LOAD BALANCING USING CLUSTERING IN WSN WITH FUZZY LOGIC TECHNIQUES

Transcription

1 Volume 119 No , ISSN: (on-line version) url: ijpam.eu LOAD BALANCING USING CLUSTERING IN WSN WITH FUZZY LOGIC TECHNIQUES 1 M. Shanmukhi, 2 G. Nagasatish 1 Department of Information Technology 2 Department of Computer Science and Engineering, BVRIT HYDERABAD College of Engineering for Women,Rajeev Gandhi Nagar, Nizampet Road, Bachupally Hyderabad Abstract-A tactical wireless sensor network (WSN) is a distributed network that facilitates wireless information gathering within a region of interest. A challenge in the deployment of WSNs is the limited battery power of each sensor node. This has a significant impact on the service life of the network. In order to improve the lifespan of the network, load balancing techniques using efficient routing mechanisms must be employed such that traffic is distributed between sensor nodes and gateway(s).therefore, Clustering is considered as one of the powerful approaches for efficient utilization of energy. The proposed Load Balancing in Zone Based Clustering approach is used to balance the load of super nodes regions by dividing the region of super nodes into levels and levels into clusters using tree-based concept to optimize Cluster Head (CH) selection using Fuzzy Logic Techniques. The Unequal Region based clustering approach is used to deploy different types of sensor nodes in different region to provide efficient utilization of coverage area. Hybrid routing is used for transmitting data to Base Station (BS). The protocol optimizes the number of CH selection and balance the load of CH. We investigate the impact of deploying single and multiple gateways on the following established energy aware load balancing routing techniques: direct routing, minimum transmission energy, low energy adaptive cluster head routing, and zone clustering. Via extensive simulations using NS-2, we analyze the effectiveness of these algorithms on network performance for single and multiple gateway scenarios and show that the EELZ algorithm maximizes network lifetime and service area coverage. I. INTRODUCTION Wireless Sensor Networks (WSNs) have gained worldwide attention in recent years due to the advances made in wireless communication, information technologies and electronics field [1]. The concept of WSNs is based on simple equation: Sensing + CPU +Radio = Thousands of potential applications [2]. The sensing circuitry measures parameters from the environment surrounding the sensor and transforms them into an electric signal. CPU processed such signal reveals some properties about objects located or event shaping. Each sensor has an onboard radio that can be used to send the collected data to interested parties. Such technological development has encouraged practitioners to envision aggregating the limited capabilities of the individual sensors in a large scale network that can operate unattended [3]. All the recent research work focuses on sensor networks that consist of identical sensors with equal capacity in term of sensing, computation, communication and power. This type of sensor networks is termed as Homogeneous Environment. The working with more than one type of sensor nodes in term of sensing, communication, computation and power within same network in mentioned as Heterogeneous [4]. Each sensor node in the WSN must have the ability to simultaneously serve as a sensing device and a wireless communication device that can exchange information with nearby nodes [5]. It is critical that information from every node is communicated to a desired destination outside the network. The focus of this paper is the deployment of tactical WSNs. Tactical WSNs, as used by the DoD, are remotely deployed in potentially hostile areas with gateway nodes located on the outskirts of these areas. The network must operate reliably and maximize sensor network coverage for the maximum amount of time in the absence of human contact. A key challenge in the deployment of tactical WSNs is the limited battery power of each sensor node. This has a significant impact on the service life of the network. In order to improve the lifespan of the network, load balancing techniques using efficient routing mechanisms to achieve energy efficiency must be employed such that traffic is distributed between sensor nodes and gateway(s). The Region based Clustering approach used in this paper for cover the large area. The total area divided into fixed number of regions and deploying different types of sensor nodes in different regions according to their energy level. The paper presents Hybrid routing approach for transmitting data in which super nodes send their data by using clustering approach, whereas normal nodes transmits their data directly to BS. For CH 61

2 formation in this paper we proposed Load Balancing in Region Based clustering approach within super nodes regions using inverted Binary tree concept. The Fuzzy inference rule used to determine the best possible combination of energy and distance factor for CH selection in clusters [6]. II. RELATED WORK In past few years in WSNs, clusters of sensor nodes are designed certain criteria to send their sensed information to elected respective CHs. The CH aggregates data and compresses it, then transmitting directly to the BS. LEACH [7] is one of the first hierarchical clustering algorithms for the efficient usage of energy in the network. In LEACH there is the assumption that every node has single-hop capability to transmit their data to CH or Base Station, therefore not suitable for large area. In article [8], the authors discussed the heterogeneity in term of energy of nodes in WSNs. The SEP protocol [9], is a two level heterogeneous protocol introducing two types of nodes, super nodes and normal nodes. The selection of CH is based on their energy. The nodes are deployed in random manner therefore the problem of coverage hole arises, and may be some normal nodes are deployed far from BS. In article [10], the Enhanced Stable Election Protocol(E- SEP) describes for the three level hierarchies. E-SEP enhance the SEP protocol by introduced the one more node that i.e. intermediate node whose energy lies between normal node and super node. By introducing intermediate node the stability of network improves. The problem of E-SEP is same as the SEP. TEEN [11] protocol is first reactive protocol for time critical applications. TEEN was proposed for homogeneous network. In [12] E-LEACH was proposed, it is energy based CH selection protocol, but it has the disadvantage of considering only the energy level, in addition to the network stuck problem that shortens the network life time. In HEED protocol [13] the CHs are distributed evenly, but the problem of large numbers of iteration occurs. In [14] Distributed Clustering Algorithm had been proposed, which assumes quasi-stationary nodes with real valued weights. And in [15] Weighted Clustering Algorithm had been adopted, it combines several properties in one parameter (weight) that is used for clustering. Other works that use properties like spanning tree clusters to produce specific properties, passive clustering for use with on demand routing and degree or lowest identifier for clustering had been proposed in [16] and [17] respectively. Some methods, merely, consider distributing the energy load to select cluster head [18, 19], such single factor based selection is not enough in complicated WSNs environments. Because the node with high energy but close to edge may be selected as cluster head, other nodes may spend more energy to deliver data to cluster head, which shortens the network life time. While other methods consider multiple factors [19, 20] but many over-restricted assumptions affect the feasibility of system model to extend. III. HYBRID ALGORITHM Consider M*M region where BS is located nearly at the center. The total area is divided into unequal regions. For providing heterogeneity uses two types of sensor nodes (super node and normal node) on the basis of their energy level. The super nodes have _ (_ > 1) more energy than the normal node. Normal nodes are placed near the BS and Super nodes are placed far away from BS. The region of Super nodes again divides into levels and in each level we do optimal number of clustering using inverted binary tree concepts. System model: In our approach, make some assumption about our network: The architecture is region based clustering. BS is stationary and unlimited energy. Two types of sensor nodes (Super nodes and normal nodes). Sensor nodes and CHs are stationary. Locations of all sensor nodes are known to each other. i) Zone based deployment An efficient routing protocol is one which consumes minimum energy and provides large coverage area this is useful for gathering information from whole network. Here, divided the whole area into regions, if there are Z p region than Z 2, Z 3. Z p region for super nodes deployment and Z p region for the normal nodes deployment. ii) Routing in network environment There are two types of nodes in the network. Normal nodes are deployed near BS and transmit their data directly to BS. On other hand, Super nodes having more energy than normal nodes are deployed far away from 62

3 BS and uses clustering approach for transmitting their data to BS. iii) Zone based clustering using Fuzzy logic techniques Cluster based routing protocol consists of four stages: CH selection, Cluster formation, data aggregation and data communication. The setup state starts by election of head selection stage, which is followed by the steady state transmission state is subdivided into data aggregation and data transmission phase. We have proposed Fuzzy based decision making techniques for selection of CHs. The fuzzy decision rules set ranked the Super node s status on the basis of memberships of input functions. Initially, all the Super nodes are deployed in the Region (Z 2, Z 3. Z p) and all the super nodes have same energy initially. Each Super node contains its ID and the value of remaining energy (Residual Energy).After Deployment it is assumed that each node is known by each other. Now, divide the each region of Super nodes into approximately equal level L1, L2... LQ, next step is to form the clusters at each level shown in Figure2. Level L1 is defined as the nearest region to BS. Formation of Clusters will be start from level LQ by using binary tree concept and BS considered root of Binary tree. Cluster Formation by dividing regions, minimizes number of dead nodes and overcome the problem of Load Balancing by using inverted Binary Tree concepts as we moving toward BS, the number of cluster will increase and their size will get smaller. 7. Formation of Levels into Regions 8. Apply, for e= 2to P for each region 9. for f=1 to Q for levels 10. Ze.L 1= Z e.l 2= = Z e.l Q 11. End. 12. Set L1 near to BS of each region Z 2,Z 3. Z P. 13. Cluster formation in each level done by using Tree based concept. 14. Number of cluster at each level x=2 y Where x= Q to 1 and y= 1 to Q. 15. Total number of cluster in each Region = y Where y= 1, 2 Q. 16. Selection using Fuzzy logic technique Cluster formation. 17. CM sense data 18. Data CH 19. Data aggregation by CH 20. CH uses inter clustering routing for transmitting the data. 21. CH BS 22. End Else. 23. End. The proposed algorithm for data transmission using hybrid routing and optimal clustering in each region using tree based approach described as follow: CH= Cluster Head; CM= Cluster Member; BS= Base Station 1. Start, a) Total area= N*N, b) Total number of nodes= nn, c) n = Fraction of total number of nodes (number of super nodes having _ (_>1) times more energy) d) m * (1-n) = number of Normal nodes e) All the sensor nodes set in Zones. f) Each node contains its ID and value of energy that it retains RE. g) Network has Z 1,Z 2,Z 3. Z P regions. h) Normal nodes deployed in Region Z 1. i) Super nodes deployed in Region Z 2,Z 3. Z P 2. If, node = normal 3. Node sense data 4. Data BS 5. End if. 6. Else, node= Super Figure1: Proposed framework 63

4 In the proposed framework Figure 1, we considered if a node is normal then transmit their data directly to BS and if not then super node uses clustering techniques follow that we divided each region into approximate equal levels and do the clustering by using tree based approach. Therefore, for CH election among super nodes let us consider an N/W of 15 super nodes. Here after clustering process, apply the fuzzy rules to take the decision. So finally efficient routing process decided by modified routing protocol based on our proposed approach. IV. EXPERIMENTAL RESULTS In our simulations, sensors and gateways are all placed on a Cartesian grid with axes x and y. Our simulations and analysis involve a grid of 21 sensors such that each sensor s x and y coordinate is modeled as a uniformly distributed random variable between 0 and 250 meters (m). The single gateway scenario employs the gateway at (x; y) = (510m, 233m), while the multi gateway simulations have gateways positioned at(x1; y1) = (72m, 329m) and (x2; y2) = (80m, 200m). In our simulation, nodes are divided into two categories. First one, super nodes are setup into network like initial energy as 100joules, transmitting threshold value is e-11 with 550m distance, receiving threshold value is e-10 with 250m distance, bandwidth is 512kb, frequency is 2.4e+9 and interfacing through physical channel with a time interval 1.0ms. The second one is normal nodes, it setup into network like initial energy is 70 joules, transmitting threshold value is with 400m, receiving threshold value is e-9 with 160m, bandwidth is 512kb, frequency is 2.4e+9 and interfacing through physical channel with a time interval 1.0ms. We employ two application layer strategies, 1) a constant bit rate (CBR) generator and 2) a data aggregation application.cbr allows each node to send an L=4096 bit message to the gateway at each round. We are not concerned with the contents of each message. Instead, we only care that messages are produced so that we may observe how energy is depleted throughout the network due to the network routing algorithm in use. experimental results of the algorithm which are introduced below. Simulation parameters are shown in Table1. PARAMETER VALUE Application Traffic CBR Transmission rate 15 packets/sec Radio range 250m Packet size 512 bytes Maximum speed 10m/s Simulation time 20s Number of nodes 21 Area 1000x1000 Routing protocol AODV Table1: System parameters Figure 2: Network Deployment Figure 3: Broadcasting in Network Evaluation results In this section, we utilize the Load Balancing in Zone Based Clustering approach to conduct numerous experiments in the sensing field with data level. According to the network process, we present 64

5 Figure 4: Divided categories of nodes based on energy Figure 8: After CH formation starts data level process Figure 5: Routing level checking through AODV Figure9: Super nodes to BS data delivery based on CH Figure6: Normal nodes to BS data delivery Figure10: Cluster files represents Figure 7: Data loss due to excess traffic at normal nodes 65

6 Loss ratio (%) DROPPING RATIO Time (s) CB-S LBZ- CAS Delay (m/s) Delivery ratio (%) Energy (%) Figure 11: Trace file set up in network End to End delay Figure12: Performance on Delay ENERGY CONSUMPTION Figure13: Energy level routing PACKET DELIVERY RATIO Figure 14: Delivery ratio Time (s) CB-S LBZ- CAS Time (s) CB-S LBZ- CAS CB-S LBZ- CAS Time (s) Figure 15: Dropping ratio in routing T In above screenshots, Figure2 shows all nodes placed in network and deployment of nodes is in network properly. Here all nodes displayed based on topology values and all properties of NAM window it should be mentioned. Figure3 shows the broadcasting occur throughout the network. Here broadcasting occurs for communication purpose. All nodes should be involved in this process. Figure4 shows and represents categories of normal nodes and super nodes based on taking energy. Figure5 shows that, all nodes participate and check the routing for request for data. Here normal nodes data aggregation process starts. Figure6 shows that, data delivery from normal nodes to base station. Here the energy levels of normal nodes immediately transfer the data to BS. Figure7 shows that data loss in network during the communication from normal nodes to BS. In this process, normal nodes have low energy. Figure8 shows that we accessing CH level process and starts the data level process. Figure9 has indicated communication between super nodes and BS through CH s. Here super nodes have more energy compare to normal nodes. Figure10 shows and represents cluster file. In this, details about CH selection and distance calculation from node to node for clustering process. Figure11 has to be measure the all attributes and values then time duration setup these all are and shows in trace file. In Figure12, graph shows and represents end2end delay and it shows a simulation time versus delay. The performance of Load Balancing in Zone Based Clustering approach improves delay time it means decrease the delay between communication nodes compare to cluster based system. Figure13 shows and represents energy consumption and it shows a simulation time versus energy. The performance of Load Balancing in Zone Based Clustering approach improves energy values compare to cluster based system. Figure14 shows and represents packet delivery ratio and it shows a simulation time versus delivery ratio. The performance 66

7 of Load Balancing in Zone Based Clustering approach improves the delivery ratio compare to cluster based system. Figure15 shows and represents dropping ratio and it show a range versus loss ratio. The performance of Load Balancing in Zone Based Clustering approach improves the tour time it means save the time interval compare to cluster based system. V. CONCLUSION The proposed Load Balancing in Zone Based Clustering approach is use to minimize the load balancing of super nodes regions. By dividing the region of super nodes into levels and levels into clusters using tree based concept to optimize CH selection using Fuzzy Logic techniques as well as re-election process time also. In this paper, we used NS-2 tool and simulate the network and increase the network lifetime. REFERENCES [1] I.F. Akyildiz, W. Su, Y Sankarasubramaniam, and E.Cayirci, A survey on sensor networks, CommunicationMagazine, IEEE, vol. 40, pp , [2] Brain Otis, Jan Rabaey, Ultra- Low Power WirelessTechnologies for Sensor Networks, Springer, [3] Ameer Ahmed Abbasi, Mohamed Younis, A survey on clustering algorithms for wireless sensor networks, Computer Communications, Elsevier, vol. 30, pp , [4] Pottie G. and Kaiser W., Wireless Integrated Network Sensors, Communication of the ACM, vol. 43, no. 5, pp.51-58, [5] W.R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, Energy efficient communication protocol for wireless microsensor networks, in Proc. of HICSS, 2000, pp [6] Kalpana Singh, A.K. Daniel, Load Balancing in Region Based Clustering for Heterogeneous Environment in WSNs Using AI Techniques, IEEE, [7] Wendi Rabiner Heinzelman, Anantha Chandrakasan, andhari Balakrishnan, Energy- Efficient Communication Protocol for Wireless Micro sensor Networks in Proceedings of the 33rd Hawaii International Conference on System Sciences, vol. 8, pp , [8] Ye M., Li C., Chen G., and Wu J., EECS: An Energy Efficient Clustering Scheme in Wireless Sensor Networks, in Proceedings of 24th IEEE International Performance, Computing; and Communications Conference (IPCCC 2005), pp , [9] Smaragdakis G., Matta I., and Bestavros A., SEP: A Stable Elcetion Protocol for clustered Heterogeneous Wireless Sensor Networks in 2nd International workshop on Sensor and Actor Network Protocols and Applications (SANPA),2004. [10] Aderohumnu, F. A., & Deng, J. D.(2009).An Enhanced Stable Election Protocol for clustered Heterogeneous WSN (No. 2009/07) Discussions Paper Series. [11] Arati Manjeshwar and Dharma P. Agrawal, TEEN: A Routing Protocol for Enhanced Efficiency in WirelessSensor Networks, in 1st International Workshop on Parallel and Distributed Computing Issues in Wireless Networks and Mobile Computing, San Francisco, [12] S. Basagni, Distributed Clustering Algorithm for Ad-Hoc Networks, Proc. Int l Symp. Parallel Architectures, Algorithms, and Networks (I- SPAN), [13] M. Chatterjee, S.K. Das, and D. Turgut, WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks, Cluster Computing, pp , [14] Mengning Yang, Dan Yan, Chao Huang, An improvement HEED clustering algorithm for Wireless Sensor Network [J [15] M. J. Handy, M. Haase, D. Timmermann, Low energy adaptive clustering hierarchy with deterministic cluster-head selection [C [16] Jian Xu, Dandan Qin A new LEACH-Based Routing Clustering Protocol in WSN, Journal of information & computational Science (2013), , [17] S.K.Gupta, N. Jain, P. Sinha Node Degree based clustering for WSN, International Journal of Computer Applications( ), Volume 40- No.16,February [18] B. McDonald and T. Znati, Design and performance of a distributed dynamic clustering 67

8 algorithm for ad-hocnetworks, in Proc. Annual Simulation Symposium, [19] M. Gerla, T. J. Kwon, and G. Pei, On demand routing in large ad hoc wireless networks with passive clustering, in Proc. WCNC, [20] M. Chatterjee, S. K. Das, and D. Turgut, Wca: A weighted clustering algorithm for mobile ad hoc networks, in Cluster Computing, 2002, pp

9 69

10 70