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An Analytical Model of Latency and Data Aggregation Tradeoff in Cluster Based Wireless Sensor Networks


Naresh Kumar , Rajasthan Institute of Engineering and Technology; Arvind Sharma, Rajasthan Institute of Engineering and Technology; Gajendra Sajedia, Rajasthan Institute of Engineering and Technology


Wireless sensor network, Data aggregation, Latency, Clustering


Sensor networks are collection of sensor nodes which co-operatively send sensed data to base station. As sensor nodes are battery driven, an efficient utilization of power is essential in order to use networks for long duration. Therefore it is needed to reduce data traffic inside sensor networks, thereby reducing the amount of data that is needed to send to base station. The main goal of data aggregation algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. In wireless sensor network, periodic data sampling leads to enormous collection of raw facts, the transmission of which would rapidly deplete the sensor power. A fundamental challenge in the design of wireless sensor networks (WSNs) is to maximize their lifetimes. Data aggregation has emerged as a basic approach in WSNs in order to reduce the number of transmissions of sensor nodes, and hence minimizing the overall power consumption in the network. Data aggregation is affected by several factors, such as the placement of aggregation points, the aggregation function, and the density of sensors in the network. In this paper, an analytical model of wireless sensor network is developed and performance is analyzed for varying degree of aggregation and latency parameters. The overall performance of our proposed methods is evaluated using MATLAB simulator in terms of aggregation cycles, average packet drops, transmission cost and network lifetime. Finally, simulation results establish the validity and efficiency of the approach.

Other Details

Paper ID: IJSRDV2I8165
Published in: Volume : 2, Issue : 8
Publication Date: 01/11/2014
Page(s): 207-216

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