[ieee 2014 international conference on computer communication and informatics (iccci) - coimbatore,...

2014 International Conference on Computer Communication and Informatics (ICCCI -2014), Jan. 03 – 05, 2014, Coimbatore, INDIA

[978-1-4799-2352-6/14/$31.00 ©2014 IEEE]

Cluster Based Routing Algorithm with Evenly Load Distribution for Large Scale Networks

Ritwik Banerjee Dept. of DOEACC, ICE(I), Techno India Group,

Kolkata, West Bengal, India [email protected]

Chandan Kr. Bhattacharyya Dept. of Computer Sc & Engineering, Techno India,

Saltlake, West Bengal, India [email protected]

Abstract—one of the major restrictions for the application areas in wireless sensor network is the threat of limited energy resources. The sensor nodes are equipped with ir-rechargable batteries as the source of power. To maximize the entire network life time it is required to optimize the energy usage for the sensor nodes in WSNs. It is already established that clustering is an efficient routing technique requires less energy consumption to transmit data to the base station with compare to the direct communication techniques. Efficiency of clustering technique is directly dependent on formation of the cluster and cluster head selection techniques. The uneven distribution of the sensors among the clusters will cause energy leakage problem for some cluster heads and may cause premature deaths to them. In this paper, we present a novel cluster based routing algorithm that ensures to form clusters with even load as well as select cluster heads in a better way using fuzzy based environment so that optimal data transmission on single or multi hop environment is maintained. The simulation results more than 40% prolonged life time for the sensor nodes.

Keywords— Wireless sensor networks; Clustering algorithm; Load distribution; Cluster Head Selection; Energy optimization; Fuzzy logic;

I. INTRODUCTION Wireless sensor network is made of large number of inexpensive sensor nodes which are generally deployed in an ad-hoc manner. The sensor nodes are equipped with sensing capacity, low computational power, and transmitting services. Generally sensors are embedded with micro integrated circuits and run by ir-rechargable battery powers. Battle field surveillances, fire monitoring systems, factory automations are some of the important application areas of WSNs [3, 4]. In most of the application areas of WSNs do not have scope for replacing the out of service sensors. Thus in WSNs sensors are often suffered from energy constraints. The maximum utilization of sensor nodes and prolonging network life time now becomes one of the most important research areas in WSNs domain [1, 12]. Such applications require thousands of sensor nodes and a very few or single base station [6]. In general the responsibility of the sensor nodes are to sense the event from the environment and transmit them to the base station. Detected data through sensors can be transmitted to the base station either by direct transmission techniques or by means of clustering techniques and it is already proved that in

terms of energy consumption clustering approaches are much more efficient rather than direct transmission techniques [10]. The clustering technique requires the sensors to transmit data to the base station through intermediate higher level nodes. Each sensor within the network must be a part of certain group of sensors, called cluster. Each cluster is leaded by a special node - the cluster head, which collects the data, transmitted from the sensors and send the processed and aggregated data to the base station. The maximum energy dissipation happens to the cluster head node as it requires maximum data communications [9]. The uneven distribution of sensor nodes during cluster formation phase leads unlimited number of members under certain cluster head. Cluster heads form those heavy loaded clusters will be having responsibility of maximum data communications and might be affected of heavy energy dissipation, hence may lead to premature death. To prolong the network life time it is require to fix the maximum number of sensors under cluster heads so that no cluster head will come to end unevenly. More to optimize the energy resource it is required to minimize the number of data communications as well as the transmission ranges with out compromising the quality of services. Thus designing an optimal clustering technique one can find the following as pivotal issues:

• Cluster formation considering the maximum number of members under certain cluster head.

• Cluster head selection through node’s current energy, distance to the base station, and distance to the entire member nodes [11].

• The clustering algorithm should optimize number of communications without compromising quality of services [12].

The rest of the paper is organized as follows. Section II illustrates related works. In section III, we present the radio and network model for our algorithm. Section IV describes the problem description whereas in section V we describe our optimal algorithm. The simulation result is presented in Section VI.

II. RELATED WORKS Forming clusters and cluster head election based on probability value was the objective of the pioneer clustering algorithms presented by LEACH [1], later cluster head election technique


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is modified by taking the node’s current energy level into account [2]. A base station assisted cluster head selection algorithm is presented in [3] where nodes with high sensing density, that is closer to the entire members will get higher chance of becoming cluster head. In [4], a novel cluster head election algorithm is presented to avoid choosing cluster head from very close zone. In [5], the nodes from border area are assisted by introducing more cluster head node. The far zone nodes are clustered with the help of Greedy K algorithm where common nodes will not communicate [6]. Cluster head election based on sensing density is implemented by simulated annealing in [7]. A novel approach of finding cluster head is presented in [8], where energy level and location of the node is given priority. In [9] cluster heads are elected from the inner area to avoid border nodes from becoming cluster heads. A novel idea for far zone nodes through two hop communications is presented in [10]. A new fuzzy logic implementation of finding cluster head based on input variables like node’s energy, distance to members, and centrality of the nodes is presented in [12].

III. NETWORK AND RADIO MODEL LEACH [1] is already established as a promising routing protocol in wireless sensor networks domain, still there are some areas for improvement to make the protocol more efficient. In this paper we present a modification on LEACH’s cluster head election algorithm to reduce and make balance the total energy dissipation of the sensors. Our sensor network architecture [1, 2, and 13] has the following properties:

A. The Network Model and Its Architecture

• The sensor nodes are of homogeneous type. • All the sensor nodes are with uniform initial

energy allocation. • The nodes are eligible to determine its

current energy level and location information through GPS service.

• All the sensor nodes are immobile and having fixed node id.

• Data aggregation is done at the cluster head only [1, 12].

• The communication channel is symmetric [1, 12].

• The base station is static, and its location is initially known to the entire sensors.

B. The Radio Model In our analysis, we use the radio model which is same as the radio model used in LEACH [1, 2, and 13]. The transmitter or receiver is used in sensors dissipates energy to run the radio. For ‘k’ bit data communication between any two sensor nodes with distance‘d’ units, the required energy cost for transmit and receive is given by following equations 1 and 2 respectively:

E T x (k, d) = Eelec * k + Eamp * k * d2 (1)

E Rx (k) = Eelec * k (2)

In our work, we assume Eelec = 50 nJ /bit to run the transmitter or receiver circuit and Eamp = 100 pJ/bit/m2 for the transmit amplifier [1, 12].

IV. PROBLEM DESCRIPTION Among the entire clustering algorithms introduced so far, it is obvious that the LEACH [1] is one of the most promising techniques. Many researchers considered the LEACH as the pioneer among all the clustering techniques, but still we can find some areas in LEACH and other established routing techniques where we can make some improvements to make an optimal energy efficient technique. Before discussing the problem statement at first we would like to illustrate the LEACH [1] as a standard clustering protocol. The LEACH [1] algorithm is designed into rounds where each round is divided into two phases – set up phase and ready state phase. The set up phase includes cluster formation, cluster head (CH) election, and TDMA schedule fixation for the non CH sensor nodes. In set up phase among the entire sensors, the cluster heads are elected randomly, based on the percentage of desired cluster head for the current round. Although node’s current energy level is taken into account in the modified version of LEACH [2]. Each node in setup phase chooses a random value between 0 and 1 and if it is smaller than a calculated threshold value T (n), the node will become a cluster head for the current round [1]. T (n) = [P /{1 − P × (r %1 / P)}] if n ∈ G Or, T (n) = 0 if n ∉ G. (3) Where ‘n’ is the node itself, ‘G’ is the set of alive node at current round ‘r’. The self elected cluster heads then broadcasts the cluster head announcements (joining request), the other non cluster head member nodes receives the joining request from one or more cluster heads, and join to closest cluster as a member. This decision is taken based on received signal strengths of one or more joining requests. The member sends a joining response packet to the respective cluster head and the setup phase is completed by transferring TDMA schedule to the cluster members form the respective cluster head. In steady phase the actual communication starts – each cluster member collects data from the environment and transmits the sensed data to the cluster head during its turn defined by the cluster head through TDMA schedule. The cluster head receives data from cluster members, aggregates the data, and then transmits the aggregated data to the base station [1, 2]. According to the problem definition some of the vital problem statements, which may arise in LEACH [1, 2], are discussed bellow. A. Cluster Head (CH) With Unlimited Load Since the responsibilities of the cluster heads are to collect data from all the member nodes and transmit the aggregated data to the base station, it is obvious that the maximum energy dissipation happens to the cluster heads only rather than member nodes. The energy dissipation amount is directly dependent on the number of member nodes under any cluster


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head. If the clustering algorithm does not take care about the maximum number of member nodes under certain cluster heads and does not specify any step to fix it up, then we may find that some cluster head are carrying a large number of members compare to other cluster heads, hence those overloaded cluster heads are going to dye very early. Consider the following figure 1, here 100 sensors are deployed in an ad-hoc manner. We apply the LEACH [2] technique to select the cluster heads in a certain round with probability value for cluster head is 5%, red spotted are elected as CH. It is clear from the figure that the Cluster ‘3’ considers maximum number of sensors, and hence the outcome is in Table1. This uneven load distribution of sensors among the cluster heads will lead to huge energy dissipations to the cluster head from the Cluster ‘3’ specifically. As an obvious the overall network life time will be affected very badly.

TABLE 1: Clusters with its Loads

Figure 1: CH with uneven load

B. Energy Dissipation and CH Selection The standard structure of sensor radio model as it was presented by LEACH [1, 2] is already declared above in this paper (equation 1 and 2). It is very clear from the above two equations that energy dissipation during the packet transmission is much more than the amount of energy dissipated during receiving a packet. The energy consumption during transmission of packet is directly in proportion with squared distance (d2), the packet traversed [10]. The more distance packet traverses, the more energy it consumes. Thus for designing energy efficient routing protocol and to prolonging network life time, it is required to optimize the number of packet transmission as well as optimize the distance traversed by the packets. In WSNs, the maximum communications are occurred between cluster head and its member nodes, so cluster head selection algorithm should

consider ‘minimum distance to the base station’ for optimizing energy dissipation during communication from CH to the BS, and ‘minimum distance to the entire cluster members’ for minimizing energy dissipation during communication from member to the CH, as two most vital restrictions. The node with high energy level, minimum distance to the entire neighbors and base station, if selected as CH then energy dissipation during data communications phase will considerably be very less.

V. PROPOSED SOLUTIONS AND ALGORITHM The proposed protocol has been divided into rounds where rounds are segregated into different phases: the initial setup phase, the regular setup phase, and the steady phase. Our proposed protocol is headed to design an energy efficient routing algorithm by considering evenly load distribution among the clusters and by selecting the base station in a better way. The proposed protocol is also aimed to reduce the total number of packet transmissions and re-clustering at each round without compromising the quality of services for the network. The initial setup phase executes at the first round, and only when the reconstruction of cluster is needed during the entire network life time. In other round the steady phase is followed by the regular setup phase. A. Initial Setup Phase The design of the initial setup phase of our proposed protocol is primarily same as the set up phase designed by the traditional LEACH [2], the only difference is that our proposed protocol does not include TDMA schedule formation at the initial setup phase. Initially each node will calculate its priority value for becoming intermediate CH by using the following equation ‘4’ [13] at the initial set up phase. T (n) = [P /{1 − P × (r %1 / P)}] × [En,r ] if n ∈ G

Or, T (n) = 0 if n ∉ G. (4) That is at the beginning each node will find its priority value to become intermediate CH by putting the value of the variables ‘n’, ‘r’, ‘p’ In the equation ‘3’. Where ‘n’ is the number of nodes inside the network, ‘r’ is the round number (initial value is 0), ‘p’ is the desired percentage of CH for the network. ‘En,r’ is the residual energy level of node ‘n’ at round ‘r’. ‘G’ is the set all alive nodes. After calculating the priority value ‘T(n)’ for any node ‘n’ will check whether this value is greater than a randomly generated threshold value (ranges between 0 and 1) or not. If the node’s priority value is greater than the random value then the node itself will declare as an intermediate CH for that initial setup phase otherwise will be waiting for the CH announcement by the other nodes. The basic idea is that node with higher energy level will be having higher chance of becoming intermediate CH for that initial setup phase. This CH announcement packet contains the location information of the CH node too. The non CH member node after receiving one or multiple CH announcement, select only the closest one as it’s intermediate CH and sends its own location information to that particular CH. Each intermediate CH node completes its work by transferring location

Cluster Id Member/Cluster

Cluster 1 4 Cluster 2 11 Cluster 3 56 Cluster 4 18 Cluster 5 6


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information (in Cartesian coordinate format) and the current energy status of the entire member nodes from its cluster in an aggregated format to the base station. After receiving those data from the intermediate CHs, the base station (BS) converts those locations to polar coordinate form (r, θ). The BS then divides the total area into sub clusters (often called as zone) based on desired percentage of cluster heads, say ‘p’ with the help of following equations. ‘MIN’ is the minimum ‘weight’ per zone, and ‘MAX’ is considered as the maximum weight per zone. Minimum number of zones, Zn= p*100 (5). And if consider p = 0.05 (means 5%), then Zn= 0.05*100 = 5. Thus initial degree range per zone, D = (360/Zn)o= 72o (6). The total area is now equally divided with respect to range D degree, starting from 0 to 360 degree polar coordinates, in anti clockwise direction. Now BS will find the number of sensors lies per zone, called weight of the zones. If in any situation it is found that any zone is having less than MIN number of nodes, then that zone is called ‘weak zone’ and will be merged with it’s predecessor or successor zone, located in anticlockwise direction. If for any zone, it is found that the weight is greater than the MAX value, and then the zone is to be partitioned again into sub zones, starting from the initial degree of the zone to until MAX number of nodes are found or half of the Zone’s weight is met or which appears earlier. The process will be continued until all zones obtain weight between the ranges [MIN, MAX]. Now for selecting the actual cluster head per zone, the BS will use a fuzzy environment by considering parameters- energy level of the nodes, node’s Euclidian distance to the base station, and node’s Euclidian distance to its all member with in the entire zone. BS selects cluster head for each zone and transmits a TDMA schedule, and cluster head id to entire the members within that zone. BS sends TDMA schedule and entire member’s id to the selected cluster head. We follow CSMA/CD technology to avoid collisions during intra cluster communications in initial set up phase. Figure 2 shows cluster formation though proposed algorithm by simulation result. B Regular Setup Phase After completion of any round, the cluster head for each zone transmits the energy status of each node within that particular zone to the BS. As we consider in network model that sensor knows its current energy status, at the last slot of packet transmission to cluster head, it includes the energy status within the data packet. Cluster head collects and transmits those energy based data at the end of the round to the BS, such that BS become able to select the most eligible cluster head for the next round. If in round BS finds that certain zone has become ‘weak zone’, due to death of some members, BS then again merge that zone with either of its neighbor zones and do the checking of zone’s weight not exceeding the MAX value, defined as in the above. C Steady Phase This phase is much similar with the steady phase designed by the LEACH algorithm [1]. Here the BS will define TDMA

schedule for the entire zone at the beginning of every round. BS sends the cluster head the TDMA schedule for intra cluster communications well as TDMA schedule of entire cluster heads for transmitting packets to the BS, and id of the entire cluster members, whereas the cluster members are used to get the TDMA schedule and the cluster head id from the BS at the beginning of any round. Members transmit the collected data to the cluster head only when its turn appears according to the schedule. This TDMA approach is used to avoid packet collisions during intra cluster, and inter cluster communication period too. The figure 2 presents the cluster formation with evenly weight distribution: here red spotted are cluster heads, node deployment is same as Figure 1, and nodes per zone is given in table 3. We consider here 100 sensor nodes with ‘p’ is 0.05, MIN = 10 and MAX = 25 (choice is application dependent). It is clear from table 3, that the cluster zones are not suffering from unlimited loading problem, and proposed protocol guarantees of having limited load for each cluster head.

Figure 2. Cluster Formation with Evenly Load Distribution

Cluster Zone Degree Range Weight/Zone Zone 1 0o -> 72o 12 Zone 2 72o -> 144o 13 Zone 3 144o -> 216o 18 Zone 4 216o -> 256o 13 Zone 5 256o -> 288o 14 Zone 6 288o -> 320o 15 Zone 7 320o -> 360o 15

TABLE 2. Clusters, Ranges (Degree), and Weight

VI. PERFORMANCE EVALUATION AND SIMULATION RESULT We evaluate the performance our proposed algorithm through simulation experiments, we have implemented the simulator through MATLAB environment. The performance of our algorithm is compared with the LEACH [1, 2] protocol. The performance metrics includes energy efficiency and prolonging network life time, cluster formation with evenly load distribution (which is already presented and explained in ‘problem discussion’ and ‘proposed solution and algorithm’ in section IV.A, and V.A, with figures 1, and 2, and tables 1, and


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2), and cluster head selection based on node’s energy, distance to BS, distance to entire cluster members. For simulation we take following parameters, presented in table 4.

Parameter Value (s) Area 100m x 100m

Number of Nodes 100 Initial Energy / Node 1J

Packet Size 2000 bits BS Location (x, y) (50, 50)

TABLE 3. Simulation Parameters

A Cluster Head Selection We have considered three vital input parameters like node’s current energy level, node’s distance to BS, and node’s distance to entire cluster members in fuzzy logic control to select the cluster head for any cluster zone at any round. The parameter ‘Energy’ has three states – poor, good, and excellent. The parameter ‘Distance to BS’ has two states – close, and far, whereas the parameter ‘Distance to Member’ has three states – close, moderate, and far. The output parameter ‘Chance’ has six states – very poor, poor, less, moderate, high, and very high (Figure 3 and 4). The membership function has 18 rules (Figure 5) and for fuzzyfication we applied the well known ‘Mamdani’ method [12] (Figure 4). The table 4 shows the comparisons between LEACH [1] and proposed algorithm with respect to chance of becoming CH. It is clear from the table that through our proposed algorithm the chance of becoming a CH for any node is drastically going downwards when the distances are too high whereas in case of LEACH [1], the chance remains unchanged. We take ranges [0, 100] for 3 inputs and the output chances. Figure 3 shows the fuzzy rules applied to proposed algorithm for selecting CH.

B. Energy Efficiency and Network Life Time To design energy efficient routing algorithm, it is required to take care on minimizing the number of transmissions as well as minimum distance covered during packet transmission period. Re-construction of clusters at every round requires heavy energy dissipation, our proposed intended to avoid cluster reconstruction at every round. Even, the cluster formation at the first round requires packet transmission from each sensor and in any successive round, the proposed algorithm does not involve packet transmission from sensors as CH selection and cluster formation (if any) is controlled solemnly by the BS itself. The proposed protocol insists the BS to create the cluster, to define the intra cluster communication TDMA schedule, to select the CH, and to transmit the TDMA schedule to the entire members, aimed to reduce the work load of CHs. In our proposed algorithm, we optimize the communications by avoiding –:

(1) cluster head announcement phase which can save up to (P * ET x(k, d) + (N-P) * ERx(k)) Joule / round, where ‘P’ is the desire percentage of CH, ETx(k, d) is the transmitting energy required for ‘k’ bit packet to traverse ‘d’ unit distance, ‘ERx(k)’ is the required energy for receiving a ‘k’

bit packet, ‘N’ is the total number of nodes within the network.

(2) It does not require ‘cluster joining request’ from the entire member nodes of each clusters, which is able to save energy up to ((N-P) * ETx(k, d) + P * ERx(k)) Joule / round.

(3) Proposed protocol does not require the TDMA scheduling announcement and can save up to P * ETx(k, d) Joule / round.

(4) This protocol requires sensors to BS two hop communication at the initial set up phase which requires energy same as first round of LEACH and which will never been again through the life time of the network because reconstruction of the clusters are not required repeatedly and can save a great amount of energy as well.

Although in every round the CHs are required to collect and send energy status of the entire members to the BS, it requires few enlargements of data packets to include energy levels in a ‘n’ bits field, where N=2n. Calculation results that proposed protocol can save up to 50% of energy where as extra packet length requires n * N bits data transmission per round, means at most 0.01% extra energy dissipation per round in compare to traditional LEACH[1]. The simulation results are presented in Figure 6.

Figure 3. Fuzzy function for CH Selection

Figure 4. CH Selection ‘Chances’ using fuzzy logic

Figure 5. Fuzzy Rules


[978-1-4799-2352-6/14/$31.00 ©2014 IEEE]

TABLE 4.Experimental Results on ‘Chance’ using Fuzzy Logic

Figure 6. Average Network Life - Proposed vs. LEACH

The figure 6 shows simulation result with considering parameters presented in table 3. It is very clear from the figure that average life time of the nodes expire after successful completion of 1302 rounds where as our proposed protocol can extend this life time up to 1899 rounds, hence the average life time is extended up to 45%.

VII. CONCLUSION This paper presents a new framework for obtaining energy efficient modification on cluster based routing protocol. Our goal was to find an optimal solution on balanced load distribution over the entire network. The probabilistic cluster head election mechanism and respective cluster formation technique may lead to uneven distribution of load among the network. We have optimized this by introducing a new novel idea which is node’s location dependent and base station assisted, and that can assure clusters with limited load. Moreover fuzzy logic implemented cluster head selection algorithm ensures of selecting cluster head by considering parameters not only node’s energy, but also node’s distance to

BS and node’s distance to other members. We have reduced the energy dissipation by reducing intra cluster communications in frequency and as well as in total distance it covers. The simulation result shows the improvements of network life time at an average 40% to 50%, in compare to the traditional LEACH. the battery health condition of the nodes as a parameter while being elected as a cluster head. Moreover the proposed protocol is applicable for the large area network with variable node’s density too as it does not require sensor to BS single hop communication or reconstruction of clusters in each round. However mobility of the sensor nodes may improve the potentiality of the routing protocols in wireless sensor network domain, although this is beyond the scope of this paper and we consider it as future work.

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communication protocol for Wireless Micro sensor Networks”, In proceedings of 33rd Hawaii International Conference on System Science – 2000.

[2] Heinzelman, Balakrishnan, Chandrakasan, application specific protocol architecture for Wireless Microsensor Networks”, IEEE Trans. Wireless Communications, vol 1, no. 4, October 2002, pp. 660-670.

[3] P Tillapart, S Thammarojsakul, T Thumathawatworn, P Saniprabhob, “An approach to hybrid clustering and routing in WSNs”, IEEEAC 2005.

[4] Lin Shen and Xiangquan SHI, “A location based clustering algorithm for wireless sensor networks”, International Journal on Intelligent Control and Systems, Vol. 13, September 2008, pp. 208-213.

[5] M Dhanraj, S Ram Murthy, “On achieving maximum network life time through optimal placement of cluster heads in WSNs”, In proceedings of IEEE conference on communication, IEEE 2007.

[6] Li-Quing Guo, Yi Xie, Chen – Hui Yang, Zhen-Wei Jing, “Improvement on LEACH by adaptive cluster head election and two hop transmission”, In proceedings 9th IEEE International conference on Machine Learning and Cybernetics, July, 2010.

[7] J Ferdous, M J Ferdous, and T dey, “ A comprehensive analysis on CBCDACP in WSNs”, Journal of Communications, Vol. 5, No. 8, August 2010.

[8] Siva D Muruganathan, D C F Ma, Rolly I Bhasin, and Abraham O., “A centralized energy efficient routing protocol for WSNs”, IEEE conference on Radio Communications, March 2005.

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[13] Ritwik Banerjee, C K Bhattacheryya, “ Energy efficient optimization in the LEACH Architecture”, IEEE International Conference AICERA-ICMICR 2013.

Experiments Energy Distance To BS

Distance To Members

Experiment 1 85.0 J 15.0 m 14.0 m Chance (LEACH)

Is 91.6 (Very High)

Chance (Proposed)

Is 89.0 (Very High)

Experiment 2 85.0 J 78.0 m 89.5 m Chance (LEACH)

Is 91.6 (Very High)

Chance (Proposed)

Is 34.6 (Very Poor)

Energy

Rounds

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