A Review on Localization Process and Localization Measurement Techniques in Wireless Sensor Networks

A Review on Localization Process and Localization Measurement Techniques in Wireless Sensor Networks

Shwetha Rani K P

Assistant Professor, Department of CSE DBIT, Bengaluru, India

Dr. S Meenakshi Sundaram

Professor and Head, Department of CSE GSSIETW, Mysuru, India

Abstract: Latest advances in radio and inserted frameworks have empowered the expansion of wireless sensor networks. WSN's are enormously being utilized as a part of diverse conditions to perform different observation and monitoring tasks in various fields. The notable uses of WSN are Military Applications, Industrial Applications and in addition Household, Disaster Relief operations, in Medical Applications and human services checking as Body Area Network, Monitor action of Sensitive territory, . For example the sensor nodes are hidden under bed at different forces for social event climatic conditions, weight and extra type of data for seeing the development of Snow. In all these, localization is a standout amongst the most imperative framework parameters. Localization is basic to report the beginning of errands, gathering inquiry of sensors, directing and to answer inquiries on the organized network. Along these lines, one of the essential difficulties in remote sensor organize is localization. This paper audits distinctive procedures of localization in wireless sensor networks. The general thought of the plans proposed by various researchers for the improvement of localization in remote sensor systems is additionally introduced.

KeywordsWSN, Localization, Anchor nodes, DV-Hop Algorithm, APIT Localization Algorithm, Amorphous Localization Algorithm

1. INTRODUCTION

   Wireless Sensor Network (WSN) is one of the most popular topic of research from the last few years. wireless sensor networks is an information-gathering prototype, in which many sensors are distributed over examination field and collect the data of interests by evaluating real-world phenomena from the physical location. wireless sensor network (WSN) has a wide range of applications, including military and medical fields, target tracking, vehicle tracking, event detection, people monitoring, forest fire detecting [1], routing and, etc. in all of these applications unless knowing the location of an event which is the location or geographical position of the sensor, the collected data are unusable. localization is an important issue in the wsn technology. localization problem refers to the process of estimating and computing the positions of sensor nodes [2]. the importance of this fact led researchers to find a solution for localization problem.one easy method ismanualconfiguration however thi s is often impractical in giant scale or once sensors area unit deployed in inaccessible area unit as like volcanoes once sensors are mobile. the central thought in localization techniques is that distinctive Some category of detector nodes that have

   information concerning theircoordinates areaunit deployed to search out the unknown nodes. such nodes area unit referred to as anchor nodes/landmarks. these nodes will be equipped with global positioning system(GPS) that send beacons with their coordinates so as to supply help to alternative nodes in order to help them to perform localization. the GPS is classical approach for localization of nodes however it is not economical. GPS has laid low with serious trees and a building as a result of it needs line-of-sight between the receiver and satellites. so, it's low accuracy attributable to poor signal reception. In extra, victimization GPS in giant scale isn't economical. Therefore, many localization algorithms introduced to unravel localization drawback. this literature categorizes localization techniques ease of use.

2. LOCALIZATION PROCESS

   A localization algorithm localizes sensor nodes based on input data. If there is any anchor available in the network, the most common inputs are the location of anchors. Other inputs are connectivity information for range free techniques, distance or angle between nodes for range based techniques that calculated based on signal modality. Generally, the output of localization algorithm for anchor based techniques is absolute coordinate and for anchor free methods is relative coordinate [1].

   Fig: Localization Process

   Signal modality influences very on the precision of area estimation. Choosing a reasonable flag sort relies upon the different factors, for example, hub equipment, the application and condition. Utilization of extra equipment parts is not cost and vitality powerful. Diverse situations effects affect the execution of area estimation. For instance, in stickiness air, acoustic performs superior to radio flag, since, dampness ingests what's more, reflect high recurrence radio, when it has

   little impact on the vibration sound. Moreover, unique applications make distinctive compels to pick a flag sort. For example, in military applications, hubs ought to confine in the noiseless way, so radio recurrence with quiet property is a decent decision. Acoustics likewise utilized as a part of numerous limitation approaches like Ultra in cricket approach [8] discernable in beam forming [9]. One other flag sort is infrared (IR) flag which is not usable for outside in view of high weakening and furthermore its trouble to peruse when daylight is accessible. All sensors have locally available radio equipment. Recurrence, stage, quality of RF is utilized for area estimation in numerous applications [10]. Light is likewise usable for some methodologies like spotlight strategy [11]. Here, the disadvantage is, observable pathway required, an intense light source and uncommon equipment for source likewise is required.

   Measurement Techniques: consists of 3 steps.

   The distance estimation phase involves measurement techniques to estimate the virtual distance between the nodes.

   The angle-of-arrival (AOA) measurement techniques can be further divided into two subclasses: those making use of the receiver antennas amplitude response and those building use of the receiver antennas phase response. Beam forming is the name given to the use of anisotropy in the response pattern of an antenna, and it is the basis of one class of AOA measurement techniques. The measurement unit can be of small size in comparison with the wavelength of the signals. The accuracy of AOA measurements is limited by the directivity of the antenna, by surveillance and by multipath reflections. How to obtain accurate AOA measurements in the presence of multipath and shadowing errors has been a subject of intensive research.

   Time based techniques, Time of arrival (TOA) method tries to estimate distances between two nodes using time based measures. Time different of arrival(TDoA)

   is a method for determining the distance between a mobile station and nearby synchronized base station. These methods can be applied to many different signals, such as RF, acoustic, infrared and ultrasound.

   TDoA methods are impressively accurate under lineof-sight conditions.

   The received signal strength indicator techniques(RSSI) are used to translate signal strength into distance. RSSI measures the power of the signal at the receiver and based on the known transmit power, the effective propagation loss can be calculated. Next by using theoretical and empirical models we can translate this loss intoa distance estimate. This method has been used mainly for RF signals.

   The Position computation consists of algorithms to calculate the coordinates of the strange node with respect to the known anchor nodes or other adjacent nodes.

   Hyprbolic trilateration: The most basic and intuitive method is called hyperbolic trilateration. It locates a node by calculating the intersection of 3 circles as shown in Fig (a).

   Triangulation: This method is used when the direction of the node instead of the distance is approximated, as in AoA systems. The node positions are calculated in this case by using the trigonometry laws of sines and cosines as shown in Fig. (b)

   Maximum Likelihood (ML) estimation: ML estimation estimates the position of a node by minimizing the differences between the measured distances and estimated distances as shown in Fig. (c )

   Fig 2: Positition Computation

   The localization algorithm, in general, determines how the information relating to distances and positions, is manipulating in order to allow most or all of the nodes of a WSN to estimate their position. Optimally the localization algorithm may involve algorithms to decrease the errors and refine the node positions. Some are as follows

   DV-Hop Algorithm

   DV-hop is institutionalized range free calculation which was proposed by D.Niculescu and B.Nath. The key idea of DV-Hop algo is that hub exchange data with its neighboring hubs. The separation between obscure hub and grapple hub are delineated by the result of normal per-bounce remove and the most brief route among the hubs. At that point trilateral strategy is utilized to recover the area data of hub.

   APIT Localization Algorithm

   APIT, a without range confinement conspire that needs a composite system where constrained percent of these gadgets are provided with powerful transmitters and position data is obtained through the medium of GPS or different procedures. Using Beacons from these grapples, APIT applies another range based instrument that achieve the area estimation. The presence of hub inside or remotely in triangular neighbourhood allows the hub to confine the space in which it exists. By utilizing the associations of stay areas, the width of the anticipated space can be diminished to provide food excellent area forecast.

   Amorphous Localization Algorithm

   Undefined calculation is undifferentiated from DV-Hop algo and the goal is to figure the jump separate amongst grapple and obscure hubs as opposed to computing direct separation between them. It comprises of three stages:-

   In the beginning, minimal hop from unknown to beacon node is determined. Then distance is estimated from unknown node to beacon node. Finally, use least square method for location information.

3. CONCLUSION

   In this paper, the basic idea of localization process with each step in wireless sensor networks is reviewed. Also the importance of localization of sensor nodes is stated.

   Various measurement techniques for different environments is discussed in brief. The plans proposed by various researchers for the improvement of localization accuracy in Wireless Sensor Networks is discussed.

4. REFERENCES

1. Chen, Manju, Xiangqian Ding, Xiaodong Wang, and XiaoweiXu. "A novel three-dimensional localization algorithm based on DV-HOP." In Signal Processing, Communications and Computing (ICSPCC), 2014 IEEE International Conference on, pp. 70-73. IEEE, 2014.

2. Gayan, Samiru, and Dileeka Dias. "Improved DV-Hop algorithm through anchor position re-estimation." In Wirelessand Mobile, 2014 IEEE Asia Pacific Conference on, pp. 126-131. IEEE, 2014.Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.

3. Lazos, Loukas, and RadhaPoovendran. "HiRLoc: high- resolution robust localization for wireless sensor networks." Selected Areas in Communications, IEEE Journal on 24, no. 2 (2006): 233-246.Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3 (Mar. 2003), 1289-1305.

4. Wu, Jiawei, Jinming Yu, AijunOu, Yiming Wu, and WujunXu. "RCDV-Hop Localization Algorithm for WSN." In Wireless Communications, Networking and Mobile Computing (WiCOM), 2012 8th International Conference on, pp. 1-4. IEEE, 2012.Y.T. Yu, M.F. Lau, "A comparison of MC/DC, MUMCUT and several other coverage criteria for logical decisions", Journal of Systems and Software, 2005, in press.

5. Li, Mo, and Yunhao Liu. "Rendered path: range-free localization in anisotropic sensor networks with holes." In Proceedings of the 13th annual ACM international conference on Mobile computing and networking, pp. 51-62. ACM, 2007.

6. Kulkarni, Raghavendra V., and Ganesh Kumar Venayagamoorthy. "Bio-inspired algorithms for autonomous deployment and localization of sensor nodes."Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 40, no. 6 (2010): 663-675.

7. Liu, Yong, Yu Hen Hu, and Quan Pan. "Distributed, robust acoustic source localization in a wireless sensor network." Signal Processing, IEEE Transactions on 60, no. 8 (2012): 4350-4359.

8. Jiang, Joe-Air, Xiang-Yao Zheng, Yu-Fan Chen, Chien-Hao Wang, Po-Tang Chen, Cheng-Long Chuang, and Chia-Pang Chen. "A distributed RSS-based localization using a dynamic circle expanding mechanism." Sensors Journal, IEEE 13, no. 10 (2013): 3754-3766.

9. Bal, Mert, Henry Xue, WeimingShen, and Hamada Ghenniwa. "A Test-Bed for Localization and Tracking in Wireless Sensor Networks." In SMC, pp. 3581-3586. 2009.

10. Aspnes,J., T. Eren, D.K. Goldenberg, A.S. Morse, W. Whiteley, Y.R. Yang, B.D. Anderson, and P.N.Belhumeur,2006. A theory of network localization:IEEE Transactions on Mobile Computing:16631678.

11. Bachrach, J. and C. Taylor, 2005. Localization in Sensor Networks: in Handbook of Sensor Networks:Algorithms and Architectures 277310.

12. Maroti, M. and B. Kusy, G. Balogh, P. V olgyesi, A. Nadas, K. Molnar, S. Dora, and A. Ledeczi, 2005. Radio Interferometric Geolocation: in Proceedings of 3rd International Conference on Embedded Netw16rked Sensor Systems (SenSys) San Diego, California, USA, Nov: 1-12.

13. atwari, N. and A. O. Hero, 2006. Indirect Radio Interferometric Localization via Pairwise Distances: in

    Proceedings of 3rd IEEE Workshop on Embedded Networked Sensors (EmNets 2006) Boston, MA,

14. Huang, R. and Gergely V. Zaruba, and Manfred Huber, 2007. Complexity and Error Propagation of

    Localization Using Interferometric Ranging: in Proceedings of IEEE International Conference on

    Communications ICC 2007 Glasgow, Scotland, June : 3063- 3069.

15. Pal, A. 2010. Localization algorithms in wireless sensor networks: Current approaches and future challenges: Network Protocols and Algorithms, (2): 4573.