ENERGY OPTIMIZATION IN WIRELESS SENSOR NETWORK USING SLEEP MODE TRANSCEIVER

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ENERGY OPTIMIZATION IN WIRELESS SENSOR NETWORK USING SLEEP MODE TRANSCEIVER

Dr. Ms.C.Umamaheswari
Dr. Ms.C.Umamaheswari K.S.R college of engineering
Mrs . J.GNANAMBIGAI
Mrs . J.GNANAMBIGAI
M.E
M.E
DOI

Abstract

Energy efficiency is a central challenge in sensor networks and the radio is a major contributor
to overall energy node consumption. These Wireless Sensor Networks have severe resource constrains
and energy conservation is very essential. The aim of this project is to reduce the energy consumption in wireless sensor networks. This paper proposes adaptive radio low-power sleep modes based on current traffic conditions in the network. It provides an analytical model to conduct a comparative study of different MAC protocols (BMAC, TMAC, SMAC, DMAC) suitable for reduction of energy consumption in wireless environment. This technique exposes the energy trade-offs of different MAC protocols.It first introduces a comprehensive node energy model, which includes energy components for radio switching,transmission, reception, listening, and sleeping for determining the optimal sleep mode and MAC protocol to use for given traffic scenarios. The model is then used for evaluating the energy-related performance of our recently proposed RFID Impulse protocol enhanced with adaptive low-power modes, and comparing it against BMAC under varying data rates. The comparative analysis confirms that RFID Impulse with adaptive low power modes provides lower energy consumption than the BMAC and DMAC in low traffic scenario. The evaluation also yields the optimal settings of low-power modes on the basis of data rates for node platform, and provides guidelines and a simple algorithm for the selection of appropriate MAC protocol, low-power mode, traffic requirements of a sensor network application.

ENERGY OPTIMIZATION IN WIRELESS SENSOR NETWORK USING SLEEP MODE TRANSCEIVER

Energy efficiency is a central challenge in sensor networks and the radio is a major contributor
to overall energy node consumption. These Wireless Sensor Networks have severe resource constrains
and energy conservation is very essential. The aim of this project is to reduce the energy consumption in wireless sensor networks. This paper proposes adaptive radio low-power sleep modes based on current traffic conditions in the network. It provides an analytical model to conduct a comparative study of different MAC protocols (BMAC, TMAC, SMAC, DMAC) suitable for reduction of energy consumption in wireless environment. This technique exposes the energy trade-offs of different MAC protocols.It first introduces a comprehensive node energy model, which includes energy components for radio switching,transmission, reception, listening, and sleeping for determining the optimal sleep mode and MAC protocol to use for given traffic scenarios. The model is then used for evaluating the energy-related performance of our recently proposed RFID Impulse protocol enhanced with adaptive low-power modes, and comparing it against BMAC under varying data rates. The comparative analysis confirms that RFID Impulse with adaptive low power modes provides lower energy consumption than the BMAC and DMAC in low traffic scenario. The evaluation also yields the optimal settings of low-power modes on the basis of data rates for node platform, and provides guidelines and a simple algorithm for the selection of appropriate MAC protocol, low-power mode, traffic requirements of a sensor network application.

Dr. Ms.C.Umamaheswari
Dr. Ms.C.Umamaheswari K.S.R college of engineering
Mrs . J.GNANAMBIGAI
Mrs . J.GNANAMBIGAI
M.E
M.E

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Dr. Ms.C.Umamaheswari. 1970. “. Global Journal of Research in Engineering – F: Electrical & Electronic GJRE-F Volume 11 (GJRE Volume 11 Issue F3): .

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Crossref Journal DOI 10.17406/gjre

Print ISSN 0975-5861

e-ISSN 2249-4596

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ENERGY OPTIMIZATION IN WIRELESS SENSOR NETWORK USING SLEEP MODE TRANSCEIVER

Dr. Ms.C.Umamaheswari
Dr. Ms.C.Umamaheswari K.S.R college of engineering
Mrs . J.GNANAMBIGAI
Mrs . J.GNANAMBIGAI
M.E
M.E

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