International journal of Com put er Science & Net w ork Solut ions Jan.2016-Volume 4.No.1
ht t p:/ / ww w .ijcsns.com ISSN 2345-3397
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A New Intelligent Cluster Head Selection
Method for Mobile Wireless Sensor Networks
MajidGhorbanJannat, Reza Javidan*,Ahmad jalili
Msc Student, Dep. of Computer Engineering & IT, Shiraz University of Technology Assistant Professor, Dep. of Computer Engineering & IT, Shiraz University of Technology
PhD Student, Dep. of Computer Engineering & IT, Shiraz University of Technology
*Corresponding author: javidan@sutech.ac.ir
Abstract
One of the most important methods for energy consumption reduction in Wireless Sensor Networks (WSNs) is clustering. In this respect, cluster head selection method can highly impact on energy saving in the overall network. In mobile scenarios, for gathering environmental information as an example, due to natural barriers in operational environment, topology isn’t fixed and changes continuously. Therefore, cluster head selection can be adapted based on physical location of sensors. This means that the nodes with fewer barriers in front of them are better to be selected for cluster head. The traditional and statistical methods can’t consider the topology of network dynamically. In this paper a new intelligent method based on Artificial Neural Network (ANN) is proposed in order to reduce energy consumption and increase the network performance by choosing the cluster head based on residual energy of nodes and physical positions of them (topology). In this method, at first the positions of nodes mapped to a 2D pattern; then this pattern feeds into ANN that has previously trained and as the output of ANN the best CH position is suggested. The proposed method is compared with the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm and the results show that it outperforms LEACH method especially when the nodes are mobile.
Keywords: Wireless sensor networks, Artificial Neural Networks, Clustering, Energy management, LEACH.
I. Introduction
Due to the increasing progress in monitoring of operational environments made by wireless sensor networks (WSNs), methods to improve the results of their applications are undoubtedly required. Gathering environmental information via WSNs while their nodes move in an environment is considerably more efficient than those for fixed nodes. There are many situations that occur in everyday life in which it is required to collect information from a specific environment, such as searching for the injured in disaster areas, or detecting fires or earthquakes. The mobile nature of nodes in WSNs enables to search a wide area using only a few sensors (Potdar et al., 2009).
The continued activity of a WSN depends upon the number of nodes which are remained active, i.e. the more, the better, and the activity of each node depends on the maintenance of energy in its battery. Therefore, in order to save energy in nodes, inefficient activities should be prevented. These activities can include repetitive or inappropriate transmissions or useless calculations. There are two ways to decide whether an activity is useful or not; using a central node which is aware of the condition of the majority of nodes, or applying distribution methods.
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this node (Dargie, 2012). The abovementioned facts clearly show that these activities quickly consume energy of the central node and put the network management in an unstable state. Due to such disadvantages, using methods with distributed computing is highly desirable. In this paper, the use of Artificial Neural Network (ANN) to achieve a distributed management in WSNs is proposed.
Because of the limitations in the nature of WSNs, such as energy and telecom power, it is required that the node activities are constantly managed and adopt appropriate responses with respect to environmental and internal changes aiming at successfully accomplish their given missions. However, as a result of these limitations, outside management of the operational environment is not possible in most cases and the nodes themselves must decide how to allocate resources as well how to cooperate. The use of smart methods can add several capabilities to nodes such that they continue acting optimally even with changing conditions and topology. One of the expected results with the use of ANNs in WSNs is the operational elongation of network nodes through smart management of energy consumption in such a way that the network lifetime is increased, compared to previous methods that are non-intelligent and statistically based (Nimbalkar, 2012; Hosseiniradet al., 2014).
In this paper, a new intelligent method for determining cluster head position in a mobile WSNs based on ANNs is proposed. The new method not only considersthe energy ofthe node but also its location is taken into account. Thus, unlike algorithms such as LEACH which use statistical rules to choose the cluster head based on the node energy, in the proposed method, nodes that are not in the eyes of the entire cluster are not selected. To achieve this,aANN is considered which is trained by key patterns and appropriate locations of the cluster head in the training phase. In the operation phase, this ANNwhich runson each sensor,accept the placement patternof the environment as input arguments and offers the perfect place to the cluster head. The simulation results on prototype data show the effectiveness of the proposed method.
The remaining of the paper is organized as follows: Section II provides a brief review on relevant literatures. The proposed method is described in Section III. Implementation of the proposed method and comparative results are explained in Section IV and Section V respectively. Finally conclusion and remarks are explained in Section VI.
II. Related Works
Algorithms that work on the basis of clustering are generally divided into two types: centralized and distributed. One of the most famous distributed type is the LEACH algorithm (Heinzelman et al., 2000)in which head cluster selection and substitutionand data combination among head clusters to manage energy consumption are considered (Akkaya et al., 2005). In spite of some advantages of LEACH distributed method, however, this algorithm does not guarantee the location of the head cluster as well as the number of head clusters which leads to unsuitable configuration to increase the lifetime of the network.
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(Muruganathan et al., 2005). Although centralized clustering algorithms are inclined to improve performance compared to distributed algorithms, this improvement is with some costs as adding hardware and high energy consumption during the initial configuration. One of the algorithms applying ANNs for clustering is Energy Based Clustering Self organizing map (EBCS).This algorithm is based on clustering and self-organizing maps to reduce energy consumption. This method can perform clustering based on some parameters such as battery and sensor coordinates in combination with self-organizing neural networks in which energy of nodes is maintained at balanced level and outage nodes in network is with the least effect on network function. In addition, a new function is proposed to select head cluster in each region of network in which the ranges are based on hierarchy analysis method (Enami et al., 2010).
In another algorithm, a routing method was presented on the basis of ANNs to increase the lifetime of WSNs. In this approach, linear programming method with determined limitations is used and the head cluster is determined by an adaptive learning method usingANN (Nehra et al., 2009).
III. The Proposed Method
A.Motivation
In ANNs, inputs are applied to the outlier nodes and the outputs of these nodes after applying some adjustable weights become inputs for the intermediate and output nodes. During learning, these weights should be adjusted to learn how response to received inputs. The main idea in this paper is that the nodes of WSNwhich participate for head cluster selection are considered asANN nodes. By using ANNs for classification of nodes, in an autonomous decision making manner, selection of the head clusterare determined as the output of such ANN with optimized energy consumption.
By selecting wireless sensors as ANN nodes and communication between them as node communication in ANN, we can achieve a model turning WSN to an ANN and taking decisions based on smart methods. Although the study regarding smart techniques in management of sensor networks is a new field, it seems that using smart techniques in this regard is unavoidable (Oldewurtel et al., 2006).
The nodes distributed in operational environments have access to limited energy resources and if the battery of a node is finished, the node is turned off and it cannot perform its duty. If such a node is an intermediate node, in retransmitting the data, the entire operation will be disturbed.Generally, energy consumption of a node in transmission and reception is much more than internal calculations. Thus, if the head cluster selected based on an optimized method, the nodes can be active for a long time and the data exchange is organized as possible (Hosseingholizadeh et al., 2009).
To reduce energy consumption, the network can be partitionedinto a series of clusters and each cluster has a head cluster. In this manner, data transmition and reception of cluster members is achieved through head cluster and then is sent from head node to the sink node for data collection. Therefore, energy consumption is reduced.Using clustering is a topological basis and it leads to local management of nodes in whichunnecessary communications are avoided.On the other hand, in operational environmental, for the following reasons, the topology of nodes will be changed and head cluster should be reselected:
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- New barriers can be in communication path of nodes and current communication is disconnected. Thus, head nodes in clusters should be selected again.
These decisions should be taken into account inside the network. Artificial techniques can be useful for such decision making and causes increasing independence of network.To select head cluster, various methods can be used. Cyclichead selection or like LEACH algorithm, head clusters are selected based on statistical methods. For example in HEED algorithm, besides the energy of node, the energy of neighboring nodes is considered in statistical calculations (Deosarkar, 2008).
B. Using anANN to determine the cluster head in mobile nodes
In this section the detail of the proposed clustering technique with head cluster selection based onANNs is explained for smart management of energy consumption. Here cluster head selection is based on the residual energy in the nodesin connection with nodes locations. If nodes are moved in the operating environment, the group arrangement of nodes is continuously changed. For example, passing through bending and narrow spaces can result loss of communication between the nodes and the cluster head. In such situation, cluster head selection base only on the residual energy of nodes is not a satisfactory method and the correct selection should also be depends upon the physical position of the nodes. GPS or other positioning systems can be used to identify the physical location of nodes (Boukerche et al., 2007).
During the training phase of ANN, some known node arrangements in an environment are given to the ANN which is trained regarding the optimum location ofthe cluster head. Following the training stage, the trained network nodes is transferred and placed inside the sensors. During the operation, each sensor can notify its presence in a region on the basis of its location. By providing information to the ANN, the proposed location of the cluster head is defined. If a node is present in a region, it notifies all cluster members that it is a cluster head candidate. If there are some nodes in this area, the candidate that notifies sooner can be a cluster head.
Although there are different types of ANN techniques, this study applied a feed-forward multilayer networkor multilayer perceptron (MLPs). Perceptron networks are one of the most important ANNsapplying in engineering and they can select the number of layers which accurately perform non-linear mapping. Such perceptron composed of three layers: an input layer, some hidden layers, and an output layer. In multi-layer networks, the signal current of network is performed in a feed-forward path starting from the input layer andleading to the output layer (Zhang, 2000; Egmont-Petersen et al., 1998;jalili et al., 2015).
We can consider the nodes of WSN like ANN nodes(Mohammadi et al., 2015). Figure 1shows that in each node add and multiply calculations are performed on input and the requiredoutput is generated. Each sensor only involves the outputof relevant nodes in
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Figure. 2.General scheme of the proposed method
calculations. The output of each node is sent tothe other nodes and to compute the result of next layer is used. Figure 2 shows the general scheme of the proposed method.
IV. Implementation
As shown in Figure 2, the proposed method is performed in two stages. In the first stage, ANN is trained with suitable models. In the second stage, this ANN is on sensor and situation of sensors is considered as input and cluster head range is proposed.
A. Training phase
In this stage, different types of arrangement of nodes in environment with good location to determine cluster head is given as input to ANN. In each stage, a model is created and the required response is given to the network.After training, the network can give response for similar models. For example, in Figure 3, there is a sensor network with 25 places. By creating aANN with 25 inputs and 25 outputs, this model is implemented. For different arrangements, the similar inputs are activated in ANN and acorresponding output is activated in ANN.Figure 3 shows four examples of input models of ANN in training stage. A good place for the cluster head is one place with red rectangle is identified.
Figure 4 shows using input model for ANN. The output of only one of 25 output nodes exists and is shown with a Red rectangle in the Figure.
B. Executive phase
To implement the proposed method, two separate plans are needed; one responsible for creation of ANNs, training, node weights adjustment; and the other is a program which runs on sensor nodes by considering the condition of each node andcomputes the a good location
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Figure. 4.Using ANN to propose suitable place
for cluster head. In operation stage, each sensor should obtain its physical location. Then by considering the current head cluster or using the mean of coordinate of each sensor, we can divide the activity of sensors as a network. Thus, each node can achieve its location in this network.
Figure 5 shows the performance of the proposed system. In part 1, a portion of operational environment is located and grey areas show the barriers in the environment while white area shows the free locations for sensorand radio waves passing. Part 2 of Figure 5 shows the sensors shown by red points working in operational environment In part 3, a virtual girding network is created wherethe center is the mean of coordinates of sensors. Here a 4*4 grid network is created. During the training, it is trained as 4*4 network, each unit of this 4*4 network can indicate zero or more which is the number of existing sensors in this unit. In part 4, each unit of this grid network which contains sensor is shown with Blue. Part 5 of Figure 5 shows the required model and this model is given as input to ANN and output is shown in part 6 which is the proposed location of cluster head.
Figure. 5.The general scheme of proposed method in operational environment
V. Simulation and Evaluation
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another simulator program.It can create the simulated operational environment with barriers, and mobile sensors. This output of the simulator is the suitable place for a head cluster during operation. In simulation program there is class of sensors which create randomly sensors that work in simulated area. They can move forward and if collision with obstacle they randomly move left or right.In Figure 6 the simulation program is showed. Sensors are drawn with red point and the NN input showed with a blue grid. This figure shows example composed of 15 sensors act in an area that obstacle draw in gray. Sensors can’t cross over gray area. The blue grid is drawn only for better understanding.
Figure. 6. The Simulation Program
In the simulation, 30 sensors were mobile in an operational environment, and the results obtained with the proposed method were compared with those obtained using the LEACH algorithm.Figure 7 shows the number of sensors with residual battery charge during operation time. The comparison of the proposed method and the LEACH algorithm shows that the former was more successful with regard to increasing the lifetime of the sensors with elimination of their battery energy than was LEACH.
The sum of consumed energy in all nodes of network can be a good measure for function and comparison of two algorithms. Figure 8 shows this comparison in a chart. This comparison shows that the proposed method in the entire network saved energy consumption and this increases life service of network.
Figure.7.The number of sensors with battery charge in the proposed method and in the
LEACH algorithm.
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Figure. 8.The sum of consumed energy in all sensors
Figure. 6.The number of packet loss
The number of packets not reaching their destination is shown in Figure 9 as a comparison. The number of sent packets of each node not reaching the data collector is shown in the vertical column, and time is shown in the horizontal column.
As shown in Figure 9, the LEACH algorithm is less capable of sending packets to data collectors when the nodes are mobile.
VI. Conclusion
In this paper a new intelligent method for determining cluster head position in a mobile WSN based on ANNwas proposed. In this method the ANN is trained by important location models and the residual energy of each sensor node given as inputs to the ANN, and the optimal node for cluster head was selected as the output.In clustering, using this method that considers the physical position of nodes, in addition to their energy, increases the decision-making capacity of sensor nodes, as compared to their physical arrangement. Suitable cluster heads are selected, and a cluster head is less likely to be located in blind areas of the operational environment, resulting in the saving of energy. The simulation results when compared to leach algorithm showed the effectiveness of the proposed method.
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The simulation results show that in proposed method energy consumption in nodes is reduced and an improvement in number of live nodes is occurred. Also in proposed method sum of all energy that is remained in nodes is always more than LEACH. Hence, it is obvious that the proposed method has lower total energy consumption. Also the simulation results show that selecting accessible nodes as CH, lead to a good improvement in pocket delivery. The pocket loss in proposed method is very less than LEACH. This result was expected because traditional algorithms don’t consider the network topology and therefore their selection always will be in wrong places. In the end we can see that the proposed method is a good candidate for improvement in performance of WSNs
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