Abstract--In Wireless Sensor Networks (WSNs), sensor nodes play the most important role. These sensor nodes are mainly un-chargeable, so it an issue regarding lifetime of the network.
The main objective of this research is concerning clustering algorithms to decrease the energy utilization of every sensor node, and maximize the sensor network lifetime of WSNs. In this paper, we propose a novel clustering algorithm for wireless sensor networks (WSN) that decrease the networks energy utilization and considerably extend its lifetime. Here main role play allocation of CHs (Cluster Heads) from corner to corner of network. Simulation result shows extensive decrease in network energy utilization and therefore increase the network lifetime.
Index Terms--Wireless Sensor Networks (WSNs), Clustering, LEACH, EnergyEfficiency.
I. INTRODUCTION
Wireless sensor network (WSN) is extensively measured as one of the significant technologies for the twenty-first century [1, 5, 7]. In the past decades, it has received tremendous attention from both industry and academia all over the world. A WSN normally consists of a large number of less-power, less-cost, and multifunctional wireless sensor nodes, by way of sensing, computation capabilities and wireless communications [2, 3, 6]. All the sensor nodes communicate above small distance via a wireless medium and collaborate to accomplish a familiar job; Wireless sensors network have become an exceptional tool for military applications, intrusion detection, perimeter monitoring, information gathering and graceful logistics support in an unidentified deployed region. Some extra applications: location detection, sensor-based personal health monitor with wireless sensor networks and progress detection [4, 18, 19, 20].
The numbers of clustering algorithms have been proposed to progress the wireless sensor network lifetime. In clustering algorithms, the wireless sensor network (WSNs) is separated into groups, is called clusters and then the one sensor node from each cluster is selected the cluster head.
All the data aggregation action has been completed within the cluster and then CH (cluster head) use to send the information of a particular cluster to the BS (base station) which is also known as sink node. To stability energy
Rajkumar is with Sambhram Institute of Technology, Bangalore, VTU Belagavi Karnataka, India
Email: [email protected]
Dr H G Chandrakanth is with the Sambhram Institute of Technology, Bangalore, VTU Belagavi Karnataka, India
Email: [email protected]
utilization in every cluster, periodic cluster head selection inside clusters is proposed [13].
The uniformly distributed CH cluster head locate can stability the energy utilization amongst sensor nodes and lastly extend network lifetime. The network through non- uniform sensor node distribution, the mechanisms used to equilibrium the power utilization and extend the network lifetime are not all the time effective. The uniformly distributed CH cluster heads allow the clusters contain the consistent cluster areas, the power utilization amongst cluster members or nodes can balanced. Though, the imbalanced power utilization still exists among CHs due to the non-uniform sensor node distribution.
In this paper, we propose a novel clustering algorithm for WSNs, this is called EDCH (Effective Distance Cluster Heads), based on the clustering algorithm of LEACH [9, 11]. We illustrate EDCH in two methods; EDCH1 and EDCH2 to emphasize different development achieved by everyone. EDCH is primarily benefitted by well-organized distribution of CH transversely the network. Here evaluation demonstrates up to the 52.35% development in power saving and in addition up to the 127.54% in enlarge the network lifetime measure up to the LEACH.
This paper ordered as shown. LEACH clustering algorithms are mention in Section II. Our novel clustering algorithms, EDCH1 and EDCH2, are presented in this Section III and performance evaluations are in this Section IV. And Section V concludes of this novel algorithm paper.
II. RELATEDWORKS
Low Energy Adaptive Clustering Hierarchy (LEACH), LEACH is the primary algorithm of clustering hierarchical routing algorithm.
All the sensor nodes in a network arrange themselves into home cluster, with one sensor node act as CH. every non-cluster head node transmit data to CH cluster head, while a CH cluster head sensor node collect data from every one cluster sensor nodes or leaf nodes, All the data aggregationaction has been completed within the cluster and transmit data to the remote BS base station. Consequently, being a CH cluster head sensor node is more power than being a non-cluster head node. When CH cluster head sensor node dies, every nodes that belong to cluster going to drop communication. The problem of LEACH algorithm is balance the energy utilization, network energy utilization.
Using LEACH algorithm we can reduce the communication energy that is dissipated by the CH cluster heads and the cluster sensor nodes as much as possible 8 times the evaluates with straight transmission & minimum transmission power routing algorithm [21].
EDCH: A Novel Clustering Algorithm for Wireless Sensor Networks
Rajkumar and H. G. Chandrakanth
LEACH procedure, it rotates the randomized elevated energy CH location such that it turn among the sensor nodes in order to restrict draining the nodes battery of any sensor node in the network. This method, the energy load associated with being a CH (cluster-head) is evenly distributed among the sensor nodes. Since the CH (cluster- head) sensor node knows every the cluster nodes, it can create a TDMA (Time Division Multiple Access) schedule that tells every node exactly when to transmit its sensed data. The process of LEACH protocol is divided into rounds. The entire round starts with a set-up phase as the clusters are organized, followed by a steady-state phase wherever numerous frames of sensed data are sent from the sensor nodes to the CH (cluster-head) and onto the BS (base station).
The set-up phase, when clusters are set and CH are elected. The initial round, all the node elected a random number in-between 0 and 1 and it compares to the threshold T(n) known in equation (1) and if the number is less than a threshold value, the node becomes the CH.
(1) Where,
p is the preferred percentage of CH (Cluster Head), r is present round,
G is set a sensor nodes that not been CH (cluster heads) inside the very last 1/p rounds.
All the rounds, elected CH broadcast a message to all the sensor nodes in the network, informing their new status.
Once the each sensor nodes received message, each of the non-cluster-head or sensor nodes can decide to which cluster they belong to based on the strength of the received signal.
The number of sensor nodes in a given cluster, that cluster’s CH generates a TDMA program, and broadcasts a transmission moment window to its CH (cluster head).
In steady state phase. All the sensor nodes start sensing a information in cluster and transmitting sensed information to their own CH cluster-head throughout a distributed transmission moment. The CH cluster-head sensor node performs the aggregating, data fusion, compressing and transfer the aggregated data to the BS (base station). BS base station is regularly far from a cluster, if a cluster head wants to communicate with the base station will consume more energy. When completed the transmission time and also completed steady state phase. The sensor node network withdraw into the setup phase and starts an alternating round, beginning with the choice a new CH (cluster heads).
Heinzelman. W et al. [10], LEACH cluster head not elected by their best possible number & locations. LEACH- C [8], is a centralized of LEACH protocol, it divides all round into two stages, a setup phase and transmission phase.
During the setup phase of LEACH-C, all the sensor nodes of WSN send their information, including the position and power level to BS (base station) or sink. Then BS (base station) or sink calculates an average power value of every sensor nodes. Which sensor node had more power than the average energy they have chance to become a CH (cluster heads). The BS employ annealing algorithm to set up
clusters. The cluster groupings are elected to minimize the power utilization needed for normal sensor nodes to broadcast data to their respective CH (cluster head). Both process of LEACH and LEACH-C are similar [12, 16]. But stimulation result shows good improvement in LEACH-C over LEACH.
Fig. 1: LEACH algorithm, (Cluster Heads and Members)
S. D. Muruganathanet al. proposed BCDCP [14], Base- Station Controlled Dynamic Clustering Protocol(BCDCP) is a centralized clustering routing algorithmfor WSNs. The clustering routing Algorithm called Base BCDCP, which consumed a high-power BS or sink to position clusters and routing pathway, implement randomized rotation of CH, and carry other power-intensive jobs. Ideas in (BCDCP) are development of impartial clusters where all CH serves an roughly the same number of sensor nodes to avoid cluster head CH overload, uniform assignment of cluster heads CH throughout the sensor field, and consumption of cluster head to cluster (CH-to-CH) routing to transfer message to BS.
BCDCP is an enhanced structure lifetime and better energy savings over the LEACH, LEACH-C and PEGASIS clustering routing algorithm [22].
M. J. Handy et al. [15, 17] it extends LEACH’s stochastic CH (cluster head) election algorithm by a deterministic component. stochastic CH (cluster-head) election will not mechanically direct to minimum power utilization during sensor data transfer for a given set of sensor nodes All the CH (cluster-heads) can be positioned near the edges of the sensor network or adjacent sensor nodes can become CH. In this case some sensor nodes have to bridge long distances to reach CH. Improve the network lifetime depending upon the network design.
G. Smaragdakiset al. SEP [24] In the (Stable Election Protocol) SEP, powerful sensor nodes have additional of a probability of being selected as CHs In the SEP the usual sensors have a smaller chance of being selected as the (cluster head) CH, This algorithm of SEP enlarge the stable- stage of the network depending on the percentage and preliminary energy of the powerful and excellent nodes.
Gupta et al. [25] and Ran et al. [26] one of the technique Fuzzy logic improve the LEACH algorithm. Fuzzy logic algorithm based CH cluster head election conducted in BS base station. BS Base Station or sink consider two election producer from sensor node which are power level and distance to the BS base station or sink to select the suitable CH (cluster head) that will prolong the first node die FND time, data stream guaranteed for each round and also increase the throughput received by the sink or BS base
station previous to FND. Now algorithm used three factors are sensors centrality, sensors density and sensors remaining energy. These two algorithms are centralized WSNs.
III. EDCH(EFFECTIVE DISTANCE CLUSTER HEADS) The section III, we explain EDCH, our novel clustering algorithm for wireless sensor networks (WSN). For the clarity reason, the EDCH algorithm described in two stages, called EDCH1 and EDCH2, to highlight dissimilar characteristics / features and achievements of all steps.
EDCH2 advance get better efficiency achieved by EDCH1 by amending the number of CH. lastly, a similar power model as the one proposed in [11, 23] is used at this point
A. EDCH1
The position of CH is quite necessary to avoid wasting of energy. This is neglected in LEACH Low Energy Adaptive Clustering Hierarchy growth and consequently there might be numerous rounds in an interval with some CH (cluster heads) either very near to or very far from all other. In this case, there will be some waste of power due to overhear signals or using long-distance transmission to reach a CH (cluster head). Our aim is to, as much as possible;
consistently distribute Cluster Heads over the whole region in order to achieve almost equal size clusters in all round with each CH (cluster head) positioned at near to the centre of the associated cluster. To do so, depending on the area size and sensor node density, a parameter d is defined as the closeness. That is, if in a particular round, the distance of two CHs (cluster heads) is less than d, those clusters are also near to all other and one of two CH (Cluster Head) should be dropped. Thus, after the election of the first CH (Cluster Head) following usual LEACH procedure, the next potential CH (Cluster Head) whose its random generated numbers is less than the threshold, checks its distance from the first CHs (cluster head) in the present round before advertising itself to other sensor nodes. When the distance is less than d, it terminates its decision to be novel CHs (cluster head) and remains a CHs (cluster head) member for future rounds.
In this case for further sensor nodes whose generated random number is less than the threshold and expected to be CHs (cluster head) in the similar round. Since all nodes have to be CHs (cluster head) in an interval, all of the remaining sensor nodes will be elected as a CH (Cluster Head) in the final round despite of their nearness to all other. This process is applied as our preliminary improvement to form clusters with almost the similar size and is called EDCH1.
Fig. 2 shows an example of a round of CHs (cluster head) elections according to EDCH1 algorithm.
Parameter d plays an essential role on the efficiency of EDCH1. The best value of d is reliant on the network area, sensor node density and also the number of CHs (Cluster Heads). To get the best value for the Effective distance, d, for a particular design, we fix new parameters in a particular designAnd observe different values of d. For example, in a network of 100 sensor nodes with p = 0.05 and region size of 50×50 square meters, the lowest energy utilization is presented when the size of d is 15 meters and MN is 25 messages, as shown in TABLE I. The initial column of this table demonstrates different inspected values for the
nearness. Second and third columns demonstrate the network power consumption in joules (J) for single round and for MN=25 and MN=250 messages all round, respectively. Using the best value of d, EDCH1 demonstrate a significant development to reduce the network power utilization and therefore to extend the network lifetime.
Fig 2: EDCH1 (Cluster Head and Cluster Member)
CE (Consumed Energy) MN (Message Number)
ED (Effective Distance)
TABLE I: The network power utilization for different value of nearness and for two different values of MN is 25 and 250 messages per round.
ED (m) CE in joules (J) CE in joules (J)
(MN = 25) (MN = 250)
0 1 2 3 4
14.47 14.30 14.10 14.00 13.98
127.54 126.60 125.70 124.53 123.37
5 13.94 122.27
10 13.69 119.89
11 13.68 119.77
12 13.68 119.86
13 13.68 119.86
14 13.72 120.13
15 13.66 119.60
16 13.70 119.96
17 13.74 120.30
18 13.73 120.20
19 13.80 120.92
20 21 22 23 24
13.89 13.90 13.98 14.10 14.22
121.80 122.72 123.66 124.34 125.18
25 14.34 126.09
Despite the important development realizable by EDCH1, there is at rest room for development. Visualize p is the best possible percentage of CHs (cluster heads) among completely sensor nodes. In EDCH1, distant from the final round of the recesses, the number of selected CHs (cluster heads) in all round is extremely likely less than p percent of the total sensor nodes in the network. Because a number of them might terminate their selection of being a CH (cluster head) due to their Effective distance to other CHs (cluster heads) and save themselves for the remaining rounds of the recess. Accordingly, the number of the clusters will be reduced compared with the most favorable number of LEACH. This leads to the bigger clusters size and extra energy usage over the intra cluster transmissions. Other hand, in the final round of the intervals, the percentage of the CHs (cluster head) is much more than the optimum
number p. Then, the number of clusters might increase and more CHs (cluster head) have to transmit their data to the BS (base station) using long distance transmission. Figure. 3 demonstrate an example of CHs (cluster heads) and cluster members in the final round of EDCH1 algorithm.
Fig. 3: Cluster Heads and Cluster Members in the last round of the EDCH1 algorithm.
This effect is removed in the EDCH2, an enhanced version of EDCH1.
B. EDCH2
The LEACH algorithm, the most excellent performance is presented by a network when the number of selected CHs (cluster heads) in each round is accurately p. This constraint is not satisfied by EDCH1, since EDCH1 most expected removed some of the CHs (cluster heads) in every round, except from the final round because of their closeness to all other. The amendment in EDCH2 is made by increasing the threshold and the number of nominated CHs (cluster heads) in every round. As a result, more than p percent of sensor nodes will be nominated as CHs (cluster head), on average, in each round to reach the optimum value, p, after dropping some of them due to closeness issue. After setting a new threshold, close to p percent of sensor nodes are finally selected as CHs (cluster head) in each round which are distributed more uniformly compared with LEACH algorithm. Now, the key question is how to enlarge the threshold to meet the optimum number in every round another time. It will be noted that is increased value for the threshold, add-on value, is not a constant value for all rounds but varies from one to another. As the value of threshold increases round to round according to Equation 1, the add-on value decreases until to reaches zero at the final round. The novel threshold value, T’ (n), can then be calculated using the following equation:
T’ (n) = T (n) + (1 − T (n)) × f. (2)
T (n)can be calculated using Equation 1 and f, the coefficient, is a constant value. This equation is used by EDCH2 to find the value of threshold at every round.
The value of coefficient, f, is very crucial to provide the optimum performance for EDCH2 algorithm. The value depends on network design and Effective distance, d, value. In this case, the network have 100 sensor nodes with p=0.05, d=15 and 150 meters, area size of50×50and500×500squaremeters, and MN of 25 and 250 messages for every round, the least energy utilization is
provided when the value of f is 0.15, as shown in TABLE II. In TABLE II first column shows different inspected values for coefficient, f. The second, third, and fourth columns show the networkenergy consumption in joules (J) for single round but in three different network designs. In Design 1, the network size is considered to be 50×50 square meters, d is 15 meters, and MN is 25 messages for each round. In Design 2 MN is changed to 250 messages per round. Finally, in the last Design 3, the network size is 500 × 500 square meters,dis 150 meters andMNis 25 messages per round.
TABLE II: The network energy utilization for different values of coefficient in three different Designs.
Coefficient design 1 Design 2 Design 3
(f) (J) (J) (J)
0.00 13.56 119.50 289.09
0.01 13.31 116.68 260.86
0.02 13.25 116.51 257.11
0.03 13.11 115.10 242.82
0.04 13.10 114.50 237.25
0.05 13.01 113.20 224.14
0.06 12.92 112.70 219.31
0.07 12.95 112.61 216.78
0.08 12.99 112.80 214.22
0.09 12.89 112.29 213.12
0.10 12.80 112.70 218.23
0.11 12.79 111.95 210.05
0.12 12.78 111.90 209.70
0.13 12.77 111.81 208.51
0.14 12.75 111.71 207.56
0.15 12.75 111.62 206.67
0.16 12.76 111.65 207.31
0.17 12.78 111.68 207.11
0.18 12.83 112.53 216.10
0.19 12.90 112.48 215.47
0.20 12.94 112.70 217.81
0.25 0.26 0.27 0.28 0.29
12.96 13.01 13.06 13.14 13.18
112.81 112.95 113.50 113.98 114.90
218.95 220.91 224.10 230.95 236.91
0.30 13.20 115.14 242.90
Using the optimum value of coefficient, f, EDCH2 shows a significant improvement to reduce the energy utilization and therefore to extend network lifetime compared with those of EDCH1 and LEACH consequently. Fig. 4 shows an example of the CHs (cluster heads) and cluster members arrangement in a round of EDCH2 algorithm.
Fig. 4: EDCH2 (Cluster Head and Cluster Member)
IV. PERFORMANCEASSESSMENTOFTHE EDCH1 ANDEDCH2
The performance study was conducted in order to evaluate the performance of our proposed clustering algorithm, EDCH, and to compare with LEACH algorithm using simulation software. Every simulation test is run for 100 sensor nodes different randomly generated topologies and the average results are presented. As mentioned previously, the energy model is exactly the same as the one employed in [10].
We conducted three groups of experiments to compare the performance of EDCH1, EDCH2, and LEACH. In the first group, the network area is 50×50 square meters when BS ( base station) is 100 meters away from the network’s edge, Fig. 5. Moreover, the number of sensor nodes is 100, p=0.05, d=15 meters, MN=25 messages, the initial energy of each node is 0.5j, and a=0.15.
Fig. 5: BS (Base Station.) with monitoring area.
In TABLE III, the total power consumed by LEACH, EDCH1, and EDCH2 at the end of different rounds is presented. In TABLE III first column shows the number round that data is collected. The second, third, and fourth columns of the TABLE III show the total network energy consumed in a different stages by LEACH, EDCH1, and EDCH2, respectively. The fifth and sixth columns in TABLE III show the achieved the energy gain by EDCH1 and EDCH2 compared with LEACH.
TABLE III: Achieved gain by EDCH1 and EDCH2 to save energy network in different rounds
Round LEACH EDCH1 EDCH2 EDCH1 Gain EDCH2 Gain
(J) (J) (J) (%) (%)
10 7.22 7.02 6.28 2.77 13.01
20 14.45 13.64 12.83 5.60 11.21
30 21.94 20.99 19.14 4.33 12.76
40 29.15 27.64 25.68 5.18 11.90
50 36.28 34.42 31.97 5.12 11.88
60 70 80
42.92 50.17 57.47
40.59 47.99 55.09
38.40 44.75 51.05
5.43 4.34 4.14
10.53 10.80 11.71
In TABLE IV shows the network lifetime in LEACH, EDCH1 and EDCH2. The first column shows in a TABLE IV, the number of died nodes out of 100 nodes. The remaining column of second, third, and fourth indicate that after how many rounds the corresponding number of nodes is died in each of these networks. The last two columns in aTABLE
IV that show the percentage gain achieved by EDCH1 and EDCH2, respectively, to extend network lifetime comparing with the LEACH.
TABLE IV: Achieved gain by EDCH1 and EDCH2 to extend network lifetime over various stages of network life
Died LEACH EDCH1 EDCH2 EDCH1 Gain EDCH2 Gain
(J) (J) (J) (%) (%)
1 39.81 40.75 51.76 2.36 30.01
5 51.29 51.23 61.78 1.83 20.45
10 56.93 57.78 65.72 1.49 15.44
20 61.36 63.46 70.79 3.42 15.36
30 40
62.85 64.88
65.43 69.47
72.60 75.72
4.10 7.07
15.51 16.70 50
60
68.41 70.80
72.16 74.67
78.88 80.67
5.48 5.46
15.30 13.94
70 72.80 76.79 83.23 6.44 15.37
80 73.64 78.94 84.11 7.19 14.21
90 75.19 80.40 86.08 6.93 14.48
95 75.80 80.85 87.34 6.66 15.22
99 76.14 81.63 88.31 7.21 15.98
100 76.42 81.96 88.58 7.25 15.91
The TABLE III and TABLE IV, compared with LEACH, EDCH1 shows up to 5.60% development in saving energy and up to 7.25% in extend the network lifetime. On the other hand, EDCH2 shows up to 13.01% improvement in saving energy and up to 30.01% in extending the network lifetime.
In the experiments of second group, we aim to examine the impact of MNs on our algorithms. We therefore increase the MN to 250 messages and also the initial energy of every node is 5.0 j. The results show up to 6.89% improvement in saving energy and up to 8.85% in prolonging the network lifetime by EDCH1 compared to those of the LEACH algorithm. Also, EDCH2 shows up to 15.88% improvement in saving energy and up to 29.95% in prolonging the network.
In the experiments third group, The Network area is increased to 500×500 square meters and its distance from the BS (base station) to 1000 meters. The d is increased to 150 meters, and initial energy of every node to 50.0 j. The results show up to 25.06% improvement in saving energy and up to 53.45% in extending the network lifetime by EDCH1 compared to those of LEACH. On the other hand, EDCH2 shows up to 38.43% improvement in saving energy and up to 128.46% in extending the network’s lifetime.
These are depicted in Fig. 6 and Fig. 7, highlighting that by increasing the network size EDCH2 significantly outperforms the LEACH algorithm in terms of energy consumption and network lifetime.
Fig. 6: Total energy consumed in the network LEACH, EDCH1 and EDCH2.
Fig. 7: Network lifetime in LEACH, EDCH1 and EDCH2
V. CONCLUSIONANDFUTUREWORK A novel clustering algorithm, called EDCH, for WSNs (wireless sensor networks) has been projected, which is based on finding suitable CHs (cluster heads) to form best possible clusters at every round. Our widespread assessment study has proved considerable enhancements achieved by EDCH compared to LEACH clustering algorithm to save sensor nodes power and to extend network lifetime.
For future works, we are considering dynamic values for the Effective distance and threshold. The two parameters of Effective distance and threshold are whose optimum values might differ from round to round depending on some supplementary parameters.
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Author Biography
Rajkumar is native of Bidar, Karnataka, India. He received his B.E Degree in Computer Science and Engineering from VEC, Bellary, Gulbarga University Gulbarga and M.Tech in Computer Engineering from SJCE Mysore, Visvesvaraya Technological University Belgaum. And currently he is pursuing his PhD from Visvesvaraya Technological University Belgaum.
Presently he is serving as Associate Professor in the department of Information Science and Engineering at Sambhram Institute Of Technology, Bangalore. His areas of interest are wireless sensor network, adhoc network and security. ([email protected])
Dr. H. G. Chandrakanth is native of Bangalore, Karnataka, India. He received B.E Degree from UVCE, Bangalore University, Bangalore, India in 1991, MS, EE from Southern Illinois University Carbondale, USA in 1994 and PhD from Southern Illinois University Carbondale, USA in 1998. Presently he is working as Principal in Sambhram Institute of Technology, Bangalore. ([email protected])
