IJEEE, Vol. 1, Issue 4 (August, 2014)
e-ISSN: 1694-2310 | p-ISSN: 1694-2426
SURVEY OF DIFFERENT LOCALIZATION TECHNIQUES OF WIRELESS SENSOR NETWORKS 1
Vimee Walia, 2Simarpreet kaur
1
Electronics and Communication Department, SPCET, Punjab, India Electronics and Communication Department, BBSBCE, Punjab, India
2 1
walia.vimee87@hotmail.com, 2simarpreet.kaur@bbsbec.ac.in
ABSTRACT: Recent advances in radio and embedded systems have enabled the proliferation of wireless sensor networks. WSN's are massively being used in different environments to perform various monitoring tasks such as research, rescue, disaster management, target finding and various tasks in smart environments. In various tasks, node localization is one of the most important system parameters. Node localization is essential to report the origin of tasks, support group query of sensors, routing and to answer questions on the network exposure. So, one of the basic challenges in wireless sensor network is node localization. This paper reviews different techniques of node localization in wireless sensor networks. The general idea of the schemes proposed by different scholars for the enhancement of localization in wireless sensor networks is also presented. I. WIRELESS SENSOR NETWORKS Sensor networks are the key to gathering the information needed by smart environments, whether in buildings, utilities, industrial, house, transportation systems automation, or elsewhere. Recent terrorist and guerrilla warfare countermeasures require distributed networks of sensors that can be deployed using, aircraft, and have self-organizing capabilities. In these applications, wires or cables are usually not viable. A sensor network is required to be fast and easy to install and maintain. Desirable functions for sensor nodes include: easy to install, self-identification, selfdiagnosis, consistency, time awareness for coordination with other nodes. Since most applications compute their positions in some fixed coordinate system, it is of great importance to design efficient localization algorithms. Localization means to determine location of nodes in a network. With the support of some communication, a node can determine its location in the network by extracting information received from the infrastructure; also, by making a node to send signals periodically, the infrastructure can calculate the location of the nodes. For example, GPS is a typical localization system. There are 24 satellites positioned at the altitude of 20200 km and distributed in 6 orbital planes. These satellites share the high accurate atomic clocks and they know exactly their coordinates. A GPS receiver can receive signals from at least 4 satellites if the receiver is not hidden from the line of sight. By matching the code International Journal of Electrical & Electronics Engineering 20
pattern in the signal, a receiver can calculate the time shift and know the distance away from that satellite by multiply the time shift to the speed of light. After that, the GPS receiver can figure out its coordinate based on some localization algorithm. II. PARAMETERS OF LOCALIZATION For the different ways of estimating location information, we have to name parameters to distinguish the similarities and differences between different techniques. In this section we present the most distinctive parameters to classify different techniques. Accuracy: Accuracy is very important in the localization of wireless sensor network. Higher accuracy is typically required in military installations, such as sensor network deployed for intrusion detection. However, for commercial networks which may use localization to send advertisements from neighbouring shops, the required accuracy may not be lower. Cost: Cost is a very challenging issue in the localization of wireless sensor network. There are very few algorithms which give low cost but those algorithms don’t give the high rate of accuracy. Power: Power is necessary for computation purpose. Power play a major role in wireless sensor network as each sensor device has partial power. Power driven from battery. Static Nodes: All static sensor nodes are homogeneous in nature. This means that, all the nodes have identical sensing capacity, computational skills, and the ability to communicate. We also assume that, the initial battery powers of the nodes are identical at deployment. Mobile Nodes: It is assumed that a few number of GPS enabled mobile nodes are part of the sensor network. These nodes are consistent in nature. But, are assumed to have more battery power as compared to the static nodes and do not drain out completely during the localization process. The communication range of mobile sensor nodes are assumed not to change drastically during the entire localization algorithm runtime and also not to change significantly within the reception of four beacon messages by a particular static node. III. LOCALIZATION TECHNIQUES Since WSN concept was introduced, localization of sensor nodes and position tracking applications has been an important study. Nowadays so many techniques www.ijeee-apm.com
and technologies have been developed till now for the off-the-shelf location systems. The location systems can be made more specific for meeting different needs and environments such as precision, indoor/outdoor environment, position techniques, ranging schemes, security, devices accessible, WSN deployment restriction, network scaling, realization cost and healthy consideration. Categorization of the location systems is divided into tree structure from technology point of view.
approaches to indoor localization using signal strengths from 802.11 routers. They also suggest that adding additional hardware or altering the model of the environment is the only alternative to improve the localization performance. Time based methods (ToA, TDoA): These methods record the time-of-arrival (ToA) or time-difference-of-arrival (TDoA). The propagation time can be directly translated into distance, based on the known signal propagation speed. 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. But this line-of-sight condition is difficult to meet in some environments. Furthermore, the speed of sound in air varies with air temperature and humidity, which introduce inaccuracy into distance estimation. Acoustic signals also show multipath propagation effects that may impact the accuracy of signal detection. Angle-of-Arrival (AoA): AoA estimates the angle at which signals are received and use simple geometric relationships to calculate node positions. Generally, AoA techniques provide more accurate localization result than RSSI based techniques but the cost of hardware of very high in AoA.
Fig. 1 Localization Techniques
Localization is most prominent research field in wireless sensor networks (WSN) and it is usually defined as the process of determination of positions of unknown nodes which are intended nodes by using information of some known nodes that are called anchor nodes dependent on measurements like distance, time of arrival (TOA), time differences of arrival (TDOA), and angle of arrival (AOA). As WSN require the awareness of sensing information that is giving rise to the issue of localization in most of the applications that are recently proposed. The localization estimation technique is a 2step process: 1.
Ranging step: In the ranging step, nodes are believed to estimate their anchors by using signal propagation time or strength of the received signal. There is need to compute the parameters precisely which is not possible because of noise and some other factors; therefore their consequences are localization algorithms are not accurate [1]. Received Signal Strength Indicator (RSSI): 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 into a distance estimate. This method has been used mainly for RF signals. RSSI is a relatively cheap solution without any extra devices, as all sensor nodes are likely to have radios. The performance, however, is not as good as other ranging techniques due to the multipath propagation of radio signals. In [20], the authors characterize the limits of a variety of
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2.
Position Estimation step: In the position evaluation step, it is done using ranging information. This can be done either by solving a set of simultaneous equations or with any optimization techniques that will reduce the localization crisis. This is a repeating method where some nodes i.e. anchor and localization procedure is iterated unless all nodes are settled or no more can be localized Hyperbolic 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. 2a. 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. 2b. 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. 2c.
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Fig. 2 (a) Hyperbolic trilateretion (b) Triangulation (c) ML estimation
IV. DIFFERENT APPROACHES of LOCALIZATION for WIRELESS SENSOR NETWORKS This section presents different proposals put forward by the research community in the areas of localization in wireless sensor networks and critiques their contributions. Research on localization in wireless sensor networks can be classified into two broad categories. Centralized Localization: Centralized localization is basically migration of inter-node ranging and connectivity data to a sufficiently powerful central base station and then the migration of resulting locations back to respective nodes. The advantage of centralized algorithms are that it eliminates the problem of computation in each node, at the same time the limitations lie in the communication cost of moving data back to the base station. As representative proposals in this category [7], [8], [9] are explained in greater detail. The techniques which are based on centralized model are explained below. MDS-MAP: The advantage of this scheme is that it does not need anchor or beacon nodes to start with. It builds a relative map of the nodes even without anchor nodes and next with three or more anchor nodes; the relative map is transformed into absolute coordinates. This method works well in situations with low ratios of anchor nodes. A drawback of MDS-MAP [12] is that it requires global information of the network and centralized computation. Localize node based on Simulated Annealing: This algorithm does not propagate error in localization. The proposed flip ambiguity mitigation method is based on neighbourhood information of nodes and it works well in a sensor network with medium to high node density. However when the node density is low, it is possible that a node is flipped and still maintains the correct neighbourhood. In this situation, the proposed algorithm fails to identify the flipped node. A RSSI-based centralized localization technique: The advantage of this scheme is that it is a practical, self organizing scheme that allows addressing any outdoor environments [13]. The limitation of this scheme is that the scheme is power consuming because it requires extensive generation and need to forward much information to the central unit. Distributed Localization: In Distributed localizations all the relevant computations are done on the sensor nodes themselves and the nodes communicate with each other to get their positions in a network. These can be broadly classified into 5 parts
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(a) Beacon-based distributed algorithms: Categorized into three parts: Diffusion: In diffusion the most likely position of the node is at the centroid [16] of its neighbouring known nodes. APIT requires a high ratio of beacons to nodes and longer range beacons to get a good position estimate. For low beacon density this scheme will not give accurate results. Bounding box: Bounding box forms a bounding region for each node and then tries to refine their positions. The collaborative multilateration enables sensor nodes to accurately estimate their locations by using known beacon locations that are several hops away and distance measurements to neighbouring nodes. At the same time it increases the computational cost also. Gradient: Error in hop count distance matrices in the presence of an obstacle. (b) Relaxation-based distributed algorithms: The limitation of this approach is that the algorithm is susceptible to local minima [3]. (c)Coordinate system stitching based distributed algorithms: The advantage of this approach is that no global resources or communications are required. The disadvantage is that convergence may take some time and that nodes with high mobility may be hard to cover. (d) Hybrid localization algorithms: The limitation of this scheme is that it does not perform well when there are only few anchors. SHARP gives poor performance for anisotropic network. (e) Interferometric ranging based localization: Localization using this scheme requires considerably larger set of measurement which limits their solution to smaller network. V. CONCLUSION Some localization schemes have fewer merits and greater demerits and some of them have less demerits and greater merits. These merits and demerits were the main source for proposing the idea of a unique approach which is the enhanced composite approach. Localization problem is an open challenge in wireless sensor network. There are many aspects where we need improvements such as how to define threshold value in wireless sensor network. The performance of any localization algorithm depends on a number of factors, such as anchor density, node density, computation and communication costs, accuracy of the scheme and so on. All approaches have their own merits and drawbacks, making them suitable for different applications. Some algorithms require beacons (Diffusion, Bounding Box, Gradient, APIT) and some do not (MDS-MAP, Relaxation based localization scheme, Coordinate system stitching). Beaconless algorithms produce relative coordinate system which can optionally be registered to a global coordinate system. Sometimes sensor networks do not require a global coordinate system. REFERENCES www.ijeee-apm.com
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