A Centroid Hierarchical Clustering Algorithm for Data Gathering in Wireless Sensor Networks.

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1 A Centroid Hierarchical Clustering Algorithm for Data Gathering in Wireless Sensor Networks. Abdullah I. Alhasanat 1, Khetam Alotoon 2, Khaled D. Matrouk 3, and Mahmood Al-Khassaweneh 4 1,3 Department of Computer Engineering/Faculty of Engineering, Al-Hussien Bin Talal University, Ma an, Jordan 2,4 Department of Computer Engineering/Hijjawi Faculty for Engineering Technology Yarmouk University, Jordan 1 abad@ahu.edu.jo Abstract Data gathering is a primary task in Wireless Sensor Networks (WSNs), which should be accomplished with the minimum energy consumption. A proper utilization of the available power resources can be achieved using clustering techniques. In this paper, a new clustering algorithm, referred to as Centroid-based Hierarchical Clustering Algorithm (Cent-HC), is proposed. This algorithm focused on reducing transmission paths between sensor nodes and sink node whilst maintaining the minimum number of multi-hop communications. Results presented in this paper showed that the Cent-HC balanced the energy consumption among all clusters and led to prolong the network lifetime significantly compared to the LEACH-C algorithm. 1. Introduction Sensed data in Wireless Sensor Networks (WSN) are gathered and sent to the sink node, which in turn sent these data to base stations for analysis and monitoring at the end systems [1]. Sensor nodes are supplied with low power resources that are inefficient to transmit data directly to the sink node [2]. Alternatively, an appropriate data gathering algorithm is required to collect data from these nodes in an energy efficient way while maximizing the network lifetime; the ultimate goal of WSNs. In addition, data gathering is more efficient with homogenous sensor networks, which is accomplished, in this case, by collecting and aggregating data from a set of sensor nodes, known as clusters. The collected data is then combined into data packets in order to be sent to the sink node. This certainly led to minimize the number of transmission by eliminating

2 redundant data and then reduced the total power consumption in the network [3]. Clustering in WSNs is an important approach to reduce the energy consumption over these networks. Many energy efficient protocols based on clustering and data aggregation have been studied [2][3]. In this paper, a new data gathering algorithm referred to as Centroid-based Hierarchical Clustering Algorithm (Cent-HC for short) is proposed. The key idea behind the Cent-HC algorithm is to recursively divide the sensor network into four partitions about the centroid node. Thereafter, a set of cluster heads in each partition are defined in order to aggregate data from its cluster members and transmit these data to cluster heads in the next level. This procedure continues until a prescribed number of sensor nodes in each partition is reached. At the end of this procedure, a set of partitions of almost equal number of nodes are produced. The advantages of the Cent-CH are threefold. Firstly, equalizing the number of sensor nodes in each partition leads to fair load distribution among sensor nodes and, therefore, results with proper utilization of the available power resources. Secondly, a set of cluster heads are assigned to each partition in each level. This step is needed to extend the network life time of cluster heads since these nodes usually consume their power more quickly compared to other normal nodes. Moreover, cluster heads do not need to send its data at long distances, as suggested in LEACH [4] where each cluster head sends the data directly to the base station. In contrast, with Cent-HC, each cluster head collects data from its cluster member nodes, computes the average and sends it to a cluster head in the next hierarchical level. The reminder of this paper is organized as follows. Next section introduces some related works in the literature. A formal definition of the problem is presented in Section 3. Section 4 and 5 discuss the algorithmic approach and performance evaluation, respectively. The summery is drawn in Section Related Works Recently, numerous clustering-based data gathering algorithms have been proposed. For example, in Low Energy Adaptive Clustering Hierarchy (LEACH) [4] protocol, sensor nodes arrange themselves into clusters for data aggregation. In each cluster one node is elected to be a cluster head. This node aggregates data from its cluster members and transmits the aggregated

3 data directly to the sink nodes. The cluster head removes redundant data and used an aggregated function to minimize the transmitted data to the sink. LEACH protocol composed of two phases: the setup and steady state phase. In the setup phase, the clusters are performed and the cluster heads are selected. Each sensor node compares a random number between 1 and 0 with a threshold T, where T is computed as = ( ( / )), (1) where is a predetermine percentage of cluster heads in the network and is the current round. If the node was not a cluster head in the previous 1/ rounds and > T, then the node become a cluster head and broadcasts a message to all other nodes informing that it is a cluster head. All non-cluster head nodes determine to which cluster head they belong based on the Received Signal Strength (RSS) of the received message. Comparing LEACH with the direct transmission and the minimum transmission-energy algorithms, LEACH had achieved a factor of eight reductions in the consumed energy [4]. In [9], the author produced LEACH Centralized (LEACH-C) which is similar to the LEACH algorithm but with the Base Station (BS) forms the clusters. During the setup phase, each sensor node sends its location information and residential energy to the BS, where BS forms clusters and defines cluster heads and then sends a broadcast message that contains the IDentification (ID) of cluster head for each sensor node. It has been proven that LEACH-C exhibited better performance than LEACH in terms of energy consumption. In the Power-Efficient Gathering in Sensor Information Systems algorithm (PEGASIS)[5], each node transmits data to its closest neighbor and only one node transmits the fused data to the sink node. The chain of nodes is built up using greedy algorithm which gathers data in each round and the chain leader aggregates data to transmit it to the sink node. On the other hand, a Distance-based Clustering Routing Protocol in WSN algorithm [6] presents a new approach to select cluster heads based on distance. In this algorithm non-cluster head nodes select the cluster head which is nearest to the mid-point between the node itself and the sink node. This method is composed of two phases; the setup and steady phases. In each round

4 the setup phase defines clusters and cluster heads as proposed in LEACH [4], and each node selects its cluster according to the distance. 3. Problem Formulation Consider that a WSN consists of sensor nodes that are uniformly and randomly distributed within an area of meter square. The sink node is fixed at (, /2). The problem considered in this research is to collect data generated from sensor nodes every seconds. To collect data from sensor nodes the cluster-heads are selected and start to receive data from its cluster s members, and transmit aggregated data directly to the sink or through other cluster heads. We consider in this research the problem of delivering data from sensor nodes to the sink node only. The network is assumed to be homogenous in that sensor nodes are required to sense the same type of information. The goal of this algorithm is to present a strategy that choosing cluster heads to minimize the total energy consumption in the network. 4. The Proposed Algorithm In this section, the Centroid-based Hierarchical Clustering Algorithm is presented. The Cent-HC algorithm is composed of two phases; setup phase and steady state phase. In the setup phase, the network is partitioning into clusters, each cluster defines its cluster heads. In the steady state phase the data transmissions is done until the network lifetime is terminated. The setup phase is done once over the network lifetime and, in setup phase a recursive algorithm is used to define cluster and cluster heads which remains fixed over the network lifetime. With the Cent-HC algorithm, the network is firstly divided into four regions of almost similar number of nodes. Then, each partition is also divided into four sub regions. This is repeated until the constraint < is satisfied, where is the number of nodes in the partition and is the maximum number of nodes allowed in each partition. There is no optimal value of such number; however it is value is selected based on the simulation. The goal of this algorithm is to define a cluster head that is close to other nodes and then reduce the transmission power required to send data from a cluster member to a cluster head. Therefore, the cluster head is selected as the centroid node of a cluster. This node can be defined as the node

5 that has the maximum sum of the pairwise RSS values with other nodes in the cluster. Hence, the cluster head for cluster in the th level is computed as =arg min (2) where is the RSS value between node and, for = 1 and is the number of members in the cluster. Level 0 Level 1 Level 2... Figure 1: The hierarchical clustering scheme used with the Cent-HC algorithm.... It is worth mentioning that for each cluster a set of cluster heads are selected so that they can share the function of gathering and sending its cluster data in a round robin scheme. Figure 1 shows the hierarchical clustering scheme used with the Cent-HC algorithm. The circle represents a network partition and the leaves resulted from such recursive process is known as a cluster. The Cent-HC algorithm can be briefly described by the following steps: Input (Network graph topology (G), Threshold number of nodes (N th ), Sink node (S)) 1. Compute number of nodes in graph (N). 2. If N greater than N th, then divide the graph into four sub graph from the centroid node and return to step If N less than N th, then define the cluster heads and each node in the area save the cluster heads. Output (network cluster, cluster heads).

6 In the steady state phase, each node maintains a table of cluster heads. In round t, the node selects cluster head c from number of cluster heads as: = mod (3) Figure 2 shows the flow chart of the Cent-HC algorithm through the setup phase. Figure 2: the flowchart of the Cent-HC algorithm Figure 3 shows 100 nodes distributed randomly in a square network area of 200x200 meter square.

7 Figure (3) 100-node distributed randomly in square network area 200mX 200m. Figure 4 shows an example on hierarchical clustering adopted by the Cent-HC algorithm. In this example, 100 nodes are randomely and uniformally distributed within 100x100 meter square and the sink node is located outside network area in (110, 50). Because the area contains small number of sensor nodes, the clusters satisfied the number of nodes constraint through the second hierarchical level. Figure 4: network area after using the Cent-HC algorithm for clustering and defining cluster heads.

8 5. Performance Evaluation In this section, the performance of the Cent-HC algorithm is assessed through 200 independent simulation runs in Matlab. The Cent-HC algorithm is compared with the LEACH-C algorithm. The performance of both algorithms was evaluated in terms of network life time, total energy dissipation and number of dead nodes. 5.1 Simulation Scenario In our experiment, 200 sensor nodes were distributed randomly in a square area of sensor network 200 x 200 meter, the sink is located outside the network area in (210,100). Each sensor node had 1 Joule initial energy and the message size (k) was set to 100 Kbits. In the radio model, the energy dissipated to run the receiver and transmitter circuitry was set to E elec = 50nJ/bit, and the transmitter amplifier Є amp =10 pj/bit/m 2.The cost to transmit a message depending on the distance between the transmitter and receiver [7]. Both the free space (i.e. power loss ~, where d is the Euliclidean distance between a transmitter and receiver) and the multipath fading (i.e.power loss ~ ) channel models were used. The multipath model is used, if the distance is greater than a threshold (d o ); otherwise, the free space model is used. The distance threshold was set 44 meters, which represents a typical communication range for IEEE std (ZigBee) nodes at environments with moderate level of obstructions [8]. Thus, the transmission cost considered in this paper is given as (, )= + Є, + Є, > (4) and the receiving cost is given as ( ) = (5) As mentioned before, with the Cent-HC algorithm there is no fixed number of clusters and it depends on the number of nodes in the network. In our experimental scenario, we used the threshold value as 50 nodes. Therefore, the number of clusters varies as a function of number of sensor nodes; nevertheless at the end of the setup phase each cluster will contain

9 almost the same number of nodes that is equal or less than. The percentage of cluster heads was set to 3% for each cluster. This value is selected based on the simulation. In each communication round in the steady state phase, one cluster head is selected in order to gather, and send the cluster data. Cluster head selection is done through round robin basis. 5.2 Energy dissipation under different network diameter It is important to examine the performance of the Cent-HC and LEACH-C algorithms for small and large network sizes. Figure 5 shows the energy dissipated at the increased area of sensor fields, from 50 to 550 meter square. It is clear from this figure that the Cent-HC algorithm dissipated lower energy than LEACH-C for most range of network diameters. It is also noticed that further improvement of energy dissipation is achieved with the Cent-HC algorithm as the diameter of the network increases. For small network diameter, however, both algorithms exhibited an identical performance. In fact, when network diameter is increased, distances between sensor nodes and cluster heads are also increased and so higher transmission power is required. Figure (5) Energy dissipation of LEACH-C and Cent-HC algorithms over varying network sizes.

10 In addition, in the presence of large inter-sensor distances, the shadowing channel model described in equation (4) is used, which greatly increases the energy consumption of the network. Despite this, partitioning the network into smaller cluster sizes as in the Cent-HC algorithm has led to reduce the communication cost between sensor nodes and cluster heads, which in effect reduced the total amount of the dissipated energy. 5.3 Number of dead nodes To evaluate the history of dead nodes in the steady state phase, we ran the experiment for 200 nodes and reported the number of dead nodes for each communication rounds, as shown in Figure 6. The results for the two algorithms indicated that the death of the first set of nodes appeared about 2000 communication rounds. After that, in contrast to the LEACH-C where nodes dead sharply, in the Cent-HC the nodes gradually die. This in turn indicates that with the Cent-HC algorithm the energy consumption is distributed over longer periods in comparison with LEACH-C; extending lifetime periods of sensor nodes. Figure 6: Total number of dead nodes over the simulation time.

11 5.4 Network lifetime with different death node percentage The network lifetime is defined as the amount of time elapsed during which the network is functioning properly. One important factor that limits the network from performing its task is that a number of nodes ran out from their energy resources which then considered as dead nodes. Determining the number of dead nodes for which the network is failed is not a trivial task; since it mostly depends on the applications, protocol being used and network type. Hence, in our simulation, we computed the lifetime of sensor network for several percentages of dead nodes. Figure 7 shows the network lifetime until 1%, 10%, 20%, 50% and 80% of nodes dead. Similarly, 100 nodes were randomly distributed within an area of 200x200 meter square. Figure 7: Network lifetime versus percentage of dead nodes As can be seen from this figure, an increased percentage of dead nodes makes the sensor nodes to live longer and this is true for the two algorithms. More interestingly, the Cent-HC showed significant increased in the network lifetime; outperforming the LEACH-C over the entire range of the percentage of dead nodes. This result emphasized the benefits of using the Cent-HC algorithm for any percentage of dead nodes.

12 6. Conclusion In this paper, we introduced a new energy efficient data aggregation protocol in wireless sensor networks, referred to as Centroid-based Hierarchical Clustering (Cent-HC) algorithm. The Cent- HC algorithm focused on reducing the overhead of dynamic clustering; minimizing the communication paths between sensor nodes and cluster head nodes, and avoiding the direct communication between cluster heads and the sink node. Simulation results of this paper showed that the Cent-HC algorithm in comparison with the LEACH-C algorithm achieved better performance in terms of energy consumption, number of dead nodes and network lifetime. References [1] Jasmine Norman ; J.Paulraj Joseph ; P.Prapoorna Roja, A Faster Routing Scheme for Stationary Wireless Sensor Networks - A Hybrid Approach, International Journal of Ad Hoc, Sensor & Ubiquitous Computing, Issn: , EIssn: ,Volume: 1,Issue: 1, 2010, pages/rec.no: [2] Ramesh Rajagopalan and Pramod K. Varshney; Data aggregation techniques in sensor networks: A survey, Communications Surveys and Tutorials, IEEE Issn: X, Volume:8, Issue: 4, 2006, pages [3] Kiran Maraiya, Kamal Kant and Nitin Gupta; Wireless Sensor Network: Areview on Data Aggregation, International Journal of Scientific & Research, Volume 2, Issue 2, [4] Wendi Rabiner Heinzelman et al. Energy-Efficient Communication Protocol for Wireless Microsensor Networks, in Proceeding of the 33 rd Hawaii International Conference on System Sciences,January 2000,pp [5] S. Lindsey, C. Raghavendra, and K.M. Sivalingam, Data gathering algorithms in sensor networks using energy metrics, IEEE Trans. Parallel and Distributed Systems, Volume 13, no. 9, September 2002, pp

13 [6] WANG Jun, Zhang, Junyuan and Zhengkun A Distance-based Clustering Routing Protocol in Wireless Sensor Networks, Communication Technology (ICCT), th IEEE International Conference, Nov [7] T. Rappaport, Wireless Communications: Principles & Practice. Englewood Cliffs, NJ: Prentice-Hall, [8] PIC microcontroller with 2.4GHz IEEE transceiver and ZigBee stack, FlexiPanel Ltd, [9] Heinzelman W. B., Chandrakasan A. P., Balakrishnan H., An application specific protocol architecture for wireless microsensor networks, IEEE Trans on Wireless Communications, Volume 1, No. 4, 2002, pp

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