A REVIEW ON DATA AGGREGATION TECHNIQUES IN WIRELESS SENSOR NETWORKS

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1 A REVIEW ON DATA AGGREGATION TECHNIQUES IN WIRELESS SENSOR NETWORKS Arshpreet Kaur 1, Simarjeet Kaur 2 1 MTech Scholar, 2 Assistant Professor, Department of Computer Science and Engineering Sri Guru Granth Sahib World University, Fatehgarh Sahib, (India) ABSTRACT Wireless sensor network (WSN)consists small light weighted wireless nodes called sensor nodes, deployed in physical or environment condition.data aggregation is the process of collecting and aggregating the useful data. In WSN data aggregation is an effective way to save the limited resources. The main goal of data aggregation algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. This paper covers the various techniques under Cluster-Based Networks for data aggregation. I INTRODUCTION A Wireless Sensor Network (WSN) typically consists of a sink node sometimes referred to as a Base Station and a number of small wireless sensor nodes. The base station is assumed to be secure with unlimited available energy while the sensor nodes are assumed to be unsecured with limited available energy. The sensor nodes monitor a geographical area and collect sensory information. Sensory information is communicated to the Base Station through Wireless hop by hop transmissions. To conserve energy this information is aggregated at intermediate sensor nodes by applying a suitable aggregation function on the received data. Aggregation reduces the amount of network traffic which helps to reduce energy consumption on sensor nodes. The model is based on an analysis of tactical issues of civil protection [1]. It however complicates the already existing security challenges for wireless sensor networks and requires new security techniques tailored specifically for this scenario. WSN were the first few works discussing techniques for data aggregation in Wireless Sensor Networks 329 P a g e

2 II VARIOUS DATA AGGREGATION TECHNIQUES IN WIRELESS SENSOR NETWORK 1. Cluster-Based data aggregation: - These Wireless sensor network is resource constraint that s why sensor cannot directly transmit data to the base station. In which all regular sensors can send data packet to a cluster head (local aggregator) which aggregates data packet from all the regular sensors in its cluster and sends the concise digest to the base station. 2.Chain Based Data Aggregation: The each sensor sends data to the closer neighbour. Power- Efficient DataGathering Protocol for Sensor Information Systems (PEGASIS) is type of chain based data aggregation. Like a heat-seeking missile, data packets are guided towards a sink node with increasing accuracy as packets approach the sink [2]. In PEGASIS, all sensors are structured into a linear chain for data aggregation. In each data-gathering round, a node receives data packet from one of its neighbours, aggregates the data with its own, and sends the aggregates data packet to its other neighbour along the chain. Eventually, the leader node in the are similar to cluster head sends the aggregated data to the base station. Figure below shows the chain based data-aggregation procedure in PEGASIS. 330 P a g e

3 3.Tree Based Data Aggregation: - All node are organized in form of tree means hierarchical, with then help of intermediate node we can perform data aggregation process and data transmit leaf node root node. Tree based data aggregation is suitable for applications which involve innetwork data aggregation. They used the theory of order statistics to show that our DV estimators are unbiased, and to establish moment formulas and sharp error bound [3].An example application is radiation-level monitoring in a nuclear plant where the maximum value provides the most useful information for the safety of the plant. One of the main aspects of treebased networks is the construction of an energy efficient data-aggregation tree. III TECHNIQUES IN CLUSTER BASED DATA AGGREGATION 1) Energy-Efficient Cluster Based Data Aggregation for Wireless Sensor Networks One of the major issues in Wireless Sensor Network is maximizing the lifetime of the network. In general, all sensor nodes directly send the information to the Base station so the energy requirement is very high. Clustering is used to decrease energy consumption and collision. In this paper, they proposed an Energy efficient Cluster Based Data Aggregation scheme for sensor networks (ECBDA). This scheme has four phases: Cluster formation, Cluster head election, Data aggregation and Maintenance. Cluster members send the data only to its corresponding local cluster head. Data generated from neighboring sensors are often redundant and highly correlated thus the cluster head performs the data aggregation to reduce the redundant packet transmission [5].In 331 P a g e

4 their scheme, clusters are formed in a non-periodic manner to avoid unnecessary setup message transmissions. Simulation results demonstrate that their approach effectively reduces the energy consumed and it helps to increase the network lifetime. 2) An Adaptive Data Aggregation Technique for Dynamic Cluster based Wireless Sensor Networks Sensor data collection through clustering mechanisms has become a common strategy in Wireless Sensor Networks (WSN). Usually in WSNs the clusters are formed dynamically and repeatedly to get uniform utilization of energy. It has been seen in many large scale dynamic cluster based WSNs that the sizes of clusters, in terms of number of nodes, show high variance. This affects the data aggregation mechanism on the cluster heads such that in a large sized clusters the collected data cannot be placed in small and fixed size packets without incurring significant losses. Therefore, they have developed a novel and an adaptive method of data aggregation that exploits the spatial correlation between the sensor nodes [6].The main feature of their proposed aggregation method is that in addition to reducing the cost of redundant data transfer in the network, it also optimally utilizes the available space in a packet at each cluster head. The simulation results have shown that in the proposed aggregation method the payload size requirement decreases to almost 25% of the non compressed payload. Also, the distortion percentage in the proposed aggregation method decreases by 16% to 41% as compared to the mean aggregation method. 3) Energy Efficient and Balanced Cluster-Based Data Aggregation Algorithm for Wireless Sensor Networks Data aggregation is an effectual approach for wireless sensor networks (WSNs) to save energy and prolong network lifetime. Cluster-based data aggregation algorithms are most popular because they have the advantages of high flexibility and reliability. But the problem of unbalanced energy dissipation is the inherent disadvantage in clusterbased WSNs. This paper addresses this problem in cluster-based and homogeneous WSNs in which cluster heads transmit data to base station by one-hop communication, and proposes an energy efficient and balanced cluster-based data aggregation algorithm (EEBCDA) [7]. It divides the network into rectangular grids with unequal size and makes cluster heads rotate among the nodes in each grid respectively, the grid whose cluster head consumes more energy has more sensor nodes to take part in cluster head rotation and share energy load, by this way, it is able to balance energy dissipation. Besides, it adopts some measures to save energy. The results of simulation show that EEBCDA can remarkably enhance energy efficiency, balance energy dissipation and prolong network lifetime. 4) Bandwidth efficient cluster-based data aggregation for Wireless Sensor Network A fundamental challenge in the design of Wireless Sensor Network (WSNs) is the proper utilization of resources that are scarce. The critical challenge is to maximize the bandwidth utilization in data gathering and forwarding from sensor nodes to the sink. The main design objective is to utilize the available bandwidth efficiently. The proposed Bandwidth Efficient Cluster-based Data Aggregation (BECDA) algorithm presents the solution for the effective data gathering with in-network aggregation [8]. It considers the network with heterogeneous nodes in terms of energy and mobile sink to aggregate the data packets. The optimal approach is 332 P a g e

5 achieved by intra and inter-cluster aggregation on the randomly distributed nodes with the variable data generation rate. The proposed algorithm uses the correlation of data within the packet for applying the aggregation function on the data generated by nodes. BECDA shows significant improvement in PDR (67.44% and 26.79%) and throughput (41.25% and 26.16%) as compared to the state-of-the-art solutions. BECDA ALGORITHM Steps in development of BECDA algorithm Details of algorithm With the assumption that node in the network has equal density and heterogeneous in energy level are parts of the network graph G. The aggregated information for the inter-cluster phase with nodes N into the cluster is obtained by the algorithm as given below: Input: Graph G (V, E) with n clusters G = {C1, C2, C3,... CN}. Output: CH with aggregated data packets and sink with network wide aggregation. Notations: HC = hop count, S Sink, Pam Parent announcement message, PCH Parent cluster head, RT Routing table, 1) Sink node generate parent announcement message (Pam). 333 P a g e

6 2) Set parent cluster head id = sink id. 3) Set first Pam = HC= 1 and previous hop = sink id. 4) Initialize the timer for periodically broadcast Pam. 5) If Pam is received by CM then update the routing table RT = Pam = previous sink id, since HC = 1. 6) Increment hop count HC++ and update Pam = current node id. 7) Else, check the sequence number in RT and update HC. 8) If new forward to PCH, else drop the packet. 9) Classify the nodes based on the rate. 10) At the end of time interval calculate rate of data received from CM. 11) Aggregate number of packets received and sends it to CH. 12) Repeat steps 4 11 till packet reaches to the sink. 13) End. IV CONCLUSION We have considered different data aggregation techniques. In Energy-Efficient Cluster Based Data Aggregation for Wireless Sensor Networks studied the problem of energy efficient data transmission in wireless sensor networks and proposed a cluster based data aggregation method which consists of four phases are studied.whereas in Energy Efficient and Balanced Cluster-Based Data Aggregation Algorithm for Wireless Sensor Networks, focus on the problem of unbalanced energy dissipation in cluster-based and homogeneous WSNs in which CHs transmit data to BS by one-hop communication, and cluster based data aggregation protocol named EEBCDA.Both gives the best possible result but Energy Efficient and Balanced Cluster-Based Data Aggregation Algorithm for Wireless Sensor Networksoverweigh the other by simulation results which showed to improve energy efficiency, EEBCDA adopts some measures to save energy. REFERENCES [1] Aschenbruck, N., Gerhards-Padilla, E., Gerharz, M., Frank, M. and Martini, P., 2007, October. Modelling mobility in disaster area scenarios. InProceedings of the 10th ACM Symposium on Modeling, analysis, and simulation of wireless and mobile systems (pp. 4-12). ACM. [2] Keally, M., Zhou, G. and Xing, G., 2009, June. Sidewinder: a predictive data forwarding protocol for mobile wireless sensor networks. In Sensor, Mesh and Ad Hoc Communications and Networks, SECON'09. 6th Annual IEEE Communications Society Conference on (pp. 1-9). IEEE. [3] Beyer, K., Haas, P.J., Reinwald, B., Sismanis, Y. and Gemulla, R., 2007, June. On synopses for distinctvalue estimation under multiset operations. InProceedings of the 2007 ACM SIGMOD international conference on Management of data (pp ). ACM. 334 P a g e

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