Distributed Fuzzy Logic-based Cluster Routing for Wireless Sensor Networks

Distributed Fuzzy Logic-based Cluster Routing for Wireless Sensor Networks Linlin LI School of Information Science and Technology, Heilongjiang University, Harbin, Heilongjiang,150080, China Abstract Energy consumption and network lifetime are main issues of Wireless Sensor Networks (WSNs). In this paper we consider distributed fuzzy logic-based cluster routing (DFLCR) for WSNs. In DFLCR protocol, sensor nodes compute the residual energy, local distance and centrality. They are considered to be fuzzy input variables. By fuzzy rules, the probability of nodes to become a CH and size of cluster are output of fuzzy logic system. DFLCR protocol forms unequal clusters to balance the energy consumption among CHs. Our simulation results show that the proposed DFLCR improves the network performance and outperforms existing routing schemes and, more specifically, DFLCR protocol yields 95.38%, 91.84% and 6.72% more average network lifetime compared with LEACH, CHEF and EAUCF respectively. Keywords- wireless sensor networks; routing protocol; clustering; fuzzy logic I. INTRODUCTION Wireless Sensor Networks(WSN) is a collection of lowcost, energy constrained, and unreliable multifunctional microsensor nodes. At present, WSN plays a key role in environmental monitoring, disaster prevention,traffic monitoring,and national border surveillance [1]. In WSN, each node sensed the environmental data,and transmit the sensed data to other sensor nodes or base station. However, due to hostile environment, the battery of each sensor node cannot be replenished, and energy resource-constrained is the most challenging aspect of WSN. In order to improve the energy utilization of sensor nodes, the researchers conducted a great deal of research, where cluster-based WSN routing protocol is good choices. The structure of cluster-based WSN is shown as Figure1. In Cluster-based WSN, those the nodes with characteristics in common or nearby nodes are grouped together to form cluster [2-3]. In each cluster, one node is selected to be a cluster head (CH), which have taken charge of gathering data from cluster member (CM) nodes, and transmit the gathering data to base station (BS). The process leads to vital reduction in the amount of transmitted data in the network, and reduce the overall energy consumption of network. In cluster-based routing, CHs have undertaken the important task. Therefore, the selection of CHs is very important. Since CHs have taken charge of collecting, integrating and transmitting data, its energy consumption speed is faster. Therefore, in general, residual energy of node is considered to be a vital metric to select CHs. Once residual energy of CHs is less than a certain value, the CHs may be unable to complete its task. In addition, fuzzy logic (FL) have been applied for solving various kind of problems in WSN [4]. FL is good technique to use in application with lots of uncertainties. Therefore, DFLCR protocol is proposed in this paper. DFLCR protocol have make use of FL system to calculate the probability of sensor node to become CH, combining the local information of sensor nodes. The node with maximum probability is considered to be CH. The residual energy, node centrality and local distance are computed. Then, they are considered to be input of FL system. Probability and size are the output of FL system. DFLCR assigns the size of a number of member nodes for a CH. Simulation results show that DFLCR protocol improves the network lifetime and energy consumption compared with its counterparts. Figure.1 Structure of cluster-based WSN II. RELATED WORK During the past decade, a large number of research efforts have been investigated in designing clustering algorithm in WSNs. Wendi [5] et.al have proposed a clustering algorithm, which is called LEACH. In LEACH, all nodes in network are organized into several clusters. The CHs are selected using optimal probability. Each node firstly generates a random number. If the random number of node is less than pre-determined threshold value, the node is successfully selected to be a CH for the current round. The threshold value is calculated by using equation (1): DOI 10.5013/ IJSSST.a.17.42.35 35.1 ISSN: 1473-804x online, 1473-8031 print

p, mg 1 Tm 1 prmod p 0, otherwise Where r represent the round. m is individual sensor node. p is the pre-determined percentage of CH. G is the set of nodes which are not elected as CH yet in previous rounds. GUO [6] have proposed an improved LEACH. It reduces the distance between CHs and transmission distance of Sink by two-double clustering. However, the improved LEACH protocol has done two-hop transmission at most, which is not good for large network. BLAND [7] has proposed clusterhead election using fuzzy logic for wireless sensor networks, which is marked as CHEF. In CHEF, the residual energy, density and centrality is the input of fuzzy logic system. Then the CH is selected by Mamdani fuzzy logic inference. However, in CHEF, the BS needs to collect information of all nodes, and most of nodes must communicate with other nodes, which consume lots of energy. In addition, JAVAID [8] have proposed central CH election-based fuzzy logic engine, but the algorithm have taken conditions of demand into account. Bagci [9] have proposed distributed clustering algorithm, which is marked as EAUCF. EAUCF protocol has used fuzzy logic approach to elect the CHs. The residual energy and distance to BS are both input of fuzzy logic system. However, EAUCF protocol has do not consider the energy consumption due to high intra cluster communication which affects the network performance. Therefore, we proposed a novel distributed clustering algorithm which used fuzzy logic system to elect the CH and control the size of cluster, and it is marked as DFLCR. The vital aim of DFLCR is to prolong the network lifetime by achieving even energy consumption. Its main features are as follows:1) DFLCR is a distributed clustering algorithm, and the clusters are not unequal;2) the probability of node to become a CH is estimated by fuzzy logic;3) residual energy, local distance and centrality are considered to be input fuzzy variables;4) the output of fuzzy logic system are probability and size. The metric reflects the ability of a node to act as CH. The latter is limit in number of cluster members in a cluster. III. SYSTEM MODEL AND HYPOTHESIS In WSNs, all sensor nodes have scattered randomly on interest area. Each sensor node has transmitted sensing data to its CH, and CHs have gathered the sensing data. Finally, these data have been transmitted to BS by CHs. In addition, the initial energy and communication radius of all sensor nodes are the same. Once deploying, nodes are not mobile. Each node can calculate the distance between nodes by RSSI (Received Signal Strength Indicator). Energy of BS is not constrained, and it is having sufficient knowledge about the network. (1) A. Time Model In DFLCR protocol, the time is divided into several rounds, as shown as Figure 2. Each round consists of clustering and data gathering process. Thus clustering process is divided into CH selection sub-process and clusterforming sub-process. The division within data gathering process is the Frame. Figure 2 Time model of DFLCR protocol B. Radio Energy Dissipation Model Communication model for energy consumption used in this evaluation is as explored in [10]. Energy model is as shown as Figure 3, the consumptive energy for transmitting a k bit data to d distance is calculated as given in the equation (2). E TX k, d ke ke d, if d d k E k E d if d d 2 elec frris co 4 elec tworay, co Where E elec is transmitter energy to run the transmitter or receiver circuitry. E frris, E tworay is the unit amplifier energy required for the transmitter in the free space, the two ground mode, respectively. This is depends on the distance. Thus d co is the crossover distance, which is considered to be a threshold distance as given in the equation (3): d co tworay (2) E frris (3) E Accordingly, energy consumed in receiving k bit data is calculated using equation (4): A. Clustering Process (1) CH-selection E k, d E k (4) Rx elec ETX kd, E kd, Rx Figure3. Radio energy dissipation model IV. DFLCR DOI 10.5013/ IJSSST.a.17.42.35 35.2 ISSN: 1473-804x online, 1473-8031 print

DFLCR protocol estimated the probability of a node to become a CH, which represents the ability of the node to act as CH. The residual energy, local distance and centrality are the input fuzzy variables, as shown Figure4. D E res C Figure 4. Fuzzy logic system model The first fuzzy output variable probability has very low, low, rather low, low medium, medium,high medium,rather high,high, very high as its five output linguistic variables as given equation (5): very low, low, rather low, low medium, medium, p high medium, ratherhigh, high, veryhigh (5) s p current energy of node. Centrality is a value that shows how central the node is among its neighbors in the entire network, its definition is as shown as equation (7): Where M j si d 2 i S i M C (7) A, j, and i j d, is the distance between node i and node j. S i is the set of neighbor nodes of node i. S i is the number of Si set. A is the sensing area. The lower value of the centrality, the lower amount of energy required by the other nodes to transmit the data through that node as CHs [11]. Local distance is the sum of the distances from a deployed node to its neighbors. For example, as shown Figure 5. In Figure 5, the local distance D i of node i : D d d d d. i 1 2 3 4 Similarly, the second output variable size has very big, big, rather big, medium, rather small, small, very small as its seven output linguistic variables as given equation (6): very big, big, rather big, medium, s rathersmall, small, verysmall (6) The input fuzzy variables are residual energy, local distance and centrality. Residual energy represents the Figure 5. A deployed node with neighbors Figure 6 membership function for fuzzy variables DOI 10.5013/ IJSSST.a.17.42.35 35.3 ISSN: 1473-804x online, 1473-8031 print

The membership function for fuzzy input variables is as shown as Figure 6, respectively. As shown Figure 6(a), less,average,hugh is the fuzzy linguistic variable for centrality. Similarly, low, moderate,high is the fuzzy linguistic variable for residual energy, which is depicted in Figure 6(b). As input variable, local distance has to take the equivalent fuzzy linguistic variable, namely, far,reachable, nearby is the fuzzy linguistic variable for local distance. In addition, DFLCR fuzzy if-then rules are depicted in Table1. TABLE 1. DFLCR FUZZY IF-THEN RULES transmitting CM _ join message, the node is waiting for receiving CM _ acceptance message from CH [12-14]. In order to limit the size of cluster, each checks the number of cluster members when it have received CM _ join message. If the number of cluster members is less than size s, which is second output variable of fuzzy logic system. The CH reply a CM _ acceptance message to the node, otherwise, it reply CM _ rejection message. Once receiving CM _ acceptance message, the node successfully join the CH, and become a cluster member. B. Data Gathering Process Once forming a cluster, each CH schedules TDMA slot for cluster members. In transmitting time slot, node transmits its sensing data to CH. In order to save energy, the node is active just in transmitting time slot, and the node is sleep in other time slot. Of course, the CH should be active in order to receive the data from its cluster members and aggregates it s the received data. Finally, CH transmits the aggregated data to BS in multi-hop way [15]. V. SIMULATION AND NUMBER ANALIYSIS In order to better analyze the performance of DFLCR protocol, MATLAB simulation software is used. In 200m 200m zone, there are 100 nodes. In order to fully analyze performance of DFLCR, three different scenes are considered in the simulation, namely, the first scene: BS is in the central of zone; the second scene: BS is at the corner of zone; the third scene: BS is outside of zone. The simulation parameters are shown as Table 2. Each simulation is independently replicated 100 times, and average value of simulation data is consider to be final simulate data. TABLE 2 SIMULATION PARAMETERS 200m 200m 1J Finally, the node with maximum probability is elected as CH. Then the CH broadcast CH _ won message in its transmission range. (2) cluster- forming The node that is not CH needs to select nearby cluster and join in the cluster. More specifically, CH broadcasts CH _ won message, and neighbor nodes may receive one or more CH _ won messages. The node is to join the cluster if it only receives a CH _ won message, and transmit CM _ join message toward the CH. Otherwise, it select a nearest CH and transmit CM _ join message toward the CH. After A. Energy Consumption Per Round The metric represent the total energy spent in data collection for the single communication round, including energy consumption of all member nodes and CHs. The simulation results are depicted in Table 3. From above Table 3, we know that LEACH protocol consume most energy, compared with LEACH, CHEF, and EAUCF. The reason is that CHs communicate with BS in direct way, and it has increased energy consumption. By contrast, energy consumption of CHEF is less than LEACH protocol. As predicted, energy consumption of DFLCR is least. This is because that DFLCR have make use of residual DOI 10.5013/ IJSSST.a.17.42.35 35.4 ISSN: 1473-804x online, 1473-8031 print

energy, local distance and centrality to select CHs. So the optimal CH is selected. This has reduced the intra-cluster communicated distance. TABLE 3 ENERGY CONSUMPTION PER ROUND B. Number of Message Transmissions In this experiment, we have analyzed the number of messages by BS received successfully when the half of node is dead. The metric represent the performance of transmitting data. Experiment results are depicted in Table 4. TABLE 4 NUMBER OF AVERAGE MESSAGE TO BS TILL HND As shown as Table 4, number of average message is least. The reason is that LEACH protocol has not limited the number of cluster members. In addition, CHEF protocol has the same problem, and it only taken residual energy into account. For the EAUCF protocol, it adaptively form cluster according to distance between node and BS, but it also have not taken the number of cluster members into account. In fact, LEACH, CHEF, and EAUCF protocol do not control the size of cluster. This may form a big cluster. Once forming a bigger cluster, it results that lots of data packets are aggregated to one data packet. This reduce the number of message to BS. Compared with EAUCF and CHEF protocol, DFLCR has good performance. C. Network Lifetime Finally, we analyzed the network lifetime. The network lifetime is the number of rounds completed by the network before the first node died (FND) and half node died (HND).The experiment results depicted in Table 5. TABLE 5 NUMBER OF ROUNDS BEFORE FND AND HND From the Table 5, we know that DFLCR protocol outperform in term of network lifetime compared with LEACH, CHEF, and EAUCF protocol. In the first scene, DFLCR protocol increase lifetime (FND) than LEACH, CHEF, and EAUCF by 71.87%, 31.91%,and 16.07%, respectively. Similarly, DFLCR protocol increase lifetime (HND) than LEACH, CHEF, and EAUCF by 31.78%, 16.74%,and 16.74%, respectively. VI. CONCLUSION For the problem of network lifetime and energy consumption in WSNs, we has proposed distributed fuzzy logic-based clustering routing (DFLCR) protocol. Compared with similar protocols, the proposed DFLCR protocol has taken residual energy and local distance, centrality into account. The DFLCR protocol is able to select the optimal node as CH, and control the size of cluster. DFLCR protocol improves the network lifetime. Simulation results show that the proposed DFLCR can increase network lifetime and improve the efficient of transmitting data. ACKNOWLEDGMENTS This work is supported by 2015 National 863 Project (2015AA040302), National High Tech Research and development program (863 Program). REFERENCES [1] Muruganathan S D, Ma D C, Bhasin R I.A centralized energyefficient routing protocol for wireless sensor networks.ieee Radio Communications, 2011,43(3):8-13. [2] F.Zhao, J.Liu. Collaborative Signal and information processing : an information directed approach[j].processing IEEE,2013,91(8):1199-1209. [3] A.Abbasi, M.F.Younis. A survey on clustering algorithms for wireless sensor networks[j].computing Communication, 2012,30(15):2826-2841. [4] B.Baranidharan,B.Santhi.DUCF:Distributed load balancing unequal clustering in wireless sensor networks using fuzzy approach[j].applied Soft Computing,2016,(40):495-506. [5] W. Wendi, A. Chandrakasan, H. Balakrishnan. An applicationspecificprotocol architecture for wireless microsensor networks[j], IEEE Transaction Wireless Communications. 2012:1 (4) : 660-670. [6] Guo xiang-ping. An improved LEACH-ED algorithm[j].chinese journal of sensors and actuators,2011,21(10):1770-1774. [7] Kim J M, PARK S H. Cluster head election mechanism using fuzzy logic in wireless sensor networks[c].//proceedings of 2008 10th IEEE International Conference on Advanced Communication Technology,2008,(1):654-659. [8] JAVAID N,QURESHI T N, KHAN A H. Enhanced developed distributed energy-efficient clustering for heterogeneous wireless sensor networks[j]. Procedia computer science,2013,(19):914-919. [9] H.Bagci,A.Yazici.An energy aware fuzzy approach to unequal clustering in wireless sensor networks[j].application Soft Computing,2013,13(4):1741-1749. [10] IZADI D, ABAWAJY J, GHANAVATI S. A new energy efficient cluster-head and backup selection scheme in WSN[C].in IEEE 14th International Conference on Information Reuse and Integration (IRI), San Francisco,USA,2013:408-415. [11] IZADI D, ABAWAJY J, GHANAVATI S. An alternative clustering scheme in WSN[J].IEEE Sensor Journal,2015:1-10. [12] CHENG C T, TSE C K, LAU F C. A clustering algorithm for wireless sensor networks based on social insect colonies[j].sensors Journal, IEEE, 2011,11(3):711-721. DOI 10.5013/ IJSSST.a.17.42.35 35.5 ISSN: 1473-804x online, 1473-8031 print

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