Data Gathering in Wireless Sensor Network using Realistic Power Control Swarup Kumar Mitra Department of ECE , M.C.K.V.I.E, LiLuah,Howrah swarup.subha@gmail.com Joydeep Banerjee 1 , M.K.Naskar 2 Advanced Digital and Embedded Systems Laboratory, Department of ETCE Jadavpur University,Kolkata 1 jogs.1989@rediffmail.com 2 mrinalnaskar@yahoo.co.in Arpita Chakraborty Department of ECE TechnoIndia, SaltLake,Kolkata carpi.technoindia@yahoo.com ABSTRACT Wireless Sensors Network consists of irreplaceable nodes after being deployed with limited energy resources. The data gathering protocol needs more realistic power consumptions model to estimate the network performance for various pervasive and ubiquitous applications. In this paper we have implemented the chain based and shortest hop data gathering and routing protocols that demonstrate how realistic power model assumptions can affect the system performance in comparison with the results obtained by considering the first order radio model. We formulated a data sheet which gives details of the energy required to transmit data packets on distance basis incorporating Log Normal Shadowing Model for obtaining the received signal strength. We also justified the reason to eradicate the power levels which are unnecessary and gives rise to complications. Moreover we have observed a comparative study of the proposed method in TinyOS platform while running the simulation in TOSSIM for realistic comparison of our model. Categories and Subject Descriptors C.2.2 [Routing Protocols]: Radio model framing for data gathering in wireless sensor network General Terms Performance, Comparison, Keywords Data gathering, Realistic Power Model, First order radio model Log Normal Shadowing Model, TOSSIM 1. INTRODUCTION Applications of sensor networks vary widely from climatic data Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. ICCCS’11, February 12–14, 2011, Rourkela, Odisha, India. Copyright 2011 ACM 978-1-4503-0464-1/11/02…$10.00. gathering, seismic and acoustic underwater monitoring to surveillance and national security, military and health care. The major resource constraint is the energy consumption in the network as the sensor nodes being equipped by irreplaceable batteries. A network with even topology is deployed randomly with sensor nodes. The sensor networks are required to transmit gathered data to the base station (BS) or sink. Each node is provided with transmit power control and omni directional antenna and therefore can vary the areas of its coverage Since communication requires significant amount of energy compared to computations, sensor nodes must collaborate in an energy- efficient manner for transmitting and receiving data so that lifetime can be enhanced and also a better “energy verses delay” [1] performance is achieved. The real issue of energy balancing in WSN can be utilized fully through an efficient power control of nodes. In this paper we have implemented the data gathering protocol using discrete power control [2]. We introduces a radio model discussed in [2], which dynamically determines which power level setting should be used to transmit between two nodes The novelty of our paper is that we have taken into consideration that the sensor nodes are limited to few discrete power levels as in Crossbow Micaz motes and given justifications for not using other power levels in the calculation [3]. Secondly we assume Log-normal Shadowing path loss model, which detects an occurrence of an event at a particular distance from the node [4], for calculation of received signal strength (RSS). We have made an analysis of the discrete model and CC2420 data sheet for determination of various power levels in term of distance and calculated the energy required to transmit data packets to a distance by taking the help of the analysis that we made to calculate the discrete power in terms of distance. We have applied our realistic power consumption model over shortest hop and chain formation data gathering algorithm. The remainder of the paper is organized as Section 2 describes the Network radio model and the radio propagation path loss, Section 3 deals with Realistic power data gathering algorithm, Section 4 contributes about simulation results and finally conclusion and future works in Section 5. 2. NETWORK RADIO MODELS A typical sensor node consists of four major components: a data processor unit, a micro-sensor unit, a radio communication subsystem that consists of transmitter/ receiver electronics,