MBC: A Multi-hop Balanced Clustering Routing ...

International Journal of Artificial Intelligence and Mechatronics Volume 2, Issue 6, ISSN 2320 – 5121

MBC: A Multi-hop Balanced Clustering Routing Protocol for Wireless Sensor Networks Shiva Rowshanrad

Manijeh Keshtgary

Reza Javidan

Computer Engineering and IT Deptt., Shiraz University of Technology (SUTECH) Shiraz, Iran Email: [email protected]



Abstract – Wireless Sensor Network consists of large number of sensors which usually deployed randomly and it is hard to replace their batteries, so one of the key parameters of these networks is energy consumption. Recently clustering routing protocols attract researchers’ attention to improve energy consumption and network lifetime. LEACH and multi-hop LEACH are the most popular clustering protocols. In this paper a multi-hop balanced clustering protocol is proposed. This protocol is based on k-means clustering and uses genetic algorithm for multi-hop communication among cluster heads. The methods which are used in the protocol cause a balanced and uniform energy dissipation in nodes and made them to have a smooth death rate, which is an advantage over multi-hop LEACH. The protocol is also compared to LEACH protocol. The simulation results show that MBC protocol has a better behavior in terms of energy consuming, network lifetime and number of nodes alive during network lifetime. Keywords – Balanced Clustering, Genetic Algorithm, KMeans Clustering, Multi-Hop Routing Protocol, Wireless Sensor Networks.

I. INTRODUCTION Wireless sensor network is a collection of distributed, self-organized, low power sensor nodes. The main tasks of sensor nodes are monitoring, sensing environment, collecting data and transmitting data to a core network node called Sink or Base Station (BS). Design of WSNs is challenging because of its energy restrictions [1, 2]. One of the main challenges in WSNs is routing protocols. A routing protocol main task is finding and maintaining path from sensor nodes to BS. Frequent dynamic topology changes in WSNs due to node failure and mobility requires flexible and adaptive routing protocols [3]which reduce energy consumptions. Many routing protocols have been proposed for WSNs. A classification of routing protocols, based on network organization is as follows:  Flat routing protocols  Hierarchical routing protocols  Location based routing protocols[3, 4] Hierarchical routing protocols are the most energy efficient routing protocols[4], in which nodes divide into groups named clusters. In each cluster one node, called Cluster Head (CH), act as a controller. CHs aggregate data sensed by member nodes and transmit them to BS. They can transmit data in a directed or multi-hop manner. In this paper a balanced multi-hop clustering (MBC) protocol is proposed which aims to reduce energy

consumption from several aspects by using machine learning methods.The rest of the paper is organized as follows: Section 2 gives an overview of related works. In section 3 the proposed protocol is described. Simulation results and performance evaluation is presented at section 4. Finally section 5 concludes the paper.

II. RELATED WORKS One of the first and the most well-known clustering protocols is LEACH[5 , 6] which uses random rotation of CHs to distribute energy consumption of nodes an prolong network lifetime. LEACH divides time into rounds consist of two phases, setup phase and steady phase. In setup phase clusters are formed and in steady phase data transfer takes place. Since cluster formation and CH selection have huge effects on power consumption of the network, many improvements on LEACH have been done. LEACH-C[7] proposed centralized LEACH. In this protocol, clustering is done by BS, based on information of nodes about their location and energy. So the most suitable nodes will be chosen as CHs and most of energy needed for clustering is consumed in BS. EWC[1] introduced a new CH selection method. It used a scoring function, made up of weighted sum of parameters such as distance between CH and member nodes, distance between CH and BS, node degree and residual energy of nodes. At the first of each round, nodes calculate their score and those with the best scores would be elected as CHs. Although this protocol is not centralized and consumes extra energy for CH selection it will reserve more energy and has a longer network lifetime than in LEACH and LEACH-C. LEACH-GA [8] used genetic algorithm (GA) for selecting CHs based on energy. GA also used in [9] for clustering formation using several parameters like direct distance to BS, Standard deviation of the distances to BS and energy consumed for transferring to BS. Both protocols were compared to LEACH and the simulation results revealed their better performance in terms of network lifetime and energy consumption. In [10] a fixed clustering scheme based on k-means is proposed. In this protocol the nearest nodes to cluster centroid, with sufficient energy are chosen as CHs respectively. This effective selection of CHs lead to better performance comparing to LEACH in terms of network lifetime and energy efficiency.

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International Journal of Artificial Intelligence and Mechatronics Volume 2, Issue 6, ISSN 2320 – 5121 Although all these protocols outperform LEACH but they become inefficient when the network diameter is increased or the BS is far away from the network. In this case the CHs lose much energy due to direct communication with BS. Multi-hop LEACH [2, 4]is another improvement on LEACH that proposed multi-hop routing between CHs to deliver data to BS. It is more energy efficient than singlehop LEACH and prolongs the network lifetime. MRLEACH [11] is another multi-hop routing protocol. At the first of each round, nodes send hello messages including their residual energy. The node with higher energy will be selected as CH. Nodes can choose to join the CH with highest RSSI. After CH selection network will be partition into multi layers according to CHs distances to BS. Each CH in each layer of the network, collaborate with adjacent layers’ CHs for transmitting data to BS. In [12] a multi-hop protocol is produced. This protocol has the same CH selection mechanism as LEACH and uses GA to construct a sink-centered multi-hop tree among CHs based on shortest path link. The networks which use MR-LEACH and the protocol proposed in [12] have a longer lifetime than LEACH networks. This paper introduces a multi-hop balanced clustering routing protocol named MBC. MBC is a centralized protocol and based on k-means clustering. It uses genetic algorithm for multi-hop communication among cluster heads. The difference of this protocol form other multihop protocols is that it finds the multi-hop path between CHs, considering not only their distances and residual energy but also the amount of energy-loss due to transmitting adjacent clusters’ data to BS. It also has a novel CH mechanism based on nodes residual energy, their distances to CH and also the members standard deviation of distances to CH. Simulations in section 4 reveals that these to schemes reduces and balances the energy consumption of nodes and clusters. More details of the proposed protocol are discussed in the rest of the paper.

III. MULTI-HOP BALANCED CLUSTERING PROTOCOL In this section MBC protocol is described in details. The main purpose of this protocol is to make energy dissipation of the network balanced and efficient especially for networks in large fields.

A. Energy Model The same energy model in [1, 4, 7, 10] is used to evaluate the performance of the protocol. It is based on the radio model shown in Fig.1. Both free space (εfs) and multipath (εmp) fading channel models are considered for energy consumption of nodes during transmitting and receiving packets. The energy dissipation of transmitting L bit packet over distance d is: 𝐸𝑒𝑙𝑒𝑐 ∗ 𝐿 + 𝜀𝑓𝑠 ∗ 𝐿 ∗ 𝑑 2 𝑑 < 𝑑𝑜 𝐸𝑇𝑋 = (1) 2 𝐸𝑒𝑙𝑒𝑐 ∗ 𝐿 + 𝜀𝑚𝑝 ∗ 𝐿 ∗ 𝑑 𝑑 ≥ 𝑑𝑜

Fig.1. Radio model Eelec is the energy required for processing 1 bit data with the electronic circuits. The threshold distance, do is calculated as follows: 𝑑𝑜 =

𝜀 𝑓𝑠 𝜀 𝑚𝑝

(2)

The energy taken to receive a packet is shown in (3): 𝐸𝑅𝑋 = 𝐸𝑒𝑙𝑒𝑐 ∗ 𝐿 (3) BS also consumes energy for data aggregation as follows: 𝐸𝑎𝑔𝑔 = 𝐸𝐷𝐴 ∗ 𝐿 (4)

B. Assumptions Clustering is done at the first of the protocol by BS. There are two phases in each round, setup phase and steady phase. Cluster head selection and multi-hop route discovery is done in the first phase. Then the BS informs nodes about their CH. it also informs CHs about their members and next CH in multi-hop path. CDMA is used for inter-cluster communication. After informing nodes, CHs assign TDMA time slots for intra-cluster communication. Following this phase, data are transmitted during steady phase. Other assumptions are as follows:  All nodes are fixed or pseudo-static.  The sensor nodes are randomly distributed.  The initial energy of all nodes is the same.  The BS can place anywhere, inside or outside of the field.

C. Algorithm Details The proposed protocol consists of 4 main stages which described in this section. The stages are:  Clustering  CH selection  Multi-hop route discovery  Data transmission The first stage is done before the beginning of the rounds. Second and third stages are in the setup phase and the last stage is in the steady phase. In first stage clustering is done by executing k-means algorithm based on Euclidian distances of nodes. At first k out of N nodes are randomly selected as centroids. Each of “N-k” nodes areplaced in a group which has the nearest centroid. After grouping the new centroids are calculated as in (5). 1 1 𝐶𝑒𝑛𝑡𝑟𝑜𝑖𝑑 𝑋, 𝑌 = ( 𝑥𝑖 , 𝑦𝑖 ) (5) 𝑐 𝑐 The new centroids, considered as virtual nodes, are at the center of their cluster. Re-clustering is executed recursively until the centroids are not changed any more [10]. Since dynamic clustering cause extra overhead, the


International Journal of Artificial Intelligence and Mechatronics Volume 2, Issue 6, ISSN 2320 – 5121 protocol uses the same clusters in all rounds. Instead a CH selection method is considered, which balances the energy consumption of nodes in each cluster and reduces the changes in it. CH selection is one of the main challenges in cluster based wireless networks. The CHs act as a local manager in their cluster and consume more energy, so sharing this role among nodes prolongs the network lifetime [13, 14]. In the proposed protocol, CH selection is done by means of weighted scoring function shown in (6). 𝑘

𝐹=

3

[ 𝑘=1

𝑖=1

(𝑤𝑖 × 𝑓𝑖 )]

(6)

The sub functions (fi) are shown in (7) to (9). 𝑁 𝑖=0 𝑑𝑡𝑜𝐶𝐻𝑖 𝑓1 = 𝜇 = (7) 𝑁 The f1 sub function is means of cluster member direct distances to CH. This can control the energy dissipation of nodes for transmitting to CH. Smaller means has lower energy dissipation. 𝑓2 = 𝑆𝐷 =

𝑛 𝑖=0 (𝜇𝑖

− 𝑑𝑡𝑜𝐶𝑕𝑖 )2

(8)

Function f2 is the Standard Deviation (SD) of direct distances to CH. Smaller SD leads to more uniform energy dissipation of nodes in cluster. E f3 = init (9) E res

The third sub function is the ratio of initial energy of node to its remaining energy. This sub function helps selecting nodes with more residual energy as CH. The weights (wi) are set arbitrary for the first round and is updated as (10): 𝑤𝑖 = 𝑤𝑖−1 + 𝑐𝑖 ∗ ∆𝑓𝑖 (10) ci and ∆fi are shown in (11) and (12) respectively: 𝑐𝑖 = 1−𝑓 𝑖 (11) 1+𝑒

∆𝑓𝑖 = 𝑓𝑖 − 𝑓𝑖−1 (12) Updating weights helps the protocol to adopt with different situations in network. The scoring function is calculated for every node in each cluster which has more energy than the threshold energy. The threshold energy is calculated as the means of energy consumed by CHs in every round. Multi-hop routing leads to load balancing and spreading energy usage among nodes [14, 15]. Energy consumption for each CH in inter-cluster communication consists of the energy for receiving packets from previous CH(s), the energy for transmitting received packets and its own packet to next CH. According to (1) and (3) this energy dissipation is calculated as follows: ECH= ERX+ETX= (2 P+1)*L*Eelec+ (P+1)*Ɛfs*L*d2 (13) P is the number of packets received from previous CH(s). It is clear that the amount of energy dissipation depends on both number of packets (P) (which is not considered in any of multi-hop protocols described in section 2 ) and the distance (d) between CHs. Genetic Algorithm (GA) is used in this paper to determine the most energy efficient multi-hop route among CHs based on (13).

Fig.2. Genetic algorithm flowchart GA is a probabilistic search algorithm based on natural selection and recombination process. It tries to find an optimized solution by creating a population of individuals and manipulate them to form next generation population. Each individual is a coded form of a possible solution, called chromosome. Each chromosome is evaluated by a fitness function relative to optimization problem. Then by using a selection method parents are selected. New population is generated after crossover and mutation operations on selected parents. Regeneration is repeated until the termination condition is satisfied [9, 16]. GA’s flowchart is shown in Fig. 2. The GA algorithm using in this paper is described below. Population: A population consists of several chromosomes which itself made up of genes. First population is generated randomly. A chromosome represents a multi-hop tree among CHs as shown in Fig.3.

Fig.3. A chromosomewhich represents a multi-hop tree

Fig.4. Crossover operation Copyright © 2014 IJAIM right reserved 166

International Journal of Artificial Intelligence and Mechatronics Volume 2, Issue 6, ISSN 2320 – 5121 Number of genes equal to number of clusters and their value is ID of next CH or BS. Four constraints should be checked to make sure that the chromosome represents a correct tree:  Single node check: gene (i) ≠ i  Single edge check: If gene (i)=j then gene (j)≠ i  Loop check  BS check: At least one BS should exist in the chromosome. Fitness: Fitness function evaluates individuals in a population based on the problem metrics. As mentioned before, here the metrics are number of packets received and transmitted by CH and it’s distance to next node in multi-hop tree. Fitness function is represented in (14): Fitness=∑ fitnessi (14) fitnessi is calculated by (13). Selection: Parents for generating next generation are determined in selection process. There are several selection methods such as “Roulette Wheel”, “Rank selection” and “Tournament selection” which is used in this paper. Crossover: This operation combines the parents to birth children. In this paper a two point crossover is used. Two random points are selected on parents. The child gets the middle gene(s) from one parent and the rest from the other as shown in Fig.4. Mutation: Mutation operation adds variation to new population. In mutation the value of a randomly selected gene is changed.

The last stage is data transmission. This stage is in steady phase. Nodes transmit their data to CH upon the schedule, and then CHs transmit aggregated data to BS via multi-hop route. The flowchart of proposed protocol is shown in Fig.5.

IV. SIMULATIONS AND ANALYSIS To evaluate the MBC protocol it is simulated using Matlab. We ran the simulations 10 times. The results are compared with LEACH. The details and results are explained in this section.

A. Simulation Scenario A network of 200 randomly distributed sensor nodes in a 200m×200m squared field is considered. The simulation parameters are summarized in Table I. It is assumed that all nodes have data to send in each round. The optimum number of clusters for MBC protocol is determined by simulation. We test the network with 5 to 25 numbers of clusters. The results are shown in Table II.This table shows that the average energy dissipations per- round are only about x×10-4 J different from each other(x is less than 10). This is 5×x×10 -7 J for each node, which is much less than energy needed for transmitting or even receiving a packet. So it doesn’t have much effect on the total energy dissipation or network behavior. This is an advantage for MBC over other clustering routing protocols which mentioned in section 2, because the number of clusters can be chosen based on WSN application or nodes ability (e.g. transmission range). In this paper we chose 17 for number of clusters which is the most optimum. Fig.6 also shows the difference of energy dissipation of different numbers of clusters.

B. Protocol Analysis 1) Multi-hop Communication

Fig.5. MBC protocol flowchart Table I: Simulation parameters Parameter Value Location of BS (200,200) Initial energy 0.5 J Eelec 50 nJ/bit 10 pJ/bit/m2 fs 0.0013 pJ/bit/m2 mp EDA 5nJ/bit/m2 Size of data packet (L) 4000bit Initial w1, w2, w3 0.5,0.3,0.2

We put a single hop chromosome in the first population of genetic algorithm process to make sure that the result of GA is better than single hop communication. The variation between best fitness and average fitness is shown in Fig.7. The sudden decrease is because of inserting the single hop chromosome. After this decreasing there are small variations in the value of best fitness function which means that GA was successful in finding the optimum path for most cases. The greater variations in average fitness are because of high fitness chromosomes which are filtered in every new generation. Best fitness values are below 1. Fig. 8 shows the best fitness variations in larger scale. Table II: Avrage energy disipation of networks with different number of clusters Average energy Number of clusters dissipation 5 0.080936 6 0.080882 7 0.080817 8 0.080784 9 0.08067 10 0.080592


International Journal of Artificial Intelligence and Mechatronics Volume 2, Issue 6, ISSN 2320 – 5121

17 18 19 20 21 22

0.080196 0.080199 0.080264 0.080313 0.080321 0.080328

23 24 25

0.080327 0.080329 0.08033

MBC without SD

MBC with SD

120 100 80 60 40 20 0 1 73 145 217 289 361 433 505 577 649 721 793 865 937 1009 1081 1153 1225 1297 1369 1441

0.080541 0.080519 0.080412 0.080297 0.080296 0.080264

Remain Energy (J)

11 12 13 14 15 16

Rounds

Fig.8. Comparing remain energy per round for MBC w/o SD MBC without SD

MBC with SD

250

2) Standard Deviation of Nodes Distances Having SD in weighted function of CH selection helps member nodes of each cluster to distribute energy among them and have uniform energy dissipation. Fig.9 and Fig.10 show that using SD improves saving energy in nodes and prolongs network lifetime. 0.081

Number of Nodes Alive

200

150

100

50

1 67 133 199 265 331 397 463 529 595 661 727 793 859 925 991 10… 11… 11… 12… 13… 13… 14…

Energy disipation per round (J)

0

0.0809

Rounds

0.0808

Fig.9. Comparing number of live nodes per round for MBC w/o SD

0.0807 0.0806 0.0805

C. Performance Evaluation

0.0804 0.0803 0.0802 0.0801 0.08

0

2

4

6

8

10 12 14 16 18 20 22 24 26 28 Number of clusters

Fig.6. Average energy disipation per round: as the number of clusters veries between 5 to 25. The differences are around 0.0001 to .0007 J.

Fig.7. Fitness variations with respect to generations

We evaluate the protocol performance in terms of network lifetime, residual energy and number of nodes alive during network lifetime. Network lifetime is defined as the number of rounds when all nodes are dead. Fig.11 shows the energy dissipation of LEACH and MBC. It shows that the energy dissipation in MBC is smoother than in LEACH. In this scenario, MBC network life time is about 1247 rounds with 90% confidence interval of (1246.946, 1247.054), and LEACH network lifetime is about 927 (926.9) with confidence interval of (924.741, 929.059). So the network lifetime of MBC is about 26% more than LEACH. Fig.12 shows the number of live nodes during simulation. As seen in this figure LEACH has a pseudo linear manner in decreasing number of live nodes and about 50% of nodes are dead when 50% of network lifetime passed. Fig.13 shows that this percentage of nodes consists of nodes farther from BS (the red dots are the dead nodes). This is due to single hop communication of LEACH CHs to BS. Fig.12 also shows that the most of MBC nodes are alive in most of the network lifetime. The number of live nodes in 25%, 50% and 75% of the network lifetime is shown in Table III. In the last two cases the live nodes in MBC are more than two times as in LEACH.


International Journal of Artificial Intelligence and Mechatronics Volume 2, Issue 6, ISSN 2320 – 5121 Table IV shows the round which all nodes in first cluster run out of energy in each repetition of MBC simulation. It shows that until approximately 97% of network lifetime, nodes are able to transmit data from all over the network area, while in LEACH the area of nodes alive, decrease as the number of rounds increase. It means that in MBC energy distribution is balanced in all over the network. Simulation result in [4] show that multi-hop LEACH outperforms LEACH in terms of network lifetime and energy dissipation but it has a similar death rate as in LEACH, So smooth death rate of MBC is an advantage over multi-hop LEACH. 100

MBC

Table III: Number of live nodes in different percentage of network Network 25% 50% 75% lifetime Number 183.9 181.6 181.6 MBC of nodes 159 69.7 7.1 LEACH alive Table IV: Death time of first cluster in 10 MBC simulations Last Lifetime Simulations Round round % 1225 1247 98.31 1 1202 1248 96.39 2 1230 1246 98.79 3 1222 1248 98.07 4 1202 1248 96.39 5 1202 1248 96.39 6 1207 1244 97.10 7 1209 1246 97.12 8 1202 1248 96.39 9 1207 1247 96.79 10 Mean 1210.8 1246 97.17

LEACH

Remain Energy (J)

90 80 70 60 50 40 30 20 0

1 89 177 265 353 441 529 617 705 793 881 969 10… 11… 12… 13… 14… 14… 15… 16… 17…

10

Rounds

V. CONCLUSION

Fig.10. Residual energy over rounds

Number of Nodes Alive

250

MBC

LEACH

200 150 100 50

1 121 241 361 481 601 721 841 961 1081 1201 1321 1441 1561 1681 1801 1921 2041 2161 2281 2401

0 Rounds

Fig.11. Number of live nodes over rounds

We proposed Multi-hop Balanced Clustering routing protocol in this paper. We tried to improve energy efficiency of the network in several aspects. MBC consists of several steps. Clustering is done at first step by using Kmeans algorithm based on their Euclidian distance. This can reduce energy consumption of each cluster. Then the CHs are selected by means of a weighted function based on SD of nodes distances, means of nodes distances and residual energy of each node. This helps to uniform and balance the energy consumption among nodes in each cluster. The last step is finding the optimum multi-hop path to BS from CHs and optimizing inter-cluster energy consumption. The simulations results show that the proposed protocol outperforms LEACH protocol in terms of network lifetime, energy dissipation. It also outperforms LEACH and multi-hop LEACH in terms of number of nodes live during performing in the network. It has balanced energy consumption during network lifetime so that there are live nodes all over the network in 97% of network lifetime. Another advantage of MBC is that the number of clusters can be chosen based on network application and nodes ability, without effecting on overall network performance or changing its behavior.

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Fig.12. Nodes’ status in half of the network lifetime in LEACH. Red dots show the dead nodes and blue “+”s show the live nodes.

[2]

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Manijeh Keshtgary is a faculty member of Dept. of Computer Eng. & IT, Shiraz University of Technology, Shiraz, Iran. She received her Master’s degree in Electrical & Computer Eng. from Colorado State University, CSU, Fort Collins, USA in 1993 and her PhD degree in Computer Eng. from Sharif University of technology in 2005. Dr. Keshtgary’s research interests include MANET, Wireless Sensor Networks and GSM security issues. Email: [email protected]

Reza Javidan was born in 1970. He is graduated from MSc Degree in Computer Engineering (Machine Intelligence and Robotics) from Shiraz University in 1996. He received Ph.D. degree in Computer Engineering (Artificial Intelligence) from Shiraz University in 2007. His major fields are artificial intelligence, image processing and sonar systems. Dr. Javidan is now assistant professor and lecturer in Department of Computer Engineering and Information Technology in Shiraz University of Technology. Email: [email protected]

AUTHOR’S PROFILE Shiva Rowshanrad is MSc. Student in Information Technology engineering (Computer Network Fields) at Shiraz University of Technology (SUTECH). Email: [email protected]
