Prolonging the lifetime of wireless sensor networks interconnected to fixed network using hierarchical energy tree based routing algorithm.

M Kalpana,P Parthiban,R Dhanalakshmi

doi:10.1155/2014/158420

Abstract

This research work proposes a mathematical model for the lifetime of wireless sensor networks (WSN). It also proposes an energy efficient routing algorithm for WSN called hierarchical energy tree based routing algorithm (HETRA) based on hierarchical energy tree constructed using the available energy in each node. The energy efficiency is further augmented by reducing the packet drops using exponential congestion control algorithm (TCP/EXP). The algorithms are evaluated in WSNs interconnected to fixed network with seven distribution patterns, simulated in ns2 and compared with the existing algorithms based on the parameters such as number of data packets, throughput, network lifetime, and data packets average network lifetime product. Evaluation and simulation results show that the combination of HETRA and TCP/EXP maximizes longer network lifetime in all the patterns. The lifetime of the network with HETRA algorithm has increased approximately 3.2 times that of the network implemented with AODV.

Highlights

Most of the wireless sensor networks (WSN) cannot be operated in isolation
hierarchical energy tree based routing algorithm (HETRA) is implemented as a new WSN routing protocol in ns 2
The results clearly indicate that the combination of HETRA with TCP/EXP has longer network lifetime compared with the other routing algorithms for all the node distribution patterns

Summary

Introduction

Most of the wireless sensor networks (WSN) cannot be operated in isolation They must be connected with fixed network for monitoring and controlling. Wireless sensor network uses many small sensor nodes for collecting information These physically small and inexpensive nodes are equipped with one or more sensors, low range RF transceiver, an embedded controller, and a battery as power source. These sensor nodes are deployed in large numbers, hundreds to thousands to form a network. These nodes cannot be configured manually due to its large number and they need to configure themselves. Examples include office building environment control, wild-life habitat monitoring, smart spaces, medical systems, robotic exploration, alarm systems, forest fire detection, and monitoring of volcanic eruptions

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