Energy Efficient Clustering Algorithm for Data ... - Semantic Scholar

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JOURNAL OF NETWORKS, VOL. 6, NO. 3, MARCH 2011

Energy Efficient Clustering Algorithm for Data Gathering in Wireless Sensor Networks Jutao Hao, Qingkui Chen, Huan Huo School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology Shanghai, China Email: {jt_hao,chen_qk,huohuan}@usst.edu.cn

Jingjing Zhao School of Electric Power and Automation Engineering, Shanghai University of Electric Power Shanghai, China Email:[email protected]

Abstract—Wireless sensor networks are characterized by centralized data gathering, multi-hop communication and many-to-one traffic pattern. These three characteristics may give rise to funneling effects that can lead to severe packet collision, network congestion, packet loss and even congestion collapse. This can also result in hotspots of energy consumption that may cause premature death of sensor nodes and even premature death of entire network. In this paper, exploiting spatial correlation of nodes to form clusters of nodes sensing similar values, and only cluster head sensor reading is transmit to sink, such can efficiently alleviates the funneling effects. A novelty clustering algorithm is proposed which can greatly reduce the number of cluster heads. Experimental results validate the effectiveness of this approach. Index Terms—wireless sensor networks, data gathering, clustering algorithm, spatial correlations

I.

INTRODUCTION

In the recent years, the rapid technological advances in microelectro-mechanical systems, low power and highly integrated digital electronics, small scale energy supplies, tiny microprocessors, and low power radio technologies have created low power, low cost and multifunctional wireless sensor devices, which can observe and react to changes in physical phenomena of their surrounding environments. These sensor devices are equipped with a small battery, a tiny microprocessor, a radio transceiver, and a set of transducers that used to acquire information that reflect the changes in the surrounding environment of the sensor node. The emergence of these low cost and small size wireless sensor devices has motivated intensive research in the last decade addressing the potential of collaboration among sensors in data gathering and processing, which led to the invention of wireless sensor networks(WSNs)[1,2]. A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions, and report the collected data Corresponding author:hao jutao [email protected]

© 2011 ACADEMY PUBLISHER doi:10.4304/jnw.6.3.490-497

through wireless interface to a center node (sink node). The areas of applications of WSNs vary from civil, healthcare, and environmental to military. Examples of applications include target tracking in battlefields [3], habitat monitoring [4], civil structure monitoring [5], and forest fire detection [6]. Although WSNs resemble conventional ad hoc networks [7] in many aspects, they have their own specific features as follows. • Sensors are deployed with a large density in a wider area compared with nodes in traditional ad hoc networks. • Naturally data communication in wireless sensor networks is mainly a multi-point to point paradigm. • Data samples sensed by sensors are spatiotemporally correlated. This correlation has been approved to have great impacts on protocol design in WSNs. • Most applications in WSNs usually require information about a specific region. Clearly, addressing each individual sensor for the available data leads a large amount of overhead which is not desired in sensor networks. One of the advantages of wireless sensors networks is their ability to operate unattended in harsh environments in which contemporary human-in-the-loop monitoring schemes are risky, inefficient and sometimes infeasible. Therefore, sensors are expected to be deployed randomly in the area of interest by a relatively uncontrolled means, e.g. dropped by a helicopter, and to collectively form a network in an ad-hoc manner [8, 9]. Given the vast area to be covered, the short lifespan of the battery-operated sensors and the possibility of having damaged nodes during deployment, large population of sensors are expected in most WSNs applications. It is envisioned that hundreds or even thousands of sensor nodes will be involved. Designing and operating such large size network would require scalable architectural and management strategies. In addition, sensors in such environments are energy


constrained and their batteries cannot be recharged. Therefore, with the specific consideration of the unique properties of sensor networks such limited power, stringent bandwidth, dynamic topology (due to node failures, adding/removing nodes, or even physical mobility), high network density and large scale deployments have posed many challenges in the design and management of sensor networks. These challenges have demanded energy awareness and robust protocol designs at all layers of the networking protocol stack [10]. Since sensor nodes are energy-constrained, the networks lifetime is a major concern; especially for applications of WSNs in harsh environments. There has been a significant interest in designing algorithms, applications, and network protocols to reduce energy usage of sensors [11]. Generally, energy conservation is dealt with on five different levels [12，13]: • efficient scheduling of sensor states to alternate between sleep and active modes; • efficient control of transmission power to ensure an optimal tradeoff between energy consumption and connectivity; • data compression (source coding) to reduce the amount of uselessly transmitted data; • efficient channel access and packet retransmission protocols on the Data Link Layer; • Energy-efficient routing, clustering and data aggregation. In this paper we will refer mainly to the sensor network model depicted in FIG. 1 and consisting of one sink node (or base station) and a (large) number of sensor nodes deployed over a large geographic area (sensing field).

Figure 1. Sensor network architecture.

Data are transferred from sensor nodes to the sink through a multi-hop communication paradigm [14]. As depicted in Figure1, data collected by sensors is transmitted to a special node equipped with higher energy and processing capabilities called “Sink Node”. The sink collects, filters and aggregates data sent by sensors in order to extract useful information. Due to their energy constraint, wireless sensors usually have a limited transmission range making multi-hop data routing toward the sink more energy-efficient than one-hop transmissions wireless networks (cellular, WLAN, etc.), The rest of the paper is organized as follows. Section II discusses the related works on this topic. In Section III

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we will briefly introduce CAG clustering technique for aggregation. The improved algorithm will be illustrated in section IV. Section V presents four experiments to validate proposed algorithm. Section Finally, conclusions and open issues are discussed in Section VI. II. RELATED WORK In this section, we provide a brief overview of some related research work. Grouping sensor nodes into clusters has been widely pursued by the research community in order to achieve the network scalability objective. In addition to supporting network scalability, clustering has numerous advantages. It can localize the route set up within the cluster and thus reduce the size of the routing table stored at the individual node [15]. Clustering can also conserve communication bandwidth since it limits the scope of inter-cluster interactions to CHs and avoids redundant exchange of messages among sensor nodes [16]. Moreover, clustering can stabilize the network topology at the level of sensors and thus cuts on topology maintenance overhead. Furthermore, Energy-efficient clustering algorithms for wireless sensor networks have been widely addressed in literature. The main goal of clustering is to efficiently maintain the energy consumption of sensor nodes by involving them in multi-hop communication within a particular cluster and by performing data aggregation and fusion in order to decrease the number of transmitted messages to the sink. Every cluster would have a leader, often referred to as the cluster-head (CH). A CH may be elected by the sensors in a cluster or pre-assigned by the network designer. A CH may also be just one of the sensors or a node that is richer in resources. Cluster formation is typically based on the energy reserve of sensors and sensor’s proximity to the CH [17]. For instance, Low-Energy Adaptive Clustering Hierarchy (LEACH) [18], one of the first clustering algorithms proposed for sensor networks, is a distributed, proactive, dynamic algorithm that forms clusters of sensors based on the received signal strength and uses local CHs as routers to the sink. Each node makes its own decision whether to become CH based on how often and the last time it has been CH but also on the optimal percentage of CHs in the network (pre-determined value). Transmissions are operated only by CHs which saves energy. LEACH provides a balance of energy consumption through a random rotation of CHs. However, CHs transmit data directly to the sink, which can be energy-consuming in large-scale sensor networks. Power-efficient GAthering in Sensor Information Systems (PEGASIS) [19] and its variation HierarchicalPEGASIS are two improvements of LEACH. Rather than forming multiple clusters, PEGASIS forms chains of sensor nodes so that each sensor transmits and receives from a neighbor and only one node is selected from that chain to convey data to the PN. Still, communication between the elected CH and the PN is one-hop, which may waste energy and prove to be unsuitable for large-

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sized networks. Weighted Clustering Algorithm (WCA) [20] is a reactive clustering algorithm where cluster election is based on the evaluation, for every sensor, of a score function called’ combined weight’. This function is a weighted linear combination of the degree, the mobility level, the transmission power and the residual energy of the sensor. Every sensor broadcasts its combined weight to its neighbors and the sensor having the lowest weight is elected CH. Hybrid Energy-Efficient Distributed Clustering (HEED) [21] is a distributed clustering protocol that uses a hybrid combination of the residual energy and the intra-cluster communication cost as attribute for cluster head selection. HEED ensures a uniform distribution of CHs across the network and adjusts the probability of CH-selection to ensure inter-CH connectivity. In its initialization phase, HEED allows sensors to compute a probability of becoming CH, proportional to its residual energy and to a pre-determined percentage of CHs. Then, during a repetition phase, sensors seek the best CH to connect to. If no CH is found, the sensor doubles its probability to become CH and broadcasts it again to its neighbors, and so forth. This phase stops either when this probability equals 1 (i.e: the sensor elects itself as CH) or when it finds a CH to connect to. Energy-efficient Strong Head clustering (EESH) [17] is a recently published clustering protocol. In EESH, nodes are promoted CHs according to their respective residual energies, their respective degrees and the distance to and the residual energy of their neighbors. For that, EESH evaluates a cost function for every sensor in the network and iteratively elects the node having the greatest cost as CH. This process terminates when all the sensors in the network are connected to at least one CH. EESH has been shown to outperform HEED and LEACH, so we used it as a comparative base in our performance evaluation. Traditionally, the sensors are deployed in a redundant fashion. Since sensor nodes might generate significant redundant data, similar packets from multiple nodes can be aggregated so that the number of transmissions would be reduced. Data aggregation combines data from different sources by using functions such as suppression (eliminating duplicates), min, max and average [22]. Some of these functions can be performed either partially or fully in each sensor node, by allowing sensor nodes to conduct in-network data reduction. Recognizing that computation would be less energy consuming than communication, substantial energy savings can be obtained through data aggregation. In the cased of allowing for an approximate result, and not requiring an exact answer, enables exploiting the correlations in the sensor data by selecting a small subset of sensor nodes called representative set whose signal data values will represents the whole sensor networks with sufficient accuracy. This technique has been used to achieve energy efficiency and traffic optimization in a number of routing protocols. YOON and SHAHABI have proposed a clustered aggregation (CAG) technique leveraging spatial



and temporal correlations in wireless sensor networks [23, 24].

III. CAG CLUSTERING TECHNIQUE FOR AGGREGATION In-network query processing and data aggregation are widely used to save energy, increase scalability, and reduce computation in many monitoring applications of WSN [25, 26]. TAG, the landmark in-network query processing system, constructs a query routing tree and performs innetwork aggregation along the tree [27]. TAG operates as follows: users pose aggregation queries from a powered, storage-rich base station. Operators that implement the query are distributed into the network by piggybacking on the existing ad hoc networking protocol. Sensors route data back towards the user through a routing tree rooted at the base station. As data flows up this tree, it is aggregated according to an aggregation function and value-based partitioning specified in the query. As an example, consider a query that counts the number of nodes in a network of indeterminate size. First, the request to count is injected into the network; Then, each leaf node in the tree reports a count of 1 to their parent; Interior nodes sum the count of their children, add 1 to it, and report that value to their parent; Counts propagate up the tree in this manner, and flow out at the root. CAG branches out from TAG for further energy saving by using spatial correlation of data to improve existing in-network aggregation mechanisms. CAG forms clusters of the sensor nodes sensing similar values and transmits only a single value per cluster as opposed to a single value per node as in TAG like schemes. Thus, CAG can significantly reduce the number of transmissions, which results in energy savings while incurring a small error in the query result. The CAG algorithm operates in two phases: query and response. During the query phase, CAG forms clusters when TAG-like forwarding tree is built using a userspecified error threshold τ . In the response phase, CAG transmits a single value per cluster. CAG is a lossy clustering method; only the cluster heads contribute to the aggregation. A user-provided error threshold, τ , is used while building clusters. Each node decides to join a cluster based on cluster head sensor Reading (CR) and My local sensor Reading(MR); if MR < CR ± CR × τ , then the sensor is included in the same cluster. The pseudo code of the CAG algorithm can be summarized as follows:


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Figure3. The problem illustration of CAG clustering algorithm

In order to be more intuitive, an example of using CAG clustering algorithm is to shown in Figure2. IV. THE IMPROVEMENT OF CAG ALGORITHM CAG algorithm seems perfect, but through carefully studying the clusters formation process of CAG, we found that where still some flaws exist in the algorithm.

Figure2. An example of CAG clustering result.

The ultimate goal of CAG is to divide the sensor network into some clusters, and a representative is selected form each cluster, which also is called cluster head. The cluster head is responsible to answer for thequeries send by base station quickly. Therefore, CAG should satisfied two requirements, one is that the cluster head can represent the whole cluster and the other is the number of cluster head should be as less as possible. This problem is illustrated in the Figure3. The whole monitoring region is classified into three classes labeled with different colors. The assumption adopted is that the readings of each sensor located in the same region are equal. According to the idea of CAG, only a sensor node is selected as cluster head from a subgraph located in the same region. But the clustering result can be seen in Figure3, every node is elected as a cluster head if its parent is located in a different region. The flaws of CAG are obviously that to many cluster heads were selected to represent the same region. © 2011 ACADEMY PUBLISHER

To overcome the disadvantages of the CAG, we proposed the improved clustering algorithm: At the every beginning, each node sends a HELLO message to build its neighborship table. Every node contain two routing items, one is that the destination is the root (base station) node, by which the node forwards the message received from its child node, the other is that the destination is its cluster head, through which the node send message to the cluster head for aggregation. When the root node prepares to collect data, it labels itself as root node and fulfill network initialization message NET_INIT and broadcasts it. The NET_INIT is a seven-tuple< QueryID, Attribute,τ,ParentID,MyID ， level, CR >, where QueryID is the query number, Attribute designates the query attribute for multi-sensor node.τ is a user-specified error threshold ,and the level is the depth of the current node in the forwarding tree. Once an intermediate node receiving the NET_INIT in the first time, it add a routing item to is route table and forward the NET_INIT message, otherwise dropped the message. For the first time receiving a broadcast message, according to clustering rule MR < CR ± τ , the node judge whether it belongs to the same cluster with its parents or not. If the reading of the node satisfy clustering rule, the node labels itself as cluster member and join the cluster. Otherwise, the node checks it neighbors for cluster head. If there is a node already becomes cluster head, and then joins the cluster, else it labels itself as cluster head. Such a process will be continued, until all of the nodes joined the routing tree. Analyzing above clustering process, we can find that in the improved algorithm when a node receive the NET_INIT message, it firstly check whether its neighbor has become a cluster heard or not. Joining its brother’s cluster is its first choice, only all of the conditions are not satisfied, the node labels itself as cluster head. So, based on the above theoretical analysis of our proposed algorithm, we can conclude that our algorithm will generate less cluster heads than CAG does. In the next section, several experiments will be conducted to validate the effectiveness of our algorithm. V.

EXPERIMENTAL RESULTS

In this section, three experiments were designed to evaluate the performance of the improved clustering algorithm. The simulating program was developed by our team using java language. In the following experiments,

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A total of 500 nodes were deployed a 1000x1000 m 2 rectangular area. The transmission ranges of all the sensor nodes are set to 100m. A. Experiment 1 The parameters in the first experiment are set as follows: threshold τ =５; the base station is located in the center of the monitoring area, dark labeled node in Fig 4(a); sensor reading generation scheme is: the node reading in the center is 50 unit, and the others reading is generated according to the rule 50 × (1 − Dist / 500) ， where Dist denotes the distance to the center. Fig.4 (b) shows the TAG routing tree. Fig.4 (c) and (d) illustrate the clustering results adopting CAG algorithm and our proposed algorithm, respectively. For the purpose of facilitating the observation, the cluster heads and the link between cluster head and their parents are marked in red color. To give a vivid cluster impression, each node is linked to its cluster head, but this link does not represent real routing path. So, the number of red line is equal to the number of cluster. In this experiment, there are 227 cluster heads existing in the networks using CAG clustering algorithm, while only 71 cluster head generated by our proposed algorithm. B. Experiment 2 The only difference between this experiment and first experiment is that the sensor reading generation mechanism. In this experiment, 10 points were randomly

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selected in the monitoring region as data centers. The difference between each center value is 10 units. The reading of each sensor node follows V × (1 − Dist / 500) ,where V is the value of the nearest data center, and Dist is the distance between them. Fig. 5(a) and (b) give the clustering results using CAG and our proposed algorithm, and the cluster heads are 191 and 66 respectively. C. Experiment 3 Parameter settings in this experiment are similar with experiment 1, and the difference is the base station is located in the upper left corner of the region. The clustering results are shown in Figure6. And the numbers of cluster heads obtained by CAG and our proposed algorithm are176 and 50, respectively. D. Experiment 4 The base station was placed in the upper left corner of the region and the node readings generation mechanism is the same as experiment 2. The clustering results are shown in Figure7. And the numbers of cluster heads obtained by CAG and our proposed algorithm are 166 and 59, respectively. E. Experiment 5 This experiment is mainly used to test the effect of threshold τ ,and τ is set to 2 and 10, respectively, The results shown in Figure7.

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(c) (d) Figure4.Results of experiment 1. (a) Nodes distribution ;(b) TAG route tree; (c) CAG clustering result(cluster head 227); (d) Clustering result using proposed algorithm (cluster head 71)



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(a) (b) Figure5.Results of experiment 2. CAG clustering result(cluster head 191); (b) Clustering result using proposed algorithm (cluster head 66)

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(d) Figure6. Results of experiment 3 (a)Nodes deployment;(b) TAG routing tree;(c)CAG clustering results(Heads:176);(d) Clustering result using the proposed method(Heads:50).

(a) (b) Figure7. Results of experiment 4. (a) CAG clustering results(Heads:166);(b) Clustering result using the proposed method(Heads:59).


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(d) Figure8. Results of experiment 5 (a) CAG clustering results(τ=10，Heads:104); (b) Clustering result using the proposed method(τ=10，Heads:66); (c) CAG clustering results(τ=2，Heads:283);(d) Clustering result using the proposed method(τ=2，Heads:171)

VI. CONCLUSTIONS A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants, at different locations. In this paper, A novelty clustering algorithm is proposed which can greatly reduce the number of cluster heads, by exploiting spatial correlation of nodes to form clusters of nodes sensing similar values, and only cluster head sensor reading is transmit to sink, such can efficiently alleviates the funneling effects. Experimental results validate the effectiveness of this approach. In the next research, we will develop a prototype system to further verify the validity of our approach and will give the exact energy consumption. ACKNOWLEDGMENT This work was supported by the National Nature Science Foundation of China (Grant No. 60970012) ,Shanghai Key Science and Technology Project in Information Technology Field (Grant No.09511501000), Shanghai Key Science and Technology Project（Grant No.09220502800),Shanghai leading academic discipline project (Grant No.S30501), Innovation Program of Shanghai Municipal Education Commission (Grant No.10YZ102) and supported by


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Hao Jutao received his PhD in computer science and engineering from Shanghai Jiaotong University in 2007 and now is a lecturer of School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology. His research interests are Mobile Ad hoc networks, image processing and pattern recognition.

Qingkui Chen was born in 1966. He is a Professor and Ph.D supervisor of Scholl of Optical-Electrical and Computer Engineering at University of Shanghai for Science andTechnology (USST), Shanghai, P. R. China. He is the Vice Dean of School of Optical-Electrical and Computer Engineering. Qingkui Chen is a senior member of China Computer Federation (CCF); is a member of Professional Committee ofOpen System of CCF; is a member of Professional Committee of Computer Support Cooperative Work of Shanghai Computer Federation in China. His research interests include networkcomputing, parallel computing, parallel database and computernetwork.He is the head of many programs which were supported by the Natural Science Foundation of China (NSFC) and the Shanghai Natural Science Foundation of China. Prof. Chen also served as program committee member ofIFIPInternational Conference on Network and parallelComputing (NPC) and as the Technical Program CommitteeCo-Chairs of ICISE.

Huo Huan received his PhD in computer science and engineering from Northeastern University in 2007 and now is a lecturer of School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology. Her research interests are web service and parallel computing.

Zhao JingJing received his PhD in Electric Engineering from Chongqing University in 2009 and now is a lecturer of School of Electric Power and Automation Engineering, Shanghai University of Electric Power. Her research interests are webGis, Power System Monitoring.