An energy efficient mechanism for data collection in wireless sensor network
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Date
2013-07
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G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand)
Abstract
A sensor network is composed of vast number of tiny sensor in a limited area. Each sensor is defined with some energy parameters and the energy based constraints. According to these constraints, as the communication is performed, each participating node loses some amount of energy. Multicast and Broadcast communication are the basic communication requirements of a sensor network. But such kind of
communication increases the network traffic extensively as well as gives large amount of energy loss. Data collection provides the solution to this problem by combining the multiple communications in single communication path. Data collection is one of the major communication approach in which multiple sources are sending data to single sink. In this present work, an agent based approach is defined to generate the effective aggregative path so that the network life and communication will be improved. The presented approach is divided in two main stages and both stages are controlled by multiple agents distributed over the network. In the very first stage, the agent will perform the analysis over the network and assign the weightage to each node based under the different parameters. Once the weights are assigned to each node the
next work of agent is to generate the aggregative path. This path generation is based on multiple parameters. The parameters considered in this work to generate the effective path are loss rate, response time and the communication delay. The presented research work is about to generate an effective communication path so that the effective communication will be performed. The path generation process is divided in two main stages, first phase is to identify frequency (load) of each node and second to generate path so that load balancing will be improved. Most frequent node here represents the heavy load node over the network. The next work is to calculate the path from source to destination by comparing loads of neighboring nodes and here the current node select that neighbor node which is having low load among list of neighboring nodes and then perform the communication over that node. The presented work is implemented in NS2 environment and obtained results shows that the presented work has improved the network throughput extensively and reduced the network delay and the data loss.
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