To Increase the Lifetime by Using Grid Optimization and Energy Efficient Clustering Scheme in Wirele

ISSN 2319 - 6629 Volume 3, No.5, August – September 2014 Tejinder Kaur et al., International Journal of Wireless Communications and Network Technologies, 3(5), August – September 2014, 82-86

International Journal of Wireless Communications and Networking Technologies Available Online at http://warse.org/pdfs/2014/ijwcnt02352014.pdf

To Increase the Lifetime by Using Grid Optimization and Energy Efficient Clustering Scheme in Wireless Sensor Network Tejinder Kaur1, Manpreet Singh2, Silki Khurana3 1

ECE, Punjab Institute of Technology, PTU Main Campus, Jalandhar, India, E-mail: khehra562@yahoo.com.

ECE, Punjab Institute of Technology, PTU Main Campus, Jalandhar, India, E-mail: reet_0987@hotmail.com.

3 ECE, Punjab Institute of Technology, PTU Main Campus, Jalandhar, India, E-mail: khuranasilkee@gmail.com.

2

ABSTRACT

Rechargingand replacement of battery of sensor nodes becomes very difficult when deployed in a sensing area for any application where it is not an easy task to access.

Therefore, it becomes very important to save energy of nodes and to prolong lifetime of sensor nodes.

An important factor while designing a wireless sensor network is network lifetime which depends upon the energy of the node which is quite limited by the battery of the sensor node. In wireless sensor network, clustering is considered to be an energy efficient strategy and LEACH is the most well known clustering technique. Random selection of the cluster leads to its inefficiency due to the unbalance load in the cluster. In this paper, we proposed a technique based on grid optimization and energy efficient clustering scheme. In this technique, the given sensing area divided into virtual grids where each grid represents a cluster. To optimize the grids mobile nodes are used so that the number of nodes in each grid should be equal which leads to balance the traffic load in each cluster and prolong the lifetime of network. In this technique, in order to balance the energy of network, the residual energy of the node and distance of the node from the sink is considered for the selection of cluster head. For comparison LEACH, LEACH-MIMO, EELEACH and EE-LEACH-MIMO schemes are used.

MATLAB is used for simulation and simulation results shows that, our proposed technique performs better when compared to the mentioned techniques in terms of energy saving, load balancing and prolonging network lifetime.

One of the energy management strategies in wireless sensor networks is clustering which divides the network into various clusters and in each cluster a sensor node is selected as cluster head. Instead of each sensor node transmitting their own data directly to the sink, sensor nodes in a cluster transmits their data to the cluster head and, then the cluster head aggregates the data received from sensor nodes and transmits further it to the sink (base station).

There are lots of energy efficient schemes which have been proposed to prolong lifetime of WSNs. Among all these various clustering based schemes used for increasing lifetime are very much popular. One of the traditional routing algorithms for wireless sensor networks is LEACH (Low-Energy Adaptive Clustering Hierarchy). It is a hierarchical based routing algorithm, which divides sensing field into various clusters and randomly selects a cluster head and data fusion which helps to save and balance the consumption of energy in the network. However, LEACH goes through lots of disadvantages [3]-[6].

Keywords: energy efficient clustering, grid optimization, LEACH, EE-LEACH, LEACH-MIMO, EE-LEACHMIMO, network lifetime, wireless sensor network.

1.

In this paper, clustering topology which is based on grids is proposed. Important points that are taken into consideration in this paper are: 1) Mobility of nodes is used to optimize grids so that more uniform distribution of nodes in each grid can be achieved. 2) Both, the residual energy of each sensor node and distance of each sensor node from the sink are considered during the selection of cluster head. 3) To provide more load balancing, saving energy and increasing network lifetime, energy efficient clustering routing algorithm is used.

INTRODUCTION

Wireless sensor networks (WSNs), used in lots of applications, so it is considered to be one of the most attractive research field in past few years [1]. Normally, the sensor nodes in such networks have resource constraints like low storage capacity, limited energy and low computing ability. However, one of the core challenges is the energy efficiency of wireless sensor networks because limited battery lifetime is provided to the sensor nodes.

Rest of the paper is organised as follows: Section 2, briefly describes the related work to increase lifetime of network in 82

Tejinder Kaur et al., International Journal of Wireless Communications and Network Technologies, 3(5), August – September 2014, 82-86 recent years; Section 3, presents the system model used;Section 4, describes the proposed technique in detail;Section 5, simulation results are shown and discussed; And at last, Section 6 finally draws the conclusion of the paper.

2.

distance and choosing the optimal cluster-head. Reference [14], GA is proposed for the reduction in the energy consumption in WSNs. In this, for reducing the total minimum communication distance, genetic algorithm allows the formation of pre-defined independent clusters. Their results show that the communication distance decreases by 80 percent by the formation of the pre-defined independent clusters when compared to the direct transmission distance.

RELATED WORK

In recent years, lots of energy efficient clustering based routing algorithms and protocol have been proposed for saving energy and enhancing lifetime of the network. Few of relevant algorithms are described as follows:

Reference [15], GAF (Geographical Adaptive Fidelity) protocol is proposed in which sensing area is divided into several grids and sensor nodes are assigned to different grid on the basis of location information. In each grid a grid head (cluster head) is elected which receives and aggregates the data transmitted by the grid member nodes, and routes the aggregated data to the base station.

One of the traditional clustering based routing algorithms is LEACH which reduces the energy consumption of the network. In LEACH, there is a rotation of cluster head to balance the energy depletion of the network [2]. However, LEACH has many drawbacks, such as unevenness in the distribution of cluster heads and also during the selection of the cluster heads, the residual energy of nodes are not considered [3]-[6]. Hence, a large number of improved algorithms over LEACH are proposed such as PEGASIS (Power Efficient Gathering in Sensor Information Systems) [8], HEED [9] and TEEN [10]. When compared with the LEACH, PEGASIS results in better network lifetime, but there is a need of dynamic topology adjustment and also end-to-end delay is significantly high, this is not suitable for large size networks. On the different side, HEED (Hybrid Energy-Efficient Distributed clustering) selects the cluster heads on the basis of the residual energy of nodes and proximity measure of the neighbour sensor nodes or node degree. The integration of LEACH protocol and MIMO technology in wireless sensor networks is proposed in literature [11] and their simulation results show that due to the of cluster heads and clusters in LEACH, in a good manner it can support cooperative MIMO transmission.

3.

SYSTEM MODEL

3.1 Network Model The system model is where N nodes are uniformly randomly deployed in M*M area of space. The following points taken into consideration during deployment: 1) The sink node (base station) is deployed outside the sensing region and has no restrictions on energy consumption.

2) The sink node is immovable and all other nodes are movable in nature and their positions can be obtained using distance formula.

3) An ideal energy-efficient MAC protocol is going to be used in the MAC layer, so that any retransmission caused due to interference and collision are not taken into consideration.

In reference [12], the combination of cooperative MIMO and EE-LEACH (energy-efficient LEACH) protocol is proposed named as an energy-efficient cooperative MIMO transmission technique. In this technique, sensing field is divided to form clusters; cooperative nodes are chosen by considering the location and cluster heads by the residual energy of the sensor nodes. Comparison shows that, EELEACH-MIMO scheme provides more network lifetime as comparison to that of above mentioned schemes. But in this scheme, still there is no problem found related to unequal distribution of nodes in each cluster.

Figure 1: Network model for optimized grid based clustering

3.2 Energy Consumption Model

There are also few evolutionary based algorithms proposed.

Reference [13], author proposed a genetic algorithm based clustering algorithm by the minimization of transmission

The model which is used to describe energy consumption for wireless communication depends upon communication 83

Tejinder Kaur et al., International Journal of Wireless Communications and Network Technologies, 3(5), August – September 2014, 82-86 distance. For the single transmission, energy consumption in k-th power path-loss channel, for the transmission of l-bit at a distance of d meters for both transmitting and receiving are respectively:

4.2 Grid Optimization Grid optimization is used so that there should be equal number of nodes in each grid and so that the load on the network can be balanced.

ETx(l,d) = lETx-elec+ lEampdk (1) 4.3 Cluster Head Selection ERx(l) = lERx-elec(2) After optimization of grids there will be selection of cluster head in each cluster. During the selection of cluster head the two important factors are considered, first the residual energy of node and secondly the distance of node from the sink.

Refer to “(1)” and “(2)”, where, ETx-elec and ERx-elecrepresents the energy consumption per bit the transmitter and receiver circuit respectively, the effect of amplifier, antenna and carrier frequency at prescribed bit error rate is denoted by factor Eamp.

4.4 Data Transmission The energy consumed by network depends on: Once the clusters are formed, and cluster head has been chosen, data transmission can start. For saving energy of the nodes, member nodes transmit data to the cluster head in their pre-assigned TDMA slot and rest of the time they remains in sleep mode. After data is collected from member nodes, cluster head fuses the data and transmit it to the base station.

1) Intra-cluster communication i.e. the transmission of data from member nodes to the cluster head of their respective clusters.

2) Inter-cluster communication i.e. the transmission of data from the cluster head to the sink.

3) Circuit operations corresponding to the transmission of data.

4.

5.

In table 2, simulation parameters are shown which is performed in MATLAB. For grid optimization, nodes are moved from one grid to another grid according to the proposed technique so that there should be equal number of nodes in each grid and load on network is well balanced.

And for the selection of cluster head (grid head) in each cluster (grid), residual energy and distance of node from the sink is considered.

PROPOSED ALGORITHM

In this section, we describe proposed technique i.e. energy efficient clustering scheme based on grid optimization using mobile nodes. The entire sensing field is divided into virtual grids then mobility of nodes is used to optimize the grids so that there should be uniform distribution of nodes in each grid. The entire operation of proposed algorithm consists of following phases as shown in table 1:

Table 2 Simulation Parameters for Proposed Technique

Table 1 Process of Proposed Algorithm

Grid Partition

Grid Optimization

Cluster head Selection

SIMULATION RESULTS

Data transmission

4.1 Grid Partition The total numbers of nodes N are deployed uniformly randomly over sensing area M*M, sensing area is divided into virtual square grids of equal size G*G which represents a cluster and the length and width of each grid is also equal in size.

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Parameter Name

Parameters

Area of network

100m*100m

Number of nodes

100 (mobile)

Position of Sink

(50,175)

Number of grids

9

ETx-elec

50 nJ/bit

ERx-elec

50 nJ/bit

Eamp

100 pJ/bit/m2

Packet size

2000 bits

Initial energy

0.5 J

Channel type

Channel/ wireless

Tejinder Kaur et al., International Journal of Wireless Communications and Network Technologies, 3(5), August – September 2014, 82-86 In this we create a scenario, where 100 nodes are randomly deployed in a sensing area of 100*100 meters and the sink is deployed outside the sensing area. Then the sensing area is divided into 9 grids i.e. 3*3 grids of equal length and width or we can say in 3 rows and 3 columns. Each node sends data to its particular cluster head and then further from cluster head to the sink. For each round, a cluster head is selected on the basis of residual energy and distance of node from the sink. And for obtaining network lifetime the number of rounds is calculated before first node dies (FND), half node dies (HND) and last node dies (LND).

It is observed from the comparison graph that, in EELEACH, FND, HND and LND prolong when compared to LEACH and LEACH-MIMO scheme. In LEACH scheme, FND, HND and LND after 571, 889 and 1094 rounds respectively. Whereas for EE-LEACH scheme values for FND, HND and LND are 932, 1119 and 1374 respectively and for LEACH-MIMO scheme values for FND, HND and LND are 758, 1080 and 1276 rounds respectively. So, it shows that EE-LEACH scheme is more energy efficient when compared to LEACH and LEACH-MIMO schemes.

By combining EE-LEACH with cooperative MIMO scheme, EE-LEACH-MIMO shows enhancement over LEACHMIMO on FND, HND and LND in terms of network lifetime and more energy efficiency. In EE-LEACH-MIMO scheme values for FND, HND and LND are 1040, 1274 and 1342 rounds respectively. Our proposed technique performs better than all the schemes mentioned in terms of more enhancements over FND, HND and LND, so it offers more energy efficiency and more networklifetime. In proposed technique values for FND, HND and LND are increased to 1391, 1876 and 2360 rounds respectively.

Figure 2 shows the network lifetime graph for proposed technique. This lifetime graph is obtained between the numbers of alive nodes with respect to the number of rounds. To evaluate this lifetime graph of proposed technique the number of round before first node dies (FND), half node dies (HND) and last node dies (LND) are considered. For this scenario, with 100 nodes and 9 grids the first node dies at 1391 round, half node dies at 1876 round and last node dies at 2360 round.

Figure 2: Lifetime Graph of Proposed Technique

Figure 3: Comparison graph of Network Lifetime of Proposed Technique with existing techniques

In the Figure 3, the comparison graph is obtained between the proposed technique and existing techniques for network lifetime obtained between number of alive nodes and number of rounds. By our results we concluded that our proposed technique has maximum lifetime when compared with that of existing techniques. To compare our results of proposed technique with the existing techniques; LEACH, EE-LEACH, LEACH-MIMO and EE-LEACH-MIMO schemes are taken as reference.

And in Table 3, the tabular representation of proposed technique and other existing techniques is represented for comparison where the values of FND, HND and LND are shown. It shows that, the lifetime of wireless sensor network is enhanced; which also helps to balance the load on network and make network more energy efficient by the grid optimization.

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Tejinder Kaur et al., International Journal of Wireless Communications and Network Technologies, 3(5), August – September 2014, 82-86 Table 3 Comparison table of lifetime for Proposed Technique with existing techniques

NUMB ER OF ROUN DS WHEN

LEA CH

EELEA CH

LEAC HMIM O

EELEA CH MIM O

PROPOS ED TECHNI QUE

FND

571

932

758

1040

1391

HND

889

1119

1080

1274

1876

LND

1094

1374

1276

1342

2360

6.

International Conference. Information Technology,pp.822827, 2010.

[5] X. Li, N. Li, L. Chen, Y. Shen, Z. Wang, and Z. Zhu, “An Improved LEACH for Clustering Protocols in Wireless Sensor Networks,” Proc. International Conference Measuring Technology and Mechatronics Automation(ICMTMA 10), pp. 496-499, 2010.

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[9] O.Younis, S.Fahmy, “HEED: a Hybrid, EnergyEfficient, Distributed clustering approach for Ad Hoc sensor networks,” IEEE Transaction on Mobile Computing 3 (2004)366–379.

[10] A.Manjeshwar, D.P.Agrawal, “TEEN: a routing protocol for enhanced efficiency in wireless sensor networks,” in: Proceedings of 15th International Workshop on Parallel and Distributed Computing Issues in Wireless Networks and Mobile Computing, San Francisco(2002)pp.2009–2015.

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[12] Yongming Qin, Qiuling Tang, Ye Liang, XiuyuYue, Xian Li, “An Energy-Efficient Cooperative MIMO Scheme for Wireless Sensor Networks Based on Clustering” 14th IEEE Conference on Computational Science andEngineering (CSE) pp. 471-474, 2011.

[13] P.C. Huruiala et al., “Hierarchical routing protocol based on evolutionary algorithms for wireless sensor networks,” in: Proceedings of the Roedunet International Conference (RoEduNet) (2010).

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[15] YaXu, John Heidemann, Deborah Estrin, “Geography informed energy conservation for ad hoc routing”, Proceedings of the Seventh Annual ACM/IEEE International Conference on Mobile Computing and Networking(ACM Mobicom),2001. IEEE–31661.

CONCLUSION

To improve the lifetime of the wireless sensor network we propose a new technique known as energy efficient clustering scheme based on grid optimization using mobility concept. Proposed scheme adopts mobility of nodes to optimize grids in order to equal the number of nodes in each grid. The residual energy and distance of nodes is considered to select cluster head to balance energy consumption of network. By comparison with other existing techniques our proposed scheme has more number of rounds when all node dies i.e., it offers improved lifetime with comparison with existing schemes such as LEACH, EE-LEACH, LEACHMIMO and EE-LEACH-MIMO over values of first node dies (FND), half node dies (HND) and last node dies (LND).

Our proposed technique is more energy efficient and load on network is also well balanced.

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