INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET)

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1 INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) ISSN (Print) ISSN (Online) Volume 4, Issue 1, January- February (2013), pp IAEME: Journal Impact Factor (2012): (Calculated by GISI) IJARET I A E M E DISTANCE BASED CLUSTER HEAD SECTION IN SENSOR NETWORKS FOR EFFICIENT ENERGY UTILIZATION Mohanaradhya 1, Andhe Dharani 2, Sumithra Devi K A 3 1 (R.V College of Engineering, Bangalore, India, 2 (R.V College of Engineering, Bangalore, India, 3 (R.V College of Engineering, Bangalore, India, ABSTRACT In wireless sensor network resource utilization is a challenging task. Handling power related issues is very difficult to manage than other resources. The energy consumption in sensor nodes happens with transmission, reception and data aggregation, by reducing any of this process without affecting the normal function of the network the lifetime of the network can be increased. The maximum amount of power is utilized in data transmission from nodes to cluster head and base station. Clustering is one of the efficient methods to increase the lifetime of WSN by efficient utilization of energy. Currently many clustering algorithm aimed for achieving the better lifetime of the network by selecting a cluster head based on residual energy, random selection, Etc. But the random selection may not give optimize number of cluster head and do not guarantee the efficient way of selecting the cluster head. This paper proposes a method which avoids the nodes within threshold distance and nodes nearer to sink become cluster head. By this no node within the transmitting range to sink and the node within the transmitting range of the other cluster head can become cluster head. The nodes nearer to the sink directly transmit to sink which avoids reception. The cluster head is selected based on the threshold distance. So it can control the nodes becoming cluster head within certain distance which improve the lifetime of the network. Keywords: Wireless sensor network, LEACH, Clustering, Cluster head. 50

2 I. INTRODUCTION Wireless sensor networks (WSNs) utilize a large number of intelligent [1] microsensor nodes with sensing, processing and wireless communicating capabilities. Energy is the scarcest resource of WSN nodes, and it determines the lifetime of WSNs. So energy efficiency has been known as the most important issue in research area of wireless sensor networks (WSN). The study and the development of reliable wireless sensor networks is very challenging. In fact, the main problems of wireless sensors are related to its powering and the communication of sensed data. Energy is a scarce and usually nonrenewable resource for such wireless sensor networks. A significant number of studies on wireless networks have focused on increasing the lifetime of sensor nodes through several strategies, involving different levels of the design hierarchy. In the case of sensor networks, energy optimization is much more complex, since it involves not only the reduction of the energy consumption of the single sensor node but also the maximization of the lifetime of the entire network. Sensor networks incorporate technologies essentially from three different research areas: sensing, communication and computing. Especially considering the highefficiency required in terms of power management, this paper focus on the communication issue which is the most expensive in the entire sensor network power budget. Reducing energy dissipation of the entire network is challenging as it entails a trade-off between energy consumption and system performances. It is necessary that all components of the system are needed to operate at as low a duty cycle as possible. In addition to low duty cycle, we expect to coordinate with the application to shut off the node for very long periods of time. Shutting down idle nodes or idle node sub units, when no interesting events occur, reduces the amount of power spent, but at the same time state transitions imply power overhead (e.g. idle listening) and reduce network connectivity increasing latency. Many proposals [2] have been proposed for the cluster head selection. These proposals can be classified into two classes according to the factors of selection. Proposals in the first class merely consider distributing the energy load to select CH [3, 4]. Such single factor based case is not adequate in complicated WSN environments. In this case, the node with high energy but close to the edge of the cluster may be selected as CH. The other nodes have to spend more energy in delivering the data to CH, which in turn shortens the network lifetime. Proposals in the second [5,6] class take multiple factors into account. Whereas the over-restricted assumptions, such as homogeneous nodes and nodes with a little or no mobility, reduce the feasibility of the system models to a large extent In order to increase the feasibility of the system model, some unnecessary constrains on the assumptions should be removed and more factors should be considered. The cluster head selection can be modeled as a multiple factors decisionmaking process. However, the different measurements units and complex interrelation between multiple factors complicate the cluster head selection process. It is difficult to find a generic metric to simplify the multiple-factors problem. Naturally, a question arises: how to incorporate multiple factors to choose a suitable CH. 51

3 II. EXISTING METHOD FOR SELECTION OF CLUSTER HEAD In this section we are describing how the cluster heads are selected in some of the existing algorithm. Low-Energy Adaptive Clustering Hierarchy (LEACH) It is a self-organizing and adaptive clustering protocol proposed by Heinzelman. The operation of LEACH [3] is divided into rounds, where each round begins with a setup phase for cluster formation, followed by a steady-state phase, where data transfers to the sink node occurs. Though LEACH uses random election of cluster heads to achieve load balancing among the sensor nodes LEACH still has some problems which are listed as follows, In LEACH, a sensor node is elected as the cluster head according to a distributed probabilistic approach. Non cluster nodes decide which cluster to join based on the signal strength [based on the distance of the cluster head and the member node].this approach insures lower message overhead, but cannot guarantee that cluster heads are distributed over the entire network uniformly and the entire network is partitioned into clusters of similar size, and the load imbalance over the cluster heads can result in the reduction of network lifetime. If the probability of selecting cluster head increases in LEACH then it can give improved efficiency than its previous probability. But it cannot guarantee the equal distribution of the cluster heads due to random selection. There is chance of selecting the nearby node as cluster heads (as shown in figure-1) which can affect the energy efficiency of the sensor networks. The probability of selection of cluster head is given by the below equation and it does not consider any distance as constraints. And there is chance that cluster head will serve the member that is nearer to the sink.(as shown in figure-3) If the node lies in between the cluster head and sink then based on the signal strength (distance) it will decide to send data to sink or cluster head. Here it is only checking the distance to sink and cluster head; it is not considering the energy required by cluster head to receive the data by this node and send the processed data to sink. This may exceed energy consumed in this mode than the member can send it directly to base station Figure below shows the selection of cluster head of 100 nodes deployed in an area of 100X100m and the probability (p) of becoming the cluster head is 0.5.In figure -1 the ellipse nodes are the cluster head which are selected very close to each other, which can affect the efficiency of the network. SEP [7]: Stable election protocol which is for an energy heterogeneity sensor networks. This protocol defines two cluster head probabilities one for advance node and another for normal node. The weighted probabilities to obtain the threshold that is used to elect the cluster head in each round is define as T(snrm) the threshold for normal nodes and T(sadv) the threshold for advanced nodes. 52

4 Figure-1: Cluster head selection in LEACH Figure-2: Cluster head selection in SEP (1) (2) Where pnrm in (1) and padv in (2) are the probability of number of advance and normal node, r =round, G and G are the set of non cluster head. The next selection procedure of cluster head is same as in LEACH. But in SEP the probability of becoming the cluster head is more in the advance node than the normal so that the energy is equally distributed in the network. But in SEP also the probability of selecting the cluster head is not based on the distance between the cluster head. The main idea of selecting the cluster head is similar to LEACH. In the figure-2 shows the how the cluster head is selected in the SEP algorithm and the ellipse nodes shows that some of advance nodes marked as * also included in nearby region to other cluster head. III. PROPOSED ALGORITH Proposed algorithm selects the cluster head based on the threshold distance calculated based on the transmitting range of the sensor nodes deployed and the probability of node that should not become cluster head until some consecutive rounds after becoming a cluster head. Working of Proposed Algorithm: The working is divided into three mechanisms first we are selecting the no cluster region, second selection of cluster head, third transmission when there is no cluster head 53

5 1. Calculating Threshold distance based on the minimum transmission range of nodes deployed: In figure-3 we can observe that O represents a node and R is the minimum transmitting range of the node which varies based on the type of node and area of network. The threshold distance r is given by the below equation, Here hexagon is considered as it can divide the network without leaving any uncovered area. Selecting No Cluster Head Region: In this we are selecting a region in which there will be no nodes which are selected as cluster head within the threshold distance of the sink. This region is called as No Cluster head Region (NCR) region. (a) (b) Figure-3 In figure 3a we can notice that there are two cluster heads one is serving only one member and another does not having any members. So by avoiding the node to becoming cluster head in the region nearer to sink can save some amount of energy by directly transmitting to the sink as shown in figure-3b This NCR [No Cluster head Region] is based on the threshold distance considered with respect to the base station. That is if the distance between the node and the sink is lesser than r means that node is not eligible for becoming as a cluster head. In the nth round of selection there may be no cluster heads selected as most of the nodes are dead in such situation the node within the threshold distance will directly transmit to the sink. 54

6 Selection of cluster head: As discussed in section II the probability method of selecting cluster head will not results in equal distribution of the cluster head. So we are selecting the cluster head based on the threshold distance r. The nodes outside the NCR region are eligible for becoming cluster head but all nodes that are outside the NCR are not selected as cluster head. Initially one node is selected as a cluster head randomly which satisfy the NCR constraints. Next in the same round the node will acquire the region r around itself and restrict the nodes present in its acquired region to become a cluster head. So the next eligible node will check whether it is in the acquired region of any other cluster head if not it will become a cluster head or else the node will join as a member to the cluster head of the acquired region. In the below figure - we can see that the cluster heads selected for 100 nodes deployed in an area of 100X100m in which the cluster head are equally distributed, and no nodes are selected as a cluster head within the threshold distance r. Figure-4: Cluster head selected by our proposed algorithm Figure 5 and 6 shows the selection of cluster head after 150 rounds, and observed that in LEACH the number of alive node is less in far away region from base station where as in proposed algorithm the numbers of alive nodes are more. Figure-5: Cluster head selected after 150rounds by LEACH Figure-6: Cluster head selected after 150rounds by proposed 55

7 IV. RESULTS AND COMPARISONS In this section we compare the simulation results of LEACH and proposed method. The graphs below are the simulation results obtain by simulating LEACH and proposed one in the Matlab. The comparison is based on same initial energy, network area, energy requires transmitting and receiving data, sink position. By this simulation we generated the graphs that show the failure nodes of LEACH and proposed algorithm as a network life time. As we can notice in figure-6, that the cluster head selected in equally distributed in the network in our proposed algorithm and there are no cluster head in NCR region and figure-7 shows the lifetime of the networks in terms of failure nodes as we simulated the results for 100 nodes and initial energy of 0.05J and simulated for a round of 200 in an area 100X100m. In LEACH the failure nodes are very less till the first node dies compare to proposed algorithm but after the first node dies the failure nodes increase drastically compared to the proposed algorithm. At the completion of 200th round LEACH as 99 failure nodes where as proposed algorithm as 91 failure node which is less than the LEACH. Hence we can say that this algorithm is 9% energy efficient than LEACH. Figure-7: Failure nodes in each round We assumed that all node as homogeneous node with initial energy of 0.05j and we are deploying a 100 number of nodes. So the overall network energy will be equal to 5J (number of nodes *Initial Energy). 56

8 Table 1 shows the comparison of Residual Energy and number of Cluster Head selected of LEACH and Proposed algorithm. Rounds Residual Energy(J) Number of CH LEACH Proposed LEACH Proposed Table-1: Residual Energy and Number of CH of LEACH and Proposed algorithm in subsequent rounds. In table-1 we can observe the residual energy decrease as number of round increases due to energy consumption for computational and transmission process. The data illustrates that residual energy is more in proposed than in LEACH.Figure-8 shows the residual energy of proposed vs. LEACH Figure-8: Residual Energy 57

9 Figure-9: Number of Cluster Head Figure 9 shows the number of cluster head selected by LEACH and proposed. Cluster head selection is consistent than LEACH. V. CONCLUSION In this paper, we have introduced a distance based cluster head selection mechanism for WSNs. This algorithm selects the cluster heads maintaining a minimum threshold distance between cluster heads. Minimum threshold distance between the cluster heads leads to efficient utilization of energy by evenly distribution and consistent number of cluster head selection. REFERENCES Journal Papers [1] ZHAO Chang-xiao, ZHOU Tian-ran, LIU Xiao-min, Xiong Hua-gang Prediction-based Energy Efficient Clustering Approach for Wireless Sensor Networks, Journal of Convergence Information Technology, Volume 6, Number 4. China,April 2011 [2] Yaoyao Yin1, Juwei Shi1, Yinong Li2, Ping Zhang2, Cluster Head Selection Using Analytical Hierarchy Process For Wireless Sensor Networks. The 17th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC'06), Beijing University of Posts and Telecommunications Beijing, China Proceedings Papers [3] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, Energyefficient communication protocol for wireless micro sensor networks, in Proc. of the 33rd Annual Hawaii International Conference on System Sciences (HICSS), Maui, HI, pp , Jan [4] M. J. Handy, M. Haase and D. Timmermann, Low energy adaptive clustering hierarchy with deterministic cluster-head selection, in Proc.4th International Workshop on Mobile and Wireless Communications Network, pp , Sept [5] M. Chatterjee, S. K. Das and D. Turgut, An on-demand weighted clustering algorithm (WCA) for ad hoc networks, IEEE GLOBECOM,vol. 3, pp , Nov [6] I Gupta, D. Riordan and S. Sampalli, Cluster-head election using Fuzzy Logic for wireless sensor network, in Proc. of the 3rd Annual Communication Networks and Services Research Conference (CNSR 05), pp , May [7] Georgios Smaragdakis Ibrahim Matta Azer Bestavros SEP: A Stable Election Protocol for clustered heterogeneous wireless sensor networks Boston University Boston. 58

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