ENMSJ : An Efficiency Filtering Technique using Bitmap Vectors for n-way Joins in Wireless Sensor Networks

In wireless sensor networks, join queries execution introduces a high energy consumption. While energy is an important factor for sensors survival, several techniques were developed to reduce it.  Sensors energy is affected by the number of transferred messages whereas query is performed. The aim of the proposed techniques was then to decrease the communicated data volume.  So, the exchanged data volume is soaring when joins are performed between many data tables. This joins type is called: n-way join query.  In this paper, we present an efficiency technique to treat n-way join queries in wireless sensor networks. This technique is named: Enhanced N-way Mediated Semi-Join (ENMSJ). ENMSJ is an improvement of a precedent strategie that we proposed: N-way Mediated Semi-Join (NMSJ). ENMSJ uses bitmap tables to more reduce transferred messages quantity.  We compared the two techniques to test their performance. Obtained results are very hopeful.

Deterministic Sampling and Range Counting in Geometric Data Streams

We present memory-efficient deterministic algorithms for constructing epsilon-nets and epsilon-approximations of streams of geometric data. Unlike probabilistic approaches, these deterministic samples provide guaranteed bounds on their approximation factors. We show how our deterministic samples can be used to answer approximate online iceberg geometric queries on data streams. We use these techniques to approximate several robust statistics of geometric data streams, including Tukey depth, simplicial depth, regression depth, the Thiel-Sen estimator, and the least median of squares. Our algorithms use only a polylogarithmic amount of memory, provided the desired approximation factors are inverse-polylogarithmic. We also include a lower bound for non-iceberg geometric queries.Comment: 12 pages, 1 figur

14-08 Big Data Analytics to Aid Developing Livable Communities

In transportation, ubiquitous deployment of low-cost sensors combined with powerful computer hardware and high-speed network makes big data available. USDOT defines big data research in transportation as a number of advanced techniques applied to the capture, management and analysis of very large and diverse volumes of data. Data in transportation are usually well organized into tables and are characterized by relatively low dimensionality and yet huge numbers of records. Therefore, big data research in transportation has unique challenges on how to effectively process huge amounts of data records and data streams. The purpose of this study is to conduct research on the problems caused by large data volume and data streams and to develop applications for data analysis in transportation. To process large number of records efficiently, we have proposed to aggregate the data at multiple resolutions and to explore the data at various resolutions to balance between accuracy and speed. Techniques and algorithms in statistical analysis and data visualization have been developed for efficient data analytics using multiresolution data aggregation. Results will be helpful in setting up a primitive stage towards a rigorous framework for general analytical processing of big data in transportation

A filtering technique for n-way stream joins in wireless sensors networks

Purpose ”“ The join operations between data streams need more time and request more energy than traditional joins. In wireless sensor networks, energy is a critical factor. The survival of the network depends on this energy, thus it is necessary to consider, for this type of queries in such networks, the reduction of the sensors’ energy consumption. While works that have been done to treat n-way join operations between data streams are rare so far, we propose a technique, named NSLSJ (N-way Stream Local Semi-Join) to perform this type of join operations. The principal aim is to considerably reduce the consumed energy. Methodology/approach/design ”“ The technique 'N-way Stream Local Semi-Join (NSLSJ) proposed in this paper is based on an in-network execution, and on filtering tuples strategy for an important gain in energy. Findings ”“ Compared to NSLJ and Sens-Join techniques, NSLSJ shows better performances in the realized tests as it consumes less energy.Purpose ”“ The join operations between data streams need more time and request more energy than traditional joins. In wireless sensor networks, energy is a critical factor. The survival of the network depends on this energy, thus it is necessary to consider, for this type of queries in such networks, the reduction of the sensors’ energy consumption. While works that have been done to treat n-way join operations between data streams are rare so far, we propose a technique, named NSLSJ (N-way Stream Local Semi-Join) to perform this type of join operations. The principal aim is to considerably reduce the consumed energy. Methodology/approach/design ”“ The technique 'N-way Stream Local Semi-Join (NSLSJ) proposed in this paper is based on an in-network execution, and on filtering tuples strategy for an important gain in energy. Findings ”“ Compared to NSLJ and Sens-Join techniques, NSLSJ shows better performances in the realized tests as it consumes less energy.Purpose ”“ The join operations between data streams need more time and request more energy than traditional joins. In wireless sensor networks, energy is a critical factor. The survival of the network depends on this energy, thus it is necessary to consider, for this type of queries in such networks, the reduction of the sensors’ energy consumption. While works that have been done to treat n-way join operations between data streams are rare so far, we propose a technique, named NSLSJ (N-way Stream Local Semi-Join) to perform this type of join operations. The principal aim is to considerably reduce the consumed energy. Methodology/approach/design ”“ The technique 'N-way Stream Local Semi-Join (NSLSJ) proposed in this paper is based on an in-network execution, and on filtering tuples strategy for an important gain in energy. Findings ”“ Compared to NSLJ and Sens-Join techniques, NSLSJ shows better performances in the realized tests as it consumes less energy

Comparative Evaluation of Techniques for n-way Stream Joins in Wireless Sensor Networks

In wireless sensor networks, sensor data are accessed using relational queries. Join queries are commonly used to retrieve the data from multiple tables stored in different parts of a wireless sensor network. However, such queries require large amounts of energy. Many studies have intended to reduce query energy consumption. However, most of the proposed techniques addressed binary joins which are performed between static tables. N-way joins between data streams were rarely considered. Join queries using data streams work continuously and require increasing energy, which is why n-way joins involving several tables consume so much energy. Thus, the challenge lies in reducing energy dissipation. Additionally, it is necessary to determine the appropriate execution order for an n-way join. The number of possible implementations of an n-way join grows exponentially with the tables’ number. In this paper, interesting approaches for n-way joins between streams of data are evaluated. The methods that have been compared are extern-join, Sens-join of Stern et al, and the two techniques NSLJ (N-way Stream Local Join) and NSLSJ (N-way Stream Local Semi-Join). Comparisons are conducted according to several parameters to determine which use case is appropriate for each technique. NSLSJ works best for join queries with low join selectivity factors, while extern-join is more suitable for queries with very high selectivity factors