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(IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 3, March 2011 A review on distance measurement and localization in wireless sensor network Kavindra Kumar Ahirwar Dr.Sanjeev Sharma(Head of department) School Of Information Technology School Of Information Technology Rajiv Gandhi Technical University Rajiv Gandhi Technical University Bhopal (MP), India Bhopal (MP), India Kavi.chak@gmail.com sanjeev@rgtu.net Abstract -Localization is the most crucial issue in wireless sensor presents a review and taxonomy of localization methods for network for operations and various application like tracking, mobile wireless sensor networks based on Zigbee. positioning, monitoring, routing. This paper focuses on study of location estimation for short range low power wireless sensor network (IEEE802.15.4 standard) based on RSSI and LQI. This II. LOCALIZATION[5 6 7] review paper is divided into three sections for detailed study. First One of the most key challenges for MWSNs is the need for section gives the overview, problem definition and taxonomy of localization. Localization is an estimation of position of sensor localization. Second section studies the distance measurement and node in terms of coordinate system with respect of time, computation based on signal strength. Last section describes two distance, received signal strength, time difference of arrival, localization methods ML (maximum likehood) and WCL (weighted angle of arrivals, theses values to locate the position of a centroaid localization) which is mostly used in ZigBee standard compatible to IEEE 802.15.4 standard. unknown sensor node. For many applications like habitat monitoring, smart buildings, failure detection, target tracking Keywords- Localization, ZigBee, Path loss model, ML, WCL. and military applications. It is more important to retrieve the position nodes with respect to a globally recognized I. INTRODUCTION coordinate system in order to record the data that is geographically useful and correct.. In this review term anchor Recent advance in wireless communication and electronics or reference node means whose location is already known, which enabled the development of low cost System on chips which help to identify location of the unknown node. (SoC) which implements low power, multifunctional, radio Generally these reference nodes is equipped with GPS or transceiver and several sensors called sensor node whose size manually deployed for extract the location information. A is small and tiny. A WSN [1,2] typically consists of a large conceptual framework of the sensor localization classification number of these tiny sensor nodes that are deployed in a is shown in Fig.1 It shows various types of localization region of interest. They communicate over a short distance via method. Localization schemes can also be characterized by a a wireless medium(RF) with key feature of Adhoc deployment set of feature pairs. The schemes differ from one another in the of sensor nodes and collaborate to achieve common way the inputs are collected. The various format of input feed applications [3] like General Engineering, Agriculture and into localization model for mathematical computational, it Environmental Monitoring, Civil Engineering, Military gives position in terms of coordinate and shows performance Applications, Health Monitoring and Surgery. Current WSNs according various parameters like error, complexity etc .The are deployed on land, underground, and underwater. nodes could be static or mobile, deployed indoor or outdoor, in Depending on the environment, a sensor network faces a 2-D or a 3-D space. Location measurements may or may not different challenges and constraints [4]. There are five types require additional hardware. The use of additional hardware in of WSNs: a node should be avoided, as it not only raises the cost, but 1. Terrestrial WSN increases both form factor and operational resource 2. Underground WSN, requirements. Furthermore, a localization approaches may be 3. Underwater WSN, centralized or distributed according to the nature of the 4. Multi-media WSN, and underlying algorithm. A centralized localization algorithm 5. Mobile WSN runs on a base station which may be a coordinator node and all participating nodes must forward their processed data to this A class of WSN where the sensors node are moving around central node. The advantage of the centralized approach is an physical environment ia called Mobile WSN. It can sense and algorithm can deployed like static node. Challenges in mobile wireless sensor be designed that as more accuracy, precision, and can network are self organization of topology, configuration, coverage, energy, maintenance, and data. process. This paper process greater amounts of data. On the other hand Distributed localization methods do not require centralized computation, 179 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 3, March 2011 and each node is capable of computing its location with only Some range based localization scheme are Centroid method limited communication with nearby nodes. According to [11], each node estimates its location by calculating the center scenario nodes are deployed indoor and outdoor region. of the locations of all seeds it hears ,APIT[12] which employs a novel area-based approach to perform location estimation by We categorize the localization problems into isolating the environment into triangular regions between mainly two groups: Range Based and Range Free. beaconing nodes or anchor nodes. Some other methods proposed in Bounding box [13], DV-HOP [14] which are range based III. LOCALIZATION IN WIRELESS SENSOR NETWORK STANDARD IEEE802.15.4 IEEE 802.15.4[15] standard is to define the PHY and MAC specifications for low data rate wireless personal area network which are portable and mobile devices with very limited battery consumption requirements typically operated in indoor environment around 10 meter. Further, the purpose of the developing this standard for ultra low complexity, ultra low cost, ultra low power consumption and low data rate wireless connectivity among inexpensive devices. The maximum and minimum raw data rates were later raised to 250 and 20 kb/s, respectively. The IEEE 802.15.4 supports the star and peer to peer connections and it support wide variety of network topologies. In this section paper studies the path loss model for distance measurement in basis of RSSI of LQI. A. Distance measurement This is the most fundamental issue in sensor network for localization purpose which uses RSSI based methods. The use of received signal strength intensity Figure 1 CLASSIFICATION OF LOCALIZATION (RSSI)[16] is one of the most common method for localization purposes. In contrast, RSS is one of the simplest methods of distance measurement and can be easily implemented in real systems. This technique is based on a standard feature found in most wireless devices, a received signal strength indicator A. Range Based method (RSSI). It is attractive because it requires no additional hardware, and is unlikely to significantly impact local power Range based scheme calculate absolute distance between two consumption, sensor size and cost. In WSN standard RSSI and nodes by using RSSI [8], ToA , TDoA [9] and AoA [10]. LQI is directly calculated without any need of hardware. After distances or angles have been measured, they can be RSSI-based localization in sensor network[17,18,19] currently used to compute locations of nodes without location has been widely used in personnel mine targeting , home information (called unknown nodes).These method provide network control , hospital patient care , ecological environment better accuracy for calculating node position. Nodes are monitoring , children tracking , and so on. The principle of equipped with radios for capturing the signal strength. In this RSSI ranging describes the relationship between transmitted paper RSSI is discussed in detail. ToA and TDoA is based on power and received power of wireless signals and the distance time where propagation time can directly be translated in a among nodes. For modeling RSSI and distance there exists distance, based on the known propagation speed. These several model but this paper studies two models these are free methods can be applied to many different signals, such as RF, space path loss model and long normal model. acoustic, infrared and ultrasound ones. While in AoA method node measures the angle of incoming signal by using antenna B. Free space pathloss model array or compass. Ranges based suffer from requiring extra When an antenna that emits a signal uniformly in all hardware and environmental condition. directions without any physical interference or obstacle called .B. Range Free Method free space. In free space, the signal power at distance d from the antenna is proportional to where is the signal In these schemes, unknown nodes collect location power at the antenna. If the distance from the antenna is information from neighboring anchor nodes (nodes withknown doubled, the signal power will be reduced by a factor of four. locations, also called anchors) and estimate their own The signal power at distance d is also a function of frequency. locations based on their anchor nodes location information. 180 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 3, March 2011 In free space, the signal power at distance d can be calculated parameter self adjusting according application it provided on from the path-loss equation (1) is [23 24]. P ( d ) = P ( 0) −10*nlog10 ( f ) −10*nlog10 ( d ) + 32.44 D. Link quality indicator (1) The LQI (25) measurement is a characterization of the strength and/or data quality of a received packet. The P(d)= signal power (in dBm) at distance d, P(0)= signal IEE802.15.4 standard (IEEE Computer Society, 2006) power (in dBm) at zero distance from the antenna, f=signal indicates that, for each received packet, the LQI shall be frequency in MHz ,d = distance (in meters) from the antenna measured and represented as an integer ranging from 0 to 255. n= path loss exponent , it is dependent on real environment for The minimum and maximum LQI values are associated with free space n is 2 . Free space model is normally used when the the lowest and highest signal qualities detectable by the transmission distance is much larger than the antenna size and receiver. The before mentioned influences during transmission the carrier wavelength and there are no obstacles between the of radio packets reduce the quality of RSSI extremely. Thus, transmitters and the receivers. localization of unknowns becomes imprecise. Another method to determine the distance is based on the link quality indicator C. Log-normal shadowing model (LQI) of the transmission. It represents a number of required It is a most commonly used propagation model that retransmissions to receive one radio packet correctly at the considers the shadowing effect, whether in outdoor or indoor receiver. For better understanding of these parameter a table is environment. This model indicates that the average received provided given below with some influencing parameter. signal strength decreases logarithmically with distance. It is also called log normal model .The calculation formula is as follows: TABLE I. RELATIONSHIP BETWEEN LQI AND RSSI di , j P i , j (d ) [ dBm] = p0, j [ dBm] − 10*n *log10 ( ) + Xσ d0 Parameters result Signal noise RSSI LQI (2) Weak absence low high P i , j (d ) = It represents the RSS at a receiving node I from weak strong present absence low high low high a transmitting node j in dB mill watts strong present high low p0, j = the received power in dB at a reference distance The LQI sometimes generated from a signal level from the transmitter j .It is considered from earth which assume determination, a signal-to-noise determination, or a 1 meter from earth combination of the two, at the discretion of the network node implementer. This enables both received signal strength n= path loss exponent indication (RSSI) and correlation-based signal quality estimators to be used. The LQI measurement is performed for X σ = is the random noise variables which mean is zero, the each received packet, and the result is reported to the MAC standard deviation is commonly 4-10. It reflects the change of sub layer as an integer ranging from 0 to 255. the received signal power in certain distance.(It is also in dB) By using above equation we obtain the relationship between E. Processing RSSI values RSSI and distance [20] This section studies the error correcting Usually d 0 =1 meter model [26 27] for getting smooth value of RSSI. When RSSI = −(−10* n *log10 (di , j ) − A ) adopting distance measurement based on RSSI, we must avoid The theoretical distance between nodes is given by the instability of RSSI, so that the RSSI value can reflect the distance of wireless signal transmission more accurately RSSI − A + X σ because of their randomness in radio signal. In this section it d i , j = 10 10*n studies the two experimental models for getting smoothness in RSSI value. In practical application environment, path loss model having (a) Statistical Mean Value Model (SMVM)-In this model problem with multi-diameters, diffraction, obstacle and so on unknown node receives a group of RSSI values and then have an impact on the wireless signal transmission[21 22] computers their mean value for obtain optimal RSSI-value .However it is indispensable to adjust A , n and X σ in the per distance. the formula is shown as model according to specific environment. In general, the 1 m ∑ RSSIi model's parameters are set based on experiences, so it does not have the self-adaptability. For better modeling of these RSSI = m i =1 181 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 3, March 2011 m is the number of the RSSI values which the unknown node receives. By adjusting the value of m, this model can balance timeliness and accuracy. When m is very big, this model can avoid instability of RSSI but communication overhead will correspondingly be high. (b) Gauss Model (GM) - When an unknown node receives n RSSI values, there must be some values which are small probability events. This value saved in array A[i].It use Gaussian distribution functions which are shown in equations to deal with the RSSI values. ( x - m )2 − 1 2σ 2 F ( x) = e (3) σ 2π Figure 2- Relationship between RSSI and distance n 1 σ2 = ∑ ( x − M )2 n − 1 i =1 (4) IV. LOCALIZATION ALGORITHIM 1 n M = ∑ xi (5) n i =1 Maximum likelihood estimation [28] - It can be used when number of reference or beacon node is equal or greater then The RSSI value which satisfies the inequality P F(x) 1 will 3.The essential idea is: if the coordinates of n reference or be stored in the array of A[i]. According to the practical anchor nodes were known as experience, we choose P as the critical point which shows the ( x1 , y1 ), ( x2 , y2 ).............( xn , yn ) (7) probability event. When the value of Gaussian distribution Their distance from unknown node D (calculated from RSSI) function is bigger than P, we figure that the corresponding RSSI values are high probability events, Otherwise we figure where d1 , d 2 ,........d n and then the following formula with that the corresponding RSSI values are small probability coordinate (x, y) of the blind node D exists : events. Generally value of P is set 0.5 or 0.6. The mean value of all optimized RSSI values can be calculated using the ( x1 − x) 2 + ( y1 − y ) 2 = d12 formula as follows (8) 1 n ( x2 − x) 2 + ( y2 − y ) 2 = d 2 2 RSSI = ∑ A[i ] (6) Let the first n−1 equations minus the last n i =1 equation in turn, and then we can obtain For getting better result of RSSI value, it has to need impose a threshold value this is called receiver sensitivity -85dB to - 90dB. x1 − xn 2 − 2( x1 − xn ) + y1 − 2( y1 − yn ) = d12 − d 2 2 xn−12 − xn2 − 2(xn−1 − xn )x + yn−12 − yn2 − 2( yn−1 − yn ) y (9) F. Relationship between RSSI and distance = d n −12 − d 2 n In this section it analyzes the relationship between The above equations can then be written as the linear type: AX RSSI and distance between two nodes considering indoor = b, where A= environment. These are the parameter which is taken in simulation. ⎛ 2( x1 − xn ), 2( y1 − yn ) ⎞ ⎜ ⎟ ⎜ 2( x2 − xn ), 2( y2 − yn ) ⎟ Constant Shadowing model- It uses constant shadowing offset whose value is 4 .In this simulator gets an input value of attenuation in dB, which will added to every transmission at ⎜ ...... ........ ⎟ every receiver. ⎜ ⎟ Lognormal Shadowing model- It uses lognormal distribution ⎝ 2( xn −1 − xn ) x, 2( yn −1 − yn ) ⎠ for the shadowing value. We calculate average value of each node at distance Where b= interval. Mobility is kept none. 182 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 3, March 2011 the area covered by the sensor nodes. In adaptive weighted x − xn + y − yn + d n − d 1 2 2 1 2 2 2 2 1 centroid localization[31]path loss exponent is adaptively estimated according to the surroundings where the target x2 2 − xn 2 + y2 2 − yn 2 + d n 2 − d 2 2 nodes situates and targeted position is calculated using WCL. …………………………………… xn −12 − xn 2 + yn −12 − yn 2 + d n 2 − d n −12 V. CONCLUSION This paper dealt with distance measurement and ⎡x ⎤ localization in IEEE802.15.4 compatible sensor network based X =⎢ ⎥ ⎣ y⎦ on RSSI. RSSI in most commonly used method for localization in this standard because of their adaptability in At last, the coordinate of node D can be written in the both indoor and outdoor environment without any extra following type by least mean square estimation: hardware .LQI combination with RSSI is used for characterizing the signal quality of receiving data and give X = ( AT A) −1 AT b better result instead of RSSI standalone. As this paper discussed two localization model for computing coordinate WCL is much better then ML in terms of complexity because . Weighted Centroid method [29]- The centroid method is a of matrix computation. The problem with RSSI with simple, efficient method that can be used in dense sensor combination of WCL is adaptation of path loss exponent for networks. This method assumes that there exists a set of real time environment, In this method it is uniform .For anchors having (i) a known position and (ii) and overlapping addressing this problem AWCL is discussed. transmission zones. The idea of the centroid method is to estimate the position of a target as the average value (i.e., the geometrical centroid) of the positions of the anchor nodes in the transmission zone of the terminal. Furthermore, the positions of the anchors can be weighted. The unknown REFERENCES position is computed as N [1] F. Akyildis, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A Survey 1 P ( x, y ) = N ∑W ( x , y ) i =1 i i i (10) on Sensor Networks,” IEEE Communications Magazine, August 2002, vol. 40, pp. 102–114. [2] Vidyasagar Potdar, Atif Sharif, Elizabeth Chang “Wireless Sensor Networks: A Survey” International Conference on Advanced Information Networking and Applications Workshops 2009 F ( x, y ) = ( xi − x) 2 + ( yi − y ) 2 (11) [3] Mohammad Ilyas And Imad Mamgoub "Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems" 2005 by CRC Press LLC [4] D. Puccinelli and M. Haenggi “WSN: Applications& Challenges “, CAS N=no of anchor nodes , (x,y) denoted the unknown node Magazine, sept. 2005 coordinate , ( xi , yi ) is the anchor node coordinate ,F(x,y) [5] Jing WANG, R. K. GHOSH, Sajal K. DAS “A survey on sensor localization” J Control Theory App 2010 8 pp 2–11 denote the error in location computing between exact and [6] Neal Patwari “LOCATION ESTIMATION IN SENSOR NETWORKS” A unknown node position. dissertation submitted of the for the degree of Doctor of Philosophy The University of Michigan 2005 The weight Wi is a constraint which depends on [7] Guoqiang Mao, Barıs¸ Fidan and Brian D.O. Anderson “Wireless Sensor Network Localization Techniques” condition of distance and characteristic of signal strength. [8] Prasan Kumar Sahoo , I-Shyan Hwang, Shi-Yao Lin “A Distributed 1 Localization Scheme for Wireless Sensor Networks”The international Wi = conference on mobile technology : application and system (mobility (d ) g conference )ACM 2008 Where d is distance between anchor and [9] Guowei Shen, Rudolf Zetik, and Reiner S. Thomä “Performance Comparison of TOA and TDOA Based Location Estimation Algorithms in unknown node. g is the degree which ensure impact of remote LOS Environment” PROCEEDINGS OF THE 5th WORKSHOP ON beacon in position determination ,higher g will increase POSITIONING, NAVIGATION AND COMMUNICATION 2008 position error .Simulation study describe that degree g=1 (WPNC’08) IEEE 2008 produces best localization results. In [30] d is considered [10] Dragos¸ Niculescu and Badri Nath “Ad Hoc Positioning System (APS) Using AOA” INFOCOM IEEE 2003 pp 1-10 energy difference between participating node. In WCL one [11] Zhen Hu ,Dongbing Gu ,Zhengxun Song, Hongzuo Li “Localization in uniform path loss exponent obtained through experiments Wireless Sensor Networks Using a Mobile Anchor Node” Proceedings of the is used to calculate the weights of nodes . It is well 2008 IEEE/ASME International Conference on Advanced Intelligent known that the path loss exponent is the essential Mechatronics [12] Tian He, Chengdu Huang, Brian M. Blum, John A. Stankovic, Tarek reflection of sensing surroundings. Therefore it is not Abdelzaher “Range-Free Localization Schemes for Large Scale Sensor appropriate that only one exponent is accepted all through Networks” mobicom 03 pp 1-15 ACM 2003 183 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 3, March 2011 [13] Peng Aiping, Guo Xiaosong, Cai Wei, Li Haibin “A Distributed and other countries. Many students of M.TECH. and PhD, are working on Localization Scheme for Wireless Sensor Networks Based on Bounding Box thesis and project work under the guidance of him. He is a active member of Algorithm” The Ninth International Conference on Electronic Measurement & various national and international journal. . His research interest is in the field Instruments ICEMI IEEE 2009 pp 1-5 of MANETs, wireless communication. [14] Wei-Wei Ji and Zhong Liu “An Improvement of DV-Hop Algorithm in Wireless Sensor Networks” IEEE2006 [15] Institute of Electrical and Electronics Engineers, Inc., “IEEE Std. Kavindra kumar ahirwar is doing M.TECH. from 802.15.4-2003 “Wireless Medium Access Control (MAC) and Physical Layer SOIT RGPV Bhopal .He is graduated in IT from JEC (PHY) Specifications for Low Rate Wireless Personal Area Networks (LR- Jabalpur in 2008.Now he is currently working on WPANs)”, New York, IEEE Press. October 1, 2003. thesis on topic distributed localization in wireless [16] Yuan Zhu, Baoli Zhang and Fengqi Yu, Shufeng Ning “A RSSI Based sensor network. 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R¨omer, H. Karl, and F. Mattern (Eds.): Springer-Verlag Berlin Heidelberg 2006” EWSN 2006, LNCS 3868, , . pp. 326– 341 [23] Mao a,, Brian D.O. Anderson , Baris Fidan” Path loss exponent estimation for wireless sensor Network localization Guoqiang” science direct 2007 [24] Jiuqiang Xu, Wei Liu, Fenggao Lang, Yuanyuan Zhang, Chenglong Wang “Distance Measurement Model Based on RSSI in WSN” Wireless Sensor Network, 2010, 2, pp 606-611 [25] Kannan Srinivasan and Philip Levis” RSSI is Under Appreciated” [26] Qingxin Zhang, Qinglong Di “A RSSI Based Localization Algorithm for Multiple Mobile Robots” CMCE IEEE 2010 [27] Zhang Jianwu, Zhang Lu “Research on Distance Measurement Based on RSSI of ZigBee” CCCM IEEE 2009 [28] Allon Rai, Sangita Ale, and Syed S. Rizvi Aasia Riasat “A New Methodology for Self Localization in Wireless Sensor Networks”IEEE2008 [29] Jan Blumenthal, Ralf Grossmann, Frank Golatowski, Dirk Timmermann “Weighted Centroid Localization in Zigbee-based Sensor Networks” IEEE2007 [30] Baozhu Li “A Low Complexity Localization Algorithm In Wireless Sensor Network” IEEE Confrence on on Innovative Computing and Communication 2010 [31] Yanjun Chen, Quan Pan, Yan Liang and Zhentao Hu “AWCL:Adaptive Weighted Centroid Target Localization Algorithm Based on RSSI in WSN” IEEE 2010 AUTHORS PROFILE Dr. Sanjeev Sharma is an Associate Professor and working as Head in the Department of in SOIT- RGPV Bhopal, Madhya Pradesh, India. He is also Deputy Registrar of RGPV University. He obtained his PhD degree from RGPV University. He has published over 50 National and International Journals & conferences various papers across India 184 http://sites.google.com/site/ijcsis/ ISSN 1947-5500

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