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Comparative Study of Radio Models for data Gathering in Wireless Sensor Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 27 - Number 4 |
| Year of Publication: 2011 |
| Authors: Joydeep Banerjee, Swarup Kumar Mitra, Mrinal Kanti Naskar |
10.5120/3433-4480
|
Joydeep Banerjee, Swarup Kumar Mitra, Mrinal Kanti Naskar . Comparative Study of Radio Models for data Gathering in Wireless Sensor Network. International Journal of Computer Applications. 27, 4 ( August 2011), 49-57. DOI=10.5120/3433-4480
Abstract
Wireless Sensor Network (WSN) consists of irreplaceable nodes which are equipped with limited energy resources. Necessity of power consumption becomes a prior importance for various pervasive and ubiquitous applications. For realistic computation of energy in accordance with available motes like Micaz, Telos, Mica2, a discrete radio model exist. In this paper we have incorporated a discrete radio model over popular data gathering algorithm. We have formulated a data sheet, which relates details of power required to transmit data packets over range of distance that includes Lognormal Shadowing model for acquiring the required received signal strength. Range of distance is the key factor for energy consumption, which justifies the reduction of power levels to a limited count. We have disseminated the power transmission into specific band with respect to distance. Our paper shows the variation of node deployment over Network lifetime, which produces a significant alteration from sparse to dense network. We have conducted a comparison of Network lifetime and Mean energy consumption for chain based, shortest-hop and load balanced energy aware routing protocols. We have conducted a comparative study of the proposed method in TinyOS platform while running the simulation in TOSSIM.
References
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- Ayon Chakraborty, Swarup Kumar Mitra, and M.K. Naskar, “An Efficient Hybrid Data Gathering Scheme in Wireless Sensor Networks”, ICDCIT 2010, LNCS 5966, pp. 98–103, 2010.
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