- Email: [email protected]
Performance Analysis of LEACH-GA over LEACH and LEACH-C in WSN
Available online at www.sciencedirect.com
ScienceDirect
Available online at www.sciencedirect.com Procedia Computer Science 00 (2018) 000–000
ScienceDirect
www.elsevier.com/locate/procedia
Procedia Computer Science 125 (2018) 248–256
6th International Conference on Smart Computing and Communications, ICSCC 2017, 7-8 December 2017, Kurukshetra, India
Performance Analysis of LEACH-GA over LEACH and LEACH-C in WSN P.Sivakumara*, M.Radhikab a
Karpagam College of Engineering, Coimbatore, Tamilnadu, 641032 , India b Anna University, Chennai, Tamilnadu,600025, India
Abstract A wireless sensor network (WSN) is a collection of sensor nodes that has an ability to support Sensing, Signal processing, Embedded Computing, Communications and Connectivity for data processing and transmit the information to far located sink nodes through intermediate nodes with the help of energy source namely batteries. However, the lifetime of the network can be analyzed with the available node energy while transmitting a data from source to destination. But the batteries used in WSN neither to be recharged nor be replaced. So it is necessary to improve the lifetime of the network for better performance. For the past few decades, researchers developed many new Hierarchical routing protocols to improve the network lifetime by minimizing the node energy consumption, which includes Genetic Algorithm based Low Energy Adaptive Clustering Hierarchical Routing Protocol (LEACH-GA). In this paper, we analyzed a network lifetime of LEACH, LEACH-C and LEACH GA by varying its Initial Energy and Cluster Probability. © 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 6th International Conference on Smart Computing and Communications. Keywords: LEACH-GA; WSN; LEACH;LEACH-C;Routing protocol
1. Introduction A wireless sensor network (WSN) is a collection of tiny sensor nodes with non rechargeable batteries for processing
* Corresponding author. Tel.: +91 9443628808. E-mail address: [email protected] (P.Sivakumar), [email protected] (M.Radhika) 1877-0509 © 2018 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the 6th International Conference on Smart Computing and Communications.
1877-0509 © 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 6th International Conference on Smart Computing and Communications 10.1016/j.procs.2017.12.034
P. Sivakumar et al. / Procedia Computer Science 125 (2018) 248–256 P.Sivakumar / Procedia Computer Science 00 (2018) 000–000
249 2
the data which includes signal processing, embedded computing, communication and connectivity and transmit the data to sink node through intermediate nodes present in the network. Wireless Sensor Networks have self organized nodes with logical interconnection. The major challenge of wireless Sensor Networks with respect to characteristics and required mechanisms [8][12][2] are Fault tolerance, Network Lifetime, Scalability, Quality of Services (QoS) which includes packet delivery ratio, reliability, delays and so on. WSN plays a vital role in many applications like military for surveillance, Intrusion, Monitoring and Targeting, Health monitoring, Industries, Natural disaster detections, Environmental Monitoring like Pollution, Habitat and Forest Fires, Agricultural for Crop, Pesticides and Water Monitoring. The lifetime of the network can be analyzed with the available node energy while transmitting a data from source to destination. But the batteries used in WSN neither be not recharged nor be replaced. So it is necessary to improve the lifetime time of the network for better performance. Based on the structure of sensor networks, routing can be classified as flat, hierarchical and location based routing [3]. Among all, one of the most energy efficient, more scalability, and minimal use of resource constrain is hierarchical routing protocol [5]. For the past few decades, researchers developed many new Hierarchical routing protocols to improve the network lifetime by minimizing the node energy consumption [13]. In this paper, we analyzed and compared the performance of LEACH-GA over LEACH and LEACH-C to have improved network lifetime. 2. Related Work In WSN, Cluster formation and selection of Cluster Heads (CHs) is one of the most challenging issues for reducing energy consumption to achieve network longevity. Still it is an open challenge for many research communities to design such energy efficient routing protocol for WSN. LEACH is one the most popular and first hierarchical routing proposed by [7]. LEACH is designed to minimize the energy consumption by distributing the nodes randomly with two phases: setup phase and steady state phase. Setup phase: Among the distributed nodes, one of the nodes will acts as a CH that receive a message from Base Station, and convey that message to all non CHs. Steady state phase: Non CHs sends information to CH, then CH aggregates the received information to sink node. LEACHC was projected by [6]. In LEACH-C, CHs is selected based on the knowledge of node energy and location awareness. LEACH-E was offered by [10]. In LEACH-E, minimum spanning tree technique is used to select CHs based on residual energy. LEACH-E is an improved version of LEACH protocol. IBLEACH was anticipated by [1]. In IBLEACH pre-stage phase is introduced in between setup phase and steady phase in order to minimize the energy consumption of the network. LEACH-EX was proposed by [16]. LEACH-EX is an improvement over LEACH-E protocol by modifying its threshold function. In LEACH-EX [11], CHs are selected based on node energy. LEACH-GA was suggested by [9]. In LEACH-GA preparation phase is introduced with the beginning of first round along with setup phase and steady state phase. CHs are elected by using optimal probability with the help of genetic algorithm. This paper is organized as Section III gives the introduction for Genetic Algorithm, Section IV represents the hierarchical protocol such as LEACH, LEACH-C and LEACH-GA. Section V represents the Analysis and Experimental result of performance of LEACH, LEACH-C and LEACH-GA with varying Initial Energy and Cluster probability. 3. Overview of Genetic Algorithm Genetic Algorithm (GA) is an adaptive heuristic search algorithm for generating solution to optimization problems. Genetic Algorithm was proposed by John Holland in the year 1970 based on the Genetic and Darwinism theory, who was the father of Genetic Algorithm. Later, Charles Darwin defined GA with an addition of fittest survival to improve the solution for optimization over depth-first, breath-first and linear programming. Genetic Algorithm is a powerful, robust and solves optimization problems based on the theoretical applications for strengthening the various troubles in real world applications. Implementation of Genetic Algorithm involves three steps [8][12] after the random selection of initial population. They are Selection, Crossover and Mutation. The selection process gives preference to better individual to equate
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the fittest survival for pass the genes to next generation. The Crossover process represents individual mating, that is creating a new off spring for next generation based on the randomly chosen bit strings. Let us consider a two string values S1 = 101010 and S2 = 010101. With a crossover point 3, it creates new mating off spring for next generation as S1’ = 010010 and S2’ = 101101. The last step in Genetic Algorithm implementation is Mutation. Mutation involves random modification on new individuals with low probability. Mutation allows the generation to maintain diversity and premature convergence. 4. Protocols 4.1. LEACH A Low Energy Adaptive Clustering Hierarchical (LEACH) protocol was proposed [7], in the year 2000. LEACH is designed to minimize the energy consumption by distributing the nodes randomly with two phases: setup phase and steady state phase. Setup phase: Among the distributed nodes, one of the nodes will acts as a CH that receive a message from Base Station, and convey that message to all non CHs. Steady state phase: Non CHs sends information to CH, then CH aggregates the received information to sink node. However, the selection of cluster head is random without considering geographical location of the node and residual energy [4]. Every node in the network produces random generation of number between 0 and 1, if the number is less than threshold value T (n), then the node is defined as cluster head [14]. (1) Where, p is the probability to become of Cluster Head, r is the selected rounds, r mod (1/p) is the number of the previous Cluster Heads and G is the set of non cluster heads in last 1/p rounds. 4.2. LEACH C LEACH-C was proposed by [6] in the year 2002. LEACH Centralized (LEACH-C) is an improved algorithm over LEACH protocol. In LEACH-C, cluster heads are randomly selected by Base station, whereas in LEACH cluster heads are selected randomly by node itself. With similar to LEACH protocol, LEACH-C has two phases: setup phase and steady state phase. In setup phase, Base Station selects the Cluster Head (CH) with the help of average node energy, location and energy level to base station. After selecting the CH and associated CH, the transmission of message takes place between nodes which contains the cluster ID. Whereas, the steady state phase of LEACH-C is similar to LEACH protocol. LEACH-C protocol improves the network lifetime over LEACH protocol. However, LEACH-C is not suitable for large network area. 4.3. LEACH GA LEACH-GA was proposed by [9] in the year 2011. In LEACH-GA preparation phase is introduced with the beginning of first round along with setup phase and steady state phase. The preparation phase of LEACH-GA is used to select the cluster head based on optimal value of cluster head probability using Genetic Algorithm (GA). Preparation phase: Initially cluster head selection is performed by all nodes by sending a message to the base station. In base station, GA is used to find the optimal probability of node to become a cluster head with minimum energy consumption for the completion of first round. Preparation phase is performed only once at the beginning of setup phase and steady state phase. The setup phase and steady state phase is same as that of the LEACH protocol. LEACH-GA minimize the energy consumption of nodes when compare to LEACH and LEACH-C. 5. Analysis and Simulation The protocols was analyzed by using first radio model of [7] as shown in Fig.1, The Energy transmitted (ETX (k,d)) by the amplifier with k-bits over a distance d is given by,
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(2) (3) Where do is the threshold value, (4)
Figure 1: First Radio Model
d2 is the free space power loss. d4 is the multipath fading power loss. Channel model use either d2 or d4 which depends on the distance between the transmitter and receiver [15]. The simulation parameters are listed below: Table 1: Simulation Parameters Network Parameters
Values
Network Size Number of nodes Packet Size, k Routing Protocol Initial Energy, Eo Transmitter Energy, ETX Receiver Energy, ERX Amplification Energy for short distance, Efs Amplification Energy for long distance, Emp Data Aggregation Energy, Eda Cluster Head probability, p Maximum number of Iteration
100 x 100 m2 100 3000 bits LEACH, LEACH-C, and LEACH-GA 0.7, 0.8 and 1 J/node 50nJ/bit 50nJ/bit 10pJ/bit/m2 0.0013pJ/bit/m2 5nJ/bit 0.05, 0.15 and 0.5 10
The Energy received (ERX (k,d)) by the amplifier with k-bits over a distance d is given by,
(5)
6. Experimental Results The comparison of LEACH, LEACH-C and LEACH-GA was done by using MATLAB simulation tool. The nodes are randomly distributed in the area of 100 x 100 m2. The base station is located at the centre point (50, 50) as yellow diamond and the cluster heads are represented as red triangle. The transmission of bits is represented by green circle and other nodes are represented by black circle. The network structure of LEACH, LEACH-C and LEACH-GA is shown in Fig. 2.
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6.1. Varying Initial Energy A. With Initial Energy 0.7 J/node Fig.3, in case of LEACH, LEACH-C and LEACH-GA the first node died at different level of rounds. The first node died at round 1291, 1656 and 3056 in LEACH, LEACH-C and LEACH-GA respectively. So the network lifetime is high in LEACH GA followed by LEACH and LEACH-C.
Figure 2: Network Structure of LEACH, LEACH C and LEACH GA
Figure 3: Comparison of network lifetime of LEACH, LEACH C and LEACH GA with Initial Energy of 0.7 J/node
Figure 4: Comparison of network lifetime of LEACH, LEACH C and LEACH GA with Initial Energy of 0.8 J/node
Figure 5: Comparison of network lifetime of LEACH, LEACH C and LEACH GA with Initial Energy of 1 J/node
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B. With Initial Energy 0.8 J/node In Fig.4, we compared the network lifetime of LEACH, LEACH-C and LEACH-GA with Initial Energy 0.8 J/node. The first node died at round 1554, 1997 and 3539 in LEACH, LEACH-C and LEACH GA respectively. So the lifetime of LEACH GA, overheads when compare to LEACH and LEACH-C. C. With Initial Energy 1 J/node In Fig.5, we compared the network lifetime of LEACH, LEACH-C and LEACH-GA with Initial Energy 1 J/node. The first node died at round 2026, 2443 and 4430 in LEACH, LEACH-C and LEACH GA respectively. So the lifetime of LEACH GA, overheads when compare to LEACH and LEACH-C. P.Sivakumar / Procedia Computer Science 00 (2018) 000–000
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From the Fig. 6, we observed that the higher the value of Initial Energy is maximizing the network lifetime. Table 2, Table 3 and Table 4 represents the comparison of LEACH, LEACH-C and LEACH-GA routing protocol with different rounds of Dead nodes with initial energy 0.7 J/node, 0.8 J/node and 1 J/node. From the comparison we can conclude that the network lifetime of LEACH-GA improved 53.35% and 44.8% over LEACH and LEACH-C protocol.
5000 4000 3000
LEACH
2000
LEACH‐C LEACH‐GA
1000 0 0.7
0.8
1
Figure 6: Comparison of network lifetime by varying Initial Energy Table 2: Comparison of Routing Protocol with Initial Energy 0.7 J/node Number of Dead nodes Rounds FND 10% 20% 30% 40% 50% LEACH 1291 1500 1578 1652 1716 1750 LEACH-C 1656 1778 1923 1952 1993 2035 LEACH-GA 3056 3279 3436 3537 3667 3768 LEACH Vs 57.75 54.25 54.07 53.29 53.2 53.56 LEACH-GA LEACH-C Vs 48.51 48.78 44.03 44.8 45.65 46 LEACH-GA Table 3: Comparison of Routing Protocol with Initial Energy 0.8 J/node Number of Dead nodes Rounds FND 10% 20% 30% 40% 50% LEACH 1554 1766 1819 1916 1954 1995 LEACH-C 1997 2119 2184 2245 2348 2391 LEACH-GA 3539 3712 3950 4011 4124 4200 LEACH Vs 56.1 52.42 53.94 52.23 52.62 52.5 LEACH-GA LEACH-C Vs 43.57 42.91 44.71 44.03 43.06 43.71 LEACH-GA
60% 1777 2089 3861 53.98
70% 1824 2167 3948 53.8
80% 1926 2193 4097 52.99
90% 2116 2488 4623 54.23
Avg
54%
45.89
45.11
46.47
46.18
46%
60% 2032 2412 4313 52.87
70% 2083 2470.5 4403 52.69
80% 2207 2539 4517 51.14
90% 2529 2654 4853 47.88
52.44
44.08
43.89
43.79
45.31
43.09
Avg
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Table 4: Comparison of Routing Protocol with Initial Energy 1 J/node Number of Dead nodes Rounds FND 10% 20% 30% 40% 50% LEACH 2026 2209 2292 2370 2411.5 2470 LEACH-C 2443 2678 2760 2811 2868 2932 LEACH-GA 4430 4792.5 4867 5019 5114.5 5215 LEACH Vs 54.26 53.91 52.91 52.78 52.85 52.64 LEACH-GA LEACH-C Vs 44.85 44.12 43.29 44 43.92 43.78 LEACH-GA
7
60% 2518 2966 5356 53
70% 2614 2990 5466 52.18
80% 2673 3062 5669 52.85
90% 2933 3315 7100 58.69
Avg
53.61
44.62
45.3
45.97
53.31
45.31
6.2. Varying Cluster Probability A. With Cluster head probability p=0.05 In Fig.7, in case of LEACH, LEACH-C and LEACH-GA the first node died at different level of rounds. The first node died at round 925, 1026 and 2146 in LEACH, LEACH-C and LEACH-GA respectively. So the network lifetime is high in LEACH GA followed by LEACH and LEACH-C. B. With Cluster head probability 0.15 In Fig.8, we compared the network lifetime of LEACH, LEACH-C and LEACH-GA with cluster probability 0.15. The first node died at round 991, 1135 and 2294 in LEACH, LEACH-C and LEACH GA respectively. So the lifetime of LEACH GA, overheads when compare to LEACH and LEACH-C. C. With Cluster head probability 0.5 In Fig.9, we compared the network lifetime of LEACH, LEACH-C and LEACH-GA with cluster probability 0.5. The first node died at round 1063, 1363 and 2419 in LEACH, LEACH-C and LEACH GA respectively. So the lifetime of LEACH GA, overheads when compare to LEACH and LEACH-C. From the Fig.10, we observed that the higher the value of cluster probability, is maximize the network lifetime. Table 5, Table 6 and Table 7 represents the comparison of LEACH, LEACH-C and LEACH-GA routing protocol with different rounds of Dead nodes with probability cluster head of 0.05, 0.15 and 0.5. From the comparison we can conclude that the network lifetime of LEACH-GA improved 53.93% and 49.82% over LEACH and LEACH-C protocol.
Figure 7: Comparison of LEACH, LEACH C and LEACH GA with cluster probability 0.05
Figure 8: Comparison of network lifetime of LEACH, LEACH C and LEACH GA with cluster probability 0.15
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Figure 9: Comparison of LEACH, LEACH C and LEACH GA with cluster probability 0.5
Figure 10: Comparison of Routing Protocol by varying cluster head probability
Table 5: Comparison of Routing Protocol with cluster head probability 0.05 Number of Dead nodes Rounds FND 10% 20% 30% 40% 50% LEACH 924 1028 1074 1149 1200 1227 LEACH-C LEACH-GA LEACH Vs LEACH-GA LEACH-C Vs LEACH-GA
255 8
60% 1251
70% 1282
80% 1341
90% 1555
Avg
1026 2146 56.94
1271 2281 54.93
1364 2352 54.34
1431 2408 52.28
1470 2472 51.46
1514 2527 51.44
1530 2596 51.81
1566 2663 51.86
1604 2736 50.99
1688 3393 54.17
53.02
52.19
44.28
42
40.57
40.53
40.08
41.06
41.19
41.37
50.25
43.35
Table 6: Comparison of Routing Protocol with cluster head probability 0.15 Number of Dead nodes Rounds FND 10% 20% 30% 40% 50% LEACH 991 1096 1147 1184 1217 1243 LEACH-C 1135 1358 1383 1401.5 1420 1449 LEACH-GA 2294 2425 2576 2625 2663.5 2722 LEACH Vs 56.8 54.8 55.47 54.89 54.3 54.34 LEACH-GA LEACH-C Vs 50.52 44 46.31 46.6 46.69 46.77 LEACH-GA Table 7: Comparison of Routing Protocol with cluster head probability 0.5 Number of Dead nodes Rounds FND 10% 20% 30% 40% 50% LEACH 1063 1207 1301 1387 1408 1451 LEACH-C 1363 1493 1526 1551 1577 1617 LEACH-GA 2419 2690 2747 2922 3070.5 3216 LEACH Vs 56.06 55.13 52.64 52.53 54.14 54.88 LEACH-GA LEACH-C Vs 43.65 44.5 44.45 46.92 48.64 49.72 LEACH-GA
60% 1284.5 1465.6 2831 54.63
70% 1342 1499 2910 53.88
80% 1416 1534 3038 53.39
90% 1574 1674 3981 60.46
Avg
55.3
48.23
48.49
49.51
57.95
48.5
60% 1497 1652 3304 54.69
70% 1554 1687 3391 54.17
80% 1711 1727 3499 51.11
90% 2111 2199 4165 59.32
Avg
53.46
50
50.25
50.64
57.2
57.6
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7. Conclusion In this paper, we have compared different hierarchical protocol deployed in WSN. Among that LEACH-GA performs better when compare to LEACH and LEACH-C. By varying the Initial Energy of the network, LEACHGA overheads the LEACH and LEACH-C protocol by 53.35% and 44.8%. Also with different cluster head probability, LEACH-GA increased by 53.93% and 49.82% over LEACH and LEACH-C protocol. Hence we conclude that LEACH-GA increases the network lifetime on the average of 54% and 47% over LEACH and LEACH-C routing protocol. Cluster Head selection based on LEACH-GA will minimize the energy consumption and gives prolonged network lifetime. In future the network lifetime of LEACH-GA can be improved by varying its cluster head selection to minimize the energy consumption for network longevity. References [1] Salim.A, Osamy.W, and Khedr.A.M. (2014) “IBLEACH: Intra-balanced Leach protocol for Wireless Sensor Networks”, Wireless Network 20 (6): 1515 – 1525 [2] Akyildiz.I.F, Su.W, Sankarasubramaniam.Y and Cayirci.E. (2002) “Wireless sensor networks: a survey”, Computer Networks: 393– 422. [3] Karaki.Al, and Kamal.A.E. (2004) “Routing techniques in Wireless Sensor Networks: A survey”, IEEE Wireless Communications: 6 – 28. [4] Anastasi.G, Conti.M, Francesco.M and Passarella. A. (2003) “Energy conversation in wireless sensor networks: A survey”, Ad Hoc Network 7(3): 537 – 568 [5] Hanef.M, and Deng.Z. (2012) “Design challenges and comparative analysis of cluster based routing protocols used in Wireless Sensor Networks for improving network lifetime”, Adv Inf Sci Serv Sci 4: 450 – 459. [6] Heinzelman.W, Chandrakasan.A and Balakrishnan.H. (2002) “An Application-Specific Protocol Architecture for Wireless Microsensor Networks”, IEEE Transactions on Wireless Communications 1(4): 660-670. [7] Heinzelman.W, Chandrakasan.A and Balakrishnan.H. (2000) “LEACH: energy efficient communication protocol for wireless microsensor networks”, in proceedings of Hawai International Conference on System Science, Maui, Hawaii: 3005 – 3014. [8] Holger Karl, and Andreas Willig. (2007) “Protocols and Architectures for Wireless Sensor Networks” - Book. John wley and sons Ltd. [9] Liu.J.L and Ravishankar.C.V. (2011) “LEACH-GA: Genetic Algorithm-based energy efficient adaptive clustering protocol for Wireless Sensor Networks”, International Journal of Machine Learning and Computing 1(1): 79 – 85. [10] Xu.J, Jin.N, Lou.X, Peng.T, Zhou.Q, and Chen.Y. (2012) “Improvement of Leach protocol for WSN”, In IEEE sponsored 9th International conference on fuzzy systems and knowledge discovery: 2174 – 2177. [11] Naregal.K. (2012) “Improved cluster routing protocol for wireless sensor network through simplification”, In 18th annual International Conference advanced Computing and Communications: 1 – 3. [12] Magdi.S, Mahmoud and Yuanquing Xia. (2015) “Networked Filtering and Fusion in Wireless Sensor Networks”, CRC Press. [13] Simmi Kansal, Tarunpeet Bhatia, and Shivani Goeal. (2015) “Performance analysis of Leach and its variants”, IEEE sponsored second International Conference on Electronics and Communication Systems: 630 – 634. [14] Bhatia.T, Kansal.S, Goel.S, and Verma.A.K. (2016) “A genetic algorithm based distance-aware routing protocol for wireless sensor networks”, Computers and Electrical Engineering, Elsevier: 1 – 15. [15] Rappaport.T. (1996) “Wireless Communications: Principles and practice”, Englewood cliffs, NJ: Prentice-Hall. [16] Anand.G and Balakrishnan.R. (2013) “Leach-Ex protocol – A comparative performance study and analysis with Leach variants of Wireless Sensor Networks”, National Conference on Frontiers &Advances in Information Science & Technology: 192 – 196.
ScienceDirect
Available online at www.sciencedirect.com Procedia Computer Science 00 (2018) 000–000
ScienceDirect
www.elsevier.com/locate/procedia
Procedia Computer Science 125 (2018) 248–256
6th International Conference on Smart Computing and Communications, ICSCC 2017, 7-8 December 2017, Kurukshetra, India
Performance Analysis of LEACH-GA over LEACH and LEACH-C in WSN P.Sivakumara*, M.Radhikab a
Karpagam College of Engineering, Coimbatore, Tamilnadu, 641032 , India b Anna University, Chennai, Tamilnadu,600025, India
Abstract A wireless sensor network (WSN) is a collection of sensor nodes that has an ability to support Sensing, Signal processing, Embedded Computing, Communications and Connectivity for data processing and transmit the information to far located sink nodes through intermediate nodes with the help of energy source namely batteries. However, the lifetime of the network can be analyzed with the available node energy while transmitting a data from source to destination. But the batteries used in WSN neither to be recharged nor be replaced. So it is necessary to improve the lifetime of the network for better performance. For the past few decades, researchers developed many new Hierarchical routing protocols to improve the network lifetime by minimizing the node energy consumption, which includes Genetic Algorithm based Low Energy Adaptive Clustering Hierarchical Routing Protocol (LEACH-GA). In this paper, we analyzed a network lifetime of LEACH, LEACH-C and LEACH GA by varying its Initial Energy and Cluster Probability. © 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 6th International Conference on Smart Computing and Communications. Keywords: LEACH-GA; WSN; LEACH;LEACH-C;Routing protocol
1. Introduction A wireless sensor network (WSN) is a collection of tiny sensor nodes with non rechargeable batteries for processing
* Corresponding author. Tel.: +91 9443628808. E-mail address: [email protected] (P.Sivakumar), [email protected] (M.Radhika) 1877-0509 © 2018 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the 6th International Conference on Smart Computing and Communications.
1877-0509 © 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 6th International Conference on Smart Computing and Communications 10.1016/j.procs.2017.12.034
P. Sivakumar et al. / Procedia Computer Science 125 (2018) 248–256 P.Sivakumar / Procedia Computer Science 00 (2018) 000–000
249 2
the data which includes signal processing, embedded computing, communication and connectivity and transmit the data to sink node through intermediate nodes present in the network. Wireless Sensor Networks have self organized nodes with logical interconnection. The major challenge of wireless Sensor Networks with respect to characteristics and required mechanisms [8][12][2] are Fault tolerance, Network Lifetime, Scalability, Quality of Services (QoS) which includes packet delivery ratio, reliability, delays and so on. WSN plays a vital role in many applications like military for surveillance, Intrusion, Monitoring and Targeting, Health monitoring, Industries, Natural disaster detections, Environmental Monitoring like Pollution, Habitat and Forest Fires, Agricultural for Crop, Pesticides and Water Monitoring. The lifetime of the network can be analyzed with the available node energy while transmitting a data from source to destination. But the batteries used in WSN neither be not recharged nor be replaced. So it is necessary to improve the lifetime time of the network for better performance. Based on the structure of sensor networks, routing can be classified as flat, hierarchical and location based routing [3]. Among all, one of the most energy efficient, more scalability, and minimal use of resource constrain is hierarchical routing protocol [5]. For the past few decades, researchers developed many new Hierarchical routing protocols to improve the network lifetime by minimizing the node energy consumption [13]. In this paper, we analyzed and compared the performance of LEACH-GA over LEACH and LEACH-C to have improved network lifetime. 2. Related Work In WSN, Cluster formation and selection of Cluster Heads (CHs) is one of the most challenging issues for reducing energy consumption to achieve network longevity. Still it is an open challenge for many research communities to design such energy efficient routing protocol for WSN. LEACH is one the most popular and first hierarchical routing proposed by [7]. LEACH is designed to minimize the energy consumption by distributing the nodes randomly with two phases: setup phase and steady state phase. Setup phase: Among the distributed nodes, one of the nodes will acts as a CH that receive a message from Base Station, and convey that message to all non CHs. Steady state phase: Non CHs sends information to CH, then CH aggregates the received information to sink node. LEACHC was projected by [6]. In LEACH-C, CHs is selected based on the knowledge of node energy and location awareness. LEACH-E was offered by [10]. In LEACH-E, minimum spanning tree technique is used to select CHs based on residual energy. LEACH-E is an improved version of LEACH protocol. IBLEACH was anticipated by [1]. In IBLEACH pre-stage phase is introduced in between setup phase and steady phase in order to minimize the energy consumption of the network. LEACH-EX was proposed by [16]. LEACH-EX is an improvement over LEACH-E protocol by modifying its threshold function. In LEACH-EX [11], CHs are selected based on node energy. LEACH-GA was suggested by [9]. In LEACH-GA preparation phase is introduced with the beginning of first round along with setup phase and steady state phase. CHs are elected by using optimal probability with the help of genetic algorithm. This paper is organized as Section III gives the introduction for Genetic Algorithm, Section IV represents the hierarchical protocol such as LEACH, LEACH-C and LEACH-GA. Section V represents the Analysis and Experimental result of performance of LEACH, LEACH-C and LEACH-GA with varying Initial Energy and Cluster probability. 3. Overview of Genetic Algorithm Genetic Algorithm (GA) is an adaptive heuristic search algorithm for generating solution to optimization problems. Genetic Algorithm was proposed by John Holland in the year 1970 based on the Genetic and Darwinism theory, who was the father of Genetic Algorithm. Later, Charles Darwin defined GA with an addition of fittest survival to improve the solution for optimization over depth-first, breath-first and linear programming. Genetic Algorithm is a powerful, robust and solves optimization problems based on the theoretical applications for strengthening the various troubles in real world applications. Implementation of Genetic Algorithm involves three steps [8][12] after the random selection of initial population. They are Selection, Crossover and Mutation. The selection process gives preference to better individual to equate
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the fittest survival for pass the genes to next generation. The Crossover process represents individual mating, that is creating a new off spring for next generation based on the randomly chosen bit strings. Let us consider a two string values S1 = 101010 and S2 = 010101. With a crossover point 3, it creates new mating off spring for next generation as S1’ = 010010 and S2’ = 101101. The last step in Genetic Algorithm implementation is Mutation. Mutation involves random modification on new individuals with low probability. Mutation allows the generation to maintain diversity and premature convergence. 4. Protocols 4.1. LEACH A Low Energy Adaptive Clustering Hierarchical (LEACH) protocol was proposed [7], in the year 2000. LEACH is designed to minimize the energy consumption by distributing the nodes randomly with two phases: setup phase and steady state phase. Setup phase: Among the distributed nodes, one of the nodes will acts as a CH that receive a message from Base Station, and convey that message to all non CHs. Steady state phase: Non CHs sends information to CH, then CH aggregates the received information to sink node. However, the selection of cluster head is random without considering geographical location of the node and residual energy [4]. Every node in the network produces random generation of number between 0 and 1, if the number is less than threshold value T (n), then the node is defined as cluster head [14]. (1) Where, p is the probability to become of Cluster Head, r is the selected rounds, r mod (1/p) is the number of the previous Cluster Heads and G is the set of non cluster heads in last 1/p rounds. 4.2. LEACH C LEACH-C was proposed by [6] in the year 2002. LEACH Centralized (LEACH-C) is an improved algorithm over LEACH protocol. In LEACH-C, cluster heads are randomly selected by Base station, whereas in LEACH cluster heads are selected randomly by node itself. With similar to LEACH protocol, LEACH-C has two phases: setup phase and steady state phase. In setup phase, Base Station selects the Cluster Head (CH) with the help of average node energy, location and energy level to base station. After selecting the CH and associated CH, the transmission of message takes place between nodes which contains the cluster ID. Whereas, the steady state phase of LEACH-C is similar to LEACH protocol. LEACH-C protocol improves the network lifetime over LEACH protocol. However, LEACH-C is not suitable for large network area. 4.3. LEACH GA LEACH-GA was proposed by [9] in the year 2011. In LEACH-GA preparation phase is introduced with the beginning of first round along with setup phase and steady state phase. The preparation phase of LEACH-GA is used to select the cluster head based on optimal value of cluster head probability using Genetic Algorithm (GA). Preparation phase: Initially cluster head selection is performed by all nodes by sending a message to the base station. In base station, GA is used to find the optimal probability of node to become a cluster head with minimum energy consumption for the completion of first round. Preparation phase is performed only once at the beginning of setup phase and steady state phase. The setup phase and steady state phase is same as that of the LEACH protocol. LEACH-GA minimize the energy consumption of nodes when compare to LEACH and LEACH-C. 5. Analysis and Simulation The protocols was analyzed by using first radio model of [7] as shown in Fig.1, The Energy transmitted (ETX (k,d)) by the amplifier with k-bits over a distance d is given by,
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(2) (3) Where do is the threshold value, (4)
Figure 1: First Radio Model
d2 is the free space power loss. d4 is the multipath fading power loss. Channel model use either d2 or d4 which depends on the distance between the transmitter and receiver [15]. The simulation parameters are listed below: Table 1: Simulation Parameters Network Parameters
Values
Network Size Number of nodes Packet Size, k Routing Protocol Initial Energy, Eo Transmitter Energy, ETX Receiver Energy, ERX Amplification Energy for short distance, Efs Amplification Energy for long distance, Emp Data Aggregation Energy, Eda Cluster Head probability, p Maximum number of Iteration
100 x 100 m2 100 3000 bits LEACH, LEACH-C, and LEACH-GA 0.7, 0.8 and 1 J/node 50nJ/bit 50nJ/bit 10pJ/bit/m2 0.0013pJ/bit/m2 5nJ/bit 0.05, 0.15 and 0.5 10
The Energy received (ERX (k,d)) by the amplifier with k-bits over a distance d is given by,
(5)
6. Experimental Results The comparison of LEACH, LEACH-C and LEACH-GA was done by using MATLAB simulation tool. The nodes are randomly distributed in the area of 100 x 100 m2. The base station is located at the centre point (50, 50) as yellow diamond and the cluster heads are represented as red triangle. The transmission of bits is represented by green circle and other nodes are represented by black circle. The network structure of LEACH, LEACH-C and LEACH-GA is shown in Fig. 2.
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6.1. Varying Initial Energy A. With Initial Energy 0.7 J/node Fig.3, in case of LEACH, LEACH-C and LEACH-GA the first node died at different level of rounds. The first node died at round 1291, 1656 and 3056 in LEACH, LEACH-C and LEACH-GA respectively. So the network lifetime is high in LEACH GA followed by LEACH and LEACH-C.
Figure 2: Network Structure of LEACH, LEACH C and LEACH GA
Figure 3: Comparison of network lifetime of LEACH, LEACH C and LEACH GA with Initial Energy of 0.7 J/node
Figure 4: Comparison of network lifetime of LEACH, LEACH C and LEACH GA with Initial Energy of 0.8 J/node
Figure 5: Comparison of network lifetime of LEACH, LEACH C and LEACH GA with Initial Energy of 1 J/node
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B. With Initial Energy 0.8 J/node In Fig.4, we compared the network lifetime of LEACH, LEACH-C and LEACH-GA with Initial Energy 0.8 J/node. The first node died at round 1554, 1997 and 3539 in LEACH, LEACH-C and LEACH GA respectively. So the lifetime of LEACH GA, overheads when compare to LEACH and LEACH-C. C. With Initial Energy 1 J/node In Fig.5, we compared the network lifetime of LEACH, LEACH-C and LEACH-GA with Initial Energy 1 J/node. The first node died at round 2026, 2443 and 4430 in LEACH, LEACH-C and LEACH GA respectively. So the lifetime of LEACH GA, overheads when compare to LEACH and LEACH-C. P.Sivakumar / Procedia Computer Science 00 (2018) 000–000
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From the Fig. 6, we observed that the higher the value of Initial Energy is maximizing the network lifetime. Table 2, Table 3 and Table 4 represents the comparison of LEACH, LEACH-C and LEACH-GA routing protocol with different rounds of Dead nodes with initial energy 0.7 J/node, 0.8 J/node and 1 J/node. From the comparison we can conclude that the network lifetime of LEACH-GA improved 53.35% and 44.8% over LEACH and LEACH-C protocol.
5000 4000 3000
LEACH
2000
LEACH‐C LEACH‐GA
1000 0 0.7
0.8
1
Figure 6: Comparison of network lifetime by varying Initial Energy Table 2: Comparison of Routing Protocol with Initial Energy 0.7 J/node Number of Dead nodes Rounds FND 10% 20% 30% 40% 50% LEACH 1291 1500 1578 1652 1716 1750 LEACH-C 1656 1778 1923 1952 1993 2035 LEACH-GA 3056 3279 3436 3537 3667 3768 LEACH Vs 57.75 54.25 54.07 53.29 53.2 53.56 LEACH-GA LEACH-C Vs 48.51 48.78 44.03 44.8 45.65 46 LEACH-GA Table 3: Comparison of Routing Protocol with Initial Energy 0.8 J/node Number of Dead nodes Rounds FND 10% 20% 30% 40% 50% LEACH 1554 1766 1819 1916 1954 1995 LEACH-C 1997 2119 2184 2245 2348 2391 LEACH-GA 3539 3712 3950 4011 4124 4200 LEACH Vs 56.1 52.42 53.94 52.23 52.62 52.5 LEACH-GA LEACH-C Vs 43.57 42.91 44.71 44.03 43.06 43.71 LEACH-GA
60% 1777 2089 3861 53.98
70% 1824 2167 3948 53.8
80% 1926 2193 4097 52.99
90% 2116 2488 4623 54.23
Avg
54%
45.89
45.11
46.47
46.18
46%
60% 2032 2412 4313 52.87
70% 2083 2470.5 4403 52.69
80% 2207 2539 4517 51.14
90% 2529 2654 4853 47.88
52.44
44.08
43.89
43.79
45.31
43.09
Avg
254
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Table 4: Comparison of Routing Protocol with Initial Energy 1 J/node Number of Dead nodes Rounds FND 10% 20% 30% 40% 50% LEACH 2026 2209 2292 2370 2411.5 2470 LEACH-C 2443 2678 2760 2811 2868 2932 LEACH-GA 4430 4792.5 4867 5019 5114.5 5215 LEACH Vs 54.26 53.91 52.91 52.78 52.85 52.64 LEACH-GA LEACH-C Vs 44.85 44.12 43.29 44 43.92 43.78 LEACH-GA
7
60% 2518 2966 5356 53
70% 2614 2990 5466 52.18
80% 2673 3062 5669 52.85
90% 2933 3315 7100 58.69
Avg
53.61
44.62
45.3
45.97
53.31
45.31
6.2. Varying Cluster Probability A. With Cluster head probability p=0.05 In Fig.7, in case of LEACH, LEACH-C and LEACH-GA the first node died at different level of rounds. The first node died at round 925, 1026 and 2146 in LEACH, LEACH-C and LEACH-GA respectively. So the network lifetime is high in LEACH GA followed by LEACH and LEACH-C. B. With Cluster head probability 0.15 In Fig.8, we compared the network lifetime of LEACH, LEACH-C and LEACH-GA with cluster probability 0.15. The first node died at round 991, 1135 and 2294 in LEACH, LEACH-C and LEACH GA respectively. So the lifetime of LEACH GA, overheads when compare to LEACH and LEACH-C. C. With Cluster head probability 0.5 In Fig.9, we compared the network lifetime of LEACH, LEACH-C and LEACH-GA with cluster probability 0.5. The first node died at round 1063, 1363 and 2419 in LEACH, LEACH-C and LEACH GA respectively. So the lifetime of LEACH GA, overheads when compare to LEACH and LEACH-C. From the Fig.10, we observed that the higher the value of cluster probability, is maximize the network lifetime. Table 5, Table 6 and Table 7 represents the comparison of LEACH, LEACH-C and LEACH-GA routing protocol with different rounds of Dead nodes with probability cluster head of 0.05, 0.15 and 0.5. From the comparison we can conclude that the network lifetime of LEACH-GA improved 53.93% and 49.82% over LEACH and LEACH-C protocol.
Figure 7: Comparison of LEACH, LEACH C and LEACH GA with cluster probability 0.05
Figure 8: Comparison of network lifetime of LEACH, LEACH C and LEACH GA with cluster probability 0.15
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Figure 9: Comparison of LEACH, LEACH C and LEACH GA with cluster probability 0.5
Figure 10: Comparison of Routing Protocol by varying cluster head probability
Table 5: Comparison of Routing Protocol with cluster head probability 0.05 Number of Dead nodes Rounds FND 10% 20% 30% 40% 50% LEACH 924 1028 1074 1149 1200 1227 LEACH-C LEACH-GA LEACH Vs LEACH-GA LEACH-C Vs LEACH-GA
255 8
60% 1251
70% 1282
80% 1341
90% 1555
Avg
1026 2146 56.94
1271 2281 54.93
1364 2352 54.34
1431 2408 52.28
1470 2472 51.46
1514 2527 51.44
1530 2596 51.81
1566 2663 51.86
1604 2736 50.99
1688 3393 54.17
53.02
52.19
44.28
42
40.57
40.53
40.08
41.06
41.19
41.37
50.25
43.35
Table 6: Comparison of Routing Protocol with cluster head probability 0.15 Number of Dead nodes Rounds FND 10% 20% 30% 40% 50% LEACH 991 1096 1147 1184 1217 1243 LEACH-C 1135 1358 1383 1401.5 1420 1449 LEACH-GA 2294 2425 2576 2625 2663.5 2722 LEACH Vs 56.8 54.8 55.47 54.89 54.3 54.34 LEACH-GA LEACH-C Vs 50.52 44 46.31 46.6 46.69 46.77 LEACH-GA Table 7: Comparison of Routing Protocol with cluster head probability 0.5 Number of Dead nodes Rounds FND 10% 20% 30% 40% 50% LEACH 1063 1207 1301 1387 1408 1451 LEACH-C 1363 1493 1526 1551 1577 1617 LEACH-GA 2419 2690 2747 2922 3070.5 3216 LEACH Vs 56.06 55.13 52.64 52.53 54.14 54.88 LEACH-GA LEACH-C Vs 43.65 44.5 44.45 46.92 48.64 49.72 LEACH-GA
60% 1284.5 1465.6 2831 54.63
70% 1342 1499 2910 53.88
80% 1416 1534 3038 53.39
90% 1574 1674 3981 60.46
Avg
55.3
48.23
48.49
49.51
57.95
48.5
60% 1497 1652 3304 54.69
70% 1554 1687 3391 54.17
80% 1711 1727 3499 51.11
90% 2111 2199 4165 59.32
Avg
53.46
50
50.25
50.64
57.2
57.6
256
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7. Conclusion In this paper, we have compared different hierarchical protocol deployed in WSN. Among that LEACH-GA performs better when compare to LEACH and LEACH-C. By varying the Initial Energy of the network, LEACHGA overheads the LEACH and LEACH-C protocol by 53.35% and 44.8%. Also with different cluster head probability, LEACH-GA increased by 53.93% and 49.82% over LEACH and LEACH-C protocol. Hence we conclude that LEACH-GA increases the network lifetime on the average of 54% and 47% over LEACH and LEACH-C routing protocol. Cluster Head selection based on LEACH-GA will minimize the energy consumption and gives prolonged network lifetime. In future the network lifetime of LEACH-GA can be improved by varying its cluster head selection to minimize the energy consumption for network longevity. References [1] Salim.A, Osamy.W, and Khedr.A.M. (2014) “IBLEACH: Intra-balanced Leach protocol for Wireless Sensor Networks”, Wireless Network 20 (6): 1515 – 1525 [2] Akyildiz.I.F, Su.W, Sankarasubramaniam.Y and Cayirci.E. (2002) “Wireless sensor networks: a survey”, Computer Networks: 393– 422. [3] Karaki.Al, and Kamal.A.E. (2004) “Routing techniques in Wireless Sensor Networks: A survey”, IEEE Wireless Communications: 6 – 28. [4] Anastasi.G, Conti.M, Francesco.M and Passarella. A. (2003) “Energy conversation in wireless sensor networks: A survey”, Ad Hoc Network 7(3): 537 – 568 [5] Hanef.M, and Deng.Z. (2012) “Design challenges and comparative analysis of cluster based routing protocols used in Wireless Sensor Networks for improving network lifetime”, Adv Inf Sci Serv Sci 4: 450 – 459. [6] Heinzelman.W, Chandrakasan.A and Balakrishnan.H. (2002) “An Application-Specific Protocol Architecture for Wireless Microsensor Networks”, IEEE Transactions on Wireless Communications 1(4): 660-670. [7] Heinzelman.W, Chandrakasan.A and Balakrishnan.H. (2000) “LEACH: energy efficient communication protocol for wireless microsensor networks”, in proceedings of Hawai International Conference on System Science, Maui, Hawaii: 3005 – 3014. [8] Holger Karl, and Andreas Willig. (2007) “Protocols and Architectures for Wireless Sensor Networks” - Book. John wley and sons Ltd. [9] Liu.J.L and Ravishankar.C.V. (2011) “LEACH-GA: Genetic Algorithm-based energy efficient adaptive clustering protocol for Wireless Sensor Networks”, International Journal of Machine Learning and Computing 1(1): 79 – 85. [10] Xu.J, Jin.N, Lou.X, Peng.T, Zhou.Q, and Chen.Y. (2012) “Improvement of Leach protocol for WSN”, In IEEE sponsored 9th International conference on fuzzy systems and knowledge discovery: 2174 – 2177. [11] Naregal.K. (2012) “Improved cluster routing protocol for wireless sensor network through simplification”, In 18th annual International Conference advanced Computing and Communications: 1 – 3. [12] Magdi.S, Mahmoud and Yuanquing Xia. (2015) “Networked Filtering and Fusion in Wireless Sensor Networks”, CRC Press. [13] Simmi Kansal, Tarunpeet Bhatia, and Shivani Goeal. (2015) “Performance analysis of Leach and its variants”, IEEE sponsored second International Conference on Electronics and Communication Systems: 630 – 634. [14] Bhatia.T, Kansal.S, Goel.S, and Verma.A.K. (2016) “A genetic algorithm based distance-aware routing protocol for wireless sensor networks”, Computers and Electrical Engineering, Elsevier: 1 – 15. [15] Rappaport.T. (1996) “Wireless Communications: Principles and practice”, Englewood cliffs, NJ: Prentice-Hall. [16] Anand.G and Balakrishnan.R. (2013) “Leach-Ex protocol – A comparative performance study and analysis with Leach variants of Wireless Sensor Networks”, National Conference on Frontiers &Advances in Information Science & Technology: 192 – 196.