal query processing over wireless sensor networks

Wireless sensor networks consist of nodes with the ability to measure, store, and process data, as well as to communicate wirelessly with nodes located in their wireless range. Users can issue queries over the network, e.g., retrieve information from nodes within a specified region, in applications such as environmental monitoring. Since the sensors have typically only a limited power supply, energy-efficient processing of the queries over the network is an important issue. In this paper, we introduce a general framework for distributed processing of spatio-temporal queries in a sensor network that has two main phases: (1) routing the query to the spatial area specified in the query; (2) collecting and processing the information from the nodes relevant to the query. Within this framework, different algorithms can be designed independently for each of the two phases. We also propose novel algorithms for this framework, one for the first phase and two for the second phase. In an extensive experimental evaluation we study the performance of these algorithms in terms of energy consumption, under varying conditions. The results allow us to recommend the most energy efficient solution, given a network and a spatiotemporal query.

Associative Classifiers for Medical Images

This paper presents two classification systems for medical images based on association rule mining. The system we propose consists of: a pre-processing phase, a phase for mining the resulted transactional database, and a final phase to organize the resulted association rules in a classification model. The experimental results show that the method performs well, reaching over 80% in accuracy. Moreover, this paper illustrates how important the data cleaning phase is in building an accurate data mining architecture for image classification.

An Analysis of Spatio-Temporal Query Processing in Sensor Networks

Sensor networks are an emerging technology that provide new means to monitor and understand various phenomena. Nodes in a sensor network typically have a limited power supply, thus energy-efficient processing of the queries over the network is a critical issue. We propose analytical models to evaluate the performance of three methods for processing historical spatio-temporal queries in sensor networks. The models allow us to better understand the tradeoffs of the investigated methods, as well as to recommend the most energy efficient one at query time.

On Join Location in Sensor Networks

We consider the problem of processing join queries in a wireless sensor network, focusing on where (which sensor node(s)) to process the join. We propose four strategies for processing such queries and investigate their performance across several scenarios. Not surprisingly, our experiments show that no single strategy performs best for all scenarios. In order to avoid the potential high cost of using a fixed strategy for processing all queries, we develop a cost-based model that can be used to select the best join strategy for the query at hand. Our experiments confirm that, given a set of queries, selecting the join strategy based on the cost model is always better than using any fixed strategy for all queries.

Exploiting redundancy in sensor networks for energy efficient processing of spatiotemporal region queries

Sensor networks are made of autonomous devices that are able to collect, store, process and share data with other devices. Spatiotemporal region queries can be used for retrieving information of interest from such networks. Such queries require the answers only from the subset of the network nodes that fall into the query region. If the network is redundant in the sense that the measurements of some nodes can be substituted by those of other nodes with a certain degree of confidence, then a much smaller subset of nodes may be sufficient to answer the query at a lower energy cost. We investigate how to take advantage of such data redundancy and propose two techniques to process spatiotemporal region queries under these conditions. Our techniques reduce up to twenty times the energy cost of query processing compared to the typical network flooding, thus prolonging the lifetime of the sensor network.

A Distributed Algorithm for Joins in Sensor Networks

Given their autonomy, flexibility and large range of functionality, wireless sensor networks can be used as an effective and discrete means for monitoring data in many domains. Typical sensor nodes are very constrained, in particular regarding their energy and memory resources. Thus, any query processing solution over these devices should consider their limitations. We investigate the problem of processing join queries within a sensor network. Due to the limited memory at nodes, joins are typically processed in a distributed manner over a set of nodes. Previous approaches have either assumed that the join processing nodes have sufficient memory to buffer the subset of the join relations assigned to them, or that the amount of available memory at nodes is known in advance. These assumptions are not realistic for most scenarios. In this context we propose and investigate DIJ, a distributed algorithm for join processing that considers the memory limitations at nodes and does not make a priori assumptions on the available memory at the processing nodes. At the same time, our algorithm still aims at minimizing the energy cost of query processing.

Adaptive processing of historical spatial range queries in peer-to-peer sensor networks

Abstract We investigate the problem of processing historical queries on a sensor network. Since data is considered to have been already collected at the sensor nodes, the main issue is exploring the spatial component of the query in order to minimize its cost represented by the energy consumption. We assume queries can be issued at any network node, i.e., there is no central base station and all nodes have only local knowledge of the network. On the one hand, a globally optimum query processing plan is desirable but its construction is not possible due to the lack of global knowledge of the network. On the other hand, while a simple network flooding is feasible, it is not a practical choice from a cost perspective. To address this problem we propose a two-phase query processing strategy, where in the first phase a path from the query originator to the query region is found and in the second phase the query is processed within the query region itself. This strategy is supported by analytical models that are used to dynamically select the best processing strategy depending on the query specifics. Our extensive analytical and experimental results show that our analytical models are accurate and that the two-phase strategy is better suited for small to medium sized queries, being up to 10 times more cost effective than a typical network flooding. In addition, the dynamic selection of a query processing technique proved itself capable of always delivering at least as good performance as the most energy efficient strategy for all query sizes.

Similarity Search and Dimensionality Reduction: Not All Dimensions Are Equally Useful

Indexing high-dimensional data is a well known problem. Techniques for dimensionality reduction which map D-dimensional objects onto a d-dimensional space (d ≪ D) are often used to speedup similarity queries. In this paper we show that one can further improve query performance by initially overestimating the reduction, i.e., reducing the dimensionality of the space to D′ dimensions, where d < D′ < D, and, at query time, automatically choosing only d′, where d′ < d, dimensions to be used – that is, using only a few good dimensions after the initial reduction of the dimensionality. By incorporating this idea within a recently proposed technique, we can process range queries up to three times faster at the expense of limited storage overhead.

An Analysis of Join Processing in Sensor Networks

Wireless sensor networks have received much attention recently. Given their autonomy, flexibility and large range of functionality, they can be used as an effective and discrete means for monitoring data in many domains. Typically the network autonomy implies a limited and relatively small amount of energy for its operation. Hence, an important challenge they pose is how to process queries, i.e., manage and communicate data, in an energy-efficient manner within the network. In this paper we consider the problem of how to process join queries in a wireless sensor network. Unlike other types of queries, join queries have received little attention in the literature, despite their importance. We propose a few strategies for processing join queries, focusing on where (which sensor node(s)) to process data, and investigate their performance across several scenarios. Not surprisingly, our experiments show that no single strategy can be considered competitive for all scenarios. In order to avoid the potential high cost of using a fixed strategy for processing all queries, we develop a cost-based model that can be used to select the best join strategy for the query at hand. Our results confirm that, given a set of queries, selecting the join strategy based on the cost model is always better than using any fixed strategy for all queries.

Efficient Indexing of High Dimensional Normalized Histograms

This paper addresses the problem of indexing high dimensional normalized histogram data, i.e., D-dimensional feature vectors H where Σ D i=1 H i = 1. These are often used as representations for multimedia objects in order to facilitate similarity query processing. By analyzing properties that are induced by the above constraint and that do not hold in general multi-dimensional spaces we design a new split policy. We show that the performance of similarity queries for normalized histogram data can be significantly improved by exploiting such properties within a simple indexing framework. We are able to process nearest-neighbor queries up to 10 times faster than the SR-tree and 3 times faster than the A-tree.