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\title{A Novel Method of Violated Constraint Prediction with Modified Spatial Analysis based Fuzzy Sorting}
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\begin{document}

             \author[1]{  K.Nithiya}

             \author[2]{A.Vinoth  Kannan}

             \affil[1]{  IFET college of engineering}

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\date{\small \em Received: 11 December 2013 Accepted: 3 January 2014 Published: 15 January 2014}

\maketitle


\begin{abstract}
        


Mobility Prediction of a Moving Node and Network Delay is an important performance characteristic of a wireless network. The Data delivery Delay of a network specifies how long it takes for a data to travel across the network from one node or endpoint to another. It is typically measured in multiples or fractions of seconds. The work presented here belongs to domain of data mining cum wireless network , the Real Time Early Prediction of network delay based on mobility is done using the proposed spatial analysis for constraint violation prediction method. A New application is presented concerning the Delivery delays of UDP packets in GPRS network. The GPS points that are collected from GPS module is analyzed using proposed spatial analysis, for future location prediction using Timestamps as primary data .

\end{abstract}


\keywords{monitoring system, delay analysis, GPRS, GPS, UDP/IP, time constraint, map matching.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
eal Time Applications usually impose strict time constraints which affect the Grade of service. Time constraints Restricts the Time gap between 2 locations. IP network delays can range from just a few milliseconds to several hundred milliseconds. When two devices communicate with each other using a packet switched network(GPRS), it takes a certain amount of time for information to transmit and receive the data. The total time that it takes for this chunk of information, commonly called a packet, to travel end-to-end is called network delay.\par
In this Proposed Violation Prediction method, Communication or ope rating delays between 2 datas are bounded and are taken into account by verifying a global time constraint. The uncertainty induced by these delays generates an uncertainty on the verification that results in a possibility measure associated with constraint verification. Freschet Distance (1999) based prediction lack of True path of Moving Object \hyperref[b0]{[1]}. The performance of Kalman filter approach depends only on the quality of electronic map data and error sources (2011) associated with positioning devices were not considered \hyperref[b12]{[13]}. Coorelation ananlysis (2012) shift the received signal by delay and multiply it with other Author ? ? : M.E Student, Assistant Professor ECE Dept Applied Electronics , IFET college of Engineering Villupuram, Tamilnadu. e-mail : Kn516894@gmail.com. series \hyperref[b11]{[12]}. Even Fuzzy Logic based matching does not consider error sources when estimating the location. Dynamic time windows (2011) based delay estimation based on Kalman Filter restricted to stastitical data \hyperref[b5]{[6]}.\par
Our Objective is therefore to study the particular problem that whenever vehicle location request is made its current position will not be retrieved accurately, instead its previous position will not be retrieved accurately, instead its previous position alone sent to requested client. For that, we suppose that communication delays between devices are bounded. This uncertainty on communication delays induces an uncertainty on the time constraint verification. The exploitation of the obtained results allows recognising in a distributed way, the occurrence of the failure symptom with a certain possibility. If a target node moves linearly, through zone prediction method we can predict the location accurately. However, on the other side, when a target does not move linearly means changes it"s direction such as though spiral way. When a device on a packet switching network sends information to another device, it takes a certain amount of time for that information, or data, to travel across the network and be received at the other end.This delay still becomes worst when using Unreliable UDP packets. 
\section[{II.}]{II.} 
\section[{Analysis on Spatial Constraints}]{Analysis on Spatial Constraints}\par
Existing localization techniques which mostly rely on GPS technology are not able to provide reliable positioning accuracy in all situations. This spatial based map matching technique will satisfy the real time constraints to reduce data delivery time and further provide accuracy than existing methods. The Important terms used are described in this section. a. A GPS log is a collection of GPS points ?? = \{??1, ??2, ? , ???? \} . Each GPS point ???? ? ?? contains latitude . ??????, longitude . ????ð??"ð??" and timestamp ???? . ??.\par
b. GPS Trajectory: A GPS Trajectory ?? is a sequence of GPS points with the time interval between any consecutive GPS points not exceeding a certain threshold ???? , i.e. ??: ??1 ? ??2 ? ? ? ???? , where ???? ? ?? , and 0 < ????+1. ?? ? ???? . ?? < Î?"?? (1 ? ?? < ??). Figure \hyperref[fig_7]{3} shows an example of GPS trajectory. ???? is the sampling interval. In this paper, we focus on low G sampling rate GPS trajectories with Î?"?? ? 5?????? and maximum Back off attempt is 15 times c. Road Segment: A road segment ?? is a directed edge that is associated with an id ??. ??????, a typical travel speed ??. ??, a length value ??. ??, a starting point ??. ??????????, an ending point ??. ?????? and a list of intermediate points that describes the road using a polyline.\par
d. Network: A network is a directed graph (??, ??), where ?? is a set of vertices representing the intersections and terminal points of the road segments, and ?? is a set of edges representing road segments.\par
e. Path: Given two vertices , ???? in a road network ??, a path ?? is a set of connected road segments that start at ???? and end at ???? f. Timestamps : Start time of each iterations represent the Timestamp of that data set. This elapsed time for each trajectory is obtained from Tic function using MATLAB. 
\section[{III.}]{III.}\par
Hardware and Software Requirements  
\section[{Proposed Architecture}]{Proposed Architecture}\par
In this spatial based location prediction method , the target location at an instance of time is predicted based on previous good locations using an iterative process. Once the zone of the target is predicted with respect a relative origin, the Previous location Points from trajectory data is used to find future location and packet delivery speed enhancement . This is done using Timestamps from which the data is sent and time of its arrival. The values are Recorded for each transmission of a packet to find out the transmission time which is the difference between Arrival and sent. WGS 84 coordinate datum is converted to Decimal Degrees as first step to enhance accuracy in prediction. Consequential Movements is given as Tj, Tj+1 TJ+1 -Tj ? Maximum Time gap. 
\section[{Proposed Spatio Temporal analysis System}]{Proposed Spatio Temporal analysis System}\par
Figure \hyperref[fig_2]{1}: Proposed Spatial Analysis Based Constraint Prediction The strict Timing constraints has to be satisfied for achieving best map matching accuracy and packet delay analysis. To fulfill this need in networks in which the topology changes frequently, these changes should not affect the Quality of Service (QoS) for data delivery. The maximum gap defined by Floating point Time difference between 2 successive GPS points.\par
The IP configuration for sending and receiving packets is carried out using UDP socket creation in DOS prompt, since this prediction is carried over Client , Server Architecture.    To determine the Time window constraints consider the observed sample and characteristics of the correctly operating system are used to create a confidence space of possible timing relationships between Gps Trajectory obtained from the system. To execute simulation for zone finding, MATLAB is used as a simulation tool. The UDP packet socket is created using Send and Receive Arguments with IP configuration.\par
From the given point P, Within radius " r" canditate projection is a line drawn from point P to the Road side of the segment. Line segment is projected from the point P to the road segment e, and named as C. shortest distance between p and c is the road that vehicle is choosen to travel as an assumption for this the proposed concept. In spatial analysis, both geometric and topological information of the road network is used to evaluate the candidate points are used for Time instance evaluation. If any value greater than max Gap is obtained ,\par
The observation probability(geometric information) is defined as the likelihood that a GPS sampling point ???? matches a candidate point ???? computed based on the distance between the two points Transmission Probability(topological information) is used to identify the true path if a cross path is located wrongly. In this if a cross path is identified wrongly, then the previous P point is compared and the path is follower regarding it. The nodal delays accumulate and give an end-to-end delay, 
\section[{Time difference = Timestamp of packet}]{Time difference = Timestamp of packet}\par
Received -Packet Sent Time End-End delay = Arrival time -Received time / number of iterations used. Here 10 The Total Number of iterations used is 10. One of the major concerns of this research is to keep track the moving target as well as stationary target. As the target can move any direction dynamic references used for applying proper geometry in triangulation based map matching method instead of stationary references. That"s"why modified spatial map matching is done here to reduce the execution time of proposed method .The iteration process earned the execution time of 0.0019 sec with respect to V. 
\section[{Proposed Algorithm based on Time Constraints}]{Proposed Algorithm based on Time Constraints}\par
The Algorithm defines the each Point"s timing relationship (Timestamps) and their Sequencing relationship.\par
STEP 1 : Initialize list of candidate points an an empty list STEP 2 : For i= 1 to n do STEP 3 : Get candidate values for observed GPS node positions. From the given point P, With in radius r canditarete projection is a line drawn from point P to the road side of the segment. Line segment is projected from the point P to the road segment e, and named as C.\par
STEP 4 : Time Difference between successive GPS points are recorded in Mantissa format to include temporally similarity with respect to current point received. ( Here VB event driven programming language is used ) STEP 5 : Line segment is projected from the point P to the road segment e, and named as C. shortest distance between p and c is the road that vehicle is choosen to travel STEP 6 : The observation probability (geometric information) is defined as the likelihood that a GPS sampling point ???? matches a candidate point ???? computed based on the Time difference between the two points STEP 7 : Transmission Probability(topological information) is used to identify the true path if a cross path is located wrongly. In this if a cross path is identified wrongly, then the previous P point is compared and the path is follower regarding it.       
\section[{Conclusion}]{Conclusion}\par
This Paper clearly examine the performance of algorithm and datas from correctly observed operating system to predict the delivery delay before the packet Reaches the destination. The time from when the packet is sent and time it received at the Receiver is recorded and it becomes crucial fator more than the threshold value here assumed Maximum Gap of 0.00019 seconds, the time difference is further applied using Fuzzy sorting to observe Error sources. So far, Spatial Analysis Results that are found is presented in this paper and membership function Estimation for Fuzzy based Interval value sorting process is in progress and presented in Future. Delays are predicted earlier within the reach of it to destination using Advanced Time Constraint violation Prediction Algorithm , at the same time by plotting the obtained values in Google map using appropriate interface, up-to date data receival with less delay and more accuracy is achieved by the proposed method. The Future Scope implies that Delay prediction is possible for GPRS, 3G Networks and delay diagnosis can be done by expanding the concept using FUZZY Sorting by proposed Algorithm by 2016.  
\section[{Global}]{Global}\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-3.png}
\caption{\label{fig_2}Figure 1 (}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-4.png}
\caption{\label{fig_4}Figure 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{8}\includegraphics[]{image-5.png}
\caption{\label{fig_5}STEP 8 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-6.png}
\caption{\label{fig_7}Figure 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{45}\includegraphics[]{image-7.png}
\caption{\label{fig_8}Figure 4 :Figure 5 :}\end{figure}
  			\footnote{© 2014 Global Journals Inc. (US)} 		 		\backmatter  			  				\begin{bibitemlist}{1}
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\end{document}
