Localization Technique

A wireless sensor network (WSN) is a group of a few to several hundreds or even thousands of sensor nodes deployed over a significant area. WSNs have a wide range of applications which include environmental monitoring, target tracking, home automation, military applications and others. To process sensor data in WSN, it is imperative to know where the data is coming from. So, knowledge of nodes locations is an essen- tial requirement for many location-aware applications including aforementioned applications, location-based services (LBS) and location-based routing. Several sur- veys discussed localization strategies and attempted to classify different localization techniques like [1],[2],[3]. Localization techniques could be classified according to all calculations being performed on a single node or distributed on all the network sensor nodes to cen- tralized localization technique (e.g. MDS-MAP [4] and Semi-Definite programming (SDP) [5]) and distributed localization technique (e.g. APIT [6]). Another new

This paper shows an experimental performance and validation of leak detection and localization technique using wireless MEMS (Micro-Electro Mechanical System) sensor network that monitors the pipe surface acceleration in between network joints in a non-invasive manner and computes the measure of acceleration change. Six pieces of accelerometer sensor nodes were placed at uniformly spaced intervals on a water pipeline to monitor water flow. When there is leakage, there will be sudden disturbance in the water flow which induces corresponding sudden change in acceleration of the pipe vibration. This change in the pipe acceleration is measured. The location of the pipe rupture can be found in the pipe network using trilateration localization algorithm. In practice, rupture was emulated in the pipe line test bed using various valves. The emulated events include opening and closing of the water discharge valves.

There is one of the most famous fault localization technique named as Tarantula [9]. In this technique there proportion of failed and passed execution exercising the statement to measure the dubious part of that statement. Naish et al [14] gave a summary for such technique. Now if we comparing such statement level technique, for locating a fault CBI [10] uses predicates as fault indicator. Which gain low complexity and high extensibility? Zhag et al. [21] empirically validate that the short circuiting has effect the predicate based technique and proposed DES [21] accordingly. They also used a non parametric predicate based statistical fault localization framework.For statistical fault localization there is a well- known impact factor is coincidental correctness which causes program runs and trigger the fault, to be marked as non-failed runs. Test suite reduction is one of a solution [8][15] to address coincidental correctness or improve test suite quality [19], but its feasibility relies on the accuracy of recognizing coincidental cases [13]. Our paper proposes a methodology to address coincidental correctness, which does not rely on the accuracy of recognizing them. In this paper, Bhattacharyya coefficient is used to measure the similarity of the predicate spectra between failed runs and non-failed runs, to rule out the fault- led predicates. After that inter and intra-class distances are often used in pattern recognition to measure the class difference [16,17].

Abstract - Localization is one of the essential problems in wireless sensor networks (WSNs), since the locations of the sensor nodes are significant to both network operations and application level tasks. Although the Global Positioning System based localization techniques can be used to determine node locations within a few meters, the cost of Global Positioning System devices and non-availability of Global Positioning System signals in limited environments avoid their utilization in large scale sensor networks. There exists an extensive body of research that aims at obtaining locations as well as spatial associations of nodes in wireless sensor networks without requiring specific hardware or employing only a limited number of anchors that are aware of their own locations. In this paper, we propose an anchor free localization technique for single hop WSN. The simulation result shows that the proposed technique outperforms its comparatives.

centered at a sensor, with their radii determined by the time of propagation of the wave from the source to the respective sensor. Jeong and Jang (2000) proposed another triangulation method, in which the time difference of arrival (TDOA) between two sensors was evaluated first, and then the damage located at the crossing point of two hyperbolas. However, there are two disadvantages of these two methods. First, these two triangulation methods are based on acoustic emission technique, which is passive sensing but not active detection. Thus they are not suitable for SHM. Second, these two methods must evaluate the arrival time or TDOA of the response waves. Due to the ambiguity of time-arrival and background noise, the measurement of time-arrival may not be accurate enough to be determined and incurs additional errors to the localization results. Although Kehlenbach and Hanselka (2003) developed an active damage localization technique by using reflected Lamb waves from damage, the time-of-flight was still needed to be evaluated in advance. In this thesis, a least- squares method uses the energy measurement of the collected elastic wave signals such that it uses all the data information including the time-of-flight. Furthermore, since the least-squares method is based on active detection technique to obtain the reflection waves, it is a promising method for the application of SHM.

In this paper, we have proposed a Particle Swarm Optimization (PSO) based energy efficient localization technique (EELT) for wireless sensor networks. The problem considered in this paper is exploration of an unknown environment with the goal of finding the nodes at an unknown location(s) using location aware (LA) nodes. This work has demonstrated the use of a distributed PSO algorithm in which the fitness function is based on the signal strength (RSSI) and angle of arrival (AoA) of the LA node for locating target(s) in high risk environments. Essentially to reduce the energy consumption, only a small number of sensors are activated to track and localize the target; while others are turned into sleep mode. The proposed method is evaluated on various mobility models and localization is performed by learning movement patterns and their parameters. By the simulation results we have shown that our proposed method increases the accuracy of localization by minimizing estimation errors

Background: Intraoperative localization of image or endoscopy-detected lesions occasionally pose surgical challenges due to the small lesion size and/or difficult anatomic exposure. Identification of such lesions can be facilitated using a hand-held gamma probe with utilization of Tc-99m macroaggregate albumen (MAA) localization technique. The radiopharmaceutical injection can be performed using ultrasound (US) or endoscopy guidance.

positioned randomly in specific area. While sending the data from source to the destination DV-Hop will identify the distance vector hop based path. this path includes various intermediate nodes so that data can be travelled through these intermediate nodes. In current research work while identifying the intermediate nodes to arrive at beacon nodes. Whichever will be having minimum number of hops will be considered the path. but the hop count should be limited to the threshold limit. Beyond this threshold limit the path to the beacon node will be rejected. The new technique based on threshold limit imparts better result in all the parameters like energy shows the 43% improvement, overhead shows that there is 21% improvement and time factor shows the improvement of 41%. So the threshold limit will stop the node to go on comparing the average weight. Which will in result reduces the time and energy requirements and also shows the improvement in overhead.

In this paper we propose an algorithm which derives the location of sensor nodes based on their connectivity information[4][6] i.e. which nodes are in communication range of each other. Based on the information about known location of certain anchor nodes and distance between neighbor nodes the location of other sensor nodes is determined by a mathematical technique , an O(n 3 ) algorithm for a network of n sensors called Multi –dimensional scaling[3]. MDS uses Euclidean principle[3] to model data proximities in geometrical shape where distance (d ij ) between points i

Ultra-wideband (UWB) technology is characterized by rel- ative bandwidths larger than 20% and absolute bandwidths of more than 500 MHz. These wide bandwidths improve the reliability of communications systems through frequency di- versity and the accuracy of positioning techniques through the high temporal resolution of the propagation channel. The most promising UWB localization approaches exploit- ing the wide bandwidth are based on time of arrival es- timation [1, 2], where the unknown position of a trans- mitter is calculated by trilateration using the estimated dis- tances to, at least, three reference receivers with known po- sitions. These methods work very accurately under line-of- sight (LOS) conditions. However, a general problem of local- ization and tracking systems using time of arrival estimates is the performance degradation under non-LOS conditions, since, the strongest and/or first arriving path may not cor- respond to the direct path [3, 4], yielding positively biased distance estimates.

Motion blur removal can also be done. Image edges are obtained for object measurement in that image. This edge obtaining process is done from the spread and the contained area within the image can be done easily. Digital Image Processing is done in one of such ways and manipulations are towards unlawful consequences. The trustworthiness of the digital images is assessed by analyzing the susceptibility of manipulations of the digital images. It also projects on to another fluctuation surrounded in the digital imagery paradigm. Digital images are both easily prone to vulnerabilities and also extend the analysis methods to overcome the mankind perceptions. Two principal research areas advance under DIF, and they are: foremost area include approaches for expounding whether the captured image is acquired by the device or to determine which device has acquired it. These methods fall under techniques of source device identification. Other technique aims at revealing the traces of manipulations that are semantically done, by learning the inconsistencies in natural image statistics.

[7] In this paper, the author proposed localization scheme called as DV-max Hop that can achieve good efficiency and precision. Authors formulated the multi-objective optimization functions they are the number of transmission in localization phase as well as to minimize location errors and estimate the performance of design using extensive simulation on several anisotropic and isotropic topologies. The proposed DV-Hop algorithm scheme can achieve dual objective of efficiency and accuracy for various scenarios. The lately proposed algorithms are persistent on localization exactness without considering efficiency in terms of energy and communication

Many methods for Barcode Localization exist; some of which come under Spatial Domain Methods while others come under Frequency Domain Methods. In the following paragraphs we will discuss both these categories of Barcode Localization. Spatial Domain Methods are those which search for group of dark lines on white background and which also have very strong directional continuity at a particular angle so as to locate barcodes [4][8]. For example, Tuinstra’s method uses basic morphological operations to localize barcodes. It basically relies on the fact that due to high intensity difference between bars and the background, gradient calculation using sobel kernel highlights the bars [2].

The path and path rate measurements between the sensors and emitter are linearly correlated with the TOA measurements. EM waves transmit through different kind of mediums while traveling along the pathway from a source to a sensor. The velocity of EM waves is constant due to the obstructed range length is much smaller than the traveling distance in the air. For representing the practical environment, noise can be divided into two sections namely; line of sight (LOS) and non-line of sight (NLOS). The true path difference and path difference rate measurements from the source to the receivers are affected with only the standard measurement noise if there is a complete LOS. This measurement noise is supposed to have a distribution that is almost similar to the Gaussian distribution, but with the fine support region. In this paper, we have focused our research to calculate the position and the velocity of emitter considering only LOS noise. Therefore, the path difference and path difference rate measurements of the position localization technique are modeled as follows:

Even through the fault circuit measured with lower current than the fault-free circuit, Figure 5 shows the current for the fault free circuit (M2) rise in a pulse, ranging from picoseconds to nanosecond time. On the other hand, the current for the faulty circuit consequence from the Ohmic defect R1, rise in mill-seconds. For fault localization technique using photo emission, the rising time effect will reflect how much proton will be emitted from the working CMOS due to the collective emission time by the failure analysis (FA) tool.

The sound localization is used to localize a sound source in three main items: first is the environment second is the noise and the third is the source itself. The environment could be quiet, noisy, and reverberant. Suppose a signal has to propagate through a medium in which noise is present. This noise could be any type either other sound or reverberations of the source itself. It is remarked that only mentioned external noise. Finally, the various attributes like directivity, constancy or the type of signal like random noise carries by sound itself. This brings a lot of uncertainties in a passive localization system. For simplification the sound source is supposed to be an Omni directional point source. Stethoscope sound localization technique is used to detect the sound of heart. The localization scheme is detect the direction of heart sound and after that we will use the sound filtering technique to differentiate heart sound from lung sound.

The amount of information stored in the web contains structured and unstructured text data. The main problem of users to find the exact information they are looking for. In existence, there are various techniques provides a good performance for web content mining. Approximate string membership checking is used to retrieve the true matches of the substring of text. For example the application areas like computational biology, text retrieval and signal processing carries many problems on spell check data querying to exact string matching. This paper focuses on overview of string matching technique research progress on the approximate matching in the World Wide Web and an improved approximate membership extraction to achieve its objective in web content mining. Which results in the proposed research combines approximate membership extraction vs. approximate membership localization technique and potential redundancy pruning techniques to retrieve the exact data and reduce the redundancy among the retrieval of information.

Melike Erol and Luiz F. M. Vieira et al. [9] explored a localization technique, Dive ‘N’ Rise (DNR) positioning for underwater acoustic sensor networks. Localization is one of the main challenges of UASNs. Acoustic channels are used which have low bandwidth, large propagation delay and high error rate. DNR positioning applies to large scale, 3-D space USN. DNR beacons learn their coordinates using GPS system when floating over water. While diving, they broadcast their coordinates which are used by sensor nodes to localize themselves. Assumed that nodes are synchronized and after hearing from several DNR beacons, they can estimate their coordinates. Sensor nodes can use boundary box or triangulation techniques to find their coordinates. Qualnet simulator is used for implementing DNR-positioning. Here performance of DNR positioning is analysed in the presence of current and without current. Performance of this technique is evaluated based on number of localized nodes and localization errors. Result showed that it can localize all nodes with small error and minimal message exchange is required.

In proposed approach an end-to-end real-time video scene text deblurring using TMD & ER base text localization technique is represent. The dictionary-based technique is common and adaptable to signify the video scene text assets than the temporary techniques in constructing video scene text image base, also extra vigorous to noises. Proposed system resolve several drawbacks of existing system. Proposed system allows rapid variations of motions and hence works better than the earlier model. Also presently working on English language only, so TMD is has limitation. In future focus is on TMD so that is will comfortable with other languages too.

and fingerprinting techniques [1]. Triangulation has two derivatives: angulation and lateration. Both require the line of sight between the transmitter and receiver. Latera- tion needs at least three measured distances between the target and references of known positions to calculate the two-dimensional (2D) location of the target. The angula- tion estimates a target’s 2D position from the measured angles between the target and at least two known loca- tions. On the other hand, the fingerprinting technique frames the positioning problem as a supervised learn- ing problem. First, a set of positions, called fingerprint positions (or sampling locations), is selected in the tar- get environment, and then, a position-dependent signal parameter is extracted at each position to define the posi- tion signature (also called the fingerprint). A fingerprint database is then constructed. Second, the target signa- ture is calculated then compared with signatures in the previously built database to estimate the position.