An Efficient Clustering Approach using Genetic Algorithm and Node Mobility in Wireless Sensor Networks Omar Banimelhem, Moad Mowafi, Eyad Taqieddin, Fahed Awad, Manar Al Rawabdeh Multimedia Networking Research Laboratory, Department of Network Engineering and Security Jordan University of Science and Technology Irbid, Jordan {omelhem, mowafi, eyadtaq, fhawad}@just.edu.jo, {
[email protected]}
Abstract—Node clustering in wireless sensor networks helps in extending the network life time by reducing the nodes’ communication energy and balancing their remaining energy. This paper presents a new genetic-based approach that improves the performance of the LEACH clustering protocol used in wireless sensor networks. The proposed approach utilizes the mobility feature of sensor nodes in order to reduce the communication distances between the cluster heads and the base station. In each round, new locations of the cluster heads are determined using a genetic algorithm. The simulation results demonstrate that the proposed approach outperforms LEACH in terms of network lifetime and average remaining energy. Keywords— Wireless Sensor Networks; LEACH protocol; Clustering; Genetic Algorithm
I.
INTRODUCTION
Wireless sensor networks (WSNs) are distributed systems, in which dedicated sensor nodes are deployed either randomly or manually in order to achieve certain tasks such as target tracking, surveillance, monitoring, and recording physical conditions of the environment [1]. After collecting the data, the sensor nodes forward these data to a base station (BS) for further processing. WSNs are becoming an essential part of our daily lives in a wide range of applications. Generally, the number of sensor nodes in any WSN depends on the application for which the WSN is used. This number can be as small as tens of sensor nodes or as large as thousands of sensor nodes as in the application of country border monitoring. Regardless of the WSN size in terms of the number of sensor nodes, the sensor nodes should be used efficiently such that the network lifetime is maximized. Efficient utilization of the sensor nodes means that the consumed power due to the sensing, processing, and relaying of data to the BS should be minimized. Since most of the energy is consumed during the transmission process of the sensed data to the BS [2], clustering approaches were proposed as efficient solutions in order to reduce the amount of energy consumed in the communication between the nodes. Low Energy Adaptive Clustering Hierarchy
978-1-4799-5863-4/14/$31.00 ©2014 IEEE
(LEACH) protocol is a well-known clustering protocol, in which the sensor nodes are self-organized into clusters [3]. For each cluster, a sensor node, called the cluster head (CH), is responsible for delivering the collected data from the sensor nodes within the same cluster to the BS. The operation of the LEACH protocol is divided into rounds and each round is composed of two phases: the setup phase and the steady state phase. In the setup phase, the CHs for the corresponding round are determined such that each sensor node generates a random number that is compared with a certain threshold in order to determine whether that node will be a CH in this round or not. In the steady state phase, each CH collects the data from the other nodes in the cluster and sends the aggregated data to the BS. The LEACH protocol was proposed assuming that the sensor nodes are stationary. However, there are several WSN applications where mobile nodes are used as a main component such as habitat monitoring [4]. The mobility feature of the sensor nodes can be employed in order to reorganize the CHs in the sensing field such that the energy consumed for communication between the CHs and the BS is minimized. In order to achieve node mobility, sensor nodes are usually carried over mobile robots. Therefore, the mobile nodes can change their locations in order to satisfy a certain objective. When there are several locations that the mobile nodes can move to, a set of these locations should be selected optimally such that the sum of the distances between the BS and these locations and the cost of movement are minimized. Artificial intelligence techniques such as a Genetic Algorithm (GA) can be employed in order to find the best locations among the available potential locations. GA is usually employed as a search algorithm to find an optimal or near-optimal solution for problem seeking optimization [5]. In this paper, we improve the functionality of the LEACH protocol using GA; assuming that the sensor nodes are mobile. The rest of the paper is organized as follows. Section II discusses the related work. Section III presents the proposed approach. Section IV discusses the simulation results, and Section V concludes the paper.
II.
RELATED WORK
Several approaches were proposed in order to improve the functionality of the LEACH protocol. The authors in [3] extended LEACH’s probabilistic cluster head selection algorithm by taking into consideration the remaining energy in each node in the CH selection equation. In [6], the authors proposed PEGASIS (Power-Efficient GAthering in Sensor Information Systems) protocol that is aimed to be more robust to node failures than LEACH. In PEGASIS, the sensor nodes transmit the data to their nearest neighbors, which in turn forward the data to the BS. In [7], the partition-based LEACH (pLEACH) protocol was proposed in order to improve the LEACH protocol. The pLEACH protocol partitions the network into an optimal number of sectors and then selects the node with the highest energy as the head for each sector. TEEN (Threshold sensitive Energy Efficient sensor Network) protocol [8] is a hierarchical routing protocol that uses two types of thresholds (hard and soft). Based on the values of these thresholds, the sensor nodes transmit their data to the CH. DEEC (Distributed Energy Efficient Clustering) protocol [9] was proposed for heterogeneous WSNs, in which the CHs are selected based on the ratio of the residual energy of the node to the average energy of the network. In [10], HEER (Hybrid Energy Efficient Reactive) protocol was proposed, which is a hybrid reactive protocol of TEEN and DEEC. In HEER, the CHs are chosen based on the residual energy as in DEEC and two hard and soft thresholds used as in TEEN. However, the above-mentioned approaches assume that the sensor nodes are stationary. There are other approaches that were proposed assuming that the sensor nodes are mobile. The authors in [11] proposed LEACH-Mobile (LEACH-M) protocol, which is similar to the LEACH protocol. However, in LEACH-M, two different algorithms in the steady-state phase are used. The mobility is taken into consideration when the data are transmitted to the CHs. If the CH does not receive data from a sensor node, which previously belonged to its cluster, the CH assumes that the node has moved away. LEACH with Virtual Force (LEACH-VF) protocol was proposed in [12]. This protocol applies virtual field force (VFF) principles to determine the proper locations for both the sensors nodes and CHs in order to maximize the coverage area and minimize the consumed energy. In [13], a clustering protocol that combines the benefits of using the k-means clustering algorithm with the LEACH-VF protocol was proposed. The k-means algorithm was employed in order to determine k centroids around which the clusters will be formed. Then, the virtual field force method is applied to these clusters to determine the most suitable positions for each node. In [14], the authors proposed SCARP (Spatial Correlation Aware Routing Protocol) that aims at reducing the number of transmissions by the sensor nodes through a spatial correlation concept and hence decreasing the consumed energy to prolong the network lifetime. In [15], RAHMoN (Routing Algorithm for Heterogeneous Mobile Networks) protocol was proposed. This protocol runs in heterogeneous mobile WSN, where the CHs use a classification of mobility levels to deliver the data to the BS. None of the aforementioned approaches considered GA to determine new locations of the CHs that are selected in each round of the LEACH protocol. In this paper, we employ GA in
order to search for optimal new locations for the CHs and move them to the new locations such that the distance between these CHs and the BS is minimized. III.
PROPOSED APPROACH
In this section, we present a GA-based approach that utilizes the mobility feature of the sensor nodes in order to improve the LEACH functionality. The proposed approach improves the operation of the LEACH protocol by adding a new stage in the setup phase that aims at determining new locations for the CHs other than their current locations. In each round and after the CHs are selected, new locations for the CHs are determined using GA. Figure 1 shows the modified LEACH protocol.
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.
Deploy N sensor nodes in the network area. Round=1 Select CHs. CHs send their current locations to the BS. BS divides the whole network area into grids. BS runs the GA in order to find the new locations of the CHs. BS informs the CHs about their new locations. CHs move to their new locations. CHs broadcast to the sensor nodes that they are CHs. Sensor nodes join CHs in clusters. CHs collect data and send the aggregated data to the BS. Round++ If the required number of rounds is achieved or the network dies, then Stop. Otherwise, go to Step 3.
Fig. 1. The modified GA-based LEACH protocol
A. GA-based New Locations Determination In Figure 1, Step 6 indicates that the GA is used in order to find the new locations of the CHs. After the CHs selection process, the proposed approach builds on a virtual division of the whole network area of the sensing field to look like a grid; based on the number of CHs. This division aims at producing a number of locations that could be new locations for the CHs. The number of these locations is greater than or equal to the number of CHs in the corresponding round. The new locations represent the intersections between the vertical and horizontal lines of the virtual grid. In each round, the sensing field is divided into a virtual grid, where the step size between the vertical and horizontal lines varies in each round; depending on the number of CHs in order to achieve the number of the desired locations. Figure 2 shows a sensing field that is divided into two different grids in different rounds. For example, if the number of CHs is 7, the sensing field is divided into nine potential locations; as shown in Figure 2(a). On the other hand, Figure 2(b) shows the same sensing field as it is divided in another round to produce 16 potential locations for 12 CHs.
population matrix represent the IDs of the generated locations after the division process.
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In order to avoid that two or more gens in any chromosome of the population matrix have the same values, the mating operator is used to replace any repeated number twice or more by another number which is selected randomly.
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C. The Fitness Function
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Given a WSN where some sensor nodes at a certain round act as CHs. Let ( X ic , Yi c ) be the current location of CH i,
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( X in , Yi n ) be the new location of CH i, and ( X BS , YBS ) be the
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BS location, then the objective function that is used in the GA aims at minimizing the sum of all distances as follows:
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The fitness value is usually the value of the objective function in the optimization problem being solved. In our proposed approach, we use a fitness function that aims to reduce the cost of moving the CHs to their new locations. This implies finding locations that minimize the communications distances between the CHs and the BS and at the same time reduce the sum of the total distance that the CHs will move.
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(b) Fig. 2. A sensing field is divided to generate (a) 9 locations and (b) 16 locations
B. Population Representation In the proposed GA-based approach, the population is represented as a matrix of the potential solutions, where each row in this matrix acts as one solution which is called a chromosome. Each chromosome is represented as integer numbers where each number represents the number of a location in the sensor field. For instance, consider Figure 3 as an illustrative example. Assume the number of CHs in a certain round is 7. The sensor field will be divided into grids where the intersections between the horizontal and vertical lines in the grid represent the new locations. As shown in Figure 3(a), 9 locations will be available as potential new locations to move the CHs to them. Figure 3(b) shows one possible solution (chromosome) which is composed of 7 gens where Figure 3(c) shows the population representation. Solutions from one population are taken and used to form a new population. This process is repeated until the GA is terminated. In each iteration, a new population will be generated using the GA operators: crossover, mutation, and mating. Solutions which are selected to form the population (offspring) are selected according to their fitness - the more suitable they are, the more chances they have to reproduce. In each round, and after the network division and the new locations IDs are determined, the (x, y) coordination for each location in the sensing field will be a real number based on intersection between the vertical and horizontal lines. As we said before, the number of these locations must be equal to or larger than the number of clusters. The integer numbers in the
(a) New locations generated by network division 2
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(c) Population Matrix Fig. 3. Chromosome and Population representation.
⎧⎪ min⎨ i ⎪ ⎩
nBS
∑i=1(Dicn + Di H
⎫⎪ )⎬ ⎪⎭
(1)
Where H is the number of CHs, Dicn is the distance between the current location and new location of CH i, and is given as:
Dicn = ( X ic − X in ) 2 + ( X ic − Yi n ) 2
(2)
and DinBS is the distance between the new location of CH i and the BS location, and is given as:
DinBS = ( X in − X BS ) 2 + (Yi n − YBS ) 2
(3)
D. Termination Criteria After producing the new locations by the GA, the CHs will be informed by the BS to move towards them. Once the CHs move to their new locations, they broadcast messages to the other sensor nodes, which in turn start joining the CHs, similar to what is done in the original LEACH protocol. After that, the steady-state phase begins. Each sensor node will sense its environment within its sensing range and transmits the sensed data to its CH. The CHs aggregate the received data from the sensor nodes and send the aggregated data to the BS. The consumed energy during the transmission and reception process is calculated as follows:
ETX (k , d ) = ( E elec × k ) + (ε amp × k × d 2 ) E RX (k , d ) = ( E elec × k )
figure, while the FDN in the original LEACH protocol occurs at round 400, it takes place at round 450 in the proposed approach. Moreover, the HDN occurs at round 841 in the original LEACH protocol while it occurs at round 950 in the proposed approach. Figure 5 shows the total remaining energy in the network for the LEACH and the proposed approach. As shown in the figure, the proposed approach always outperforms the original LEACH protocol. TABLE I.
Parameter Network Area Number of Nodes CH Probability Initial energy of sensor nodes Packet Size Base Station Location GA number of iterations
(4) (5)
Number of rounds at half-dead nodes Number of nodes
LEACH
50 75 100 125
800 827 841 856
SIMULATION RESULTS
We compare between the proposed approach and the original LEACH protocol in terms of the network lifetime and the remaining energy. The network lifetime can be measured in different metrics, such as first-dead node (FDN), half-dead nodes (HDN), and last-dead node (LDN). In our experiment, we used the FDN and HDN metrics as a measure for the network lifetime. Table II shows the number of rounds when half of the nodes are dead using the proposed approach and the original LEACH. As shown in Table II, the proposed approach outperforms the LEACH protocol for various number of nodes. An improvement of more than 12% was recorded for the four cases in the table. Figure 4 shows the network lifetime in terms of the number of live nodes for the LEACH and the proposed approach when the number of nodes in the network is 100. As shown in the
Proposed Approach 950 932 950 968
Improvement (%) 18.75 12.7 13.0 13.1
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Proposed Approach LEACH
80 Number of Live Nodes
An experiment for performance comparison between the proposed approach and the original LEACH protocol was conducted. The simulation parameters that were used in the experiment are shown in Table I.
Value 100 m x 100 m 50, 75, 100, 125 0.05 0.5 J 1000 bits (100,100) 1000
IMPROVEMENT IN THE NETWORK LEIFETIME IN TERMS OF TABLE II. HDN FOR DIFFERENT NUMBER OF NODES
Where Eelec is the transmission energy of one bit which is equal to 50n/b, k is the number of bits, ℰamp is the transmission amplifier unit, and d is the distance between any sensor node and the CH, or between any CH and the BS [3]. IV.
SIMULATION PARAMETERS FOR PERFORMANCE EVALUATION
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Fig. 4. Number of live nodes when the number of sensor nodes is 100.
REFERENCES 50 45
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Fig. 5. Total remaining energy when the number of sensor nodes is 100.
V.
CONCLUSION
An new approach that improves the functionality of the original LEACH protocol in WSNs is proposed. The main idea in this approach is to use the mobility feature of the sensor nodes such that the cluster heads in each round are asked to move to new locations other than their current locations, where the new locations of the cluster heads are determined using GA. The candidate locations that the CHs will be asked to move to are determined by dividing the sensing field into grid where the intersections between vertical and horizontal lines of this grid represent the potential new locations. Simulation results in terms of the network lifetime and the total remaining energy in the network showed that the proposed approach outperforms the original LEACH protocol. As a future work, new fitness functions will be investigated in the GA in order to increase the performance of the proposed approach.
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
ACKNOWLEDGMENT This work was funded by the Jordan University of Science and Technology, Research Project No. 148/2012.
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