ICFL-BEENISH: Inter-Cluster Fuzzy Logic Balanced Energy Efficient Network Integrated Super Heterogeneous Protocol for Wireless Sensor Network

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1 ICFL-BEENISH: Inter-Cluster Fuzzy Logic Balanced Energy Efficient Network Integrated Super Heterogeneous Protocol for Wireless Sensor Network RAVINDER KAUR 1, SANDEEP KAUR DHANDA 2 1 M.Tech Research Scholar, Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib, Punjab, India 2 Assistant Professor, Department of CSE, Baba Banda Singh Bahadur Engineering College/, Fatehgarh Sahib, Punjab, India ABSTRACT-Today, wireless sensor networks have become most interesting field for development of sensor nodes communication. Wireless sensor networks has be strictly controlled by storage capacity, energy and computing power of sensor nodes communication. So it is essential to design effective and energy aware protocol to increase the lifetime of network. Clustering is a key of routing technique used to reduce energy consumption. The proposed technique is ICFL- BEENISH (Inter-clustering Fuzzy Logic- Balanced Energy Efficient Network Integrated Super Heterogeneous Protocol) is energy aware clustering protocol for heterogeneous WSNs, having four types of nodes with fuzzy cost in it cluster head has elected with fuzzy logic. Simulation results show that it performs better than existing clustering protocol. ICFL- BEENISH achieves longer stability, lifetime and throughput of the network. Experiments using realistic network scenarios in MATLAB environment, this research provided better results to increase this network lifetime by using parameters like last node dead, packet send to BS and CH, alive nodes etc. Keywords: - WSNs, Sensor Nodes, Clustering Protocols, lifetime. I INTRODUCTION Wireless Sensor Network (WSN) is expanding field with different type of applications. Today it has extensive applications in the field of defense security, civilian applications and medical research, have lots of researches are ongoing [22,9]. Wireless sensor network is self-possessed of sensor nodes organized in joint network. The WSN is used for assemble data from the environment and drive to the base station. Energy is main constraint in the design of all node and network components of WSNs. That s why, it becomes crucial to design energy efficient algorithm for enhancing power against node failures and it has extending the lifetime of WSNs [3]. Sensor nodes have takes self the decisions to complete sense tasks, construct network topologies and routing techniques. Many new routing protocols have been designed for the sensor networks where facts is essential considered for it. The routing techniques have used for sent the data between sensor nodes and base station for communication in the network. The routing techniques are classified as:-based on mode of functioning and type of target application i.e. Proactive Routing (LEACH), Reactive Routing (TEEN) and Hybrid Routing (APTEEN); According to the participation fashion of nodes i.e. Direct Communication (SPIN), Flat (RUMOR routing), the Clustering Protocols (BEENISH, LEACH etc), Depending on the network structure i.e. Data Centric (SPIN), Hierarchical (TEEN, APTEEN), the Location Based (GEAR). The wireless sensor network (WSN) has different type of applications in the different domain, and also have several different restrictions such that it s limited energy, communication abilities. So in the wireless sensor network, it is important to minimize the energy consumption to increase the performances of network [15, 3]. ISSN All Rights Reserved 2014 IJEETE Page 118

2 To maximize the lifetime of the network we use different type of clustering schemes. Because clustering schemes are valuable way to increases the energy efficiency of the WSNs [25]. In previously heterogeneous protocols consider two or three energy levels in the network. But in newly invented protocols have four energy levels of nodes like BEENISH (Balanced Energy Efficient Integrated Super Heterogeneous) protocols [22]. In this protocol CH are selected on the bases of residual energy level of the nodes. We introduced modified protocol from BEENISH that is called ICFL-BEENISH (Inter-Cluster Fuzzy Logic BEENISH), which contain four level of energy in the nodes. In this proposed protocol fuzzy logic is used to select the cluster head and inter-clustering is used to data aggregation [1, 10]. Cluster Nodes Base Station Cluster head Fig.1 Wireless Sensor Network (EDEEC), BEENISH are protocols for heterogeneous WSNs [12,11,32,23]. Fuzzy logic, has potential for dealing with conflicting situations and imprecision in data using heuristic human reasoning without needing complex mathematical model. It is very well suited for implementing routing and clustering heurist and optimizations, like link or cluster head (CH) quality classification. RADIO DISSIPATION MODEL The energy consumption rate of sensor network based on the protocols that sensor nodes used for communication. The main target of cluster-based routing has to maintain the energy consumption of sensor nodes by involving them in multi-hop communication within a cluster. And by performing inter cluster data aggregation and fusion in order to decrease the transmitted message to the bases station and transmission distance of the sensor nodes in the network. The radio energy model describes that 1-bit message has transmitted over a distance as in [22, 27, 34]. By using this model, system trades could be carried out by users in system planning and power management for sensor network operation, so it achieves optimized energy-efficient solution and to provide range of system parameters that are operable under various operations. Clustering can be formed in two kind of networks i.e., homogenous and heterogeneous. WSNs having nodes of same energy level are called homogeneous WSNs. Low Energy Adaptive Clustering Hierarchy (LEACH)[26], Power Efficient Gathering in Sensor Information Systems (PEGASIS) are examples of cluster based protocols which are designed for homogeneous WSNs. These algorithms weakly execute in heterogeneous regions. Nodes have less energy will expire faster than high energy nodes because these homogenous clustering based algorithms are unable to treat every node with respect to energy. In heterogeneous WSNs, nodes are deployed with different initial energy levels. Heterogeneity in WSN may be the result of re-energizing of WSN in order to extend the network lifetime. Stable. Election Protocol (SEP), Distributed Energy Efficient Clustering (DEEC), Developed DEEC (DDEEC), Enhanced DEEC k bit packet K bit packet Transmit Electronics Receive Electronics Tx Amplifier Fig.2 Radio Energy Dissipation model d ISSN All Rights Reserved 2014 IJEETE Page 119

3 (1) Where is energy used per bit to run transmitter or receiver circuit. Free space model is used if distance is in less than threshold otherwise multi path model. Now, total energy dissipated in the network during a round is given below, (2) Where, k= number of clusters, = Data aggregation cost expended in CH = Average distance between CH and BS = Average distance between cluster members and CH Assuming all nodes has uniformly distributed over network so, and can be calculated: (3) By finding the derivative of with respect to to zero, we get the optical number clusters. (4) = II RELATED PROTOCOLS In large sensor networks there is energyconstrained, so it is inefficient for sensors to transmit the data directly to base station. Clustering reduce the energy consumption and achieve the best results. In this approach whole network is divided into several clusters and each cluster has cluster head. Cluster head aggregate the data from each cluster node and transmit result to the sink. Grouping of nodes into near groups increases network lifetime and balances loads. The following are related protocols which are used in wireless sensor network. 1) PANEL: The Position-based Aggregator Node Election protocol (PANEL) has a position-based clustering routing protocol for WSNs. Like the other CH selection protocols, PANEL wires asynchronous network applications where the sensor node reading has be fetched by the sink node. The main target of PANEL is to elect aggregators, i.e., CHs, for consistent and constant data storage applications. PANEL assumes that the nodes are deployed in a bounded area, which is partition into geographical clusters. The clustering is resolute before the deployment of the network, and each node is pre-loaded with the geographical information of the cluster to which it belongs. The PANEL introduce a notion of state point. At the beginning of each epoch, a reference point is computed in each cluster j by the nodes in a spread manner in terms of the epoch number. Fig.3: Clustering in PANEL Reference point Elected Aggregator Sensor Node (2) Distributed Energy Efficient Clustering scheme: A Distributed Energy Efficient Clustering scheme (DEEC) for heterogeneous wireless sensor networks, has proposed by [21] in order to elected the cluster head through a probability based on the proportion between remaining power of every sensor node and the average power of the whole network. For the sensor nodes, cluster heads are distinct according to their early and remaining power in the network. DEEC is based on low the energy adaptive clustering hierarchy. In order to expend power reliability of cluster head is rotated among all sensor nodes. It has considered two levels of heterogeneity. The solution for multi-level heterogeneity is attaining. For avoiding that each sensor node requires to have the entire knowledge of the sensor networks, the ideal value of the lifetime of network is approximate by DEEC that ISSN All Rights Reserved 2014 IJEETE Page 120

4 is utilized to calculate the reference power which each sensor node should expend during each round. (3) Developed Distributed Energy Efficient Clustering scheme: A Developed Distributed Energy Efficient Clustering scheme (DDEEC) for heterogeneous wireless sensor networks has proposed by [6] which is based on varying dynamically and the election probability of cluster head with more efficiency. The same method as that of DEEC is implemented by DDEEC. The initial and remaining power level of sensor nodes is used to define the cluster heads. For avoiding that each sensor node requires to have the entire knowledge of the sensor networks, the perfect value of the lifetime of network is estimated by DDEEC that is utilized to calculate the reference power which each sensor node should expend during a round. In DDEEC, the whole network is divided into clusters, and the information from cluster member nodes is collected by cluster head and the aggregated information is sent to the base station directly. In addition, the topology of network is fixed and remains constant. The only distinction between DDEEC and DEEC is found in the expression that shows the probability of being a cluster head for normal and advanced sensor nodes. (4) Threshold Distributed Energy Efficient Clustering scheme: The Threshold Distributed Energy Efficient Clustering scheme (TDEEC) for heterogeneous wireless sensor networks, has proposed by [24]. It is assumed that homogeneously randomly deployed sensor nodes are location-unaware having same processing and communication capabilities. The nodes have different initial power and some nodes have more power as compared to normal nodes. In TDEEC, the threshold is adjusted according to which a sensor node takes decision to be a cluster head or not, on the basis of amount of remaining power and average power of that round with respect to the optimum cluster heads. Threshold function is used to select the cluster head in the wireless sensor network. (5) Enhanced Distributed Energy Efficient Clustering scheme: An Enhanced Distributed Energy Efficient Clustering scheme (EDEEC) for heterogeneous wireless sensor networks is proposed by [23]. It has contained three types of nodes: normal, advanced and super nodes. The performance of this scheme is better as compared to other protocols in terms of prolonging the lifetime of network and power utilization. The same idea as that of DEEC has implemented by EDEEC in order to approximate average power of the network and the algorithm for selecting cluster head that is based on remaining power in the network. (6) Enhanced Developed Distributed Energy Efficient Clustering scheme: An Enhanced Developed Distributed Energy Efficient Clustering scheme (EDDEEC) for heterogeneous wireless sensor networks, has proposed by [9]. It also uses the concept of three level of energy in the network like EDEEC. It has utilizes the similar scheme like that of DEEC to calculate the residual power of nodes, average power of network and the cluster head selection schemes. For altering the cluster head election possibility with drive, a novel clustering routing technique is utilized. Here, network lifetime is enhanced, stability period is long and number of messages transmitted to sink has incremented in comparison to DEEC, DDEEC and EDEEC. (7) Energy Efficient Heterogeneous Clustered scheme: An Energy Efficient heterogeneous clustered scheme (EEHC), has presented by [11] for heterogeneous wireless sensor networks in order to select the cluster heads in a distributed way in hierarchical wireless sensor networks. The residual power of a node weighs the election probability of cluster heads relative to that of the other sensor nodes which are present in the sensor network. The algorithm is based on low energy adaptive clustering hierarchy. Its results show that, it has increases the lifetime of the network as compare to the LEACH (8) Balanced Energy Efficient Network Integrated Super Heterogeneous Protocol: The Balanced Energy Efficient Network Integrated Super Heterogeneous protocol contain four energy levels in the sensor network. It has proposed by [22]. BEENISH mechanism has similar to the concept of DEEC, because its cluster head has selecting on the base of remaining energy level of the nodes with respect to average energy of sensor network. DEEC has based on two types of nodes; normal and advance ISSN All Rights Reserved 2014 IJEETE Page 121

5 nodes. But BEENISH uses the concept of four types of nodes that are normal nodes, advance nodes, super nodes and ultra-super nodes [22]. During process of sensor network all the nodes does not have the same remaining energy. So, if the time has kept equal for all the nodes as used in LEACH then energy is not efficiently distributed and nodes having low energy die before the superior energy nodes. BEENISH choose different time period for the different nodes with respect to their remaining energy (r) in the sensor network. High energy nodes have more chance to elected CH as compare to low energy nodes. So, which nodes have high energy elected as cluster head in network. In BEENISH ultra-super nodes are largely elected as CH as compare to super nodes, advance nodes and normal nodes, and so, on. In this way energy consumed by all nodes has uniformly distributed. In BEENISH, average energy of round can be obtained as follows and as supposed in DEEC: (7) most recent then it belongs to set. Random number between 0 and 1 is selected by nodes belonging to set. If the number is less than threshold, the node will be CH for that current round. [22] In reality, WSNs have been greater than two or three energy levels of nodes. In WSN due to random CH selection, large range of energy levels is formed. So, as much more energy levels it define diverse probability for every energy level will lead to as much better results and lead to energy efficiency. In BEENISH, it uses the concept of four level heterogeneous network having: Normal Nodes Advance Nodes Super Nodes Ultra-Super Nodes The BEENISH, protocol has four energy level nodes which are deployed in the wireless sensor network. The probabilities for four types of nodes are given below: is showing total rounds from the start of network to the all nodes die and can be estimated as in DEEC and given as under: (8) is the energy dissipated in a network during single round. To achieve the number of CH at start of each round, node decided whether to become a CH or not based on probability threshold calculated by expression in the following equation. (10) Threshold is calculated for CH selection of normal, advanced, super and ultra-super nodes by putting above values in equation below. (11) (9) Where is the set of nodes eligible to become CH. If a node has not been CH in the In this equation, they find that nodes with greater remaining energy at round r are more probably to become CH as compare to low energy nodes [22] ISSN All Rights Reserved 2014 IJEETE Page 122

6 III LITERATURE SURVEY Quershi et al. (2013) [22] has studied that the clustering is an effective way to increase energy efficiency. Mostly, heterogeneous protocols consider two or three energy level of nodes. In actuality, heterogonous WSNs contain large range of energy levels. By analyzing communication energy consumption of the clusters and large range of energy levels in heterogeneous WSN, they have proposed BEENISH (Balanced Energy Efficient Network Integrated Super Heterogeneous) Protocol which containing four energy levels of nodes. At this point, Cluster Heads (CHs) are elected on the bases of residual energy level of nodes in the network. So, which nodes have high energy they become more chance to get selected as CH. They proved that BEENISH is most efficient protocol as compare to other protocols like DEED, EDEEC etc. M.Mohammad et al. (2012) [17] has explained the Challenge of WSN routing has to deal with is the energy efficiency and prolonging network lifetime. Hierarchical communication among sensor nodes by clustering them is more scalable, energy efficient, lower in latency, better in terms of network lifetime than flat communication. A hierarchical clustering routing protocol proposed in literature for data gathering application in WSN called Energy Aware routing Protocol (EAP), meets several important requirements for a clustering algorithm. It is proved that EAP achieves a good performance in terms of lifetime. Pin Nie et al. (2011[18] has presented a cluster-based aggregation data middleware in WSN for SHM. By leveraging an energy- aware clustering protocol, they built a role-based, threelevel aggregation architecture to filter unwanted samples, to extract features from raw measurements and to combine multiple measurements from different locations. This prototype demonstrates the advantages of performing flexible aggregation data for energyefficient and monitoring. Wendi Rabiner Heinzelman et al.(2000)[8]has study LEACH (Low Energy Adaptive Clustering Hierarchy),a clusteringbased protocol that utilize randomized rotation of local cluster base stations (cluster heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the sink. P.Saini et al. (2010) [23] they presented an improved protocol Enhanced Distributed Energy Efficient Clustering (EDEEC). EDEEC comes up with three kind of sensor nodes called normal nodes, advanced nodes and super nodes that can be used to increase the life and stability of the WSNs. EDEEC follow the rules of DEEC and extends it by three level heterogeneity in the WSNs by commence of the super sensor nodes having more energy than the normal and advanced nodes. EDEEC protocol has more stability time and life span than SEP. It enhances the heterogeneity and energy level of the WSNs. EDEEC received more data packets at BS than SEP. Guisheng Yin et al. (2008) [32] has studied about the novel routing algorithm which combines with hierarchical routing and geographical routing. Based on the hierarchical network architecture, the process of forwarding packets between the normal nodes in the target region and the sink consists of two phases intercluster routing and intra-cluster routing, a greedy algorithm is adopted in the process of the intercluster routing and an multi-hop routing algorithm based on the forwarding restriction angle is designed for the intra- cluster routing. Motahareh Bahrami Zanjani et al.(2010) [34] has studied about a highly secured data aggregation method for WSN, which ensures that information of all live nodes inside the network is reachable inside the base station node with the minimum redundancy. In this scheme, data of all live nodes stored in the base station have accessible inside the sink even if the wireless channel is polluted with noise and interference. In this paper sensor nodes aggregate the data and then transfer to the base station in the WSNs. Shuo Shi et al. (2012) [26] explained an energy-efficiency Optimized LEACH-C. First, we select a group of cluster heads using LEACH- C. Next, taking retransmission and ISSN All Rights Reserved 2014 IJEETE Page 123

7 acknowledgment into consideration, they create a model of cluster head energy consumption. They calculate the quadratic sum of the distances from each cluster head to its member nodes in the optimal solution. Finally, the largest energy consumption for a single cluster head in the next round will be estimated, and all nodes with residual energy larger than the calculated consumption would be taken to a new round of finding a better solution. Thus, loss of the cluster head for each round could be minimized, and the WSN life could be extended ultimately. Xuxun Liu (2012) [15] explained a comprehensive and fine grained survey on clustering routing protocols proposed in the literature for WSNs. They outline the advantages and objectives of clustering for WSNs, and develop a novel taxonomy of WSN clustering routing methods based on complete and detailed clustering attributes. In particular, they systematically analyze a few major WSN clustering routing protocols and compare these different approaches according to our taxonomy and several significant metrics. Jau-Yang et al. (2012) [5] has explained the Saving Energy Clustering Algorithm (SECA) to provide efficient energy consumption 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 uniform cluster concepts. In order to make an ideal distribution for sensor node clusters, they calculate the average distance between the sensor nodes and take into account the residual energy for selecting the appropriate cluster head nodes. The lifetime of wireless sensor networks has extended by using the uniform cluster location and balancing the network loading among the clusters. Haifeng Jiang et al. (2013) [10] has explained based on analysis of energy consumption for data transceiver, single-hop forwarding scheme is proved to consume less energy than multihop forwarding scheme within the communication range of the source sensor or a current forwarder, using free space energy consumption model. They adopt the social welfare function to predict inequality of residual energy of neighbours after selecting different next hop nodes. Based on energy inequality, the method is designed to compute the degree of energy balance. Parameters such as degree of nearness of node to the shortest path, degree of closeness of node to Sink, and degree of energy balance are put into fuzzy logic system. Fuzzylogic-based energy optimized routing algorithm has proposed to achieve multiparameter, fuzzy routing decision. Jia Xibei et al. (2010) [31] has focused on DCDA-LEACH makes improvements to LEACH with data correlation and data aggregation as the core, introducing the thinking of regionalization. It divides the nodes into regions in unity with the relevant characteristics of the data to improve the data correlation level within the clusters. Clustering process works with the regional restrictions to enhance data aggregation efficiency and reduce energy consumption. Multiskip routing has used to reduce the number of cluster-heads which communicate with base station directly. The protocol, therefore, can balance the node energy and prolong the network life.. Buttyán et al. (2010) [4] has explained the PANEL a position-based aggregator node election protocol for wireless sensor networks. The novelty of PANEL with respect to other aggregator node election protocols is that it supports asynchronous sensor network applications where the sensor readings has fetched by the base stations after some delay. PANEL ensures load balancing, and it supports intra and inter-cluster routing allowing sensor-toaggregator, aggregator-to-aggregator, base station-to aggregator, and aggregator to-base station communications. Jin-Shyan Lee et al. (2012) [14] has explained, a fuzzy-logic-based clustering approach with an extension to the energy predication to prolong the lifetime of WSNs by evenly distributing the workload. Fuzzy clustering algorithms use fuzzy logic for blending different clustering parameters to select cluster heads. They explained the heuristic fuzzy rule generation method is used with the principle: A ISSN All Rights Reserved 2014 IJEETE Page 124

8 node which holds more residual energy and more ERE has a higher probability to become a CH. Imad S. AlShawi et al. (2012) [1] has explained, about a new routing method for WSNs to extend network lifetime using a combination of a fuzzy approach and an A-star algorithm. They determined an optimal routing path from the source to the destination by favouring the highest remaining battery power, minimum number of hops, and minimum traffic loads. To demonstrate the effectiveness method in terms of balancing energy consumption and maximization of network lifetime, they compare approach with the A-star search algorithm and fuzzy approach using the same routing criteria in two different topographical areas. Adwitiya Sinha and Daya Krishan Lobiyal (2013) [25] has explained the data aggregation on the basis of entropy of the sensors. The entropy is computed from the local and global probability models. The models provide assist in extracting high accuracy data from the sensor nodes. They have been also proposed an energy efficient method for clustering the nodes in the network. At first, sensors sensing the same category of data are placed within a distinct cluster. The remaining unclustered sensors estimation their disagreement with respect to the clustered neighbors and ultimately join the leastdivergent cluster. Mohammed A. Abuhelaleh and Khaled M. Elleithy (2010) [2] has focused on how to achieve the highest possible level of security by applying new key management technique that can be used during wireless sensor networks communications. They introduced about more effective and applicable to a large number of wireless sensor networks applications, they work on a special kind of architecture cluster hierarchy of wireless sensor networks and they pick one of the most interesting protocols that used for this kind of architecture, which is LEACH. Vikash Kumar et al. (2014) [13] has focused only on the challenges related to the security of Wireless Sensor Network. This paper begins by introduce the concept of Wireless Sensor Network (WSN). The introductory section gives brief information on the WSN components and its architecture. Then it deals with some of the major security issues over wireless sensor networks (WSNs). Further, as security being vital to the acceptance and use of sensor networks for many applications; they has made an in depth threat analysis of Wireless Sensor Network. John Paul Walters et al. (2006) [30] has explained the four main aspects of wireless sensor network security: obstacles, requirements, attacks, and defences. Within each of those categories they also sub-categorized the major trust, denial of service, and so on. It provide both a general overview of the rather broad area of wireless sensor network security, and give the main citations such that further review of the relevant literature can be completed by the interested researcher N. Javaid, T.N. Qureshi et al. (2013) [9] has proposed enhanced developed distributed energy efficient clustering scheme (EDDEEC) for heterogeneous networks. This protocol is adaptive power aware. The probability of sensor nodes for becoming a cluster head in an efficient manner has been altered dynamically in order to distribute same amount of power between sensor nodes. Simulations have been performed for checking the efficiency of new protocol. For this analysis, the selected performance parameters are lifetime, stability period and data transmitted to the base station. From the results, it has been shown that EDDEEC is more efficient and reliable as compared to DEEC, DDEEC and EDEEC. IV METHODOLOGY The proposed algorithm will function in following stages i.e. Cluster formation (Selection of cluster head), inter-cluster aggregation (grouping of Cluster heads for transmission of aggregated data packets to the relay node) and compression of data. In ICFL-BEENISH, Interclustering data aggregation reduces the power consumption and increase the lifetime of network ISSN All Rights Reserved 2014 IJEETE Page 125

9 INITIALIZE FOR EACH SENSOR IF NODE (i) YES SELECT NODE AS CH BASED ON FUZZY-BASED T(n) FUNCTION ASSOCIATE MEMBER NODES WITH THEIR RESPECTIVE APPLY INTER-CLUSTER DATA AGGREGATION TO SPANNING TREE COUNT DEAD IS ALL DEAD END NO YES will be selected by the cluster heads on the basis of distance. After that, the inter-cluster data aggregation will be applied using additive function at the relay node. Step 7: Evaluate energy. Inter- cluster will be reducing the data packet count at the base station. It will be reduce the actual energy essential, which will be result in increase in the network lifetime. Step 8: Update remaining energy of each node (i). The remaining energy of the WSNs is updated and repeat the above steps from step 3. V IMPLEMENTATION The clustering routing technique has used to reduce the energy consumption in the wireless sensor network. In this section, we evaluate the ICFL-BEENISH protocol scheme through MATLAB. EVALUATE ENERGY DISSIPATE UPDATE REMAINING ENERGY OF EACH NODE (i) Fig.4 Flow Chart for ICFL- BEENISH Step 1: First of all network is initialized using the required characteristics of WSNs. Step 2: Repeat the following steps for each sensor node i until all nodes become dead. Step 3: If given node has energy less than or equal to zero (0) then node is dead and continue step 2 else continue step3. It count the dead nodes if all the nodes is dead then working of system is stop otherwise it again repeat the step 2. Step 4: Select node as cluster head (CH) based on fuzzy logic network based T( ) function. Selects cluster head and broadcast the message. Step 5: Associate member nodes with their respective CHs. Each cluster member is connected with its cluster heads (CHs). CHs collect the data from the cluster members. Step6: Apply inter-cluster data aggregation for collecting data to form spanning tree among CHs. CHs assemble data from each cluster members to achieve the additional aggregation for transmission with the base station. A relay node A. Simulation Setup This section consider, sensor nodes are randomly distributed in the field with dimensions 100m x 100m and with base station 50, 50. It has considered n = 100,120,130, 140,.300 nodes both for normal, advanced, super and ultra-super nodes in the sensor network. It means that the X - axis and Y-axis coordinates of the sensor nodes have randomly selected between the 0 and the maximum value of the field in the network. Its initial energy E=0.1 and its probability, p=0.1. B. Simulation Scenario Design In the scenario design all the nodes of the sensor network randomly distributed and from these nodes it select the cluster head with the help of fuzzy logic. These cluster head collect the by inter-cluster data aggregation and transfer it to the base station. By using inter-clustering and fuzzy logic it reduce the energy consumption in the network and increase the lifetime of network. It removes the redundancy data from the network and transfers the relevant data to the base station. With the help of our experiment, we can calculate the energy consumption for transmission and receiving the data in the wireless sensor network table.1. ISSN All Rights Reserved 2014 IJEETE Page 126

10 Parameters Values Area (x, y) 100, 100 Base Station (x, y) 50, 50 Nodes (n) 100 to 300 Probability (p) 0.1 Initial Energy 0.1 J Transmit Energy J/bit (Tx_energy) Receive Energy J/bit (Rx_energy) Free Space J/bit/ (amplifier) Multipath (amplifier) J/bit/ Maximum Lifetime 6000 (max_time) Data_cost J/bit/signal (DA_cost) x1 (fraction of 0.1 advanced node) x (fraction of super 0.3 node) m (fraction of ultra 0.3 super node) a (energy factor 3 between normal and advance node) b (energy factor 2 between normal and super node) sp (energy factor 4 between normal and ultra super node) Table 1. The screen is divided into various regions that are called clusters. Each cluster thus formed has a cluster head, normal, advance, super nodes and ultra super node. In the centre there is base station that is responsible for collection of data from the active nodes in the network. Cyan triangle nodes are representing the normal sensor nodes and blue circle nodes are representing the advance sensor nodes. Magenta diamonds are representing the super sensor node. Green stars are representing ultra super sensor nodes. Red lines are representing how data communicate to the base station. Red circles are representing cluster head. Dash line shows the inter-cluster data aggregation. Fig.6 When some nodes are dead Fig.6 shows, the network of ICFL-BEENISH protocol. In this, red triangles represent the dead nodes in the network. A node is known as dead if it has zero energy that is it is no longer available for communication. VI RESULT AND DISCUSSION Fig.5 Alive Nodes Fig.5 shows the active environment of ICFL-BEENISH protocol. This section evaluates the performance of different clustering schemes in wireless sensor network. After describing our implementation and simulation setup, we evaluate the impact of different clustering schemes like BEENISH, FUZZY BEENISH AND ICFL BEENISH on the parameter like energy consumption, packet send to base station, packet sent to cluster head,first node dead, tenth node dead, last node dead and average remaining energy of wireless sensor network. In the ICFL-BEENISH reduce the ISSN All Rights Reserved 2014 IJEETE Page 127

11 energy consumption and increase the lifetime the network. A. Remaining Energy the packet sent to BS. It shows that data sent to base station is more in ICFL-BEENISH then others. It clearly shows that total number of data packets received by BS has increased because inter-clustering and fuzzy logic increased the life of network as compare to other. C. Packet to Cluster Head Fig.7Comparison on the basis of Remaining Fig.7 shows the comparison of remaining energy of BEENISH, FUZZY BEENISH and ICFL-BEENISH protocols. X-axis is representing total number of rounds. Y-axis is representing the energy. It shows that ICFL-BEENISH has more remaining energy than BEENISH and FUZZY BEENISH. Thus this figure shows that the ICFL- BEENISH has higher energy efficiency as compare to the schemes. B. Packet to Base Station Fig.9 Comparison on the basis of Packet Fig.9 shows the comparison of total number of packets sent to cluster head of BEENISH, FUZZY BEENISH and ICFL- BEENISH protocols. X-axis is representing the total number of rounds. Y-axis is representing the number of nodes. It indicates that data sent to cluster head is more for ICFL-BEENISH then others. In this figure ICFL-BEENISH shows best throughput than BEENISH and FUZZY BEENISH. D. Network Lifetime Fig.8 Comparison on the base of Packet It measured the rate of data sent all over the network, the rate of data sent from cluster heads to the BS as well as the rate of data sent from the nodes to their cluster heads. Fig.7 shows the comparison of throughput of BEENISH, FUZZY BEENISH and ICFL- BEENISH protocols. X-axis has representing the total number of rounds. Y-axis has representing Fig.10 Comparison on the base of Network Lifetime ISSN All Rights Reserved 2014 IJEETE Page 128

12 Fig.10 shows ICFL-BEENISH has better performance as compare to other. It has been the time interval from the start of the operation until the death of the last alive node. It shows the comparison of total number of dead nodes of BEENISH, FUZZY BEENISH and ICFL- BEENISH protocols. X-axis represents the total number rounds. Y-axis represents the total number of nodes. E. Comparison on First Node Dead In Fig 12, X-axis represents number of nodes and Y-axis represents the time. The graph shows the variation of tenth node dead in the BEENISH, FUZZY BEENISH and ICFL BEENISH. It clearly shows that the ICFL- BEENISH is most efficient than other two. In it tenth node dead for BEENISH, FUZZY BEENISH and FUZZY BEENISH at 579, 530, 1073 round respectively when n=100. This way we can take different values for n (nodes) and measure the lifetime of the nodes. G. Comparison on Last Node Dead Fig.11 First Node Dead (FND) In Fig 11, X-axis represents number of nodes and Y-axis represents the time. The graph shows the variation of first node dead in the BEENISH, FUZZY BEENISH and ICFL BEENISH. It clearly shows that the ICFL- BEENISH is most efficient than other two. In this first node die for BEENISH, FUZZY BEENISH and FUZZY BEENISH at 400, 415, 545 round respectively when n=100. This way we can take different values for n (nodes) and measure the lifetime of the nodes. F. Comparison on Tenth Node Dead (TND) Fig.13. Last Node Dead In Fig 13, X-axis represents number of nodes and Y-axis represents the time. The graph shows the variation of last node dead in the BEENISH, FUZZY BEENISH and ICFL BEENISH. It clearly shows that the ICFL- BEENISH is most efficient than other two. In it last node dies for BEENISH, FUZZY BEENISH and FUZZY BEENISH at, 4660, 4385, 4600 round respectively when n=100. This way we can take different values for n (nodes) and measure the lifetime of the nodes. It clearly represents that the ICFL-BEENISH is most efficient than BEENISH and FUZZY BEENISH in terms of network lifetime. This way ICFL-BEENISH is more efficient then other clustering schemes. It increases the lifetime of network. From above graphs, it clearly shows that ICFL-BEENISH has more stability period as compare to BEENISH and FUZZY BEENISH. Fig.12. Tenth Node Dead ISSN All Rights Reserved 2014 IJEETE Page 129

13 VII CONCLUSION AND FUTURE SCOPE In wireless sensor networks, energy and the bandwidth of the sensors network are valued resources and critical to consume adeptly. Data aggregation at the sink by individual nodes causes flooding of the data which cost in maximum energy consumption in the network. To moderate this problem a new data aggregation technique has been introduced which uses inter-cluster data aggregation and fuzzy-logic based system. It has be improved the performance of the Balanced Energy Efficient Network Integrated Super Heterogeneous Protocol (BEENISH) WSNs. The improved protocol is ICFL-BEENISH. The proposed scheme has reduced the energy consumption problem in the network and also inter-cluster data aggregates and transmits the data in efficient manner. In addition, the proposed technique has used the additive and divisible data aggregation function at cluster head (CH) as in-network processing to reduce energy consumption in WSNs. The cluster head communicates with the inter-cluster data aggregated information to base station and cluster head nodes communicate data to cluster nodes in the network. The proposed scheme has been designed and simulated in the MATLAB tool. The comparative analysis has shown that the proposed ICFL-BEENISH is increase the lifetime of the WSNs. This works has not considered the use of mobile sink and also 3-D wireless environment for experimental purpose. So in near future ICFL- BEENISH will be modified in such a way. That it will be work on either mobile or stationary sink based environment. And also it will work for 3-D wireless environment. REFERENCES [1] AlShawi, Imad S., Lianshan Yan, Wei Pan, and Bin Luo. "Lifetime enhancement in wireless sensor networks using fuzzy approach and A-star algorithm." Sensors Journal, IEEE 12, no. 10 (2012): [2] Abuhelaleh, Mohammed A., and Khaled M. Elleithy. "Security in wireless sensor networks: Key management module in sooawsn." International Journal of Network Security & Its Applications (IJNSA) 2, no. 4 (2010): [3] Alazzawi, L., and A. Elkateeb. "Performance evaluation of the WSN routing protocols scalability." Journal of Computer Systems, Networks, and Communications 2008 (2009). [4] Buttyán, Levente, and Péter Schaffer. "Position-based aggregator node election in wireless sensor networks." International Journal of Distributed Sensor Networks 2010 (2010). [5] Chang, Jau-Yang, and Pei-Hao Ju. "An efficient cluster-based power saving scheme for wireless sensor networks." EURASIP Journal on Wireless Communications and Networking 2012, no. 1 (2012): [6] Elbhiri, Brahim, R. Saadane, S. El Fkihi, and D. Aboutajdine. "Developed Distributed Energy-Efficient Clustering (DDEEC) for heterogeneous wireless sensor networks." In I/V Communications and Mobile Network (ISVC), th International Symposium on, pp IEEE, [7] Granjal, Jorge, Edmundo Monteiro, and Jorge Sá Silva. "Security Issues and Approaches on Wireless M2M Systems." In Wireless Networks and Security, pp Springer Berlin Heidelberg, [8] Heinzelman, Wendi Rabiner, Anantha Chandrakasan, and Hari Balakrishnan. "Energy-efficient communication protocol for wireless microsensor networks." In System sciences, Proceedings of the 33rd annual Hawaii international conference on, pp. 10-pp. IEEE, [9] Javaid, Nadeem, T. N. Qureshi, A. H. Khan, Adeel Iqbal, E. Akhtar, and M. Ishfaq. "EDDEEC: enhanced developed distributed energy-efficient clustering for heterogeneous wireless sensor networks." Procedia Computer Science 19 (2013): [10] Jiang, Haifeng, Yanjing Sun, Renke Sun, and Hongli Xu. "Fuzzy-Logic-Based Energy Optimized Routing for Wireless Sensor Networks." International Journal of Distributed Sensor Networks 2013 (2013). [11] Kumar, Dilip, Trilok C. Aseri, and R. B. Patel. "EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor ISSN All Rights Reserved 2014 IJEETE Page 130

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15 [28] Tang, ShaoJie, Jing Yuan, XiangYang Li, Yunhao Liu, GuiHai Chen, Ming Gu, Jizhong Zhao, and Guojun Dai. "DAWN: energy efficient data aggregation in WSN with mobile sinks." In Quality of Service (IWQoS), th International Workshop on, pp IEEE, [29] Walters, John Paul, Zhengqiang Liang, Weisong Shi, and Vipin Chaudhary. "Wireless sensor network security: A survey." Security in distributed, grid, mobile, and pervasive computing 1 (2007): 367. [30] Xibei, Jia, Zhang Huazhong, and Zhang Jingchen. "Research of data aggregation routing protocol in WSN data-related applications." In Computer Science and Information Technology (ICCSIT), rd IEEE International Conference on, vol. 1, pp IEEE, [31] Yin, Guisheng, Guang Yang, Yang Wu, Bingyang Zhang, and Wenjin Jin. "An energy-efficient routing algorithm for wireless sensor networks." In Internet Computing in Science and Engineering, ICICSE'08. International Conference on, pp IEEE, [32] Yick, Jennifer, Biswanath Mukherjee, and Dipak Ghosal. "Wireless sensor network survey." Computer networks 52, no. 12 (2008): [33] Zanjani, Motahareh Bahrami, Reza Monsefi, and Arash Boustani. "Energy efficient/highly secure data aggregation method using treestructured orthogonal codes for Wireless Sensor Networks." In Software Technology and Engineering (ICSTE), nd International Conference on, vol. 2, pp. V IEEE, Er. Sandeep Kaur Dhanda: She received her Master`s degree in computer science and engineering from Thapar University, Patiala, Punjab, India. Presently, she`s a assistant professor at computer science and engineering department of Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib, India. Her research area includes parallel computing. AUTHOR S BIBLOGRAPHY Ravinder Kaur: She received her B.Tech. degree in information technlogy from S.Sukhjinder Singh Engineering & Technology College, India and his Master`s degree in computer science and engineering (specialization in E Security) from Baba Banda Singh Bahadur Engg. College Fategarh sahib, Punjab, India. Her research area includes energy efficiency in wireless network (ICFL BEENISH). ISSN All Rights Reserved 2014 IJEETE Page 132