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International Journal of Computer Networking, Wireless and Mobile Communications (IJCNWMC) ISSN 2250-1568 Vol. 3, Issue 4, Oct 2013, 141-148 Š TJPRC Pvt. Ltd.

ENERGY OPTIMIZATION CLUSTERING ALGORITHM FOR WIRELESS SENSOR NETWORK SHITAL DABHADE & PALLAVI ZAMBARE Department of Computer Engineering, Smt. Kashibai Navale College of Engineering, Pune, Maharashtra, India

ABSTRACT In this article, efficient energy saving scheme and related algorithm have to be developed and intended in order to provide reasonable energy consumption and to get better the network life span for wireless sensor network systems. The cluster-based method is one of the approaches to reduce energy utilization in wireless sensor networks. In this article, we propose an energy optimization clustering algorithm to offer efficient energy utilization in such networks. The main idea of this article is to reduce data transmission distance of sensor nodes in wireless sensor networks by using the identical cluster concepts. In order to make an ideal allotment for sensor node clusters, we compute the average distance among the sensor nodes and take into account the left over energy for selecting the suitable cluster head nodes. The life span of wireless sensor networks is extended by using the identical cluster position and stabilizing the network loading along with the clusters For the Simulation considering the following parameters throughput, energy consumption, residual energy, End to end packet delay.

KEYWORDS: Cluster, Distance, Energy Utilization, Life Span, Wireless Sensor Networks INTRODUCTION Microsensor network consist of several spatially scattered sensors, which are used to observe various kinds of ambient conditions like temperature, humidity, etc and then convert them into electric signal. A sensor is prepared with a radio transceiver, a small microcontroller, and an energy source, generally a battery. Usually sensors are physically small and inexpensive. Small sensors are not as reliable as more expensive macrosensors, but small size and small cost of an individual sensor, allow making and operation in large numbers. A wireless sensor network contains hundreds or thousands of these sensor devices that have capability to communicate either directly to the Base Station (BS) or between each other. The nodes in WSNs are usually battery operated sensing devices with restricted energy resources and replacing or replenishing the batteries is usually not an option. Thus energy efficiency is one of the most important issues and designing power efficient protocols is critical for prolonging the life span. Generally, sensor nodes are scattered in the sensing field, being the area where we want to monitor some ambient conditions. Sensor nodes have to manage among themselves to get information about the physical environment. The information together by sensor nodes is routed to the Base Station either directly or through other sensor nodes. The Base Station is a set node or mobile node, which is able to connect the sensor network to an infrastructure networks or to the Internet where users can access and process data. Routing in WSNs is extremely tricky due to the specific characteristics that differentiate WSNs from other wireless networks such as wireless ad hoc networks or cellular networks. Many new algorithms have been projected, taking into account the inherent features of WSNs along with the application and architecture requirements. Based on the network formation adopted, routing protocols for WSNs can be classify into flat network routing, hierarchical network routing, location-based network routing [3].


Shital Dabhade & Pallavi Zambare

In flat network routing, all nodes have the identical functionality and they work mutually to perform sensing and routing tasks. Hierarchical network routing divides the network into clusters to accomplish energy-efficient, scalability and one of the well-known hierarchical network routing protocol is low-energy adaptive clustering hierarchy (LEACH) [1]. In location-based network routing, position information of nodes is used to calculate the routing path. This information can be obtained from global positioning system (GPS) devices attached to each sensor node. Examples of location-based network routing protocols contain geography adaptive routing (GAF) [1] and Geographic and Energy-Aware Routing (GEAR) [6]. During the establishment of network topology, the method of setting up routes in WSNs is usually influenced by energy considerations. Because the power reduction of a wireless link is proportional to square or even higher order of the distance between the sender and the receiver, multi-hop routing is assumed to use less energy than direct communication. though, multi-hop routing establishes significant overhead to maintain the network topology and medium access control. In the case that all the sensor nodes are close sufficient to the BS, direct communication could be the best alternative for routing since it reduces network overhead and have a very easy nature. But in most cases, sensor nodes are randomly distributed so multi-hop routing is conclusively real. Several research projects and papers have shown that the hierarchical network routing and specially the clustering mechanisms make significant improvement in WSNs in reducing energy utilization and overhead [7, 8] also have to note that most of clustering protocols projected for WSNs assume that nodes are fixed. The reason for sensor nodes to be taken as fixed is the assumption of simple network topology. Clustering protocols can reduces signaling overhead since they do not have to handle the mobility pattern or position information of sensor nodes. As a result, it allows nodes saving more energy most important to a longer network life span. However, with some applications such as animal tracking, search and rescue activities this assumption is not very sensible; hence there are raising demands for clustering protocols to support mobile nodes. Clustering network is efficient and scalable way to arrange WSNs [1, 2]. A cluster head responsible for assigning any information gathered by the nodes in its cluster and may collective and compress the data before transmitting it to the sink. on the other hand, this added responsibility results in a higher rate of energy use up at the cluster heads. One of the most popular clustering mechanisms, LEACH, addresses this by probabilistically rotate the role of cluster head among all nodes. However, except each node selects its probability of becoming a cluster head sensibly, the performance of the network may be distant from optimal. The rest of this article is organized as follow. In Section 2 we are describing related work. In Section 3, we present the system model of wireless sensor networks. In Section 4, we illustrate the proposed scheme in detail. Finally, some conclusions are given in Section 5.

RELATED WORK Clustering is the method by which sensor nodes in a network classify them selves in to hierarchical formations. By doing this, sensor nodes can use the restricted network resources such as radio resource, battery power more efficiently. Within a particular cluster, data aggregation and data fusion are performed at cluster- head to reduce the quantity of data transmitting to the base station. Cluster configuration is generally based on available energy of sensor nodes and sensor’s closeness to cluster-head [1]. Non cluster-head nodes decide their cluster-head right after operation and convey data to the cluster-head. The job of cluster-head is to forward these data and its own data to the base station subsequent to performing data aggregation and data fusion. LEACH is one of the former hierarchical routing protocols for WSNs.

Energy Optimization Clustering Algorithm for Wireless Sensor Network


LEACH Low energy adaptive clustering hierarchy[9] uses the clustering method to allocate the energy utilization right from the start its network. Here, based on data gathering, network is separated into Clusters and Cluster heads are chosen randomly. The cluster head collects the data from the nodes which are coming under its cluster. Let us see the steps involved in every round in the LEACH protocol. 

Set-Up Phase This is first phase in which two steps are involved .they are as follows, o

Advertisement Phase

This is the first step in LEACH protocol. The suitable cluster head nodes will be issuing a announcement to the nodes coming under its range to become a cluster member in its cluster. The nodes will be accepting the proposal based upon the Received Signal Strength (RSS). o

Cluster Set-Up Phase

In this step the nodes will be responding to their chosen cluster heads. 

Steady State Phase In Steady state phase following two steps are involved, o

Schedule Creation Phase

Scheme and return to its cluster members to intimate them when they have to pass their data to it. o

Data Transmission Phase

The data together by the individual sensors will be given to the cluster head during its time period and on all other time the cluster members radio will be off to reduce it energy utilization. Here in the LEACH protocol multi cluster interference problem. It helps to prevent energy drain for the same sensor nodes which has been chosen as the cluster manager, using Randomization for each time cluster head would be changed. The cluster head is responsible for collecting data from its cluster members and combine it. Finally each cluster head will be forwarding the merged data to the base station. When compared with its previous protocols LEACH have shown a considerable improvement. Although LEACH protocol acts in a good manner, it suffers from various drawbacks such like:  CH election is arbitrarily, that does not obtain into account energy utilization.  It can't cover a large area.  CHs are not uniformly distributed; where CHs can be situated at the boundaries of the cluster. Since LEACH has many drawbacks, many researchers have been done to make this protocol performs improved. LEACH-C As previously stated, the weakness to LEACH is that the number of cluster head nodes is slight uncertain to calculate. LEACH-C [5]. Has been proposed to clarify this difficulty. LEACH-C provides an efficient clustering


Shital Dabhade & Pallavi Zambare

relationship algorithm, in which a best possible cluster head is selected with minimization of data transmission energy between a cluster head and other nodes in a cluster. In LEACH-C, the base station receives information regarding to remaining node energy and node positions at the set up phase of each one round. The received data can calculate an average remaining energy for each and every one node. The nodes with less than average energy are disqualified in election of cluster heads. Among the nodes that have more than average energy, cluster heads are elected with use of the simulated annealing algorithm. The base station sends all nodes a message of the best possible cluster head IDs (Identifiers). The node, the ID of which is the same as the optimum cluster head ID, is designated as a cluster head and prepares a TDMA schedule for data transfer. Other nodes wait for the TDMA schedule from their cluster heads.

ENERGY MODEL In system design is composed of a BS and a number of sensor nodes. We categorize all sensor nodes into noncluster head nodes and cluster head nodes. The non-cluster head nodes work in sensing mode to monitor the surroundings information and transmit data to the cluster head node. Also, the sensor node becomes a cluster head to get together data, compresses it and forwards to the BS in cluster head In wireless sensor networks, data communications use a large quantity of energy. The total energy utilization consists of the average energy degenerate by data transmission of the non-cluster head nodes and the cluster head nodes. In addition, the energy utilization for data collection and aggregation of cluster head nodes is measured. Figure 1illustrates the energy dissipation model in wireless sensor networks [5-7]. In this model, to exchange a P-bit message between the two sensor nodes, the energy consumption can be calculated by (1) (2) where d is the distance between the two sensor nodes, ESx(P, d) is the sender energy consumption, and ERx(P) is the receiver energy consumption. Eelec is the electronics energy consumption per bit in the sender and receiver sensor nodes. εamp is the amplifier energy consumption in sender sensor nodes, which can be calculated by

εamp =


Where d0 is a threshold value. If the distance d is less than d0, the free-space broadcast model is used. Otherwise, the multipath fading channel model is used. εfs and εmp are communication energy parameters. Using the previously described in the literature [5,6], the εfs is set as 10 pJ/bit/m2 and εmp is set as 0.0013 pJ/bit/m4. Also, the energy for data aggregation of a cluster head node is set as EDA = 5 nJ/bit/signal and the initial energy of a sensor node is set as Einit = 2 J. Suppose that a non-cluster head node N transmits PN bits to the BS. Let dN, CH be the distance between the non-cluster head node N and its cluster head node CH. Let d CH, BS be the distance between the cluster head node CH and the BS. Because of the multi-hop communication, a noncluster head node only sends data to its cluster head node. The remaining energy of the non-cluster head node N is equal to Einit - ESx(PN, dN, CH). In addition, the residual energy of the cluster head node CH is equal to Einit - ERx(PN) - EDA - ESx(PN, dCH, BS), because the cluster head node must collect and process the information of non-cluster head nodes in the cluster, and then send data to the BS.

Energy Optimization Clustering Algorithm for Wireless Sensor Network


Figure 1: Energy System Model It is clear that the data transmission between sensor nodes takes a large amount of the energy utilization in the wireless sensor networks. Taking into account the energy utilization of sensor nodes, the data transmission distance have to be reduced and the packets delay should be avoided. Hence, the energy consumption and routing design become an imperative issue in the wireless sensor networks.

PROPOSED METHOD: EOCA In order to reduce energy utilization and extend the life span of the sensor nodes in wireless sensor networks, energy optimization clustering algorithm must be developed and designed. Based on the centralized clustering approach, we propose an EOCA to provide efficient energy utilization and enhanced network life span in the wireless sensor networks. In the proposed system, we assume that the BS receives the information of position and remaining energy for each sensor node and the average remaining energy can be calculated. When the remaining energy of sensor node is higher than the average remaining energy, the sensor node becomes a candidate of cluster leader. We modify k-means algorithm to make an ideal allocation for sensor node clusters by using the information of position and remaining energy for all sensor nodes [2, 3]. In this algorithm, the operation includes two phases: set-up and steady-state phases. Set-up Phase The key objective of this phase is to generate clusters and find cluster leader nodes. During the set-up phase, the BS collects the information of the position and energy level from all sensor nodes in the networks. Based on the characteristics of stationary sensor nodes, the appropriate preliminary means of points for clusters can be obtained. Let C be the center position for all sensor nodes. If there are n sensor nodes in the wireless sensor networks, C can be calculated by



Where Zi is the coordinate of sensor node i. Let R be the average distance between C and all sensor nodes, which can be calculated by (5) According to C and R, the positions of initial mean of point mi(mix ,miy ) for the cluster i is calculated by



Shital Dabhade & Pallavi Zambare

Where k is the number of clusters and i = 1, 2,..., k. the example of the initial means of points, where k is equal to 3. The preliminary value k must be determined in the initial set-up phase. Along with the definition of optimum number of clusters in LEACH-C [6], Where M is the side of the given square field. The d to BS is the average space from the cluster leader nodes to the BS which is defined in LEACH-C. However, the cluster leader nodes are elected by creating some clusters in our proposed algorithm. Hence, we re-define d to BS which is the average distance from the all sensor nodes to the BS. The location of initial means of points is very important. It can decrease the iteration time for creating clusters extensively. After the initial means of points are set, based on the position of all sensor nodes, the BS forms some clusters. We use the k-means algorithm to division the n sensor nodes into k clusters in which each sensor node are in the right places to the cluster with the nearest mean of point Where each one sensor node link s exactly one cluster. The main aim of this is to decide which cluster the sensor node j belongs to in the jth execution. When the classification of all nodes is done, the new mean of point is created. For the reason that the means of points are changed, all sensor nodes are re-classified by executing iteratively to obtain the minimum average distance between the means of points and the sensor nodes for all clusters. The last clusters are created when each sensor node is fixed in the cluster. Figure 2 shows the flowchart of the initial cluster processing for our proposed scheme. The cluster leader is a sensor node which is nearer to the final mean of point and the remaining energy of the sensor node is higher than the average remaining energy in each cluster. Finally, the cluster architecture is created. The BS broadcasts the routing information of the clusters to all sensor nodes. Hence, each every sensor node has its own routing table and knows its task (e.g., cluster leader or non-cluster leader). Besides each every sensor node knows the distances from any other sensor node in its cluster and thereby calculates the transmission power. Based on the number of the sensor nodes within the cluster, the cluster leader node creates a schedule based on Time Division Multiple Access (TDMA) to allocate the time for the cluster members.

Figure 2: Flowchart of the Initial Cluster Working

Energy Optimization Clustering Algorithm for Wireless Sensor Network


Steady-State Phase Once the clusters are formed and the TDMA schedule is set, information transmission can start. The non-cluster leader nodes send information to cluster leader node during their allocated transmission time. When all the data have been received, the cluster leader node performs signal processing to compress the data into a single signal. Then, this signal is sent to the BS. The amount of information is reduced due to the data aggregation done at the cluster leader node. This round is done and the next round begins with set-up and steady-state phases repeatedly. To avoid unnecessary nodes control messages transmission and control overhead of the BS, the clusters are re-created only when the sensor node cannot work in a certain round. So, the calculating overhead is only cluster leader selecting in the most set-up phase.

CONCLUSIONS The energy saving is a demanding subject in the wireless sensor networks. To increase energy efficiency and expand the life span of sensor node, new and efficient energy reduction schemes must be developed. In the proposed system, we compute the average distance between the sensor nodes and take into account the remaining energy for selecting the suitable cluster head nodes. The life span of wireless sensor networks is extended by using the identical cluster position and balancing the network loading among the clusters For the Simulation considering the following parameters throughput, energy consumption, Residual energy, End to end packet delay


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