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Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks
Author’s Accepted Manuscript Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks T. Shankar, S. Shanmugavel, A. Rajesh www.elsevier.com/locate/swevo
PII: DOI: Reference:
S2210-6502(16)30001-3 http://dx.doi.org/10.1016/j.swevo.2016.03.003 SWEVO208
To appear in: Swarm and Evolutionary Computation Received date: 13 August 2015 Revised date: 7 March 2016 Accepted date: 9 March 2016 Cite this article as: T. Shankar, S. Shanmugavel and A. Rajesh, Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor n e t w o r k s , Swarm and Evolutionary Computation, http://dx.doi.org/10.1016/j.swevo.2016.03.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Hybrid HSA and PSO Algorithm for Energy Efficient Cluster Head Selection in Wireless Sensor Networks T. Shankar1, S. Shanmugavel2, A. Rajesh1 1
School of Electronics Engineering VIT University, Vellore, India
2
National Engineering College [email protected]
Abstract Energy efficiency is a major concern in wireless sensor networks as the sensor nodes are battery-operated devices. For energy efficient data transmission, clustering based techniques are implemented through data aggregation so as to balance the energy consumption among the sensor nodes of the network. The existing clustering techniques make use of distinct Low-Energy Adaptive Clustering Hierarchy (LEACH), Harmony Search Algorithm (HSA) and Particle swarm optimization (PSO) algorithms. However, individually, these algorithms have exploration-exploitation tradeoff (PSO) and local search (HSA) constraint. In order to obtain a global search with faster convergence, a hybrid of HSA and PSO algorithm is proposed for energy efficient cluster head selection. The proposed algorithm exhibits high search efficiency of HSA and dynamic capability of PSO that improves the lifetime of sensor nodes. The performance of the hybrid algorithm is evaluated using the number of alive nodes, number of dead nodes, throughput and residual energy. The proposed hybrid HSA-PSO algorithm shows an improvement in residual energy and throughput by 83.89% and 29.00%, respectively, than the PSO algorithm. Keywords: Particle Swarm Optimization; Harmony Search Algorithm; LEACH; Wireless Sensor Network 1. Introduction Wireless Sensor Networks (WSN) has emerged as an essential and popular way of providing pervasive computing for various applications. Since the WSN makes use of tiny sensor nodes that run on battery, their energy has to be optimally utilized. Clustering is one of the traditional approaches for energy efficient transmission of data from sensor node to cluster head [1-13]. Clustering is the process of dividing the geographical area into small sectors and assigning one node as a head for the cluster, termed as Cluster Head (CH) [17].
The selection of the cluster head plays a vital role for energy efficient data transmission. In practical network, the cluster head is changed during definite iteration for better performance [7]. The number of cluster heads within the network and the amount of nodes per cluster can be variable or fixed based on the application scenario. Cluster heads can also form a second tier network, i.e. creating another level of hierarchy or they can just pass on the data to the base station [8]. During the method of clustering, there exist few limitations, such as, additional overhead during CH selection and improper assignment of cluster construction process. These limitations reduce the lifespan of sensor nodes. This motivated many researchers to investigate various aspects like, appropriate CH selection, low power protocols [19-20], network establishments and routing protocol [21-23] of wireless sensor networks. This paper also discusses some widely explored clustering algorithms in WSN and proposes a meta–heuristics optimization algorithm for CH selection for WSN. In situations where the search for an optimal solution becomes exhaustive, we incline to choose meta-heuristic algorithms [8]. For meta-heuristic algorithms to be efficient, it has to cover the solution space where there is global optimum and also should generate new and improved solutions. Also, the meta-heuristic algorithm should escape from the local optimum. In literature, there are meta-heuristic algorithms, such as, Particle Swarm Optimization (PSO) and Cuckoo Search that aim at global optimization (Exploration) and algorithms like Simulated Annealing (SA) and Harmony Search Algorithm (HSA) that get confined to the local optima (Exploitation) [9, 10, 31-32]. For better solution, there must be a balance between the exploration and the exploitation. This motivated us in combining the prevalent meta-heuristic algorithms, namely, HSA and PSO. The reason for the superior performance of the proposed hybrid HSA-PSO algorithm than the existing algorithms is rationalized as follows: In the hybrid HSA-PSO, with the ordinariness of PSO, it allows the particles to move from one region to another by updating the position and velocity at the end of each round. As PSO face high dimensional optimization limitation, it’s difficult to explore every possible region of the search space. Under such circumstances, HSA is utilized that has the high searching computational capability. It provides a new way to produce particles that generates a new vector after considering all of the existing vectors.
The HSA has the limitation of being restricted to only a certain region. This is being eradicated in PSO, which moves from one region to another in search for an optimum solution but still faces the problem of exploration and exploitation in high dimensional problems and takes a longer time to achieve a local maxima or minima. Hence, the proposed hybrid approach makes use of high searching efficiency of HSA combined with the dynamic nature of PSO. The rest of the paper is organised as follows: Section 2 describes the works related to heuristic protocols, clustering algorithms and meta-heuristic algorithms. Section 3 provides the wireless sensor network model. The hybrid HSA-PSO algorithm is given in Section 4. The results and discussion of the proposed and existing algorithm are described in Section 5 and the Section 6 concludes the major findings of the proposed algorithm. 2. Related Work In literature [24], a detailed study has been carried out on the classical protocols, such as, Direct Diffusion (DD), Energy Aware Routing (EAR), etc., location-based protocols such as Geographic and Energy-aware Routing (GEAR), Energy-aware WSN Geographic Routing Protocol (EAGRP), etc., hierarchical protocols such as Low-energy Adaptive Clustering Hierarchy (LEACH), Balanced-clustering Energy-efficient hierarchical routing protocol (BCEE) [33], etc., and swarm based hierarchical protocols such as Multi-sink Swarm-based Routing Protocol (MSRP), Probabilistic Zonal and Swarm-inspired System for Wild Fire Detection (PZSWiD), etc.. It has been found that the use of swarm based hierarchical protocols is promising in improving the energy efficiency of sensor nodes. The authors in [25] has proposed an improved Binary Particle Swarm Optimization (BPSO) algorithm with modified connected dominating set that utilizes residual energy for discovery of optimal number of clusters and cluster head. Although this method improves the number of clusters, the number of nodes alive and remaining average energy is reduced to zero beyond 800 rounds. The authors in [26] have suggested a modified Genetic algorithm (GA) based evolutionary approach for load balancing so as to reduce the energy consumption in WSN. The authors in [27] have identified the additional energy consumption at cluster head due to improper formation of clusters in WSN. This leads to rapid death of cluster head as they are overloaded with large number of sensor nodes. Hence, they suggest a modified
differential evolution (DE) based clustering algorithm to improve the lifetime of the cluster head in WSN. The authors in [28] have suggested a hybrid algorithm to extend the lifetime of sensor nodes in WSN. Here, the hybrid technique combines two routing methods, namely, flat multihop routing and hierarchical multihop routing. The authors in [29] combined the ant colony optimization with greedy migration mechanism to improve the life time of sensor nodes under energy hole problem in grid based WSN. However, the hybrid techniques in [28-29] do not consider the clustering in WSN. A hybrid clustering technique named Hybrid Distributed Hierarchical Agglomerative Clustering (H-DHAC) has been proposed in [30] that combines quantitative location and binary qualitative connectivity of data in clustering. However, the number of alive nodes starts reducing as the number of rounds reaches 600. The LEACH utilizes a dynamic clustering method, where nodes ‘n’ are selected as cluster head randomly based on a threshold value as given in Equation (1). Here, r represents the round, G is the set of all the nodes that are eligible to become cluster heads and p is the probability that each of the nodes will become the cluster heads. Here, the nodes that are chosen as cluster heads cannot be cluster heads in the next p rounds. Hence, each node has a probability of 1/p to become the cluster head. p 1 T (t ) 1 p *(r mod ) p 0
; nG
(1) ; nG
After (1/p)-1 round, the threshold is assigned as one for any nodes that have not yet been elected as cluster heads. Hence, in LEACH, all nodes become eligible to become cluster heads [2]. However, through extensive simulations it has been found that the LEACH suffers from the possibility of none of the sensor node in the network being selected as cluster head during some rounds. Also, there is a possibility of concentration of all the selected cluster heads in a particular part of the network. Hence, cluster head selection is not energy adaptive and the cluster formation during each round consumes more energy at the sensor nodes. Further, nodes near the cluster boundary consume more energy as compared to other sensor nodes in the network. The authors in [17] have proposed an improved clustering algorithm than LEACH, which is termed as Energy Efficient Clustering Scheme (EECS). It suits the periodical data
gathering applications. Here, the network is partitioned into several clusters and communication is carried out between the CH and the base station. In EECS, CH candidates compete for the ability to elevate to CH for a given round. As compared to LEACH for cluster formation, EECS differs by the dynamic sizing of clusters based on cluster distance from the base station. In [18], Threshold Sensitive Energy Efficient Sensor Network (TEEN) is proposed where a CH sends its members a hard threshold and a soft threshold. The hard threshold tries to reduce data communications by allowing the nodes to transmit only when the sensed attribute is in the range of interest. The soft threshold further reduces the data communications as there is little or no change in the sensed attribute. To overcome the limitation of the above mentioned algorithms, in literature, metaheuristic algorithms have been analyzed for the optimal selection of efficient cluster heads. There are two important components in meta-heuristics, namely, intensification and diversification. For an algorithm to be efficient and effective, it must be able to generate a diverse range of solutions including the potentially optimal solutions so as to explore the whole search space effectively, while it intensifies its search around the neighborhood of an optimal or nearly optimal solution. In order to do so, every part of the search space must be accessible, though, not necessarily visited during the search [16]. The HSA is a music-based meta-heuristic optimization algorithm [3]. It was inspired by the observation that the aim of music is to search for a perfect state of harmony. This harmony in music is analogous to find the optimality in an optimization process. Harmony Search (HS) has been just such a successful example by transforming the qualitative improvisation process into some quantitative rules by idealization, and thus turning the beauty and harmony of music into an optimization procedure through the search for a perfect harmony [6]. There are various advantages of HSA in terms of searching efficiency: (a) HS algorithm imposes fewer mathematical requirements and does not require initial value settings of the decision variables. (b) As the HS algorithm uses stochastic random searches, derivative information is also unnecessary. (c) The HS algorithm generates a new vector, after considering all of the existing vectors. These features increase the flexibility of the HS algorithm and produce better solutions. The HS algorithm is effectively directed using two parameters, namely, Harmony Memory Considering the Rate (HMCR) and Pitch Adjusting Rate (PAR). In the formulated problem, it is necessary to choose the best set of CHs that minimize energy consumption. The
sets of CHs presented in Harmony Memory (HM) are improved by the estimated residual energy. In each set of CHs, the high residual energy node is searched within its clusters. If any node is found with greater residual energy than the current CH, the corresponding node is replaced as CH and the existing CH is replaced as its member node in each set. In [9], cuckoo search is used for optimal ratio between the global and the local search. In [10], the authors make use of the eagle strategy and cuckoo search for multimodal objective functions. As exploration is often linked to randomness, it should have the ability to jump from the local solution space to the global solution space. Hence, this paper discusses the way evolutionary algorithms are affected by exploration and exploitation and a proper balance between them is attempted to achieve better energy efficiency. The summary of various clustering algorithms in wireless sensor networks is given in Table 1. Table 1 Summary of various clustering algorithms in wireless sensor networks Algorithm LEACH BCEE EECS
TEEN PSO HSA
BPSO Cuckoo Search Hybrid HSA-PSO
Authors / References Heinzelman et al., [2] Xiaoyan Cui et al., [33] Mao Ye et al., [17] Arati Manjeshwar et al., [18] Buddha Singh et al., [13] D.C. Hoang, et al., [6] Shanmugasun daram Thilagavathi et al., [25] Xin-She Yang et al., [10] This Work
Distribution of Cluster Head
Cluster Head Selection
Cluster Stability
Explo ration
Exploit ation
Global Maxim um
Global Minimu m
Energy Efficient
Non-Uniform
Random
Low
No
No
No
No
Low
Uniform
Random
Medium
No
No
No
No
Low
Uniform
Probability / Feature based
Low
No
No
No
No
Medium
Non-Uniform
Probability
Moderate
No
No
No
No
Medium
Non-Uniform
Probability
High
More
Less
Less
More
High
Non-Uniform
Random
High
Less
More
More
Less
Medium
Uniform
Random
Moderate
Less
More
High
Uniform
Random
Low
More
Less
Medium
Non-Uniform
Random / Probability
High
Balanced
Less
More
Balanced
Balanced
Very High
3. Network Model The network model considered in this paper is a free space model, which consists of the transmitter and receiver section with a distance of separation, d. The transmission section consists of transmit electronics with transmission amplifier and the receiving section consists of receive electronics as part for information to be transmitted in terms of bits. It is assume as
a set of sensors is dispersed on a rectangular field [15]. We also assume the following properties about the sensor network:
The nodes in the network are considered to be quasi-stationary
The energy consumption is not uniform for all the nodes and depends on the distance from the base station or the cluster head
Nodes are unaware of the location
All the nodes are homogeneous in nature
The nodes are self-organizing and need not be monitored after deployment
Each node has a fixed number of power levels 2 lEelec l fs d , d d 0 ETx (l , d ) 4 lEelec l mp d , d d 0
ERx lEelec
(2) (3)
where Eelec is the energy consumed to send one bit of data; fs is the amplification coefficient of the transmission amplifier for the free space model and it sends one bit of information, which is considered if the distance is lesser than the threshold distance and mp is used when the distance is greater than the threshold; l is the amount of information being sent. In the considered application scenario, n sensor nodes are deployed randomly into a field with an area ‘M×N’ m2. To optimize the selection of cluster heads and to find optimal ‘k’ cluster head, the following objective function is used to compute the optimum (Hoang et al 2010):
fobj f1 1 f 2
(4)
where f1 and f2 are given by
d nodei , CH k f1 max k Clusterk nodeiCk
(5)
N
f2
E node i
i 1 k
E CH j 1
j
(6)
where is the scaling factor that ranges between 0 and 1. In this objective function, ‘ ’ is the maximum of the Euclidean distance of nodes ‘ cluster heads ‘ ‘
’and ‘
’. Meanwhile
’ to their
’is the number of nodes that belong to cluster is the ratio of initial energy of all nodes alive,
network with the total current energy of the cluster head ‘ (
(
) in the
)’ in the current round. The
constant α indicates the contribution of ‘ ’ and ‘ ’ in the objective function ‘
’.
4. Proposed HSA-PSO Algorithm The proposed scheme called hybrid HSA-PSO is based on the common characteristics of both PSO and HSA algorithms. An HSA can produce new solutions and the parameters of HMCR and PAR are introduced to allow the solution to escape from local optima and to improve the global optimum prediction of the algorithm. Due to this reason HSA is merged with the PSO to overcome exploration and exploitation in high dimensional problems. In hybrid algorithm, with the help of PSO, it allows the particles to move from one region to another by updating the position and velocity at the end of each round. The dynamic nature of PSO allows to search in more than one region of the space combined with higher searching efficiency at each region with the help of HSA. The operation of the algorithm is partitioned into rounds. In each round, the cluster heads collect data from all cluster members and transfer to the BS. To implement the hybrid HSAPSO algorithm, the steps to be followed are as follows: Step 1:
Initializing the Parameters Initial wireless sensor network with the appropriate energy of the nodes is created
according to the parameters given in the Table 1. Step 2:
Initializing the particle position and velocity, fitness function and hybrid matrix The particle position and velocities are generated randomly in the range [X min,
Xmax] and [Vmin, Vmax]. The initialization of swarm size and harmony memory in hybrid HSA-PSO algorithm is termed as Particle Harmony Memory (PHM). The initial PHM consists of a number of randomly generated solutions for the optimization problem under consideration. The solution of the formulated problem is an index of the ‘ ’ numbers of
cluster heads. PHM is computed as shown in Equation 7. Each row of the PHM is a random solution for the optimization problem and then calculates the fitness function for each Hybrid vector using Equation 4.
I11 2 I1 I1HMS Step 3:
I 21 I 22 I
HMS 2
I k1 f obj _1 F 1 ... I k2 f obj _ 2 F 2 HMS HMS f ... I k obj _ n F
...
(7)
Improvise a new hybrid harmony from the PHM After defining the PHM as shown in Equation 7, the improvisation of the PHM is
done by generating a new harmony vector [ harmony vector
]. Each component of the new
is generated using Equation 8 based upon the HMCR defined.
{
(
(8)
)
HMCR is defined as the probability of selecting a component from the PHM members, and (1-HMCR) is, therefore, the probability of generating it randomly. If
is
generated from the PHM, then it is further modified or mutated according to PAR. It determines the probability of a candidate from the PHM to be mutated and (1-PAR) is the probability of doing otherwise. The pitch adjustment for the selected
{
where
(
)
is given by,
(9)
is nearest node whose energy is greater than the energy of present CH in the cluster.
Step 4:
Updating the hybrid harmony matrix The newly generated hybrid harmony vector is evaluated in terms of the objective
function value at each row’s p_best. If the objective function value of the new hybrid harmony vector is better than the objective function value for the worst harmony in the PHM then new harmony is included in the PHM and the existing worst harmony is excluded from the PHM.
Step 5:
Updating particle position and velocity The optimal position of the cluster heads is determined by taking a swarm of
particles and determining the best particle in the swarm with least cost function. The best particle solution for that swarm is taken as the p_b (particle best position). These swarms are created for ‘n’ number of iterations, p_b is determined for all the iterations, and the best of all p_b is taken as the g_b (global best position). In WSN scenario, the nodes that coordinates with the global best positions are taken and the nodes that are nearest to these coordinates are made cluster heads. The velocities of all nodes are calculated and updated for every round [12]. The velocity of each particle can be calculated using the following information, namely, the current velocity, the distance between the current position besides p_b and the distance between the current positions besides g_b. This can be formulated as follows:
Vij r 1 w r Vij r c1 r1 p _ bij X ij r c2 r2 g _ bij X ij r
(10)
X ij r 1 X ij r Vij r 1
(11)
where Vij(r) and Xij(r) are particle velocity and position at r, i and j represents ith particle and dimension, w(r) is inertia weight factor, c1 and c2 are acceleration constants, r1 and r2 are uniform random values between [0,1], where p_b and g_b are particle’s local and global best positions [5]. The use of linearly decreasing inertia weight factor ‘w’ provides improved performance in the considered scenario. Its value is decreased linearly from about 0.9 to 0.4 during each run. A suitable selection of the inertia weight provides a balance between global and local exploration and exploitation, and results in less iteration on average to find a sufficiently optimal solution. Its value is set as follows,
t w t wu wu wl T max
(12)
where Tmax is the total number of iterations, t is the current iteration, wu =0.9, wl=0.4. Thus, the particle-swarm with least cost function is considered as the p_b and the best solution of p_b of all iterations is considered as the g_b. The nodes nearest to the g_b values are taken as cluster heads. The velocity and position of each row of PHM are calculated and updated for every round using Equation 7. Step 6:
Go to step 3 until the termination criterion is reached If the number of rounds reaches maximum rounds, stop the algorithm, otherwise,
continue the algorithm to search for optimum result. The flow of the proposed algorithm is illustrated in Figure 1.
Figure 1 Flowchart of hybrid HSA-PSO algorithm
5. Simulation Results and Analysis In this section we evaluate the performance of each algorithm based on the following metrics, namely, residual energy of the network, the number of dead and alive nodes and throughput of the network. The required simulation parameter [6, 13, 15, 21, 26-27] for various algorithm is shown in Table 2. The existing algorithms and the proposed hybrid algorithm are experimented using MATLAB 2014b on Intel Core i5 processor with 2.30 GHz CPU and 8 GB RAM. Table 2 Simulation parameters of hybrid HSA-PSO algorithm Parameter
Value
Sensor field region (m2)
(100*200)
Base station location (m)
(50,150)
Number of nodes (n)
100
Initial energy of a node (Eo) (J)
0.5
Data packet length (k1) (bits)
4096
Eelec (nJ/bit)
70
Eamp (pJ/bit/m2)
120
Energy data aggregation (nJ)
5
Number of rounds (rmax)
2000
Swarm size
15
Harmonic Memory Considering Rate
0.95
Pitch Adjustment Rate
0.8
Number of Cluster Heads (k)
5
Scaling Factor (ε)
0.5
Number of iterations
5
Particle Position [Xmin,Xmax]
[0,200]
Particle Velocity [Vmin,Vmax] (m/s)
[0,200]
Acceleration Constants [0.25,0.5] [c1min,c1max], [c2min,c2max]
As shown in Figure 2, it is evident that the hybrid scheme designed using HSA and PSO exhibit best results for energy optimization in clustered WSN. It is inferred that in direct transmission the residual energy drops to zero at an early stage (around 300 rounds). The reason being all nodes is involved in Direct Transmission (DT) to the base station which also increases the redundancy. But LEACH algorithm gives better performance than direct transmission to a certain extent because of being a cluster head algorithm, it involves the random selection of cluster heads, but still doesn’t last for long and survives for only 650 rounds approximately. It can also be seen that HSA algorithm lasts for a long time, i.e. around 780 rounds
the reason being
its higher searching efficiency and as well as
considering multiple criteria’s for cluster head selection. It generates a new vector from an existing vector which makes exploration and exploitation achieved at a faster rate, but it includes the limitation of being restricted to only a certain region. This problem is being eradicated in PSO, which moves from one region to another in search for an optimum solution but still faces the problem of exploration and exploitation in high dimensional problems and takes a longer time to achieve a local maxima or minima and hence lasts for only approximately 830 rounds. Hence, the design of a hybrid HSA-PSO, which makes use of high searching efficiency of HSA combined with the dynamic nature of PSO and acquires a better result, which is clearly visible in the graph the residual energy dropping to zero after almost 1744 rounds.
Figure 2 Residual energy of the network for various algorithms
Figure 3 Alive nodes in the network for various algorithms
Figure 4 Dead Nodes of the Network for Various Algorithms
The number of nodes alive with DT, LEACH, HSA, PSO and hybrid algorithm is shown in Figure 3. An alive node directly indicates the lifespan of the network. This implicates the lifetime of the network is increased as long as more number of the nodes are alive. The number of alive nodes in DT method starts dropping as the number of rounds reaches 110 and it becomes zero as the round becomes 285. Alternatively, the LEACH method try to improve the number of alive node and the nodes start to die after 255 rounds and the total number of nodes die after 710 rounds. Among HSA and PSO, the number of live nodes in PSO is increased by 30.83 % as compared to HSA. However, the proposed hybrid algorithm improves the number of alive nodes by 49.69% and 37.5 % than HSA and PSO methods, respectively. As equal cluster head selection policy is enforced during cluster head selection, the number cluster heads during each round correspond to the number of alive nodes of the network. Also, 10 % of the total nodes act as cluster head. Hence, at any point of the round one could infer the number of cluster heads in the network. For instance, in LEACH method, at the 430th round, the number of alive nodes corresponds to 70. Hence, the number of cluster head equal to 7.
Figure 5 Throughput of the Network for Various Algorithms
From the Figure 4 the first node and last node death for DT occurs around 43 and 300 rounds respectively the reason being all nodes involved in direct transmission, whereas in
LEACH the first node and last node death occurs around 240 and 650 rounds respectively the reason of it being a cluster head algorithm. HSA and PSO almost give same performance the reason being the optimum searching efficiency of HSA and dynamic capability of PSO. The first node death for HSA and PSO occurs around 119 and 293 rounds, respectively, and the last node death for HSA and PSO occurs around 780and 830 rounds, respectively. However, the hybrid algorithm exhibit better results as the first node death occurs around 1304 rounds and last node death around 1744 rounds.
Figure 6 Mean residual energy of the network for various sink positions
The throughput of the WSN for DT, LEACH, HSA, PSO and hybrid algorithm is shown in Figure 5. The throughput of the network improves as the number of alive node in WSN increases. As seen from the curves, an maximum throughput of 0.41 Mbps is observed with the various algorithms. However, the throughput reduces as the number of rounds increases. For instance, at 150 rounds, the throughput in DT is reduced by 51.21 % as compared with the other algorithms when throughput equal to 0.2 Mbps. However, the throughput in LEACH is increased by 380 rounds from 150 rounds as compared with DT. Although the throughput in HSA is maintained till 850 rounds, it starts reducing from 910 rounds. On the other hand, the proposed hybrid algorithm maintains the throughput of 0.41 Mbps for more than 1500 rounds. The reason for hybrid algorithm performing much superior than other
algorithms is being it combines the searching efficiency of HSA with the dynamic capability of PSO.
Figure 7 Mean throughput of the network for various sink positions
Figure 8 Standard deviation of residual energy of the network for various sink positions
Figure 9 Standard deviation of throughput of the network for various sink positions
Figure 10 Mean residual energy of the network for varying number of nodes
Figure 11 Mean throughput of the network for varying number of nodes
Figure 12 Standard deviation of residual energy of the network for varying number of nodes
Figure 13 Standard deviation of throughput of the network for varying number of nodes
Table 3 Performance Comparison of the Implemented Algorithms First node death
Last node death
Residual energy(J) after 300 rounds
Throughput (bits/round)
DT
43
297
0
0
LEACH
243
661
9
300000
HSA
119
783
13
280000
PSO
293
831
26
409600
Hybrid HSA-PSO
1304
1744
39
415000
Algorithm
Comparison between DT, LEACH, HSA, PSO and Hybrid PSO-HSA algorithms is shown in Table 3. The comparison is made among all the implemented algorithms on the basis of first node death, last node death, residual energy, throughput of the network and the number of alive and dead nodes. The residual energy, throughput of the network, number of alive nodes and the number of dead nodes are obtained after 350 rounds. The performance of the network is measured from all the above parameters mentioned in the table directly. As
shown in Table 3 it is evident that hybrid PSO using HSA is the best performing algorithm for energy optimization in clustered WSN. The mean residual energy and throughput of the network for various sink positions and varying number of nodes is shown in Figure 6, Figure 7 and Figure 10, Figure 11, respectively. As seen from the charts, the proposed hybrid HSA-PSO algorithm performs best than the existing algorithms for varying sink positions. This can also be verified from the standard deviation of the residual energy and throughput results as shown in Figure 8, Figure 12 and Figure 9, Figure 13, respectively. From these results it can be concluded that the proposed hybrid algorithm exhibit better results among other existing algorithms due to the high searching efficiency of HSA combined with the dynamic exploration nature of PSO. The standard deviation of the residual energy for various sink positions and number of nodes signify that the variation in hybrid HSA-PSO is less as compared to the existing HSA and PSO algorithms. Alternatively, it is high as compared to DT and LEACH methods. The reason is due to the non-usage of the cluster head concept among nodes in DT and the usage of random probabilistic cluster head selection concept in LEACH. 6. Conclusion In this paper, selection of cluster head algorithms such as LEACH, HSA, PSO and hybrid HSA-PSO have been analyzed and implemented for energy optimization in WSN. In HSA, both energy and distance are considered at the time of CH selection and it also performs searching at a faster rate making exploration and exploitation faster. Because of this reason the results are improved with respect to conservative LEACH in terms of first node death and last node death. However, it restricts it’s searching to only a particular region. Also, with PSO implementation, it allows dynamic capability to move from one region to another in search for an optimal solution and achieve a faster convergence. But, in high dimensional problems the convergence rate starts to decrease and hence making exploitation and exploration difficult. In order to overcome the problems resulted by HSA and PSO, a hybrid HSA-PSO is proposed, which combines the best part of HSA and PSO. It utilizes the higher searching efficiency of HSA, which is due to the reason it creates a new solution from the present solution and the dynamic capability of PSO, which allows moving from one region to another in search for an optimum solution and hence it give better results than other algorithms.
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Elsevier Journal of Ad Hoc Networks (3) (3) (2005) 325–349.
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Dr. T. Shankar received a Ph.D., Degree from Anna University, Tamil Nadu, India in 2014. He obtained M.E in Applied Electronics from Anna University, Tamil Nadu, India in 2005. He obtained Bachelor of Engineering degree in Electronics and Communication Engineering from University of Madras, Tamil Nadu, India in 1999. He is a Life member in ISTE (Indian Society for Technical Education). His research interests are in the area of mobile ad-hoc networks, software router design and systems security. Presently, he is working as Associate Professor in the School of Electronics Engineering, VIT University, Vellore. His current research interest is in the areas of WSN, WLAN, Heterogeneous Networks and Emerging Wireless Networks.
Dr. S. Shanmugavel graduated from Madras Institute of Technology in electronics and communication engineering in 1978. He obtained his Ph.D. degree in the area of coded communication and spread spectrum techniques from the Indian Institute of Technology (IIT), Kharagpur, in 1989. He joined the faculty of the Department of Electronics and Communication Engineering at IIT, Kharagpur, as a Lecturer in 1987 and became an Assistant Professor in 1991. Presently, he is a Professor in the Department of Electronics and Communication Engineering, College of Engineering, Anna University, Chennai, India. He has published more than 68 research papers in national and international conferences and 15 research papers in journals. He has been awarded the IETE-CDIL Award in September 2000 for his research paper. His areas of interest include mobile ad hoc networks, ATM networks, and CDMA engineering.
Dr. A. Rajesh received B.Tech, M.Tech and Ph.D degrees in Electronics and Communication Engineering from Pondicherry University in 2005, 2008 and 2014 respectively. He has worked at Indian Telephone Industries (Govt. of India) and Tata Consultancy Services. He is a recipient of gold medal at PG level from Pondicherry University and Pondicherry Engineering College. He has been awarded INSPIRE fellowship under AORC from the Department of Science and Technology (DST), Ministry of Science and Technology (MST), Government of India. He is a member of IEEE, IET, IETE, OSA, OSI, IEICE and life member of ISTE. Presently, he is working as Associate Professor in the School of Electronics Engineering, VIT University, Vellore. His current research interest is in the areas of LTE-A, WiMAX, WLAN, Heterogeneous Networks, IMS, and Emerging Wireless Networks.
PII: DOI: Reference:
S2210-6502(16)30001-3 http://dx.doi.org/10.1016/j.swevo.2016.03.003 SWEVO208
To appear in: Swarm and Evolutionary Computation Received date: 13 August 2015 Revised date: 7 March 2016 Accepted date: 9 March 2016 Cite this article as: T. Shankar, S. Shanmugavel and A. Rajesh, Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor n e t w o r k s , Swarm and Evolutionary Computation, http://dx.doi.org/10.1016/j.swevo.2016.03.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Hybrid HSA and PSO Algorithm for Energy Efficient Cluster Head Selection in Wireless Sensor Networks T. Shankar1, S. Shanmugavel2, A. Rajesh1 1
School of Electronics Engineering VIT University, Vellore, India
2
National Engineering College [email protected]
Abstract Energy efficiency is a major concern in wireless sensor networks as the sensor nodes are battery-operated devices. For energy efficient data transmission, clustering based techniques are implemented through data aggregation so as to balance the energy consumption among the sensor nodes of the network. The existing clustering techniques make use of distinct Low-Energy Adaptive Clustering Hierarchy (LEACH), Harmony Search Algorithm (HSA) and Particle swarm optimization (PSO) algorithms. However, individually, these algorithms have exploration-exploitation tradeoff (PSO) and local search (HSA) constraint. In order to obtain a global search with faster convergence, a hybrid of HSA and PSO algorithm is proposed for energy efficient cluster head selection. The proposed algorithm exhibits high search efficiency of HSA and dynamic capability of PSO that improves the lifetime of sensor nodes. The performance of the hybrid algorithm is evaluated using the number of alive nodes, number of dead nodes, throughput and residual energy. The proposed hybrid HSA-PSO algorithm shows an improvement in residual energy and throughput by 83.89% and 29.00%, respectively, than the PSO algorithm. Keywords: Particle Swarm Optimization; Harmony Search Algorithm; LEACH; Wireless Sensor Network 1. Introduction Wireless Sensor Networks (WSN) has emerged as an essential and popular way of providing pervasive computing for various applications. Since the WSN makes use of tiny sensor nodes that run on battery, their energy has to be optimally utilized. Clustering is one of the traditional approaches for energy efficient transmission of data from sensor node to cluster head [1-13]. Clustering is the process of dividing the geographical area into small sectors and assigning one node as a head for the cluster, termed as Cluster Head (CH) [17].
The selection of the cluster head plays a vital role for energy efficient data transmission. In practical network, the cluster head is changed during definite iteration for better performance [7]. The number of cluster heads within the network and the amount of nodes per cluster can be variable or fixed based on the application scenario. Cluster heads can also form a second tier network, i.e. creating another level of hierarchy or they can just pass on the data to the base station [8]. During the method of clustering, there exist few limitations, such as, additional overhead during CH selection and improper assignment of cluster construction process. These limitations reduce the lifespan of sensor nodes. This motivated many researchers to investigate various aspects like, appropriate CH selection, low power protocols [19-20], network establishments and routing protocol [21-23] of wireless sensor networks. This paper also discusses some widely explored clustering algorithms in WSN and proposes a meta–heuristics optimization algorithm for CH selection for WSN. In situations where the search for an optimal solution becomes exhaustive, we incline to choose meta-heuristic algorithms [8]. For meta-heuristic algorithms to be efficient, it has to cover the solution space where there is global optimum and also should generate new and improved solutions. Also, the meta-heuristic algorithm should escape from the local optimum. In literature, there are meta-heuristic algorithms, such as, Particle Swarm Optimization (PSO) and Cuckoo Search that aim at global optimization (Exploration) and algorithms like Simulated Annealing (SA) and Harmony Search Algorithm (HSA) that get confined to the local optima (Exploitation) [9, 10, 31-32]. For better solution, there must be a balance between the exploration and the exploitation. This motivated us in combining the prevalent meta-heuristic algorithms, namely, HSA and PSO. The reason for the superior performance of the proposed hybrid HSA-PSO algorithm than the existing algorithms is rationalized as follows: In the hybrid HSA-PSO, with the ordinariness of PSO, it allows the particles to move from one region to another by updating the position and velocity at the end of each round. As PSO face high dimensional optimization limitation, it’s difficult to explore every possible region of the search space. Under such circumstances, HSA is utilized that has the high searching computational capability. It provides a new way to produce particles that generates a new vector after considering all of the existing vectors.
The HSA has the limitation of being restricted to only a certain region. This is being eradicated in PSO, which moves from one region to another in search for an optimum solution but still faces the problem of exploration and exploitation in high dimensional problems and takes a longer time to achieve a local maxima or minima. Hence, the proposed hybrid approach makes use of high searching efficiency of HSA combined with the dynamic nature of PSO. The rest of the paper is organised as follows: Section 2 describes the works related to heuristic protocols, clustering algorithms and meta-heuristic algorithms. Section 3 provides the wireless sensor network model. The hybrid HSA-PSO algorithm is given in Section 4. The results and discussion of the proposed and existing algorithm are described in Section 5 and the Section 6 concludes the major findings of the proposed algorithm. 2. Related Work In literature [24], a detailed study has been carried out on the classical protocols, such as, Direct Diffusion (DD), Energy Aware Routing (EAR), etc., location-based protocols such as Geographic and Energy-aware Routing (GEAR), Energy-aware WSN Geographic Routing Protocol (EAGRP), etc., hierarchical protocols such as Low-energy Adaptive Clustering Hierarchy (LEACH), Balanced-clustering Energy-efficient hierarchical routing protocol (BCEE) [33], etc., and swarm based hierarchical protocols such as Multi-sink Swarm-based Routing Protocol (MSRP), Probabilistic Zonal and Swarm-inspired System for Wild Fire Detection (PZSWiD), etc.. It has been found that the use of swarm based hierarchical protocols is promising in improving the energy efficiency of sensor nodes. The authors in [25] has proposed an improved Binary Particle Swarm Optimization (BPSO) algorithm with modified connected dominating set that utilizes residual energy for discovery of optimal number of clusters and cluster head. Although this method improves the number of clusters, the number of nodes alive and remaining average energy is reduced to zero beyond 800 rounds. The authors in [26] have suggested a modified Genetic algorithm (GA) based evolutionary approach for load balancing so as to reduce the energy consumption in WSN. The authors in [27] have identified the additional energy consumption at cluster head due to improper formation of clusters in WSN. This leads to rapid death of cluster head as they are overloaded with large number of sensor nodes. Hence, they suggest a modified
differential evolution (DE) based clustering algorithm to improve the lifetime of the cluster head in WSN. The authors in [28] have suggested a hybrid algorithm to extend the lifetime of sensor nodes in WSN. Here, the hybrid technique combines two routing methods, namely, flat multihop routing and hierarchical multihop routing. The authors in [29] combined the ant colony optimization with greedy migration mechanism to improve the life time of sensor nodes under energy hole problem in grid based WSN. However, the hybrid techniques in [28-29] do not consider the clustering in WSN. A hybrid clustering technique named Hybrid Distributed Hierarchical Agglomerative Clustering (H-DHAC) has been proposed in [30] that combines quantitative location and binary qualitative connectivity of data in clustering. However, the number of alive nodes starts reducing as the number of rounds reaches 600. The LEACH utilizes a dynamic clustering method, where nodes ‘n’ are selected as cluster head randomly based on a threshold value as given in Equation (1). Here, r represents the round, G is the set of all the nodes that are eligible to become cluster heads and p is the probability that each of the nodes will become the cluster heads. Here, the nodes that are chosen as cluster heads cannot be cluster heads in the next p rounds. Hence, each node has a probability of 1/p to become the cluster head. p 1 T (t ) 1 p *(r mod ) p 0
; nG
(1) ; nG
After (1/p)-1 round, the threshold is assigned as one for any nodes that have not yet been elected as cluster heads. Hence, in LEACH, all nodes become eligible to become cluster heads [2]. However, through extensive simulations it has been found that the LEACH suffers from the possibility of none of the sensor node in the network being selected as cluster head during some rounds. Also, there is a possibility of concentration of all the selected cluster heads in a particular part of the network. Hence, cluster head selection is not energy adaptive and the cluster formation during each round consumes more energy at the sensor nodes. Further, nodes near the cluster boundary consume more energy as compared to other sensor nodes in the network. The authors in [17] have proposed an improved clustering algorithm than LEACH, which is termed as Energy Efficient Clustering Scheme (EECS). It suits the periodical data
gathering applications. Here, the network is partitioned into several clusters and communication is carried out between the CH and the base station. In EECS, CH candidates compete for the ability to elevate to CH for a given round. As compared to LEACH for cluster formation, EECS differs by the dynamic sizing of clusters based on cluster distance from the base station. In [18], Threshold Sensitive Energy Efficient Sensor Network (TEEN) is proposed where a CH sends its members a hard threshold and a soft threshold. The hard threshold tries to reduce data communications by allowing the nodes to transmit only when the sensed attribute is in the range of interest. The soft threshold further reduces the data communications as there is little or no change in the sensed attribute. To overcome the limitation of the above mentioned algorithms, in literature, metaheuristic algorithms have been analyzed for the optimal selection of efficient cluster heads. There are two important components in meta-heuristics, namely, intensification and diversification. For an algorithm to be efficient and effective, it must be able to generate a diverse range of solutions including the potentially optimal solutions so as to explore the whole search space effectively, while it intensifies its search around the neighborhood of an optimal or nearly optimal solution. In order to do so, every part of the search space must be accessible, though, not necessarily visited during the search [16]. The HSA is a music-based meta-heuristic optimization algorithm [3]. It was inspired by the observation that the aim of music is to search for a perfect state of harmony. This harmony in music is analogous to find the optimality in an optimization process. Harmony Search (HS) has been just such a successful example by transforming the qualitative improvisation process into some quantitative rules by idealization, and thus turning the beauty and harmony of music into an optimization procedure through the search for a perfect harmony [6]. There are various advantages of HSA in terms of searching efficiency: (a) HS algorithm imposes fewer mathematical requirements and does not require initial value settings of the decision variables. (b) As the HS algorithm uses stochastic random searches, derivative information is also unnecessary. (c) The HS algorithm generates a new vector, after considering all of the existing vectors. These features increase the flexibility of the HS algorithm and produce better solutions. The HS algorithm is effectively directed using two parameters, namely, Harmony Memory Considering the Rate (HMCR) and Pitch Adjusting Rate (PAR). In the formulated problem, it is necessary to choose the best set of CHs that minimize energy consumption. The
sets of CHs presented in Harmony Memory (HM) are improved by the estimated residual energy. In each set of CHs, the high residual energy node is searched within its clusters. If any node is found with greater residual energy than the current CH, the corresponding node is replaced as CH and the existing CH is replaced as its member node in each set. In [9], cuckoo search is used for optimal ratio between the global and the local search. In [10], the authors make use of the eagle strategy and cuckoo search for multimodal objective functions. As exploration is often linked to randomness, it should have the ability to jump from the local solution space to the global solution space. Hence, this paper discusses the way evolutionary algorithms are affected by exploration and exploitation and a proper balance between them is attempted to achieve better energy efficiency. The summary of various clustering algorithms in wireless sensor networks is given in Table 1. Table 1 Summary of various clustering algorithms in wireless sensor networks Algorithm LEACH BCEE EECS
TEEN PSO HSA
BPSO Cuckoo Search Hybrid HSA-PSO
Authors / References Heinzelman et al., [2] Xiaoyan Cui et al., [33] Mao Ye et al., [17] Arati Manjeshwar et al., [18] Buddha Singh et al., [13] D.C. Hoang, et al., [6] Shanmugasun daram Thilagavathi et al., [25] Xin-She Yang et al., [10] This Work
Distribution of Cluster Head
Cluster Head Selection
Cluster Stability
Explo ration
Exploit ation
Global Maxim um
Global Minimu m
Energy Efficient
Non-Uniform
Random
Low
No
No
No
No
Low
Uniform
Random
Medium
No
No
No
No
Low
Uniform
Probability / Feature based
Low
No
No
No
No
Medium
Non-Uniform
Probability
Moderate
No
No
No
No
Medium
Non-Uniform
Probability
High
More
Less
Less
More
High
Non-Uniform
Random
High
Less
More
More
Less
Medium
Uniform
Random
Moderate
Less
More
High
Uniform
Random
Low
More
Less
Medium
Non-Uniform
Random / Probability
High
Balanced
Less
More
Balanced
Balanced
Very High
3. Network Model The network model considered in this paper is a free space model, which consists of the transmitter and receiver section with a distance of separation, d. The transmission section consists of transmit electronics with transmission amplifier and the receiving section consists of receive electronics as part for information to be transmitted in terms of bits. It is assume as
a set of sensors is dispersed on a rectangular field [15]. We also assume the following properties about the sensor network:
The nodes in the network are considered to be quasi-stationary
The energy consumption is not uniform for all the nodes and depends on the distance from the base station or the cluster head
Nodes are unaware of the location
All the nodes are homogeneous in nature
The nodes are self-organizing and need not be monitored after deployment
Each node has a fixed number of power levels 2 lEelec l fs d , d d 0 ETx (l , d ) 4 lEelec l mp d , d d 0
ERx lEelec
(2) (3)
where Eelec is the energy consumed to send one bit of data; fs is the amplification coefficient of the transmission amplifier for the free space model and it sends one bit of information, which is considered if the distance is lesser than the threshold distance and mp is used when the distance is greater than the threshold; l is the amount of information being sent. In the considered application scenario, n sensor nodes are deployed randomly into a field with an area ‘M×N’ m2. To optimize the selection of cluster heads and to find optimal ‘k’ cluster head, the following objective function is used to compute the optimum (Hoang et al 2010):
fobj f1 1 f 2
(4)
where f1 and f2 are given by
d nodei , CH k f1 max k Clusterk nodeiCk
(5)
N
f2
E node i
i 1 k
E CH j 1
j
(6)
where is the scaling factor that ranges between 0 and 1. In this objective function, ‘ ’ is the maximum of the Euclidean distance of nodes ‘ cluster heads ‘ ‘
’and ‘
’. Meanwhile
’ to their
’is the number of nodes that belong to cluster is the ratio of initial energy of all nodes alive,
network with the total current energy of the cluster head ‘ (
(
) in the
)’ in the current round. The
constant α indicates the contribution of ‘ ’ and ‘ ’ in the objective function ‘
’.
4. Proposed HSA-PSO Algorithm The proposed scheme called hybrid HSA-PSO is based on the common characteristics of both PSO and HSA algorithms. An HSA can produce new solutions and the parameters of HMCR and PAR are introduced to allow the solution to escape from local optima and to improve the global optimum prediction of the algorithm. Due to this reason HSA is merged with the PSO to overcome exploration and exploitation in high dimensional problems. In hybrid algorithm, with the help of PSO, it allows the particles to move from one region to another by updating the position and velocity at the end of each round. The dynamic nature of PSO allows to search in more than one region of the space combined with higher searching efficiency at each region with the help of HSA. The operation of the algorithm is partitioned into rounds. In each round, the cluster heads collect data from all cluster members and transfer to the BS. To implement the hybrid HSAPSO algorithm, the steps to be followed are as follows: Step 1:
Initializing the Parameters Initial wireless sensor network with the appropriate energy of the nodes is created
according to the parameters given in the Table 1. Step 2:
Initializing the particle position and velocity, fitness function and hybrid matrix The particle position and velocities are generated randomly in the range [X min,
Xmax] and [Vmin, Vmax]. The initialization of swarm size and harmony memory in hybrid HSA-PSO algorithm is termed as Particle Harmony Memory (PHM). The initial PHM consists of a number of randomly generated solutions for the optimization problem under consideration. The solution of the formulated problem is an index of the ‘ ’ numbers of
cluster heads. PHM is computed as shown in Equation 7. Each row of the PHM is a random solution for the optimization problem and then calculates the fitness function for each Hybrid vector using Equation 4.
I11 2 I1 I1HMS Step 3:
I 21 I 22 I
HMS 2
I k1 f obj _1 F 1 ... I k2 f obj _ 2 F 2 HMS HMS f ... I k obj _ n F
...
(7)
Improvise a new hybrid harmony from the PHM After defining the PHM as shown in Equation 7, the improvisation of the PHM is
done by generating a new harmony vector [ harmony vector
]. Each component of the new
is generated using Equation 8 based upon the HMCR defined.
{
(
(8)
)
HMCR is defined as the probability of selecting a component from the PHM members, and (1-HMCR) is, therefore, the probability of generating it randomly. If
is
generated from the PHM, then it is further modified or mutated according to PAR. It determines the probability of a candidate from the PHM to be mutated and (1-PAR) is the probability of doing otherwise. The pitch adjustment for the selected
{
where
(
)
is given by,
(9)
is nearest node whose energy is greater than the energy of present CH in the cluster.
Step 4:
Updating the hybrid harmony matrix The newly generated hybrid harmony vector is evaluated in terms of the objective
function value at each row’s p_best. If the objective function value of the new hybrid harmony vector is better than the objective function value for the worst harmony in the PHM then new harmony is included in the PHM and the existing worst harmony is excluded from the PHM.
Step 5:
Updating particle position and velocity The optimal position of the cluster heads is determined by taking a swarm of
particles and determining the best particle in the swarm with least cost function. The best particle solution for that swarm is taken as the p_b (particle best position). These swarms are created for ‘n’ number of iterations, p_b is determined for all the iterations, and the best of all p_b is taken as the g_b (global best position). In WSN scenario, the nodes that coordinates with the global best positions are taken and the nodes that are nearest to these coordinates are made cluster heads. The velocities of all nodes are calculated and updated for every round [12]. The velocity of each particle can be calculated using the following information, namely, the current velocity, the distance between the current position besides p_b and the distance between the current positions besides g_b. This can be formulated as follows:
Vij r 1 w r Vij r c1 r1 p _ bij X ij r c2 r2 g _ bij X ij r
(10)
X ij r 1 X ij r Vij r 1
(11)
where Vij(r) and Xij(r) are particle velocity and position at r, i and j represents ith particle and dimension, w(r) is inertia weight factor, c1 and c2 are acceleration constants, r1 and r2 are uniform random values between [0,1], where p_b and g_b are particle’s local and global best positions [5]. The use of linearly decreasing inertia weight factor ‘w’ provides improved performance in the considered scenario. Its value is decreased linearly from about 0.9 to 0.4 during each run. A suitable selection of the inertia weight provides a balance between global and local exploration and exploitation, and results in less iteration on average to find a sufficiently optimal solution. Its value is set as follows,
t w t wu wu wl T max
(12)
where Tmax is the total number of iterations, t is the current iteration, wu =0.9, wl=0.4. Thus, the particle-swarm with least cost function is considered as the p_b and the best solution of p_b of all iterations is considered as the g_b. The nodes nearest to the g_b values are taken as cluster heads. The velocity and position of each row of PHM are calculated and updated for every round using Equation 7. Step 6:
Go to step 3 until the termination criterion is reached If the number of rounds reaches maximum rounds, stop the algorithm, otherwise,
continue the algorithm to search for optimum result. The flow of the proposed algorithm is illustrated in Figure 1.
Figure 1 Flowchart of hybrid HSA-PSO algorithm
5. Simulation Results and Analysis In this section we evaluate the performance of each algorithm based on the following metrics, namely, residual energy of the network, the number of dead and alive nodes and throughput of the network. The required simulation parameter [6, 13, 15, 21, 26-27] for various algorithm is shown in Table 2. The existing algorithms and the proposed hybrid algorithm are experimented using MATLAB 2014b on Intel Core i5 processor with 2.30 GHz CPU and 8 GB RAM. Table 2 Simulation parameters of hybrid HSA-PSO algorithm Parameter
Value
Sensor field region (m2)
(100*200)
Base station location (m)
(50,150)
Number of nodes (n)
100
Initial energy of a node (Eo) (J)
0.5
Data packet length (k1) (bits)
4096
Eelec (nJ/bit)
70
Eamp (pJ/bit/m2)
120
Energy data aggregation (nJ)
5
Number of rounds (rmax)
2000
Swarm size
15
Harmonic Memory Considering Rate
0.95
Pitch Adjustment Rate
0.8
Number of Cluster Heads (k)
5
Scaling Factor (ε)
0.5
Number of iterations
5
Particle Position [Xmin,Xmax]
[0,200]
Particle Velocity [Vmin,Vmax] (m/s)
[0,200]
Acceleration Constants [0.25,0.5] [c1min,c1max], [c2min,c2max]
As shown in Figure 2, it is evident that the hybrid scheme designed using HSA and PSO exhibit best results for energy optimization in clustered WSN. It is inferred that in direct transmission the residual energy drops to zero at an early stage (around 300 rounds). The reason being all nodes is involved in Direct Transmission (DT) to the base station which also increases the redundancy. But LEACH algorithm gives better performance than direct transmission to a certain extent because of being a cluster head algorithm, it involves the random selection of cluster heads, but still doesn’t last for long and survives for only 650 rounds approximately. It can also be seen that HSA algorithm lasts for a long time, i.e. around 780 rounds
the reason being
its higher searching efficiency and as well as
considering multiple criteria’s for cluster head selection. It generates a new vector from an existing vector which makes exploration and exploitation achieved at a faster rate, but it includes the limitation of being restricted to only a certain region. This problem is being eradicated in PSO, which moves from one region to another in search for an optimum solution but still faces the problem of exploration and exploitation in high dimensional problems and takes a longer time to achieve a local maxima or minima and hence lasts for only approximately 830 rounds. Hence, the design of a hybrid HSA-PSO, which makes use of high searching efficiency of HSA combined with the dynamic nature of PSO and acquires a better result, which is clearly visible in the graph the residual energy dropping to zero after almost 1744 rounds.
Figure 2 Residual energy of the network for various algorithms
Figure 3 Alive nodes in the network for various algorithms
Figure 4 Dead Nodes of the Network for Various Algorithms
The number of nodes alive with DT, LEACH, HSA, PSO and hybrid algorithm is shown in Figure 3. An alive node directly indicates the lifespan of the network. This implicates the lifetime of the network is increased as long as more number of the nodes are alive. The number of alive nodes in DT method starts dropping as the number of rounds reaches 110 and it becomes zero as the round becomes 285. Alternatively, the LEACH method try to improve the number of alive node and the nodes start to die after 255 rounds and the total number of nodes die after 710 rounds. Among HSA and PSO, the number of live nodes in PSO is increased by 30.83 % as compared to HSA. However, the proposed hybrid algorithm improves the number of alive nodes by 49.69% and 37.5 % than HSA and PSO methods, respectively. As equal cluster head selection policy is enforced during cluster head selection, the number cluster heads during each round correspond to the number of alive nodes of the network. Also, 10 % of the total nodes act as cluster head. Hence, at any point of the round one could infer the number of cluster heads in the network. For instance, in LEACH method, at the 430th round, the number of alive nodes corresponds to 70. Hence, the number of cluster head equal to 7.
Figure 5 Throughput of the Network for Various Algorithms
From the Figure 4 the first node and last node death for DT occurs around 43 and 300 rounds respectively the reason being all nodes involved in direct transmission, whereas in
LEACH the first node and last node death occurs around 240 and 650 rounds respectively the reason of it being a cluster head algorithm. HSA and PSO almost give same performance the reason being the optimum searching efficiency of HSA and dynamic capability of PSO. The first node death for HSA and PSO occurs around 119 and 293 rounds, respectively, and the last node death for HSA and PSO occurs around 780and 830 rounds, respectively. However, the hybrid algorithm exhibit better results as the first node death occurs around 1304 rounds and last node death around 1744 rounds.
Figure 6 Mean residual energy of the network for various sink positions
The throughput of the WSN for DT, LEACH, HSA, PSO and hybrid algorithm is shown in Figure 5. The throughput of the network improves as the number of alive node in WSN increases. As seen from the curves, an maximum throughput of 0.41 Mbps is observed with the various algorithms. However, the throughput reduces as the number of rounds increases. For instance, at 150 rounds, the throughput in DT is reduced by 51.21 % as compared with the other algorithms when throughput equal to 0.2 Mbps. However, the throughput in LEACH is increased by 380 rounds from 150 rounds as compared with DT. Although the throughput in HSA is maintained till 850 rounds, it starts reducing from 910 rounds. On the other hand, the proposed hybrid algorithm maintains the throughput of 0.41 Mbps for more than 1500 rounds. The reason for hybrid algorithm performing much superior than other
algorithms is being it combines the searching efficiency of HSA with the dynamic capability of PSO.
Figure 7 Mean throughput of the network for various sink positions
Figure 8 Standard deviation of residual energy of the network for various sink positions
Figure 9 Standard deviation of throughput of the network for various sink positions
Figure 10 Mean residual energy of the network for varying number of nodes
Figure 11 Mean throughput of the network for varying number of nodes
Figure 12 Standard deviation of residual energy of the network for varying number of nodes
Figure 13 Standard deviation of throughput of the network for varying number of nodes
Table 3 Performance Comparison of the Implemented Algorithms First node death
Last node death
Residual energy(J) after 300 rounds
Throughput (bits/round)
DT
43
297
0
0
LEACH
243
661
9
300000
HSA
119
783
13
280000
PSO
293
831
26
409600
Hybrid HSA-PSO
1304
1744
39
415000
Algorithm
Comparison between DT, LEACH, HSA, PSO and Hybrid PSO-HSA algorithms is shown in Table 3. The comparison is made among all the implemented algorithms on the basis of first node death, last node death, residual energy, throughput of the network and the number of alive and dead nodes. The residual energy, throughput of the network, number of alive nodes and the number of dead nodes are obtained after 350 rounds. The performance of the network is measured from all the above parameters mentioned in the table directly. As
shown in Table 3 it is evident that hybrid PSO using HSA is the best performing algorithm for energy optimization in clustered WSN. The mean residual energy and throughput of the network for various sink positions and varying number of nodes is shown in Figure 6, Figure 7 and Figure 10, Figure 11, respectively. As seen from the charts, the proposed hybrid HSA-PSO algorithm performs best than the existing algorithms for varying sink positions. This can also be verified from the standard deviation of the residual energy and throughput results as shown in Figure 8, Figure 12 and Figure 9, Figure 13, respectively. From these results it can be concluded that the proposed hybrid algorithm exhibit better results among other existing algorithms due to the high searching efficiency of HSA combined with the dynamic exploration nature of PSO. The standard deviation of the residual energy for various sink positions and number of nodes signify that the variation in hybrid HSA-PSO is less as compared to the existing HSA and PSO algorithms. Alternatively, it is high as compared to DT and LEACH methods. The reason is due to the non-usage of the cluster head concept among nodes in DT and the usage of random probabilistic cluster head selection concept in LEACH. 6. Conclusion In this paper, selection of cluster head algorithms such as LEACH, HSA, PSO and hybrid HSA-PSO have been analyzed and implemented for energy optimization in WSN. In HSA, both energy and distance are considered at the time of CH selection and it also performs searching at a faster rate making exploration and exploitation faster. Because of this reason the results are improved with respect to conservative LEACH in terms of first node death and last node death. However, it restricts it’s searching to only a particular region. Also, with PSO implementation, it allows dynamic capability to move from one region to another in search for an optimal solution and achieve a faster convergence. But, in high dimensional problems the convergence rate starts to decrease and hence making exploitation and exploration difficult. In order to overcome the problems resulted by HSA and PSO, a hybrid HSA-PSO is proposed, which combines the best part of HSA and PSO. It utilizes the higher searching efficiency of HSA, which is due to the reason it creates a new solution from the present solution and the dynamic capability of PSO, which allows moving from one region to another in search for an optimum solution and hence it give better results than other algorithms.
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Dr. T. Shankar received a Ph.D., Degree from Anna University, Tamil Nadu, India in 2014. He obtained M.E in Applied Electronics from Anna University, Tamil Nadu, India in 2005. He obtained Bachelor of Engineering degree in Electronics and Communication Engineering from University of Madras, Tamil Nadu, India in 1999. He is a Life member in ISTE (Indian Society for Technical Education). His research interests are in the area of mobile ad-hoc networks, software router design and systems security. Presently, he is working as Associate Professor in the School of Electronics Engineering, VIT University, Vellore. His current research interest is in the areas of WSN, WLAN, Heterogeneous Networks and Emerging Wireless Networks.
Dr. S. Shanmugavel graduated from Madras Institute of Technology in electronics and communication engineering in 1978. He obtained his Ph.D. degree in the area of coded communication and spread spectrum techniques from the Indian Institute of Technology (IIT), Kharagpur, in 1989. He joined the faculty of the Department of Electronics and Communication Engineering at IIT, Kharagpur, as a Lecturer in 1987 and became an Assistant Professor in 1991. Presently, he is a Professor in the Department of Electronics and Communication Engineering, College of Engineering, Anna University, Chennai, India. He has published more than 68 research papers in national and international conferences and 15 research papers in journals. He has been awarded the IETE-CDIL Award in September 2000 for his research paper. His areas of interest include mobile ad hoc networks, ATM networks, and CDMA engineering.
Dr. A. Rajesh received B.Tech, M.Tech and Ph.D degrees in Electronics and Communication Engineering from Pondicherry University in 2005, 2008 and 2014 respectively. He has worked at Indian Telephone Industries (Govt. of India) and Tata Consultancy Services. He is a recipient of gold medal at PG level from Pondicherry University and Pondicherry Engineering College. He has been awarded INSPIRE fellowship under AORC from the Department of Science and Technology (DST), Ministry of Science and Technology (MST), Government of India. He is a member of IEEE, IET, IETE, OSA, OSI, IEICE and life member of ISTE. Presently, he is working as Associate Professor in the School of Electronics Engineering, VIT University, Vellore. His current research interest is in the areas of LTE-A, WiMAX, WLAN, Heterogeneous Networks, IMS, and Emerging Wireless Networks.