Research Paper THRESHOLD BASED TOWARD ENERGY EFFICIENT BIG DATA GATHERING IN WIRELESS SENSOR NETWORK

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1 Research Paper THRESHOLD BASED TOWARD ENERGY EFFICIENT BIG DATA GATHERING IN WIRELESS SENSOR NETWORK 1 T. Sujithra, 2 R.Venkatesan Address for Correspondence Department of Computer Science and Engineering, PSG College of Technology, Coimbatore - India ABSTRACT Handling massive volume of data is one of the key issues in wireless sensor network. In this paper, we propose an enhanced version of Toward Energy Efficient Big Data gathering (TEEBD) protocol called Threshold based Toward Energy Efficient Big Data Gathering (T-TEEBDG). It incorporates the features of TEEBD with threshold. It classifies the cluster members, of TEEBD as active cluster members and passive cluster members based on the threshold. Threshold value controls communication between the cluster members and the mobile element. The cluster members reach the threshold value called active cluster members that are only allowed to communicate with the mobile element while others goes to the sleep mode immediately. Thus transceiver scheduling increases the lifetime of the sensor network. The main idea of this paper is to reduce the data gathering latency by limiting the amount of data which is to be transmitted to the mobile element based on the cluster member classification. It results in reduced data traffic. From the simulation results, we show that the proposed approach reduces the energy consumption, data gathering latency and packet loss. KEYWORDS: mobile element, threshold, cluster member classification. 1. INTRODUCTION In recent years, Wireless Sensor Network (WSN) has come out as a new data gathering paradigm. WSN consist of hundreds or even thousands of sensors which are tiny, are low powered and have limited storage and transmission capability [1]. Mostly, WSN collects environmental data via the deployed sensor nodes in the area of interest. Sensor nodes have the capability of sensing different kinds of stimulus such as temperature, magnetic, humidity, light, etc. The collected data in the sensor nodes has to be delivered to a special node, called sink, which has the capability of transferring the augmented data from WSN to a remote central to be processed. The technology for sensing and control has the potential for significant advances, not just in science and technology, but equally important for a wide range of applications relating to wellness maintenance, energy, critical infrastructure protection and security, environment, food safety, production processing, quality of life and economy [2]. Mostly wireless sensor nodes are powered up with replaceable batteries and also its capacity is small [3]. Owing to this nature, the lifetime of the wireless sensor network is becoming a challenging issue. Furthermore, once the sensor nodes are deployed, it is hard to recharge due to either the huge volume of sensor node deployment or human non-intervention characteristics of the deployment area. In a wireless sensor network, most of the energy is consumed for communication purpose. The amount of energy consumed in wireless communication is directly proportional to the communication distance. Therefore, single-hop communication is not preferred. Usually, the data collection in large geographical area is accomplished via multi-hop communication. Furthermore, the expenditure of energy in multi-hop communication is heterogeneous as nodes near the base station (BS) drain out very quickly. Because, these nodes are forced to forward the data of other nodes in the network to BS. In order to address the non-uniform energy consumption problem among the sensor nodes, recently researchers have been introduced mobile element (ME) [4]. ME could be a mechanical agent equipped with a powerful transceiver and battery. It directly collects the data from the sensors in the sensing environment via single-hop communication when traversing its transmission range and finally delivers the collected data to the remote central. However, this approach increase the network lifetime, data gathering latency is high. Data gathering latency not only degrades the timeliness of the data, but also may result in the buffer overflow of sensor nodes. The data gathering latency, mainly determined by the mobility and scheduling of ME, i.e., how they traverse through the sensing field and when they collect data from which sensor. The main contribution of this paper is To improve data gathering latency and packet delivery ratio by receding the nodes that are communicating with the mobile element. To improve network lifetime by scheduling transceiver states of the cluster members. The rest of the paper is organized as follows i) Section 2 describes the related Work ii) Section 3 defines the problem statement and network model iii) Section 4 discusses about the proposed scheme iv) Section 5 deals with the experimental results and v) Section 6 gives the conclusion. 2. RELATED WORK Daisuke Takaishi et al. proposed Toward Energy Efficient Big Data Gathering [TEEBD] in densely distributed sensor networks [5]. The main idea of this paper, is to find the optimum number of clusters for large scale data gathering. In which, single mobile element is used for entire network data gathering. At the outset, the entire sensing area is partitioned into equal sized regions. Nodes within the region are grouped together based on the degree of dependence called responsibility. K-Means algorithm is used to find the centroid of the cluster. Data is gathered from the centre of the cluster. Mobile element visits every centroid of the cluster and gathers the data from all of its cluster members. Travelling Salesmen Problem (TSP) algorithm is used to find the optimum travelling path of the mobile sink. Even it simplifies the data gathering process, data gathering latency is high. Because single mobile element takes the responsibility of gathering the data from the entire sensing region. Packet loss due to buffer overflow is also not taken into account. Arati Manjeshwar et al.

2 presented a routing protocol for enhanced efficiency in wireless sensor networks, called Threshold sensitive Energy Efficient sensor Network (TEEN) [6]. It is a reactive network mainly focused for criticality based application. The main idea of this paper is to limit the amount of data which is to be transferred to the cluster head based on the threshold value. Threshold is a critical value that causes an occurrence of the event. Once node senses this value it should immediately switch on its transmitter and report the occurrence of the event to the cluster head. Report time and threshold value are broadcast by the base station to all of its sensor nodes in the sensing environment at the time of cluster formation. However, it is energy efficient and identification of the node failure is difficult to find out. W.R. Heinzelman et al. introduced Low Energy Adaptive Clustering Hierarchy (LEACH) [7]. In this paper, clustering is done in two phases, namely set up phase and steady state phase. Cluster formation and cluster head election is carried out in the set up phase. Data transmission is carried out in the steady state phase. Role of cluster head is rotated to balance the energy consumption. Steady state phase uses Time Division Multiple Access (TDMA) schedule for synchronizing the sensor nodes and the cluster head which give an effective data communication. During the cluster head election, the energy level of the node and distance between CH and BS is not taken into account. It leads to unnecessary re-clustering. Agrawal et al. presented hybrid protocol for efficient routing and comprehensive information retrieval in wireless sensor networks (APTEEN) [8]. It inherits the features of LEACH and TEEN. It works in reactive mode when the sensor node reaches the hard threshold. Otherwise, it works based on the LEACH protocol, i.e., it sends the data to the Base Station (BS) periodically what it receives from the environment. Sasikumar et al. presented K-Means clustering in wireless sensor networks [9]. It focuses on finding centroids of the cluster using K-Means algorithm. The cluster head election process uses both the Euclidean distance and hybrid energy level of the node as a selection criterion. Node with minimum distance to the centroid of the cluster and having maximum energy is appointed as CH. At the time of cluster head gets depleted, a node which is nearest to the depleted cluster head with maximum energy is elected as CH. Nakayama et al. proposed fault resilient sensing in wireless sensor networks [10]. In this paper, mobile element is used to collect the data from its cluster members, it uses K-Means and TSP mobility for finding an optimized route. Mohamed Hefeeda et al. presented forest fire modeling and early detection using wireless sensor networks [11]. The main idea is to design a system for early forest fire detection using Fire Weather Index (FWI) System. FWI is one of the fire rating systems used in North America. Temperature and humidity are considered in analyzing the fire index behavior. Indexes of FWI system are Initial Spread Index (ISI), Build Up Index (BUI) and Fire Weather Index. FWI is calculated based on ISI and BUI. It mainly concentrates on two major components such as Fine Fuel Moisture Code (FFMC) and FWI. FFMC provides the early warning of potential fire. FWI gives the magnitude and intensity of the forest fire. Range of FFMC and corresponding fire intensity as per their results is presented as below. Table1. Intensity Level of Fire based on FFMC 3. PROBLEM STATEMENT AND NETWORK MODEL 3.1 Problem Statement In [5], single mobile element is used to collect the information from large scale environment. Furthermore, all of its cluster members in the sensing environment are allowed to report the occurrence of the event to the mobile element via multi-hop communication. Due to this, it increases the data gathering latency, packet loss because of buffer overflow, and affects the lifetime of the network because of multi-hop communication. Now, as an extension to this work, we propose a novel approach named Threshold based Toward Energy Efficient Big Data Gathering (T-TEEBDG). It incorporates the features of TEEBD with the threshold. The main idea of this paper, is to control the communication between the cluster members and mobile element based on the cluster member classification. It classifies the cluster members, of TEEBD based on the threshold value, namely active cluster members and passive cluster members. Cluster members sensing value that reaches the threshold value are called active cluster members. Cluster members sensing value that is below the threshold value are called passive cluster members. Active cluster members are only allowed to communicate with the mobile element. It reduces the data traffic inside the cluster. Other nodes immediately go to the sleep mode. It increases the lifetime of the sensor network. Because energy consumption of the sleep mode is very less compared to transmit/receive mode. The threshold value is broadcast to all the nodes in the sensing environment at the time of cluster formation. Whenever the requirement changes, the threshold value is refreshed. 3.2 Network Model Consider MXN sensing area (A) encompasses one mobile element which knows about the location details of all of its cluster centroids in the sensing environment and N number of sensor nodes that are distributed randomly over the network. Localization method is used for location finding. The nodes which are able to communicate with each other are grouped together. Data gathering is done at the centroid of the cluster. Mobile element visits all of its cluster centroids only once over the path computed by using TSP (Travelling Salesman Problem). It gathers the information only from the active cluster members through multi-hop fashion. 4. PROPOSED PROTOCOL The proposed approach is designed based on the following observations. Assume that a WSN has been designed for forest fire detection application. WSN has two mandatory components: sensor nodes and mobile element. Some of their characteristics in WSN. Sensing environment is partitioned into equal sized regions.

3 Nodes within the region are clustered based on the degree of dependence. ME is the base station fixed on a moving object. ME moves with a fixed velocity on a path computed by TSP algorithm. ME knows the location of all of its cluster centroids. Cluster centroids act as data collection points. Sensor nodes can not be recharged after deployment. The lowest value of FFMC in table.1 is set as the threshold value. T-TEEBDG is an enhanced version of TEEBD. The main idea of this paper is to control the communication between the cluster members and the mobile element based on the cluster member classification. It is discussed in detail as follows. 4.1 Group Formation For easier network maintenance, the nodes in the MXN sensing area are partitioned into equal sized groups. It denoted as (4.1) Denotes the total number of sensor nodes in the sensing environment and denotes the total number of groups in the sensing environment. Nodes which are able to communicate with each other are grouped together. It is done by using (4.2) Where ε denotes a communication range of the node. indicates the distance between the nodes n i and n j.the above operation is repeated until all the nodes are grouped. Responsibility value ( ) varies from 0 to 1 where μ k denotes the position vector of the k th cluster centroid and Σ k denotes 2X2 covariance matrix of k th cluster as given in [5]. K and indicate the total number of clusters and mixing coefficients of k th cluster. x denotes the position vectors of all the nodes. 4.3 Centre of gravity Calculation of center of gravity is very important because the data gathering is done at the centre of the cluster. Initially, the mobile element places the cluster centroid μ to random location, after that it is repositioned to centre of gravity. For finding the center of gravity, it takes nodes responsibility ( ) as a weight of the sensor node. Value of log likelihood is calculated as in [5] by using (4.4) It continues until cluster centroid becomes optimal. 4.4 Threshold A value that we require to measure from the environment is set as the threshold. Threshold value varies from one application to another. It is application dependent. For implementation purposes, we take the reference of forest fire detection application to set a threshold value. The lowest value of FFMC in Table.1 is set as the threshold. This value broadcasts to all the nodes in the sensing environment at the time of cluster formation by ME. Whenever the requirement changes, the threshold value is refreshed. 4.5 Cluster Member Classification In TEEBD, single ME is used for entire sensor network data gathering. All of its cluster members located in the sensing environment are allowed to report the occurrence of the event to ME which increases the data traffic inside the cluster and ME data gathering latency. In order to overcome this, cluster members of, TEEBD are classified based on the threshold value namely, active cluster member and passive cluster member as in Fig 1. Sensing value of the cluster member reaches the threshold value called as an active cluster member. Sensing value of the cluster member does not reach the threshold value named as passive cluster member. It is represented as (4.5) Fig.1 PROPOSED SCHEME CLUSTER MEMBER CLASSIFICATION AND ITS DATA GATHERING 4.2 Cluster Formation For simplifying the data gathering process, groups are further refined into clusters. Nodes within the group are clustered based on the node dependence ( ) value called responsibility. Let us consider two clusters c 1, c 2 in a group, if the nodes are having higher dependence on c 1, then it will be added to c 1. Else it will be added to c 2. The node dependence value is calculated by using (4.3) (4.6) where {v 1,v 2,v 3,.,v k } denotes the sensing value of the cluster member, CM a is set of all active cluster members, CM p is set of all passive cluster members, t h denotes the threshold value. 4.6 Data gathering Threshold value schedules the states of the transceiver. Data gathering within the cluster is limited based on the cluster member classification. Active cluster members are only allowed to retain in the transmit mode and allowed to transmit the data to ME. Others go to the sleep mode immediately. It limits the number of nodes communicating with the mobile element. This results in increased the network lifetime and reduced data traffic inside the cluster. 4.7 Traversal pattern of the mobile element

4 TSP algorithm is used to find the optimum travelling path of the mobile element. Mobile element visits all the cluster centroid and gathers the data only from the active cluster members using the path computed by TSP. 4.8 Proposed Algorithm 1. Initially N numbers of sensor nodes are randomly distributed over the sensing environment. 2. Repeat Partition nodes in the sensing area (A) into groups N G. Until N={} 3. Cluster the nodes within the group based on 4. Calculate center of gravity do increases the lifetime of the sensor node and automatically reduces the data traffic of TEEBD. Fig. 2.c shows the performance of T-TEEBDG and TEEBD as a function of density of sensor nodes in terms of data gathering latency. In TEEBD, information is gathered by single ME from all of its cluster members in the sensing environment. It gives increased delay in the data gathering even it finds the optimum location for data gathering. In our algorithm, ME collects the information only from the active cluster members instead of the whole. It reduces the 25% of the delay than TEEBD. Until p new =P opt 5. Classify cluster members based on the threshold value Until N={} 6. Allow CM a to transmit the data to ME. 5. Experimental Results Performance of our protocol is tested using Network Simulator NS2. For Testing T-TEEBDG, nodes are deployed randomly in 5000X5000 metre square network area and the nodes are static. Communication range is set to metres. Only one mobile element is used to gather the data from all of its cluster members over the path computed by using TSP. Performance is measured in terms of data gathering latency, packet delivery ratio, and energy consumption by varying number of nodes as 20,30,40,50,60,70,80,90,100. Our results are compared with TEEBD. The simulation parameters are shown in Table-2. Table-2. Simulation Parameters Fig. 2.a Energy Consumption Vs Number of Nodes Fig. 2.b Packet delivery ratio Vs Number of Nodes The following metrics have been used to evaluate the performance of the proposed protocol. 1. Data gathering latency - It is the time taken by ME to collect the information from the data sources. 2. Packet delivery ratio - It is defined as the number of data packets received successfully with the total number of packets sent. 3. Energy - It is the amount of energy consumed for packet transmission, reception and listening. Fig 2.a, 2.b. shows proposed approach improves the performance in terms of energy consumption, and packet delivery ratio than TEEBD. The reason behind this is, all the nodes in the sensing environment of TEEBD are allowed to transmit the data to the mobile element. It enables the transceiver of all the nodes to be in the transmit mode. So energy consumption is high. In our paper, the transceiver state of the node is controlled based on threshold value. IE., only the active cluster members are allowed to retain in the transmit mode, remaining nodes goes to the sleep mode immediately. Hence, it Fig 2.c Data gathering latency Vs Number of Nodes 6. CONCLUSION In this paper, we deal with different performance metrics such as energy consumption, data gathering latency, and packet delivery ratio in large scale application. For improving performance of TEEBD, we propose T-TEEBDG that controls the communication between the cluster members and the mobile element based on the cluster member classification. Extensive simulations have been carried out to prove the efficiency of the protocol. The results shows that the proposed approach obtain 50% improvement on network lifetime compared with TEEBD. Data gathering latency is reduced by 25% than TEEBD and packet delivery ratio is improved by 20%. REFERENCES 1. Jennifer Yick, Biswanath Mukherjee and Dipak Ghosal, Wireless sensor Network Survey, In. Comput.Netw.,Elsevier., Vol.52, 2008, p I.F.Akyildiz,W.Su, Y.Sankarasubramaniam, E.Cayirci, Wireless sensor networks: a Survey, Comput.Netw., Elsevier, Vol.38, 2002, p

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