A Hybrid Localization Scheme for Underwater

Wireless Sensor Networks

Dr. C. Rama Krishna# I

# Associate Professor, Department of CSE

NITTTR, Chandigarh, India

lrkc 98@hotmail.com

Abstract- In past few years a rapid growth of interest in

Underwater Wireless Sensor Networks (UWSNs) have been

observed due to its vital range of scientific exploration like early

warning system for natural disasters, ecosystem monitoring, oil

drilling, tactical military surveillance, undersea explorations and

assisted navigation. Now research orientation is towards solving

the issues related to the large scale UWSNs such as reliable

transport, routing and localization. In this paper we propose a

hybrid localization scheme, which is integration of two different

localization schemes. The aim of scheme is to achieve high

localization ratio with relatively low localization error. The

performance comparison of proposed scheme with integrating

schemes will be done via simulation results.

Keywords- Least square method; Area localization

schem;, Ordinary node localization; Anchor node localization;

Localization error, Localization ratio; Range-based scheme;

Range-free scheme.

I. INTRODUCTION

A UWSNs deployed in the sea uses sensors equipped with acoustic modems that enable them to communicate wirelessly with one another. Acoustic communications [1] is used rather than Radio Frequency because Radio signals cannot travel far underwater due to absorption losses. Propagation characteristics differ consistently due to the factors that influence acoustic communication like transmission loss, impaired channel due to noise, sparse deployment of sensor, multi path propagation due to node mobility, high propagation delay, frequency translation due to doppler spreading. Acoustic signals attenuate less, and they are able to travel further distances than radio signals and optical signals. However the bandwidth of the acoustic channel is low, hence the data rates are lower than they are in terrestrial WSNs. UWSNs can use static sensors or some vehicles deployed in the mid of sea to cooperate in sensing and monitoring area of interest and send their data to a surface sink in a single-hop or multi-hop manner for real-time processing . .

II. LOCALIZATION

Localization is a problem of estimation of location of a node. It can be done locally by position information relative to other nodes or globally by latitude, longitude, altitude information. Location information is needed for data tagging, without node location information the data received in the sink

978-1-4799-2900-9/14/$31.00 ©2014 IEEE

Pankaj Singh Yadav *2

* PG Scholar, Department of CSE

NITTTR, Chandigarh, India

2pankajsahil17@gmail.com

node cannot be identified where it comes from and becomes meaningless to the application. Nodes without location information are known as blind or ordinary nodes and with location information are known as beacon or anchor nodes. Localization is not only needed for tagging of an object it is also needed for finding optimum coverage of an area and finding optimal routes in geographic routing. Location information can be used to design efficient networking and management protocols. Localization problem is closely dependent on how nodes are deployed in environment. Depending on network size and area to be monitored nodes can be deployed manually or by some Autonomous Underwater Vehicle (AUV). Manually deployed networks are human-accessible and nodes register their locations during deployment. In more complex cases when the area is not human-accessible and there are many nodes in the network, then nodes should be deployed by a vehicle. Localization needs several objects like Anchors, DET (Detachable elevator transceiver), for location estimation [2] and distance or angle

measurements between these anchors and ordinary nodes.

III. RELATED WORK

Localization techniques can be categorized according to Input Data (range-free and range-based), Accuracy (fine­grained and coarse grained), Positioning (relative and absolute), Node placement (mobile and fixed), Topology (sparse and dense), Tracking (cooperative and passive target) and Beacons (beacon-free and beacon-based). Localization is started with initial step as Location Discovery followed by Position Computation [3] and finally localization. Location discovery is possible only by estimation between two different nodes either distance or angle.

It is found in literature survey that most of localization schemes are either range-based or range-free. Range-based schemes uses ToA (Time-of-Arrival), TDoA (Time­Difference-of-Arrival), RSSI(Received Signal Strength Indicator) [4] etc. method for fine estimation of location whereas range-free schemes [5] uses DV-Hop, ALS(Area Localization Scheme), APIT(Approximate Point In Triangle) etc. method for coarse estimation of location. Both the schemes can use distributed or centralized localization algorithm. Centralized algorithms collect information in a single server to process whereas in distributed approach

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localization algorithm is executed on each node so that they can locate themselves relative to their neighbours. After this relative localization phase, a local and global map is created. This is usually achieved by merging local maps and transforming coordinate systems with respect to known node positions. Two steps localization is done, first for anchor node then for ordinary node. Fine estimation at anchor node is essential whereas we can use coarse estimation at ordinary nodes.

Any scheme that relies on ToA [6] or TDoA requires tight time synchronization [7] between the transmitter and the receiver clocks [8]. Distance can be calculated using difference in propagation times of acoustic and radio signals. RSSI-based localization schemes need to take into account multi path effects and attenuation losses. Hop-Count based method needs a classical distance vector [9] exchange method to get distance in number of hops. If node distribution is non­uniform then density aware hop-count method is used for localization. Area localization method [10] estimates location within certain area, rather than exact coordinate of sensor. Based on power levels and ranges of anchor nodes grid is divided into multiple smaller areas.

IV. PROPOSED HYBRID LOCALIZATION SCHEME

The hybrid scheme consists of four types of nodes which are Surface Buoys, Detachable Elevator Transceiver (DETs), Anchor Nodes and Ordinary Nodes. Surface buoy will be equipped with Global Positioning System (GPS) on the water surface for off-shore base station communication. A DET is attached to a surface buoy that can dive and rise to broadcast its beacon (position) signal at regular interval. The anchor nodes will compute their positions based on the position information from the DETs by applying least square method. Now anchor nodes got the location information after some time they will start broadcasting. Ordinary node will receive signal from anchor nodes and area localization method [10] will be applied for ordinary node localization. Now ordinary nodes are localized, they have their real location and they can transmit information of their purpose. It is assumed in network architecture (Fig. 1 ) that underwater sensors are equipped with pressure sensor, which will provide depth (z-coordinate) of nodes. Anchor nodes are placed at the comers of square grid.

Fig. I Network Architecture

We can use Ranger 2 or Scout for acoustic positioning that is based on a technique known as Ultra-Short Base Line (USBL) positioning. USBL offers high accuracy performance (0.1 % to 0.2% slant range error) combined with efficient subsea tracking operations.

V. COMPUTATION METHODOLOGY

The proposed work is divided in two steps. First is anchor node localization where range-based distributed method will be used that is time based infrastructure dependent [11] method. Least square method will be used for location estimation. Second is ordinary node localization where range­free centralized method will be used, that is, area localization method. Because of the use of both range-free centralized method and range-based distributed method, the proposed scheme is known as hybrid localization scheme.

Initialize no. of buoy, B = 0, maximum no. of buoy, B = n

Buoys start broadcasting of signals

Anchor node records coordinate

signal and distance from

different buoy, B = B + 1

No B�n

Yes

Using B = 1 as a linearization tool generate

system of equation in Ax-B form

Least square method

Anchor nodes localized

Anchor nodes start

broadcasting of signals

Record frequency (j) of signal power levels

generated by different anchor nodes

No

No Is there any power

End

Fig. 2. Computation Flow Diagram

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VI. ANCHOR NODE LOCALIZATION

Least square method (LSM):- Let (Xj, yj) are the coordinate values received from n different buoys and A(x, y) is the unknown position of anchor node and distance between them is rj so:-

(x - xji + (y - yi = r? (i = 1, 2, . . . ,n) (I)

For linearization we can use any of coordinate as linearization tool, let us use /h coordinate, adding and subtracting the coordinate:-

(X- Xj + Xj - xl + (y - Yj + Yj - yl = rj2

(i = 1, 2, .... , j+l, j-I, . . , n)

After resolving and simplification equation will be:-

(2)

Here rj and ri are the distances between anchor node and jth buoy and ith buoy, dij is the distance between jth and ith buoy.

1 Let us write 2" [rj2 - ri2 +dij2] = bij so:-

(4)

As we have already told we can use any coordinate as linearization tool, let j= I and putting the value of i=2,3,4, ...... ,n.

(X- XI) (X2 -XI) + (y -YI) (Y2 -YI) = b21

(X- Xl) (x3-xd+ (y-yd(Y3-yd= b31

(X- Xl) (Xn -xd + (y -yd (Yn -yd = bnl

(5)

(6)

(7)

System of equations can be written in matrix form, Ax = B, Now we have to minimize the value of IIAx-BI12 to get the best fit value of coordinate and minimize the error associated with values. Putting first order derivative of equation equal to zero will give values of A and B.

VII. ORDINARY NODE LOCALIZATION

Area localization method (ALS):- Now anchor nodes having location information will start broadcasting of signal. Ordinary node will listen every signal from different anchor nodes and record them. Ordinary node at particular location will get localization signals from anchor node at different power levels. A threshold value is set to determine which power levels should be accepted from anchor nodes depending on the frequency of occurrence, for our simulation this value is set to 70%, if there is no power level with frequency more than threshold value all the power levels will be considered. If the number of anchor nodes is large naming scheme can be used. Signal coordinate representation is used to show the location of ordinary node and contour lines are drawn to indicate the power levels of anchor nodes. Now either ideal signal model or irregular signal model can be used to estimate the location of ordinary nodes.

VIII. PERFORMANCE EVALUATION

Simulation setting:- Our proposed hybrid scheme has been implemented in Omnet++ (Open network simulator). In our simulation experiments, it is assumed that the anchor nodes and the ordinary sensor nodes are static and have pressure sensor which provides depth (z coordinate), 6 buoys equipped with GPS are placed at fixed position on water surface. Distance measurements between nodes are assumed to follow normal distributions, with real distances as mean values and standard deviations to be one percent of real distances. Every Buoy is attached with DET, which uses acoustic transceiver for communication with anchor nodes. 500 ordinary sensor nodes are distributed randomly underwater, of which 30% are anchor nodes with communication range 500m. Area to be covered is lkm X lkm X lkm. Anchor nodes calculate their coordinates by least square method and ordinary nodes calculate their coordinates by area localization method. Confidence threshold is set to 70%.

IX. SIMULATION RESULT ANALYSIS

In our simulation, we consider two performance metrics: Localization ratio and Localization error to compare our hybrid scheme with Area localization scheme (ALS) and Least square method (LSM) scheme.

A. Localization ratio

Localization ratio is the ratio of number of sensor nodes that can be localized by the broadcast messages of different anchor nodes to the total number of nodes. Localization ratio will depend on transmission range of anchor node and anchor node percentage.

0 � & 5 � rl � r. " 0 ..J

12 ...-----------------

0 . 8

0.6

0.4

0.2

-+-ALS

Hybrid

...... LSM

10 0 15 0 2 0 0 2 5 0 lOa 350 400 450 500

NlUubl'l' of s(,lIsor lIodes Anchortransmissionrange = 500m Anchor percentage = 30%

Fig. 3. Localization ratio versus node density

Fig. 3. shows that approximate 90% ordinary nodes can be localized above anchor density 9 and does not increase much with the node degree increase.

B. Localization error

Localization error is the ratio of number of faulty localized ordinary node to total number of nodes. Localization error significantly decreases with increased number of sensor nodes.

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Fig. 4. Localization error versus node density

Fig.4. shows that our scheme outperforms than other two schemes, above ordinary node density 200 and achieves localization error to .05, which is quite small and is acceptable. Conclusion and future work In this paper, we presented a hybrid localization scheme for UWSNs. In this approach, the localization process consists of two sub-processes: anchor node localization and ordinary node localization. Simulation results shows that our proposed scheme can achieve high localization ratio with relatively small localization error. Performance metrics like Distance error, Average accuracy, Localization coverage can also be tested in future for proposed method. Proposed scheme uses one hop broadcast message, in future we can check performance of our proposed scheme in multi-hop environment.

x. CONCLUSION AND FUTURE WORK

In this paper, we presented a hybrid localization scheme for UWSNs. In this approach, the localization process consists of two sub-processes: anchor node localization and ordinary node localization. Simulation results shows that our proposed scheme can achieve high localization ratio with relatively small localization error. Performance metrics like Distance error, Average accuracy, Localization coverage can also be tested in future for proposed method. Proposed scheme uses one hop broadcast message, in future we can check performance of our proposed scheme in multi-hop environment.

REFERENCES

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[2] Jose Esteban Garcia, "Positioning of Sensors in Undetwater Acoustic Networks," IEEE, OCEANS'OS, September 200S, vol. 3, pp. 2088-2092.

[3] Chang Ho Yu, Kang Hoon Lee, Hyun Pil Moon, Jae Weon Choi, Young Bong Seo, "Sensor Localization Algorithms in Undetwater Wireless Sensor Networks," IEEE, ICROS-SICE'09, August 2009, pp.1 760-1 764.

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[S] Tian He, Chengdu Huang, Brian M. Blum, John A. Stankovic, Tarek Abdelzaher, "Range-Free Localization Schemes for Large Scale Sensor Networks," ACM, MobiCom '03, September 2003, pp. 81-9S.

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[7] John Heidemann, Wei Ye, Jack Wills, Affan Syed, Yuan Li, "Research Challenges and Applications for Underwater Sensor Networking," IEEE, WCNC'06, April 2006, vol. 4, pp. 228-23S.

[8] Affan A. Syed, John Heidemann, "Time Synchronization for High Latency Acoustic Networks," IEEE, INFOCOM'06, April 2006, pp 1-12.

[9] Drago S Niclescu, Badri Nath, "DV Based Positioning in Ad Hoc Networks," IEEE, Telecommunication Systems, 2003, pp. 267-280.

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