Design of PSO Based Localization Technique for Mobile Sinks in Wireless Sensor Networks

(1)

International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459,ISO 9001:2008 Certified Journal, Volume 4, Issue 8, August 2014)

Design of PSO Based Localization Technique for Mobile

Sinks in Wireless Sensor Networks

Prerana Shrivastava

1

, Dr. S. B Pokle

2

, Dr. S. S. Dorle

3

1

Research Scholar, Electronics Department, G.H.Raisoni College of Engineering, Nagpur University, India

2_{Electronics & Communication Department, RCOEM, Nagpur University, India} 3_{Electronics Department, GHRCE, Nagpur University, India}

Abstract – In today’s wireless Sensor Networks, the location information of the various sensors that are deployed in the network is the basic requirement. Localization plays a very vital and challenging role where it becomes very important to find the location of the various sensors with respect to the geographical extent of the entire network. Here we have proposed a localization technique for mobile sink based on the hop development in order to estimate the position of the sensors and also we have employed the particle swarm optimization technique in order to reduce the errors that may accumulate due to the iterative process. Moreover our proposed technique will try to overcome and minimize the problems that are related with the increased hardware cost and the accuracy of the Localization scheme. The proposed localization strategy has been simulated in NS-2.32 and the evaluation of the proposed technique has been done based on the various performance metrics of the network. The simulation results obtained showed that our proposed localization strategy improves the performance metrics like Energy, Drop, Delivery Ratio and Delay as compared to the traditional method of localization which is the range based Localization technique.

Keywords– Wireless Sensor Network (WSN), Particle Swarm Optimization Localization Strategy (PSOLS), Range based Localization Technique (RBLT)

I. INTRODUCTION

A Wireless Sensor Network comprises of large number of sensors that are closely organized. Basically the sensors are the sensing devices which sense and monitor the physical conditions such as temperature, pressure, motion, sound, vibration at a number of locations. Generally the sensors are battery operated tiny devices and therefore have limited memory processing, power and communication capabilities. Also they may not have global identification [1], [2].

In case of wireless sensor networks localization techniques plays a very important role in determining the co-ordinates of the sensors and the accurateness of any localization scheme is highly desirable[3].In various WSN applications like monitoring, tracking, geographic routing the location information is the primary requirement.

In these applications the acquired information would be irrelevant if they are not associated with the location of the sensors [4].The traditional method of the localization algorithm refers the input data in order to estimate the location or position of the sensors. For this it uses the information that is provided by the neighboring sensors in order to calculate the position [5].

The Localization Techniques are mostly classified into two groups. They are range based localization techniques and range free localization techniques. The range based localization techniques calculate the position of the sensor by using the information provided by the other sensors in its surrounding area. This technique is based on the assumption that the accurate distance between the sender and the receiver can be predictable by one or more features of communication signal from sender to the receiver. The various range based localization techniques obtain the range information by mapping the angles between their neighbors or by translating the signal strength into distance. Another method used is the time of arrival method where in order to obtain the range information the signal propagation from source to destination is calculated. The range information can also be acquired by using the time difference of arrival where an ultrasound is used to calculate the distance between the sensor and the source [6].

On the other hand the range free localization techniques never tries to approximate the fixed point to point distance on the basis of the received signal strength or any other features of the received communication signal like time, angle etc.Majority of the range free localization methods are based on the ad hoc positioning system and uses a hop by hop algorithm to estimate the position of the sensors that are deployed in the network [7].

II. RELATED WORK

(2)

International Journal of Emerging Technology and Advanced Engineering

The authors developed and compared two different sensor selection schemes for static source localization. The first method iteratively turns on those non-anchor sensors that capitalize the mutual information between source location and the quantized sensor measurements. The second method is the sensor choice method where at each iteration several non-anchor sensors are turned on whose quantized data is reduced. The performance metric discussed in this paper is the energy efficiency. However in this system the computational complexity of sensor selection increases linearly when the number of activated sensors per iteration is increased.

Hongyang Chen, Qingjiang Shi et al. [9] discussed the problems related to the localization. The authors presented a new co-operative localization scheme which can attend high localization accuracy in mobility assisted wireless sensor networks when obstruction exists. The authors have developed optimal localization accuracy. This scheme can achieve high localization accuracy by including a mobile element. However there is a need to improve the proposed localization scheme using real sensors in mobility assisted wireless sensor networks.

Yun Wang, Xiaodong Wang et al. [10] proposed an algorithm based on the analysis of hop progress in a wireless sensor network with randomly organized sensors and uninformed node density. By deriving the predictable hop progress from a network model for WSN in terms of network parameters, the distance between any pair of sensors can be accurately computed by this algorithm. This algorithm attained better performance and less communication overhead. But however this system is very complex and needs to be discussed related with the delay of the signal in order to improve the performance of the system.

III. PROBLEM IDENTIFICATION

In case of wireless sensor networks, the sensors are battery operated devices. Thus communication, processing and sensing are expensive since they actively decrease the life span of the sensor. Localization must cost as little as possible and at the same time produce reasonable results. Hence there is an urgent need to develop the localization algorithms that will reduce the power cost, Hardware cost and the deployment cost. Also the localization algorithms that depend on beacon my fail if the beacon concentration is not highly adequate in a particular region. Environmental obstacles and terrain irregularities can also cause devastation on localization. Large rocks can deflect the line of sight thereby preventing TDOA ranging or may interfere with the radios thereby introducing error into RSSI ranges and producing wrong hop count ranges.

The main focus for designing any localization technique is to minimize the hardware cost, increase the localization accuracy and reduce the error accumulation which results from the iterative process.

IV. PROPOSED METHODOLOGY

For overcoming the various problems identified we are proposing a particle swarm optimization (PSO) based techniques for the mobile elements assisted cooperative localization scheme. In this scheme we have made use of the estimated step forward metric where in along with the transmission radius and the current position the estimated step count is also broadcasted by the mobile elements. After this the PSO is applied for estimating the locations of unknown sensors by using this information. By using the estimated step forward metric the distance between any pair of source and destination sensors within the same transmission range in a sparsely deployed network can be computed and mathematically it is given by,

d s = S * E ( S) (1)

Where S is the step count and E(S) is the estimated step forward metric.

The PSO based localization algorithm is implemented for the following scenario:

At the time of search process each particle update its estimated step forward function E (S) i and position Xi

according to following two functions:

E(S)i (t+1) = w⋅ E(S)i + c1⋅rand()⋅(Pibest (t)- Xi(t))

+ c2⋅rand(). (Pgbest (t) - Xi (t)) (2)

Xi (t+1) = Xi (t) + E (S) i (t) (3)

Where,

t is the iterative step,

E (S)i and Xi are velocity and position of particle i at step

t, Pi best (t) is the fitness position of particle i at step t,

Pg best (t) is the fitness position of group at step t,

E (S) i (t+1) is the expected distance of particle i at step t+1,

Xi (t+1) is the position of particle i at step t+1,

rand is any random number between 0 and 1, c1 and c2 are constants and

w is the weight.

Considering that the unknown sensor have enough neighbor anchor to estimate its position and after obtaining the number of existing sensors the mobile sensor starts collecting the information. Then the proposed algorithm can be explained in the following steps:

(3)

International Journal of Emerging Technology and Advanced Engineering

Step 2: Each Mobile sensor (MS) goes on collecting the location information which is broadcasted by neighbor anchor N.

Step 3: When any unknown MS discovers 3 or more anchors in its neighborhood, it will use a beacon signal strength measurement and E (S) metrics to measure the location of the discovered anchors.

Based on the localization algorithm for calculating the fitness function we assume that MS engage the PSO in order to estimate its coordinate (XS, YS) and E (S) i is the

calculated distance D and the anchor i. The error equation for real and estimated location of D can be given as below:

2 2 2

)

(

)

(

)

(

E

S

X

Xs

Y

Ys

e

i



i



i





i



(4)

The above error equation can be written as follow if the sensor S has n neighbor anchors:



 









n i i S i i n i

i

E

S

X

Y

Ys

e

1 2 2 2 1

)

(

)

(

)

(

(5)

In order to improve location error accumulation problem resulting from iterative process, we add a weighted value (1/ E (H) i) to adjust error equation as below:

i n i m i i i n i i

S

E

Y

Xm

X

S

E

e

)

(

1 )

)

(

)

(

)

(

1 2 2 2 1













  (6)

Here n is the number of anchors and m is the particle from 1 to K and E(S) i is the ranging distance of the

anchors. In case if any inaccurate ranging data exist, we can make use of shortest distance to constrain the estimated location and hence location errors can be reduced. Each particle will compute the fitness value according to equation (6) in the searching process and update Pbest by the

location with the smallest error. The particle with the smallest location error is the fittest particle which is closest to real location. Hence the location error is taken as fitness value and Gbest is set as the Pbest having the smallest fitness

value. The proposed algorithm provides more accuracy in localization of the nodes in the sparse WSN.

V. SYSTEM MODEL AND PARAMETERS

The performance evaluation of the proposed PSO based Localization technique is done through NS 2.34.For this we have deployed the sensors in a bounded region of 1000 x 1000 sq.m. using uniform distribution. We have used the distributed coordination function of IEEE 802.11 as the medium access control for wireless LAN’s.

The power levels are assigned to the sensors such that the transmission and sensing range is 250m.For the mobile hosts, the channel capacity is set to 2Mbps.The simulated traffic used is the constant bit rate and the routing protocol used is AODV.

VI. SIMULATION RESULTS

The TCL script for our proposed algorithm is run under NS-2.32 environment. The NAM output will give us the visualization of our network model. For tracing and monitoring our simulation we will run our trace file which we have set in the TCL script. The trace values obtained will be analyzed by making use of the trace data analyzer that is the X-graph .The evaluation of our PSO based Localization strategy (PSOLS) has been done with the traditional method of localization Scheme (TMLS) which is based on the range based method. The performance metrics considered were the Packet Delivery Ratio, End to End Delay and the Energy consumption. The X graph were observed for the packet delivery ratio, End to End Delay and the energy consumption by varying the number of the sensors as well as the transmission rate and the mean value was computed.

TABLE I

MEAN VALUES FOR PERFORMANCE METRICS BY VARYING NUMBER OF

SENSORS AND DATA TRANSMISSION RATE

Perform ance Metrics Localizat ion Strategy µ value with varying number of sensors % Improv ement µ value with varyin g data rates % Impro veme nt Packet Delivery Ratio Existing TMLS 2.08835 2 61.02 % 1.9311 242 65.31 % Proposed PSOLS 8.56646 5 75.62 % 6.9660 466 72.27 % End to

End Delay Existing TMLS 2.21230 4

(4)

International Journal of Emerging Technology and Advanced Engineering

[image:4.612.48.291.133.306.2]

Fig 1 : Percentage Improvement with respect to µ value by varying number of sensors

Fig 2 : Percentage Improvement with respect to µ value varying Data Rates

From the above table 1 and fig.1 and fig.2 we observe that the proposed PSOLS shows an improvement in the packet delivery ratio, minimizes the end to end delay and achieves a significant amount of reduction in the average energy consumption as compared to the existing TMLS.The observations were made by varying the number of sensors and the data transmission rate.

VII. CONCLUSION

In this paper we have presented the benefits of employing the PSO based localization strategy in case of Wireless Sensor Networks which reduces the hardware cost since our proposed scheme is range free technique.

In our proposed strategy the sensors position is first estimated by the estimated step forward metric and then the particle swarm optimization algorithm is used to minimize the localization errors. This results in accurate localization information about the various sensors. The simulation results showed that the various performance metrics like the packet delivery ratio, end to end delay and energy consumption showed a significant amount of improvement as compared to the traditional method of localization Scheme. The work can further be extended by considering the impact of PSOLS on node density, node connectivity and location estimation error.

REFERENCES

[1] Mohamed K.Watfa, Sesh Commuri, “An Energy Efficient Approach to Dynamic Coverage in Wireless Sensor Networks”, Journal of Networks, vol.1, No.4, August 2006.

[2] Khin Thanda Soe,” Increasing Lifetime of Target Tracking Wireless Sensor Networks”, World Academy of science, Engineering and Technology, vol.44, August 2008.

[3] Amitangshu Pal, “Localization Algorithms in Wireless Sensor Networks: Current Approaches and Future Challenges”, Network Protocols and Algorithms, vol.2, No.1, 2010.

[4] Vibha Yadav, Manas Kumar Mishra, A.K. Singh, M.M. Gore, “ Localization Scheme for three dimensional wireless sensor networks using GPS Enabled Mobile Sensor Nodes”, International Journal of Next Generation,vol.1,No.1,December 2009.

[5] Ewa Niewiadomska Szynkiewicz,Michal Marks,Mariusz Kamola,” Localization in wireless sensor networks using heuristic Optimization Techniques”, Journal of Telecommunications and Information Technology, pp. 55-64, 2011.

[6] Po Jen Chuang, Cheng Pei Wu,”Employing PSO to enhance RSS Range Based Node Localization for wireless sensor networks”, Journal of Information Science and Engineering, pp.1597-1611, December 2011.

[7] Cesare Alippi, Giovanni Vanini, Politecnico di Milano,” A RSSI based and calibrated centralized localization technique for wireless sensor networks”, proceedings of IEEE International Conference on Pervasive Computing and Communications, 13-17 March 2006. [8] Engin Mas Azade,Ruixin Niu,Pramod K.Varshney, Mehmet

Keskinoz, “Energy Aware Iterative source Localization for wireless sensor networks”, IEEE Transaction on Signal Processing, vol.58, No.9, September 2010.

[9] Hongyang Chen, Qingjiang Shi, Rui Tan,H. Vincent Poor, Kaoru Sezaki, “Mobile Element Assisted Co-operative Localization for wireless sensor networks with obstacles”,IEEE Transaction on wireless communications,vol.9,No.3,March 2010.

[10] Yug Wang, Xiaodong Wang, Demin Wang,Dharma P.Agrawal, “Range free localization using expected Hop progress in wireless sensor networks”, IEEE Transaction on Parallel and distributed Systems, vol.20, No.10, October 2009.

[image:4.612.48.290.334.505.2]

(5)

International Journal of Emerging Technology and Advanced Engineering

AUTHOR’S PROFILE

Mrs. Prerana Shrivastava obtained her Bachelor’s degree in Electronics Engineering from University of Nagpur, India. Then she obtained her Master’s degree in Electronics Engineering and currently pursuing her PhD in Wireless Sensor Networks from G.H.Raisoni College of Engineering, Nagpur, India. She is working as an Assistant Professor in Electronics Department at Lokmanya Tilak College of Engineering, University of Mumbai, India. Her specializations include Electromagnetic Wave Theory, Image Processing and Computer Networks. Her current research interests are Wireless Adhoc Networks, Wireless Sensor Networks and Network Security.