Project Code : JNW 01

Energy Efficient Reverse Skyline Query Processing over Wireless Sensor Networks

TARGETJ SOLUTIONSREAL TIME PROJECTS IEEE BASED PROJECTSEMBEDDED SYSTEMSPAPER PUBLICATIONSMATLAB PROJECTStargetjsolutions@gmail.com(0)9611582234, (0)9945657526

Abstract:Reverse skyline query plays an important role in many sensing applications, such as environmental monitoring, habitat monitoring, and battlefield monitoring. Due to the limited power supplies of wireless sensor nodes, the existing centralized approaches, which do not consider energy efficiency, cannot be directly applied to the distributed sensor environment. In this paper, we investigate how to process reverse skyline queries energy efficiently in wireless sensor networks. Initially, we theoretically analyzed the properties of reverse skyline query and proposed a skyband-based approach to tackle the problem of reverse skyline query answering over wireless sensor networks. Then, an energy-efficient approach is proposed to minimize the communication cost among sensor nodes of evaluating range reverse skyline query. Moreover, optimization mechanisms to improve the performance of multiple reverse skylines are also discussed. Extensive experiments on both real-world data and synthetic data have demonstrated the efficiency and effectiveness of our proposed approaches with various experimental settings

Introduction AS an important and popular query operator for multiple criteria

decision making, skyline and its variants, such as constrained skyline dynamic skyline and reverse skyline have been applied in many applications. Given a data set P, a traditional skyline retrieves all the points in P that are not dominated by others. A point p1 dominates another point p2, if p1 is not worse than p2 for each dimension I nd p1 is better than p2 for at least one dimension j. Fig. 1a shows an example of traditional skyline in 2D space and points p1, p2, and p6 are the skyline points In addition to traditional skyline, dynamic skyline with respect to qðdenoted as DSðq; PÞÞ has been proposed to retrieve all the points that are not dynamically dominated by others. For the sake of simplicity, we adopt the definition of dynamic attributes proposed in . A point p1 dynamically dominates p2 with respect to qðdenoted as p1 _q p2Þ, if it holds that: 1) jp1½i_ _ q½i_j _ jp2½i_ _ q½i_j, for each dimension i 2 D, and 2) there exists at least one dimension j 2 D, such that jp1½j_ _ q½j_j < jp2½j_ _ q½j_j.

Literature Survey Goal of Project Our goal is to carefully and properly adjust the relation of

semidominance, such that the modified relation becomes transitive (in turn, the query becomes decomposable).

After that, we are able to take the advantage of in-network aggregation techniques to efficiently get the query results. This way, the base station can calculate reverse skyline without introducing false dismissals.

In order to achieve the goal above, we introduce another novel concept, namely full dominance, between any two data points

Existing System:Recently it is found that wireless sensor networks (WSNs) offer a very economic and effective platform to monitor the environment. To satisfy different application demands, we conduct various types of queries over WSNs, for example min, max top-k, and skyline. The de facto limit to processing queries in WSNs is the energy constraint, since sensor nodes are generally battery powered, and in many WSNs (e.g., an unattended and hard-to-reach environment), it is impossible or at least very difficult to change their batteries. Wireless communication is the major energy consumer in WSNs, and therefore, if we can reduce the amount of communication during query processing, the energy consumption can be significantly reduced and the lifetime of the WSNs as a whole can be prolonged.

Dis-AdvantageAlmost all existing work has focused on

reducing the communication cost for a specific type of query.

As one of the important operators in WSN applications, the energy-efficiency of RS query processing also needs to be studied in depth.

Therefore, in this work, we study energy-efficient approaches to answer reverse skyline (RS) queries over WSNs

Proposed System:We propose an energy-efficient approach to evaluate reverse skyline query in WSNs, based on the full skyband which contains all necessary information for the base station to reconstruct the reverse skyline. Then, the transmission of nonfull skyline points is avoided by pointing out which full skyline points belong to the reverse skyline. Furthermore, the proposed approaches are also extended to support range reverse skyline and multiple reverse skyline queries. 2. We theoretically analyze the relationship between reverse skylines on different dimensional spaces or query ranges, and propose two optimization mechanisms, vertical and horizontal optimizations, to improve the performance of multiple reverse skyline evaluation in WSNs. 3. Lastly, our extensive experimental studies using both real-world data and synthetic data show that the proposed approach can significantly reduce the communication cost among sensor nodes and save the energy consumption during the evaluation of RS queries in WSNs

Advantages of Proposed System:1. it is impossible or at least very difficult to

change their batteries. Wireless communication is the major energy consumer in WSNs, and therefore,

if we can reduce the amount of communication during query processing, the energy consumption can be significantly reduced and the lifetime of the WSNs as a whole can be prolonged

Requirement Analysis:Operating System : Windows XP professional

Front End : Microsoft Visual Studio .Net 2005Language : Visual C#.Net

Back End : SQL Server 2000

Software Requirements :

Processor : Pentium III / IVHard Disk : 40 GB

Ram : 256 MB

Monitor : 15VGA ColorMouse : Ball /

OpticalKeyboard : 102

Keys

Hardware Requirements :

System Design Details User & Admin login

Access the database continuously

Due to continuous access & more user access DB reaches critical limit

Sensor 1 will give alert signal for critical limit

Sensor 2 will continuously monitor the critical limit point

MODULE DECRIPTION:Reverse Skyline Query: In all kind of applications, different users may have different

preferences, and it is quite common to have multiple queries in different subspaces which are posed into the WSN (Wireless Sensor Networks) simultaneously to gather interesting information. Skyline queries over databases have many important applications such as sensor data monitoring and business planning. Skyline queries accurately and efficiently over data has become increasingly important. Reverse skyline queries, which retrieve a set of objects whose dynamic skyline contains a given query point, are useful and valuable for many applications such as business location and environmental monitoring applications. Though there are several methods for processing reverse skyline queries, they are based on pre-processing. Since they waste time and space to pre-compute necessary data and to manage the

pre-computed data, they are not feasible for some applications. 

Optimization:We implement two kinds of Optimization here

one is Vertical Optimization and another one is Horizontal Optimization.

Vertical Optimization:The Vertical Optimization technique is used

for multiple queries by using the relationship between reverse skylines with respect to different subspaces but the same query range.

Horizontal Optimization:The Horizontal Optimization technique is

used for the relationship between reverse skylines with the same subspace but different query ranges.

Trace the Database by using Sensors:The two sensors have been implemented to monitor

the entire database activities.Out of this two sensors one sensor will monitor the

entire databases, in real time applications simultaneously more number of users will access the databases, due to this problem there might have a chance for any database to get its critical point, to know the critical point alert one sensor is continuously monitoring the database. Once any database reaches the critical point it will give alert signal. The role of another sensor is to monitor the critical point limit databases due to this we can avoid major problems occurs in databases .